The effect of intrauterine blood glucose exposure on the eruption of deciduous teeth within one year after birth in offspring: a prospective birth cohort study

The effect of intrauterine blood glucose exposure on the eruption of deciduous teeth within one year after birth in offspring: a prospective birth cohort 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 The effect of intrauterine blood glucose exposure on the eruption of deciduous teeth within one year after birth in offspring: a prospective birth cohort study Qinwen Xu, Liqin Zhang, Yanran Li, Jie Yu, Feng Zhang, Xujuan Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3886931/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 Objective: The aim of this study is to explore the effect of intrauterine blood glucose exposure on the eruption of deciduous teeth in offspring within one year after birth, and to further investigate the impact of different degrees of intrauterine blood glucose exposure on the eruption of deciduous teeth in offspring by grouping them according to the maternal serum insulin levelsin the third trimester . Materials and Methods: This study is a prospective cohort study. From July 2021 to January 2023, pregnant women who regularly checked in the antenatal clinic were selected as the research objects by the convenient sampling method of multiple centers. All the research objects were tested for oral glucose tolerance at 24-28 weeks of pregnancy. Among them, fasting serum insulin levels of pregnant women with abnormal glucose tolerance were measured after 32 weeks of pregnancy. The research objects were divided into the group unexposed to diabetes,exposed to GDM without IR and exposed to GDM with IR. Their offspring were followed up to the age of one year. The main results are the eruption timing of the first deciduous tooth in the offspring, the total number of teeth at the age of one year, and the teeth growth rate. Results： This study included a total of 312 mothers and infants,which consisted of 108 children of mothers unexposed to diabetes, 97 children of mothers exposed to GDM without IR and 107 children of mothers exposed to GDM with IR. Univariate analysis of variance found no intergroup differences in the eruption time of the first deciduous tooth and the total number of teeth at the age of one year among the three groups. However, the group exposed to diabetes including the group exposed to GDM without IR and the group exposed to GDM with IR, had an average monthly tooth emergence rate faster than the group unexposed to diabetes, P<0.05. Spearman correlation analysis showed a significant positive correlation between the mother exposed to GDM and the eruption time and average monthly eruption speed of the first deciduous tooth in male offspring. Multiple linear regression analysis showed that gestational age and delivery method affect the eruption time of the first deciduous tooth in offspring, delivery method affects the total number of teeth in offspring at 12 months, and gestational age affects the teeth growth rate of offspring (P<0.05). Conclusions： The first deciduoustooth eruption time of male offspring with maternal intrauterine blood glucose exposure was later than that of male offspring in the normal control group. Intrauterine blood glucose exposure increased the eruption speed of offspring, but there was no significant correlation between the eruption time and eruption speed of offspring's first deciduous tooth and the degree of maternal intrauterine blood glucose exposure. Clinical Relevance: The eruption of deciduous teeth in offspring is related to maternal blood glucose exposure. Therefore, obstetrics and child health departments should pay attention to the impact of maternal blood glucose exposure on the development of baby teeth in offspring and develop corresponding intervention measures. Cohort study Gestational diabetes Insulin resistance Offspring Deciduous teeth Figures Figure 1 Figure 2 Introduction The concept of "Developmental origins of health and disease (DOHaD)" is now widely accepted, indicating that adverse intrauterine environments during critical early stages of life can lead to disease risks in later stages of life [ 1 ]. Intrauterine hyperglycemia caused by gestational diabetes is considered to be the most important "teratogenic factor" of developing fetus [ 2 ], and hyperglycemic environment will lead to serious degenerative changes in target organs. The development of teeth is a long and complex biological process. From the 4th week of embryonic development to the age of over 20, tooth growth is gradually completed, while the development of deciduous teeth starts from 2 months of embryonic development and ends around 3 years old. The eruption time of the first deciduous tooth is influenced by multiple factors, with genetic factors accounting for the highest proportion. However, race and gender also have important effects on the eruption time of deciduous teeth [ 3 ]. Premature tooth eruption may have a negative impact on oral health as it can affect enamel. Ameloblasts are responsible for the formation of enamel. When teeth appear, this process stops and ameloblasts disappear, making further enamel remodeling impossible. Therefore, infants with rapidly developing teeth may erupt before their teeth are fully mineralized, leading to the occurrence of dental caries [ 4 ]. The formation and mineralization of the coronal and root systems, as well as the surrounding tissues, begin to develop long before the birth of infants, which is the stage of craniofacial complex development [ 5 ]. A large number of genetic and environmental factors affect the parity, sequence, and symmetry of tooth eruption [ 6 ]. The effects of parental age, premature birth, and high birth weight on tooth eruption have been confirmed [ 7 , 8 ]. Dentin formation is a multifactorial process, in which the tooth germ from the ectoderm interacts with undifferentiated mesenchymal cells. Highly differentiated cells, such as ameloblasts, are sensitive to even small changes in the environment [ 9 ]. Due to the fact that the process of enamel formation begins at the 15th week of fetal development, maternal metabolic disorders can affect the formation and maturation of enamel in offspring [ 10 , 11 ]. Previous studies have found that diabetes has a significantly higher risk of pathological changes in the teeth of adolescents with type 1 diabetes, pregnant women with diabetes, and pregnant women with type 1 diabetes compared with normal people [ 12 , 13 ].Epidemiology and animal models show that mother diabetes affects the process of tooth development by damaging the eruption and mineralization of teeth in offspring and changing tooth size [ 14 – 17 ]. Previous studies based on animal models have shown that fetal teeth may be another target organ modified by GDM. Maternal diabetes significantly affects the proliferation and apoptosis of offspring tooth germ cells. Maternal intrauterine hyperglycemia is accompanied by fetal growth throughout pregnancy, so we speculate that gestational diabetes may have adverse effects on the growth and development of teeth in offspring. A cross-sectional study in the United States found a correlation between vitamin D deficiency and insulin resistance [ 18 ]. diabetes patients with vitamin D deficiency have a higher risk of insulin resistance [ 19 ]. Vitamin D is an important factor in tooth mineralization.Because vitamin D can only be transported from the mother to the fetus through the placenta [ 20 ], if the mother lacks calcium during pregnancy, it will affect the placental transport of calcium, leading to poor bone and tooth development in the fetus. Calcium deficiency in the mother's body during pregnancy can also affect the calcium content of postpartum breast milk, which may lead to delayed teething and uneven tooth arrangement in newborns [ 21 ]. Methods Study design and participants This study is a prospective cohort study, using a multicenter convenience sampling method to select mid pregnancy (24-28W) women recruited from Nantong University Affiliated Hospital, Nantong Maternal and Child Health Hospital, and Nantong Third People's Hospital prenatal clinics from July 2021 to January 2023 according to inclusion and exclusion criteria. Among them, 32W pregnant women with abnormal glucose tolerance were measured for fasting serum insulin before delivery, and all offspring were followed up from birth to 12 months. Investigate the effects of maternal intrauterine blood glucose exposure or insulin resistance on the eruption of deciduous teeth in their offspring. The project has been reviewed by the Ethics Committee of Nantong University Affiliated Hospital (2021-K004-01) and registered with the Chinese Clinical Trial Registration Center (ChiCTR210042814).Obtaining written informed consent from research participants. Inclusion and Exclusion criteria We enrolled women that fulfill the following inclusion criteria: 1) Age ≥ 18 years old and ≤40 years old; 2)No serious complications during pregnancy; 3)Singleton pregnancy; 4)Has communication and understanding skills; 5)Informed consent and voluntary participation in this study. We excluded women if she has any of the following situation: 1)High risk of chromosomal abnormalities in prenatal screening of fetuses (suspicious or confirmed); 2)Women with severe cognitive impairment and mental illness; 3)Women who have used drugs such as tetracyclines that affect the development of fetal teeth and bones during pregnancy.We excluded women if her child have any of the following situation:1)In perinatal period, there were complicated asphyxia, apnea and hypoxic ischemic encephalopathy; 2)Infants with congenital or serious physical illnesses. Data Collection and Variable Definition Maternal Characteristi cs The investigator obtained sociodemographic data such as the names, ages, places of residence, and educational levels of the mothers included in the study through a data survey form when their mothers were 24-28W pregnant. Among them, GDM pregnant women undergo fasting serum insulin concentration measurement from 32W pregnancy to delivery. With the consent and support of the hospital nursing department director and ward nurse, the investigator informs GDM pregnant women of the purpose of blood extraction. After explaining the research purpose, their consent is obtained, and an informed consent form is filled out. On the basis of the pregnant woman's consent, blood samples are taken to measure fasting serum insulin concentration. Data such as parity, gestational age, prepregnancy BMI, gestational weight gain, delivery method, baby gender and birth weight, were obtained from the maternal hospitalization medical records. Serological indicator detection methods Specimen collection and testing: GDM pregnant women who meet the requirements should draw peripheral venous blood on an empty stomach for at least 8 hours in the late stage of pregnancy (32 weeks to delivery), inject it into EDTA anticoagulant tubes, and send it to the laboratory. Centrifuge the blood sample at 3000rpm for 10 minutes, take 1ml of the upper plasma and store it in a -80 ℃ freezer. Thaw it to 4℃ for insulin measurement and analyze the fasting plasma insulin concentration within 48 hours. The reagent kit was purchased from Roche Elecsys Diagnostic Products Co., Ltd. in Shanghai, China, and insulin detection was performed using electrochemiluminescence. Calculation formula: Insulin Resistance Index (IRI)=Fasting Blood Glucose (FPG) mmol/L × Fasting insulin (FINS) μ U/mL/22.5 GDM and IR diagnostic criteria: Diagnosis is made through the Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of pregnancy. Its diagnostic criteria [22]: When any of the following values are met or exceeded at 24-28 weeks of pregnancy: fasting PG (0h) ≥ 5.10 mmol/L, 1h PG ≥ 10.00 mmol/L, 2h PG ≥ 8.50 mmol/L, any blood glucose value that meets or exceeds the above criteria can be diagnosed as GDM. Pregnant women are divided into GDM group and non GDM group. Insulin resistance rapidly increases from 24 to 28 weeks of pregnancy and reaches its peak after 32 weeks. According to HOMA-IR, the critical value is set to 2.0. If HOMA-IR ≥ 2, it can be diagnosed as IR[23]. The research subjects were ultimately divided into three groups:the group unexposed to diabetes,the group exposed to GDM without IR and the group exposed to GDM with IR. Offspring Characteristics The status of infant vitamin D supplementation, daily outdoor activity time, time for adding complementary foods, postpartum breastfeeding, pacifier use, time of first deciduous tooth eruption, and total number of deciduous teeth at 12 months were obtained through telephone or WeChat follow-up. Forming a WeChat group for all pregnant women who meet the inclusion criteria and have informed consent is beneficial for further tracking the development of deciduous teeth in later generations. Inform parents to take their baby to the children's health clinic for routine physical examination. During the initial physical examination, the nurse from the hospital's children's health department will train parents on the method of examining the eruption of deciduous teeth. The eruption of teeth is judged by any part of the crown appearing on the gums and being visible and palpable in the oral cavity. Confirm mastery through a model test. Follow up by phone at 4, 6, 9, and 12 months to record the time of the first deciduous tooth eruption. The age identification is accurate to the day (for example, 5 months and 15 days are recorded as 5.5 months).At the age of 12 months, the offspring will undergo a physical examination at the Children's Health Stomatology Department to evaluate their teeth and oral development status, record the number of teeth, and calculate the teeth growth rate (number/month)=12 months of total teeth/(12 months of age - the age of the first deciduous tooth eruption). Statistical Analysis The data was double entered using EpiData software and subjected to consistency testing. Statistical analysis was conducted using SPSS 25.0 software. The chi square test and analysis of variance were used to explore the differences in maternal and infant characteristics between three group for categorical and continuous variables, respectively. Continuous variables were expressed as mean±standard deviation（X̅±S）, while categorical variables were expressed as frequency (percentage). The correlation between different degrees of blood glucose exposure during pregnancy and the eruption time of the first deciduous teeth in offspring, the total number of teeth at 12 months, and the average monthly teething speed was analyzed using Spearman correlation analysis. Use multiple linear regression to analyze the influencing factors of offspring deciduous tooth eruption. P<0.05 shows statistically significant differences. Results This study included 312 pregnant women who were divided into three groups based on their maternal glucose tolerance during pregnancy. Among them, there were 108 cases (34.6%) in the group unexposed to diabetes, 97 cases (31.1%) in the group exposed to GDM without IR, and 107 cases (34.3%) in the group exposed to GDM with IR. Table 1 details the baseline characteristics of mother child pairs divided by mother's diabetes status. There are significant differences among the three groups in terms of mother's age, delivery mode, gestational week, prepregnancy BMI and weight gain during pregnancy (p<0.05).Compared with mothers without GDM, mothers with GDM were more likely to have advanced maternal age, cesarean delivery,shorter gestation weeks,higher BMI before pregnancy and more weight gain during pregnancy.Other parameters were homogeneously distributed. Table 2 further divides three groups of offspring into six groups based on gender. Firstly, the inter group differences in ETFPT among male offspring from different blood glucose groups were analyzed. The results showed that there was a critical difference in ETFPT among male offspring from different blood glucose groups (P=0.058). From the table, it can be observed that the ETFPT of male offspring in the group exposed to GDM without IR (7.00±1.45) and the group exposed to GDM with IR (6.80 ± 1.34) is later than that of male offspring in the group unexposed to diabetes (6.43±1.20). There was no significant intergroup difference in ETFPT among the three groups of female offspring (P>0.05). As shown in Figure 1, the peak period for the eruption of the first deciduous tooth in offspring is mainly between 5 and 8 months, while the peak period for the eruption of the first deciduous tooth in the offspring exposed to GDM without IR and the offspring exposed to GDM with IR is later than that in the offspring unexposed to diabetes. This can also be observed in the cumulative distribution of the eruption time of the first deciduous tooth shown in Figure 2. The research subjects were divided into two groups based on whether they were exposed to blood glucose: the GDM group and the normal group. The total number of teeth at 12 months in their offspring was compared, and there was no statistically significant difference (P>0.05). Further comparison between the group exposed to GDM without IR and the group exposed to GDM with IR revealed no significant difference in the total number of teeth at 12 months between the two groups (P>0.05). Comparing the teeth growth rate of their offspring, it was found that the GDM group, including the the group exposed to GDM without IR (1.61±0.63) and the group exposed to GDM with IR (1.63±0.77), had a faster average monthly teething speed (tooth per month) than the the normal group (1.46±0.56), with a statistically significant difference (P<0.05). According to whether the research object was IR, the offspring were further divided into two groups: the the group exposed to GDM without IR and the group exposed to GDM with IR. It was found that there was no statistically significant difference in both the total number of teeth at 12 months and the teeth growth rate between the two groups (P>0.05), as shown in Table 3. Spearman correlation analysis was conducted on the correlation between maternal blood glucose exposure during pregnancy and the eruption of deciduous teeth in offspring. The results showed that maternal blood glucose exposure during pregnancy was significantly positively correlated with male offspring ETFPT and teeth growth rate (tooth per month). Further Spearman correlation analysis was conducted on the correlation data between gestational blood glucose exposure accompanied by IR and the eruption of offspring deciduous teeth. The results showed that there was no significant relationship between gestational blood glucose exposure accompanied by IR and the eruption of offspring deciduous teeth, as shown in Table 4. Considering the inter group differences in the baseline characteristics of the mothers of the three study subjects, the eruption of deciduous teeth in the offspring was used as the dependent variable, and confounding factors including maternal age, gestational age, prepregnancy BMI, pregnancy weight gain, and delivery method were used as independent variables for multiple linear regression analysis. The results showed that gestational age and delivery method were the main influencing factors on the eruption time of the first baby tooth in offspring. The regression coefficient of gestational age is -0.164<0, P=0.010<0.05, which means that gestational age can significantly negatively affect offspring ETFPT. The older the gestational age, the earlier teeth will appear, while the smaller the gestational age, the later teeth will appear. The regression coefficient of delivery method is 0.453>0, P=0.015<0.05, indicating that delivery method can significantly positively affect offspring ETFPT, and offspring born after cesarean section have teeth earlier than those born through vaginal delivery, as shown in Table 5. Multiple linear regression analysis was conducted with the total number of teeth at 12 months in the offspring as the dependent variable, and the results showed that delivery method was the main influencing factor on the total number of teeth at 12 months in the offspring. The regression coefficient of delivery method is -0.665<0, P=0.012<0.05, indicating that delivery method can significantly negatively affect the total number of teeth in offspring at 12 months. The total number of teeth in offspring at 12 months after cesarean section is lower than that of offspring at 12 months after vaginal delivery, as shown in Table 5. Multiple linear regression analysis was conducted using the average monthly tooth emergence rate of offspring as the dependent variable, and the results showed that gestational age was the main influencing factor on the teeth growth rate of offspring. The regression coefficient of gestational age is -0.094<0, P=0.000<0.05, which means that gestational age can significantly negatively affect the teeth growth rate of offspring. The larger the gestational age, the slower the teeth growth rate, while the smaller the gestational age, the faster the teeth growth rate, as shown in Table 5. Discussion The normal eruption and development of deciduous teeth are not only beneficial for infant nutrition intake and absorption, but also closely related to the development of the maxillofacial region and language[24]. Currently, many studies have explored various influencing factors on the eruption of baby deciduous teeth. Ntani G et al. [25] conducted a 2-year follow-up study on 2915 mother infant pairs in the UK and found an association between gestational age and the eruption of deciduous teeth. The shorter the gestational time, the later the initial eruption of deciduous teeth. This study also reached the same conclusion. Wang XZ et al. [26] studied the eruption of deciduous teeth in 2230 children in China and found a positive correlation between the total number of deciduous teeth eruption and birth weight. Ntani G et al. [25] also found a negative correlation between birth length and the time when baby baby deciduous teeth erupt. The longer the birth height, the more deciduous teeth erupt at the age of 1 and 2. Yang Changyou and other scholars [27] proposed that the eruption of deciduous teeth is related to physical development indicators such as age specific body length Z-value and age specific body weight Z-value.The relationship between breastfeeding and the eruption of deciduous teeth is contradictory. Some reports show that different infant feeding methods are not significantly associated with the eruption time of deciduous teeth [28, 29]. Alnemer KA et al [30] found that infants who are exclusively breastfed have teeth eruption earlier than those who are not exclusively breastfed Żądzińska E et al [31] reached the opposite conclusion in their study. There is currently controversy over the relationship between the eruption of deciduous teeth and infant gender. Kariya P [32] and Burgueño Torres L [33] found that deciduous teeth erupt earlier in male infants than in female infants, while Al Batyneh OB et al. [34] found that deciduous teeth erupt earlier in female infants. However, most studies have shown that there is no statistically significant difference in the eruption time of deciduous teeth between males and females, only in specific deciduous teeth. Some scholars have conducted relevant studies on macrosomia and deciduous tooth eruption, but the results are controversial. Some researchers [35] have pointed out premature eruption of deciduous teeth in macrosomia, while Khuraseva has pointed out delayed eruption of deciduous teeth in macrosomia. These controversies can be explained by many perinatal factors, many maternal endocrine factors can lead to the birth of macrosomia, resulting in fetal weight gain and potentially affecting the conditions for tooth eruption. Therefore, this study explores the maternal endocrine factors that lead to the development of macrosomia. This study found no significant intergroup differences in ETFPT among the three groups of offspring, but the ETFPT of male offspring in the three groups showed borderline significance. The ETFPT of male offspring in the GDM group, including the group exposed to GDM without IR and the group exposed to GDM with IR, was later than that of male offspring in the normal control group. In addition, this study used a line chart to more intuitively display the time distribution and cumulative distribution of the first deciduous tooth eruption within 12 months in offspring of different blood glucose exposure groups. The results showed that the non GDM group had earlier overall tooth eruption, and the cumulative time distribution curves of tooth eruption in the group exposed to GDM without IR and the group exposed to GDM with IR were similar. Therefore, prenatal blood glucose exposure can indeed have a certain impact on offspring ETFPT, but the presence of IR has little effect on offspring ETFPT. Animal studies have found that a high glucose environment inhibits the proliferation of oral epithelial stem cells and promotes their apoptosis. During the process of tooth formation, these oral epithelial stem cells produce inner enamel epithelium, intermediate layer, stellate reticulum, and outer enamel epithelium. Therefore, a high sugar environment can lead to incomplete enamel development and increase the risk of future dental caries in individuals [36].This study conducted a Spearman correlation analysis on the correlation between GDM during pregnancy and the eruption of deciduous teeth in offspring. GDM during pregnancy was significantly positively correlated with ETFPT and average monthly tooth emergence rate in male offspring. A study [37] found that the permanent teeth of children with diabetes emerge faster than those of normal children, and the risk of dental caries is higher. The results of this study are similar to those of this study. Although all offspring in this study were only exposed to intrauterine hyperglycemia and were not diagnosed as diabetes, the results showed that only intrauterine hyperglycemia exposure would lead to accelerated eruption of deciduous teeth after birth. The rapid eruption of primary teeth may indicate that the permanent teeth in childhood are also growing too fast. Therefore, this study tells us that we should not only pay attention to the teeth health of permanent teeth in children with diabetes, but also pay attention to the early development of primary teeth in the offspring of mothers exposed to blood glucose during pregnancy. Finally, a multiple linear regression analysis was conducted on confounding factors including maternal age, gestational age, prepregnancy BMI, weight gain during pregnancy, mode of delivery, and the eruption of deciduous teeth in offspring. It was found that gestational age was negatively correlated with ETFPT, and the total number of teeth in offspring born after cesarean section at 12 months was less than that of offspring born after natural delivery. The average monthly tooth emergence rate was negatively correlated with gestational age. After adjusting for confounding factors, there was no significant correlation between maternal intrauterine blood glucose exposure and the eruption of deciduous teeth in offspring. Therefore, the results suggest that compared to gestational age and delivery method, the impact of intrauterine blood glucose exposure on the early eruption of deciduous teeth in offspring is not significant, and further evidence is needed by increasing the sample size. Conclusion The first deciduous tooth eruption time of male offspring with maternal intrauterine blood glucose exposure was later than that of male offspring in the normal control group. Intrauterine blood glucose exposure increased the eruption speed of offspring, but there was no significant correlation between the eruption time and eruption speed of offspring's first deciduous tooth and the degree of maternal intrauterine blood glucose exposure. Strengths and weaknesses of the study Our study has several strengths This study innovatively studied the intergenerational impact of mother's intrauterine blood glucose exposure on the eruption of baby teeth. Previously, it was mostly about the impact of individual diabetes on tooth development. The results of this study can provide corresponding theoretical guidance and take corresponding preventive measures for staff in the child health department. Several limitations should be noted.Firstly, due to the limitations of research time and survey scope, this study did not conduct a large-scale study, and further expansion of the sample size is needed to support the results of this study. Secondly, the data related to the eruption of deciduous teeth in this study were collected by the guardian, which may have some memory bias. Abbreviations GDM,Gestational diabetes mellitus; IR,Insulin resistance; ETFPT,The eruption timing of the first primary tooth;BMI,Body mass index Declarations A.Author Contribution Guarantor of integrity of entire study Study concepts：Qinwen Xu Study design：Qinwen Xu、 Feng Zhang Literature research：Qinwen Xu、Yanran Li Clinical studies：Jie Yu、 Liqin Zhang Experimental studies：Qinwen Xu、Yanran Li Data acquisition：Xujuan Xu、Liqin Zhang、Qinwen Xu Data analysis/interpretation ：Qinwen Xu Statistical analysis ：Qinwen Xu、Feng Zhang Manuscript preparation：Qinwen Xu Manuscript defnition of intellectual content：Feng Zhang Manuscript editing ：Qinwen Xu Manuscript revision/review ：Xujuan Xu、Feng Zhang Manuscript fnal version approval ：Xujuan Xu B.Ethics Approval and Consent to Participate The project has been reviewed by the Ethics Committee of Nantong University Affiliated Hospital (2021-K004-01) and registered with the Chinese Clinical Trial Registration Center (ChiCTR210042814).Obtaining written informed consent from research participants. 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Al-Batayneh O B, Shaweesh A I, Alsoreeky E S. Timing and sequence of emergence of deciduous teeth in Jordanian children[J]. Arch Oral Biol, 2015,60(1):126-133. Garmash O. Dependence of Deciduous Tooth Eruption Terms and Tooth Growth Rate on the Weight-Height Index at Birth in Macrosomic Children over the First Year of Life[J]. Acta Medica (Hradec Kralove), 2019,62(2):62-68. Chen G, Sun W, Liang Y, et al. Maternal diabetes modulates offspring cell proliferation and apoptosis during odontogenesis via the TLR4/NF-κB signalling pathway[J]. Cell Prolif, 2017,50(3). Lal S, Cheng B, Kaplan S, et al. Accelerated tooth eruption in children with diabetes mellitus[J]. Pediatrics, 2008,121(5):e1139-e1143. Tables Table 1 Baseline Characteristics of Study Participants by Maternal Diabetes Mellitus Status Variable Unexposed to diabetes（n=108） Exposed to GDM without IR（n=97） Exposed to GDM with IR（n=107） F/c 2 P Maternal characteristics Maternal age at childbirth, years 27.95±3.03 30.08±4.05 30.72±4.00 16.294 0.000 Place of residence Rural 64（59.3） 52（53.6） 54（50.5） 1.719 0.423 Urban 44（40.7） 45（46.4） 53（49.5） Type of birth Cesarean section 46（42.6） 56（57.7） 64（59.8） 7.560 0.023 Vaginal delivery 62（57.4） 41（42.3） 43（40.2） Maternal parity，times 0.33±0.53 0.34±0.52 0.40±0.60 0.499 0.608 Gestational age，weeks 38.84±1.45 38.36±1.42 38.46±1.40 3.357 0.036 Prepregnancy BMI（kg/m2） 21.42±3.14 22.52±3.67 25.25±4.38 29.353 0.000 Gestational weight gain（kg） 11.68±5.13 13.05±5.43 15.33±5.00 13.132 0.000 Vitamin D supplementation for mothers during pregnancy Yes 81（75.0） 70（72.2） 84（78.5） 1.109 0.574 No 27（25.0） 27（27.8） 23（21.5） Child’s characteristics Sex Male 72（66.7） 57（58.8） 57（53.3） 4.049 0.132 Female 36（33.3） 40（41.2） 50（46.7） Birth weight, g 3466.85±397.16 3367.94±499.51 3515.79±560.79 2.384 0.094 Birth height, cm 50.05±1.09 49.81±1.69 50.27±1.62 2.416 0.091 Feeding patterns Formula feeding 23（21.3） 21（21.6） 17（15.9） 2.053 0.726 Mixed feeding 34（31.5） 31（32.0） 41（38.3） Exclusive breastfeeding 51（47.2） 45（46.4） 49（45.8） Complementary feeding, month 5.81±0.38 5.75±0.42 5.85±0.44 1.477 0.230 Average outdoor activity time per day, h 1.98±1.20 1.78±1.24 1.78±1.18 0.919 0.400 Signifcant p-values are shown in bold GDM,Gestational diabetes mellitus; IR,Insulin resistance. Table 2 Associations between maternal diabetes and the eruption timing of the first primary tooth in offspring Groups and subgroups Unexposed to diabetes（n=108） Exposed to GDM without IR （n=97） Exposed to GDM with IR（n=107） P-Value a P-Value b Male Female Male Female Male Female Sample sizes 72 36 57 40 57 50 ETFPT, month 6.43±1.20 7.09±1.35 7.00±1.45 7.00±1.35 6.80±1.34 6.98±1.44 0.058 * 0.814 a Intergroup differences in ETFPT in male offspring from different blood glucose exposure groups b Intergroup differences in ETFPT in female offspring from different blood glucose exposure groups * Borderline significance ETFPT,The eruption timing of the first primary tooth;GDM,Gestational diabetes mellitus; IR,Insulin resistance. Table 3 Number of teeth at the age of one year and teeth growth rate of offspring exposed to different degrees of blood glucose Variables Unexposed to diabetes （n=108） Exposed to GDM without IR （n=97） Exposed to GDM with IR （n=107） P1 P2 Number of teeth at the age of one year 7.06±2.17 7.20±2.37 7.17±2.22 0.638 0.624 Teeth growth rate, teeth per month 1.46±0.56 1.61±0.63 1.63±0.77 0.045 0.750 Signifcant p-values are shown in bold P1:Compare group unexposed to diabetes with group exposed to GDM P2:Compare group exposed to GDM without IR with group exposed to GDM with IR Table 4 The correlation between different degrees of blood glucose exposure during pregnancy and tooth eruption in offspring Groups and subgroups ETFPT Number of teeth at the age of one year Teeth growth rate, teeth per month Male Female Male Female Male Female Exposed to GDM 0.148 * 0.000 0.021 0.061 0.195 ** 0.110 Exposed to GDM with IR -0.036 -0.021 -0.002 -0.037 -0.025 -0.043 **P＜0.01 *P＜0.05 Table 5 Multiple linear regression analysis of ETFPT,number of teeth at the age of one year and teeth growth rate in offspring Variables B S.E β t P Multiple linear regression analysis of ETFPT in offspring (Constant) 12.512 2.756 4.540 0.000 Maternal blood glucose levels 0.295 0.210 0.087 1.401 0.162 Maternal age at childbirth 0.021 0.024 0.051 0.864 0.388 Gestational age -0.164 0.064 -0.147 -2.586 0.010 Prepregnancy BMI -0.022 0.023 -0.056 -0.941 0.347 Gestational weight gain -0.009 0.017 -0.029 -0.492 0.623 Type of birth 0.453 0.185 0.141 2.445 0.015 Multiple linear regression analysis of number of teeth at the age of one year in offspring (Constant) 5.449 3.906 1.395 0.164 Maternal blood glucose levels 0.158 0.298 0.034 0.531 0.596 Maternal age at childbirth -0.032 0.035 -0.056 -0.923 0.357 Gestational age 0.099 0.090 0.063 1.099 0.273 Prepregnancy BMI -0.011 0.033 -0.021 -0.343 0.732 Gestational weight gain -0.003 0.025 -0.008 -0.135 0.893 Type of birth -0.665 0.263 -0.148 -2.532 0.012 Multiple linear regression analysis of teeth growth rate in offspring (Constant) 1.072 0.702 1.528 0.132 Maternal blood glucose levels 0.003 0.005 0.056 0.544 0.589 Maternal age at childbirth 0.000 0.004 -0.003 -0.030 0.976 Gestational age -0.037 0.015 -0.337 -2.372 0.021 Prepregnancy BMI 0.004 0.001 0.005 0.049 0.961 Gestational weight gain 0.050 0.039 0.198 1.274 0.208 Type of birth 0.001 0.003 0.069 0.395 0.695 Signifcant p-values are shown in bold Additional Declarations No competing interests reported. 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Child Health Hospital Affiliated to Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Liqin","middleName":"","lastName":"Zhang","suffix":""},{"id":274025653,"identity":"2ed410f8-b16b-49c6-8813-6557a4c0245d","order_by":2,"name":"Yanran Li","email":"","orcid":"","institution":"Medicine School of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Yanran","middleName":"","lastName":"Li","suffix":""},{"id":274025654,"identity":"17f3f45b-3adf-4212-a365-0529bcd0ea09","order_by":3,"name":"Jie Yu","email":"","orcid":"","institution":"Maternal and Child Health Hospital Affiliated to Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yu","suffix":""},{"id":274025655,"identity":"8bc6f1b3-64cf-4583-83c2-1967c3986b89","order_by":4,"name":"Feng Zhang","email":"","orcid":"","institution":"Medicine School of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhang","suffix":""},{"id":274025656,"identity":"3b330850-36c4-4ccb-87fb-dcf4cebf6cdc","order_by":5,"name":"Xujuan Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHCCBIYEAxtmfmbmgw+I1/KgII1dsp0t2YBoaxgffDjMb3Cex0yAKOUGNxIeAB3GLG18mMGMgaHGJpqgFsmeAyC/sBmbHWZIe8BwLC23gZAWfvYGkBaeZKCW4waMDYcJa2FjBoeYRP3mZsY2CaK0QG0xYDZgZmYjTgvULwnMEofZmA0SiPGLwY2cBMYff/4z8/ef//jgQ40NYS0MDDzpP+DsBMLKQYD9AHHqRsEoGAWjYOQCAMzqOaoC3H0mAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Xujuan","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-01-22 04:45:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3886931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3886931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51511808,"identity":"796a5c1f-39a3-4d00-b5e3-a5e45c114fd6","added_by":"auto","created_at":"2024-02-22 21:10:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33380,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the eruption time of the first tooth in offspring exposed to different blood glucose levels\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3886931/v1/e4f497acdabcd892665163d5.png"},{"id":51511805,"identity":"c87b76c3-a354-4823-a0d7-1f3daa6dd731","added_by":"auto","created_at":"2024-02-22 21:10:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26705,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative distribution of the eruption time of the first tooth in offspring exposed to different blood glucose levels\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3886931/v1/aac14f488c07e9dca4f1a17d.png"},{"id":58132767,"identity":"1ee2862a-eff5-41ab-b039-e43099eaf6fd","added_by":"auto","created_at":"2024-06-11 14:54:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":680612,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3886931/v1/22ffa5b2-e286-4a10-9da0-a1f1d9256581.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of intrauterine blood glucose exposure on the eruption of deciduous teeth within one year after birth in offspring: a prospective birth cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe concept of \"Developmental origins of health and disease (DOHaD)\" is now widely accepted, indicating that adverse intrauterine environments during critical early stages of life can lead to disease risks in later stages of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Intrauterine hyperglycemia caused by gestational diabetes is considered to be the most important \"teratogenic factor\" of developing fetus [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and hyperglycemic environment will lead to serious degenerative changes in target organs.\u003c/p\u003e \u003cp\u003eThe development of teeth is a long and complex biological process. From the 4th week of embryonic development to the age of over 20, tooth growth is gradually completed, while the development of deciduous teeth starts from 2 months of embryonic development and ends around 3 years old. The eruption time of the first deciduous tooth is influenced by multiple factors, with genetic factors accounting for the highest proportion. However, race and gender also have important effects on the eruption time of deciduous teeth [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Premature tooth eruption may have a negative impact on oral health as it can affect enamel. Ameloblasts are responsible for the formation of enamel. When teeth appear, this process stops and ameloblasts disappear, making further enamel remodeling impossible. Therefore, infants with rapidly developing teeth may erupt before their teeth are fully mineralized, leading to the occurrence of dental caries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The formation and mineralization of the coronal and root systems, as well as the surrounding tissues, begin to develop long before the birth of infants, which is the stage of craniofacial complex development [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A large number of genetic and environmental factors affect the parity, sequence, and symmetry of tooth eruption [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The effects of parental age, premature birth, and high birth weight on tooth eruption have been confirmed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDentin formation is a multifactorial process, in which the tooth germ from the ectoderm interacts with undifferentiated mesenchymal cells. Highly differentiated cells, such as ameloblasts, are sensitive to even small changes in the environment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Due to the fact that the process of enamel formation begins at the 15th week of fetal development, maternal metabolic disorders can affect the formation and maturation of enamel in offspring [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have found that diabetes has a significantly higher risk of pathological changes in the teeth of adolescents with type 1 diabetes, pregnant women with diabetes, and pregnant women with type 1 diabetes compared with normal people [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Epidemiology and animal models show that mother diabetes affects the process of tooth development by damaging the eruption and mineralization of teeth in offspring and changing tooth size [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous studies based on animal models have shown that fetal teeth may be another target organ modified by GDM. Maternal diabetes significantly affects the proliferation and apoptosis of offspring tooth germ cells. Maternal intrauterine hyperglycemia is accompanied by fetal growth throughout pregnancy, so we speculate that gestational diabetes may have adverse effects on the growth and development of teeth in offspring. A cross-sectional study in the United States found a correlation between vitamin D deficiency and insulin resistance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. diabetes patients with vitamin D deficiency have a higher risk of insulin resistance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Vitamin D is an important factor in tooth mineralization.Because vitamin D can only be transported from the mother to the fetus through the placenta [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], if the mother lacks calcium during pregnancy, it will affect the placental transport of calcium, leading to poor bone and tooth development in the fetus. Calcium deficiency in the mother's body during pregnancy can also affect the calcium content of postpartum breast milk, which may lead to delayed teething and uneven tooth arrangement in newborns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a prospective cohort study, using a multicenter convenience sampling method to select mid pregnancy (24-28W) women recruited from Nantong University Affiliated Hospital, Nantong Maternal and Child Health Hospital, and Nantong Third People\u0026apos;s Hospital prenatal clinics from July 2021 to January 2023 according to inclusion and exclusion criteria. Among them, 32W pregnant women with abnormal glucose tolerance were measured for fasting serum insulin before delivery, and all offspring were followed up from birth to 12 months.\u0026nbsp;Investigate the effects of maternal intrauterine blood glucose exposure or insulin resistance on the eruption of deciduous teeth in their offspring.\u0026nbsp;The project has been reviewed by the Ethics Committee of Nantong University Affiliated Hospital (2021-K004-01) and registered with the Chinese Clinical Trial Registration Center (ChiCTR210042814).Obtaining written informed consent from research participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and Exclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled\u0026nbsp;women\u0026nbsp;that fulfill the following inclusion criteria:\u0026nbsp;1)\u0026nbsp;Age \u0026ge; 18 years old and \u0026le;40 years old; 2)No serious complications during pregnancy; 3)Singleton\u0026nbsp;pregnancy; 4)Has communication and understanding skills; 5)Informed consent and voluntary participation\u0026nbsp;in this study.\u003c/p\u003e\n\u003cp\u003eWe excluded women if she has any of the following situation: 1)High risk of chromosomal abnormalities in prenatal screening of fetuses (suspicious or confirmed); 2)Women with severe cognitive impairment and mental illness; 3)Women who have used drugs such as tetracyclines that affect the development of fetal teeth and bones during pregnancy.We\u0026nbsp;excluded\u0026nbsp;women\u0026nbsp;if her child have any of the following situation:1)In perinatal period, there were complicated asphyxia, apnea and hypoxic ischemic encephalopathy; 2)Infants with congenital or serious physical illnesses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Variable Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal Characteristi\u003c/strong\u003e\u003cstrong\u003ecs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigator obtained sociodemographic data such as the names, ages, places of residence, and educational levels of the mothers included in the study through a data survey form when their mothers were 24-28W pregnant.\u0026nbsp;Among them, GDM pregnant women undergo fasting serum insulin concentration measurement from 32W pregnancy to delivery. With the consent and support of the hospital nursing department director and ward nurse, the investigator informs GDM pregnant women of the purpose of blood extraction. After explaining the research purpose, their consent is obtained, and an informed consent form is filled out. On the basis of the pregnant woman\u0026apos;s consent, blood samples are taken to measure fasting serum insulin concentration.\u0026nbsp;Data such as parity, gestational age, prepregnancy BMI, gestational weight gain, delivery method, baby gender and birth weight, were obtained from the maternal hospitalization medical records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerological indicator detection methods\u003c/strong\u003e\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eSpecimen collection and testing: GDM pregnant women who meet the requirements should draw peripheral venous blood on an empty stomach for at least 8 hours in the late stage of pregnancy (32 weeks to delivery), inject it into EDTA anticoagulant tubes, and send it to the laboratory.\u0026nbsp;Centrifuge the blood sample at 3000rpm for 10 minutes, take 1ml of the upper plasma and store it in a -80 ℃ freezer. Thaw it to 4℃ for insulin measurement and analyze the fasting plasma insulin concentration within 48 hours. The reagent kit was purchased from Roche Elecsys Diagnostic Products Co., Ltd. in Shanghai, China, and insulin detection was performed using electrochemiluminescence.\u003c/li\u003e\n \u003cli\u003eCalculation formula: Insulin Resistance Index (IRI)=Fasting Blood Glucose (FPG) mmol/L\u0026nbsp;\u0026times;\u0026nbsp;Fasting insulin (FINS)\u0026nbsp;\u0026mu;\u0026nbsp;U/mL/22.5\u003c/li\u003e\n \u003cli\u003eGDM and IR diagnostic criteria: Diagnosis is made through the Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of pregnancy. Its diagnostic criteria [22]: When any of the following values are met or exceeded at 24-28 weeks of pregnancy: fasting PG (0h) \u0026ge; 5.10 mmol/L, 1h PG \u0026ge; 10.00 mmol/L, 2h PG \u0026ge; 8.50 mmol/L, any blood glucose value that meets or exceeds the above criteria can be diagnosed as GDM. Pregnant women are divided into GDM group and non GDM group. Insulin resistance rapidly increases from 24 to 28 weeks of pregnancy and reaches its peak after 32 weeks. According to HOMA-IR, the critical value is set to 2.0. If HOMA-IR \u0026ge; 2, it can be diagnosed as IR[23]. The research subjects were ultimately divided into three groups:the group unexposed to diabetes,the group exposed to GDM without IR and the group exposed to GDM with IR.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOffspring Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe status of infant vitamin D supplementation, daily outdoor activity time, time for adding complementary foods, postpartum breastfeeding, pacifier use, time of first deciduous tooth eruption, and total number of deciduous teeth at 12 months were obtained through telephone or WeChat follow-up.\u0026nbsp;Forming a WeChat group for all pregnant women who meet the inclusion criteria and have informed consent is beneficial for further tracking the development of deciduous teeth in later generations.\u003c/p\u003e\n\u003cp\u003eInform parents to take their baby to the children\u0026apos;s health clinic for routine physical examination. During the initial physical examination, the nurse from the hospital\u0026apos;s children\u0026apos;s health department will train parents on the method of examining the eruption of deciduous teeth. The eruption of teeth is judged by any part of the crown appearing on the gums and being visible and palpable in the oral cavity. Confirm mastery through a model test.\u003c/p\u003e\n\u003cp\u003eFollow up by phone at 4, 6, 9, and 12 months to record the time of the first deciduous tooth eruption. The age identification is accurate to the day (for example, 5 months and 15 days are recorded as 5.5 months).At the age of 12 months, the offspring will undergo a physical examination at the Children\u0026apos;s Health Stomatology Department to evaluate their teeth and oral development status, record the number of teeth, and calculate the teeth growth rate (number/month)=12 months of total teeth/(12 months of age - the age of the first deciduous tooth eruption).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data was double entered using EpiData software and subjected to consistency testing. Statistical analysis was conducted using SPSS 25.0 software. The chi square test and analysis of variance were used to explore the differences in maternal and infant characteristics between three group for categorical and continuous variables, respectively. Continuous variables were expressed as mean\u0026plusmn;standard deviation（X̅\u0026plusmn;S）, while categorical variables were expressed as frequency (percentage). The correlation between different degrees of blood glucose exposure during pregnancy and the eruption time of the first deciduous teeth in offspring, the total number of teeth at 12 months, and the average monthly teething speed was analyzed using Spearman correlation analysis. Use multiple linear regression to analyze the influencing factors of offspring deciduous tooth eruption. P\u0026lt;0.05 shows statistically significant differences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 312 pregnant women who were divided into three groups based on their maternal glucose tolerance during pregnancy. Among them, there were 108 cases (34.6%) in the group unexposed to diabetes, 97 cases (31.1%) in the group exposed to GDM without IR, and 107 cases (34.3%) in the group exposed to GDM with IR.\u0026nbsp;Table 1 details the baseline characteristics of mother child pairs divided by mother\u0026apos;s diabetes status. There are significant differences among the three groups in terms of mother\u0026apos;s age, delivery mode, gestational week, prepregnancy BMI and weight gain during pregnancy (p\u0026lt;0.05).Compared with mothers without GDM, mothers with GDM were more likely to have advanced maternal age, cesarean delivery,shorter gestation weeks,higher BMI before pregnancy and more weight gain during pregnancy.Other parameters were homogeneously distributed.\u003c/p\u003e\n\u003cp\u003eTable 2 further divides three groups of offspring into six groups based on gender. Firstly, the inter group differences in ETFPT among male offspring from different blood glucose groups were analyzed. The results showed that there was a critical difference in ETFPT among male offspring from different blood glucose groups (P=0.058). From the table, it can be observed that the ETFPT of male offspring in the group exposed to GDM without IR (7.00\u0026plusmn;1.45) and the group exposed to GDM with IR\u0026nbsp;(6.80 \u0026plusmn; 1.34) is later than that of male offspring in the group unexposed to diabetes\u0026nbsp;(6.43\u0026plusmn;1.20). There was no significant intergroup difference in ETFPT among the three groups of female offspring (P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1, the peak period for the eruption of the first deciduous tooth in offspring is mainly between 5 and 8 months, while the peak period for the eruption of the first deciduous tooth in the offspring\u0026nbsp;exposed to GDM without IR and the offspring exposed to GDM with IR is later than that in the offspring unexposed to diabetes. This can also be observed in the cumulative distribution of the eruption time of the first deciduous tooth shown in Figure 2.\u003c/p\u003e\n\u003cp\u003eThe research subjects were divided into two groups based on whether they were exposed to blood glucose: the GDM group and the normal group.\u0026nbsp;The total number of teeth at 12 months in their offspring was compared, and there was no statistically significant difference (P\u0026gt;0.05).\u0026nbsp;Further comparison between\u0026nbsp;the group exposed to GDM without IR\u0026nbsp;and the group exposed to GDM with IR\u0026nbsp;revealed no significant difference in the total number of teeth at 12 months between the two groups (P\u0026gt;0.05).\u0026nbsp;Comparing the\u0026nbsp;teeth growth rate\u0026nbsp;of their offspring, it was found that the GDM group, including the\u0026nbsp;the group exposed to GDM without IR\u0026nbsp;(1.61\u0026plusmn;0.63) and\u0026nbsp;the group exposed to GDM with IR\u0026nbsp;(1.63\u0026plusmn;0.77), had a faster average monthly teething speed (tooth per month) than the the normal group (1.46\u0026plusmn;0.56), with a statistically significant difference (P\u0026lt;0.05). According to whether the research object was IR, the offspring were further divided into two groups: the\u0026nbsp;the group exposed to GDM without IR\u0026nbsp;and\u0026nbsp;the group\u0026nbsp;exposed to GDM with IR. It was found that there was no statistically significant difference in both the total number of teeth at 12 months and the\u0026nbsp;teeth growth rate\u0026nbsp;between the two groups (P\u0026gt;0.05), as shown in Table 3.\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis was conducted on the correlation between maternal blood glucose exposure during pregnancy and the eruption of deciduous teeth in offspring. The results showed that maternal blood glucose exposure during pregnancy was significantly positively correlated with male offspring ETFPT and\u0026nbsp;teeth growth rate\u0026nbsp;(tooth per month). Further Spearman correlation analysis was conducted on the correlation data between gestational blood glucose exposure accompanied by IR and the eruption of offspring deciduous teeth. The results showed that there was no significant relationship between gestational blood glucose exposure accompanied by IR and the eruption of offspring deciduous teeth, as shown in Table\u0026nbsp;4.\u003c/p\u003e\n\u003cp\u003eConsidering the inter group differences in the baseline characteristics of the mothers of the three study subjects, the\u0026nbsp;eruption of deciduous teeth\u0026nbsp;in the offspring\u0026nbsp;was used as the dependent variable, and confounding factors including maternal age, gestational age, prepregnancy BMI, pregnancy weight gain, and delivery method were used as independent variables for multiple linear regression analysis.\u0026nbsp;The results showed that gestational age and delivery method were the main influencing factors on the eruption time of the first baby tooth in offspring.\u0026nbsp;The regression coefficient of gestational age is -0.164\u0026lt;0, P=0.010\u0026lt;0.05, which means that gestational age can significantly negatively affect offspring ETFPT. The older the gestational age, the earlier teeth will appear, while the smaller the gestational age, the later teeth will appear.\u0026nbsp;The regression coefficient of delivery method is 0.453\u0026gt;0, P=0.015\u0026lt;0.05, indicating that delivery method can significantly positively affect offspring ETFPT, and offspring born after cesarean section have teeth earlier than those born through vaginal delivery, as shown in Table 5.\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analysis was conducted with the total number of teeth at 12 months in the offspring as the dependent variable, and the results showed that delivery method was the main influencing factor on the total number of teeth at 12 months in the offspring.\u0026nbsp;The regression coefficient of delivery method is -0.665\u0026lt;0, P=0.012\u0026lt;0.05, indicating that delivery method can significantly negatively affect the total number of teeth in offspring at 12 months. The total number of teeth in offspring at 12 months after cesarean section is lower than that of offspring at 12 months after vaginal delivery,\u0026nbsp;as shown in Table 5.\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analysis was conducted using the average monthly tooth emergence rate of offspring as the dependent variable, and the results showed that gestational age was the main influencing factor on the teeth growth rate of offspring. The regression coefficient of gestational age is -0.094\u0026lt;0, P=0.000\u0026lt;0.05, which means that gestational age can significantly negatively affect the teeth growth rate of offspring. The larger the gestational age, the slower the teeth growth rate, while the smaller the gestational age, the faster the teeth growth rate, as shown in Table 5.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe normal eruption and development of deciduous teeth are not only beneficial for infant nutrition intake and absorption, but also closely related to the development of the maxillofacial region and language[24]. Currently, many studies have explored various influencing factors on the eruption of baby deciduous teeth. Ntani G et al. [25] conducted a 2-year follow-up study on 2915 mother infant pairs in the UK and found an association between gestational age and the eruption of deciduous teeth. The shorter the gestational time, the later the initial eruption of deciduous teeth. This study also reached the same conclusion. Wang XZ et al. [26] studied the eruption of deciduous teeth in 2230 children in China and found a positive correlation between the total number of deciduous teeth eruption and birth weight. Ntani G et al. [25] also found a negative correlation between birth length and the time when baby baby deciduous teeth erupt. The longer the birth height, the more deciduous teeth erupt at the age of 1 and 2. Yang Changyou and other scholars [27] proposed that the eruption of deciduous teeth is related to physical development indicators such as age specific body length Z-value and age specific body weight Z-value.The relationship between breastfeeding and the eruption of deciduous teeth is contradictory. Some reports show that different infant feeding methods are not significantly associated with the eruption time of deciduous teeth [28, 29]. Alnemer KA et al [30] found that infants who are exclusively breastfed have teeth eruption earlier than those who are not exclusively breastfed\u0026nbsp;Żądzińska E\u0026nbsp;et al [31] reached the opposite conclusion in their study. There is currently controversy over the relationship between the eruption of deciduous teeth and infant gender. Kariya P [32] and Burgueño Torres L [33] found that deciduous teeth erupt earlier in male infants than in female infants, while Al Batyneh OB et al. [34] found that deciduous teeth erupt earlier in female infants. However, most studies have shown that there is no statistically significant difference in the eruption time of deciduous teeth between males and females, only in specific deciduous teeth.\u003c/p\u003e\n\u003cp\u003eSome scholars have conducted relevant studies on macrosomia and deciduous tooth eruption, but the results are controversial.\u0026nbsp;Some researchers [35] have pointed out premature eruption of deciduous teeth in macrosomia, while Khuraseva has pointed out delayed eruption of deciduous teeth in macrosomia.\u0026nbsp;These controversies can be explained by many perinatal factors, many maternal endocrine factors can lead to the birth of macrosomia, resulting in fetal weight gain and potentially affecting the conditions for tooth eruption.\u0026nbsp;Therefore, this study explores the maternal endocrine factors that lead to the development of macrosomia.\u003c/p\u003e\n\u003cp\u003eThis study found no significant intergroup differences in ETFPT among the three groups of offspring, but the ETFPT of male offspring in the three groups showed borderline significance. The ETFPT of male offspring in the GDM group, including\u0026nbsp;the group exposed to GDM without IR and the group exposed to GDM with IR, was later than that of male offspring in the normal control group. In addition, this study used a line chart to more intuitively display the time distribution and cumulative distribution of the first deciduous tooth eruption within 12 months in offspring of different blood glucose exposure groups. The results showed that the non GDM group had earlier overall tooth eruption, and the cumulative time distribution curves of tooth eruption in\u0026nbsp;the group exposed to GDM without IR and the group exposed to GDM with IR\u0026nbsp;were similar. Therefore, prenatal blood glucose exposure can indeed have a certain impact on offspring ETFPT, but the presence of IR has little effect on offspring ETFPT.\u003c/p\u003e\n\u003cp\u003eAnimal studies have found that a high glucose environment inhibits the proliferation of oral epithelial stem cells and promotes their apoptosis.\u0026nbsp;During the process of tooth formation, these oral epithelial stem cells produce inner enamel epithelium, intermediate layer, stellate reticulum, and outer enamel epithelium. Therefore, a high sugar environment can lead to incomplete enamel development and increase the risk of future dental caries in individuals [36].This study conducted a Spearman correlation analysis on the correlation between GDM during pregnancy and the eruption of deciduous teeth in offspring. GDM during pregnancy was significantly positively correlated with ETFPT and average monthly tooth emergence rate in male offspring.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A study [37] found that the permanent teeth of children with diabetes emerge faster than those of normal children, and the risk of dental caries is higher. The results of this study are similar to those of this study. Although all offspring in this study were only exposed to intrauterine hyperglycemia and were not diagnosed as diabetes, the results showed that only intrauterine hyperglycemia exposure would lead to accelerated eruption of deciduous teeth after birth. The rapid eruption of primary teeth may indicate that the permanent teeth in childhood are also growing too fast. Therefore, this study tells us that we should not only pay attention to the teeth health of permanent teeth in children with diabetes, but also pay attention to the early development of primary teeth in the offspring of mothers exposed to blood glucose during pregnancy. Finally, a multiple linear regression analysis was conducted on confounding factors including maternal age, gestational age, prepregnancy BMI, weight gain during pregnancy, mode of delivery, and the eruption of deciduous teeth in offspring. It was found that gestational age was negatively correlated with ETFPT, and the total number of teeth in offspring born after cesarean section at 12 months was less than that of offspring born after natural delivery. The average monthly tooth emergence rate was negatively correlated with gestational age. After adjusting for confounding factors, there was no significant correlation between maternal intrauterine blood glucose exposure and the eruption of deciduous teeth in offspring. Therefore, the results suggest that compared to gestational age and delivery method, the impact of intrauterine blood glucose exposure on the early eruption of deciduous teeth in offspring is not significant, and further evidence is needed by increasing the sample size.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe first deciduous tooth eruption time of male offspring with maternal intrauterine blood glucose exposure was later than that of male offspring in the normal control group. Intrauterine blood glucose exposure increased the eruption speed of offspring, but there was no significant correlation between the eruption time and eruption speed of offspring's first deciduous tooth and the degree of maternal intrauterine blood glucose exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and weaknesses of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several strengths\u0026nbsp;This study innovatively studied the intergenerational impact of mother's intrauterine blood glucose exposure on the eruption of baby teeth. Previously, it was mostly about the impact of individual diabetes on tooth development. The results of this study can provide corresponding theoretical guidance and take corresponding preventive measures for staff in the child health department.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be noted.Firstly, due to the limitations of research time and survey scope, this study did not conduct a large-scale study, and further expansion of the sample size is needed to support the results of this study.\u0026nbsp;Secondly, the data related to the eruption of deciduous teeth in this study were collected by the guardian, which may have some memory bias.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGDM,Gestational diabetes\u0026nbsp;mellitus; IR,Insulin resistance; ETFPT,The eruption timing of the first primary tooth;BMI,Body mass index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA.Author Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuarantor of integrity of entire study Study concepts：Qinwen Xu\u003c/p\u003e\n\u003cp\u003eStudy design：Qinwen Xu、\u0026nbsp;Feng Zhang\u003c/p\u003e\n\u003cp\u003eLiterature research：Qinwen Xu、Yanran Li\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical studies：Jie Yu、\u0026nbsp;Liqin Zhang\u0026nbsp;\u003cbr\u003e\u0026nbsp;Experimental studies：Qinwen Xu、Yanran Li\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData acquisition：Xujuan Xu、Liqin Zhang、Qinwen Xu\u003c/p\u003e\n\u003cp\u003eData analysis/interpretation\u0026nbsp;：Qinwen Xu\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u0026nbsp;：Qinwen Xu、Feng Zhang\u003c/p\u003e\n\u003cp\u003eManuscript preparation：Qinwen Xu\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManuscript defnition of intellectual content：Feng Zhang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManuscript editing\u0026nbsp;：Qinwen Xu\u003c/p\u003e\n\u003cp\u003eManuscript revision/review\u0026nbsp;：Xujuan Xu、Feng Zhang\u003c/p\u003e\n\u003cp\u003eManuscript fnal version approval\u0026nbsp;：Xujuan Xu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.Ethics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project has been reviewed by the Ethics Committee of Nantong University Affiliated Hospital (2021-K004-01) and registered with the Chinese Clinical Trial Registration Center (ChiCTR210042814).Obtaining written informed consent from research participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Jiangsu Province Graduate Research and Practice Innovation program (KYCX21-3123) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Conflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eP S P, K P A, Z G M, et al. Developmental origins of health and disease (DOHaD).[J]. Jornal de pediatria, 2007,83(6).\u003c/li\u003e\n \u003cli\u003eChen G, Sun W, Liang Y, et al. Maternal diabetes modulates offspring cell proliferation and apoptosis during odontogenesis via the TLR4/NF-\u0026kappa;B signalling pathway[J]. Cell Prolif, 2017,50(3).\u003c/li\u003e\n \u003cli\u003eHughes T E, Bockmann M R, Seow K, et al. Strong genetic control of emergence of human primary incisors[J]. J Dent Res, 2007,86(12):1160-1165.\u003c/li\u003e\n \u003cli\u003eNtani G, Day P F, Baird J, et al. Maternal and early life factors of tooth emergence patterns and number of teeth at 1 and 2 years of age[J]. J Dev Orig Health Dis, 2015,6(4):299-307.\u003c/li\u003e\n \u003cli\u003eSuri L, Gagari E, Vastardis H. 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Time and sequence of eruption of primary teeth in relation to breastfeeding in sudanese children[J]. Brazilian Dental Science, 2014,17(3).\u003c/li\u003e\n \u003cli\u003eAlnemer K A, Pani S C, Althubaiti A M, et al. Impact of birth characteristics, breast feeding and vital statistics on the eruption of primary teeth among healthy infants in Saudi Arabia: an observational study[J]. BMJ Open, 2017,7(12):e18621.\u003c/li\u003e\n \u003cli\u003eŻądzińska E, Sitek A, Rosset I. Relationship between pre-natal factors, the perinatal environment, motor development in the first year of life and the timing of first deciduous tooth emergence[J]. Ann Hum Biol, 2016,43(1):25-33.\u003c/li\u003e\n \u003cli\u003eKariya P, Tandon S, Singh S, et al. Polymorphism in emergence of deciduous dentition: A cross-sectional study of Indian children[J]. J Investig Clin Dent, 2018,9(1).\u003c/li\u003e\n \u003cli\u003eBurgue\u0026ntilde;o T L, Mourelle M M, Di\u0026eacute;guez P M, et al. Sexual dimorphism of primary dentition in Spanish children[J]. Acta Odontol Scand, 2018,76(8):545-552.\u003c/li\u003e\n \u003cli\u003eAl-Batayneh O B, Shaweesh A I, Alsoreeky E S. Timing and sequence of emergence of deciduous teeth in Jordanian children[J]. Arch Oral Biol, 2015,60(1):126-133.\u003c/li\u003e\n \u003cli\u003eGarmash O. Dependence of Deciduous Tooth Eruption Terms and Tooth Growth Rate on the Weight-Height Index at Birth in Macrosomic Children over the First Year of Life[J]. Acta Medica (Hradec Kralove), 2019,62(2):62-68.\u003c/li\u003e\n \u003cli\u003eChen G, Sun W, Liang Y, et al. Maternal diabetes modulates offspring cell proliferation and apoptosis during odontogenesis via the TLR4/NF-\u0026kappa;B signalling pathway[J]. Cell Prolif, 2017,50(3).\u003c/li\u003e\n \u003cli\u003eLal S, Cheng B, Kaplan S, et al. Accelerated tooth eruption in children with diabetes mellitus[J]. Pediatrics, 2008,121(5):e1139-e1143.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline Characteristics of Study Participants by Maternal Diabetes Mellitus Status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003eUnexposed to diabetes（n=108）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003eExposed to GDM without IR（n=97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003eExposed to GDM with IR（n=107）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003eF/c\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\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=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\" valign=\"top\"\u003e\n \u003cp\u003eMaternal age at childbirth, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e27.95\u0026plusmn;3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e30.08\u0026plusmn;4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e30.72\u0026plusmn;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e16.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\" valign=\"top\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e64（59.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e52（53.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e54（50.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e44（40.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e45（46.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e53（49.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eType of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eCesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e46（42.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e56（57.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e64（59.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e7.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e62（57.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e41（42.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e43（40.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eMaternal parity，times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u0026plusmn;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eGestational age，weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e38.84\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e38.36\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e38.46\u0026plusmn;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e3.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003ePrepregnancy BMI（kg/m2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e21.42\u0026plusmn;3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e22.52\u0026plusmn;3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e25.25\u0026plusmn;4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e29.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eGestational\u0026nbsp;weight\u0026nbsp;gain（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e11.68\u0026plusmn;5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e13.05\u0026plusmn;5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e15.33\u0026plusmn;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e13.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eVitamin D supplementation for mothers during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e81（75.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e70（72.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e84（78.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e27（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e27（27.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e23（21.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eChild\u0026rsquo;s characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e72（66.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e57（58.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e57（53.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e4.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e36（33.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e40（41.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e50（46.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eBirth weight, g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e3466.85\u0026plusmn;397.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e3367.94\u0026plusmn;499.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e3515.79\u0026plusmn;560.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e2.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eBirth height, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e50.05\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e49.81\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e50.27\u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e2.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eFeeding\u0026nbsp;patterns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eFormula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e23（21.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e21（21.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e17（15.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e2.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eMixed feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e34（31.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e31（32.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e41（38.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eExclusive\u0026nbsp;breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\"\u003e\n \u003cp\u003e51（47.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e45（46.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e49（45.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eComplementary feeding, month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e5.81\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e5.75\u0026plusmn;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e5.85\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e1.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.505709624796086%\"\u003e\n \u003cp\u003eAverage outdoor activity time per day, h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.128874388254488%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u0026plusmn;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.461663947797716%\" valign=\"top\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97226753670473%\" valign=\"top\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignifcant p-values are shown in bold\u003c/p\u003e\n\u003cp\u003eGDM,Gestational diabetes mellitus; IR,Insulin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; Associations between maternal diabetes and the eruption timing of the first primary tooth in offspring\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"683\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.105417276720353%\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups and subgroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.52269399707174%\" colspan=\"2\"\u003e\n \u003cp\u003eUnexposed to diabetes（n=108）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.815519765739385%\" colspan=\"2\"\u003e\n \u003cp\u003eExposed to GDM without IR\u0026nbsp;（n=97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.37628111273792%\" colspan=\"2\"\u003e\n \u003cp\u003eExposed to GDM with IR（n=107）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.809663250366032%\" rowspan=\"2\"\u003e\n \u003cp\u003eP-Value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" rowspan=\"2\"\u003e\n \u003cp\u003eP-Value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.478555304740407%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.704288939051917%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.381489841986458%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.252821670428894%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.704288939051917%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.478555304740407%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.08187134502924%\"\u003e\n \u003cp\u003eSample\u0026nbsp;sizes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.67251461988304%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.818713450292398%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.818713450292398%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.67251461988304%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.7953216374269%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.35672514619883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.08187134502924%\"\u003e\n \u003cp\u003eETFPT,\u0026nbsp;month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.67251461988304%\"\u003e\n \u003cp\u003e6.43\u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.818713450292398%\"\u003e\n \u003cp\u003e7.09\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\"\u003e\n \u003cp\u003e7.00\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e7.00\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.818713450292398%\"\u003e\n \u003cp\u003e6.80\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.67251461988304%\"\u003e\n \u003cp\u003e6.98\u0026plusmn;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.7953216374269%\"\u003e\n \u003cp\u003e0.058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.35672514619883%\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ea\u0026nbsp;Intergroup differences in\u0026nbsp;ETFPT\u0026nbsp;in male offspring from different blood glucose exposure groups\u003c/p\u003e\n\u003cp\u003eb\u0026nbsp;Intergroup differences in\u0026nbsp;ETFPT\u0026nbsp;in\u0026nbsp;female offspring from different blood glucose exposure groups\u003c/p\u003e\n\u003cp\u003e* Borderline significance\u003c/p\u003e\n\u003cp\u003eETFPT,The eruption timing of the first primary tooth;GDM,Gestational diabetes mellitus; IR,Insulin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e \u0026nbsp;Number of teeth at the age of one year and teeth growth rate of offspring exposed to different degrees of blood glucose\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.218241042345277%\"\u003e\n \u003cp\u003eUnexposed to diabetes\u003c/p\u003e\n \u003cp\u003e（n=108）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.241042345276874%\"\u003e\n \u003cp\u003eExposed to GDM without IR\u0026nbsp;（n=97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.172638436482085%\"\u003e\n \u003cp\u003eExposed to GDM with IR\u003c/p\u003e\n \u003cp\u003e（n=107）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.120521172638437%\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.446254071661238%\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\"\u003e\n \u003cp\u003eNumber of teeth at the age of one year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.218241042345277%\"\u003e\n \u003cp\u003e7.06\u0026plusmn;2.17\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.241042345276874%\"\u003e\n \u003cp\u003e7.20\u0026plusmn;2.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.172638436482085%\"\u003e\n \u003cp\u003e7.17\u0026plusmn;2.22\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.120521172638437%\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.446254071661238%\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\"\u003e\n \u003cp\u003eTeeth growth rate,\u0026nbsp;teeth per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.218241042345277%\"\u003e\n \u003cp\u003e1.46\u0026plusmn;0.56\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.241042345276874%\"\u003e\n \u003cp\u003e1.61\u0026plusmn;0.63\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.172638436482085%\"\u003e\n \u003cp\u003e1.63\u0026plusmn;0.77\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.120521172638437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.446254071661238%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSignifcant p-values are shown in bold\u003c/p\u003e\n\u003cp\u003eP1:Compare group unexposed to diabetes\u0026nbsp;with group\u0026nbsp;exposed to GDM\u003c/p\u003e\n\u003cp\u003eP2:Compare group exposed to GDM without IR\u0026nbsp;with group exposed to GDM with IR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u0026nbsp; The correlation between different degrees of blood glucose exposure during pregnancy and tooth eruption in offspring\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.23076923076923%\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups and subgroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" colspan=\"2\"\u003e\n \u003cp\u003eETFPT\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" colspan=\"2\"\u003e\n \u003cp\u003eNumber of teeth at the age of one year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" colspan=\"2\"\u003e\n \u003cp\u003eTeeth growth rate, teeth per month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.541353383458645%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.791979949874687%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.541353383458645%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.791979949874687%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.541353383458645%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.791979949874687%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eExposed to GDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e0.148\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e0.195\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.23076923076923%\"\u003e\n \u003cp\u003eExposed to GDM with IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.360323886639677%\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e**P＜0.01\u0026nbsp; \u0026nbsp;*P＜0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e \u0026nbsp;Multiple linear regression analysis of ETFPT,number of teeth at the age of one year and \u0026nbsp;teeth growth rate in offspring\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u003cem\u003eS.E\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple linear regression analysis of ETFPT in offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e12.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e2.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e4.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal blood glucose levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal age at childbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-2.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003ePrepregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational\u0026nbsp;weight\u0026nbsp;gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eType of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e2.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple linear regression analysis of number of teeth at the age of one year\u0026nbsp;in offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e5.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e3.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal blood glucose levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal age at childbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003ePrepregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational\u0026nbsp;weight\u0026nbsp;gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eType of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-2.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003eMultiple linear regression analysis of teeth growth rate\u0026nbsp;in offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal blood glucose levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eMaternal age at childbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e-2.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003ePrepregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eGestational\u0026nbsp;weight\u0026nbsp;gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.338028169014084%\"\u003e\n \u003cp\u003eType of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.732394366197184%\" valign=\"top\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSignifcant p-values are shown in bold\u003c/p\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":"Cohort study, Gestational diabetes, Insulin resistance, Offspring, Deciduous teeth","lastPublishedDoi":"10.21203/rs.3.rs-3886931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3886931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The aim of this study is to explore the effect of intrauterine blood glucose exposure on the eruption of deciduous teeth in offspring within one year after birth, and to further investigate the impact of different degrees of intrauterine blood glucose exposure on the eruption of deciduous teeth in offspring by grouping them according to the maternal serum insulin levelsin the third trimester .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e This study is a prospective cohort study. From July 2021 to January 2023, pregnant women who regularly checked in the antenatal clinic were selected as the research objects by the convenient sampling method of multiple centers. All the research objects were tested for oral glucose tolerance at 24-28 weeks of pregnancy. Among them, fasting serum insulin levels of pregnant women with abnormal glucose tolerance were measured after 32 weeks of pregnancy. The research objects were divided into the group unexposed to diabetes,exposed to GDM without IR and exposed to GDM with IR. Their offspring were followed up to the age of one year. The main results are the eruption timing of the first deciduous tooth in the offspring, the total number of teeth at the age of one year, and the teeth growth rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003eThis study included a total of 312 mothers and infants,which consisted of 108 children of mothers unexposed to diabetes, 97 children of mothers exposed to GDM without IR and 107 children of mothers exposed to GDM with IR. Univariate analysis of variance found no intergroup differences in the eruption time of the first deciduous tooth and the total number of teeth at the age of one year among the three groups. However, the group exposed to diabetes including the group exposed to GDM without IR and the group exposed to GDM with IR, had an average monthly tooth emergence rate faster than the group unexposed to diabetes, P\u0026lt;0.05. Spearman correlation analysis showed a significant positive correlation between the mother exposed to GDM and the eruption time and average monthly eruption speed of the first deciduous tooth in male offspring. Multiple linear regression analysis showed that gestational age and delivery method affect the eruption time of the first deciduous tooth in offspring, delivery method affects the total number of teeth in offspring at 12 months, and gestational age affects the teeth growth rate of offspring (P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions：\u003c/strong\u003eThe first deciduoustooth eruption time of male offspring with maternal intrauterine blood glucose exposure was later than that of male offspring in the normal control group. Intrauterine blood glucose exposure increased the eruption speed of offspring, but there was no significant correlation between the eruption time and eruption speed of offspring's first deciduous tooth and the degree of maternal intrauterine blood glucose exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Relevance:\u003c/strong\u003eThe eruption of deciduous teeth in offspring is related to maternal blood glucose exposure. Therefore, obstetrics and child health departments should pay attention to the impact of maternal blood glucose exposure on the development of baby teeth in offspring and develop corresponding intervention measures.\u003c/p\u003e","manuscriptTitle":"The effect of intrauterine blood glucose exposure on the eruption of deciduous teeth within one year after birth in offspring: a prospective birth cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:10:30","doi":"10.21203/rs.3.rs-3886931/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":"aa83b9e5-aa76-4a13-9ae1-5b42f18858f4","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-26T03:18:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 21:10:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3886931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3886931","identity":"rs-3886931","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}