New Method For Safety Prediction Of Mothers And Infants With Gestational Diabetes Mellitus Based On Genetic Susceptibility And Single Nucleotide Polymorphism

Background:Gestational diabetes mellitus(GDM)refers to any degree of impaired glucose tolerance first diagnosed during pregnancy.The incidence of GDM in China is as high as18.9%,and it is a common pregnancy complication that affects the short-term and longterm health of both mother and child.Early prevention and treatment of GDM has always been a hot clinical direction,but no authoritative screening method for GDM in early pregnancy has been established.As is known to all,the pathogenic factors affecting GDM are complex,including maternal age,overweight or obesity,poor dietary habits and family history,etc.,especially genetic factors play an important role in this process.Several studies have shown that single nucleotide polymorphism(SNP)are closely associated with the incidence of type 2 diabetes and GDM.Therefore,this research proposed to 1)establish the GDM susceptible SNPs mode by case-control study,2)build prediction model for GDM in early pregnancy combined with clinical characteristics,and prospectively verify its predictive effects and the feasibility of clinical application,3)further explore the influence of GDM genetic susceptibility SNPs on maternal and infant pregnancy outcomes in GDM women with different abnormal glucose metabolism.Based on the principle of precision medicine,this study can provide effective help for the early prevention and treatment of GDM and its pregnancy outcome.Part ⅠScreening for SNPs of genetic susceptibility to gestational diabetes mellitus in Chinese pregnant women.ObjectivesThe genetic susceptible SNP sites of GDM were screened for subsequent screening of pregnant women at high risk of GDM.MethodsA total of 1000 pregnant women who completed oral glucose tolerance test(OGTT)and agreed to collect DNA samples at the research center from December 2017 to September 2019 were involved in this study,including 500 GDM women and 500 normal control women,which formed the model construction cohort.Blood samples were collected for DNA extraction,and genotyping for SNP was performed by Snapshot method.Chi-square test was used to preliminarily explore the distribution differences of genotype frequency and allele frequency of 16 candidate SNPs in GDM women and normal control women.Additive genetic model,dominant genetic model and recessive genetic model were used to explore the influence of susceptibility genotypes of each SNP on the incidence risk of GDM under different genetic models,so as to screen out GDM susceptible SNPs in Chinese pregnant women.Results1.The genotype frequency and allele frequency distribution of MTNR1 B rs10830963,C2CD4A/B rs1436953 and rs7172432 were significantly different between GDM and normal control women(P < 0.05),while the frequency distribution of other SNPs were not statistically significant.2.In the additive genetic model,MTNR1 B rs10830963,C2CD4A/B rs1436953 and rs7172432,CMIP rs16955379 were significantly positively correlated with the incidence of GDM(OR >1,P < 0.05),while no significant correlation was found with other SNPs.3.In the dominant genetic model,MTNR1 B rs10830963,C2CD4A/B rs1436953 and rs7172432,were significantly positively correlated with the incidence of GDM X(OR >1,P < 0.05),while no significant correlation was found with other SNPs.4.In the recessive genetic model,MTNR1 B rs10830963 and C2CD4A/B rs1436953 were significantly positively correlated with the incidence of GDM(OR >1,P <0.05),while no significant correlation was found with other SNPs.ConclusionsThis study found MTNR1 B rs10830963,C2CD4A/B rs1436953 and rs7172432,CMIP rs16955379,which were related to the incidence of GDM in Chinese pregnant women.We plan to select these four SNPs as follow-up research objects to the screening of pregnant women at high risk of GDM in early pregnancy.Part Ⅱ Establishment and validation of the prediction model for early pregnancy of gestational diabetes mellitus based on genetic factors and clinical characteristicsObjectivesTo evaluate the influence of four candidate SNPs and clinical characteristics on the risk of GDM,and construct and verify the prediction model of GDM in early pregnancy.MethodsBased on the model construction cohort,475 GDM and 487 normal control women with complete genotyping results and clinical data were included in this study to construct the prediction model of GDM in early pregnancy.Clinical data of the subjects were collected,and the genotypes of each SNP were scored according to the additive genetic model.The T test and chi-square test were used to compare the clinical data.Univariate logistic regression analysis was used to explore the correlation between clinical characteristics and GDM.Logistic regression analysis was performed to evaluate the influence of each SNP on the risk of GDM with or without adjustment of clinical features.Multiple linear regression analysis was used to explore the correlation between each SNP and glucose metabolism-related indexes.The model construction cohort was randomly divided into trail cohort(70%)and test cohort(30%).Multivariate logistic regression analysis was used to construct the prediction model in the trail cohort,and the test cohort was used to verify the predictive effect of the prediction model internally.Receiver operating characteristic(ROC)area under the curve(AUC)was used to evaluate the model discrimination,and HosmerLemeshow test was used to evaluate the model calibration.Then,pregnant women who underwent OGTT in our research center and agreed to collect DNA samples between November 2019 and October 2020 were randomly included to establish a model validation cohort.Finally,985 pregnant women with complete 4 SNP genotyping results and clinical data were used for external validation of GDM early pregnancy prediction model,with the same verification method as above.Results1.Maternal age,gravidity,parity,BMI and family history of diabetes were significantly associated with GDM(OR >1;P < 0.001).Assisted reproduction was also a critical risk factor for GDM(OR=1.553,P=0.055).Therefore,the six clinical characteristics of maternal age,gravidity,parity,BMI,family history of diabetes and conception way were finally included in the construction of GDM early pregnancy prediction model.2.No matter whether the clinical characteristics were adjusted,MTNR1 B rs10830963,C2CD4A/B rs1436953 and rs7172432,CMIP rs16955379 were significantly correlated with the incidence of GDM(AOR >1,P < 0.05).In addition,we also observed that these four SNPs were linearly correlated with the increase of blood glucose level,Hb A1 c and HOMA-IR in the second trimester,respectively.Thus,these four genetic susceptible SNPs of GDM was included in the construction of early pregnancy prediction model for GDM by means of genetic risk score.3.In the trial cohort(332 GDM and 339 normal control women),a predictive model of GDM in early pregnancy was constructed,in which genetic risk score was independently associated with GDM(AOR=2.061,P < 0.001)and was the most effective predictor except for family history of diabetes.The ROC-AUC of the XII prediction model was 0.727(95%CI: 0.690-0.765),and the sensitivity and specificity were 69.9% and 64.0%,respectively.4.In the test cohort(143 GDM and 148 normal control women),the prediction model was internally validated.The ROC-AUC was 0.776(95%CI: 0.722-0.830),and the sensitivity and specificity were 71.3% and 75.0%,respectively.5.The prediction model was externally validated in the model validation cohort.The ROC-AUC was 0.620(95%CI: 0.573--0.667),and the sensitivity and specificity were 52.4% and 68.8%,respectively.ConclusionsBased on the genetic variants and clinical characteristics,this study developed and verified the early pregnancy prediction model of GDM.This model can help screen out the high-risk population of GDM in early pregnancy,and lifestyle intervention such as reasonable diet and exercise can be timely performed for them in early pregnancy.It is expected to reduce the risk of GDM,delay the progress of GDM,reduce the incidence of adverse pregnancy outcomes,and thus protect the life and health of mother and child in two generations.Part Ⅲ Relationship between genetic susceptible SNPs and adverse pregnancy outcomes in gestational diabetes mellitus ObjectivesTo explore the relationship between genetic susceptible SNPs of GDM and adverse pregnancy outcomes in pregnant women with different abnormal glucose metabolism.MethodsOn the basis of model construction cohort,we followed up the data of antenatal care and delivery outcomes of pregnant women.A total of 452 GDM and 439 normal control women were included in the statistical analysis.GDM were further divided into two groups: one group with impaired fasting glucose(n=115)and one group with impaired glucose tolerance(n=337).ANOVA and chi-square test were used to compare the clinical data and frequency distribution of SNPs among groups.Logistic regression analysis was used to explore the correlation between SNPs and adverse pregnancy outcomes of GDM.Results1.Rs10830963(AOR=1.387,95%CI: 1.112-1.730)and rs7172432(AOR=1.338,95%CI: 1.058-1.691)were independently positively associated with impaired glucose tolerance,while rs16955379 was independently positively associated with impaired fasting glucose(AOR=1.571,95%CI: 1.042-2.367).2.For normal control women as a reference,the risk of cesarean section was increased by 2.621-fold(95%CI: 1.529-4.491)in women with impaired fasting glucose,and the risk of intrahepatic cholestasis during pregnancy was increased by 3.303-fold(95%CI: 1.358-8.030)in women with impaired glucose tolerance.However,there was no significant association between abnormal glucose metabolism and pregnancy induced hypertension.At the same time,the statistical analysis of adverse pregnancy outcomes in offspring(including preterm,macrosomia,low body weight infant,large for gestational age,small for gestational age)did not find a significant relationship with impaired fasting glucose and impaired glucose tolerance.3.In normal control pregnant women,rs10830963 was an independent risk factor for pregnancy induced hypertension(AOR=2.358,95%CI: 1.036-5.367),intrahepatic cholestasis during pregnancy(AOR=2.239,95%CI: 1.084-9.681)and cesarean section(AOR=1.364,95%CI: 1.013-1.838).4.In pregnant women with impaired fasting glucose,rs10830963 was an independent protective factor for large for gestational age(AOR=0.330,95%CI: 0.155-0.703),rs7172432 was an independent risk factor for large for gestational age(AOR=1.933,95%CI: 1.018-3.670),and rs16955379 was an independent protective factor for pregnancy induced hypertension(AOR=0.133,95%CI: 0.030-0.589).5.In pregnant women with impaired glucose tolerance,only rs16955379 was found to be an independent risk factor for pregnancy induced hypertension(AOR=3.143,95%CI: 1.119-8.238).ConclusionsIn GDM pregnant women with different abnormal glucose metabolism,there was a heterogeneous relationship between GDM genetic susceptible SNPs and adverse pregnancy outcomes.It suggested that stratified management and precise intervention can be carried out for GDM according to their classification and different SNPs,so as to improve the pregnancy outcome of GDM and reduce maternal and infant complications.Summary:Under the guidance of precision medicine,this study explored a new method for maternal and infant safety prediction of GDM based on the genetic variants of GDM,so as to provide a certain direction for early prevention and treatment of GDM and its complications.The research results will provide reference for the early detection,early intervention and accurate management of GDM,and are expected to have great significance for improving maternal and infant outcomes of GDM and preventing the occurrence of long-term diseases in the two generations from the source.