BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestationa...BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.展开更多
Background:Large-for-gestational age(LGA)newborns can increase the risk of metabolic syndrome.Previous studies have shown that the levels of maternal blood lipids,connecting peptide(C-peptide),insulin and glycosylated...Background:Large-for-gestational age(LGA)newborns can increase the risk of metabolic syndrome.Previous studies have shown that the levels of maternal blood lipids,connecting peptide(C-peptide),insulin and glycosylated hemoglobin(HbA_(1c))were significantly different between LGA and appropriate-for-gestational age(AGA)newborns.This study aimed to determine the effect of the levels of maternal lipids,C-peptide,insulin,and HbA_(1c) during late pregnancy on LGA newborns.Methods:This study comprised 2790 non-diabetic women in late pregnancy.Among their newborns,2236(80.1%)newborns were AGA,and 554(19.9%)newborns were LGA.Maternal and neonatal characteristics were obtained from questionnaires and their case records.The levels of maternal fasting serum apolipoprotein A1(ApoA1),triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),C-peptide,insulin and blood HbA_(1c) were measured.The chi-square and Mann-Whitney U test were used to analyze categorical variables and continuous variables between the AGA and LGA groups,respectively.Binary logistic regression analysis was made to determine the independent risk factors for LGA newborns.Results:Maternal TG,C-peptide,insulin and HbA_(1c) levels were signifi cantly higher in the LGA group than in the AGA group(P<0.05).The LGA group had signifi cantly lower levels of maternal TC,HDL-C and LDL-C than the AGA group(P<0.05).After adjustment for confounding variables,including maternal age,pre-pregnancy body mass index,education,smoking,annual household income,amniotic fluid volume,gestational hypertension,newborn gender and gestational age at blood collection,high maternal TG levels remained signifi cantly associated with LGA newborns(P<0.05).Conclusion:High maternal TG level during late pregnancy is signifi cantly associated with LGA newborns.展开更多
基金Supported by National Natural Science Foundation of China,No.81870546Nanjing Medical Science and Technique Development Foundation,No.YKK23151Science and Technology Development Foundation Item of Nanjing Medical University,No.NMUB20210117.
文摘BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.
基金supported by grants from the"11th Five-Year Plan"and the"12th Five-Year Plan"from the National Science and Technology Issues Research,China(2009BAI80B03,2012BAI02B03)the Innovation Program for Early Screening and Intervention of Birth Defects,Zhejiang Province(2010R50045)the National Key Scientifi c Research Projects of China(973 Program)(2012CB944900).
文摘Background:Large-for-gestational age(LGA)newborns can increase the risk of metabolic syndrome.Previous studies have shown that the levels of maternal blood lipids,connecting peptide(C-peptide),insulin and glycosylated hemoglobin(HbA_(1c))were significantly different between LGA and appropriate-for-gestational age(AGA)newborns.This study aimed to determine the effect of the levels of maternal lipids,C-peptide,insulin,and HbA_(1c) during late pregnancy on LGA newborns.Methods:This study comprised 2790 non-diabetic women in late pregnancy.Among their newborns,2236(80.1%)newborns were AGA,and 554(19.9%)newborns were LGA.Maternal and neonatal characteristics were obtained from questionnaires and their case records.The levels of maternal fasting serum apolipoprotein A1(ApoA1),triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),C-peptide,insulin and blood HbA_(1c) were measured.The chi-square and Mann-Whitney U test were used to analyze categorical variables and continuous variables between the AGA and LGA groups,respectively.Binary logistic regression analysis was made to determine the independent risk factors for LGA newborns.Results:Maternal TG,C-peptide,insulin and HbA_(1c) levels were signifi cantly higher in the LGA group than in the AGA group(P<0.05).The LGA group had signifi cantly lower levels of maternal TC,HDL-C and LDL-C than the AGA group(P<0.05).After adjustment for confounding variables,including maternal age,pre-pregnancy body mass index,education,smoking,annual household income,amniotic fluid volume,gestational hypertension,newborn gender and gestational age at blood collection,high maternal TG levels remained signifi cantly associated with LGA newborns(P<0.05).Conclusion:High maternal TG level during late pregnancy is signifi cantly associated with LGA newborns.
