Cargando…

A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy

OBJECTIVE: Gestational diabetes mellitus (GDM) is a serious threat to maternal and child health. However, there isn’t a standard predictive model for the disorder in early pregnancy. This study is to investigate the association of blood indexes with GDM and establishes a practical predictive model i...

Descripción completa

Detalles Bibliográficos
Autores principales: Kang, Mei, Zhang, Hui, Zhang, Jia, Huang, Kaifeng, Zhao, Jinyan, Hu, Jie, Lu, Cong, Shao, Jiashen, Weng, Jianrong, Yang, Yuemin, Zhuang, Yan, Xu, Xianming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695875/
https://www.ncbi.nlm.nih.gov/pubmed/34956091
http://dx.doi.org/10.3389/fendo.2021.779210
_version_ 1784619678436425728
author Kang, Mei
Zhang, Hui
Zhang, Jia
Huang, Kaifeng
Zhao, Jinyan
Hu, Jie
Lu, Cong
Shao, Jiashen
Weng, Jianrong
Yang, Yuemin
Zhuang, Yan
Xu, Xianming
author_facet Kang, Mei
Zhang, Hui
Zhang, Jia
Huang, Kaifeng
Zhao, Jinyan
Hu, Jie
Lu, Cong
Shao, Jiashen
Weng, Jianrong
Yang, Yuemin
Zhuang, Yan
Xu, Xianming
author_sort Kang, Mei
collection PubMed
description OBJECTIVE: Gestational diabetes mellitus (GDM) is a serious threat to maternal and child health. However, there isn’t a standard predictive model for the disorder in early pregnancy. This study is to investigate the association of blood indexes with GDM and establishes a practical predictive model in early pregnancy for GDM. METHODS: This is a prospective cohort study enrolling 413 pregnant women in the department of Obstetrics and Gynecology in Shanghai General Hospital from July 2020 to April 2021.A total of 116pregnantwomen were diagnosed with GDM during the follow-up. Blood samples were collected at early trimester (gestational weeks 12-16) and second trimester(gestational weeks 24-26 weeks). A predictive nomogram was established based on results of the multivariate logistic model and 5-fold cross validation. We evaluate the nomogram by the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCAs). RESULTS: Significant differences were observed between the GDM and normal controls among age, pre-pregnancy BMI, whether the pregnant women with complications, the percentage of B lymphocytes, fasting plasma glucose (FPG), HbA1c, triglyceride and the level of progesterone in early trimester. Risk factors used in nomogram included age, pre-pregnancy BMI, FPG, HbA1c, the level of IgA, the level of triglyceride, the percentage of B lymphocytes, the level of progesterone and TPOAb in early pregnancy. The AUC value was 0.772, 95%CI (0.602,0.942). The calibration curves for the probability of GDM demonstrated acceptable agreement between the predicted outcomes by the nomogram and the observed values. DCA curves showed good positive net benefits in the predictive model. CONCLUSIONS: A novel predictive nomogram was developed for GDM in our study, which could do help to patient counseling and management during early pregnancy in clinical practice.
format Online
Article
Text
id pubmed-8695875
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-86958752021-12-24 A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy Kang, Mei Zhang, Hui Zhang, Jia Huang, Kaifeng Zhao, Jinyan Hu, Jie Lu, Cong Shao, Jiashen Weng, Jianrong Yang, Yuemin Zhuang, Yan Xu, Xianming Front Endocrinol (Lausanne) Endocrinology OBJECTIVE: Gestational diabetes mellitus (GDM) is a serious threat to maternal and child health. However, there isn’t a standard predictive model for the disorder in early pregnancy. This study is to investigate the association of blood indexes with GDM and establishes a practical predictive model in early pregnancy for GDM. METHODS: This is a prospective cohort study enrolling 413 pregnant women in the department of Obstetrics and Gynecology in Shanghai General Hospital from July 2020 to April 2021.A total of 116pregnantwomen were diagnosed with GDM during the follow-up. Blood samples were collected at early trimester (gestational weeks 12-16) and second trimester(gestational weeks 24-26 weeks). A predictive nomogram was established based on results of the multivariate logistic model and 5-fold cross validation. We evaluate the nomogram by the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCAs). RESULTS: Significant differences were observed between the GDM and normal controls among age, pre-pregnancy BMI, whether the pregnant women with complications, the percentage of B lymphocytes, fasting plasma glucose (FPG), HbA1c, triglyceride and the level of progesterone in early trimester. Risk factors used in nomogram included age, pre-pregnancy BMI, FPG, HbA1c, the level of IgA, the level of triglyceride, the percentage of B lymphocytes, the level of progesterone and TPOAb in early pregnancy. The AUC value was 0.772, 95%CI (0.602,0.942). The calibration curves for the probability of GDM demonstrated acceptable agreement between the predicted outcomes by the nomogram and the observed values. DCA curves showed good positive net benefits in the predictive model. CONCLUSIONS: A novel predictive nomogram was developed for GDM in our study, which could do help to patient counseling and management during early pregnancy in clinical practice. Frontiers Media S.A. 2021-12-09 /pmc/articles/PMC8695875/ /pubmed/34956091 http://dx.doi.org/10.3389/fendo.2021.779210 Text en Copyright © 2021 Kang, Zhang, Zhang, Huang, Zhao, Hu, Lu, Shao, Weng, Yang, Zhuang and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Kang, Mei
Zhang, Hui
Zhang, Jia
Huang, Kaifeng
Zhao, Jinyan
Hu, Jie
Lu, Cong
Shao, Jiashen
Weng, Jianrong
Yang, Yuemin
Zhuang, Yan
Xu, Xianming
A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title_full A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title_fullStr A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title_full_unstemmed A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title_short A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy
title_sort novel nomogram for predicting gestational diabetes mellitus during early pregnancy
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8695875/
https://www.ncbi.nlm.nih.gov/pubmed/34956091
http://dx.doi.org/10.3389/fendo.2021.779210
work_keys_str_mv AT kangmei anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhanghui anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhangjia anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT huangkaifeng anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhaojinyan anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT hujie anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT lucong anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT shaojiashen anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT wengjianrong anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT yangyuemin anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhuangyan anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT xuxianming anovelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT kangmei novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhanghui novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhangjia novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT huangkaifeng novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhaojinyan novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT hujie novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT lucong novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT shaojiashen novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT wengjianrong novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT yangyuemin novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT zhuangyan novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy
AT xuxianming novelnomogramforpredictinggestationaldiabetesmellitusduringearlypregnancy