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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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 |
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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 |
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