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An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China

OBJECTIVES: To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy. METHODS: Four hundred seventy five women with GDM and 487 women without, as a control, w...

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Autores principales: Wu, Qi, Chen, Yanmin, Zhou, Menglin, Liu, Mengting, Zhang, Lixia, Liang, Zhaoxia, Chen, Danqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785509/
https://www.ncbi.nlm.nih.gov/pubmed/35073990
http://dx.doi.org/10.1186/s13098-022-00788-y
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author Wu, Qi
Chen, Yanmin
Zhou, Menglin
Liu, Mengting
Zhang, Lixia
Liang, Zhaoxia
Chen, Danqing
author_facet Wu, Qi
Chen, Yanmin
Zhou, Menglin
Liu, Mengting
Zhang, Lixia
Liang, Zhaoxia
Chen, Danqing
author_sort Wu, Qi
collection PubMed
description OBJECTIVES: To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy. METHODS: Four hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model. RESULTS: We found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, 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 with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690–0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort. CONCLUSIONS: Based 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 population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-022-00788-y.
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spelling pubmed-87855092022-01-24 An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China Wu, Qi Chen, Yanmin Zhou, Menglin Liu, Mengting Zhang, Lixia Liang, Zhaoxia Chen, Danqing Diabetol Metab Syndr Research OBJECTIVES: To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy. METHODS: Four hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model. RESULTS: We found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, 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 with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690–0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort. CONCLUSIONS: Based 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 population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-022-00788-y. BioMed Central 2022-01-24 /pmc/articles/PMC8785509/ /pubmed/35073990 http://dx.doi.org/10.1186/s13098-022-00788-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wu, Qi
Chen, Yanmin
Zhou, Menglin
Liu, Mengting
Zhang, Lixia
Liang, Zhaoxia
Chen, Danqing
An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title_full An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title_fullStr An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title_full_unstemmed An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title_short An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China
title_sort early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785509/
https://www.ncbi.nlm.nih.gov/pubmed/35073990
http://dx.doi.org/10.1186/s13098-022-00788-y
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