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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.
Afiliação
  • Wu Q; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Chen Y; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Zhou M; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Liu M; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Zhang L; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Liang Z; Obstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, 1 Xueshi Road, Hangzhou, 310006, China.
  • Chen D; Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Los Angeles, United States of America.
Diabetol Metab Syndr ; 14(1): 15, 2022 Jan 24.
Article em En | MEDLINE | ID: mdl-35073990
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article