Open Access

A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings

  • Authors:
    • Ya‑Zhong Zhang
    • Lei Zhou
    • Luobing Tian
    • Xin Li
    • Guyue Zhang
    • Jiang‑Yuan Qin
    • Dan‑Dan Zhang
    • Hui Fang
  • View Affiliations

  • Published online on: April 27, 2020     https://doi.org/10.3892/etm.2020.8690
  • Pages: 293-300
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Although previous studies have proposed predictive models of gestational diabetes mellitus (GDM) based on maternal status, they do not always provide reliable results. The present study aimed to create a novel model that included ultrasound data of maternal fat distribution and serum inflammatory factors. The clinical data of 1,158 pregnant women treated at Tangshan Gongren Hospital and eight other flagship hospitals in Tangshan, including the First Hospital of Tangshan Gongren Hospital group, Ninth Hospital of Tangshan Gongren Hospital group, Tangshan Gongren Hospital group rehabilitation hospital, Tangshan railway central hospital, Tangshan Gongren Hospital group Fengnan hospital, Tangshan Gongren Hospital group Qianan Yanshan hospital, Tangshan Gongren Hospital group Qianxi Kangli hospital and Tangshan Gongren Hospital group Jidong Sub‑hospital, were analyzed following the division of subjects into GDM and non‑GDM groups according to their diagnostic results at 24‑28 weeks of pregnancy. Univariate analysis was performed to investigate the significance of the maternal clinical parameters for GDM diagnosis and a GDM prediction model was established using stepwise regression analysis. The predictive value of the model was evaluated using a Homer‑Lemeshow goodness‑of‑fit test and a receiver operating characteristic curve (ROC). The model demonstrated that age, pre‑pregnancy body mass index, a family history of diabetes mellitus, polycystic ovary syndrome, a history of GDM, high systolic pressures, glycosylated hemoglobin levels, triglyceride levels, total cholesterol levels, low‑density lipoprotein cholesterol levels, serum hypersensitive C‑reactive protein, increased subcutaneous fat thickness and visceral fat thickness were all correlated with an increased GDM risk (all P<0.01). The area under the curve value was 0.911 (95% CI, 0.893‑0.930). Overall, the results indicated that the current model, which included ultrasound and serological data, may be a more effective predictor of GDM compared with other single predictor models. In conclusion, the present study developed a tool to determine the risk of GDM in pregnant women during the second trimester. This prediction model, based on various risk factors, demonstrated a high predictive value for the GDM occurrence in pregnant women in China and may prove useful in guiding future clinical practice.
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July-2020
Volume 20 Issue 1

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Copy and paste a formatted citation
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Spandidos Publications style
Zhang YZ, Zhou L, Tian L, Li X, Zhang G, Qin JY, Zhang DD and Fang H: A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings. Exp Ther Med 20: 293-300, 2020
APA
Zhang, Y., Zhou, L., Tian, L., Li, X., Zhang, G., Qin, J. ... Fang, H. (2020). A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings. Experimental and Therapeutic Medicine, 20, 293-300. https://doi.org/10.3892/etm.2020.8690
MLA
Zhang, Y., Zhou, L., Tian, L., Li, X., Zhang, G., Qin, J., Zhang, D., Fang, H."A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings". Experimental and Therapeutic Medicine 20.1 (2020): 293-300.
Chicago
Zhang, Y., Zhou, L., Tian, L., Li, X., Zhang, G., Qin, J., Zhang, D., Fang, H."A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings". Experimental and Therapeutic Medicine 20, no. 1 (2020): 293-300. https://doi.org/10.3892/etm.2020.8690