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. 2023 Dec 12;14:1283539. doi: 10.3389/fendo.2023.1283539

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Copyright © 2023 Li, Wang, Yang, Zhong, Huang, Liang and Yu

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.

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Establishment and validation of nomogram model for predicting GDM risk. (A) A nomogram model constructed using FPG, HbA1c, DBP, and rs1595066, based on binary logistic regression. The value of each variable was scored on a point scale from 0 to 100, after which the scores for each variable were added. That sum is located on the total points axis, which enables us to predict the probability of GDM risk; (B) the calibration curve was evaluated by Hosmer–Lemeshow goodness-of-fit testing with a p-value of 0.837; (C) the area under the receiver operating characteristic curve (ROC) and the scoring system for identifying GDM; (D) the decision curve analysis (DCA) with higher clinical bet benefit in probability threshold in 0.12 to 0.82.