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Obstetrics

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors

, , , , , & show all
Article: 2372665 | Received 12 Jan 2023, Accepted 21 Jun 2024, Published online: 04 Jul 2024

Figures & data

Figure 1. Flowchart.

Figure 1. Flowchart.

Table 1. Basic characteristics of women.

Table 2. Multivariate logistic regression analysis results.

Table 3. Strength of association between SNPs and postpartum diabetes mellitus among women with gestational diabetes mellitus history.

Figure 2. Bayesian model with incorporation of traditional and genetic factors for predicting GDM. BP: blood pressure; CHOL: cholesterol; GDM: gestational diabetes mellitus; IGT: impaired glucose tolerance; LDL: low-density lipoprotein. rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657 are SNP loci.

Figure 2. Bayesian model with incorporation of traditional and genetic factors for predicting GDM. BP: blood pressure; CHOL: cholesterol; GDM: gestational diabetes mellitus; IGT: impaired glucose tolerance; LDL: low-density lipoprotein. rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657 are SNP loci.

Figure 3. Receiver operating characteristic curves of the model. (A) Internal evaluation; (B) external validation.

Figure 3. Receiver operating characteristic curves of the model. (A) Internal evaluation; (B) external validation.

Figure 4. Calibration curves of the model. (A) Internal evaluation; (B) external validation.

Figure 4. Calibration curves of the model. (A) Internal evaluation; (B) external validation.
Supplemental material

Supplemental Material

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Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.