A Cross-Sectional Analysis of Maternal Cardiac Autonomic Function in Kazakh Pregnant Women with Gestational Diabetes

Figures

• 

  Figure 1. Comparison of demographic characteristics between healthy pregnant women and pregnant women with GDM, including age (a), education (b), work status (c), number of pregnancies, excluding this pregnancy (d), previous occurrences of macrosomic fetuses (e), missed pregnancies (f), obesity (g), and mode of delivery (h). Lines above the columns show significant differences between columns. Statistically significant differences observed in age (p=0.005), previous occurrences of macrosomic fetuses (p=0.038), missed pregnancies (p=0.000), and obesity (p=0.000).

• 

  Figure 2. Comparison of average and standard deviation heart rate variability parameters between healthy pregnant women and those with gestational diabetes mellitus (GDM): heart rate (HR) (a), mean heart rate (b), standard deviation of normal-to-normal intervals (SDNN) (c), root mean square of successive differences (RMSSD) (d), proportion of NN50 intervals (pNN50) (e), low frequency power (LF) (f), high frequency power (HF) (g), very low frequency power (VLF) (h), and stress index (SI) (i). No significant differences in these heart rate variability metrics were detected between the two groups (p < 0.05).

• 

  Figure 3. Comparison of mean and standard deviation laboratory parameters between healthy pregnant women and those with gestational diabetes mellitus (GDM), including HbA1c (a), systolic (b), diastolic blood pressure (c), hemoglobin (d), RBC (e), and platelets (f). Line above the columns shows significant differences between columns. HbA1c levels were significantly higher in patients with GDM (p=0.000).

• 

  Figure 4. Nomogram for assessing the risk of gestational diabetes mellitus (GDM). This tool uses nine parameters: number of missed pregnancies, BMI before delivery, peak glycated hemoglobin, highest systolic and diastolic blood pressure, lowest red blood cell (RBC) count, stress index (SI), low frequency (LF), and very low frequency (VLF) to predict GDM risk. The differences in patient distribution among subgroups are illustrated by the size of the rectangles.

• 

  Figure 5. (A) Receiver Operating Characteristic (ROC) Curves for GDM Prediction. The ROC curve, along with the AUC value, was used to evaluate the model’s ability to discriminate between patients with and without GDM. The AUC of 0.7847 reflects the model’s performance. (B) Calibration Plots illustrate the alignment between the predicted probabilities of GDM from the nomogram and the actual observed outcomes in the cohort. The y-axis represents the observed cumulative incidence of GDM, while the x-axis shows the predicted probability based on the model. A line close to the ideal line indicates better predictive accuracy. Calibration was further evaluated using the Hosmer-Lemeshow chi-square statistic, which produced a value of −0.18642 (P = 1).