The Role of Placental Growth Factor in the Prediction of Carbohydrate and Thyroid Disorders during Pregnancy.
Vesselina YanachkovaRadiana StaynovaEmilia Krassimirova NasevaZdravko A KamenovPublished in: Medicina (Kaunas, Lithuania) (2022)
Background and objectives: To assess whether placental growth factor (PlGF) levels may have a predictive value for the onset of gestational diabetes mellitus (GDM) and thyroid dysfunction during pregnancy. Materials and Methods: This single-center retrospective analysis was conducted at the Specialized Hospital for Active Treatment in Obstetrics and Gynecology "Dr. Shterev", Sofia, Bulgaria, from December 2017 to December 2019. Using pregnant women's electronic records, we analyzed and compared the data of 412 women diagnosed with GDM and 250 women without evidence for carbohydrate disorders. Thyroid function was tested in all patients at the time of performing GDM screening. The following measurements were compared and assessed: body mass index (BMI), fasting blood glucose levels, thyroid-stimulating hormone levels (TSH), free thyroxine, and triiodothyronine (FT4 and FT3) levels, and serum placental growth factor (PlGF). The sensitivity and specificity of PlGF as a predictive marker for GDM and thyroid dysfunction were analyzed using receiver operating characteristic (ROC) curves. Results: There were no significant differences between GDM and control groups in terms of age and BMI ( p > 0.05). In patients with established GDM, the PlGF corrected multiple of the median (MoM) was significantly higher compared to the control group (0.9 vs. 0.7, p < 0.001). The ROC-AUC for the prediction of GDM and thyroid dysfunction during pregnancy was 0.68 (95% CI 0.64-0.72) and 0.61 (95% CI 0.57-0.65), respectively. Conclusions: Our results underscore the potential role of PlGF as a biomarker in the prediction and diagnosis of GDM and thyroid dysfunction during pregnancy.
Keyphrases
- growth factor
- body mass index
- blood glucose
- pregnant women
- oxidative stress
- polycystic ovary syndrome
- pregnancy outcomes
- healthcare
- type diabetes
- palliative care
- risk assessment
- emergency department
- weight gain
- insulin resistance
- blood pressure
- big data
- electronic health record
- glycemic control
- artificial intelligence
- human health