Early pregnancy hemoglobin is associated with the risk of gestational diabetes mellitus: a retrospective cohort study

Role of placental barrier on trace element transfer in maternal fetal system and hypertensive disorders complicating pregnancy and gestational diabetes mellitus

BACKGROUND

Hypertensive disorders complicating pregnancy (HDCP) and gestational diabetes mellitus (GDM) can affect the placental barrier function to varying degrees. However, current studies show that the transfer and distribution characteristics of trace elements in the maternal-fetal system are still unclear. This study investigated the effect of the placental barrier on the transfer of trace elements from mother to fetus and its relationship with HDCP and GDM.

METHODS

A case-control method was used in this study. 140 pairs of samples were collected; 60 were from healthy pregnant women, and 80 were from patients with pregnancy complications. The contents of trace elements in paired samples were determined by inductively coupled plasma-mass spectrometry (ICP-MS). SPSS software was used to analyze the differences in trace element levels in matched samples of each group. The correlations were analyzed based on Pearson's correlation factor (r).

RESULTS

The distribution characteristics of Fe content in the pathological group (HDCP group and GDM group) were the same as those in the normal group (umbilical cord blood > maternal blood > placenta), but there was no significant difference in the iron content in maternal blood and cord blood of pathological group. The distribution characteristics of Mn content in the pathological group (placenta > umbilical cord blood > maternal blood) were changed compared with those in the normal group (placenta > maternal blood > umbilical cord blood). In addition, the placental Cr content and cord blood Cr and Ni content of the pathological group were higher than those of the normal group. HDCP placental Cr and GDM placental Fe levels were significantly correlated with the Apgar score.

CONCLUSIONS

The transfer of Fe and Mn and the placental barrier function of Cr and Ni in the maternal-fetal system of HDCP and GDM are significantly altered, which directly or indirectly increases the maternal and fetal health risk.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM.

AIM

To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention.

METHODS

The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified.

RESULTS

Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (P < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model (P < 0.05).

CONCLUSION

The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.