Pregnancy outcome in women with gestational diabetes - A longitudinal study of changes in demography and treatment modalities

Third trimester HbA1c and the association with large-for-gestational-age neonates in women with gestational diabetes

Objective

To evaluate the association between HbA1c levels measured in the third trimester and the risk for large for gestational age (LGA) in neonates of mothers affected by gestational diabetes mellitus (GDM). Secondarily, we aimed to identify an ideal cut-off for increased risk of LGA amongst pregnant women with GDM.

Methods

Observational retrospective review of singleton pregnant women with GDM evaluated in a diabetes and pregnancy clinic of a tertiary and academic hospital. From January/2011 to December/2017, 1,085 pregnant women underwent evaluation due to GDM, of which 665 had an HbA1c test in the third trimester. A logistic regression model was performed to evaluate predictors of LGA. A receiver-operating-characteristic (ROC) curve was used to evaluate the predictive ability of third trimester HbA1c for LGA identification.

Results

A total of 1,085 singleton pregnant women were evaluated during the study period, with a mean age of 32.9 ± 5.3 years. In the multivariate analysis, OGTT at 0 minutes (OR: 1.040; CI 95% 1.006-1.076, p = 0.022) and third trimester HbA1c (OR: 4.680; CI 95% 1.210-18.107, p = 0.025) were associated with LGA newborns. Using a ROC curve to evaluate the predictive ability of third trimester HbA1c for LGA identification, the optimal HbA1c cut-off point was 5.4% where the sensitivity was 77.4% and the specificity was 71.7% (AUC 0.782; p < 0.001).

Conclusion

Few studies in the Mediterranean population have evaluated the role of HbA1c in predicting neonatal complications in women with GDM. A third trimester HbA1c > 5.4% was found to have good sensitivity and specificity for identifying the risk of LGA.

Hyperglycemia in pregnancy: sleep alterations, comorbidities and pharmacotherapy

OBJECTIVE

To investigate sleep alterations and associated factors in pregnant diabetic women (n=141).

METHODS

Sleep profile, sociodemographics and clinical information were collected. Poor sleep quality (Pittsburgh Sleep Quality Index >5) and excessive daytime sleepiness (Epworth Sleepiness Scale ≥10), sleep duration (h), sleep latency (min), frequent sleep interruption and short sleep (≤6 h) were assessed in type 1 diabetes mellitus (16.3%), type 2 diabetes mellitus (25.5%) and gestational diabetes mellitus (58.2%).

RESULTS

Poor sleep quality was found in 58.8% of patients and daytime sleepiness in 25.7%, regardless of hyperglycemia etiology. No correlation existed between daytime sleepiness and poor sleep quality (Pearson correlation r=0.02, p=0.84). Short sleep duration occurred in 1/3 of patients (31.2%). Sleep interruptions due to frequent urination affected 72% of all and sleep interruptions due to any cause 71.2%. Metformin was used by 65.7% of type 2 diabetes mellitus and 28.7% of gestational diabetes mellitus. In gestational diabetes mellitus, parity number was independently associated with poor sleep quality (p=0.02; OR=1.90; 95%CI 1.07-3.36) and metformin use was also independently associated with poor sleep quality (p=0.03; OR=2.36; 95%CI 1.05-5.29).

CONCLUSIONS

Our study originally shows that poor sleep quality and excessive daytime sleepiness are frequent in diabetic pregnancy due to different etiologies. Interestingly, only in gestational diabetes mellitus, metformin therapy and higher parity were associated with poor sleep quality.

Reduced glycodeoxycholic acid levels are associated with negative clinical outcomes of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is characterized by glycemia and insulin disorders. Bile acids (BAs) have emerged as vital signaling molecules in glucose metabolic regulation. BA change in GDM is still unclear, which exerts great significance to illustrate the change of BAs in GDM. GDM patients and normal pregnant women were enrolled during the oral glucose tolerance test (OGTT) screening period. Fasting serums were sampled for the measurement of BAs. BA metabolism profiles were analyzed in both pregnant women with GDM and those with normal glucose tolerance (NGT). Delivery characteristics, delivery gestational age, and infant birthweight were extracted from medical records. GDM patients presented distinctive features compared with NGT patients, including higher body mass index (BMI), elevated serum glucose concentration, raised insulin (both fasting and OGTT), and increased hemoglobin A1c (HbA1c) levels. Higher homeostasis model assessment of insulin resistance (HOMA-IR) and decreased β-cell compensation (i.e., oral disposition index (DIo)) were also prevalent in this group. Total BAs (TBAs) remained stable, but glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) levels declined significantly in GDM. GDCA was inversely correlated with HOMA-IR and positively correlated with DIo. No obvious differences in clinical outcome between the GDM and NGT groups were observed. However, GDM patients with high HOMA-IR and low DIo tended to have a higher cesarean delivery rate and younger delivery gestational age. In conclusion, GDCA provides a valuable biomarker to evaluate HOMA-IR and DIo, and decreased GDCA levels predict poorer clinical outcomes for GDM.

Predictability of Macrosomic Birth based on Maternal Factors and Fetal Aneuploidy Screening Biochemical Markers in Hyperglycemic Mothers

Background: Macrosomic birth weight has been implicated as a significant risk factor for developing various adult metabolic diseases such as diabetes mellitus and coronary heart diseases; it has also been associated with higher incidences of complicated births. This study aimed to examine the predictability of macrosomic births in hyperglycemic pregnant women using maternal clinical characteristics and serum biomarkers of aneuploidy screening performed in the first half of pregnancy. Methods: A retrospective observational study was performed on a cohort of 1,668 pregnant women who 1) had positive outcomes after undergoing 50-g oral glucose challenge test (OGCT) at two university-based hospitals and 2) underwent any one of the following maternal biomarker screening tests for fetal aneuploidy: triple test, quadruple test, and integrated test. Logistic regression-based models for predicting macrosomic births using maternal characteristics and serum biomarkers were developed and evaluated for prediction power. A nomogram, which is a graphical display of the best predictable model, was then generated. Results: The study cohort included 157 macrosomic birth cases defined as birth weight ≥3,820 g, which was equivalent to the top 10 percentile of the modeling cohort. Three primary models solely based on serum biomarkers achieved area under curves (AUCs) of 0.55-0.62. Expanded models, including maternal demographic and clinical factors, demonstrated an improved performance by 25% (AUCs, 0.69-0.73). Conclusion: Our prediction models will help to identify pregnancies with an elevated risk of macrosomic births in hyperglycemic mothers using maternal clinical factors and serum markers from routine antenatal screening tests. Prediction of macrosomic birth at mid-pregnancy may allow customized antenatal care to reduce the risk of macrosomic births.