Elevated blood pressure in pregnant women with gestational diabetes according to the WHO criteria: importance of overweight

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

Introduction

Gestational diabetes mellitus (GDM) is a common complication during pregnancy that poses considerable risks to both maternal and fetal health. However, its effect on cardiac autonomic function, measured by heart rate variability (HRV), remains uncertain. This study aims to investigate potential alterations in cardiac autonomic function in women diagnosed with GDM.

Methods

In this cross-sectional study, 80 Kazakh pregnant women in their third trimester with GDM were enrolled from the endocrinology department of Aktobe Medical Center between January and April 2023. A control group of 30 third-trimester pregnant women without GDM was also selected from outpatient clinics in Aktobe City. HRV was measured with participants in a seated position. A nomogram was developed to predict GDM risk, integrating relevant parameters associated with the condition.

Results

Women with GDM were found to be older than those in the control group (p=0.005), though there were no significant differences in education level, employment status, or parity between the two groups. GDM was associated with larger fetal size (p=0.035) and a higher incidence of miscarriages and abortions (p<0.05) compared to the control group. Additionally, obesity was more prevalent among women with GDM (p<0.05). HRV parameters showed no significant differences between the GDM group and healthy pregnant women. The nomogram demonstrated good predictive accuracy, with an area under the curve of 0.7847 in the training cohort.

Conclusion

The nomogram developed in this study may prove useful for clinicians and patients in making informed clinical decisions and assessing outcomes. Notably, no significant differences in HRV were observed between women with uncomplicated pregnancies and those with GDM.

Sensitive months for green spaces' impact on macrosomia and interaction with air pollutants: A birth cohort study

Macrosomia poses significant health risks to mother and fetuses, yet the protective sensitive window for the effects of green space resources on the risk of macrosomia remains unexplored. This study identified sensitive windows of green space exposure and examined the interactions with air pollutants. In a study of 221,380 full-term newborns delivered at the Hospital, from 2017 to 2021, Normalized Difference Vegetation Index (NDVI) and atmospheric pollutant concentrations were matched to participants based on their residences in the Ningxia region. A Cox proportional hazards model was utilized to estimate the association between green space exposure and macrosomia and analyze the differences between the macrosomia (<4500 g) and macrosomia (≥4500 g) groups. Green space exposure for each month of pregnancy was employed to identify possible sensitive windows. Possible interactions between green spaces and air pollutants were tested on additive and multiplicative scales. Across 250, 500, 1000, and 2000-m buffers, increased NDVI exposure and range throughout the pregnancy were linked to a lower macrosomia risk, with the strongest association in the macrosomia (≥4500 g) group. The key window for the protective effect of green spaces was in late pregnancy, with the most pronounced protective effect noted in the 9th month of pregnancy. We also found a consistent combined effect between low green space and the air pollutants (NO2 and SO2). The research highlights the beneficial impact of increased green space during late pregnancy and the combined effect of low green space and elevated air pollutant levels on macrosomia risk, which can support government initiatives in urban green space development and public health protection.

Serum Ferritin Combined with Glycated Hemoglobin for Early Prediction of Gestational Diabetes Mellitus: A Retrospective Cohort Study

OBJECTIVE

To investigate the value of serum ferritin (SF) in conjunction with glycated hemoglobin (HbA1c) for the early prediction of gestational diabetes mellitus (GDM) and to provide insights that could enhance health care standards for women and newborns.

METHODS

A retrospective cohort study was conducted involving 650 pregnant women who received regular prenatal check-ups at our institution from January 2019 to April 2024. Participants were categorized into four groups based on their SF concentration quartiles during the 11th to 13th weeks of gestation. Logistic regression analyses were conducted to assess the predictive value of early GDM risk factors, with the lowest quartile group serving as a reference.

RESULTS

The incidence rate of GDM rose progressively with increasing SF concentrations at 11-13 weeks of gestation, with rates of 18.79%, 21.25%, 24.38%, and 25.45% respectively. Notably, the incidence rate in the highest quartile group (quartile 4) was significantly higher compared to the lowest (quartile 1), with an odds ratio of 1.48 and a 95% confidence interval of 1.12 to 1.93. Additionally, the predictive model incorporating both SF concentration and HbA1c (Model 2) outperformed the model with SF alone (Model 1), indicating a heightened predictive accuracy for GDM when these two biomarkers are used in combination.

CONCLUSION

The findings of this study highlight the potential utility of SF and HbA1c as early predictors of GDM risk, especially when employed in combination.

Novel insights into the genetic architecture of pregnancy glycemic traits from 14,744 Chinese maternities

Glycemic traits are critical indicators of maternal and fetal health during pregnancy. We performed genetic analysis for five glycemic traits in 14,744 Chinese pregnant women. Our genome-wide association study identified 25 locus-trait associations, including established links between gestational diabetes mellitus (GDM) and the genes CDKAL1 and MTNR1B. Notably, we discovered a novel association between fasting glucose during pregnancy and the ESR1 gene (estrogen receptor), which was validated by an independent study in pregnant women. The ESR1-GDM link was recently reported by the FinnGen project. Our work enhances the findings in East Asian populations and highlights the need for independent studies. Further analyses, including genetic correlation, Mendelian randomization, and transcriptome-wide association studies, provided genetic insights into the relationship between pregnancy glycemic traits and hypertension. Overall, our findings advance the understanding of genetic architecture of pregnancy glycemic traits, especially in East Asian populations.

Impact of postpartum weight change on metabolic syndrome and its components among women with recent gestational diabetes mellitus

BACKGROUND

While postpartum weight changes may affect the levels of metabolic parameters, the direct effects of weight changes in the postpartum period on changes in the prevalence rates of metabolic syndrome and its components remain unstudied. This study aimed to investigate the effects of postpartum weight changes between 6 weeks and 6 months on changes in the prevalence rates of metabolic syndrome and its components in women who have recently experienced gestational diabetes mellitus.

METHODS

This prospective cohort study included 171 postpartum women with recent gestational diabetes mellitus, who underwent serial weight and metabolic risk factor assessments at 6 weeks and 6 months postpartum. Weight changes between these time points were classified as weight loss (> 2 kg), weight stability (± 2 kg), or weight gain (> 2 kg). Metabolic syndrome comprised the following metabolic risk factors: large waist circumference, elevated blood pressure, elevated fasting plasma glucose levels, high triglyceride levels, and low high-density lipoprotein cholesterol levels.

RESULTS

Of the 171 women in our cohort, 30 women (17.5%) lost > 2 kg of body weight, while 85 (49.7%) maintained a stable weight and 56 (32.8%) gained > 2 kg. The weight loss group experienced significant changes in the prevalence rates of the following metabolic risk factors compared to the weight stability and weight gain groups: large waist circumference (% change: - 26.7 vs - 5.9 vs 5.4, respectively; p = 0.004), elevated fasting plasma glucose levels (% change: - 3.4 vs 18.9 vs 26.8, respectively; p = 0.022), and high triglyceride levels (% change: - 30.0 vs 0 vs - 7.2, respectively; p = 0.024). A significantly greater decrease in the prevalence of metabolic syndrome was also found in the weight loss group than in the other two groups (% change: - 20.0 vs 11.8 vs 14.2, respectively; p = 0.002).

CONCLUSIONS

Weight changes from 6 weeks to 6 months postpartum significantly altered the prevalence rates of metabolic syndrome and its components in women with recent gestational diabetes mellitus. Early postpartum weight loss can reverse metabolic risk factors and reduce the prevalence of metabolic syndrome.

TRIAL REGISTRATION

Thai Clinical Trials Registry: Registration no. TCTR20200903001. Date of registration: September 3, 2020. Date of initial participant enrolment: September 7, 2020.

Psychometric evaluation of a novel tool for assessing gestational diabetes and hypertension care: knowledge, attitudes, and practices of midwives and nurses

While standardized assessment of knowledge, attitudes, and practices (KAP) related to gestational diabetes and hypertension is possible with a valid tool, existing research remains limited. This prospective validation study aimed to develop and validate a novel tool to assess the KAP of midwives and obstetric nurses. We included 125 midwives and obstetric nurses who routinely care for patients with gestational diabetes and hypertension. The tool demonstrated good internal consistency (Cronbach's alpha): knowledge (0.729, 95% CI, 0.654-0.776), attitude (0.756, 95% CI, 0.690-0.814), and practices (0.925, 95% CI, 0.905-0.943). Difficulty indices (d) ranged from 0.38 to 0.99 (knowledge), 0.41 to 0.99 (attitudes), and 0.41 to 0.93 (practices), indicating appropriate item difficulty. Discrimination indices (D) confirmed items could differentiate between respondents with low and high knowledge levels (D range: 0.02-0.77 for knowledge, 0.06-0.64 for attitudes, 0.20-0.84 for practices). The robust psychometric properties of this tool support its use in future research on KAP related to diabetes and gestational hypertension management in midwives and nurses. This instrument has the potential to be valuable in various settings, including baseline assessment before educational programs or evaluation of learning outcomes after interventions.

Serotonin and Interleukin 10 Can Influence the Blood and Urine Viscosity in Gestational Diabetes Mellitus and Pregnancy-Specific Urinary Incontinence

Serotonin and interleukin 10 (IL-10) may play a role in gestational diabetes mellitus. Hyperglycemic environment, the detrusor musculature of the bladder and pelvic floor muscles may become damaged, leading to urination problems and urine viscosity in pregnant women with gestational diabetes mellitus and pregnancy-specific urinary incontinence. Urine and blood samples were collected from pregnant women between 24 and 28 weeks of gestation. The serotonin concentration and cytokine IL-10 levels were evaluated in plasma and urine. In the total blood and urine, the viscosity was evaluated in the presence and absence of exogenous serotonin and IL-10. The plasma serotonin levels decreased, while the urine serotonin levels increased in the normoglycemic incontinent (NG-I), hyperglycemic continent (GDM-C), and hyperglycemic incontinent (GDM-I) groups. The IL-10 in the plasma decreased in the GDM-I group and was higher in the urine in the NG-I and GDM-I groups. The blood viscosity was higher, independently of urinary incontinence, in the GDM groups. The serotonin increased the blood viscosity from women with GDM-C and urine in the NG-I, GDM-C, and GDM-I groups. Blood and urine in the presence of IL-10 showed a similar viscosity in all groups studied. Also, no difference was observed in the viscosity in either the blood or urine when in the presence of serotonin and IL-10. These findings suggest that serotonin and IL-10 have the potential to reduce blood viscosity in pregnant women with gestational diabetes and specific urinary incontinence, maintaining values similar to those in normoglycemic women's blood.

Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm

OBJECTIVE

To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.

METHODS

A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models.

RESULTS

A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none.

CONCLUSIONS

The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.