Prediction of insulin treatment in women with gestational diabetes mellitus

Do maternal haemodynamics have a causal influence on treatment for gestational diabetes?

BACKGROUND

Arterial stiffening is believed to contribute to the worsening of insulin resistance, and factors which are associated with needing pharmacological treatment of gestational diabetes (GDM), such as maternal obesity or advanced age, are associated with impaired cardiovascular adaptation to pregnancy. In this observational study, we aimed to investigate causal relationships between maternal haemodynamics and treatment requirement amongst women with GDM.

METHODS

We assessed maternal haemodynamics in women with GDM, comparing those who remained on dietary treatment with those who required pharmacological management. Maternal haemodynamics were assessed using the Arteriograph® (TensioMed Ltd, Budapest, Hungary) and the NICOM® non-invasive bio-reactance method (Cheetah Medical, Portland, Oregon, USA). A graphical causal inference technique was used for statistical analysis.

RESULTS

120 women with GDM were included in the analysis. Maternal booking BMI was identified as having a causative influence on treatment requirement, with each unit increase in BMI increasing the odds of needing metformin and/or insulin therapy by 12% [OR 1.12 (1.02 - 1.22)]. The raw values of maternal heart rate (87.6 ± 11.7 vs. 92.9 ± 11.90 bpm, p = 0.014) and PWV (7.8 ± 1.04 vs. 8.4 ± 1.61 m/s, p = 0.029) were both significantly higher amongst the women requiring pharmacological management, though these relationships did not remain significant in causal logistic regression.

CONCLUSIONS

Maternal BMI at booking has a causal, rather than simply associational, relationship on the need for pharmacological treatment of GDM. No significant causal relationships were found between maternal haemodynamics and the need for pharmacological treatment.

Promoting breastfeeding in women with gestational diabetes mellitus in high-income settings: an integrative review

BACKGROUND

Breastfeeding provides many short- and long-term health benefits for mothers and their infants and is a particularly relevant strategy for women who experience Gestational Diabetes Mellitus (GDM) during pregnancy. However, breastfeeding rates are generally lower amongst this group of women than the general population. This review's objective is to identify the factors that influence breastfeeding by exploring the experiences and outcomes of women in in high-income health care contexts when there is a history of GDM in the corresponding pregnancy.

METHODS

A comprehensive search strategy explored the electronic databases Medline, CINAHL, Web of Science and Scopus for primary studies exploring breastfeeding practices for papers published between January 2011 and June 2023. All papers were screened independently by two researchers with included papers assessed using the Crowe Critical Appraisal tool. Findings were analysed using a narrative synthesis framework.

RESULTS

From an initial search result of 1037 papers, 16 papers representing five high-income nations were included in this review for analysis - the United States of America (n = 10), Australia (n = 3), Finland (n = 1), Norway (n = 1), and Israel (n = 1). Fifteen papers used a quantitative design, and one used a qualitative design. The total number of participants represented in the papers is 963,718 of which 812,052 had GDM and 151,666 did not. Women with an immediate history of GDM were as likely to initiate breastfeeding as those without it. However, they were more likely to have the first feed delayed, be offered supplementation, experience delayed lactogenesis II and or a perception of low supply. Women were less likely to exclusively breastfeed and more likely to completely wean earlier than the general population. Maternity care practices, maternal factors, family influences, and determinants of health were contextual and acted as either a facilitator or barrier for this group.

CONCLUSION

Breastfeeding education and support need to be tailored to recognise the individual needs and challenges of women with a history of GDM. Interventions, including the introduction of commercial milk formula (CMF) may have an even greater impact and needs to be very carefully considered. Supportive strategies should encompass the immediate and extended family who are major sources of influence.

The CHANGED Score-A New Tool for the Prediction of Insulin Dependency in Gestational Diabetes

Gestational diabetes (GDM) is a frequent complication during pregnancy. We aimed to develop a score to predict future insulin dependency in gestational diabetes (GDM). Data from 1611 patients from Charité Berlins gestational diabetes clinic from 2015 to 2022 were utilized. A stepwise backwards regression, including patient characteristics obtained at the initial presentation, was performed. Predictors examined included age, fasting blood glucose level, blood glucose levels one and two hours after oral glucose tolerance test, pre-pregnancy BMI, number of previous pregnancies and births, and fetal sex. The ideal cutoff value between high and low risk for insulin dependency was assessed and the score was internally validated. There were 1249 (77.5%) women diagnosed with dietary GDM and 362 (22.5%) were diagnosed with insulin-dependent GDM. The CHarité AssessmeNt of GEstational Diabetes (CHANGED) Score achieved an area under the curve of 0.77 (95% confidence interval 0.75-0.80; 0.75 in internal validation). The optimal cutoff value was calculated at a score value of 9 (72% sensitivity, 69% specificity). We developed an easily applicable tool to accurately predict insulin dependency in gestational diabetes. The CHANGED Score is routinely available and can potentially improve maternal and fetal outcomes.

Social Vulnerability and Initiation of Pharmacotherapy for Gestational Diabetes Mellitus in a Medicaid Population

OBJECTIVE

Our study examines the association between social vulnerability index (SVI) and pharmacotherapy initiation for gestational diabetes mellitus (GDM).

METHODS

We studied a retrospective cohort of pregnant patients with GDM, enrolled in Tennessee Medicaid, who gave birth between 2007 and 2019. Enrollment files were linked to birth and death certificates, state hospitalization registries, and pharmacy claims. SVI, measured at the community level and determined by residential census tract, ranged from 0 to 100 (low to high vulnerability). Multivariable logistic regression assessed the association between SVI and the odds of initiating the most common pharmacotherapies for GDM-insulin, glyburide, or metformin-and adjusted for relevant covariates. SVI was modeled with restricted cubic splines to account for nonlinear associations, using the median Tennessee SVI as a reference. Secondary analysis assessed associations with the SVI subthemes.

RESULTS

Among 33,291 patients with GDM, 21.7% (7,209) initiated pharmacotherapy during pregnancy. Patients from areas with higher SVI were more likely to be non-Hispanic Black with higher body mass index, whereas those with lower SVI were more likely to be nulliparous. Multivariable modeling demonstrated a complex nonlinear association between SVI and GDM pharmacotherapy initiation, relative to the reference. Higher SVI was associated with elevated odds of GDM pharmacotherapy initiation (e.g., odds ratio 1.11 [95% confidence interval 1.02-1.22] for SVI 80) and low to medium SVI had variable nonsignificant associations with GDM pharmacotherapy initiation, relative to the reference (lower odds of initiation for values 25-50, higher odds of initiation for values < 25). Secondary analysis demonstrated a nonlinear association between subtheme 3 and the odds of GDM pharmacotherapy initiation.

CONCLUSION

Social vulnerability is associated with initiation of pharmacotherapy for GDM, highlighting the possible role of social determinants of health in achieving glycemic control.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.