Alison Md Stuebe

Aims/hypothesis: Women with a history of gestational diabetes mellitus (GDM) have an elevated risk of type 2 diabetes in their later life, yet the underlying mechanisms of this remain unclear. We aimed to investigate the long-term impact of GDM on gut microbiota and related metabolites and to explore whether such alterations may contribute to type 2 diabetes risk. Methods: Among parous women from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we identified microbial species associated with a history of GDM (visit 2, 2014–2017, n =1525), and serum metabolites associated with both a history of GDM (visit 1, 2008–2011, n =2968) and GDM-related microbiota (visit 2, n =391). We further examined prospective associations of the GDM-related microbiome (visit 2, n =925) with incident type 2 diabetes over 6 years of follow-up, and of microbial-related metabolites (visit 1, n =2341) with incident type 2 diabetes over 12 years. Results: Among 1525 US Hispanic/Latino parous women (median age: 58 years), seven species differed between women with and without a history of GDM, including higher abundances of four species (e.g. Parabacteroides merdae CAG:48 , a proinflammatory taxon) and lower abundances of three species (e.g. Dialister sp. CAG:588 , a short-chain fatty acid producer). Fifteen metabolites were associated with both a history of GDM and the GDM-related microbiome in a consistent direction, nine of which (e.g. saturated sphingomyelins and unsaturated fatty acids) were associated with glycaemic traits and incident type 2 diabetes. Using these microbial-related metabolites as proxy measures, proxy analysis suggested a positive relationship between the GDM-related microbiome and type 2 diabetes ( r =0.55, p =0.036). A metabolite score derived from the nine microbial-related metabolites mediated an estimated 20% (95% CI 9%, 42%) of the association between a history of GDM and type 2 diabetes. Conclusions/interpretation: A history of GDM is associated with an unfavourable gut microbiota and related metabolites in US Hispanic/Latino women, suggesting a potential role of the gut microbiota in GDM-related type 2 diabetes.

Key Points: In the United States, one in three pregnant women receiving maintenance dialysis experienced severe maternal morbidity from 2000 to 2020. History of severe maternal morbidity was associated with higher mortality and a longer time to kidney transplantation in the years following delivery. Background: Pregnant women receiving maintenance dialysis are at high risk for adverse pregnancy outcomes. Little is known about their rates of severe maternal morbidity and the association of severe maternal morbidity with future kidney transplant wait times and mortality. Methods: This is a cohort study of women who gave birth while receiving maintenance dialysis from January 1, 2000, to December 31, 2019, as captured in the United States Renal Data System. Nonkidney severe maternal morbidity, defined by the US Centers for Disease Control and Prevention, was assessed from 4 weeks before birth to 6 weeks postpartum. Multivariable regression models examined risk factors independently associated with severe maternal morbidity. Multivariable Cox proportional hazards were stratified by the presence or absence of severe maternal morbidity to examine time to mortality, and, separately, the receipt of a kidney transplant. Results: We identified 1082 births among 986 women who were receiving dialysis at the time of birth. In total, 332 women (34%) and 342 births (32%) experienced at least one nonkidney severe maternal morbidity. The most common severe maternal morbidity events were blood transfusion (16% of all births), pulmonary edema/acute heart failure (10%), sepsis (6%), and acute respiratory distress syndrome (5%). In multivariable analyses, risk factors independently associated with severe maternal morbidity included Black or other/unknown race, body mass index >35 kg/m ² , and dialysis initiation within 1 year before giving birth. Patients who experienced severe maternal morbidity had a higher risk of mortality than those who did not (adjusted hazard ratio, 1.69; 95% confidence interval, 1.30 to 2.19) and were less likely to receive a kidney transplant than those who did not (adjusted subhazard ratio, 0.76; 95% confidence interval, 0.59 to 0.97). Conclusions: In the United States, one in three pregnant women receiving maintenance dialysis experienced severe maternal morbidity with risk factors that included recent dialysis initiation. In the following years, the history of severe maternal morbidity was associated with higher mortality and a longer time to kidney transplantation.

Objective: Severe maternal morbidity and mortality are higher in the United States than in other high-income countries, and unacceptable disparities persist. To facilitate research on these outcomes, we developed a standardized approach for extracting perinatal data from electronic health records (EHRs). Materials and Methods: To support data model building and validation, we harmonized perinatal EHR data in a common data model, building on lessons learned from multiple prior projects. Results: We developed an Entity Relationship Diagram (ERD) that aggregates perinatal EHR data at appropriate granularities (ie, mothers, infants, encounters) with indexing of observations to gestational age and time from delivery. We then developed a standard approach to extract, transform, and load pregnancy-related observations from EHRs for inclusion in PCORnet® Common Data Model tables. Discussion: Our ERD can facilitate cross-institutional research to identify populations at risk and prompt interventions to improve perinatal outcomes. Conclusion: A structured approach can accelerate the use of EHR data for perinatal research.

A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes and enable proactive intervention to prevent maternal mortality. Current evidence syntheses of such ML approaches either focus only on specific maternal outcomes, aspects other than risk prediction, or do not consider the full pipeline of studies from the development to implementation in clinical practice. The goal of this scoping review is to document evidence for the use of ML models for predicting the risk of maternal morbidity and mortality outcomes (research objective [RO1]), the translation of such models into applications for clinical use by providers (RO2), and factors associated with the implementation of clinical applications in practice (RO3). The review was limited to studies in health care settings, using data from EMRs. A detailed search string was developed in collaboration with a health sciences librarian and implemented on February 20, 2023, on PubMed, CINAHL Plus, Scopus, Embase, and IEEE Xplore. Two reviewers independently reviewed titles and abstracts for inclusion, and a third reviewer resolved conflicts. Only full-length journal articles published in English were included. Studies using non-EMR data exclusively were excluded. Two reviewers independently reviewed full texts for inclusion, and a third reviewer resolved conflicts. A structured template was used for data extraction, and findings were summarized descriptively. From 480 deduplicated studies identified from the search, 142 studies were included for full-text review, and 39 studies were included in the review. More than half of the included studies were conducted in 2022, and 34 studies were from just 3 countries (United States, China, and Israel). More studies focused on identifying the risk of pregnancy and delivery outcomes compared with postpartum outcomes. The top 3 most common outcomes for risk prediction were cardiovascular risks and hypertensive disorders of pregnancy (9 studies), gestational diabetes (7 studies), and postpartum hemorrhage (6 studies). Data were labeled with computable phenotypes in 30 studies, and the most often used method in ML models was boosting methods (18 studies). The most common metric used to assess model performance was area under the precision-recall curve (AUPRC; 33 studies). No studies described clinical applications of ML models for providers (RO2) or associated implementation factors (RO3). Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice.

Task-sharing and telemedicine can increase access to effective psychotherapies. Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT) is pragmatic, multisite, noninferiority, four-arm trial that tested the non-inferiority of provider (non-specialist vs. specialist providers) and modality (telemedicine vs. in-person) in delivering psychotherapy for perinatal depressive symptoms. Across three university-affiliated networks in the United States and Canada, pregnant and postpartum adult participants were randomized 1:1:1:1 to each arm (472 nonspecialist telemedicine, 145 nonspecialist in-person, 469 specialist telemedicine and 144 specialist in-person) and offered weekly behavioral activation treatment sessions. The primary outcome was depressive symptoms (Edinburgh Postnatal Depression Scale (EPDS)) and the secondary outcome was anxiety (Generalized Anxiety Disorder (GAD-7)) symptoms at 3 months post-randomization. Between 8 January 2020 and 4 October 2023, 1,230 participants were recruited. Noninferiority was met for the primary outcome comparing provider (EPDS: nonspecialist 9.27 (95% CI 8.85–9.70) versus specialist 8.91 (95% CI 8.49–9.33)) and modality (EPDS: telemedicine 9.15 (95% CI 8.79–9.50) versus in-person 8.92 (95% CI 8.39–9.45)) for both intention-to-treat and per protocol analyses. Noninferiority was also met for anxiety symptoms in both comparisons. There were no serious or adverse events related to the trial. This trial suggests compelling evidence for task-sharing and telemedicine to improve access to psychotherapies for perinatal depressive and anxiety symptoms. ClinicalTrials.gov NCT04153864

This policy brief discusses North Carolina’s Care Management for High Risk Pregnancy Program (CMHRP), the state’s primary strategy for supporting Medicaid enrolled pregnant individuals at elevated risk for adverse maternal and birth outcomes. Research and survey results show that CMHRP improves health outcomes, is acceptable to Medicaid clients and staff, and aligns with local health department (LDH) missions, while also highlighting concerns about sustainability and resource constraints.

Perinatal mental illness is a leading cause of death during pregnancy and the first postpartum year in the United States. Although better acute care services for mental health conditions are desperately needed, urgent services alone cannot create the conditions to thrive. Cultivating well-being requires a sustained commitment to reproductive justice, “the human right to maintain personal bodily autonomy, have children, not have children, and parent the children we have in safe and sustainable communities.” To support reproductive justice for pregnant and birthing people, the Rippel Foundation’s Vital Conditions for Health and Well-Being framework offers a holistic approach comprising seven domains: a thriving natural world; basic needs for health and safety; humane housing; meaningful work and wealth; lifelong learning; reliable transportation; and, central to all of these, belonging and civic muscle. Here we review the evidence for each of the vital conditions as key drivers of perinatal mental health, and we outline how this public health approach can advance well-being across generations.