Importance Assessment of cardiovascular health (CVH) during pregnancy may unmask latent metabolic vulnerability and indicate long-term disease risk. However, the prognostic value of the American Heart Association’s Life’s Essential 8 (LE8) framework during pregnancy remains uncertain. Objective To evaluate CVH during pregnancy using a modified LE8 (mLE8) score in association with time to incident cardiometabolic disease. Design, Setting, and Participants This cohort study used electronic medical record (EMR) surveillance for 7 years post partum (August 2018 to March 2026) and included singleton pregnancies in individuals aged 18 to 44 years without preexisting diabetes or cardiovascular disease (CVD) from a large academic medical system in South Carolina. Data were analyzed from December 2024 to April 2026. Exposures A 7-component mLE8 score assessed during pregnancy, incorporating hypertensive disorders of pregnancy (HDP), 50 g glucose tolerance test results, early pregnancy body mass index, smoking status, sleep adequacy, diet quality, and physical activity. Scores ranged from 0 to 100, with higher scores indicating more favorable CVH. Main Outcomes and Measures Postdelivery incident cardiometabolic conditions captured through EMRs and classified as chronic hypertensive conditions, chronic metabolic conditions (eg, dyslipidemia, impaired glucose regulation), and CVD (eg, cardiac arrest, cardiomyopathy). Adjusted accelerated time-to-failure models estimated mLE8 associations with incident conditions. Time to incident diagnosis was measured in days from delivery. Results Among 1225 pregnancies (mean [SD] age, 25.0 [5.3] years), 499 incident cardiometabolic events occurred over a median (IQR) follow-up of 6.2 (2.8) years. Each 10-point higher mLE8 score was associated with a longer time to incident diagnosis of chronic hypertensive conditions (time ratio [TR], 1.26; 95% CI, 1.11-1.42) and chronic metabolic conditions (TR, 1.20; 95% CI, 1.11-1.29). Healthier HDP (1.06 [1.03-1.10]), glucose (1.15 [1.10-1.21]), body mass index (1.07 [1.04-1.11]), and sleep (1.05 [1.00-1.09]) scores were associated with longer time to diagnosis of chronic metabolic conditions. Associations were generally similar after excluding individuals with gestational diabetes or HDP. Conclusions and Relevance In this cohort study of 1225 pregnancies, better CVH during pregnancy was associated with a longer time to incident postdelivery diagnosis of cardiometabolic conditions. Pregnancy-based CVH assessment may help identify individuals with elevated and emerging cardiometabolic risk who could benefit from early, targeted intervention and enhanced longitudinal surveillance.

ABSTRACT Maternal diet quality and perinatal depression significantly impact maternal and child health, yet their relationship remains underexplored in low‐resource settings. This cross‐sectional study examined the association between overall diet quality and risk of depression during the third trimester among 296 pregnant women receiving antenatal care at Dhulikhel Hospital, Nepal (August 2023–January 2024). Depression risk was assessed using the Edinburgh Postnatal Depression Scale (EPDS), with scores ≥ 12 indicating elevated symptoms. Diet quality was measured using an adapted Nepali version of the 23‐item PrimeScreen questionnaire, generating a Prime Diet Quality Score (PDQS) ranging from 0 to 46. Multivariable logistic regression models were used to estimate the association between PDQS and depression risk, adjusting for age, education, ethnicity, occupation, parity, gestational week, physical activity, and pre‐pregnancy BMI. The mean PDQS was 24.7 (SD = 3.1), and 22.3% of participants screened positive for depression. Each 1‐point increase in PDQS was associated with 16% lower odds of depression (adjusted OR: 0.84; 95% CI: 0.70–0.90; p = 0.002). These findings suggest that higher overall diet quality is associated with a reduced likelihood of third trimester depression. Further longitudinal studies are warranted to assess causality and inform targeted nutritional interventions. If supported by further studies, incorporating brief dietary assessments like PrimeScreen into antenatal care may potentially offer a feasible strategy to identify women with suboptimal diet quality and co‐occurring depressive symptoms in low‐ and middle‐income countries.

Objective:Hypertensive disorders of pregnancy (HDP) contribute to maternal mortality and morbidity globally. Mobile health technologies may improve HDP management through patient education, facilitating patient-provider communication, and supporting blood pressure self-monitoring through tailored feedback and reminder prompts. Our objective was to understand the digital health needs of women with HDP from low-socioeconomic backgrounds. Methods:An interactive HDP management digital prototype was developed and evaluated through usability and acceptability testing. Participants included nine pregnant or postpartum women with diagnosed HDP and three maternal-fetal medicine specialists, recruited from two clinics in a predominantly low-income city, Newark, N.J., in 2024 The Technology Acceptance Model was used to guide the assessment of the prototype's acceptability and usability. Data were collected from interviews, a digital literacy questionnaire, and a system usability questionnaire, with quantitative data analyzed descriptively and qualitative data through content analysis. Results:The median gestational age among pregnant women was 22.0 (17.0, 29.0) weeks, with 89 % identifying as Black/African American. Most women (78 %) reported moderate or high digital health literacy. The mean System Usability score was 81 ± 17, indicating good usability. Three themes were identified: high acceptability and usability, the importance of tailored feedback, and the need for real-time provider-patient communication to support treatment decisions. Conclusions:These findings indicate a high acceptability and usability of a digital application for HDP management and home blood pressure monitoring among pregnant and postpartum women diagnosed with HDP and their providers in a low-income urban setting.

Introduction: Gestational diabetes mellitus is among the most common pregnancy complications with various adverse maternal and fetal outcomes. Mobile health technology offers new opportunities to enhance its care and support self-management. This study aims to assess the ‘Garbhakalin Diabetes athawa Madhumeha- Dhulikhel Hospital’ application app’s usability, acceptability and satisfaction to support the treatment and self-management of gestational diabetes mellitus among Nepalese women. Methods: A cross-sectional study among 46 women of an intervention arm of a parent randomized controlled trial was conducted. The ethical/institutional review boards of Rutgers University (Pro2019001883) and the Nepal Health Research Council approved the study (Ref number: 735/2019). Perceived usability and acceptability of the application was assessed using the System Usability Scale and mobile Health Application Usability Questionnaire. To assess satisfaction with gestational diabetes mellitus care, the Oxford Maternity Diabetes Treatment Satisfaction Questionnaire was used. Frequencies and percentages are reported for categorical variables, and means and standard deviation for continuous variables. Results: The average time spent on the application was 37 minutes/per day, with 12 minutes of engagement per session. The mean score on System Usability Scale was 72.12±4.78; 44 (95.56%) participants liked using the application, 45 (97.76%) found it easy, and 40 (86.91%) praised its functional integration. The mean score on the mobile Health Application Usability Questionnaire and Oxford Maternity Diabetes Treatment Satisfaction Questionnaire were 118.32 ±8.84 and 15.82±4.09 respectively. Conclusions: The mobile application demonstrated strong usability and was well-accepted by Nepalese women with gestational diabetes mellitus, suggesting it is a promising tool for self-management support.

Objective: Continuous glucose monitoring (CGM) provides a novel approach to monitor postprandial glucose responses (PPGR) in persons with gestational diabetes mellitus (GDM). We sought to develop a machine learning (ML) framework to identify PPGRs automatically from CGM data to address challenges of manual meal logging. Methods: Adults 18+ years diagnosed with GDM were enrolled at 24-32 gestational weeks and wore a blinded Freestyle Libre CGM and logged meals for up to 14 days each over 3 visits. A random forest ML algorithm was trained to identify the first meal of the day from raw daily CGM profiles. Comparing self-recorded and ML-predicted PPGRs, the primary outcome was difference in start time of PPGR while the secondary outcomes were the ratio of the corresponding 2-hr and 3-hr area under the PPGR curves. Results: We analyzed data from 19 participants with CGM and self-recorded meal logs (35 ± 6 years, 47% Asian or Black/African-American, 37% Hispanic/Latino). The ML algorithm predicted start time of PPGRs to within a median 30 [19,45] minutes of self-logged meal time (Fig 1). The median ratio of the corresponding 2-hr PPGR AUCs was 1.0 [0.98,1.03] and 3-hr AUCs was 1.0 [1.00,1.01]. Conclusion: An ML algorithm showed promising performance in identifying PPGRs accurately from CGM data in persons with GDM, enabling a new automated approach to meal logging and analyzing postprandial glucose patterns. Disclosure S. Barua: None. T. Sangmo: None. A. Khan: None. L. Berube: None. L. Li: None. S. Williams: None. T. Rosen: None. S. Rawal: Stock/Shareholder; Merck & Co., Inc., LabCorp, Pfizer Inc. Research Support; National Institutes of Health. Stock/Shareholder; Bristol-Myers Squibb Company. Funding Rutgers SHP Dean's Intramural Grant

Gestational weight gain (GWG) is linked to pregnancy outcomes, such as birth weight and delivery mode, though research in low-income countries like Nepal is limited. We examined the association of GWG rate with infant birth weight and cesarean delivery in a prospective cohort of 191 pregnant women in Nepal, using data collected from August 2018 to August 2019 at a peri-urban hospital in Dhulikhel. Participants included women with singleton, full-term live births, with GWG rate calculated from weight gain between the second and late pregnancy stages, divided by the weeks in between. GWG rate categories-adequate, inadequate, or excessive-were defined by pre-pregnancy Body Mass Index (BMI) specific to GWG recommendations from the 2009 Institute of Medicine report. Ethical approval was obtained from Institutional Review Board of Kathmandu University and Rutgers University. Statistical analyses in SPSS and Stata revealed that 52.4% of mothers exceeded the recommended GWG rate, particularly among overweight and obese women (0.4 ± 0.2 kg/week and 0.5 ± 0.2 kg/week, respectively). The average birth weight was 2964.9 ± 407.0 grams, with 12% of infants classified as low-birth-weight. Cesarean delivery was recorded in 45% of the women. After controlling for factors like age, education, ethnicity, occupation, parity and BMI, each 1 kg/week increase in GWG from the second to third trimester correlated with a 392-gram increase in birth weight (β = 391.9, 95%CI = 67.2-716.7, p = 0.01), while excessive GWG rate led to a 148-gram increase over adequate GWG rate (β = 148.1, 95%CI = 8.7-287.5, p = 0.03). However, GWG rate was not significantly linked to cesarean delivery. These findings suggest that maternal GWG rate positively affects infant birth weight but not cesarean delivery, underscoring the need for larger studies to explore GWG rate's effects on maternal and neonatal outcomes.

BackgroundMobile apps can aid with the management of gestational diabetes mellitus (GDM) by providing patient education, reinforcing regular blood glucose monitoring and diet/lifestyle modification, and facilitating clinical and social support. ObjectiveThis study aimed to describe our process of designing and developing a culturally tailored app, Garbhakalin Diabetes athawa Madhumeha—Dhulikhel Hospital (GDM-DH), to support GDM management among Nepalese patients by applying a user-centered design approach. MethodsA multidisciplinary team of experts, as well as health care providers and patients in Dhulikhel Hospital (Dhulikhel, Nepal), contributed to the development of the GDM-DH app. After finalizing the app’s content and features, we created the app’s wireframe, which illustrated the app’s proposed interface, navigation sequences, and features and function. Feedback was solicited on the wireframe via key informant interviews with health care providers (n=5) and a focus group and in-depth interviews with patients with GDM (n=12). Incorporating their input, we built a minimum viable product, which was then user-tested with 18 patients with GDM and further refined to obtain the final version of the GDM-DH app. ResultsParticipants in the focus group and interviews unanimously concurred on the utility and relevance of the proposed mobile app for patients with GDM, offering additional insight into essential modifications and additions to the app’s features and content (eg, inclusion of example meal plans and exercise videos).The mean age of patients in the usability testing (n=18) was 28.8 (SD 3.3) years, with a mean gestational age of 27.2 (SD 3.0) weeks. The mean usability score across the 10 tasks was 3.50 (SD 0.55; maximum score=5 for “very easy”); task completion rates ranged from 55.6% (n=10) to 94.4% (n=17). Findings from the usability testing were reviewed to further optimize the GDM-DH app (eg, improving data visualization). Consistent with social cognitive theory, the final version of the GDM-DH app supports GDM self-management by providing health education and allowing patients to record and self-monitor blood glucose, blood pressure, carbohydrate intake, physical activity, and gestational weight gain. The app uses innovative features to minimize the self-monitoring burden, as well as automatic feedback and data visualization. The app also includes a social network “follow” feature to add friends and family and give them permission to view logged data and a progress summary. Health care providers can use the web-based admin portal of the GDM-DH app to enter/review glucose levels and other clinical measures, track patient progress, and guide treatment and counseling accordingly. ConclusionsTo the best of our knowledge, this is the first mobile health platform for GDM developed for a low-income country and the first one containing a social support feature. A pilot clinical trial is currently underway to explore the clinical utility of the GDM-DH app.

Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic–High TG (n = 149); Dyslipidemic–High FFA (n = 180); Insulin Resistant (IR)–Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine–acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant–Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk.

Background: Evidence has indicated that polyunsaturated fatty acids (PUFAs)-enriched diet could reduce inflammation because of thyroid autoimmunity in vivo, and therefore, enhance thyroid function. Objectives: We investigated whether early pregnancy plasma phospholipid PUFAs could benefit maternal thyroid function across pregnancy, which is critical to fetal brain development and growth in pregnancy. Methods: Within the National Institute of Child Health and Human Development Fetal Growth Studies-Singleton Cohort, we collected plasma samples longitudinally from 214 subjects [107 with gestational diabetes mellitus (GDM) matched with 107 controls] with a singleton pregnancy. We measured 11 PUFAs at early pregnancy (10-14 wk) and 5 thyroid biomarkers at 10-14, 15-26, 23-31, and 33-39 wk, including free thyroxine (fT4), free triiodothyronine (fT3), thyroid -stimulating hormone, antithyroid peroxidase, and antithyroglobulin. Associations of PUFAs with thyroid function biomarkers and relative risk (RR) of gestational hypothyroidism (GHT) during pregnancy were assessed using generalized linear mixed models and modified Poisson regression, respectively. Results: After sample weighting because of subjects with GDM over -representing in the analytic sample with biomarkers, eicosapentaenoic acid (EPA) at early pregnancy was associated with a reduction of 0.24 pmol/L (95% confidence intervals: -0.31, -0.16) in fT3 across gestation per standard deviation (SD) increment, whereas docosahexaenoic acid (DHA) at early pregnancy was associated with an increment of 0.04 ng/dL (0.02, 0.05) in fT4 across gestation per SD increment. Furthermore, EPA and docosatetraenoic acid (DTA) were associated with lower risks of persistent GHT (EPA-RR: 0.13; 0.06, 0.28; DTA-RR: 0.24; 0.13, 0.44) per SD increment. All significant associations remained robust in sensitivity analysis and multiple testing. Conclusions: Certain plasma phospholipid PUFAs were associated with optimal levels of thyroid biomarkers and even lower risk of GHT throughout pregnancy, which might be potentially targeted for maternal thyroid regulation in early pregnancy. Clinical Trial Registry: This trial was registered at https://beta.clinicaltrials.gov/study/NCT00912132?distance=50&term=NCT00912132&rank=1 as NCT00912132.

Background: Pregnancy is a unique stage of the life course characterized by trade-offs between the nutritional, immune, and metabolic needs of the mother and fetus. The Camden Study was originally initiated to examine nutritional status, growth, and birth outcomes in adolescent pregnancies and expanded to study dietary and molecular predictors of pregnancy complications and birth outcomes in young women. Methods: From 1985-2006, 4765 pregnant participants aged 12 years and older were recruited from Camden, NJ, one of the poorest cities in the US. The cohort reflects a population under-represented in perinatal cohort studies (45% Hispanic, 38% non-Hispanic Black, 17% White participants; 98% using Medicaid in pregnancy). Study visits, including questionnaires, dietary assessments, and biospecimen collection, occurred in early and late pregnancy as well as at delivery. Medical records were abstracted, and a subset of mothers and infants participated in a six-week postpartum visit. Results: Findings from the Camden Study have added to the understanding of adolescent and young adult maternal health and perinatal outcomes. These include associations of adolescent linear growth while pregnant with smaller neonatal birth size, low dietary zinc intake in early pregnancy with increased risk of delivery <33 gestational weeks, and higher circulating fatty acid levels with greater insulin resistance. More recent analyses have begun to unpack the biochemical pathways in pregnancy that may be shaped by race as an indicator of systemic racism. Conclusions: The Camden Study data and biorepositories are well-positioned to support future research aimed at better understanding perinatal health in under-represented women and infants. Linkages to subsequent health and administrative records and the potential for recontacting participants over 18-39 years after initial participation may provide key insights into the trajectories of maternal and child health across the life course.

Loss of taste and smell is one of the most troubling symptoms of long COVID and may be permanent for some. Correlation between subjectively and objectively assessed olfactory and gustatory impairment is low, leading to uncertainty about how many people are affected, how many recover, and to what extent. We prospectively investigated the effects of COVID-19 on long-term chemosensory function in a university and hospital-based cohort in NJ. We followed 856 participants from March 2020 through April 2022, of which 58 were diagnosed with COVID-19 and completed the NHANES 2013–2014 taste and smell protocol, including chemosensory questionnaire, whole-mouth taste tests, and 8-item odor identification test at and/or before acute COVID-19 infection. Of these, 29 repeated taste and smell assessments at 6 months (183.0 ± 54.6) follow-up. Total overall smell score significantly improved from baseline to 6-month follow up (6.9 ± 1.4 vs 7.6 ± 0.8; p = .01). Taste intensity also improved across 6 months, but not significantly. Participants self-reported improved taste and smell but noticed improvements in taste (78%) more than improvements in smell (56%). Our study is the first to show psychophysically-assessed and self-reported long-term recovery of olfactory and gustatory function in the same population after acute COVID-19.

Objectives: Determine the association between diet quality and abdominal adiposity among postmenopausal women with overweight and obesity. Methods: Cross-sectional analysis of baseline data from a subset of participants (n=105) in the Minnesota Green Tea Trial classified at enrollment as overweight or obese (BMI = 25 - 40kg/m2) who had complete health history, dietary intake, anthropometrics, and body composition data. Dietary intake data from a 124-item validated food frequency questionnaire was used to calculate the Healthy Eating Index (HEI)-2015 scores which measured diet quality. Abdominal adiposity was assessed with wait-to-hip ratio (WHR), and dual X-ray absorptiometry scan estimated visceral adipose tissue (VAT) mass and VAT volume. Adjusting for covariates, general linear regression model analyzed the associations between total HEI-2015 score and WHR, VAT mass, and VAT volume. Results: Mean (±SD) age was 60.35 (±4.94) years, the majority of participants were white (96.20%), non-Hispanic (99.04%) and had some college or higher level of education (68.60%). Median (IQR) VAT mass, VAT volume and WHR were 0.97 kg (0.57, 1.33), 1,019.00 cm3 (595.00, 1,412.50) and 0.86 (0.83, 0.91). Abdominal obesity classified as WHR ≥ 0.85 was found in 61.90% of participants. Mean (±SD) total HEI-2015 score was 71.65 (±7.32) out of 100. The study sample achieved maximum HEI-2015 component scores for whole fruits, total vegetables, greens and beans, and refined grains which were the main contributors to their total score. A significant inverse association was observed between total HEI-2015 score and VAT mass (β = -0.015, 95% CI: -0.029, -0.002, P = 0.029) in the unadjusted model, however, no significant associations were found between total HEI-2015 score and WHR, VAT mass and VAT volume after adjusting for demographic, dietary intake, and lifestyle characteristics. Conclusions: In our study sample of postmenopausal women with overweight and obesity whose diet quality was characterized as high in fruits, vegetables, greens and beans and low in refined grains, no significant associations were observed between total HEI-2015 score and WHR, VAT mass, or VAT volume. Funding Sources: No funding was received for this study; National Institutes of Health/National Cancer Institute (Grant R01 CA127236) funded the parent trial.

BackgroundThe prevalence of gestational diabetes mellitus (GDM) is increasing, particularly in low- and middle-income countries (LMICs) like Nepal. GDM self-management, including intensive dietary and lifestyle modifications and blood glucose monitoring, is critical to maintain glycemic control and prevent adverse outcomes. However, in resource-limited settings, several barriers hinder optimal self-management. Mobile health (mHealth) technology holds promise as a strategy to augment GDM treatment by promoting healthy behaviors and supporting self-management, but this approach has not yet been tested in any LMIC. ObjectiveThis report describes the protocol to develop a culturally tailored mHealth app that supports self-management and treatment of GDM (GDM–Dhulikhel Hospital [GDM-DH] app, phase 1) and test its usability and preliminary efficacy (phase 2) among patients with GDM in a periurban hospital setting in Nepal. MethodsThe study will be conducted at Dhulikhel Hospital in Dhulikhel, Nepal. In the development phase (phase 1), a prototype of the GDM-DH app will be developed based on expert reviews and a user-centered design approach. To understand facilitators and barriers to GDM self-management and to gather feedback on the prototype, focus groups and in-depth interviews will be conducted with patients with GDM (n=12), health care providers (n=5), and family members (n=3), with plans to recruit further if saturation is not achieved. Feedback will be used to build a minimum viable product, which will undergo user testing with 18 patients with GDM using a think-aloud protocol. The final GDM-DH app will be developed based on user feedback and following an iterative product design and user testing process. In the randomized controlled trial phase (phase 2), newly diagnosed patients with GDM (n=120) will be recruited and randomized to either standard care alone or standard care plus the GDM-DH app from 24-30 weeks gestation until delivery. In this proof-of-concept trial, feasibility outcomes will be app usage, self-monitoring adherence, and app usability and acceptability. Exploratory treatment outcomes will be maternal glycemic control at 6 weeks post partum, birth weight, and rates of labor induction and cesarean delivery. Qualitative data obtained from phase 1 will be analyzed using thematic analysis. In phase 2, independent 2-tailed t tests or chi-square analyses will examine differences in outcomes between the 2 treatment conditions. ResultsAs of July 2024, we have completed phase 1. Phase 2 is underway. The first participant was enrolled in October 2021, with 99 participants enrolled as of July 2024. We anticipate completing recruitment by December 2024 and disseminating findings by December 2025. ConclusionsApp-based lifestyle interventions for GDM management are not common in LMICs, where GDM prevalence is rapidly increasing. This proof-of-concept trial will provide valuable insights into the potential of leveraging mHealth app–based platforms for GDM self-management in LMICs. Trial RegistrationClinicalTrials.gov NCT04198857; https://clinicaltrials.gov/study/NCT04198857 International Registered Report Identifier (IRRID)DERR1-10.2196/59423