A feasibility study using motivational interviewing and a smartphone application to promote physical activity (+Stay-Active) for women with gestational diabetes.

BACKGROUND: Physical activity (PA) interventions have an encouraging role in gestational diabetes mellitus (GDM) management. Digital technologies can potentially be used at scale to support PA. The aim of this study was to assess the feasibility and acceptability of + Stay-Active: a complex intervention which combines motivational interviewing with a smartphone application to promote PA levels in women with GDM. METHODS: This non-randomised feasibility study used a mixed methods approach. Participants were recruited from the GDM antenatal clinic at Oxford University Hospitals. Following baseline assessments (visit 1) including self-reported and device determined PA measurements (wrist worn accelerometer), women participated in an online motivational interview, and then downloaded (visit 2) and used the Stay-Active app (Android or iOS). Women had access to Stay-Active until 36 weeks' gestation, when acceptability and PA levels were reassessed (visit 3). The primary outcome measures were recruitment and retention rates, participant engagement, and acceptability and fidelity of the intervention. Secondary outcome measures included PA levels, app usage, blood glucose and perinatal outcomes. Descriptive statistics were performed for assessments at study visits. Statistics software package Stata 14 and R were used. RESULTS: Over the recruitment period (46 weeks), 114 of 285 women met inclusion criteria and 67 (58%) enrolled in the study. Mean recruitment rate of 1.5 participants/clinic with 2.5 women/clinic meeting inclusion criteria. Fifty-six (83%) received the intervention at visit 2 and 53 (79%) completed the study. Compliance to accelerometer measurement protocols were sufficient in 78% of participants (52/67); wearing the device for more than 10 h on 5 or more days at baseline and 61% (41/67) at 36 weeks. There was high engagement with Stay-Active; 82% (55/67) of participants set goals on Stay-Active. Sustained engagement was evident, participants regularly accessed and logged multiples activities on Stay-Active. The intervention was deemed acceptable; 85% of women rated their care was satisfactory or above, supported by written feedback. CONCLUSIONS: This combined intervention was feasible and accepted. Recruitment rates were lower than expected. However, retention rates remained satisfactory and participant compliance with PA measurements and engagement was a high. Future work will explore the intervention's efficacy to increase PA and impact on clinical outcomes. TRIAL REGISTRATION: The study has received a favourable opinion from South Central-Hampshire B Research Ethics Committee; REC reference: 20/SC/0342. ISRCTN11366562.

Association of common maternal infections with birth outcomes: a multinational cohort study.

PURPOSE: It is unclear whether common maternal infections during pregnancy are risk factors for adverse birth outcomes. We assessed the association between self-reported infections during pregnancy with preterm birth and small-for-gestational-age (SGA) in an international cohort consortium. METHODS: Data on 120,507 pregnant women were obtained from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA. Self-reported common infections during pregnancy included influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections, cystitis, urinary tract infection, and the symptoms fever and diarrhoea. Birth outcomes included preterm birth, low birth weight and SGA. Associations between maternal infections and birth outcomes were first assessed using Poisson regression in each cohort and then pooled using random-effect meta-analysis. Risk ratios (RR) and 95% confidence intervals (CI) were calculated, adjusted for potential confounders. RESULTS: Vaginal infections (pooled RR, 1.10; 95% CI, 1.02-1.20) and urinary tract infections (pooled RR, 1.17; 95% CI, 1.09-1.26) during pregnancy were associated with higher risk of preterm birth. Similar associations with low birth weight were also observed for these two infections. Fever during pregnancy was associated with higher risk of SGA (pooled RR, 1.07; 95% CI, 1.02-1.12). No other significant associations were observed between maternal infections/symptoms and birth outcomes. CONCLUSION: Vaginal infections and urinary infections during pregnancy were associated with a small increased risk of preterm birth and low birth weight, whereas fever was associated with SGA. These findings require confirmation in future studies with laboratory-confirmed infection diagnosis.

Validating the ratio of insulin like growth factor binding protein 4 to sex hormone binding globulin as a prognostic predictor of preterm birth in Viet Nam: a case-cohort study.

OBJECTIVE: To validate a serum biomarker developed in the USA for preterm birth (PTB) risk stratification in Viet Nam. METHODS: Women with singleton pregnancies (n = 5000) were recruited between 19+0-23+6 weeks' gestation at Tu Du Hospital, Ho Chi Minh City. Maternal serum was collected from 19+0-22+6 weeks' gestation and participants followed to neonatal discharge. Relative insulin-like growth factor binding protein 4 (IGFBP4) and sex hormone binding globulin (SHBG) abundances were measured by mass spectrometry and their ratio compared between PTB cases and term controls. Discrimination (area under the receiver operating characteristic curve, AUC) and calibration for PTB <37 and <34 weeks' gestation were tested, with model tuning using clinical factors. Measured outcomes included all PTBs (any birth ≤37 weeks' gestation) and spontaneous PTBs (birth ≤37 weeks' gestation with clinical signs of initiation of parturition). RESULTS: Complete data were available for 4984 (99.7%) individuals. The cohort PTB rate was 6.7% (n = 335). We observed an inverse association between the IGFBP4/SHBG ratio and gestational age at birth (p = 0.017; AUC 0.60 [95% CI, 0.53-0.68]). Including previous PTB (for multiparous women) or prior miscarriage (for primiparous women) improved performance (AUC 0.65 and 0.70, respectively, for PTB <37 and <34 weeks' gestation). Optimal performance (AUC 0.74) was seen within 19-20 weeks' gestation, for BMI >21 kg/m2 and age 20-35 years. CONCLUSION: We have validated a novel serum biomarker for PTB risk stratification in a very different setting to the original study. Further research is required to determine appropriate ratio thresholds based on the prevalence of risk factors and the availability of resources and preventative therapies.

REduced-Carbohydrate intervention for managing Obesity and Reduction of gestational Diabetes (RECORD): A randomized controlled feasibility trial.

AIM: To test the feasibility and acceptability of a reduced-carbohydrate dietary program, intended to reduce the risk of gestational diabetes. MATERIALS AND METHODS: Fifty-one pregnant women at <20 weeks' gestation, with body mass index ≥30 kg/m2 , and a normal baseline oral glucose tolerance test (OGTT), were randomized 2:1 to an intervention or control group and followed-up until delivery. The dietary intervention aimed at providing 130-150 g carbohydrate/day. Feasibility outcomes assessed at 24-28 weeks' gestation, included adoption of the reduced-carbohydrate diet by the intervention group, and retention of all participants, assessed by completion of a second OGTT. Changes in glycemia, weight gain and dietary intake, and the maternal and neonatal outcomes were also assessed. Participants were interviewed about their experience of the intervention and the study. RESULTS: Forty-nine of 51 participants attended the follow-up OGTT, a retention rate of 96% (95% confidence interval [CI] 86.8%-98.9%). In the intervention group, carbohydrate intake at follow-up was 190.4 (95% CI 162.5-215.6) g/day, a reduction of -24.6 (95% CI -51.5-2.4) g/day from baseline. Potentially favourable effects of the intervention on glucose control, weight gain and blood pressure were observed, but the study was not powered to detect significant differences in these. Participants found the intervention acceptable, and were content with the study processes, but some reported barriers to sustained adherence, mainly pertaining to competing priorities. CONCLUSIONS: Retention was high, suggesting the study processes are feasible, but the carbohydrate reduction in the intervention group was small, and did not meet progression criteria, limiting the likelihood of achieving the desired goal to prevent gestational diabetes. TRIAL REGISTRATION NUMBER: ISRCTN16235884.

A life-course approach to tackling noncommunicable diseases in women.

Women's health has been critically underserved by a failure to look beyond women's sexual and reproductive systems to adequately consider their broader health needs. In almost every country in the world, noncommunicable diseases are the leading causes of death for women. Among these, cardiovascular disease (including heart disease and stroke) and cancer are the major causes of mortality. Risks for these conditions exist at each stage of women's lives, but recognition of the unique needs of women for the prevention and management of noncommunicable diseases is relatively recent and still emerging. Once they are diagnosed, treatments for these diseases are often costly and noncurative. Therefore, we call for a strategic, innovative life-course approach to identifying disease triggers and instigating cost-effective measures to minimize exposure in a timely manner. Prohibitive barriers to implementing this holistic approach to women's health exist in both the social arena and the medical arena. Recognizing these impediments and implementing practical approaches to surmounting them is a rational approach to advancing health equity for women, with ultimate benefits for society as a whole.

An expert review of environmental heat exposure and stillbirth in the face of climate change: Clinical implications and priority issues.

Exposure to extreme heat in pregnancy increases the risk of stillbirth. Progress in reducing stillbirth rates has stalled, and populations are increasingly exposed to high temperatures and climate events that may further undermine health strategies. This narrative review summarises the current clinical and epidemiological evidence of the impact of maternal heat exposure on stillbirth risk. Out of 20 studies, 19 found an association between heat and stillbirth risk. Recent studies based in low- to middle-income countries and tropical settings add to the existing literature to demonstrate that all populations are at risk. Additionally, both short-term heat exposure and whole-pregnancy heat exposure increase the risk of stillbirth. A definitive threshold of effect has not been identified, as most studies define exposure as above the 90th centile of the usual temperature for that population. Therefore, the association between heat and stillbirth has been found with exposures from as low as >12.64°C up to >46.4°C. The pathophysiological pathways by which maternal heat exposure may lead to stillbirth, based on human and animal studies, include both placental and embryonic or fetal impacts. Although evidence gaps remain and further research is needed to characterise these mechanistic pathways in more detail, preliminary evidence suggests epigenetic changes, alteration in imprinted genes, congenital abnormalities, reduction in placental blood flow, size and function all play a part. Finally, we explore this topic from a public health perspective; we discuss and evaluate the current public health guidance on minimising the risk of extreme heat in the community. There is limited pregnancy-specific guidance within heatwave planning, and no evidence-based interventions have been established to prevent poor pregnancy outcomes. We highlight priority research questions to move forward in the field and specifically note the urgent need for evidence-based interventions that are sustainable.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

Impact of COVID-19 on gestational diabetes pregnancy outcomes in the UK: A multicentre retrospective cohort study.

OBJECTIVE: To determine the impact of implementing emergency care pathway(s) for screening, diagnosing and managing women with gestational diabetes (GDM) during COVID-19. DESIGN: Retrospective multicentre cohort. SETTING: Nine National Health Service (NHS) Hospital Trusts/Health boards in England and Scotland. POPULATION: 4915 women with GDM pre-pandemic (1 April 2018 to 31 March 2020), and 3467 women with GDM during the pandemic (1 May 2020 to 31 March 2021). METHODS: We examined clinical outcomes for women with GDM prior to and during the pandemic following changes in screening methods, diagnostic testing, glucose thresholds and introduction of virtual care for monitoring of antenatal glycaemia. MAIN OUTCOME MEASURES: Intervention at birth, perinatal mortality, large-for-gestational-age infants and neonatal unit admission. RESULTS: The new diagnostic criteria more often identified GDM women who were multiparous, had higher body mass index (BMI) and greater deprivation, and less frequently had previous GDM (all p < 0.05). During COVID, these women had no differences in the key outcome measures. Of the women, 3% were identified with pre-existing diabetes at antenatal booking. Where OGTT continued during COVID, but virtual care was introduced, outcomes were also similar pre- and during the pandemic. CONCLUSIONS: Using HbA1c and fasting glucose identified a higher risk GDM population during the pandemic but this had minimal impact on pregnancy outcomes. The high prevalence of undiagnosed pre-existing diabetes suggests that women with GDM risk factors should be offered HbA1c screening in early pregnancy.

A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.

Evaluation of Maternal Infection During Pregnancy and Childhood Leukemia Among Offspring in Denmark.

Importance: Maternal infection is common during pregnancy and is an important potential cause of fetal genetic and immunological abnormalities. Maternal infection has been reported to be associated with childhood leukemia in previous case-control or small cohort studies. Objective: To evaluate the association of maternal infection during pregnancy with childhood leukemia among offspring in a large study. Design, Setting, and Participants: This population-based cohort study used data from 7 Danish national registries (including the Danish Medical Birth Register, the Danish National Patient Registry, the Danish National Cancer Registry, and others) for all live births in Denmark between 1978 and 2015. Swedish registry data for all live births between 1988 and 2014 were used to validate the findings for the Danish cohort. Data were analyzed from December 2019 to December 2021. Exposures: Maternal infection during pregnancy categorized by anatomic locations identified from the Danish National Patient Registry. Main Outcomes and Measures: The primary outcome was any leukemia; secondary outcomes were acute lymphoid leukemia (ALL) and acute myeloid leukemia (AML). Offspring childhood leukemia was identified in the Danish National Cancer Registry. Associations were first assessed in the whole cohort using Cox proportional hazards regression models, adjusted for potential confounders. A sibling analysis was performed to account for unmeasured familial confounding. Results: This study included 2 222 797 children, 51.3% of whom were boys. During the approximately 27 million person-years of follow-up (mean [SD], 12.0 [4.6] years per person), 1307 children were diagnosed with leukemia (ALL, 1050; AML, 165; or other, 92). Children born to mothers with infection during pregnancy had a 35% increased risk of leukemia (adjusted hazard ratio [HR], 1.35 [95% CI, 1.04-1.77]) compared with offspring of mothers without infection. Maternal genital and urinary tract infections were associated with a 142% and 65% increased risk of childhood leukemia, with HRs of 2.42 (95% CI, 1.50-3.92) and 1.65 (95% CI, 1.15-2.36), respectively. No association was observed for respiratory tract, digestive, or other infections. The sibling analysis showed comparable estimates to the whole-cohort analysis. The association patterns for ALL and AML were similar to that for any leukemia. No association was observed for maternal infection and brain tumors, lymphoma, or other childhood cancers. Conclusions and Relevance: In this cohort study of approximately 2.2 million children, maternal genitourinary tract infection during pregnancy was associated with childhood leukemia among offspring. If confirmed in future studies, our findings may have implications for understanding the etiology and developing preventive measures for childhood leukemia.

Development and testing of a reduced carbohydrate intervention for the management of obesity and reduction of gestational diabetes (RECORD): protocol for a feasibility randomised controlled trial.

INTRODUCTION: Previous trials of dietary interventions to prevent gestational diabetes mellitus (GDM) have yielded only limited success. Low-carbohydrate diets have shown promise for the treatment of type 2 diabetes, but there is no evidence to support their use in pregnancy. The aim of this study is to explore the feasibility of a moderately reduced-carbohydrate dietary intervention delivered from mid-pregnancy alongside routine antenatal care. METHODS AND ANALYSIS: This is a feasibility randomised controlled trial (RCT) with embedded qualitative study. Sixty women who are pregnant <20 weeks' gestation, with body mass index ≥30 kg/m2 at their antenatal booking appointment, will be randomised 2:1 intervention or control (usual care) and followed up until delivery. The intervention is a moderately reduced-carbohydrate diet (~130-150 g total carbohydrate/day), designed to be delivered alongside routine antenatal appointments. Primary outcomes are measures of adoption of the diet and retention of participants. Secondary outcomes include incidence of GDM, change in markers of glycaemic control, gestational weight gain, total carbohydrate and energy intake. Process outcomes will examine resources and management issues. Exploratory outcomes include further dietary changes, quality of life, maternal and neonatal outcomes, and qualitative measures. ETHICS AND DISSEMINATION: This trial was reviewed and approved by the South-Central Oxford B Research Ethics Committee NHS National Research Ethics Committee and the Health Research Authority (Reference: 20/SC/0442). The study results will inform whether to progress to a full-scale RCT to test the clinical effectiveness of the RECORD programme to prevent GDM in women at high risk. The findings will be published in peer-reviewed journals and presented at conferences. TRIAL REGISTRATION NUMBER: ISRCTN16235884.

Study protocol: use of a smartphone application to support the implementation of a complex physical activity intervention (+Stay Active) in women with gestational diabetes mellitus-protocol for a non-randomised feasibility study.

INTRODUCTION: Physical activity (PA) interventions have a promising role in the management of gestational diabetes mellitus (GDM). Digital technologies can support PA at scale and remotely. The protocol describes a study designed to determine the feasibility and acceptability of a complex intervention; known as +Stay Active. +Stay Active combines motivational interviewing with a bespoke behaviour change informed smartphone application (Stay-Active) to augment PA levels in women with GDM. METHODS AND ANALYSIS: This is a non-randomised feasibility study using a mixed methods approach. Participants will be recruited from the GDM antenatal clinic at the Women Centre, John Radcliffe Hospital, Oxford. Following baseline assessments (visit 1) including self-reported and device determined PA assessment (wearing a wrist accelerometer), women will be invited to participate in an online motivational interview, then download and use the Stay-Active app (Android or iOS) (visit 2). Women will have access to Stay-Active until 36 weeks gestation, when engagement and PA levels will be reassessed (visit 3). The target sample size is 60 women. Primary outcomes are recruitment and retention rates, compliance and assessment of participant engagement and acceptability with the intervention. Secondary outcomes are assessment of blood glucose control, self-reported and device determined assessment of PA, usage and structured feedback of participant's attitudes to +Stay Active, assessment of health costs and description of maternal and neonatal outcomes. This study will provide key insights into this complex intervention regarding engagement in smartphone technology and the wearing of accelerometers. These data will inform the development of a randomised controlled trial with refinements to intervention implementation. ETHICS AND DISSEMINATION: The study has received a favourable opinion from South Central-Hampshire B Research Ethics Committee; REC reference: 20/SC/0342. Written informed consent will be obtained from all participants. Findings will be disseminated through peer-reviewed journals, conferences and seminar presentations. TRIAL REGISTRATION NUMBER: ISRCTN11366562.

Preeclampsia prediction with blood pressure measurements: A global external validation of the ALSPAC models.

OBJECTIVE: The prediction of preeclampsia in pregnancy has resulted in a plethora of prognostic models. Yet, very few make it past the development stage and most fail to influence clinical practice. The timely identification of high-risk pregnant women could deliver a tailored antenatal care regimen, particularly in low-resource settings. This study externally validated and calibrated previously published models that predicted the risk of preeclampsia, based on blood pressure (BP) at multiple time points in pregnancy, in a geographically diverse population. METHODS: The prospective INTERBIO-21st Fetal Study included 3,391 singleton pregnancies from Brazil, Kenya, Pakistan, South Africa, Thailand and the UK, 2012-2018. Preeclampsia prediction was based on baseline characteristics, BP and deviation from the expected BP trajectory at multiple time points in pregnancy. The prediction rules from the Avon Longitudinal Study of Parents and Children (ALSPAC) were implemented in the INTERBIO-21st cohort. RESULTS: Model discrimination was similar to the development cohort. Performance was best with baseline characteristics and a BP measurement at 34 weeks' gestation (AUC 0.85, 95 % CI 0.80-0.90). The ALSPAC models largely overestimated the true risk of preeclampsia incidence in the INTERBIO-21st cohort. CONCLUSIONS: After recalibration, these prediction models could potentially serve as a risk stratifying tool to help identify women who might benefit from increased surveillance during pregnancy.

Machine Learning-Based Risk Stratification for Gestational Diabetes Management.

Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention. However, appropriate assessment, management, and treatment have been shown to reduce the complications of GDM. This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health record (EHR) data from GDM patients. We internally trained and validated our model on a cohort of 1148 pregnancies at Oxford University Hospitals NHS Foundation Trust (OUH), and performed external validation on 709 patients from Royal Berkshire Hospital NHS Foundation Trust (RBH). We trained linear and non-linear tree-based regression models to predict the proportion of high-readings (readings above the UK's National Institute for Health and Care Excellence [NICE] guideline) a patient may exhibit in upcoming days, and found that XGBoost achieved the highest performance during internal validation (0.021 [CI 0.019-0.023], 0.482 [0.442-0.516], and 0.112 [0.109-0.116], for MSE, R2, MAE, respectively). The model also performed similarly during external validation, suggesting that our method is generalizable across different cohorts of GDM patients.

Physiological mechanisms of the impact of heat during pregnancy and the clinical implications: review of the evidence from an expert group meeting.

Many populations experience high seasonal temperatures. Pregnant women are considered vulnerable to extreme heat because ambient heat exposure has been linked to pregnancy complications including preterm birth and low birthweight. The physiological mechanisms that underpin these associations are poorly understood. We reviewed the existing research evidence to clarify the mechanisms that lead to adverse pregnancy outcomes in order to inform public health actions. A multi-disciplinary expert group met to review the existing evidence base and formulate a consensus regarding the physiological mechanisms that mediate the effect of high ambient temperature on pregnancy. A literature search was conducted in advance of the meeting to identify existing hypotheses and develop a series of questions and themes for discussion. Numerous hypotheses have been generated based on animal models and limited observational studies. There is growing evidence that pregnant women are able to appropriately thermoregulate; however, when exposed to extreme heat, there are a number of processes that may occur which could harm the mother or fetus including a reduction in placental blood flow, dehydration, and an inflammatory response that may trigger preterm birth. There is a lack of substantial evidence regarding the processes that cause heat exposure to harm pregnant women. Research is urgently needed to identify what causes the adverse outcomes in pregnancy related to high ambient temperatures so that the impact of climate change on pregnant women can be mitigated.

Complex Perinatal Syndromes Affecting Early Human Growth and Development: Issues to Consider to Understand Their Aetiology and Postnatal Effects.

Complex perinatal syndromes (CPS) affecting pregnancy and childhood, such as preterm birth, and intra- and extra-uterine growth restriction, have multiple, diverse contexts of complexity and interaction that determine the short- and long-term growth, health and development of all human beings. Early in life, genetically-guided somatic and cerebral development occurs alongside a psychism "in statu nascendi," with the neural structures subjected to the effects of the intra- and extra-uterine environments in preparation for optimal postnatal functioning. Different trajectories of fetal cranial and abdominal growth have been identified before 25 weeks' gestation, tracking differential growth and neurodevelopment at 2 years of age. Similarly, critical time-windows exist in the first 5-8 months of postnatal life because of interactions between the newborn and their environment, mother/care-givers and feeding practices. Understanding these complex relational processes requires abandoning classical, linear and mechanistic interpretations that are placed in rigid, artificial biological silos. Instead, we need to conduct longitudinal, interdisciplinary research and integrate the resulting new knowledge into clinical practice. An ecological-systemic approach is required to understand early human growth and development, based on a dynamic multidimensional process from the molecular or genomic level to the socio-economic-environmental context. For this, we need theoretical and methodological tools that permit a global understanding of CPS, delineating temporal trajectories and their conditioning factors, updated by the incorporation of new scientific discoveries. The potential to optimize human growth and development across chronological age and geographical locations - by implementing interventions or "treatments" during periods of greatest instability or vulnerability - should be recognized. Hence, it is imperative to take a holistic view of reproductive and perinatal issues, acknowledging at all levels the complexity and interactions of CPS and their sensitive periods, laying the foundations for further improvements in growth and development of populations, to maximize global human potential. We discuss here conceptual issues that should be considered for the development and implementation of such a strategy aimed at addressing the perinatal health problems of the new millenium.

Clinical consequences of gestational diabetes mellitus and maternal obesity as defined by asian BMI thresholds in Viet Nam: a prospective, hospital-based, cohort study.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is common in South East Asia, occurring at relatively lean Body Mass Index (BMI). Outside pregnancy, cardiometabolic risks increase at lower BMI in Asian populations, justifying Asian-specific thresholds for overweight and obesity. We aimed to explore the effects of GDM and obesity on perinatal outcomes using a WHO expert consultation-recommended Asian-specific definition of obesity. METHODS: This is a secondary analysis of a prospective, hospital-based, cohort study in Ho Chi Minh City. Participants were recruited from antenatal clinics between 19+ 0-22+ 6 weeks gestation and followed until delivery. GDM screening occurred between 24 and 28 weeks using WHO criteria. Obesity was defined as BMI ≥ 27.5 kg/m2, based on weight and height at recruitment. We assessed associations between GDM (singly, and in combination with obesity) and perinatal outcomes. Participants were categorised into four groups: no GDM/non-obese (reference group), GDM/non-obese, no GDM/obese and GDM/obese. Outcomes included primary caesarean section, hypertensive disorders of pregnancy (HDP), large-for-gestational-age (LGA), birth weight, preterm birth, and composite adverse neonatal outcome. Logistic and linear regressions were performed with adjustment for differences in baseline characteristics. RESULTS: Among 4,970 participants, 908 (18%) developed GDM. Compared to women without GDM, GDM increased risks for preterm birth (OR: 1.40, 95% CI: 1.09-1.78), higher birthweight (birthweight z-score 0.16 versus 0.09, p = 0.027), and LGA (OR 1.14, 0.89-1.46). GDM without obesity was associated with an increased risk of preterm birth (OR 1.35, 1.04-1.74). Obese women without GDM were more likely to deliver by caesarean section and have an LGA baby (1.80, 1.33-2.44 and 2.75, 1.88-4.03). The highest risks were observed amongst women with both GDM and obesity: caesarean Sect. (2.43, 1.49-3.96), LGA (3.36, 1.94-5.80) and preterm birth (2.42, 1.32-4.44). CONCLUSIONS: GDM was associated with an increased risk of preterm birth and larger newborn size. Using an Asian-specific definition of obesity, we demonstrate obese women with GDM are at the highest risk of adverse outcomes. Using a BMI threshold in pregnancy of 27.5 kg/m2 (between 19 and 22 weeks gestation) for Asian women can identify women who will benefit from intensified diabetes, nutritional, and obstetric care. This has relevance for obstetric service delivery within Asia, and other health systems providing pregnancy care for Asian expatriate women.

Understanding the relationship between social determinants of health and maternal mortality: Scientific Impact Paper No. 67.

Within this document we use the terms pregnant woman and women's health. However, it is important to acknowledge that it is not only people who identify as women for whom it is necessary to access care. Obstetric and gynaecology services and delivery of care must therefore be appropriate, inclusive and sensitive to the needs of those individuals whose gender identity does not align with the sex they were assigned at birth.

Common maternal infections during pregnancy and childhood leukaemia in the offspring: findings from six international birth cohorts.

BACKGROUND: Previous epidemiological studies have found positive associations between maternal infections and childhood leukaemia; however, evidence from prospective cohort studies is scarce. We aimed to examine the associations using large-scale prospective data. METHODS: Data were pooled from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA (recruitment 1950s-2000s). Primary outcomes were any childhood leukaemia and acute lymphoblastic leukaemia (ALL); secondary outcomes were acute myeloid leukaemia (AML) and any childhood cancer. Exposures included maternal self-reported infections [influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections and urinary tract infection (including cystitis)] and infection-associated symptoms (fever and diarrhoea) during pregnancy. Covariate-adjusted hazard ratio (HR) and 95% confidence interval (CI) were estimated using multilevel Cox models. RESULTS: Among 312 879 children with a median follow-up of 13.6 years, 167 leukaemias, including 129 ALL and 33 AML, were identified. Maternal urinary tract infection was associated with increased risk of any leukaemia [HR (95% CI) 1.68 (1.10-2.58)] and subtypes ALL [1.49 (0.87-2.56)] and AML [2.70 ([0.93-7.86)], but not with any cancer [1.13 (0.85-1.51)]. Respiratory tract infection was associated with increased risk of any leukaemia [1.57 (1.06-2.34)], ALL [1.43 (0.94-2.19)], AML [2.37 (1.10-5.12)] and any cancer [1.33 (1.09-1.63)]; influenza-like illness showed a similar pattern but with less precise estimates. There was no evidence of a link between other infections and any outcomes. CONCLUSIONS: Urinary tract and respiratory tract infections during pregnancy may be associated with childhood leukaemia, but the absolute risk is small given the rarity of the outcome.