There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother–child dyads from the longitudinal Southampton Women’s Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6–7 and 8–9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman’s = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.

Exposure to adversity in childhood is associated with elevations in numerous physical and mental health outcomes across the life course. The biological embedding of early experience during periods of developmental plasticity is one pathway that contributes to these associations. Dimensional models specify mechanistic pathways linking different dimensions of adversity to health and well-being outcomes later in life. While findings from existing studies testing these dimensions have provided promising preliminary support for these models, less agreement exists about how to measure the experiences that comprise each dimension. Here, we review existing approaches to measuring two dimensions of adversity: threat and deprivation. We recommend specific measures for measuring these constructs and, when possible, document when the same measure can be used by different reporters and across the lifespan to maximize the utility with which these recommendations can be applied. Through this approach, we hope to stimulate progress in understanding how particular dimensions of early environmental experience contribute to lifelong health.

Advances in technology enabled the development of a web-based, pictorial FFQ to collect parent-report dietary intakes of 7-year-old children in the Growing Up in Singapore Towards healthy Outcomes study. This study aimed to compare intakes estimated from a paper-FFQ and a web-FFQ and examine the relative validity of both FFQ against 3-d diet records (3DDR). Ninety-two mothers reported food intakes of their 7-year-old child on a paper-FFQ, a web-FFQ and a 3DDR. A usability questionnaire collected participants’ feedback on the web-FFQ. Correlations and agreement in energy, nutrients and food groups intakes between the dietary assessments were evaluated using Pearson’s correlation, Lin’s concordance, Bland–Altman plots, Cohen’s κ and tertile classification. The paper- and web-FFQ had good correlations (≥ 0·50) and acceptable-good agreement (Lin’s concordance ≥ 0·30; Cohen’s κ ≥ 0·41; ≥ 50 % correct and ≤ 10 % misclassification into same or extreme tertiles). Compared with 3DDR, both FFQ showed poor agreement (< 0·30) in assessing absolute intakes except micronutrients (web-FFQ had acceptable-good agreement), but showed acceptable-good ability to classify children into tertiles (κ ≥ 0·21; ≥ 40 % and ≤ 15 % correct or misclassification). Bland–Altman plots suggest good agreement between web-FFQ and 3DDR in assessing micronutrients and several food groups. The web-FFQ was well-received, and majority (81 %) preferred the web-FFQ over the paper-FFQ. The newly developed web-FFQ produced intake estimates comparable to the paper-FFQ, has acceptable-good agreement with 3DDR in assessing absolute micronutrients intakes and has acceptable-good ability to classify children according to categories of intakes. The positive acceptance of the web-FFQ makes it a feasible tool for future dietary data collection.

The incidence of preterm birth (PTB), delivery before 37 completed weeks of gestation, is rising in most countries. Several recent small clinical trials of myo-inositol supplementation in pregnancy, which were primarily aimed at preventing gestational diabetes, have suggested an effect on reducing the incidence of PTB as a secondary outcome, highlighting the potential role of myo-inositol as a preventive agent. However, the underlying molecular mechanisms by which myo-inositol might be able to do so remain unknown; these may occur through directly influencing the onset and progress of labour, or by suppressing stimuli that trigger or promote labour. This paper presents hypotheses outlining the potential role of uteroplacental myo-inositol in human parturition and explains possible underlying molecular mechanisms by which myo-inositol might modulate the uteroplacental environment and inhibit preterm labour onset. We suggest that a physiological decline in uteroplacental inositol levels to a critical threshold with advancing gestation, in concert with an increasingly pro-inflammatory uteroplacental environment, permits spontaneous membrane rupture and labour onset. A higher uteroplacental inositol level, potentially promoted by maternal myo-inositol supplementation, might affect lipid metabolism, eicosanoid production and secretion of pro-inflammatory chemocytokines that overall dampen the pro-labour uteroplacental environment responsible for labour onset and progress, thus reducing the risk of PTB. Understanding how and when inositol may act to reduce PTB risk would facilitate the design of future clinical trials of maternal myo-inositol supplementation and definitively address the efficacy of myo-inositol prophylaxis against PTB.

Initiatives to optimise preconception health are emerging following growing recognition that this may improve the health and well-being of women and men of reproductive age and optimise health in their children. To inform and evaluate such initiatives, guidance is required on indicators that describe and monitor population-level preconception health. We searched relevant databases and websites (March 2021) to identify national and international preconception guidelines, recommendations and policy reports. These were reviewed to identify preconception indicators. Indicators were aligned with a measure describing the prevalence of the indicator as recorded in national population-based data sources in England. From 22 documents reviewed, we identified 66 indicators across 12 domains. Domains included wider (social/economic) determinants of health; health care; reproductive health and family planning; health behaviours; environmental exposures; cervical screening; immunisation and infections; mental health, physical health; medication and genetic risk. Sixty-five of the 66 indicators were reported in at least one national routine health data set, survey or cohort study. A measure of preconception health assessment and care was not identified in any current national data source. Perspectives from three (healthcare) professionals described how indicator assessment and monitoring may influence patient care and inform awareness campaign development. This review forms the foundation for developing a national surveillance system for preconception health in England. The identified indicators can be assessed using national data sources to determine the population’s preconception needs, improve patient care, inform and evaluate new campaigns and interventions and enhance accountability from responsible agencies to improve preconception health.

Recent studies implicate maternal gestational diabetes mellitus (GDM) in differential methylation of infant DNA. Folate and vitamin B12 play a role in DNA methylation, and these vitamins may also influence GDM risk. The aims of this study were to determine folate and vitamin B12 status in obese pregnant women and investigate associations between folate and vitamin B12 status, maternal dysglycaemia and neonatal DNA methylation at cytosine-phosphate-guanine sites previously observed to be associated with dysglycaemia. Obese pregnant women who participated in the UK Pregnancies Better Eating and Activity Trial were included. Serum folate and vitamin B12 were measured at the oral glucose tolerance test (OGTT) visit. Cord blood DNA methylation was assessed using the Infinium MethylationEPIC BeadChip. Regression models with adjustment for confounders were used to examine associations. Of the 951 women included, 356 (37.4%) were vitamin B12 deficient, and 44 (4.6%) were folate deficient. Two-hundred and seventy-one women (28%) developed GDM. Folate and vitamin B12 concentrations were not associated with neonatal DNA methylation. Higher folate was positively associated with 1-h plasma glucose after OGTT (β = 0.031, 95% CI 0.001–0.061, p = 0.045). There was no relationship between vitamin B12 and glucose concentrations post OGTT or between folate or vitamin B12 and GDM. In summary, we found no evidence to link folate and vitamin B12 status with the differential methylation of neonatal DNA previously observed in association with dysglycaemia. We add to the evidence that folate status may be related to maternal glucose homoeostasis although replication in other maternal cohorts is required for validation.