Early timing of puberty has established wide relevance for the child & family (stress, mental health, behavioural issues, and risk-taking behaviours), their educational performance, and for public health (obesity, Type 2 diabetes, cardiovascular disease, and several types of cancer). Hence, understanding the determinants of puberty timing is an important topic. Children who show rapid weight gain during early life and are overweight or obese are more likely than others to progress through puberty early. However, there is scarce and inconsistent evidence regarding specific foods or nutrients associated with the timing of puberty (1).
A previous analysis of ALSPAC in 2010 (prior to available puberty questionnaire data) was limited to a categorical assessment of age at menarche only (2). That analysis found that higher total protein intakes at age 3 and 7 years were associated with higher likelihood of reaching menarche by age 12 years 8 months (2), but two other studies found no association between total protein intake and the timing of onset of puberty (1). Also, inconsistent associations between total fat, monounsaturated fat and polyunsaturated fat intakes and puberty timing have been reported (1, 2). No association between total carbohydrate intake and puberty timing was found (1), nor in ALSPAC with specific carbohydrates (i.e. starch, sugar and fibre intakes) (2). In contrast, a recent study found that more frequent sugar-sweetened beverage intakes was associated with earlier menarche (1).
The present research will analyse diets in children at ages prior to the onset of puberty. In ALSPAC, these diets were assessed by parental report (food frequency questionnaire and diet diaries) and objective biomarkers of specific intakes (red cell membrane fatty acids, as markers of dairy fat, and fish and seafood intakes). The main outcome will be the timing of puberty development: onset, completion and duration of puberty. Both boys and girls will be included, and analysed separately.

References:
1) Villamor, E., & Jansen, E. C. (2016). Nutritional determinants of the timing of puberty. Annual review of public health, 37, 33-46.
2) Rogers, I. S., Northstone, K., Dunger, D. B., Cooper, A. R., Ness, A. R., & Emmett, P. M. (2010). Diet throughout childhood and age at menarche in a contemporary cohort of British girls. Public health nutrition, 13(12), 2052-2063.

My hypothesis is that interactive play between caregivers and their infants could improve growth, development and wellbeing through nurturing care, and through improving movement.
The aim of interactive play is that it encompasses two key elements – a positive interaction between caregiver and infant, and promotion of healthy movement behaviours in the child. From my formative work, I have been able to explore and develop novel methodology for objectively measuring infant movement using 24-hour accelerometry, and have consequently been able to measure and interpret detailed physical activity over a seven-day period in this population. Caregiver-infant interactions have primarily been measured using questionnaires or third party observation; however, these measures are limited by their subjectivity and intrusiveness. First person observation using headcams provide more comprehensive measures of caregiver-infant interaction, and the analysis of these measures has been optimised by Dr Pearson (Bristol, UK). Even with these novel ways of assessing movement and caregiver-infant interaction, I still need to determine relationships between these two behaviours, as well as optimal levels of interactive play. This project proposes to add accelerometry to a subsample of the ALSPAC-G2 cohort, in which headcams are already being used. This will be replicated by the cohort I am establishing in South Africa in order to allow for cross-context comparisons.

Understanding how genetic variations interact with an environmental exposure (GxE) is a critical step in predicting long-term health. For example, genetic differences between individuals influence the likelihood to develop adult mental health disorders triggered by adversity in early-life. The development of such disorders is hypothesized to be linked to environmentally-induced epigenetic modifica-tions, such as DNA methylation, at regulatory regions of genes linked to adult stress responses. DNA variations (polymorphisms) between individuals at these key regions could determine those sensitive to developing long-term effects following early-life adversity. However, it is difficult to identify these key regulatory regions in humans as we are all uniquely individual both genetically and environmentally.
We will develop and test a strategy to discover polymorphisms regulating GxE using a translational mouse model to control for genetic variation under a regulated environment together with an integrated genetic and epigenetic genome-wide approach. As proof of principle we will perform association studies with polymorphisms and DNA methylation at homologous regions in the longitudinal ALSPAC study of early environmental outcomes.

The number of preterm born children, defined as birth before the completed 37th week of gestation, has increased globally, with currently around 10% of all children worldwide being born preterm. Improvement in the management of risk pregnancies and neonatal care procedures in the last four decades have increased survival rates of all preterm born neonates and particularly those born very preterm. However, preterm birth has been linked to many long-term sequels related to cognitive function and mental health which are associated with gestational age at birth. Negative consequences are more likely among those born very preterm (<32nd week gestation), compared to moderate preterm (32nd-33rd weeks gestation) and late preterm children (34th-36th weeks gestation) but the latter two groups have less frequently been studied. In this project we study positive and negative aspects of wellbeing in preterm children, adolescents, and young adults and the role of protective factors for wellbeing potentially leading to resilience. Positive wellbeing involves high self-esteem and mental wellbeing, while negative wellbeing is defined by emotional and behavioural problems. We are studying protective factors on the individual level (academic skills, personality factors) and micro-system factors (family climate, parental mental health, peer relations). To answer these questions we analyse simultaneously data from six cohort studies (ALSPAC, British Cohort Study, Bavarian Longitudinal Study, Basel Study of Preterm Children, Millennium Cohort Study, and National Child Development Study) from UK, Germany, and Switzerland. Comparisons between the cohorts allow investigating differences between countries and epochs with different neonatal care procedures.

The Wellcome Trust Longitudinal Population Studies (LPS) Strategy encourages integration of data from a variety of resources, including commercial data on shopping habits; and states that research methods are needed to underpin efficient linkages of multiple datasets. Our project addresses this by building on core Avon Longitudinal Study of Parents and Children transactional data linkage program, seeking to develop a methodology for efficient integration of transactional data into LPS research.

This 14 months project will create a methodology, a software package, guidelines and three proof-of-concept studies to the use of linked transactional data in LPS. The guidelines will include the description of the full cycle of data linkage from workshops and surveys with participants about feasibly and acceptability of such data linkage, through consent campaigns, data linkage and a framework of working with linked dataset. As a part of the project we will create a software package that can integrate the data into LPS and make it usable for LPS researchers. Further, we will run exploratory and dissemination workshops with LPS researchers to first investigate utility of data linkage and second provide them with the tools to set up transactional data linkages and conduct research with transactional data. The framework of using linked transactional data with LPS developed in this research project can be rolled out to be used in other cohort studies (e.g., through Cohort and Longitudinal Studies Enhancement Resources, CLOSER), making possible a harmonised longitudinal population studies approach for studying public health with transactional data.

Childhood recurrent abdominal pain (RAP) is very common, affecting around 10% of children, and highly co-morbid with anxiety. The link between anxiety and eating disorders is well established, and clinically I have noticed that many adolescents with eating disorders suffered from RAP in childhood. A theory underpinning childhood RAP is that children develop a fear of normal somatic gut sensations, and similarly one of the core features of eating disorders, such as anorexia nervosa and bulimia, is a fear of somatic sensations. I would like to analyse the ALSPAC dataset to assess whether childhood RAP predicts later eating disorder cognitions and behaviours.

It is highly likely that there is a broad and reproducible footprint of variation in body composition (lean and fat mass) on the circulating metabolome. Technological developments are now such to allow a broad characterisation of circulating metabolome using an approach called mass spectrometry which can allow for an analysis of the cell derived products of metabolic processes by exposures of interest. Here the exposure of interest is body mass index (BMI) - or weight given height.

The problem with looking at the direct relationship between BMI and measures of the metabolome is that BMI is related to many other biological and non-biological things. Therefore, when one analyses the relationship between BMI measured directly and health outcomes (like metabolites), then it is the case that BMI and those other things are jointly assessed as they are correlated. To avoid this, it is possible to conduct randomised controlled trials which shuffle up these correlations before assessing the impact of a risk factor - however you cannot randomise to BMI. Therefore, by taking genetically predicted BMI, it is possible to avoid this problem and to assess the impact of BMI variation on metabolite measures in ALSPAC.

We will do this by using the ALSPAC genetic data to generate a score for the burden of genetic contributions to higher and lower BMI, then select samples from storage for metabolite measurement at a company called Metabolon.

The main analysis of the data generated by this experiment will be focused on characterising the impact of BMI on the human metabolome. In principle, the two groups generated in this sample (high and low BMI as prescribed by being at the ends of a distribution of BMI genetic risk) will be compared.