Studies have suggested that waist (cm)/height (cm) ratio (WC/H) is a useful alternative measure of central adiposity and obesity related metabolic risk in adults and children than body mass index (BMI) (1-3). In practice, BMI has shown poor performance in certain cohorts and does not indicate an individual’s distribution of fat. WC/H focuses on central adiposity, a known cardiometabolic risk factor for adults and children. Furthermore, calculating BMI requires weighing equipment, a calculator and potentially software/charts. WC/H only needs a tape measure, uses a simple calculation and has a pre-defined ratio to define risk across participant types (cited as >0.5).

NICE have recently recommended the use of WC/H alongside BMI to assess and predict weight-related conditions. There is currently a paucity of research describing the life course patterns of WC/H or demonstrating that it provides equal or improved prediction in metabolic health from childhood to adult life compared to BMI. There is also little evidence relating WC/H to outcomes or intermediates related to central adiposity in particular – e.g., hypertension and non-alcoholic fatty liver disease.

This study aims to 1) chart the trajectory of WC/H compared to BMI through ages 7, 11, 15 and 24, 2) examine associations of WC/H or BMI, and blood pressure and liver health at these ages and 3) compare these associations to explore if WC/H is more discriminatory in defining risk. This will provide additional evidence for the recommendation of an easier measure to assess clinical need for weight management interventions across childhood and adolescence.

References
1. Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than using a 'matrix' based on BMI and waist circumference. BMJ Open. 2016 Mar 14;6(3):e010159.
2. Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, Georgiou C, Kafatos A. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord. 2000 Nov;24(11):1453-8.
3. Khoury M, Manlhiot C, McCrindle BW. Role of the waist/height ratio in the cardiometabolic risk assessment of children classified by body mass index. J Am Coll Cardiol. 2013 Aug 20;62(8):742-51.

Exposure to air pollution has been associated with poorer cardiovascular health and cardiovascular disease, and inflammation is one of the postulated mechanisms of this relationship. Most studies have investigated the association between air pollution and inflammation using C-reactive protein (CRP), particularly in adulthood. Evidence in younger ages and using other inflammatory markers is limited. This study will explore the association between air pollution and inflammation from childhood to early adulthood using different inflammatory markers.

This project aims to assess the association between DNA methylation and breastfeeding using data from ALSPAC and other birth cohorts with similar data. Breastfeeding has both short and long term beneficial effects on child health. DNA methylation is a molecular mechanism which might mediate the relationship between breast feeding and beneficial health effects in children but we do not yet have good evidence to show if DNA methylation is associated with breast feeding. This research aims to answer this knowledge gap.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort, based in and around Bristol, UK. Maintaining data security and participant anonymity and confidentiality are key principles for the study, hence why data access is restricted to bona fide researchers and ALSPAC data are not openly available. Despite these valid reason for restricting data availability, this position is somewhat in conflict with emerging best scientific practices, which encourage making data openly available to facilitate reproducible and replicable open scientific research.

Given the rich nature of the resource, ALSPAC data may also be valuable as a educational tool, such as for teaching methods such as longitudinal modelling or approaches to modelling missing data. To aid these efforts, we want to assess methods for generating and making openly-available synthesised ALSPAC data; these synthesised datasets are modelled on the original ALSPAC data, thus maintaining variable distributions and relations among variables, while at the same time preserving participant anonymity and confidentiality. We will explore how data can be synthesised using the ‘synthpop’ package in the R statistical programming language, and aim to present a list of guidelines which all researchers wishing to release such synthesised ALSPAC data must follow.

Crime in adulthood is a well-established long-term consequence of childhood adversity, but the exact mechanism underlying this process is not well known. One mechanism suggested by research is that childhood adversity results in impaired neurobiology, emotional and executive functioning, and self-regulation. Disrupted emotional processing is linked to behavioural problems such as conduct disorders due to an impaired ability to recognise certain emotions, namely anger and fear, with the latter being linked to increased violence. Impaired executive functioning is hypothesized to lead to decreased inhibitory control and cognitive flexibility, while disrupted self-regulation is linked to increased aggressiveness due to an inability to regulate one’s thoughts, actions, and emotions. These deficiencies and their outcomes have been linked to crime, especially violent crime. However, other factors such as genetics, socioeconomic status and alcohol use can increase the likelihood of experiencing additional adversity while also influencing the nature and extent of these impairments. The resulting differences in cognitive and emotional impairments may result in different types of crimes being committed. We hope that better understanding of the processes mediating adverse childhood experiences (ACEs) in children and crime in adulthood can inform more precise therapy employed by clinicians treating these ACEs, as well as more cost-effective measures for policymakers to target the effects of these ACEs in vulnerable populations.

This project aims to understand growth during and after a pregnancy in which the placenta is faulty and less able to nourish the fetus, and its impact on the key developmental brain function of sleep, via mathematical modelling.

Growth is a crucial part of healthy development. So, it is important to understand what happens when growth is impaired. In some pregnancies, the placenta cannot transfer as much oxygen and sugar to the fetus as required. To deal with this, the fetus prioritises its most important organ - the brain - and directs most oxygen and sugar there. This results in the size of the brain relative to the body being larger than usual.

Sleep is a key developmental brain function, supporting the formation of memories. This is why babies spend up to 97% of their time asleep. Because sleep occurs to help our brain but also the rest of our body, the size of the brain relative to the body explains for how long we need to sleep. We believe that the brain growing too large relative to the body may disorganise sleep, and make it less good at helping to form memories. This could explain why infants who grow like this sometimes have later learning difficulties.

In this project, we are going to collate and collect data which will allow us to mathematically test our theory that faulty growth impairs sleep. The results will help to understand this condition better, and could suggest new ideas for personalised therapies, e.g. that strengthen sleep.

The National Mental Health Intelligence Network (NMHIN) has been commissioned by NHSE to determine whether an estimate of mental health need for 0–4-year-olds can be produced, with the aim of being able to produce national and local area prevalence estimates for this age group.

This work included an initial feasibility assessment which was advised by an expert reference group (ERG) of experts in the field. The ERG gave a strong steer of using an existing cohort study that assesses the mental health of the children in this age group, considering limited availability of data around the topic which can be generalized at national level.

Currently, we are looking at developing an interim national prevalence that gives an estimate of mental health need among this age group. This will be done by selecting a study sample from ALSPAC, looking at the mental health outcomes of the study group and then applying the prevalence at a national and local level.

Individuals with Attention deficit hyperactivity disorder (ADHD) may be more likely to suffer from mental health problems than the general population. We aim to investigate the relationship between ADHD and depression using data from a longitudinal study of over 14000 children born in the early 90s with ongoing data collection. We will investigate whether specific risk factors such as bullying and childhood adversity may influence this relationship and act as modifiable targets for intervention.

Attention-Deficit Hyperactivity Disorder (ADHD) has symptoms of inattention, hyperactivity, or both. However, patients with ADHD often experience symptoms of other disorders such as autism spectrum disorder (ASD), epilepsy, conduct disorder, or anxiety symptoms. Previously, it was reported that there are shared heritability and cognitive process across the conditions, implying that there are common underlying biopsychological factors that have not been identified.Epigenetic biomarkers, especially blood DNA methylation (DNAm) became significantly important in understanding neurodevelopmental disorders such as ADHD. Even though the associations between DNAm and each condition were investigated, there is a limited amount of research on DNAm biomarkers across ADHD-related phenotypes (ADHD and co-occurring symptoms). We hypothesized that the underlying biological factors would be specific to clusters of conditions and could be used as a diagnosis factor for children. Therefore, we propose to investigate the relationships between each of the individual traits linked to ADHD and DNAm to reveal their epigenetic similarities and differences. The epigenetic similarities and differences will give in-depth insight to understand ADHD. We will use a series of computational methods including EWAS and machine learning to investigate the DNAm biomarkers of complex ADHD-related phenotypes from around 1500 participants from the Avon Longitudinal Study of Parents and Children.