Between 2015 and 2016, obesity affected approximately 13.7 million children and adolescents in the United States. Children with obesity are more likely to have high blood pressure, abnormal lipids and insulin resistance even though overt cardiovascular disease may not develop for decades.Additionally, children with obesity are at increased risk for musculoskeletal and mental health disorders, as well as certain cancers. This project aims to to better understand the development and progression of the metabolic derangement seen with obesity in children as well as the associations with development of comorbidities. Inflammation is considered to be the common link between obesity and its progression to various comorbidities. If we are able to identify an early signal in children with obesity who are more likely to develop diseases such as asthma, cardiovascular disease and cancers we will be able to address the problem in a timely fashion and offer focused intervention and treatment to these high risk children

Mental illness accounts globally for one third of years lived with disability, leading to tremendous loss of human, societal and economic potential. Most symptoms (e.g., depression, anxiety, conduct problems) emerge before adulthood, highlighting the first two decades of life as an important period of heightened vulnerability. Epigenetics has emerged as a biological system that captures the underlying genetic and environmental risk factors, but we do not yet understand the developmental dynamics in this system over time. Such knowledge, however, is critical if we are to understand how mental health disorders develop, and thus how they may be prevented.

The worldwide prevalence of obesity has tripled since 1975, and nearly a third of the world's population is now classified as overweight or obese. This rising prevalence of obesity is not only due to single genetic or environmental factors, but largely attributed to complex gene-environment interactions. Genetically predisposed individuals may be more prone to obesity in an obesogenic environment. Previous studies have focused on the identification of specific environmental factors that interact with genetic predisposition to obesity. The results indicate that physical activity, diet, age, gender and ethnicity could modulate the risk for obesity. Socioeconomic position (SEP) is relevant to all realms of behaviors and lifestyles, is a key factor that determines health across the lifespan, and may carry over to subsequent generations. The fact that BMI inequalities have persisted across different generations means that SEP is a significant factor to consider when understanding the role of environment. Parental SEP could influence the offspring’s risk of obesity through shared lifestyles such as dietary profile, home environment, social networks, and physical activity patterns early in life, which may be exacerbated by predisposition to obesity. Emerging studies have started to focus on SEP mobility across the life course, or intergenerational SEP mobility across two generations. The findings show evidence that higher parental education may be favorable in lowering obesity risk in offspring, especially for women. Longitudinal research should minimize reverse causation and allow us to investigate the dynamic interplay between one’s social strata of origin and own achieved social strata on obesity. However, it is still unclear how early the ancestors’ influence emerge and to what extent susceptibility to obesity is attenuated by SEP mobility.
We hypothesize that higher grandparental SEP, and upward SEP mobility across generations would diminish the grandchildren’s risk of obesity, compared to those who are always in social disadvantaged strata. The approach to consider ancestors’ SEP as a modifier in the heritability of BMI will add in tailoring appropriate interventions in future work.

Different parts of our brains communicate with one another as we learn new information during the day, then continue to communicate “offline” as we sleep. This overnight brain activity helps file memories for long-term storage, but the process is complex and delicate: lots of genes influence brain activity in ways we do not yet understand. We also need to establish why and how this process is disrupted in disorders like schizophrenia, which are associated with impaired sleep-dependent brain activity and memory. This study will investigate links between a set of schizophrenia-associated genes and brain function during sleep.

Participants will be asked to wear a ‘fitbit’-like device for 2 weeks of normal activity, so we can track when and how much they sleep. We will then use a comfortable sleep cap that contains an array of recording devices to monitor EEG brainwaves while participants sleep at home for 2 nights. By analysing the EEG data using machine learning methods (similar to those used for speech recognition), we will identify patterns of activity that make up their personal “sleep fingerprint”. This will allow us to test whether these fingerprints vary according to genetics in a healthy population, without interference from things like medication that complicate patient studies.

The main outcomes will be (1) development of novel analysis methods allowing us to capture brain activity, (2) a deeper understanding of the mechanisms that determine variation in patterns of brain activity during sleep and (3) a route towards understanding mechanisms of schizophrenia liability.

Many of the ALSPAC mums and fathers/partners may have also volunteered to take part in the UK Biobank cohort study. It is important that the study Data Managers and the researchers understand who is in both studies. This is because studies such as ALSPAC and UKBiobank are often used together in order to study rare events or small associations (where you need large numbers of participants for the statistical tests to work) or they are used to check and confirm whether findings in one study are also seen in another study. Finding the same patterns means there can be more confidence the findings are genuine, rather than occurring by chance or due to error. In both cases, the statistical tests assume the people in one study are different to the people in the other study. However we now know there is substantial overlap between participants in ALSPAC and UK Biobank (and possibly other studies).

ALSPAC and UKBiobank are ensuring that any duplication is flagged so researchers can take account of this (without knowing the identities of the participants). To inform thinking on how to best deal with this issue, it is necessary to produce descriptive statistics describing the characteristics of the ALSPAC participants who are in UKBiobank and how these differ from ALSPAC participants who are not in UKBiobank.

This project will explore the perception of environmental conditions on the face shape of 15 year old children.

The question that will be addressed does environmental surroundings influence face shape?

We now know that children who have asthma and are also obese are more likely to require an urgent visit to the doctor for breathing problems than those who are not obese. Obese children are thought to have a different type of asthma, which is more severe and harder to control. This may be because the current treatments we use to manage daily symptoms do not work very well for this type of asthma. There are many theories that could explain why this is happening. One possible reason could be that the child’s genetic makeup may also play a role in the development of obesity as well as influence their response to treatment.

To address this problem, my proposed project aims to investigate how obesity can affect the response to treatment in children with asthma. In the first phase, I will examine whether obese children with asthma are less likely to respond to standard treatments than those who have normal weight using cutting-edge statistical methods. In the second phase, I will investigate whether certain genetic changes influence the children’s ability to respond to asthma treatments and if these same changes are also affected by excess weight.

To do this, I will analyze data that has already been collected from a large number of children with asthma in many different countries, including ALSPAC. The data that will be used for this project forms part of an international collaboration in pediatric asthma, called the Pharmacogenomics in Childhood Asthma (PiCA) Consortium. PiCA is the largest pediatric asthma consortium in the world and has all the necessary data to successfully carry out this research.

I expect the results of this project to help doctors and scientists understand why obese children with asthma are suffering with more severe symptoms than others. Identifying the reasons why they are severe will promote the development of new targeted treatments for children with asthma who have excess weight, with the ultimate goal of improving the management and quality of life for these children.

Exposure to natural environments has been shown to be associated with child development and outcomes later in life. This will be investigated by measuring exposure to the natural environment using a combination of linked spatial environmental indicators and self-reported data. For example, normalized difference vegetation index (NDVI) measurements will be used alongside subjective measurements of parks visits and outdoor time. This will assess exposure multi-dimensionally, by measuring how much an individual visits natural environments as well as the abundance of vegetation in their living environments.

The primary outcome of interest will be emotional, social and behavioural development, primarily assessed using the Strengths and Difficulties Questionnaire. It will be assessed at multiple time points and corroborated from parent and teacher-reported sources. Other mental wellbeing/developmental measures will be used as secondary outcomes. The project will also investigate potential intermediate variables such as air pollution, maternal wellbeing, biomarkers of stress and birth outcomes. Access to the natural environment is often distributed
by social class / socio-economic status. Therefore detailed and multiple dimensional indicators will be used to account for confounding.

Reducing childhood obesity is a major global public health challenge. However, interventions designed at changing individual or family behaviours often do not show an impact. It is important therefore to better understand the causes of childhood obesity. Whilst there is some evidence that factors in pregnancy are associated with obesity, it is unclear whether these are causes. It is also unclear whether different factors affect obesity at different ages.

For example, there is evidence that children whose mothers had gestational diabetes may have a greater risk of childhood obesity. Studies in Europe and South Asia have also reported that this effect may not emerge until later on in childhood. There is also evidence that infants born preterm are about two-fold more likely to be obese later in life than those born at term. However few studies have examined the association between preterm birth and BMI in later childhood.

In terms of socioeconomic factors, lower family socioeconomic position (SEP) has been associated with higher BMI from as early as 9 months. However, there is inconsistent evidence on the extent as to whether which the effect of SEP increases, decreases or remains constant over time. There is also evidence that neighbourhood exposures in the post-natal period (such as exposure to green spaces and area-level deprivation) are also associated with childhood BMI; however to our knowledge the effect of these exposures before birth has not been investigated.

The LifeCycle project is an EU initiative to harmonise key data from a number of EU studies, including ALSPAC. To maintain participant anonymity, we will be using innovative software called DataSHIELD to analyse the data. DataSHIELD allows the remote analysis of summaries of this harmonised data, but prevents the access of individual data.

The LifeCycle project provides a unique opportunity to examine how early life factors might relate to childhood BMI. In this ‘proof of principle’ study we will use DataSHIELD to carry out remote analyses of LifeCycle cohorts, selecting variables that have evidence for some effect on childhood BMI (maternal and paternal education, area level deprivation, access to greenspace, gestational diabetes and gestational age at birth). We will conduct analysis on BMI at different points throughout childhood to explore how the effect of these factors emerges over childhood.