Chronic pain impacts multiple aspects of the lives of people who experience it with up to 10% of young people experiencing chronic pain into early adulthood, including symptoms of musculoskeletal pain, recurrent abdominal pain, and headaches. Importantly, it is estimated that up to 72% of those who experience chronic pain also experience significant levels of depression and anxiety (also referred to as common mental health problems hereafter). Despite these shocking estimates, few studies have sought to understand the causes, mechanisms, and longitudinal relationship between symptoms of chronic pain and common mental health problems.

The overarching aim of this project is to investigate the direction of the relationship between chronic pain and common mental health across a developmental period, from childhood to young adulthood, and explore potential mechanisms which may elucidate individual differences in the development of symptoms. To do this I will apply advanced statistical methods to large, genetically informative, longitudinal population-based studies to test the relationship between chronic pain symptoms including headaches, backache, and abdominal pain and common mental health problems, including anxiety and depression. Findings will provide a deeper understanding into the causal relationship between symptoms of chronic pain and common mental health across a key developmental period.

Autism spectrum disorder is a neurodevelopmental condition characterised by difficulties with reciprocal social communication, restricted interests, repetitive behaviours, and sensory difficulties. In addition to these core features autistic people are at extremely high risk of developing additional mental health difficulties. For example, between 40% to 78% of children with autism have at least one anxiety disorder, almost four times the rate observed in children without the diagnosis. In addition, rates of depression and ADHD are extremely high. Adding to this complex picture is the fact that an autistic person will often experience more than one of these additional diagnoses. Current statistical approaches to the study of the co-occurrence of mental health conditions in autism fail to consider of important associations at a symptom level both within and between conditions, potentially preventing vital insights into key problem areas which could be targeted by interventions. To address this, we plan to apply an alternative and novel approach (Network analysis) to understanding the underlying structure of co-occurring conditions in this highly complex and heterogeneous population.

Many people feel their emotional wellbeing is affected by their social media use, in positive and/or negative ways. However, scientific research has made little progress in uncovering the mechanisms by which social media could affect mental health. We now know that social media use can be modelled as a reward-learning process, whereby people engage with social media partly in order to gain ‘rewards’ such as ‘Likes’ and followers. However, we do not know which individual differences cause people to pursue and react to these rewards differently, nor whether different responses to these ‘rewards’ might determine the effects of social media on wellbeing.

We propose to investigate, for the first time, reward-learning behaviours as a potential mechanism linking social media and mental health. We intend to use the unique Twitter-data linkage ALPSAC has established for its participants in recent years. We will link the multidimensional questionnaire data regarding interindividual differences and developmental trajectories in the ALSPAC cohort, to data from these individuals’ Twitter profiles. We will then use computational models of reward-learning to model the Twitter data. This will allow us to associate behavioural and emotional trajectories with reward-learning behaviours on Twitter.

We will attempt to answer the following questions:
- Are behavioural and emotional characteristics, and other individual differences, associated with reward-learning behaviours on Twitter?
- Do reward-learning behaviours, such as the extent to which people alter their posting in response to the numbers of ‘Likes’ they receive, predict mental health trajectories over subsequent years?

There is considerable evidence that if the mother eats fish during pregnancy, the developing fetus benefits in a number of ways, including in behaviour intellectual abilities and aspects of vision. However, although a number of confounders have been taken into account there has always been a suspicion of residual confounding. The aim of this study is to use the exposome techniques to determine which features of the maternal
background, including features of her childhood, best predict whether she consumes fish during pregnancy. In parallel, aspects of the father's background will be assessed to determine whether similar factors predict his likelihood of eating fish. The results will be used to determine the features to be taken into account when looking at the possible consequences of eating fish.
The outcomes to be considered will be (a) the mother's physical health; (b) the mother's mental health; (c) the mother's beliefs and behaviours; (d) the child's development; (e) the child's health and behaviour; (f) the child's adolescence and early adulthood; (g) the father's physical and mental health, beliefs and behaviours.
All analyses will take account of the features identified in the exposome, as well as of assessing that the results are not due to the individual eating a healthy diet overall.

The COVID-19 pandemic has had an irrevocable impact on healthcare both in the UK and worldwide. To date, almost 20 million cases had been recorded in the England and 164,000 deaths[1]. As we make advances in understanding how we manage this disease clinically, it is also important that we use this time to better understand who is more susceptible to COVID infection and why. Understanding the impact of religion and spiritual beliefs/behaviours (RSBB) is a novel assessment of factors that may influence COVID-19 transmission and therefore incidence. In this study we will determine whether (and how) behaviours associated with religion or spiritual beliefs such as attending a place of worship, attending a faith school or belief in a divine power has any impact on COVID incidence in our cohort. Our analysis will be conducted using data collected before and after covid from mothers and partners from the ALSPAC cohort and classes of religiosity determined by latent class analysis on the same population (highly religious, moderately religious, agnostic, and atheist)[2]. These four groups have been determined using a range of religious belief indicators and socioeconomic risk factors. By conducting this analysis, we hope to contribute to the emerging description of COVID populations alongside other known risk factors such as social deprivation and co-morbidity.

[1]coronavirus.data.gov.uk.(n.d.).Official UK Coronavirus Dashboard. [online]Available at: https://coronavirus.data.gov.uk/details/deaths?areaType=nation&areaName=....
[2]Halstead I, Heron J and Joinson C. Identifying patterns of religiosity in adults from a large UK cohort using latent class analysis[version 1; peer review: awaiting peer review].Wellcome Open Res 2022, 7:192(https://doi.org/10.12688/wellcomeopenres.17969.1)

Evidence suggests that erratic (highly-variable) blood pressure, as well as high mean average blood pressure, could be associated with cardiovascular disease (CVD). As such, a better understanding of the associations of genes with blood pressure variability could provide further insight into the development of CVD. However, whilst there has been considerable research identifying the genetic factors associated with mean average blood pressure, little is known about the associations of genetic factors with blood pressure variability. The project aims to address this by analysing data from ALSPAC to investigate associations between blood pressure variability and genes.

Hypertension is one of the strongest risk factors for cardiovascular disease, which is the leading cause of death worldwide (1). Early-life exposures have been associated with blood pressure and the development of hypertension (2), with differential DNA methylation suggested as a potential underlying mechanism (3). Two large epigenome-wide association studies (EWAS) on systolic and diastolic blood pressure in adults have identified associations with DNA methylation (DNAm) levels of multiple Cytosine-phosphate-Guanine (CpG) sites (4,5). In the CHARGE consortium, one large-scale multi-ethnic EWAS on blood pressure in n=9,828 adults identified 13 CpGs annotated to 8 genes that were associated with blood pressure (4). A DNAm risk score based on these CpGs explained 1.4% and 2.0% of the inter-individual variation in systolic and diastolic BP, respectively. Another, more recent, EWAS on 4,820 multi-ethnic adults (6), identified 33 CpGs that explained an additional 3.3% and 4.0% of the inter-individual variation in systolic and diastolic blood pressure, respectively, beyond the traditional blood pressure risk factors age, sex, and body mass index (BMI), with 3 CpGs overlapping with the CHARGE study. The majority of the discovered CpG sites have been linked with other metabolic phenotypes including obesity, lipids, CRP, insulin resistance, and type 2 diabetes mellitus by previous epigenome-wide association studies (6), indicating that DNAm may be one of the common factors related to the concurrence of multiple cardiometabolic abnormalities.

Much less is known about the associations between DNAm and blood pressure in children. It is also not known if blood pressure associations with DNAm found in adults are already apparent in early childhood, late childhood and adolescence. Therefore, in this project, we aim to test the ability of a methylation risk score, developed using CpGs previously identified to be associated with SBP and DBP in adult EWAS meta-analysis by Richard (4) and Huang (6), to predict blood pressure at multiple timepoints in childhood and adolescence, and its added value compared to a risk score constructed only from the basic covariates age, sex and BMI.
This weighted methylation risk score was developed using the CpGs from the adult studies that survived a penalized elastic net regression in the ALSPAC adult data.

1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessmentof burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet (London, England).2012;380(9859):2224-60.
2. Lackland DT. Fetal and Early Life Determinants of Hypertension in Adults. Hypertension. 2004;44(6):811-2.
3. Wang X, Falkner B, Zhu H, Shi H, Su S, Xu X, Sharma AK, Dong Y, Treiber F, Gutin B, et al.. A genome-wide methylation study on essential hypertension in young African American males. 2013; 8:e53938.
4. Richard MA, Huan T, Ligthart S, Gondalia R, Jhun MA, Brody JA, Irvin MR, Marioni R, Shen J, Tsai PC,Montasser ME, Jia Y, Syme C, Salfati EL, Boerwinkle E, Guan W, Mosley TH Jr, Bressler J, Morrison AC,Liu C, Mendelson MM, Uitterlinden AG, van Meurs JB; BIOS Consortium, Franco OH, Zhang G, Li Y, Stew-art JD, Bis JC, Psaty BM, Chen YI, Kardia SLR, Zhao W, Turner ST, Absher D, Aslibekyan S, Starr JM,McRae AF, Hou L, Just AC, Schwartz JD, Vokonas PS, Menni C, Spector TD, Shuldiner A, Damcott CM,Rotter JI, Palmas W, Liu Y, Paus T, Horvath S, O'Connell JR, Guo X, Pausova Z, Assimes TL, SotoodehniaN, Smith JA, Arnett DK, Deary IJ, Baccarelli AA, Bell JT, Whitsel E, Dehghan A, Levy D, Fornage M. DNAMethylation Analysis Identifies Loci for Blood Pressure Regulation. Am J Hum Genet. 2017 Dec7;101(1):888-902.
5. Kazmi N, Elliott HR, Burrows K, Tillin T, Hughes AD, Chaturvedi N, et al. (2020) Associations between highblood pressure and DNA methylation. PLoS ONE 15(1): e0227728.
6. Huang Y, Ollikainen M, Muniandy M, Zhang T, Dongen Jv, Hao G, et al. Identification, Heritability, and Re-lation With Gene Expression of Novel DNA Methylation Loci for Blood Pressure. Hypertension.2020;76(1):195-205.
7. Gervin, K., Salas, L.A., Bakulski, K.M. et al. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data. Clin Epigenet 11, 125 (2019).

With the current cost of living crisis, it is important to include relevant questions on financial difficulties in the next questionnaires going out to both G0 and G1 in order to understand the impact this will have on their lives.

Obesity and Type 2 diabetes (T2D) (together, called 'diabesity') has an extensive economic burden, a negative impact on healthy life expectancy and is linked to lower quality of life particularly in children and young people (CYP). Diabesity is also strongly linked to health inequity: CYP who are from ethnic minorities, with lower socio-economic position, have much higher risk of diabesity and the poor health associated with it. We have recently shown, using data from primary care records, sharp rises in pre-diabetes and T2D in CYP; incidence rates of T2D in CYP have increased almost threefold from 2005 to 2019. There is an urgent need to invest in diabesity prevention and the development and implementation of specific plans to improve the care of CYP with diabesity. Obesity is the main risk factor for T2D which we are able to modify. Risk of T2D rises with increasing BMI, and only 1.5% of children with T2D have a healthy weight. Interventions to manage weight and physical activity have the potential to reduce T2D risk. Whilst we have identified a number of risk factors for diabesity, we do not know enough about how these multiple factors interact together and across time. This is known as a complex system, with multiple factors interacting and developing together across time. Complex systems make it difficult to identify prevention or intervention strategies that will work for everyone, because of the wide variation of possibilities in the system. This means that, for complex health problems like diabesity, we need to better understand and map the complex system before we can determine what prevention or intervention services would work for who, and when, and at what cost. This would also us to 'tailor' prevention programmes to specific groups to make them as effective as possible as well as estimating their economic costs and benefits.

In this project we will map the complex system of diabesity in CYP, integrating the existing literature with data collected in primary care and birth cohort studies (ALSPAC, Born in Bradford and the Millennium cohort), as well as expert stakeholder input. We will then use a simulation technique called agent-based modeling, which is used to study complex systems and allows us to use artificial intelligence to model what would happen if we introduce a prevention programme with specific features at specific time points to specific groups of people. We will also include health economic data in the model so that economic costs and benefits of programs can be modelled.

The final output of the project will be an open access decision making 'dashboard': a state-of-the-art multi-agent systems simulation tool for Type 2 diabetes prevention in children and young people with which policy makers, commissioners, service providers and clinicians can simulate tailored prevention programmes and strategies for their target population. We will produce training and support materials to maximise usability and uptake. The 'Diabesity Dashboard' will support commissioning of real-life programs tailored for specific groups that are most likely to work and be cost effective.