Bipolar disorder (BD) is a debilitating mental health condition, characterised by severe shifts in mood, that can range from disabling highs (i.e., mania/hypomania) to extreme lows (i.e., depression). Approximately 1% of the population are affected by bipolar (Pini et al., 2005), with most people experiencing the onset of mood symptoms prior to their 20s (Geoffroy et al., 2013). Despite this, little is known about the predictors to bipolar disorder and hypomania symptoms, particularly among young people. Intervening early in the development of bipolar is a top clinical priority, and one that may have the potential to limit its functional and symptomatic impact on those affected. Thus, predicting the onset of bipolar/hypomania prior to its onset, may help clinicians/researchers to develop novel, tailored preventative strategies and interventions for young people.

Health and care services are increasingly planned and provided using patient data shared securely across multiple health-care settings. However, we know that people’s health is influenced by a wide range of things, including social, cultural, and economic factors. Unfortunately, we don’t currently have data resources that link information about these things to improve service planning, individual care and research.
Our project will fund the information technology and people to bring together information from multiple sources, including, local councils and other services such as the police, as well as the detailed data collected as part of research studies. We will do this using secure technologies and protecting patients’ confidentiality.
By combining and analysing data, we will be able to work with public services to help identify people at higher risk of a condition or disease, and deliver better, more joined up care that is both more effective and offers better value.

The impact of the public health measures adopted to control the COVID-19 pandemic on young infants and family interactions is unknown. New information is vital to inform future policies and recovery for families and aid infant development. Infants may show more unsettled and
restless behaviours even if they are not aware of the situation, however, they may also show positive behaviours benefiting from more parental attention if parents are home. Understanding of both is important to manage further transitions in an ever changing home environment. Furthermore , young children will have been separated from wider family and friends. Online chats may provide a helpful substitute to retain attachments, but how young infants respond to such interactions is unknown . Using our existing methods to code indepeth parent and infant verbal and non-verbal behaviours we can compare parent and infant behaviours in interactions at this time to already collected and coded interactions of ALSPAC-G2 families pre-pandemic. We can also compare infant behaviours towards parents in the same room and a mimicked online interaction (where the one parent joins a chat from another room).

Infection by the sars-cov-2 virus (coronavirus) causes a range of flu-like symptoms which can be mild or serious. To date, testing for coronavirus in the Bristol area has focussed on people with severe symptoms. This means that existing test results do not give a full picture of how many people in the Bristol area have already had coronavirus infection.

Rather than using tests, it may be possible to track the spread of coronavirus over time by looking at how many people are experiencing flu-like symptoms. This analysis plans to divide flu-like symptoms into two groups (those which are specific to coronavirus and those which are common in many types of cold or flu) and compare how the rate of these has changed between October 2019 and June 2020. This may give more information about the posible true rate of mild infection in the Bristol area, as well as how the number of people with coronavirus is changing over time.

Quantifying social contacts is essential for understanding how a disease will spread in a population. We have measured contact patterns using surveys that have formed the basis of predictive modelling presented at the UK Government Modelling advisory group on COVID-19. Here, we will measure social contact patterns during this unprecedented time of social distancing in the UK and compare to other studies and settings.

The on-going childhood obesity epidemic is accompanied by dramatic increases in childhood metabolic disorders, such as paediatric type 2 diabetes and a cluster of metabolic complications. However, children with obesity are not all equally prone to developing these metabolic disorders; research has shown that there is a subset of obese children who have a more ‘favourable’ health profile, including good insulin sensitivity and normal blood pressure, glucose regulation and blood lipid levels. This has been termed metabolically healthy obesity (MHO), which contrasts with metabolically unhealthy obesity (MUO), where excess body fat is accompanied by metabolic disorders. Since most of the research to date on MHO and MUO has focused on adults, the specific determinants of MHO and MUO in paediatric groups are still not clearly understood.

The concept of metabolically unhealthy and metabolically healthy profiles can also be applied to individuals of ‘normal’ weight, since normal-weight individuals can also have the metabolic and/or inflammatory abnormalities commonly observed in obese people. This group has been termed normal-weight metabolically unhealthy (NWMU). NWMU adults are also at increased risk of cardiometabolic diseases, however, little is known about the health profile and predictive characteristics of NWMU in children.

The relatively high proportion of metabolically healthy individuals in the obese (ranging from 4-60%) and metabolically unhealthy individuals in the lean population suggests that besides from total calories, diet quality could be an important influencing factor on metabolic health. However, few studies have looked at how dietary patterns in children might influence the sub-groups of obesity. Therefore, this study aims to identify which dietary patterns, lifestyle behaviours and socio-demographic factors in children are related to MHO and MUO, as well as NWMU in adolescents. Gaining a clearer understanding of the health profile, characteristics and potential determinants of MHO and MUO in paediatric groups could be valuable in developing more efficient and targeted treatment approaches for these groups of children with obesity.

Acute lymphoblastic leukaemia (ALL) is the most common cancer in children. Whilst a range of genetic abnormalities have been identified as crucial in disease initiation, these abnormalities alone are not sufficient for transformation. Maternal exposures during pregnancy such as smoking, or folate intake have the potential to impact on offspring DNA methylation. This epigenetic mark has also been shown to be altered in ALL. We have previously identified 5 CpG sites that have altered DNA methylation due to maternal smoking and folate intake and we aim to identify whether changes to DNA methylation at these sites affects risk of ALL.

Children born with the ability of self-regulated cues of hunger and satiety. During early life, this ability is developed in a good way will facilitate healthy eating behaviours, which may be associated appropriate weight gain. In the first two years, milk feeding and complementary feeding are main feeding aspects，which could have impact on children’s self-regulation of hunger and satiety cues. Previous studies showed that breastfeeding duration and timing of solid food introduction were associated with children’s eating behaviours(eg. enjoyment of food, food fussiness, picky eating, eating slowly, overeating and so on) and weight growth. For example, Samantha L. Rogers found that breastfeeding duration was related to slower weight gain during 1 to 6 month and 1 to 12 month, and that breastfeeding duration was associated with slowness in eating at 12 m. Wang jing found that introducing complementary foods before 4 months of age compared to at 4~6 months was associated with and increased risk of being overweight and obesity during children. These findings indicated that infant feeding practice may exert an important role on development of children’s weight health However, the evidence about the relationship between concrete feeding mode such as bottle feeding, breast feeding, feeding on demand and children’s eating behaviors and weight growth were limited. Whether children’s eating behaviors and related gene variant play a mediated role on the relationship between feeding practices and weight gain also deserves exploring from a longitudinal perspective.

School closures have been a central component of many countries’ response to contain COVID-19; however, we don’t know:
• What children do during unplanned school closures
• Whether children are infectious
• Whether other strategies could be equally effective.
Much of the policy is based on influenza, which does affect children more than SARS-CoV-2 [1].

Preliminary evidence suggests that children are able to become infected with SARS-CoV-2, but are either asymptomatic or show mild symptoms, with a minority of cases progressing to disease [2]. The role of healthy children in transmitting SARS-CoV-2 remains uncertain. It is of interest to define how many children do/don’t experience COVID-19 symptoms and have evidence of having had SARS-CoV-2 infection, this may have implications for transmission dynamics and policy decisions. Along with many European countries, the UK decided to close schools from March 23rd in an effort to slow SARS-CoV-2 transmission - only some vulnerable children and those of key workers remain in school. We do not know what children’s contact patterns are during this unplanned school closure, but there is evidence that contacts inside and outside the home might continue, especially for older children and where parents don’t agree with closures [3]. We propose to deploy a survey to G2 ALSPAC children to capture data on symptoms and contact patterns during the COVID-19 pandemic, as well determine evidence of SARS-CoV-2 infection through saliva antibody detection methods.