Family environments including relationships between parents and children are crucial for psychological adjustment and mental-health across childhood and adolescence. Parent reports as well as parent and child behaviours during their interpersonal interactions ("transactional" dynamics) are seen as particularly important. Moreover, the onset of mental health problems can disrupt previously healthy parent-child transactional dynamics. The aim of this project, which is an extension of the existing project B2159, is to explore family relationships (siblings and parents) from early to late development and their importance for children's adjustment.

Inflammation is key to the development of cardiovascular and metabolic (cardiometabolic) diseases. However, it is largely unknown how inflammation markers relate to early measures of cardiometabolic risk, or how these relationships change as people get older. Using existing data collected by large international cohorts from infancy to old age, we will investigate the cross-sectional relationship between inflammation markers, metabolic health, and pre-clinical cardiovascular phenotypes at different stages of life. Data collected in mothers and offspring as part of ALSPAC will be an important addition to LIQUID. This project will help identify inflammation markers that are most informative for early cardiometabolic risk, which is critical for unlocking opportunities to intervene prior to the development of irreversible vascular damage.

90% of liver disease is preventable with the main causes in the UK being damage from alcohol and obesity. Sadly, liver disease is now a leading cause of death in 35-49 year olds. By the time people come to hospital with liver disease, 1 in 6 will die during their stay. However, it takes many years to develop liver disease, often with no symptoms. That gives us a chance to detect it before serious complications develop.
I am working with one of the biggest birth cohorts in the world, the Children of the 90s. They have looked for liver disease in their members aged 17 and 24years old. When they were 24years, I found 1 in 5 had a fatty liver and 1 in 40 already had liver scarring, mainly due to obesity and alcohol.
I plan to repeat this with our members, now 30years, with liver scans and blood tests. An Academy Starter Grant funding would help with analysis of 2000 blood tests from our members. We want to understand why some adults are developing liver disease earlier. Are there factors in childhood and adolescence we can tackle? By understanding who is developing liver disease early, and why, we can help with public health messages we provide. We can also target people who most need our support. Finding people with liver disease early and getting them seen by a specialist will reduce the chance of them getting sick and dying from liver disease.

Asthma is the most common chronic disease affecting children. Although we do not fully understand what causes asthma, there is good evidence that social conditions early in a child’s life play an important role in its development. Children from poorer backgrounds are more likely to develop asthma, suffer from asthma attacks, require hospitalisation and die from their asthma.
In this study I will unpick why asthma disproportionately impacts poorer children in the UK. I will explore the reasons for socio-economic differences in asthma outcomes in the UK and examine the conditions in children’s lives which gives rise to such differences, such as, exposure to second-hand smoke and poor housing conditions.
To do this, I will use data from ALSPAC and other UK birth cohorts that have collected detailed information on asthma and allergy from participating children at regular intervals in their lives.
Findings from this research will help to establish how to reduce unfair differences in asthma outcomes for children, informing policy and practice to deliver services and interventions in the populations that are most affected by asthma. The impact of this will be to reduce inequalities in children’s health over the lifecourse and costs to healthcare systems.

The mother's internal and external environment during the developmental stages of the fetus influences the health of the offspring. According to the evolutionary origins of health and disease theory,
environmental factors influence offspring and also affect health in adulthood. Recently, studies based on this theory have attracted attention for their clinical utility in identifying risk groups for various diseases.
Neurodevelopmental disorders (NDD) such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are on the rise globally and may be caused by exposure to certain
environments, lifestyle (including diet) prenatally during pregnancy. Researching the determinants of prenatal environment and lifestyle on the mechanism of NDD onset serves to fill a useful knowledge gap
in preventing the increase of these disorders and disseminating preventive medicine.
The goal of this research is to find out how different types of prenatal data (like lifestyle, nutrition, sociodemographics, and environment) interact to make a prediction algorithm (conversion score) for how
a child's brain develops. In this way, the statistical analysis of the dataset will try to find the most accurate predictors of how children's neurodevelopment and NDD will turn out. We'll make a machine learning
algorithm (like a support vector machine or a random forest) to rank the predictors by how well they can predict and choose a weighted average of that power.

Depression is the leading cause of disability worldwide and is a major contributor to the overall global burden of disease. Further, depression is one of the most common mental health problems in young people, with an estimated prevalence of 5.6%. Most importantly, depression in young people is frequently an indicator of recurrent or chronic depression which stretches into adulthood. Thus, to recognise, prevent, and treat depression in young ages is crucial. However, a number of important issues need further investigation to aid early intervention in depression in young people. And among these, it is crucial to identify those risk factors that have greatest impact in the development of depression in young people, as these are the factors that should be targeted when designing early intervention in young people. Therefore, the aim of this study is to identify relevant risk factors for the development of depression in young people. To do this, we will use secondary data analyses, using the Avon Longitudinal Study of Parents and Children (ALSPAC), which comprises around 14,000 individuals recruited at birth. Risk factors to be investigated in this study include sleep, cognitive function, diet, substance abuse, childhood abuse, parenting style, academic performance, social relationships, school connectedness, or self-esteem, among others. Further, we will focus on depression symptoms occurring at several time points across adolescence (from 13 to 21 years old), using a validated questionnaire on depression (the Short Mood and Feelings Questionnaire). Expected statistical analyses to apply include correlations, regression analyses and path analyses, using SPSS.

Depression is the leading cause of disability
worldwide and is a major contributor to the overall global burden of disease.
Further, depression is one of the most common mental health problems in young
people, with an estimated prevalence of 5.6%. Most importantly, depression in
young people is frequently an indicator of recurrent or chronic depression which
stretches into adulthood. Thus, to recognise, prevent, and treat depression in
young ages is crucial. However, a number of important issues need further
investigation to aid early intervention in depression in young people. And among
these, it is crucial to identify those risk factors that have greatest impact in the
development of depression in young people, as these are the factors that should be
targeted when designing early intervention in young people. Therefore, the aim of
this study is to identify relevant risk factors for the development of depression in
young people. To do this, we will use secondary data analyses, using the Avon
Longitudinal Study of Parents and Children (ALSPAC), which comprises around
14,000 individuals recruited at birth. Risk factors to be investigated in this study
include sleep, cognitive function, diet, substance abuse, childhood abuse,
parenting style, academic performance, social relationships, school connectedness,
or self-esteem, among others. Further, we will focus on depression symptoms
occurring at several time points across adolescence (from 13 to 21 years old),
using a validated questionnaire on depression (the Short Mood and Feelings
Questionnaire). Expected statistical analyses to apply include correlations,
regression analyses and path analyses, using SPSS.

Depression is the leading cause of disability
worldwide and is a major contributor to the overall global burden of disease.
Further, depression is one of the most common mental health problems in young
people, with an estimated prevalence of 5.6%. Most importantly, depression in
young people is frequently an indicator of recurrent or chronic depression which
stretches into adulthood. Thus, to recognise, prevent, and treat depression in
young ages is crucial. However, a number of important issues need further
investigation to aid early intervention in depression in young people. And among
these, it is crucial to identify those risk factors that have greatest impact in the
development of depression in young people, as these are the factors that should be
targeted when designing early intervention in young people. Therefore, the aim of
this study is to identify relevant risk factors for the development of depression in
young people. To do this, we will use secondary data analyses, using the Avon
Longitudinal Study of Parents and Children (ALSPAC), which comprises around
14,000 individuals recruited at birth. Risk factors to be investigated in this study
include sleep, cognitive function, diet, substance abuse, childhood abuse,
parenting style, academic performance, social relationships, school connectedness,
or self-esteem, among others. Further, we will focus on depression symptoms
occurring at several time points across adolescence (from 13 to 21 years old),
using a validated questionnaire on depression (the Short Mood and Feelings
Questionnaire). Expected statistical analyses to apply include correlations,
regression analyses and path analyses, using SPSS.

Maternal overweight or obesity (defined by an elevated body mass index exceeding 25.0–29.9 or 30.0–34.9 kg/m2, respectively) has been associated with autism and attention-deficit hyperactivity disorder (ADHD) in the offspring. However, little is known on whether these associations may be causal, considering that observational investigations may be limited by confounding and reverse causation. Epidemiological approaches integrating genotype information have the potential to overcome the limitations of traditional observational approaches and provide evidence of potential causality.