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.

Use outcome data on asthma and atopy in children across different settings over a wide age range. Combine these outcomes with information on potential risk factors from questionnaires to identify those that are associated with atopic asthma and non-atopic asthma.

Individuals with mood disorders have cognitive biases towards disorder-related cues in the environment. Emotion recognition ability is distorted in mood disorders. People with mood disorders tend to perceive ambiguous facial expressions more negative compare to healthy controls. Working memory capacity is decreased in mood disorders which is related to the perception of emotions. Also, mood disorders are related to memory deficits. The aim of this project is to examine the relationship between emotion recognition, visual memory, memory, working memory capacity, and mental health. Also, replicate previous findings of distorted emotion recognition and decreased working memory capacity in mood disorders in a large sample.

Depression is a debilitating condition that significantly impacts on the physical, emotional, and social wellbeing of individuals and their relatives. Unfortunately, effective treatments for depression are struggling to keep up and we need to seek other ways of supporting and treating those at risk. However, we first need to more fully understand how the various risk and protective factors for depression might be working on a more biological level.

One key brain region associated with depression is the hippocampus, which is also where hippocampal neurogenesis (HN, i.e., the birth of new neurons) occurs. It is a key form of brain plasticity, vital for learning and memory, responding to stress, and regulating emotions. Importantly, depression has been associated with changes in HN, which is thought to occur throughout life in response to important social and health factors including diet, exercise, stress, and inflammation – all also key risk and/or protective factors for depression.

Until now, it has been impossible to test the effects of these factors on human HN. However, using an in vitro neurogenesis cellular assay (which involves adding participant serum to human hippocampal cells) we can generate proxy readouts of neurogenesis for each individual and then explore of how key risk and protective factors for depression influence these HN readouts.

As such, this Fellowship project will look at the relationship between HN and depression and explore the impact of key risk and protective factors for depression on neurogenesis across early life.

Intimate Partner Violence and Abuse (IPVA; whether cohabiting or not) is a widespread public health issue which often results in serious physical and psychological harm. In the last 50 years, research has been dedicated to understanding IPVA, including its effects and why it takes place. Research has highlighted that individuals who experience or witness IPVA during childhood are more likely to become perpetrators or survivors of IPVA in the future which is referred to as intergenerational transmission. However, the mechanisms behind this are debated. There are several theories in the literature including Social Learning Theory and biological theory, which are among the more prominent. The social learning approach theorises that offspring learn the violent attitudes/behaviour from their environment, in particular their parent(s). However, this theory does not account for the offspring in violent homes who do not exhibit IPVA in their relationships, or those who do not grow up in violent homes who become perpetrators or survivors in the future. Recent findings suggest that IPVA is passed from parent to offspring through environmental factors, psychological factors, and underlying biological mechanisms including a genetic component. The studies on genetic liability and IPVA outcomes to date have largely involved twin studies with preliminary results. This study will expand on previous research and use a novel approach to explain why some offspring are at increased risk of perpetrating or suffering IPVA in their future relationships.

Health related physical fitness has 3 main subcomponents: body composition, cardiorespiratory fitness (CRF) and muscular fitness. Body composition refers to the percentage of muscle, fat, bone and water in the body; CRF refers to the ability of the heart and lungs to supply oxygen to muscles during physical activity; muscular fitness refers to the ability to do work against a load (and is usually judged by muscle strength). The 3 components of physical fitness are affected by physical activity and are associated with important health outcomes like cardiovascular disease and frailty. Body composition, CRF and muscular fitness develop and change over a lifetime, and, in adulthood, meaningful changes in them are achievable in the general population. Thus, if we want to develop strategies to maintain our health for as long as possible, we need to understand the causes and consequences of these 3 aspects of fitness as well as how they are related to each other.
Behaviours can influence the components of fitness, e.g., higher intensity physical activity is associated with subsequent higher CRF. So, it is unsurprising that government guidelines recommend doing activities to improve/maintain CRF (i.e., moderate-to-vigorous intensity activity) and muscle strength (i.e., strength building activities). However, the general population’s perception of physical activity is skewed towards aerobic activity which mostly benefits CRF. Muscle strengthening activities are often referred to as ‘forgotten’ guidelines. To demonstrate and emphasise the unique added value of the different types of physical activity recommended by the guidelines, we need to understand the independent effects of CRF and muscle strength on health. Moreover, the fitness components are interrelated and can influence each other, but questions remain unanswered, e.g., we do not know whether duration of exposure to obesity is important for CRF or the extent to which CRF influences muscle strength and vice-versa. Understanding interrelationships with obesity in particular is crucial, because, compared with older generations, younger generations are accumulating greater exposure to obesity throughout their lives, and the impact of living longer with obesity is potentially enormous.
‘Real life’ is complex and outside of a lab or a randomised trial it is difficult to assess cause. However, some extremely important health questions like ‘how much of the effect of obesity on poor health could be avoided if everyone was strong?’ cannot be answered in a lab or by doing a trial. This is where the approach we will adopt is valuable: we will use different analytical methods and different datasets to answer our questions. The datasets and methods we will use have different strengths and weaknesses and taken together they overcome weaknesses of studying cause and effect in a single dataset with a single method. Therefore, our adopted approach is extremely powerful in terms of triangulating evidence for (or against) causality when results from the different datasets and analysis methods are considered collectively. We will use the 5 different national cohorts to address 3 specific knowledge gaps. We aim to better understand the (i) interrelations between components of physical fitness (muscular fitness, CRF, and body composition), how they affect each other and subsequently cause poor health; and to improve understanding of influences over a life-time on, and the development of, (ii) CRF and (iii) muscular fitness. Our work will promote the physical activity guidelines regarding specificity of types of exercise that should be encouraged at the population level and provide evidence for when an ideal life stage might be to intervene to promote maintenance of high levels of CRF and muscular fitness for as long as possible. This work is therefore of relevance, and, will have impact on, health policy. Finally, our work will demonstrate how to make best use of existing data resources.