Congenital heart disease (CHD) is a group of diseases with defects in the heart that occur during fetal life when the heart is formed. CHD occurs in 1% of births each year with causes not fully known. Most cases are believed to be caused by effects of both genes and risk factors that could be changed to prevent CHD (1,2). We are interested in identifying these modifiable risk factors. Several risk factors have been suggested including maternal diet intake (3). We would like to investigate the possible association between maternal vitamin D status and the risk of CHD in the child. A study from 2018 found that low maternal vitamin D was associated with a higher risk of CHD in the offspring (2). That study used blood markers of vitamin D measured approx. 15-months after birth as a proxy for mother’s vitamin D in early pregnancy. Because of the strong effects of sunlight and season on vitamin D levels the measures might not be a good indicator of the mother’s vitamin D in early pregnancy. We would like to use ALSPAC data to investigate the association of mother’s blood vitamin D in early pregnancy on their child’s risk of CHD. ALSPAC is an ideal study to improving our understanding of whether ensuring pregnant women have sufficient vitamin D could prevent CHD. It has measures of blood vitamin D (4), details of all cases of CHD including those that were diagnosed after birth (5), and information on factors that could confound the results.

Using a recall-by-genotype design, we have recently shown increases in BMI to be causally related to elevations in systolic - and to a lesser extent - diastolic blood pressure. While these changes are often attributed to the effects of excess adiposity, increases in BMI are known to arise from increases in both fat and underlying lean tissue, both of which may drive changes in blood pressure. This student project aims to investigate the extent to which these differing tissue types may contribute to changes in blood pressure commonly observed in the young.

The last decade has seen a dramatic improvement in our understanding of how our genes affect our height, body mass index (BMI), mental health, cancer risk, and many other traits. This has been facilitated by technological developments which allow us to measure a persons’ epigenetic data accurately and economically. Almost all epigenetic studies investigate traits collected at a single timepoint (e.g. adult height), and the epigenetic sites associated with these traits are then found using an epigenome-wide association study (EWAS). However, some traits such as BMI change over time, and the epigenetics of these repeatedly measured traits remain poorly understood. This project will apply new approaches for epigenetic analysis of longitudinal traits - in particular BMI measured repeatedly from birth to adulthood and depressive symptoms from later childhood through adolescence.

Cardiovascular disease (CVD, e.g. heart attack, stroke) is the leading cause of death in women. Yet women with CVD remain understudied, under-recognised, underdiagnosed and undertreated. Therefore, women have not experienced the same decline in CV rates that has occurred in men in recent decades.
General practitioners currently use CVD risk prediction tools that use traditional risk factors such as blood pressure and cholesterol. We propose that a CVD risk assessment tool which incorporates menstrual data could improve the prediction/prevention of CVD in women.
Recent research suggests that menstrual symptoms such as heavy periods may predict future CVD. Examination of our local electronic health records revealed that women having surgery for heavy menstrual bleeding (HMB) had a seven times greater risk of death from a heart attack than the general female population. Women undergoing hysterectomy for benign causes (e.g. HMB) also had increased CVD, even if the ovaries were not removed.(2).
There are many underlying causes of HMB. These may be non-structural (normal womb at scan) or structural (fibroids/adenomyosis) causes and may be influenced by genetics. However, the relationship between the symptom of HMB, its underlying cause and CVD risk has not been systematically evaluated.
This project will evaluate: (1) if there is an association between the symptom or underlying cause of HMB and future CVD by examining anonymised electronic health records and longitudinal population-based cohort studies of women across the life-course. This will determine if menstrual data would be useful in the prediction of future CVD. (2) if inclusion of relevant menstrual data (informed by our population data studies) and/or other female-specific risk factors (from published literature) in current CV risk prediction tools improves their prediction of CVD. We will use two independent population studies from the UK and Australia to validate our findings. (3) if adoption of a CV risk prediction tool that incorporates female-specific risk factors is feasible in clinical practice to improve the prevention of CVD. We will ask three general practices to use the new female-specific tool on pre-menopausal women. We will assess the number of women it is used in, how user friendly the tool is and also examine the anonymised electronic health records of those it is used in to determine the rate of prescription of CV preventative medicines (e.g. blood pressure medications, statins). These results will inform the design of a future, larger trial to assess the effectiveness of this new tool versus the currently used tools.
This project combines the expertise of gynaecologists, cardiologists and epidemiologists to analyse data from populations to inform clinical practice. We aim to inform the inclusion of relevant menstrual data in CVD risk assessment tools and inform future studies to evaluate their performance in the identification of pre-menopausal women at risk of CVD, allowing early implementation of effective preventative strategies to reduce CV disease and death in women.

The aim of this paper is to leverage repeated measurements from the ALSPAC birth cohort to describe the timing, chronological sequence, and interrelationships between the maturational processes and events of puberty, and to investigate the associations of key prenatal stressors with these derived indicators of puberty timing (i.e., with all early to late signs of puberty).

We will use multiple types of data from children in ALSPAC and other childhood cohorts to ask what factors, already evident in children, are causal precursors of future disease. We will focus on molecules that circulate in the blood (metabolites), factors inherited in the genome (genetic variants), and special modifications of the genome (epigenetics, methylation). By combining data from children for genetic variants, epigenetics and metabolites, we can ask whether there are particular metabolites or epigenetic factors that are causal precursors of diseases like obesity, coronary artery disease (CAD), and type 2 diabetes (T2D). Understanding the early causal factors of future disease could guide earlier and therefore potentially more effective interventions.

The symptoms experienced by young people with mental health difficulties rarely fit neatly into one diagnosis. For more than half of young people with difficulties, the symptoms experienced can be divided into two, or even more, diagnoses, while for others, there is no diagnostic category into which they fit. These problems with our diagnostic categories make it difficult for researchers and doctors who want to know how and why, some people develop mental health difficulties, and how best to support them.

To solve these problems, we need to look at mental health difficulties in a different way. In the last five years, researchers have started using ‘person-centred’ statistical techniques, which produce an alternative to the diagnostic categories we use at the moment, to look at mental health difficulties in teenage groups. Person-centred techniques look for patterns in data that show how symptoms might group in certain ways for some individuals, and in different ways for others. For example, having poor concentration might be grouped together with feeling anxious and being withdrawn in some young people. In others, poor concentration might, instead, be grouped together with feeling restless and breaking rules. With traditional diagnoses, both sets of young people might be diagnosed with ADHD, but using a person-centred perspective we can find these different groupings that reflect the variety of difficulties being experienced. This should give researchers a better chance of understanding what might make it more (or less) likely that somebody has the problems they do, and what type of support might help them most.

The challenge we face is that person-centred techniques are still quite new and we don’t yet know whether the new groupings they produce are consistent, or any more representative or useful than the current ways we diagnose. This is what this project aims to assess. At the same time, we want to get input from young people with experience of mental health difficulties as we do this. Phase 1 will start by understanding more from young people about their experiences of mental health difficulties and the diagnostic journey. We want to know what has been important to them, what they think might have influenced the symptoms they live with, and what sorts of things help make life easier. Researchers will use the understanding we gain from these sessions to plan how they will do the statistical analysis in phase two to make sure that it captures what is important to young people. Phase 2 will be the statistical analysis, shaped by Phase 1. We will use ALSPAC data, which has followed up thousands of people from childhood and through the teenage years, and use person-centred techniques to find new groupings with shared patterns of symptoms. We will then test the statistical strength and consistency of the new groupings, and use understanding gained from Phase 1 to see whether the new groupings can help us to learn more about the factors that impact mental health. In Phase 3, we want to come back to young people to see how far they feel the new perspectives on mental health difficulties that were generated in Phase 2 fit with their experiences. They can help us to see what will and won’t be an improvement over existing diagnoses, and can consider with us how the findings can be used to improve the diagnostic journey. During this phase, we also want to discuss the findings with groups who make decisions about how CAMHS runs, to identify positive ways to use the knowledge that has been generated.

There is a growing interest in the use of "genetic scores" to investigate the contribution of traits such as obesity and blood pressure to disease and disease risk. However, scores developed using individuals of one ancestry (e.g. Europeans) typically are less useful when applied to populations from another (e.g. East Asians). We are investigating the performance of scores generated using data from multiple ancestries, compared with those from a single ancestry, and have found that there can be substantial improvements. As part of this, to make sure that our results are reproducible, we need to do analyses in sets of individuals who were not used in generating the scores. This project tests how our "improved" scores perform in ALSPAC mothers and fathers.

Cognitive and behavioral disorders among children are on the increase. Exposures to metabolic disorders during pregnancy may influence the offspring’s psychopathology and neurodevelopment. This project will assess the relationship between diabetes before and during pregnancy on various aspects of child cognitive and behavioral development