Systemic inflammation is associated with increased risk in the development of atherosclerosis as well as other cardiovascular diseases (CVD). Recent trials have thus investigated whether immunotherapies may decrease the risk of CVD (Ridker et al. 2017; Tardif et al. 2019; Liberale et al. 2021). However, there remains a dearth of knowledge regarding which inflammatory proteins may play an aetiological and/or mediating role, and thereby may be potential therapeutic targets in the prevention of CVD. We seek to investigate the associations of the 92 Olink inflammatory proteins, measured in approximately 3000 mothers, 3000 children at age 9 and another 3000 children at age 24, with changes in carotid intima-media thickness (cIMT), pulse wave velocity (PWV) and blood pressures. We will also perform the analyses on the inflammatory biomarkers Glyoprotein Acetyls (GlycA) and C-reactive protein (CRP), as well as IL-6 measured in the 9-year-olds via clinical chemistry, since IL-6 is the only protein measured by Olink that has been measured previously in ALSPAC to be used as a validation.

The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium invites ALSPAC and Bristol researchers’ to take part in a large GWAS of lipid traits (incl. high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides). The main aims of CHARGE’s GWAS are to (1) identify novel loci for lipids trait separately by sex; (2) identify gene-alcohol consumption interaction effects on lipid traits separately by sex. These analyses will follow the ‘Phase II Analysis Plan for Alcohol and Lipids’ version 2.0 provided by CHARGE. Only GWAS summary statistics will be shared with CHARGE. Based on the GWAS results, subsequent analyses (e.g. validation of the gene-by-environment effects using longitudinal data of lipid traits, and Mendelian randomization) would be suggested by CHARGE.

Previous studies have demonstrated that loneliness is related to poorer health outcomes across a range of health conditions and traits. However, it is unclear whether these represent causal relationships i.e., increased loneliness leads to poorer health outcomes and what the direction of effect is if so. In addition, further investigation is needed to examine the extent of the impact of loneliness across a range of physical and mental health outcomes. In this project we will examine this further.

Do official records and self-reports of offending identify the same individuals? And does this depend on the context and/or individual characteristics? These are critical questions at the heart of crime and violence research. Official records and self-reports represent the two primary methods for measuring offending. However, official records only capture the “tip of the iceberg” as not all crimes are reported to the police. Additionally, there is bias in which individuals and which offences are decided to be recorded as criminal incidents by the police, and there are examples of misidentification errors. On the other hand, self-reports of offending via confidential questionnaires or interviews can introduce different biases, including social desirability (e.g., response falsification), recall error, and selection bias – as individuals who have engaged in criminal activity are less likely to respond to research surveys and interviews.

Few studies have compared the two measures of offending, with most of these coming from the US. While this evidence suggests that both measures are generally concordant, self-reports identify higher rates of criminal offending and a significant proportion of individuals with criminal records fail to self-report. The strength of agreement between official records and self-report has been found to vary by crime types and sociodemographic groups. Cultural context may also represent an important factor influencing the agreement between official records and self-reports given known discrepancies in the prevalence of crime and violence in low- and middle-income countries (LMIC) compared to high-income countries (HIC). However, due to a lack of studies with both official records and self-reports of crime from countries outside of the US, the role of national context is poorly understood.

This project examines protection from word reading problems in children with environmental risk for atypical reading development. We focus on genetic and brain structure variables that are hypothesized to buffer against socioeconomic status risks. We will examine how the risk for reading problems changes within adverse environments across genetic and brain structure variables. The research is expected to identify specific developmental environments where genetics and brain structure can support at least normal reading development.

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.

(1) Blood Pressure Variability
Outside pregnancy, how much blood pressure varies is associated with the risk of heart attack, stroke, and further health problems. In the CHIPS Trial (Control of Hypertension in Pregnancy Study), of 987 women with high blood pressure that was there before pregnancy or that developed in pregnancy, we found that the more blood pressure varied, the greater the risks for the mother, with possible reduced risks for the baby. We are seeking to confirm whether these findings can be confirmed in another group of patients.

(2) Blood Pressure Thresholds
During pregnancy, high blood pressure (diagnosed when the top number [‘systolic’] is 140mmHg or more, or the bottom number [‘diastolic’] is 90mmHg or more) is associated with more risks for the mother (such as early birth or Caesarean delivery) and for the baby (such as restricted growth). Recent research has indicated that a lower cut-off of 130/80mmHg for high BP may identify more mothers and babies at risk during pregnancy. In this study, we aim to confirm if these findings can be replicated in another group of women.

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.