Current guidelines use body mass index (BMI) to predict which women have increased risk of developing pregnancy complications and require additional care. BMI is an inaccurate measure to predict risk and leads to many women receiving inappropriate care, makes health outcomes worse and wastes NHS resources. Being overweight (BMI between 25 and 29) or obese (BMI 30 or above) is associated with pregnancy complications including diabetes, preterm birth, and death of mother or baby. Guidelines recommend that obese women receive costly high-risk care including additional referrals, tests and closer monitoring. This is unnecessary for half of obese women who don’t develop pregnancy complications, approximately 87,000 women/year in England. Almost half of overweight women do develop complications, approximately 103,000 women/year. Using obese BMI in guidelines wastes resources and increases anxiety for obese women inaccurately labelled as high-risk, and overlooks many overweight women who do require additional care. BMI does not assess where body fat is stored. When fat is stored in the upper-body there is an increased risk of developing certain diseases. Someone with a “healthy” BMI who stores fat around their waist can have a higher risk than someone with obesity who stores fat on their hips. Alternative measures can identify where body fat is stored (e.g. waist size, ultrasound examination). These are called adiposity measures. This research will look at whether adiposity measures can predict which women will develop complications in pregnancy to help the NHS target care to women who really need it.

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.

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.

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.

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.