The ability of environmental conditions to influence phenotypes in future generations requires that environmental exposures induce changes in the epigenome of male gametes via the transmission of aberrant sperm epigenetic marks following fertilization. Studies have demonstrated that exposure to cannabis and tobacco products alter sperm DNA methylation. Thus, it is important to investigate environmental exposures, including cigarettes and cannabis, and their effect on the male gametes during the crucial pre and periconception window. In addition, there is a need to determine whether these methylation marks are heritable and associated with health outcomes in the progeny, especially given that men are the predominant cannabis and tobacco product consumers, and their use is increasing. Our machine learning-based DNA methylation score is based on cord blood measurements of DNA methylation (Illumina’s Infinium HumanMethylation450K BeadChip) and will reflect exposure to paternal smoking pre and during pregnancy.

Type 2 diabetes (T2D) is a heritable disease leading to an abnormal glucose metabolism, influenced by multiple genetic variants across the genome. Although common in adults, T2D was previously rare in childhood, but its prevalence in this age group has been increasing in the past two decades as a result of the childhood obesity pandemic, accounting for up to 45% of cases of diabetes in youth in certain at-risk populations. The prevalence of prediabetes (an early stage of T2D, which is characterized by elevated blood sugar below the threshold to diagnose T2D) is even higher among obese children. Furthermore, individuals who develop both prediabetes and T2D in childhood and adolescence develop early microvascular complications, whose treatment failure is high. Thus, prevention of young-onset T2D using targeted lifestyle modification presents a particularly important yet difficult challenge. As such, identifying children at increased risk early in their lives is critical for these targeted interventions. Additionally, quantifying the genetic liability to youth-onset T2D could help better understand the respective contributions of environment vs genetics in the etiology of this disease. For instance, it is crucial to understand if among children with increased genetic risk for T2D, a favorable environment can prevent the development of T2D.
T2D has a polygenic nature, with an important heritable component explaining between 20% and 80% of the risk to develop this disease. Polygenic risk scores (PRS) have been demonstrated to have an improving ability to identify adult individuals at significantly high/low predisposition towards polygenic diseases. Therefore, it has become possible to identify adults who will lie at the extreme distribution of a trait, such as risk of T2D. T2D is causally linked to obesity. Obesity, as expressed by the measures of body mass index (BMI), is also a highly heritable polygenic trait. A PRS for adult BMI (Khera et al, Cell 2019) has been able to predict differences in body weight in ALSPAC children as early as at birth, with increasing predictive performance as individuals grow older.
Therefore, we posit that, similar to the PRS for BMI, a PRS for adult T2D, in combination with clinical risk factors (such as nutrition and physical activity) may be able to effectively predict individuals presenting abnormal glycemic traits in childhood. Since T2D and prediabetes in both adults and children is causally linked to obesity, we will also test if a PRS for adult BMI predicts abnormal glycemic traits in children. We will then compare the predictive performance of the PRSes and combine them with traditional non-genetic risk factors to enhance T2D risk prediction in youth. Finally, among children at the extremities of the PRS distribution (ie children with the highest and lowest genetic risk for T2D), we will seek to identify which environmental factors mitigate the effects of this genetic risk, and either contribute or prevent the development of T2D. To do this, we will use a large pediatric cohort representing the general population (ALSPAC), as well as a population of children at risk of obesity (QUALITY), both of predominantly European ancestry.

The coronavirus disease 2019 (COVID-19) pandemic is having a profound effect on all aspects of society. To reduce the spread of the infection, the UK government imposed strong restrictive measures across England, Scotland, and Wales, including the lockdown announced on 23rd March 2020, as well as strict physical distancing rules. These infection control measures, as well as the diversion of resources towards COVID-19 services has resulted in substantial disruption to UK health care and delivery.

Recent reports from NHS digital indicate a 153-fold increase in those waiting 12 months or more for elective treatments, compared to 2020.1 Furthermore, despite a decrease in those attending A&E services, the number of patients waiting over 12 hours for admission was 34% higher in January 2021 than January 2020. Furthermore, disruption to pharmacological treatments has also been reported with delays to accessing medication.

Although restrictive measures were implemented universally to the UK population, it has become apparent that not all those are affected equally, with some individuals impacted more than others.

As a result of the health inequalities, which have been well documented for decades, exist based on sex, ethnicity and socioeconomic status, there is an increased concern that these groups will be disproportionately impacted in terms of healthcare disruption.

The aim of this project is to investigate sociodemographic predictors of healthcare disruption during the COVID-19 pandemic.

This project will analyse data already transferred under project: B3258 STOP: Science and Technology in childhood
Obesity Policy

Ultra-processed food (UPF) has become increasingly common in the diets of children, particularly in the UK. Consumption of UPF has been linked with obesity, cardio-metabolic disease and some cancers, independently of total energy intake and nutritional composition. Mechanisms through which UPF effects health remain unclear. DNA methylation can be used to compute a measure of biological age using methods developed by Horvath and others. Age acceleration (AA, having a higher DNA methylation age than chronological age) measured in children may reflect developmental processes and epigenetic stability and is strongly related to child obesity. We propose to explore the effects of UPF on AA in ALSPAC children and its role in the development of obesity

Since 2003, novel birth cohorts arose and prevalences of asthma, allergy and eczema have not been studied. Combining these data might lead to better understanding of a.o. the impact of these conditions. The aim of our project is to describe the harmonization process of asthma, allergy, and eczema, and relevant related data. We will perform a meta-analysis using individual participant data of cohorts participating in the EU Child Cohort Network and describe the prevalences of wheezing, asthma, upper and lower respiratory tract infections, lung function values, inhalant allergy, inhalant allergic sensitization, food allergy, food allergic sensitization, itchy rash and eczema measured at each age from birth until adolescence while taking country of cohort and main subject characteristics into account. Additionally,we provide a framework for further research into early life stressors, genetic, epigenetic, and microbiome pathways on the risk of developing respiratory and related outcomes.

During pregnancy, the immune system faces a particular challenge of protecting mother and vulnerable offspring. Mothers rely on nutrients to maintain their physiological condition and immune system, as well as to nourish developing young. A key question is: when mothers face challenges to their physiological state, how do they adjust energy allocation to protect themselves and their young? When does this result in adverse outcomes, such as pre-term birth? To date, most research on pregnancy and immunity involves longitudinal studies in humans or experiments on laboratory rodents. We have a solid understanding of how nutrition or inflammation in pregnancy influences birth timing, offspring physiology and behaviour. Surprisingly few studies have, however, considered the interaction between nutrition and inflammation. This project will aim to fill this gap by using a diverse toolkit: evolutionary models, experiments in insect models of pregnancy, and analyses of human cohort studies.

Smoking is the world's leading preventable cause of morbidity and mortality, killing over seven million people annually. Cigarettes contain nicotine, which is highly addictive. E-cigarettes are less harmful than smoking, can successfully deliver nicotine, and can help some smokers quit. However, their long-term health effects are unknown, and there are concerns about e-cigarette use among non-smokers, including long-term use, nicotine dependence and potentially transitions to smoking.

This project aims to examine the patterns and predictors of e-cigarette use and smoking among young people in ALSPAC, with a focus on perceptions and attitudes towards use.

Mental health problems represent one of the leading drivers of overall disease burden. Half of all lifetime psychiatric disorders present before adulthood, with a point prevalence of between 10% and 20% of children and adolescents experiencing mental health difficulties. In addition, more than 40 percent of youths with a lifetime psychiatric disorder go on to develop at least one additional mental illness concurrently or later in life. This adds significant complexity to diagnosis and interventions and further increases the likelihood of negative outcomes, such as criminality, low educational attainment and unemployment. A developmental perspective that investigates the interrelations between multiple mental health issues from early life up until adulthood is likely to offer important insights into why mental health problems commonly co-occur and can consequently inform prevention strategies. In the current project, using state-of-the art statistical techniques, we propose to analyse the developmental relations of mental health problems. We will further examine potential factors linking mental health problems together such as genetic predispositions to mental health problems, perinatal risk factors, and school problems. The results of this project will have important clinical implications. In particular, they will shed light on potential risk factors that drive the development of co-occurring mental health problems, give insights into which symptoms are likely to precede other symptoms and further help identify other factors that might exacerbate the development of co-occurring mental health problems. Thus, findings will inform early intervention strategies for preventing the development of secondary mental health disorders.

Often a large number of equally plausible possible analysis options are available to researchers. Multiverse analysis is a proposed way of overcoming this problem where all plausible analyses are conducted and the results of all are interpreted in the context of each other. This information can be used as a measure of the reliability of a result, which in turn is useful for scientific advancement and deciding policy. However, multiverse analysis can be time consuming and complex. This project will use ALSPAC data to test and inform the building of an R/python package to conduct multiverse analysis, which is currently being built. This package will make multiverse analysis easier to conduct, encouraging greater adoption of this technique across the health sciences.