DNA methylation is a chemical change to our DNA that influences the extent to which our genes are expressed. DNA methylation has previously been used to accurately predict age, and the difference between a person's age and the age predicted by their DNA methylation has been shown to be associated with mortality and various health outcomes. Recently, this approach has also been used to identify a set of DNA methylation markers that predict gestational age at birth (epigenetic gestational age (EGA)). EGA has been shown to be associated with birth weight, independently of gestational age, sex and ancestry. In this project, we will look at whether EGA is associated with childhood growth trajectories.

Each of us leaves many digital traces. For example, when we buy things in supermarkets and use loyalty cards to get benefits the supermarket records our purchases, creating a representation of our habits and preferences. Companies have been using aggregates of these data to track sales of their products, and to understand aspects of context that can impact sales levels, and to target marketing and promotions. But can these data be utilized for higher public impact helping individuals, economies and society at large? For example, can we learn about causes and consequences of mental health illnesses and routes to wellbeing through objectively tracked real world behaviours and choices? Up to now longitudinal cohort studies – like ALSPAC – allowed to use rich genetic, biomedical and early environment data to explain real world outcomes such as mental health illnesses and wellbeing. However, these outcomes often are self-reported which makes it challenging to unpick relationships in the data. This project aims to link objective measures of behaviour traced through retail and banking data to provide a set of variables reflecting objectively measured real world behaviours and decisions. These can be then linked to rich genetic, bio-medical and early environment data already collected on longitudinal cohorts and allow to investigate causes and consequences of real world outcomes such as mental health issues and wellbeing.

A recent study has identified 136 genetic markers associated with all three allergic diseases (asthma, hay-fever, eczema). Another study has shown that patients fall into different groups of allergic disease, for example some may have only asthma and eczema, and others may have only eczema and hay-fever etc. This project is aiming to find out if any of these genetic markers are associated with any particular groups of disease.

Although many studies have found associations between maternal depression and impaired child growth, the relationship between parental anxiety and child growth has not been thoroughly investigated.
By using the Avon Longitudinal Study of Parents and Children (ALSPAC) data, our aim is to investigate the relationship between parental anxiety and child growth.

The measurement of circulating Haemoglobin A1C (glycated haemoglobin; Hba1c) is a good proxy of the mean plasma glucose concentration over a period of the past 2 months. Haemoglobin A1C levels are commonly used as a screening test for diabetes diagnosis and monitoring. Using freshly collected blood samples, circulating levels of Hba1c can be accurately measured by Boronate affinity high performance liquid chromatography (HPLC). However, it is currently unclear if this method can measure levels of Hba1c accurately using frozen hemolysates. Hba1c prepared hemolysates have previously been collected from ALSPAC mums at various clinics (FOM1, FOM2, FOM3 and FOM4). This proposal is for a pilot study to test the feasibility of measuring Hba1c using frozen Hba1c prepared hemolysates. This project will enable the assessment of whether Hba1c can be measured in frozen Hba1c hemolysates. If this is feasible, Hba1c levels of mums in ALSPAC can be measured and will be useful for future research linked to different health outcomes such as diabetes and cancer.

Associations between circulating sex hormones in blood serum and DNA methylation have previously been found in ALSPAC children in both childhood and adolescence. Such associations may tag biological pathways involved in endocrine-related diseases, with important implications for the study of hypo- or hyper- gonadism, as well as various metabolic disorders known to be regulated by sex hormones. Whether such associations also exist between maternal levels of sex hormones and the mothers growing foetus is not known. The project will look at the effects of sex hormones in mothers on DNA methylation in children, using both observational and genetic evidence. Maternal sex hormone genotype will be used as a proxy of circulating sex hormone levels in genetic analyses enabling true causal associations to be unpicked from those due to confounding. Paternal genotype will be used as negative control to determine whether associations are due to in utero exposure to maternal sex hormones or simply shared genotype between mother and offspring.

Missing data is a common problem in epidemiology where participant drop out can substantially reduce the sample size of initially large cohorts. One method of dealing with missing data is to use multiple imputation (MI) in which copies of the dataset are created and missing values are replaced in each dataset using an imputation model. An analysis model is then fitted to each imputed dataset and the point estimates of model parameters are combined using Rubin’s Rules. Variables included in the imputation model but not the analysis model are known as auxiliary variables.

A common question among researchers and reviewers is what proportion of missing data warrants the use of MI. A lower threshold of 5% missingness has been suggested as a point below which MI provides negligible benefit. At the opposite end some reviewers suggest an upper threshold of 50% missingness above which MI should not be attempted.

The fraction of missing information (FMI) is a measure able to quantify the loss of information to missingness while accounting for the amount of information retained by other variables within the dataset. It can be thought of as the fraction of the total variance of a MI model that is attributable to the between imputation variance. The FMI can take values between 0 and 1 with low values being preferable.

In a simulation study proportions of missing data in a multivariate normal dataset were increased using a missing completely at random pattern. Multiple imputation was then used and its performance compared to complete case analysis. Imputation models with varying amounts of auxiliary information were investigated in terms of the bias and precision of parameter estimates, confidence interval coverage and FMI.

An empirical example, using ALSPAC data, will now be used to support the findings of the simulation study.

Epilepsy is a common disorder that presents with recurrent seizures often from early childhood. It can affect children’s learning and school achievement and it is often related to other conditions such as autism, while significantly inflicting a burden on the affected children and their families. Amongst the known causes are genetic factors and birth complications. However, the mechanism by which epilepsy develops is unclear. Recent studies have found epigenetic alterations being linked with several neurological and psychiatric diseases, including scarce evidence of a link with epilepsy. Epigenetic modifications are chemical changes in the DNA molecule that maintain intact the inherited genetic information while altering, sometimes permanently, the functionality of the interested genes. Investigating these changes in the peripheral blood of children that experience seizures might help develop early biomarkers and understand the biological mechanisms behind epilepsy. In this project we aim to investigate the link between the epigenome at birth, during childhood and adolescence with the experience of seizures and we will investigate its potential consequences for neurodevelopment and mental health.

Promoting good mental health and well-being across the population is a strategic priority within the United Kingdom [1] and across the globe [2]. Good mental health influences the capacity to thrive and flourish, while conversely, poor mental health affects indices as diverse as physical well-being and health, work satisfaction and achievements, and a range of interpersonal relationships. Yet, poor mental health characterizes up to a quarter of the UK adult population [3]. Given that many adult psychosocial difficulties (e.g. depression, substance use, unemployment) can be traced to neurodevelopmental difficulties in childhood [4], investigating early risk pathways is crucial for early detection and prevention [5]. There is good evidence in animal studies, and increasing evidence in humans, that early-life adversity can promote chronic, low-grade inflammation [6] and vulnerability for later mental health problems - and may do so through the immune system’s role in brain development. The “hidden wounds hypothesis” [7] indeed proposes that early psychosocial adversity translate into biological risk for mental illness by negatively affecting the role of the immune system in brain development [8]. Of interest, inflammatory markers can cross into the brain through several routes (e.g. afferent fibers, vagus nerve, permeable blood brain barrier) and (in animals) modulate synaptogenesis, synaptic refinement and survival, as well as myelination [8, 9]. Recent evidence also suggests that DNA methylation, a type of epigenetic modification that regulates gene expression, may be a biological mechanism by which adversity can result in low-grade chronic inflammation through affecting the regulation of genes involved in inflammation [10]. However, in humans, little is known about how adversity in different developmental periods may affect immune-related DNA methylation, which in turn could influence neurodevelopmental and mental health problems.

What we have done so far:
Using DNA methylation loci from a recent independent epigenome-wide study of serum C-reactive protein (CRP) [11] – a biomarker of chronic inflammation – we [12] created inflammation-related polyepigenetic risk scores (i-ePGS; similar to polygenic risk scores) in the Avon Longitudinal Study of Parents and Children. Of interest, we replicated the previous study [11] by showing that i-ePGS scores associated with serum CRP. We then examined prospective associations with cognitive function and mental health problems. We found that higher i-ePGS at birth associated with lower cognitive function (i.e. WISC-III), which, in turn, associated with higher mental health problems between ages 7 – 15. Persisting i-ePGS in childhood (age 7) further associated with higher internalizing problems. We therefore have provided preliminary evidence that i-ePGS scores can associate with mental health problems and that the brain may be involved.

Why additional research is needed:
i) a better understanding of adversity and immune system variation with age. The changing immune-related risk environment (e.g. prenatal: maternal depression/obesity; neonatal: housing adequacy/breast feeding, etc.) coincides with a developing immune system (i.e. innate and adaptive). Environmental factors can influence adaptive changes in the immune system (e.g. potentially evidenced through DNA methylation: see [13]) and may help to explain age-correlated inflammatory responses to stress [14]. For example, in neonates only, IL-8 is shown to have specific T cell effector functions [i.e. proinflammatory; 15]. In contrast, increased concentrations of other pro-inflammatory cytokines (e.g. IL-6) are found in the circulation of adolescents/adults [14]. Research is needed to examine age-correlated “biological embedding” of environmental factors that might affect the development and long-term functioning of the immune system. ii) a more fine-tuned understanding of developmental variation in immune markers associate with changes in brain structure. Animal models show that during pregnancy and after birth peripheral inflammation can modulate the development and function of the brain for better and for worse [9]. Similar patterns are evident in humans: intrauterine inflammation associates with altered expression of placental tissue genes known to associate with cognitive impairment [16]. Adversity-related infection and inflammation are associated with impaired child cognitive function and language [17], and increased risk for depression [18]. Indeed, in an in vitro study, Prof Pariante’s group [19] reported that higher level of inflammatory markers (i.e. INF-alpha, IL-6) decreased neurogenesis and increased apoptosis in hippocampal cells – results that are highly relevant for depression. Yet, there are currently few published neuro-imaging studies. A recent paper [20] based on a small sample (n = 31) of female university students suggested increased IL-6 in response to a stressor associated with heightened connectivity in brain areas implicated in depression (e.g. amygdala, dorsomedial prefrontal cortex). Research based on large-scale epidemiological samples is needed to replicate and extend these findings. iii) testing degree to which variability in brain development help can explain (i.e. mediate) the relationship between immune function, DNA methylation and mental health. Research examining the degree to which brain-imaging can be predictive of subsequent mental health is in early stages and with mixed results [21, 22]. A recent adult brain imaging study, however, reported that an association between higher CRP levels and higher depressive symptoms was mediated by decreased functional connectivity between the ventromedial prefrontal cortex and the ventral striatum [23]. Hence, data support the idea that adversity-related inflammation can shape neurodevelopment, which in turn, can increase vulnerability for emotional problems.

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