Proposal summaries
B1536 - The association of ADHD symptoms in childhood to self-harm in adolescence - 09/05/2013
Aims: To investigate the relationship between ADHD and self-harm, in order to see if self-harm can be considered to be a risk factor, and to explore any possible gender effects.
Hypothesis: That self-harm will be more prevalent in individuals with ADHD, particularly in females with ADHD, compared to controls with ADHD.
Method
We propose to obtain participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) which is an ongoing population-based study investigating a wide range of environmental and other influences on the health and development of children. Of the core cohort of 13,988 infants, 10% were randomly selected to be examined in more detail encompassing 10 examinations between 4 months and 5 years - the Children in Focus (CiF) clinics.
Outcome Variables
Diagnostic information (ADHD diagnosis) will be derived from the Development and Well-being Assessment (DAWBA) which was administered to all children remaining in the CiF cohort at 91 months of age (7 years 9 months). All children who were given a diagnosis of ADHD using the DAWBA will be included in the clinical experimental group (currently unknown figure). There will be a sex and age-matched control group. The number in this control group will be twice the amount in the clinical/experimental group.
We are interested in seeking data on self harm in the questionnaire at 16 years 6 months for all individuals that we have DAWBA information on (Section L: Deliberate self-harm, p. 39-43). Can be downloaded from the ALSPAC site: http://www.bristol.ac.uk/alspac/researchers/resources-available/data-det...
Confounding Variables
Associations between diagnosis of ADHD as a predictor for self-harm will be calculated using Chi square analysis. We will control for potential confounders in a logistical regression analysis exploring ADHD and self-harm: presence of learning difficulties and behavioural problems (DSM-IV items on Oppositional Defiant Disorder (ODD); anxiety, social class (of parents); gender and substance abuse. Both ODD and learning difficulties will be evaluated since both have significant impact on functioning and frequently co-occur with ADHD, however they are relatively unexplored as a potential confounding factor in ADHD studies.
B2014 - Suicide attempt and self harm and its association with adolescent sexuality A UK community based study - 09/05/2013
Aims and Objectives:
To determine whether there is an association with minority sexual orientation in adolescence and self-harm or suicide attempt.
B2012 - Estimating features of trajectories in the diurnal BP of adolescents - 09/05/2013
Background: In many epidemiological scenarios, understanding change is crucial. Repeated measures (or longitudinal) data are one of the pillars of this understanding. Cohort studies and randomised controlled trails produce such data since a group of individuals are followed over time with repeated measurement of key exposure(s) or outcome(s). A plethora of methods exist for modelling such data, with mixed or multi-level models being at the forefront. However there is a lack of approaches capable of estimating features of trajectories borne out of such data. To accurately estimate a feature (such as the minimum or maximum of a trajectory) requires a descriptive approach not restricted by parametric assumptions. Furthermore, many features of a trend can only be extracted through derivative estimation. For example, derivative estimates are needed to obtain the maximum rate of change of a biological process or the time at first decline of a biomarker.
There is a strong positive association of systolic and diastolic blood pressure (SBP and DBP) across most of their distribution with increased cardiovascular disease risk. Higher SBP and DBP measured in adolescence and early adulthood are associated with increased coronary heart disease and stroke risk with magnitudes of association that are similar to those seen when blood pressure is measured in middle-life.
Blood pressure varies, by up to 20%, over the day (with lower levels during rest and in particular in deep sleep) and in response to different stimuli, one of which is physical activity. Variability in blood pressure across the day, and the magnitude of the difference between day- and night-time blood pressure have been proposed as independent cardiovascular risk factors over and above the mean level of blood pressure. Relatively little is known about these patterns and their correlates in healthy adolescents.
Aim: The aim of this proposal is to develop methods to accurately extract features of diurnal BP for individuals and for the sample. These may then be used to investigate associations with outcomes.
Hypothesis: Specific features of diurnal SBP and DBP trajectories in adolescents are associated with cardiovascular risk factors
Exposure: Extracted features of diurnal SBP and DBP. Linear mixed models are one commonly used approach for modelling continuous repeated measures. These allow separate modelling of the variability between members of the cohort and the variability within individuals. To handle non-linear trajectories over time, alterations to the linear mixed model are available, such as transforming the outcome, allowing for polynomial trends of the outcome over time (fractional polynomials) or allowing the outcome trend to change in different segments of time (regression splines). These can be useful if statistical inference is of primary concern. At the other end of the spectrum, when the question of interest is to describe the trend over time, these simple alterations can prevent goodness of fit because of the restrictions of a fully parametric model. In particular, when interest lies in identifying a feature of repeated measurements, a fully parametric model is not sufficiently flexible to obtain reliable estimates.
Flexible methods which borrow from the fields of mixed models and non-parametric smoothing come under the umbrella of functional data analysis (FDA). FDA encompasses the various modelling methods for these non-linear repeated measures data. Unfortunately, in the situation where measurement times are irregular across individuals, many methods under the umbrella of FDA become inefficient. In epidemiology such data are common since measurement often occurs within routine GP visits. Three approaches to modelling non-linear irregular repeated measures data have been identified, namely semiparametric mixed models, P-Spline mixed models and Principal components Analysis through Conditional Expectation (PACE). These have not been used to their full potential in the epidemiological literature.
In a range of disciplines it is often the case that the derivative, or rate of change, of observed data is of primary interest. In the situation where data are observed over time, the first derivative will correspond to velocity, the second to acceleration. In a LMM the rate of change is, by definition, constant for both the individual and cohort. Where the LMM contains a complex polynomial of time, this polynomial can be differentiated to give the rate of change of the biomarker at any timepoint, both on average and for each individual. When non-linear methods such as P-Splines are used, it can be difficult to obtain derivative estimates and their standard error analytically. Derivative estimation is a difficult problem for several reasons. Firstly, with no observations for derivatives, testing goodness of fit does not exist. Secondly, modelling the change of a variable over time is more sensitive to measurement error and outliers than modelling observed data. Third, standard errors can be difficult to obtain since many approaches use numerical methods to estimate the derivative as a by-product. However, derivative estimates can be very useful. For instance, when a first derivative estimate of a biomarker over time is below zero, the process is declining. Using the standard error to create variability bands, we obtain an analogous confidence interval comparison for evidence of decline. Derivative estimates allow us to obtain features of trend such as the maximum/minimum velocity or acceleration. These have been used in physiology as a marker for endurance and in child growth as a marker for puberty. Derivative estimates and their standard errors have yet to be obtained for several approaches to longitudinal data.
Outcome: Cardiovascular risk factors
Confounders: Age, sex, socio-economic status, smoking status, weight, height, fat mass, physical activity.
B2011 - Rural/urban differences in dietary intake in a UK cohort of children - 09/05/2013
Clear differences in dietary intake have been reported in residents of urban compared to rural areas. This has been frequently reported in developing countries (Bowen et al, 2012) but also in the USA and Canada (Downs et al, 2012). Differences have been reported in children/adolescents as well as adults. However, little is known about any differences in the UK.
It has generally been shown that residents of rural areas have a poorer nutrient intake compared to residents of urban areas. In developed countries, the primary reason put forward to explain these differences is down to the the food environment whereby food is generally reported to be more expensive in rural communities, with less availability and variety. It is unlikely that families living in rural communities in the UK exclusively purchase food in their immediate suroundings and we hypothesise that there will be no obvious differences in dietary profile. However, differences have been reported in obesity levels in the UK in rural versus urban areas through secondary analyses of the Health Survey for Englanddary analyses of the Health Survey for nhtat dietary intake is a driverand it is possible that dietary intake is a driver in this, so warrants attention. In addition, previous studies examining areas of residence and dietary intake suffer from residual confounding; not taking into account familial factors such as socio-economic status.
Exposures:
We plan to use the Rural/Urban Definition (England and Wales), a detailed rural/urban indicator defined by the Rural Evidence Research Centre at Birkbeck College (RERC) based on 2001 Census data and will be derived from our address information. The use of this indicator will allow us to distinguish between rural and urban areas using eight categories: Urban, Town and Fringe, Village, Hamlet and Isolated Dwellings; each on two sparsity levels) to examine detailed rural and urban residential effects on dietary patterns.
Given the heavily urban-weighted spatial distribution of the ALSPAC cohort and the potential lack of geographical representation of data in more remote areas, we also request Output Area (from which the Rural/Urban Indicator is built) and Super Output Area (Lower & Middle) to permit analysis in the event of unsatisfactory statistical power at the preferred (OA) scale.
Outcomes:
At each time point (7, 10 and 13 years) we will examine differences in selected energy-adjusted macro- and micronutrients and food group intakes derived from diet diaries according to the divisons described above using t-tests/ANOVAs as appropriate and will use general linear models to adjust for a variety of potential confounding factors.
We will also examine any differences in the clusters of dietary patterns previously described (Northstone 2012). When examining effects at ages seven and thirteen, population changes will be used to assess the accuracy of using the 2001 data for earlier/later years and extrapolating data appropriately if necessary.
If possible (dependent on relevant numbers) we will determine whether any changes in rural/urban indicator lead to any changes in dietary intake over time in an attempt to infer causality.
References
Downs SM, Fraser SN, Storey KE, Forbes LE, Spence JC, Plotnikoff RC, Raine KD, Hanning RM, McCargar LJ. Geography influences dietary intake, physical acitivity and weight status of adolescents. Journal of Nutrition and Metabolism 2012; doi:10.1155/2012/816834.
Bowen L, Ebrahim S, De Stavola B, Ness A, Kinra S, Bharathi AV, Prabhakaran D, Reddy KS. Dietary Intake and Rural-Urban Migration in India: A Cross-Sectional Study. PLoS ONE 6(6):
e14822. doi:10.1371/journal.pone.0014822.
K Northstone, AD Smith, PK Newby, and P Emmett. Longitudinal comparisons of dietary patterns derived by cluster analysis in 7 to 13 year old children. Br J Nutr 2012; 15: 1-9.
B2008 - Biomedical Research Informatics for Data Gathering and Exploitation in Systems Medicine BRIDGES - 09/05/2013
Margaret Thatcher was famous for her need for little sleep, though nobody knows with any accuracy, how little. Now we have the sensor technology to capture many such types of lifecourse data in real-time, to passively stream those data directly to databases, and to connect 'research,' 'health' and 'clinical' data seamlessly together. This opens the potential for highly integrated systems?medicine research encompassing many disease domains in conjunction with highly multidimensional data -for example, linking data for sleep, environment, genetics, diabetes and cardiovascular health, and neuropsychiatry for an holistic approach to reearch. Such data not only encompass simple tabulatable information but also complex data types that cannot be represented in standard formats, for example, MRI, continuous monitoring/signal processing, quantitative proteomics and next-generation sequencing. Novel data types rely on emergent database technologies and as yet many different types of issues tend to confine individuals' data into isolated and lost 'pockets' - in the NHS, in research units or simply unrecorded. There might, for example, be signature relationships between sleep duration, neuroanatomy, genetic and 'omic determinants, lifecourse, and health outcomes - we need the technology and data silos to be joined up to find out.
Our proposal capitalises on unique data-orientated developments in the co-applicants' institutions and aims to integrate, structure global access, innovate real-time sampling of patient data, facilitate complex analyses across and within diseases and train a new cohort of medical informaticians to pave the way for personalised and systems medicine of the future. The institutions are already engaged with large scale multilevel data (e.g. MRC Centres in Bristol and Cardiff; ALSPAC and 1958 birth cohort in Bristol; diabetes collections in Exeter; BRU's in Leicester) and technological developments in the vanguard for database integration, access and analysis challenges. They also have large scale funding for sensor technology development (EPSRC - Bristol and collaborators), for omics integration (EU - Leicester), for graduate training in complexity (EPSRC) and analysis bioinformatics (MRC), for integrated biostats/bioinformatics (BBU - Cardiff) and importantly, for splicing or searching biomedical data types together flexibly and securely (e.g. BRISSKit, SHRINE, DataSHIELD - Bristol and Leicester). The deep complementarities between the co-applicant centres, form the basis of the proposal's workpackages which work toward seamless streaming, integration and analysis of data for biomedical research driven by the context of our specific health and disease studies.
B2010 - The developmental role of Behavioural and Neurobiological Dimensions in predicting Eating Disorders in adolescence/young - 08/05/2013
Eating Disorders (ED) are serious mental health disorders affecting approximately 5-10% of adults (Hudson et al., 2007; Swanson et al., 2011) and have a peak of onset in adolescence between the ages of 15-19 (Micali et al., 2013; Field et al., 2012). The etiology of ED remains poorly understood, though and interplay of genetic and environmental factors is likely to be at play. Research into the clarification of risk factors has been hampered by uncertainties about clear phenotypic distinctions across ED categories, and by a lack of integrative studies using a longitudinal approach to clarify risk for ED. This study aims to focus on how cognitive, emotional and social processes cause ED behaviors in adolescence and young adulthood in interaction with environmental factors from infancy onwards.
Cross-sectional studies have identified cognitive, emotional and social difficulties that are associated with ED.
There is cross-sectional evidence that anorexia nervosa (AN) is associated with emotional and social communication difficulties and that specific cognitive profiles characterize bulimic type disorders.
AN shares common features with anxiety (Silberg & Bulik, 2005; Micali et al, 2011); and a range of social communication deficits, including interpersonal problems and poor emotion recognition, are present in individuals with AN (Tchanturia et al., 2012; Treasure et al., 2012).
Bulimic-type ED are cross-sectionally associated with specific cognitive profiles characterized by poor attention (Dobson & Dozois, 2004; Faunce, 2002) and low inhibition (Galimberti, et al., 2012; Rosval et al., 2006).
Most of the studies cited above are cross-sectional and have often focused on one specific area of behavior or cognition.
In relation to genetic risk for ED poor replication of early candidate genes studies has led to newer approaches heralding possible successful identification of risk markers. Large genome-wide studies of psychiatric disorders have highlighted genetic similarities across disorders, indicating either poor specificity of genetic markers or poor specificity of categorical classification systems. It also remains to be determined what the role of genetic mechanisms that affect gene expression, such as epigenetic processes, is in relation to ED.
The purpose of this study is to investigate the contribution of specific Research Domain Criteria (RDoC) constructs (across behavioral/self report and biological units of analyses (genetic, epigenetic and biomarkers data)) and their interaction with environmental factors to the risk for ED behaviors (restrictive eating, excessive exercise, bingeing, purging at ages 13,14, 16, 18, 24) in a longitudinal developmental fashion. We aim to develop a risk prediction algorithm for ED behaviors that incorporates these predictors.
We propose to use data collected prospectively (and carry out a new wave of data collection) from a unique ongoing cohort study: the Avon Longitudinal Study of Parents and Children (ALSPAC), based in the United Kingdom, to investigate the prospective association of anxiety and social communication and AN-type behaviors (restrictive eating and excessive exercise); and cognitive control and attention and BN-type behaviors (bingeing, purging).
The availability of biological (blood samples and cord blood for DNA at birth, age 7/9 and 15/17 years), neuropsychological (between ages 1 and 5, 8 and 13 years), behavioral (every two years from 3 1/2 until 12 years and more detailed at 7,10 and 13 years), social (ages 1 and 5, ages 8-10, 12 years) data at repeated time points in 7,000-9,000 adolescents/young adults over a period of 20 years makes this cohort a unique resource.
We will investigate the following specific aims:
1. To determine whether anxiety and social communication difficulties:
a) are longitudinally predictive of developing AN-type behaviors (restrictive eating, excessive exercise) in adolescence/young adulthood (ages 13-24);
b) interact with life events to predict AN-type behaviors (restrictive eating, excessive exercise)
We hypothesize that high childhood anxiety and poor social communication will be predictive of restrictive eating and excessive exercise in adolescence/young adulthood; and that they will interact with childhood life events.
2. To determine whether low cognitive control and poor attention:
a) are longitudinally predictive of developing BN-type behaviors (bingeing, purging) in adolescence/young adulthood (ages 13-24);
b) interact with life events/bullying to predict bingeing and purging from ages 13 to 24.
We hypothesize that low cognitive control and poor attention will be predictive of bingeing and purging in adolescence/young adulthood; and will interact with life events/bullying.
3. To build a risk model across domains and derive a risk prediction algorithm that can be used to identify high-risk individuals.
Exploratory aim: Exploratory analyses on the effect of poor emotion recognition on restrictive eating and excessive exercise in 500 young males, using functional and structural neuroimaging will complement the above aims.
We will look at whether these patterns and associations vary by gender. We propose to use one of the largest population-based longitudinal study in the world with a long enough follow-up and data available across most units of analyses, to investigate longitudinal associations between specific constructs and ED behaviors in adolescence/young adulthood. The size of our sample and the repeat and comprehensive assessments will allow an investigation of risk for ED across specific cognitive, social and behavioral constructs and their interaction with environmental factors in a unique and cost-effective way.
Methodology
Outcomes:
ED behaviours collected at ages 13,14,16,18. A questionnaire will be included in the 24/25 clinic to assess ED. We will use the same questionnaire used at age 14.
Predictors:
This grant focuses on using dimensional behavioural/cognitive predictors recently identified in the Research Domain Criteria (RDoC) proposal (one of the strategic aims of NIMH). RDoC focuses on understanding specific neurobiological dimensional phenotypes by studying each across a series of indicators-called units of analyses (including available biomarkers, self-report measures and behavioural observation).
Data collected throughout childhood on anxiety/emotional disorders, social communication and neuropsychological tasks (constructs) will be used as predictors. Additionally we will use data on evironmental risk factors, such as life events throughout childhood and bullying/teasing as predictors.
Genetic data already collected on ALSPAC participants will be used to investigate the role of genome and epigenome across the constructs under study. In particular a polygenic risk score will be generated using results (top SNPs) from current MEGA-analyses from two consortia. Genome-wide methylation data available from ARIES will also be used.
Biomarkers, i.e. salivary cortisol at age 11/12 will also be used as predictors.
Confounders:
data collected throughout childhood on socio-demographic data. Basic demographic characteristics and an overall environmental adversities index will be constructed from mothers' questionnaire data as collected from pregnancy through to child age 11. Information on gender, ethnicity, SES, employment, education, income, housing, family type and size, mother age at birth, gestational age and birth weight will be recorded.
Child IQ collected at age 8 will also be used as a confounder.
Data analyses:
After initial dscriptive analyses across each variable a stepped approach will be used.
Firstly each construct under study will be investigated across its units of analyses using factor analysis or by deriving latent variables.
Secondly the association between hypothesised constructs and outcomes will be investiagated using univariable analyses.
Fourthly multivariable models will be applied.
In relation to aim 3. we will use a multivariable Cox proportional hazard regression backward elimination model for each outcome under study, by including variables identified in objective 1 and 2 to build a riks prediction model.
B1537 - The genetics of wound healing Collection of scar phenotypes following common insult and their genetic analysis - 29/04/2013
Where adult tissue is damaged, a complex repair process is taking place involving regeneration and acute phase immunological response. Unlike embryonic tissues, adult repair always leads to the formation of a fibrotic scar where the wound has healed, which ultimately can disable proper tissue function [1]. In recent years, research was able to link several genes to the event of scar formation . Knockdown of Ostepontin (OPN) in mice for example resulted in reduced granulation tissue formation and scarring [2]. It also has been indicated that TGF-beta1 in conjunction with Connective tissue growth factor (CTGF) is promoting scar formation [3]. Most of this data comes from mouse model studies, in humans however, less is known.
BCG injections were routinely given in schools since the 1960s (up until 2005) and lead to a charateristic scar, which individually differs in size and character. Before injection, a skin test is performed to test for exsiting Tuberculosis antibodies. If the test is negative, BCG injections are given [4,5]. In the age group of ALSPAC mothers, up to 70% were immunised each year during childhood, making it a great read-out for assessing population wide scarring outcomes.
By collecting information about different levels of BCG scarring outcomes in ASLPAC mothers by telephone interviews and combining this data with genome data we hope to perform a genomewide associaiton study (and possible rare variant analysis) for scar type. For ethical approval this research proposal will be submitted to ALEC. Please find attached documents summarising the infornation and SOP to be summarised and used during the course of proposed telephone interviews. These are in the process of development before submission to ALEC.
Analysis plan:
(i) develop cover letter, info sheet, protocol and consent form for the BCG scar study for use within a telephone interview
(ii) submitt proposal to ALEC for ethical approval
(iii) collect phenotypic data - coordinated effort with Kate Sherlock and tele-contact team
(iv) submit data for processing by ALSPAC team
(v) unite both genetic and phenotypic data to undertake tests of association
between genetic variation and phenotypic characterisation.
(vi) test selected genes plus surrounding regions locally for variant associated with scarring outcome
Genetic analysis will be supported by Nic Timpson.
1. Stramer, B.M., R. Mori, and P. Martin, The inflammation-fibrosis link? A Jekyll and Hyde role for blood cells during wound repair. J Invest Dermatol, 2007. 127(5): p. 1009-17.
2. Mori, R., T.J. Shaw, and P. Martin, Molecular mechanisms linking wound inflammation and fibrosis: knockdown of osteopontin leads to rapid repair and reduced scarring. J Exp Med, 2008. 205(1): p. 43-51.
3. Shi-Wen, X., A. Leask, and D. Abraham, Regulation and function of connective tissue growth factor/CCN2 in tissue repair, scarring and fibrosis. Cytokine Growth Factor Rev, 2008. 19(2): p. 133-44.
4. NHS (1996) Immunisation against infectious disease - The green book 1996 edition.
5. Shaaban, M. A., Abdul Ati, M., Bahr, G. M., Standford, J. L., Lockwood, D. N. and McManus, I. C., Revaccination with BCG: its effects on skin tests in Kuwaiti senior school children. Eur Respir J, 1990. 3(2): 187-91.
B1352 - Participation in meta-analysis consortium CREAM Consortium for Refractive Error and Myopia - 26/04/2013
ALSPAC myopia GWAS methodology
The YP phenotypes analysed were those collected at the TF3 clinic (refractive error, myopia case/control status, astigmatism case/control status, eye axial length, and corneal curvature).
The only mother's phenotype we have analysed to date has been myopia case/control status (inferred from questionnaire responses). We also plan to analyse the mother's quantitative refractive error (data on a subset of mothers collected from opticians or from assessments at the TF3 clinic). [Note that Exec approval for analysis of the mother's data was kindly granted recently to allow us to check for replication of our YP GWAS results].
Outliers, defined as values 4 standard deviations from the mean, were excluded. Quantitative traits were transformed to normal deviates prior to analysis. Autosomes: quantitative traits and case/control status were analysed with mach2qtl and mach2dat, respectively, using sex as a covariate (for datasets of imputed genotypes previously prepared by John Kemp). Chromosome X: analyses were run in PLINK. SNPs with an imputation quality Rsqless than 0.3 or MAFless than 0.01 were excluded.
We have not yet attempted joint analyses of YP + mother's data. If this is done, we will take account of the non-independence of the genotypes and phenotypes in parents/offspring (e.g. perform analysis in GenABEL using the mmscore function).
ALSPAC myopia GWAS results
We found no evidence of population stratification (l~1.00; ditto QQ plots). In YPs, we replicated a locus within the PDGFRA gene associated with corneal curvature in Asians [6]. In mothers, we replicated a locus associated with refractive error and myopia at 15q14 [4]. However, only a single SNP had a P-value below 5x10E-8 (an imputed SNP on the X chromosome with a MAF =0.06....and we note our X-chromosome analysis did not properly account for genotype imputation uncertainty).
Plans for meta-analysis
Most members of the CREAM consortium, including ALSPAC, have already participated in a replication of the 15q14 and 15q25 myopia loci [as approved by the Exec; manuscript currently in revision].
Meta-analysis will be carried out using standard procedures [10]. An Executive Committee will set specific objectives, consider applications for consortium membership, and resolve conflicts. To accelerate progress, analyses/manuscript writing will be distributed to three Working Groups (Refractive Error/Ocular Biometry/Astigmatism). Each working group will be co-chaired by individuals with expertise in statistical genetics and/or the phenotype of interest. Working Groups will be expected to organise conference calls at approximately monthly intervals to monitor progress. CW and/or JG will represent the ALSPAC myopia team in each working group (this option will also be open to GM & BSP if other demands on their time permit).
As well contributing directly to the meta-analysis, the ALSPAC cohort is ideal for examining the effects of genetic variants across childhood (e.g. following the strategy of Sovio et al. [11]) and their interaction with environmental exposures (e.g. following the strategy of Kilpelainen et al. [12]).
As mentioned above, a draft MOU has been prepared to cover the operation of the consortium (attached).
We would be grateful for the Exec's opinion on the MOU and any amendments that would be required before it could be given approval.
If the MOU were to be approved, we would also be grateful for advice on how it should be processed through the University, e.g. via RED?
References
1. Bamashmus MA, Matlhaga B, Dutton GN. Causes of blindness and visual impairment in the West of Scotland. Eye 2004; 18:257-261.
2. Hysi PG, Young TL, Mackey DA, et al. A genome-wide association study for myopia and refractive error identifies a susceptibility locus at 15q25. Nature Genet 2010; 42:902-905.
3. Sanfilippo PG, Hewitt AW, Hammond CJ, Mackey DA. The heritability of ocular traits. Surv Ophthalmol 2010; 55:561-583.
4. Solouki AM, Verhoeven VJ, van Duijn CM, et al. A genome-wide association study identifies a susceptibility locus for refractive errors and myopia at 15q14. Nature Genet 2010; 42:897-901.
5. Fan Q, Zhou X, Khor CC, et al. Genome-wide meta-analysis of five Asian cohorts identifies PDGFRA as a susceptibility locus for corneal astigmatism. PLoS Genet 2011; 7:e1002402.
6. Han S, Chen P, Fan Q, et al. Association of variants in FRAP1 and PDGFRA with corneal curvature in three Asian populations from Singapore. Hum Mol Genet 2011; 20:3693-698.
7. Yang J, Manolio TA, Pasquale LR, et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Genet 2011; 43:519-U44.
8. Lango Allen H, Estrada K, Lettre G, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010; 467:832-838.
9. Dudbridge F, Gusnanto A. Estimation of significance thresholds for genomewide association scans. Genet Epidemiol 2008; 32:227-234.
10. Thompson JR, Attia J, Minelli C. The meta-analysis of genome-wide association studies. Briefings Bioinformat 2011; 12:259-269.
11. Sovio U, Mook-Kanamori DO, Warrington NM, et al. Association between Common Variation at the FTO Locus and Changes in Body Mass Index from Infancy to Late Childhood: The Complex Nature of Genetic Association through Growth and Development. PLoS Genet 2011; 7.
12. Kilpelainen TO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: A meta-analysis of 218,166 adults and 19,268 children. PLoS Medicine 2011; 8.
B2005 - A comparison of twin and singleton data for early life characteristic - 23/04/2013
Aims/Hypothesis:
Twin data are valuable for quantifying genetic and environmental contributions to phenotypes, including both disease risk (e.g. type 2 diabetes) as well as quantitative traits (e.g. body mass index). However, twins can differ from singletons insofar as they tend to be born earlier (37 weeks rather than 40 weeks), and smaller (lower birth weight). The extent to which twins differ from singletons on a range of other early life characteristics (e.g. adiposity, growth, appetite and illness) is unclear. It is important to establish the extent to which twin data are comparable to singleton data, in order to ensure that results derived from twin studies may be generalisable to singletons.
The primary aim of this project is to establish the extent to which twins are similar to singletons on a variety of early life characteristics, by comparing twin data from two UK-based paediatric cohorts (the Twins Early Development Study, and Gemini - Health and Development in Twins), with singleton data from a number of large UK-based cohorts including ALSPAC. We hypothesize that twins will not differ from singletons on early life characteristics, thereby supporting the generalisability of findings from twin studies.
Our secondary aim is to test if the relationship between different early life characteristics is the same for twins and singletons.
Exposure variable:
We do not have exposure variables as such, as we are comparing summary statistics across different cohorts. 'Twinness' or 'singleton' (captured by the cohorts themselves) will be the primary 'exposures' of interest.
Outcome variables:
Our primary interest is anthropometric data. Primarily, we will compare anthropometric data from the singletons in ALSPAC to twin data from the Gemini cohort (at a number of different age points from birth to 5 years) and the Twins Early Development Study (TEDS) (at 7, 10, 11 and 12 years).
In addition to anthropometric variables, we will be comparing the twins and the singletons on appetitive data. In the Gemini cohort we have data from the Baby Eating Behaviour Questionnaire (BEBQ) and the Child Eating Behaviour Questionnaire (CEBQ) and would be looking for a comparator measure for appetite within ALSPAC. We also hope to compare feeding behaviour; encompassing breast versus bottle feeding to ascertain, for example, whether having multiple babies would make breast feeding more difficult. Other variables that we will examine are illness (to establish if the shorter gestational period of twins effects their general health in the early years of their life), infant temperament, physical activity and sleep.
One of the primary interests of the Gemini Study is to understand the predictors of weight gain in early life. As such, we have established relationships between appetite and weight, and sleep and weight, using the twin data. We will therefore examine if the magnitude of the association between appetite and weight, and between sleep and weight is the same for twins and singletons.
We are also planning to compare our twin data to singleton data from the Cambridge Baby Growth Study cohort and the Millennium Cohort Study.
Confounding variables:
When making comparisons between the cohorts we will take account of gestational age, postnatal age, gender, parental age at birth, parental health, ethnicity, and sociodemographic information.
B2002 - Role of selected endocrine disrupting chemicals on sex-hormone levels at age 15 girls enrolled in the ALSPAC study - 22/04/2013
Plasma levels of testosterone and sex hormone-binding globulin are associated with risk of diabetes. Several cross-sectional studies have reported positive associations between exposures to endocrine disrupting chemicals (EDCs) with BMI or insulin resistance. There are no longitudinal studies, however, exploring associations of gestational concentrations of EDCs during pregnancy with endogenous sex hormones known to predict obesity. For this proposal we want to use data on concentrations of sex hormones and insulin resistance generated by Hany Lashen on a sample of girls at age 15 to explore associations with EDCs.
The goals of this proposal are to explore feasibility of testing study associations with current data and development of future studies on this topic.
B2001 - Effects of vitamin B12 intake during pregnancy on offspring cognitive ability - 22/04/2013
Background: Vitamin B12 is an essential nutrient and it is required in one-carbon metabolism, the biochemical pathway that leads to DNA methylation. Maternal vitamin B12 and more generally the one-carbon metabolism are associated with neurodevelopment although the evidence is scarce (e.g. Bhate et al., 2008, Bonilla et al., 2012). Maternal vitamin B12 status seems to affect offspring's DNA methylation (McKay et al., 2012) suggesting a role for DNA methylation in the association between vitamin B12 and neruocognitive development.
Aim: To examine the causal association between vitamin B12 during pregnancy and offspring cognitive development, using genetic variants previously associated with one-carbon metabolism as proxies for vitamin B12 in a two-way Mendelian Randomization study. To assess the role of DNA methylation as a mediator in the association.
Hypotheses: We hypothesize that alleles which increase the levels of vitamin B12 during pregnancy will be associated with better cognitive performance. We hypothesize that DNA methylation mediates this association.
Exposure variables: Maternal and offspring genotypes for polymorphisms associated with vitamin B12 status and DNA methylation. Maternal vitamin B12 intake during pregnancy and cord blood levels.
Outcome variables: SCDC, WISC, WASI, CCC, DAWBA conduct problems.
Confounders and mediators: Maternal education, social class, age at delivery, parity, smoking during pregnancy, alcohol and folate assumption during pregnancy, infections during pregnancy. Child's date of birth, birth weight, sex, gestational age. Breastfeeding duration. Child's DNA methylation status.
Bhate V, Deshpande S, Bhat D, Joshi N, Ladkat R, Watve S, Fall C, de Jager CA, Refsum H, and Yajnik C. Vitamin B12 status of pregnant Indian women and cognitive function in their 9-year-old children. Food Nutr Bull. (2008) 29: 249-254.
Bonilla C, Lawlor DA, Taylor AE, Gunnell DJ, Ben-Shlomo Y, Ness AR, Timpson NJ, St Pourcain B, Ring SM, Emmett PM, Smith AD, Refsum H, Pennell CE, Brion MJ, Smith GD, Lewis SJ. Vitamin B-12 status during pregnancy and child's IQ at age 8: a Mendelian randomization study in the Avon longitudinal study of parents and children. PLoS One. (2012) 7:e51084. doi: 10.1371/journal.pone.0051084.
McKay JA, Groom A, Potter C, Coneyworth LJ, Ford D, Mathers JC, Relton CL. Genetic and non-genetic influences during pregnancy on infant global and site specific DNA methylation: role for folate gene variants and vitamin B12. PLoS One. (2012) 7:e33290. doi: 10.1371/journal.pone.0033290.
B2003 - Sexual orientation and problem drinking in adolescence - 22/04/2013
Aims:
1. To establish whether sexual orientation (at 15) is associated with problem drinking in late adolescence;
2. To assess evidence that any link between sexual orientation and problem drinking in late adolescence may be mediated by high levels of depressed mood and/or anxiety;
3. To test whether there are gender-specific differences (females vs. males);
4. To establish the prevalence of substance use (i.e., tobacco and illicit substances) in non-heterosexual vs. heterosexual adolescents.
Adolescent alcohol use represents a main public health concern as it causes short and long-term health problems and antisocial behaviour, which can lead to self-harm or harm to others (Coleman & Carter, 2005). Alcohol use is widespread in the UK where 88% of 15 year-olds report having drunk alcohol in the past 12 months and 1% of 14-16 year olds drink nearly every day.
Adolescents who identify themselves as gay, lesbian or bisexual (i.e. sexual minority adolescents) are at increased risk of involvement in problem drinking compared to their heterosexual counterparts, with studies reporting earlier age of onset of alcohol use, more frequent and heavier (i.e., binge) drinking, and more alcohol-related problems than heterosexuals (Bergmark, 1999; Corliss et al., 2008). Although there is evidence of differences in risk of problem drinking between males and females from a sexual minority background, the pattern remains unclear. Some studies have observed more alcohol problems among females than males while others have observed the opposite pattern (Ziyadeh et al., 2007).
Evidence also shows that sexual minority adolescents experience more mental health issues (i.e., depression and anxiety) than their heterosexual peers (Fergusson, Horwood & Beautrais, 1999; Hatzenbueller et al., 2008). Greater levels of anxiety and depressive symptoms may be elicited by gay-related stressors, such as the stress of coming out (Elze, 2002; Hatzenbueller et al., 2008). Symptoms of depression and anxiety have been found to predict elevated drinking and alcohol-related problems among non-heterosexual youth. However, to our knowledge, no study has attempted to establish whether these risk factors may explain the developmental relationship between sexual orientation and drinking. Our study aims to fill this gap by assessing whether the relationship between sexual orientation and problem drinking is explained by internalising disorders. We will also include risk factors of problem drinking, identified in the general population, in order to establish if they can further help us to understand this relationship. The risk factors we intend to use are school satisfaction, relationships with friends and parents, parental monitoring and alcohol attitudes (Sareceno et al., 2010).
Few studies also identified greater substance use and misuse among sexual minorities although less research has been dedicated to tobacco and illicit drug use in this sub-group (Hefferman, 1998). Thus, our final aim is to assess substance use prevalence in the non-heterosexual group compared to their heterosexual counterparts.
Hypotheses:
1. Adolescents who identify as not heterosexual report an earlier onset age of alcohol use and greater problem drinking in later adolescences;
2. We expect that the relationship between sexual orientation and problem drinking may be stronger for those experiencing internalising disorders;
3. There are gender-specific differences in these patterns. Although there is evidence in the general population that rates of depression are higher in female than male adolescents (Thapar et al., 2012), the findings for non-heterosexual adolescents are unclear. Thus, these analyses will be exploratory in nature
4. We expect higher engagement with other substances among non-heterosexual individuals.
Exposure variable(s): Sexual orientation assessed as sexual identity and also as sexual behaviour (i.e., gender of sexual partner); depressive and anxiety symptoms experienced by the teenager; relationship within the family members; parental monitoring; relationships with peers; school satisfaction; attitudes to alcohols
Outcome variable(s): Alcohol use: age of onset; drinking patterns and alcohol-related problems. Tobacco and illicit drug use.
Covariates: Family socio-economic status (SES); parental alcohol and substance use; parental depression; sensitivity to alcohol; conduct problems (i.e., Strengths and Difficulties Questionnaire)
Analyses:
We will conduct preliminary correlations and univariate regression models between sexual orientation and problem drinking as well as substance misuse to examine the association at baseline (i.e., age 15; cross-sectional data). Moreover, we will use structural equation modelling (SEM) to assess these relationships over time while controlling for relevant covariates (e.g., family SES, parental depression and alcohol misuse). Finally, we will use SEM to test the effects of theorised moderators and mediators (i.e., depressed mood and anxiety) on this association and assess whether they explain the direction and/or strength of the relationship. The model will also be adjusted for variables which have been identified as risk factors for both alcohol and substance misuse to assess whether they further explain this mechanism (e.g., relationship with parents and peers; attitudes to alcohol). We will conduct Multiple Imputation to account for missing data.
B2000 - Effects of pubertal timing on childrens bullying behaviour and mental health - 15/04/2013
Aim:
To investigate whether pubertal timing has an impact on bullying and victimisation at school for males and females and whether the relationship between peer victimisation and mental health problems (such as depression symptoms, psychosis symptoms, anxiety or self-harming behaviour) would be exacerbated by early maturation (girls) or late maturation (boys).
Hypotheses:
1) Early-maturing girls will be more likely to be victimised.
Outcome Variables:
Bullying status groups according to child, mother and teacher report at ages 8 to 18.
Exposure Variables:
Puberty measures from 8 years to 17 years
Confounding variables:
Demographics: gender, ethnicity, family social and economic status, home ownership;
Family factors: domestic violence and harsh parenting, maternal mental health status, child abuse and maltreatment;
Psychological factors: any mental health problems using DAWBA, internalizing and externalizing problems using SDQ, depression using SMFQ and MFQ;
Individual factors: sex, IQ, sexual relationships
2) Late-maturing boys will be more likely to be victimised.
Outcome, exposure and confounding variables:
Listed as above.
3) Off-time puberty will mediate/moderate the relationship between bullying involvement (at ages 8, 10 and 12) and menteal health problems (ie.depression, psychotic experiences, anxiety and self-harm) and sexual relationships.
Outcome, exposure and confounding variables:
Listed as above.
Outcome:
Depression, psychotic experiences and anxiety at 18 years and self-harming behaviour at 16.5 years and sexual relationship in adolescence.
Analysis:
Logistic regression analysis, multiple mediation model and structural equation modelling will be used to test the hypotheses. Given the potential impact of attrition on the findings, the data may be weighted for each part of the analysis. A logistic regression approach to weighting will be employed with bullying data available versus not available specified as the dependent variable and factors likely to predict attrition (e.g., gender, ethnicity, parental social class, family environment) included as independent variables and predicted probabilities (pprob) obtained. The inverse probability weight will then be calculated as 1/pprob.
B1549 - Association of maternal smoking and tooth eruption in ALSPAC - 11/04/2013
In a recent study we looked at the relationship between tooth eruption and adolescent anthropometric measures in ALSPAC. We found primary tooth eruption to be associated with height and weight at 17 years (unpublished Fatemifar et al. 2013). To gain a better understanding of factors effecting tooth development we believe that it is also important to understand exposures that may alter the timing of eruption and subsequently number of primary teeth at childhood. Smoking during pregnancy is known to reduce fetal development (1). We therefore hypothesise there will be relationship between maternal smoking during pregnancy and timing of tooth eruption.
Aims:
We aim to investigate the relationship between maternal smoking and primary tooth eruption. In doing so we need to take into account any confounders and mediators. We then aim to use a variant in a known nicotine receptor to conduct some instrumental variable analysis, in which we look at the relationship between maternal smoking and primary tooth eruption using the nicotine receptor variant as an instrument for maternal smoking.
Exposure variables:
Maternal Smoking
Paternal Smoking
Outcome variables:
Age at first tooth (15 months)
Number of teeth (15 months)
Confounding variables:
Birth weight
Maternal Education
Breast Feeding
Gestational Age
Maternal Age
Parity
1. Jaakkola,J.J.K. and Gissler,M. (2004) Maternal smoking in pregnancy, fetal development, and childhood asthma. American journal of public health, 94, 136-40.
B1548 - Identifying common genetic variants and putative genes associated with facial attractiveness - 11/04/2013
Facial attractiveness plays a crucial role in a variety of human interactions, including human mate choice. People prefer to date and marry facially attractive individuals. The preference for more attractive partners is warranted from an evolutionary perspective, given that facially attractive individuals have higher reproductive success than their less attractive counterparts and facial attractiveness is generally thought to indicate genetic quality in terms of disease resistance. People are also more likely to ascribe positive personality attributes to form same-sex appliances, employ and even vote for facially attractive individuals. Despite historical beliefs, facial attractiveness is not merely an arbitrary cultural convention. People from different cultures show strong agreement in what is considered facially attractive. Even young infants who have not been exposed to cultural norms, prefer to look at faces that adults describe as facially attractive. Previous studies have identified several facial cues (eg sexual dimorphism and symmetry) and hormones (eg testosterone and cortisol) that play a role in facial attractiveness. Yet despite the high estimated heritability of facial attractiveness, very few studeis have accessed the genetic variation underlying facial attractiveness. To our knowledge, only the human leukocyte antigen (HLA) genes have been investigated as candidate genes for facial attractiveness. Roberts et al found that HLA heterozygous men (ie men that have differenc copies of the HLA genes) were considered more attractive than HLA homozygous men (ie men that have similar copies of the HLA genes). Follow up studies have replicated this association in male, but not female subjects. Recent studies have shown that genome wide association (GWA) studies can successfully identify common genetic variants and genes which regulate quantitative heritable traits such as height and facial morphology. GWA studies therefore provide a more robust approach to identifying common genetic variants that are associated with facial attractiveness compared to candidate gene approaches that have been used for HLA).
The primary aimof this study is to identify common genetic variants, and ultimately putative genes, that are associated with facial attractiveness using GWA methodologies. We specifically chose the ALSPAC dataset because it is, to our knowledge, the largest dataset with both facial images and GWA data. To accomplish this aim, 3D facial images obtained from the ALSPAC image set will be standardised for size and orientation. 30 (15 male) caucasian students from the United Kingdom (UK) will rate all the images for attractiveness on a seven point Likert scale over eight one-hour sessions (rate calculated from previous work). 30 raters are sufficient to produce an accurate measure of facial attractiveness. We request permission to have the images rated at the Perception Lab, University of St Andrew's (UK), because of the well-established image rating facilities, large participation pool and streamlined workflow; images are rated in the UK to reduce cross-cultural variation in attractiveness judgements. Attractiveness ratings will be averaged for each image. The ALSPAC team have cleaned and imputed a GWA study dataset consisting of 8365 individuals with genotype calls for ~2.5 million common variants spread across the genome. We will use this resource to conduct a 2 stage (discovery and replication) genome-wide association study. Initially the discovery phase will include analysing ~5000 individuals that have both genotypic data and facial images. Power analysis (PowerGwas/QT version 1.0) indicate a sample size of 5000 is adequate to provide 80% statistical power to detect single nucleotide polymorphisms (SNPs) that explain as little as 0.8% of the variance in facial attractiveness. The association between each of the ~2.5 million SNPs (exposure variables) and facial attractiveness (outcome variable) will be independently tested in the ALSPAC cohort using linear additive regression, while controlling for pubertal development. SNP associations that exceed the standard significance threshold for genome-wide significance (pless than 5 x 10-8), will be identified and replicated independently in additional cohorts, making up the second replication phase for the GWA study. To determine which genes (and pathways) are most likely associated with facial attractiveness we will conduct a range of post-hoc analyses including (a) assigning SNPs to genes, (b) epistasis modelling and (c) pathway analyses. Briefly, multiple genes are ascribed to each SNP and these genes are then prioritised using epistasis modelling and pathway analysis, allowing us to further identify which biological processes regulate facial attractiveness. In addition, we will calculate a more accurate heritability estimate of facial attractiveness; do a GCTA analysis to estimate the amount of phenotypic variance in attractiveness common SNPs explains; test the relationship between admixture and attractiveness; test the relationship between genome-wide heterozygosity and attractiveness using the ~2000 ALSPAC individuals who have whole genome sequencing data; and test the association between previously imputed classical HLA alleles and facial attractiveness separately for males and females. Based on previous work we predict that HLA alleles will be associated with male, but not female attractiveness. All GWA analyses will be conducted at the University of Bristol.
The second aimis to determine the association between health measures (exposure variables) and facial attractiveness (outcome variable). The health measures will be divided into prenatal risk factors (eg parental age, presence of gestational diabetes) and childhood health measures (eg body mass index, blood pressure and self-reported health). Facial attractiveness is generally assumed to serve as a 'health certificate', but studies testing this assumption mostly utilise a few self-reported health measures and small sample size (~N=40-200). The size and quality of the ALSPAC dataset, especially the wide range of physiological measurements, provides us with the ideal opportunity to test the association between health indices and facial attractiveness in male and female faces respectively. Based on previous research and work currently under review, we predict that facial attractiveness will be significantly associated with health measures, but more so for male than for female subjects.
The third aimis to determine whether SNPs associated with facial attractiveness are also associated with other traits proposed to indicate overall quality, specifically increased height, body mass index (BMI) within the health BMI range, increased sexual dimorphism (eg facial masculinity/femininity) and facial symmetry. To do so we will calculate morphometric or perceptual measures of sexual dimorphism and facial symmetry before testing the relationship between allelic scores of SNPs for facial attractiveness and these traits.
B1547 - DGKK variants and fetal growth - 11/04/2013
DGKK was the main gene found to be associated with hypospadias in a genome-wide association study (GWAS) (van der Zanden et al., 2011), which we replicated in a further study (Carmichael et al., 2013). Given that DGKK is expressed in the placenta (we know little else about it), and hypospadias is associated with fetal growth retardation (Carmichael et al., 2012), we hypothesized that DGKK variants would be associated with fetal growth. Preliminary data among 930 non-malformed, male, population-based controls that were part of our DGKK-hypospadias study suggest that DGKK variants are associated with increased risk of low birthweight (less than 2500 gm) among term infants (37 or more weeks gestation). However, the sample size was relatively limited (only 20 subjects were term and low birthweight). ALSPAC offers an excellent opportunity to examine this hypothesis in more depth and in a much larger sample (approximately 7,500 subjects).
Our hypothesis is that DGKK is associated with fetal growth. The aim of the proposed analysis is to examine the association of fetal growth with infant genetic variants (SNPs) in DGKK that were included in the Illumina 317 genotyping (GWAS) panel, which has been run on ALSPAC samples. Parameters reflecting fetal growth, as well exclusion criteria for genotyping data, will largely follow methods developed for use of ALSPAC data for the meta-analysis of GWAS data on fetal growth by Freathy et al. (2010). That is, we will examine the association of birthweight standardized to z scores adjusted for gestational age, as a continuous measure and as a dichotomy (ie, less than 10th percentile versus higher), and we will examine birth length, head circumference and ponderal index, all among singleton term infants. The Freathy et al. study did not examine growth among preterm infants, but we propose to also examine the association of the DGKK variants with preterm delivery and with birthweight adjusted for gestational age among infants born preterm. All analyses will be stratified by infant sex since DGKK is on the X chromosome. Potential covariates to consider inclue maternal age, parity, prepregnancy body mass index, smoking, and education (as a marker of socioeconomic status). We will restrict analyses to singletons.
References
Carmichael SL, Mohammed N, Ma C, Iovannisci D, Choudhry S, Baskin LS, Witte JS, Shaw GM, Lammer EJ. Diacylglycerol kinase K variants impact hypospadias in a California study population. J Urol 2013;189:305-11.
Carmichael SL, Shaw GM, Lammer EJ. Environmental and genetic contributors to hypospadias: a review of the epidemiologic evidence. Birth Defects Res A Clin Mol Teratol 2012;94:499-510.
Freathy RM, Mook-Kanamori DO, Sovio U, Prokopenko I, Timpson NJ, et al. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat Genet 2010;42:430-5.
van der Zanden LF, van Rooij IA, Feitz WF, Knight J, Donders AR, et al. Common variants in DGKK are strongly associated with risk of hypospadias. Nat Genet. 2011;43:48-50.
B1535 - Comparison of methods to relate GWG to child BMI at age 7 - 28/03/2013
There is increasing emphasis in medical research on fetal and childhood antecedents of chronic disease risk, and how these interact with other exposures throughout the lifecourse to influence later-life conditions (1). Answering questions about the relative importance of speed, magnitude and timing of growth, behaviour and health status for longer-term outcomes requires appropriate analyses of longitudinal data. For example, to understand how maternal weight gain during pregnancy (gestational weight gain, GWG) influences later cardiovascular disease risk for the child, we might seek to describe relationships between GWG and BMI at age 7, and how any relationships are mediated through birthweight and changes in weight during childhood.
Analysis of lifecourse data poses several statistical problems (2). Analysis of a repeated outcome must account for dependencies between multiple observations on the same person: methods to do this (e.g. multilevel models, (3) (4)) are now widely available in standard statistical software packages (e.g. Stata (5)). Within-individual variation may vary over time (e.g. absolute measurement error in weight will be larger in later childhood than at birth) and there will usually be dropout due to non-response, death, illness, emigration, etc. Where several repeated measures are used as exposures in one regression model for a later-life outcome, standard regression models may be affected by their multicolinearity. Disentangling the genuine associations between GWG, birth weight and later outcomes is important; a negative correlation between birth weight and later cardiovascular risk would imply a public health focus on increasing average birth weight, whereas if the relationship were largely between subsequent growth and later cardiovascular risk, this would imply a focus on minimising excess weight gain in children (6).
We consider methods for relating growth (exemplified here by GWG) to later outcomes (here, BMI at age 7). Methods to be used include: SITAR (7); multivariate multilevel growth models (8); latent class models (9); mediation models (10)including structural equation models (11) and lifecourse models (12). We propose to use the associations between GWG, birthweight, childhood growth and BMI at age 7 in ALSPAC as an example dataset on which to compare these statistical methods.
B1546 - Social inequalities in allostatic load in childhood - 28/03/2013
Background:
Social inequalities in health, with people experiencing progressively worse health with increasing deprivation, are present throughout the world. Inequalities in health are not limited to mortality and life expectancy, with the incidence of physical and mental conditions being higher for individuals with lower socioeconomic position (SEP), including most cancers, heart disease, diabetes, depression and multimorbidity. However, the pathways, and particularly the underlying biological processes, linking poorer SEP and ill health are not well understood. Understanding the causal links between SEP and health are essential if inequalities are to be reduced in the UK and elsewhere.
Given the wide range of conditions that vary by SEP, it has been proposed that there are some common biological pathways in how SEP can 'get under the skin'. Through the exposure to environmental, psychosocial and behavioural factors that SEP results in, the body is put under demands that it can adapt to in the short-term (normal system regulation). However, if these exposures persist, dysregulation can occur. The 'wear and tear' on the body that will occur over long spells of such dysregulation is typically irreversible, eventually increasing the risks of poor health and functioning.
Cumulative physiological burden and dysregulation that occurs across multiple physiological systems throughout the lifecourse can be captured using the concept of allostatic load. The most widely used construct of allostatic load has been developed by Seeman and colleagues, where it is conceptualised using biomarker measures across an array of systems including the cardiovascular, metabolic and inflammatory systems. Allostatic load has been shown to predict the risk of major health outcomes including heart disease and all-cause mortality. Importantly, many of the individual components of allostatic load are not risk predictors for the same health outcomes associated with allostatic load. Assessing these biomarkers together as allostatic load helps us to understand the synergistic nature of the physiological burden on the body imposed by exposure to damaging environmental stressors. To date, there has been consistent (albeit small in number) evidence for lower SEP to be associated with higher allostatic load. Given the associations identified between SEP and allostatic load, and allostatic load and health, it is hypothesised that allostatic load is a mediator in the pathway between SEP and health. However, we are missing evidence for how SEP and allostatic load are associated throughout the lifecourse, how these associations can differ over time and place and if allostatic load is indeed a link between SEP and health. If allostatic load is a predictor of health, it would be expected that similar patterns of inequality would be seen in allostatic load as those seen with life expectancy and diseases like CHD. Greater knowledge on the relationship between SEP and allostatic load (and subsequent risk prediction of ill health) could be important for targeting interventions aimed at reducing inequalities in health that will have the broadest impact across the population. Understanding how the relationship between SEP and allostatic load differs according to factors such as age, gender and geographical location could be an important step in ensuring that these interventions are targeted correctly and efficiently.
Preliminary work:
I have recently led on a study paper looking at the relationship between SEP over the lifecourse and allostatic load (to be submitted April 2013). This study used a structured modelling approach comparing various theoretical models of the influence of SEP on health across the lifecourse that encompass the accumulation of risk, critical/sensitive periods and social mobility models. We found that the accumulation model of lifecourse SEP had the best model fit for the association with allostatic load in men and women aged approximately 35, 55 and 75 from the West of Scotland Twenty-07 Study (although the results were less convincing at older ages). However, the results also indicated that childhood was a particularly important time-point for the link between SEP and allostatic load. Since March 2013 I have also been leading on a study investigating some of the potential mediators (behavioural, psychosocial and material factors) between SEP and allostatic load using the Twenty-07 Study. This work has been funded by a six-month MRC Centenary Award.
Aims:
The overall aim of the fellowship is to examine if allostatic load, as a measure of cumulative physiological burden, is a mediator in the association between lower SEP and poorer health outcomes, including physical and mental health and mortality. The specific aim of the project using ALSPAC data is to examine allostatic load inequalities by SEP in children. Very little evidence exists in the development of allostatic load in childhood and its association with SEP, although there is good evidence for social patterning in adolescence. In addition, our preliminary work above has indicated the importance of childhood as a possible critical period in the development of adult allostatic load. Other data on childhood circumstances will also help answer if any patterning is driven by factors such as disease in infancy.
Hypotheses:
Lower SEP (based on a latent parental SEP construct) will be associated with higher allostatic load scores in children aged 9 from the ALSPAC study. This association will be partly mediated by disease in infancy and poorer conditions at birth (e.g. low birth weight, small for gestational age).
Exposure variables:
Parental SEP (education, income, social class, housing tenure, financial difficulties and area deprivation)
Outcome variables:
Allostatic load - a score produced from several bookmarkers (blood pressure, pulse rate, cholesterol, glycated haemoglobin (diabetes marker), waist-hip ratio, C - reactive protein (inflammation marker) and IL-6 (inflammation marker))
Confounding variables:
Sex, disease, birth weight, gestational age, ethnicity
Please note, any data provided will be stored on secure drive spaces at SPHSU, which only I have access to.
B1545 - The Changing Nature of Lone Parenthood and its consequences - 28/03/2013
Research Aims
The study will focus on four inter-related research questions.
1. Who becomes a lone-parent and what are the consequences?
How does lone-parenthood today differ from the past when it was relatively rare? Are lone-parents an increasingly (or decreasingly) "selected" group of the population? How might we expect lone-parents families to fare (in terms of employment, income and poverty) had they not become lone-parents?
2. How long does lone-parenthood last and what is the nature of parents' relationships before and after periods of lone-parenthood?
Most mothers who become lone-parents at the time of their child's birth will have partnered by the time their child is five years old, whilst many married / cohabiting relationships will founder resulting in lone-parenthood for older children. How have these patterns evolved? Do they offer important information for the variation in children's experiences?
3. Are lone-parents becoming more heterogeneous?
Is lone-parenthood becoming increasingly polarized, as has been found in US, with some mothers managing to maintain their incomes through work and maintenance while others fare poorly? Or is lone-parenthood a dominant characteristic leading to poor outcomes for parents and children regardless of background? What are the longer term consequences of lone-parenthood for mothers - does it leave a long term scar even after re-partnering or children leaving home?
4. How does lone-parenthood influence children's outcomes and has this changed over time?
How does family structure, including lone-parenthood and re-partnering (step-parenthood), influence children's outcomes? How does this map onto the patterns in the variation in lone-parenthood described in the preceding questions? To what extent does the background of lone-parents (e.g. age, education) matter in determining children's outcomes? And how does lone-parenthood affect children's social mobility?
Estimation Techniques (Exposure and Outcome Variables)
The analysis will use simple descriptive statistics and panel data techniques to address the research questions set out above. Our first step will be to examine lone-parent status across all data sets. Family status will be defined at the child's birth, at early school age (around age 6), end of primary school (age 11) and end of compulsory schooling (age 16). Re-partnering will be treated as a separate status to intact partnerships from birth. Much of our analysis will focus on lone mothers, who constitute over 90 percent of the lone parent population, although we will also examine lone fathers as a single distinct category where sample sizes allow.
The data will allow us to provide a description of the growth of lone-parenthood over time and across the cohorts. It will also allow us to measure duration of lone-parenthood for children at various ages in the birth cohorts. To do this we will add information of the duration of the relationship prior to the birth to form a typology of lone-parenthood by age of child, duration of lone-parenthood and stability of surrounding relationships. This typology will then be mapped across the cohorts and changes over time will be compared as well as onto child outcomes at ages 6, 11 and 16 to gauge the size of educational deficits at each age for children in or have moved through lone-parent families into re-partnered families. This can also be extended to adult economic and social outcomes, including marriage, fertility and lone-parenthood. So we will assess patterns on social mobility for children growing up in lone-parent families for the early cohorts.
As we are particularly interested in the diversity of experience of lone-parenthood an important question is whether lone-parenthood is more damaging to women's economic position depending on their route into lone-parenthood. We will investigate the influence of education and labour market experience on outcomes for lone-parents; routes into lone-parenthood (including past relationship histories, age of children on becoming a one parent and labour market attachment) and the duration of lone-parenthood. In addition, as paid work has increasingly become the "social norm" for women, has a greater divide developed between lone-parents with strong labour market attachment and earnings and will examine how lone-motherhood has changed in response to increased female labour market opportunities. We will use regression based approaches to condition on observable differences prior to lone-parenthood but it is difficult to also predict what the effects of extremely unhappy relationships would have had if families remained intact. So a number of approaches can be undertaken, each with strengths and weaknesses so as to give as robust a picture as possible. As the data are longitudinal this will allow the use of "fixed effects" estimators, or their equivalents in forms other than linear regression, to further condition out unobservable characteristics of parents and children. Propensity score matching can be used to remove observable differences in socio-economic origins between lone-parent and couple families (including step-parent families), and also to match families by duration of relationships.
The data on children will provide insights into the relationship between the components of our typology and the test scores, so that we can identify which elements of the lone-parent typology appear to lower test scores. We will use regression based approaches to condition on observable differences. In particular we would envisage using propensity score matching to remove observable differences in socio-economic origins between lone-parent and couple families. For those children where lone-parenthood occurs after age 6 or so, and so a pre-lone-parenthood observation of test scores is available a value added model structure will be employed.
B1479 - The determinants of child/adolescent well-being and psychological resilience - 28/03/2013
There is a growing body of literature that has analysed cross-sectional data which shows that there is a strong relationship between the current characteristics of the individual and their wellbeing. However, policymakers need to know about how these characteristics arose in order to determine the point in the life-cycle when interventions would be most cost effective. To answer this question, a life-course model is needed that captures in a quantitative way the impact of different influences (at different points in time) upon subsequent wellbeing.
Our previous research has identified that adult life satisfaction is mainly dependent on seven other contemporaneous dimensions of a person's adult characteristics. These are a person's education, employment, and earnings, plus their mental health, physical health, family status and criminal records. However, our central interest is in how these seven measures are determined and thus on how we can influence them (and thus wellbeing). We know that both a person's family background and childhood development will be important. But, little is known about the relative importance of specific components of a person's family background and childhood development. There is also little understanding about the timing of when these components begin to matter. Our primary research aims to address both of these areas.
Thus, in our primary research project, we will first test the hypothesis that a person's family background and childhood development affect adolescent mental health, by responses to Strenghts and Difficulties Questionnaire (SDQ), the Short Mood and Feelings Questionnaire (SMFQ) and by the Development and Wellbeing Assessment (DAWBA), mainly through their effect on our seven other contemporaneous dimensions. If our hypothesis is confirmed, we will then seek to isolate the causal effect of a number of specific components of a person's family background and childhood development, which are caused by exogenous mechanisms such as the raising of the school leaving age (RoSLA) and life events, to identify their relative importance and also how they interact with time.
In our second project, we would like to investigate the early determinants of psychological resilience which can be broadly defined as the person's ability to withstand exogenous negative shocks in life. There is a growing body of literature over the last decade on the causes and consequences of human's psychological resilience, especially people's ability to bounce back from significant bad life events (Rayo and Becker, 2007; Graham and Oswald, 2010; Perez-Truglia, 2012). This recent surge of interest is fuelled by the releases of new longitudinal evidence of hedonic adaptation to negative life events, including adaptation to unemployment, disability, and bereavement (Clark et al, 2008; Oswald and Powdthavee, 2008). Yet little is known, either by economists or psychologists, why certain individuals are better than others at bouncing back from a bad life event and why they are initially hurt less by such a shock.
Thus, in our second project, we wish to test the hypothesis that a person's psychological resilience can be determined early on in childhood. To do this, we would first need to measure the emotional trauma from bad shocks in their life events. We would then try to find the important determinants of this estimated measure of psychological resilience in children. We believe our research using ALSPAC data can provide us with new scientific evidence on whether or not the components of long-term well-being (e.g., individual's ability to cope with bad life shocks) are determined early on in childhood, or more specifically before the child was born.
Finally, in our third project we wish to use the ALSPAC data to develop a dynamic factor model of child and adolescent well-being. More precisely, we wish to look at the development of cognitive, non-cognitive and health capabilities in a dynamic framework where abilities are the result of parental environments and investments at different stages of childhood and adolescence(Cunha et al, 2010). We believe that the unique wide range of cognitive, non-cognitive and health measurements in the ALSPAC data will allow us to refine our current understanding of the dynamics of child development.
References:
Clark, A.E., Diener, E., Georgellis, Y. and Lucas, R.E. 2008. Lags and leads in life satisfaction: a test of the baseline hypothesis. Economic Journal, 118(529), F222-F243.
Cunha, F., J.J. Heckman, and Susanne M. Schennach (2010) "Estimating the technology of cognitive and noncognitive skill formation" Econometrica, Econometric Society, vol. 78(3), 883-931, 05.
Graham, L. and Oswald, A.J. 2010. Hedonic capital, resilience and adaptation. Journal of Economic Behavior & Organization, 76, 372-384.
Oswald, A.J., and Powdthavee, N. 2008. Does happiness adapt? A longitudinal study of disability with implications for economists and judges. Journal of Public Economics, 92(5-6), 1061-1077.
Perez-Truglia, R. 2012. On the causes and consequences of hedonic adaptation. Journal of Economic Psychology. In press
Rayo, L. and Becker, G.S. 2007. Evolution efficiency and happiness. Journal of Political Economy, 115(2), 302-337.