Attention Deficit Hyperactivity Disorder (ADHD) otherwise known as Hyperkinetic disorder (ICD 10) is an extremely disabling condition, affecting 1.4-6% of children. It is now the commonest reason for follow-up in Child and Adolescent Mental Health Services as well as being seen in Community Child Health Services. Behavioural, social and educational problems as well as increased rates of adult difficulties including criminality and drug misuse are associated with ADHD in children with normal intelligence. In contrast, virtually nothing is known about ADHD in children with learning disability (LD/mental retardation) as sub average intelligence (IQless than 70) has traditionally been used as an exclusion criterion in studies of ADHD. Neither the DSM-IV nor the ICD-10 gives detailed consideration to the diagnosis of ADHD/Hyperkinetic disorder among children with LD.

At one time, clinical lore suggested that ADHD did not occur in children with LD, and any inappropriate behaviour children with LD showed was secondary to mental impairment. That view is not supported by current evidence. More recent studies have shown that ADHD occurs more commonly in these children but may be underdiagnosed due to issues such as '' diagnostic overshadowing'', the tendency by which clinicians tend to overlook additional psychiatric diagnosis once a diagnosis of learning disability is made; or ''masking'' in which the clinical characteristics of a mental disorder are masked by a cognitive, language or speech deficit.While available evidence suggests that ADHD may be more common among children with learning disability, research has been hindered as most studies of ADHD in children with LD do not use standardised diagnostic interviews and criteria. Also this sub-group of children with ADHD and learning disability are excluded from virtually all aetiological and treatment studies. Thus it is not known whether the clinical presentation, pattern of correlates and aetiology of ADHD differs in children with learning disability. This may have ethical implications as not much is known about the diagnosis and treatment in this group of children and hence they are frequently undiagnosed and untreated.

Although there is evidence that ADHD does occur in those with LD a crucial clinical issue is whether or not ADHD in this group is the same entity as that found in those without learning disability; that is can those with both ADHD and LD be viewed as having "true" ADHD. As this sub-group is almost always excluded from research, it remains unknown whether they are similar to children with ADHD and normal IQ in terms of clinical and aetiological correlates. Our preliminary pilot study analyses using a sample of children with ADHD with low IQ test scores showed that lower IQ, is associated with increased ADHD symptom severity and conduct disorder symptoms. This suggests that ADHD and LD may represent a clinically more severe variant of ADHD.On the basis of pilot data, we hypothesise that children with ADHD and LD will show a similar clinical presentation and pattern of genetic and psychosocial correlates to those found in children with ADHD and IQ within the normal range. If our hypothesis is correct, then children with ADHD and LD are likely to benefit from stimulant and other treatments that are known to be effective in those with ADHD without LD and our work will provide impetus for therapeutic trials in this important and disadvantaged group. Moreover, the findings will further highlight that children with ADHD and learning disability should not necessarily be excluded from aetiological and other clinical studies.

AIMS OF THE STUDY:

The aim of this study is to

1) Collect and comprehensively assess a sample of 100 children with ADHD and learning disability.

2) Compare this group with two other groups-a well-characterised sample of children with ADHD who have normal intelligence (IQgreater than 70) that has already been collected and a control group of children with low IQ alone on a) clinical characteristics b) genetic and psychosocial correlates that are known to be associated with ADHD.

The control group of children with low IQ and no clinical comordity have been difficult to recruit from a clinical setting and the ALSPAC would be ideally placed to provide the clinical and cognitive data required for this group of children.

Current data requested:

ADHD -

DAWBA data on Attention & Activity from parent questionnaires at ages 91mths and 128 months

DAWBA data on Attention, Activity and Impulsiveness from teacher questionnaires at Yr 3 & Yr 6

Antisocial Behaviour -

DAWBA data on Awkward & Troublesome Behaviour from parent questionnaires at ages 91mths and 128 mths

DAWBA data on Awkwardness & Troublesome Behaviour from teacher questionnaires at Yr 3 & Yr 6

Antisocial Behaviour questions from Focus@8 and Focus@10 from the child

Cognitive measures -

WISC IQ measure from Focus@8, including digit span subtest, full scale IQ, pe rformance IQ and verbal IQ measure

Other Measures:

Demographic measures -

Sex,

Age at each assessment,

whether from a multiple birth

Social class (maternal education and housing type),

Ethnicity.

We will provide a list of 235 SNPs that have been found to be associated with performance in our cohort. We genotyped our samples using the Illumina HumanHap550 or 610 and will pick the top 10 (approximately) associations from each of the 23 phenotypes examined in our cohort for followup in the ALSPAC cohort. We would like the p-values, betas, standard errors, and direction of effect by allele for a linear regression between each of these SNPs and each of the cognition phenotypes available in the ALSPAC cohort (with covariates such as age and gender included in the model). Since our cohort is composed of individuals over the age of 18, we would prefer the cognition data to be from a timepoint past childhood.

Our analysis was done in a sample of 291 Caucasians for the following tests: Green Story Recall Immediate and Delayed, TrailsA, TrailsB, WAIS Digit Span Forward, WAIS Digit Span Backward, Controlled Oral Word Association, Animals, WAIS Digit Symbol, WAIS Symbol Search, Stroop Color-Word, and the first principal component of these 11 phenotypes. Analysis was performend in a partially overlapping sample of 656 Caucasians and Asians for the following tests from Cantab: paired associates learning (PAL) (2 phenotypes from test), spatial working memory (SWM) (2 phenotypes from test), verbal recognition memory - immediate recall (VRM), pattern recognition memory (PRM), intra-extradimensional set shifting (IED), rapid visual processing (RVP), spatial span (SSP), spatial recognition memory (SRM), and the first principal component from these 10 phenotypes. Cognition phenotypes that are most similar to these measures would be ideal for comparison.

Multiple genome wide and candidate gene analysis have been performed in order to identify genetic varaints that explain the inter-individual differences in intelligence(Butcher, L. M. et al., 2005; Butcher, L. M., Davis, O. S., Craig, I. W., & Plomin, R., 2008; Plomin, R., 1999; Plomin, R. et al., 2004; Plomin, R. et al., 2001; Buyske, S. et al., 2006; Dick, D. M. et al., 2006; Luciano, M. et al., 2006; Posthuma, D. et al., 2005, for a review see Posthuma and de Geus (Posthuma, D. & de Geus, E. J. C., 2006)) but so far results are far from clear. Of all reported genetic association studies in the literature, only 4% have shown replicable association according to a 2002 search (Hirschhorn, J. N., Lohmueller, K., Byrne, E., & Hirschhorn, K., 2002). Using data from the IMAGE project, we performed a GWAS on IQ using data from 947 European Caucasian nuclear families (2844 individuals) from eight countries (Belgium, England, Germany, Holland, Ireland, Israel, Spain, and Switzerland) that were included in the analysis. Families were recruited based on having one child with ADHD (Attention-Deficit/Hyperactivity Disorder) and another who would provide DNA and quantitative trait data. In addition, both parents had to be available for DNA-sampling. IQ scores were available for 634 probands (for which we also had genotyping data), of which 554 are males, with a mean age of 10.99 (SD 2.74). IQ was measured with WISC-III-R (Wechsler Intelligence scales for children) (Wechsler, D, 1991) or the WAIS-III-R (Wechsler Adult Intelligence Scale) (Wechsler, D, 1997) when appropriate (for children aged 17 and older). The vocabulary, similarities, picture completion and block design subtests from the WISC were used to obtain an estimate of child's IQ (prorated following procedures described by Sattler) (Sattler, J. M., 1992). Age-appropriate national population norms were available for each participating site and these were used to derive standardized estimates of intelligence (Sonuga-Barke, E. J. et al., 2008). Standardized Full Scale IQ scores ranged from 56 to 154, with a mean of 100.7 (SD 15.6).

The IMAGE consotium makes part o the Genetic Association Information Network (GAIN) a public-private partnership of FNIH (Foundation for the National Institutes of Health, Inc.) that currently involves NIH, Pfizer, Affymetrix, Perlegen Sciences, Abbott, and the Eli and the Edythe Broad Institute (of MIT and Harvard University (http,//www.fnih.org)). Genotyping was conducted at Perlegen Sciences using their genotyping platform, which comprises approximately 600,000 tagging SNPs designed to be in high linkage disequilibrium with untyped SNPs for the HapMap populations. Genotype data were cleaned by NCBI (The National Center for Biotechnology Information). Quality Control analyses were processed using the GAIN QA/QC Software Package (version 0.7.4) developed by Goncalo Abecasis and Shyam Gopalakrishnan at the University of Michigan. Details of the genotyping and data cleaning process for the ADHD GAIN study (Study Accession, phs000016.v1.p1) have been reported elsewhere (Neale, B. M. et al., 2008a). Briefly, we selected only SNPs with minor allele frequency (MAF) >= 0.05 and Hardy-Weinberg equilibrium (HWE) (P >= 1x10-6). Genotypes causing Mendelian inconsistencies were identified by PLINK and removed (http,//pngu.mgh.harvard.edu/purcell/plink/) (Purcell, S. et al., 2007a). We additionally removed SNPs that failed the quality control metrics for the other two GAIN Perlegen studies (for Major Depression Disorder (dbGAP Study Accession, phs000020.v1.p1) and Psoriasis (dbGAP Study Accession, phs000019.v1.p1). With this filtering, 384.401 SNPs were retained in the final dataset. To increase coverage in the targeted genomic areas, we used the imputation approach implemented in PLINK (v1.04), which imputes genotypes of SNPs that are not directly genotyped in the dataset, but that are present on a reference panel. The PLINK algorithm is an extension of multimarker tagging. The reference panel used consisted of 2,543,285 polymorphic autosomal SNPs genotyped on the 60 HapMap CEU founders which are publicly available for download from the HapMap website (http://www.hapmap.org). A threshold of 0.80 confidence level was set for a hard call to be included in further association testing. Most likely genotypes on imputed SNPs were then included in association analyses. Gene coverage was determined by the sum of the typed and imputed SNPs as well as the tagged SNPs divided by the total known common SNPs within a gene, using WGAviewer (Ge, D. et al., 2008). Association analysis of 634 ADHD probands was conducted using the linear procedure implemented in PLINK using the prorated IQ score as quantitative outcome and adjusting for the language in which the IQ tests were done. Initial findings show several SNPs with interesting (non geneome wide significant) association p values (p less than 10E-06) including a haplotype block on chromosome 3 (rs7643566, rs6800161, rs9860985, rs7642425, rs1602, rs480668, rs6583190 and rs553783 (uncorrected p values = 5.32E-6)) also SNP rs11594325 (uncorrected p value = 6.43 E-6) in the PRKG1 gene on chromosome 10 and SNP rs2134947 (uncorrected p value=9.47E-7) in the CNTN5 genme on chromosme 11. In order to validate these findings, an independent replication sample is needed. We propose to use the genetic and IQ information available from the ALSPAC study in order to replicate these association findings. ALSPAC, as a birth cohort is comparable to an ADHD sample as IMAGE in terms og IQ because the selection criteria applied for the IMAGE participants only required that the IQ was equal or higher than 70, meaning that the distribution of the trait in our ADHD sample is perfectly normal (overall mean IQ [all probands] = 100.0, SD=15.4). Also, the proportion of IQ values lower that this threshold is low, skewing our IQ distribution only slightly, skewness 0.103, SE=0.070), this will not influence our comparison with cohort such as ALSPAC.

Mean heart rate at eleven years of age will be correlated against genome-wide imputed snp data on 1500 ALSPAC children. The summary level association statistics will be provided to Ruth Loos (Cambridge) who is chairing a consortium, aiming to meta-analyse the results of several cohorts with the heart rate measure. We also request that the heart rate measures and DNA be made available for follow up single SNP genotyping for genome-wide significant SNPs from the meta-analysis.

The present proposal is a a follow up of our current B598 project, which aims to investigate the gene x gene and gene x environment interactions at the basis of dyslexia and related disorders. Preliminary data from the analysis conducted so far are encouraging. We were able to replicate in the ALSPAC sample some associations of genetic markers we have idetified in our selected samples for dyslexia and SLI (Paracchini et al, 2008 , Newbury et al, submitted). The B598 has been designed as a short 2-years pilot study and we would now like to take forward and extend its format while the aims remain the same.

Aims of the project are:

1) To replicate in the ALSPAC sample previously reported genetic association at the basis of dyslexia and SLI

2) To investigate whether the effect of dyslexia and SLI susceptibility variants can be modulated by different genetic factors (gene x gene interactions).

3) To investigate the role of environmental factors in modulating the effect of genes that contribute to measures of reading, speech and language (gene x environment interactions).

4) To test whether shared genetic or environmental factors can explain co-morbidity between dyslexia and SLI and whether it is possible to identify a correlation between genetic/environment background and specific sub-groups of phenotypes.

We would like to expand the current analysis by genotyping additional SNP, genotyping the mother cohort and possibly the fathers. We will also expand, consequentially, the statistical analysis.

We would like to include in the analysis additional genes that have been implicated in SLI, dyslexia and ADHD as well as interesting gene implicated in regulating cognitive function. We are also cunducting an extensice functional characterisation of the KIAA0319, focusing in particular on the identification of interacting proteins. We have identify and verified several protein interacting with KIAA0319 and we would like to include these in our analysis. We are in the process of testing for association SNPs within these genes and we planning to include in ALSPAC only significantly associated SNPs. Therefore at this stage we cannot speficy any rs number for these genes.

For some genes we are planning to investigate parent-of-origin effect. Therefore we would like to include in this proposal the genotypic analysis of the available parents.

Details of resources requested:

- Biological materials

We have enough DNA for the children cohort to carry out this project. we will perform the genetic analysis here at the Wellcome trust Centre for Human genetics using the I-plex sequenom system. We are planning to analyse in the parents only specific SNPs and, if possible, we would like to genotype these through the K-Biosciences service.

-Existing data

We would like to include in this project the analysis of genes already genotyped in the ALSPAC sample by different research groups. In some case we would like to use the existing genotypic data (providing the quality is good) and in other cases we would to genotype additional markers.

- Phenotypic measures

We would like to include in this project all the outcomes that we have already accessed through the our previous project DTA (B566) and the ones we will finalise for the B598 project. I anticipate that we might add some specific extra measures but the the basic "concepts" will be the same.

- Data linking

We would like the genotypic data to be linked to the database including the genotypes generated in our previous projects. If possible we would like to use the same old codes otherwise we are happy to get a new code as long as all the genotypes will be included.

-Statistical analysis

We would like to choose the option of performing the staitical analysis in our laboratory.

Details of funding

This project will be part of my next fellowship applications. I am planning to apply to the Wellcome Trust, MRC, Royal Society and European Council aiming to a project starting date in Oct 2010. I will submit to ALSPAC copies of my application for your approval before submitting to funding bodies.

A susceptibility locus on chromosome 9p21 has become the most consistently reproduced genetic risk marker for myocardial infarction1, 2. The locus has also now been associated with an increased risk of aneurysm formation via different underlying pathological processes, in distinct vascular beds; the abdominal aorta and intracranial arteries3. Together these observations point towards an underlying biological mechanism that operates at the level of the arterial wall4.

We have completed a comprehensive vascular phenotype study in around 2500 individuals to study the impact of genotype on the arterial wall in young people and compared the findings to those with established coronary artery disease. As well as associations with vascular phenotypes we have demonstrated an unexpected association between blood pressure and genotype in young people (based on a sample of 1354 adolescents from the Ten Towns Study) (see below). While unexpected, the direction of effect fits with our observations of vascular phenotype in young subjects. Other studies in older subjects have not demonstrated associations between this genotype and conventional risk factors, including blood pressure.

We wish to undertake a collaborative project to establish whether we can replicate this finding in a second cohort of young people of similar age. At the same time the lack of association with other risk factors during childhood could be verified. Our analysis has been based on the rs2891168 SNP (which effectively tags the 9p21 risk haplotype4) and we propose the joint analysis with ALSPAC be based on the same SNP or the most closely associated proxy SNP within the available ALSPAC genome wide association study dataset of 2000 subjects. We propose that we study associations with blood pressure levels at both age 11 and during adolescence (as performed for the Ten Towns Study) and with other risk factors at these time points.

This collaboration would provide a unique opportunity to present data from two different populations on the association between this specific genetic locus and risk factors during childhood. Furthermore, the collaboration with ALSPAC provides the possibility for rapid analysis based on existing data for both blood pressure and genome-wide association.

Adolescence

AA

AG

GG

p

Number of male (%)

199 (55)

350 (55)

193 (55)

0.9

Median age in years (range)

15 (13-16)

15 (13-16)

15 (13-16)

0.7

Systolic blood pressure in mmHg

122.6 (14.1)

120.9 (13.2)

120.4 (13.2)

0.03

Diastolic blood pressure in mmHg

67.8 (7.8)

66.8 (7.2)

66.5 (7.1)

0.02

Aged 10-11 years

AA

AG

GG

p

Systolic blood pressure in mmHg

113.3 (11.2)

112.4 (11.1)

111.9 (10.8)

0.1

Diastolic blood pressure in mmHg

66.1 (6.8)

65.5 (7.0)

65.0 (6.9)

0.03

Values expressed as mean (SD) unless otherwise stated. p represents the significance level of an ANOVA.

1. Samani NJ, Erdmann J, Hall AS, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. Aug 2 2007;357(5):443-453.

2. McPherson R, Pertsemlidis A, Kavaslar N, et al. A common allele on chromosome 9 associated with coronary heart disease. Science. Jun 8 2007;316(5830):1488-1491.

3. Helgadottir A, Thorleifsson G, Magnusson KP, et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet. Feb 2008;40(2):217-224.

4. Broadbent HM, Peden JF, Lorkowski S, et al. Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p. Hum Mol Genet. Mar 15 2008;17(6):806-814.

The mammalian brain has undergone significant expansion in its evolution to man, and is over 3 times bigger than our closest primate relatives (1). The longterm research interests of the Jackson laboratory are in brain size. Head circumference is highly correlated with brain size, and is used clinically as a proxy measurement for brain volume. To date our major work has been in the identification and functional analysis of genes causing marked reduction in head circumference/brain size (Primary Microcephaly/Seckel syndrome,ref. 2-6). Several genes have been identified, all of which have centrosomal functions (6-8). Two of these, ASPM and MCPH1 have been found to undergo significant adaptive evolution in primates (9,10). However, to date, variants in these genes have not been found to be associated with normal population variation in head circumference/brain size (11).

Following on from the identification of Mendelian genes for these disorders of extreme reduction in head circumference (-4 to -10sd), it is now of interest to identify the factors responsible for variation in head circumference and brain size in the general population.

Within the Human Genetics Unit, head circumference has been measured as a QTL in ongoing GWAS studies in Croatia and Orkneys. To increase the power of these studies we now plan to perform metaanalyses in conjunction with other cohorts to identify SNPs associated with determining head circumference in the general population. The ALSPAC cohort provides an ideal cohort to combine with our studies, particularly given that genotyping has been performed on the same 317 platform, and the availability of a larger cohort for replication/confirmation, of positive hits. (If required, such validation work through genotyping of candidate SNPs would be performed by Nic Timpson on the ALSPAC cohort for within cohort replication/confirmation)

Once identified, and validated, such genes contributing to variation in head cirucmference, will be functionally characterised at the cellular level and in model organisms, using approaches already established in ongoing work in the lab to characterise the MCPH1 and PCNT genes. Such work, will address whether such variants are acting in developmental pathways already implicated by mendelian genetics in brain size determination.

1. Ponting, C. & Jackson, A.P. Evolution of primary microcephaly genes and the enlargement of primate brains. Curr Opin Genet Dev 15, 241-8 (2005).

2. Griffith, E. et al. Mutations in pericentrin cause Seckel syndrome with defective ATR-dependent DNA damage signaling. Nat Genet 40, 232-6 (2008).

3. Brunk, K. et al. Microcephalin coordinates mitosis in the syncytial Drosophila embryo. J Cell Sci 120, 3578-88 (2007).

4. Alderton, G.K. et al. Regulation of mitotic entry by microcephalin and its overlap with ATR signalling. Nat Cell Biol 8, 725-33 (2006).

5. Trimborn, M. et al. Mutations in Microcephalin Cause Aberrant Regulation of Chromosome Condensation. Am J Hum Genet 75, 261-266 (2004).

6. Jackson, A.P. et al. Identification of microcephalin, a protein implicated in determining the size of the human brain. Am J Hum Genet 71, 136-42 (2002).

7. Bond, J. et al. Protein-Truncating Mutations in ASPM Cause Variable Reduction in Brain Size. Am J Hum Genet 73, 1170-7 (2003).

8. Bond, J. et al. A centrosomal mechanism involving CDK5RAP2 and CENPJ controls brain size. Nat Genet 37, 353-5 (2005).

9. Evans, P.D. et al. Adaptive evolution of ASPM, a major determinant of cerebral cortical size in humans. Hum Mol Genet 13, 489-94 (2004).

10. Evans, P.D., Anderson, J.R., Vallender, E.J., Choi, S.S. & Lahn, B.T. Reconstructing the evolutionary history of microcephalin, a gene controlling human brain size. Hum Mol Genet 13, 1139-45 (2004).

11. Timpson, N., Heron, J., Smith, G.D. & Enard, W. Comment on papers by Evans et al. and Mekel-Bobrov et al. on Evidence for Positive Selection of MCPH1 and ASPM. Science 317, 1036; author reply 1036 (2007).

ALSPAC proposes to use the funding from DCSF (£30,000 in the first year and £55,000 in the second year) towards increasing participation in the ALSPAC study.

In June 2009, ALSPAC executive commissioned the Social Marketing Department at University of West of England to conduct research into how ALSPAC could increase participation in the study. A literature search was carried out which revealed that recruiting and retaining 16-18 year olds in longitudinal cohort studies is a common problem. This is because it is a difficult time for them with hugely conflicting demands over their time and energies. They tend to be very busy and prefer not to adhere to strict timetables or commit to long-term research projects. This has been evident in our recent participation rates for clinic and questionnaires which shows a marked difference from even a year ago. The literature review confirms that although retention is challenging, it can be done, as long as there is an in depth understanding of what moves and motivates young people.

Focus groups were held with participants from the ALSPAC study to try and find out more about what motivates them and what would keep them engaged in the project. Those interviewed were 17 or 18 years old and were from a diverse range of social and study participation backgrounds. Of particular interest were the participants who had completely disengaged from the study. The interviews were analysed and the findings were used to develop strategies that could increase participation. A plan has been devised to implement these strategies over the next two years. This coincides with the remainder of the DCSF funding data collection, Focus at 17 and the 18 year questionnaire.

Findings from these initiatives will be disseminated amongst the research community and hold many benefits for other longitudinal cohort studies, e.g. LSYPE. As previously mentioned, attrition is a major problem in all longitudinal studies, especially amongst young people, and if ALSPAC can show that certain strategies are successful in retaining young people in the study then these techniques can be transferred to LSYPE and other research projects. The DCSF have invested a considerable amount of money into the development of the ALSPAC data set, and through data linkage ALSPAC will continue to collect further education data from the National Pupil Database. The proposed engagement initiatives will enable future data collection to add outcome data to the resource. This significant data set will enable the DCSF to continue to track the young people and their educational achievements and aspirations which could inform their policy making.

ALSPAC intend to fully evaluate each of the initiatives described below with a view to assessing which was the most successful in terms of increasing the participation rate and why. Firstly, we will undertake background research to examine the effectiveness andefficiencyof similar initiatives elsewhere and learn from the most successful of these. Secondly, we will clearly identify the aims and objectives of each initiative and judge the outcome of the activity against these aims and objectives. We will regularly run focus groups and other participatory activities to sound out our participants views on the various activities we are running and if appropriate adapt the activity accordingly.

We believe that the following initiatives are those most likely to improve participation in the study over the next two years.

Year One (November 2009 - March 2010)

1. New Media techniques

In the first year the focus will be on using marketing and new media techniques in order to 'reach out' to the young people in the cohort. The social marketing report highlighted very strongly that "the internet is a social crutch of huge importance" to the cohort members. They communicate primarily through social networking sites such as Facebook. To maintain contact and credibility with the teenagers, it is essential that ALSPAC fully engages with this online world. In addition to straightforward communication, we need to build online communities which will maintain cohort loyalty, particularly now that the members are dispersing geographically as they get jobs or go to college. ALSPAC already has a presence on Facebook and YouTube, but to make the most of the enormous potential of the internet we need specialist help to develop, execute and monitor a long-term online strategy. This work would include establishing and moderating networks and forums, orchestrating 'viral' and 'buzz' marketing, and creating unique games. Marketing research stressed the importance of these techniques within this age group. It would also include constant monitoring of the ever-changing online world and how the age group inhabits it. Facebook leads the field at the moment - but in a few months it may fall out of fashion. ALSPAC need to stay one step ahead and to keep the cohort members engaged. Specialist youth/digital marketers will ensure that ALSPAC always puts across the right messages in the right media. One idea is to use 'key peers' to engage with the cohort. The key peers will be the 'popular' young people who have lots of influence over a wide range of other young people including those who are disengaged in the study. They will be commissioned to provide content for the ALSPAC facebook site, to comment on ALSPAC on their Facebook page, as well as upload photos of ALSPAC events. Through the use of age appropriate materials and the involvement of influential peers we aim to encourage their 'friends' on Facebook to become interested in what ALSPAC is all about and to get them re-engaged.

2. Targeted Messages

In order that ALSPAC reaches out to the whole cohort it is important to target the young people as three separate groups; the engaged, recently dropped off, the disengaged. The report made it very clear that involving these three groups in the study would require three different strategies. For example, the young people who were engaged with the study wanted to find out more about the 'Science', whereas the recently dropped off and disengaged could be motivated by incentives. We would like to commission specialist help in assisting us with formulating targeted messages to each of these groups and the way in which this message should be delivered. Funding is also necessary in order to give these incentives; they are likely to range from vouchers for Amazon.co.uk to offering custom 'fitness testing' sessions based on ALSPAC scientific expertise. This would make sure that the three separate groups were catered for.

Year Two (April 2010 - March 2011)

1. Club Membership

The focus for the funding in the second year will be on introducing 'club membership' to all those originally eligible for the study as they turn 18 years of age. This is something that all of the young people taking part in the focus groups were very enthusiastic about. The idea behind this is to keep all of the YPs engaged in the study and to give ALSPAC more of a presence in their everyday lives. It would be similar to an NUS card but will be free to join. The young people will be given a membership card which will entitle them to discounts on various items that could include: nationwide shops/cinemas, a free bus/rail card etc and entry to ALSPAC events (e.g. CV writing, fitness assessment, parties and comedy nights) and competitions. More importantly, it will sustain a link between ALSPAC and them. ALSPAC will be able to keep them updated with what they are doing and what clinics/questionnaires are currently running. For instance, using their membership card, they will be able to use their 'username' to access online questionnaires etc. Evidence has shown that questionnaires are becoming less popular than they were; especially with boys. Short online questionnaires are one way in which to address this problem and if it can be linked in with a membership scheme where there will be incentives to fill out the questionnaire then this will hopefully keep the young people on board with the study. The DCSF funding will go towards setting this up and the ongoing development of the scheme.

Costs

These initiatives will cost £30,000 in Year 1 and £55,000 in Year 2. The costs are broken down below:

Year 1

• New media techniques - £10,000
• Specialist expertise - £5,000
• Incentives - £15,000

Year 2

• Club membership - £45,000
• Continuing new media techniques - £10,000

Summary

ALSPAC is at a critical phase of its development. There is serious concern that as the study participants enter young adulthood they will desert the study. We are very encouraged by the findings of this recent work we have undertaken as it shows that attrition is not inevitable and that with further funding there are strategies that can be put in place to increase participation. These initiatives are likely to attract particpants across the social spectrum which has major advantages in terms of representiveness of the study.

Background

Obesity in children, and adults, is a rapidly growing problem in the UK and worldwide and has been increasing at accelerating rates in more recent years. It is associated with a number of co-morbidities in childhood and with increased risk of adult disease, particularly cardiovascular disease, hypertension and type 2 diabetes. Obesity related diseases account for a substantial proportion of costs of health care resources worldwide (WHO, 2004). The Select Committee Report on Obesity (2004) estimated that the total cost of treating obesity in the UK was £3.3-3.7 billion in 2002, increasing to £7 billion by 2020, and the latest estimates (Foresight report, 2007) put it at £45.5 billion by 2050.

A dietary risk factor for obesity is a high consumption of high fat, salt and sugar (HFSS) foods, and in particular "fast foods" (energy dense, nutrient poor, foods) (Reidpath et al, 2002; Mendoza et al, 2007; Procter, 2007a). The popularity of fast foods has increased over recent years and consumption by children has risen 300% over the last twenty years (St-Onge et al, 2003). It has been shown that on days when children eat fast food, then their energy and fat intake is likely to be higher, and fruit and vegetable intake lower, than normal (Bowman et al, 2004). Also children who eat fast food frequently consume more total energy, more energy per gram of food, more total fat, more total carbohydrate, more added sugars, and less fibre, less milk, fewer fruit and vegetables than children who eat fast food infrequently (Bowman et al, 2004; Speiser et al, 2005). Accordingly, it may not be the consumption of fast food, per se, that leads to obesity (as both lean and obese people consume fast food), but the fact that overweight consumers of fast food are less likely to adjust their daily energy intake to take account of an energy dense fast food meal than their lean counterparts (Ebbeling et al, 2004). This "passive over-consumption" is due to the weak innate ability of humans to identify energy dense foods and thus do not correspondingly reduce the amount of food eaten to achieve energy balance (Prentice & Jebb, 2003).

Household income has been shown to be a significant predictor of obesity (inverse relationship) (Strauss & Knight, 1999; Stamatakis et al, 2005), as has deprivation (Kinra et al, 2000; Kinra et al, 2005) and low socio-economic status (SES) (Parsons et al, 1999; Hardy et al, 2000; Okasha et al, 2003; Monden et al, 2006). The increased prevalence of obesity from more deprived backgrounds could be due to a multitude of factors: dietary differences are often apparent; lack of opportunity / funds for activities, so TV viewing is the primary leisure activity by default; constraints on calories per pound, which focuses purchases on energy dense foods. Also the association between SES and obesity may be due to SES acting as a proxy for the effect of multiple adverse circumstances, which are then manifesting as obesity in the long term (Power & Parsons, 2000). For example, it has been shown that there is a higher density of fast food outlets in poorer areas, which may (partially) explain the phenomenon (Reidpath et al, 2002). Obesity and deprivation may be connected due to the routine consumption of a high energy dense, low cost diet (Drewnoski, 2003). Energy dense diets are associated with lower diet quality and lower costs, and vice versa (Cade et al, 1999; Darmon et al 2004; Andrieu et al, 2006; Drewnoski et al, 2007). Research shows that low income households are associated with a high energy dense diet (Mendoza et al, 2006).

There is a large literature contending that the environment, particularly that of our place of residence or school/work, impacts on health related behaviour and therefore health outcomes (Macintyre et al, 2002; Mohan et al, 2005). This increased interest in the effect of place on health seems to stem from the publication of the Black Report some twenty-five years ago (Black et al, 1980). Since then many authors have shown that deprivation is related to mortality as well as to specific health outcomes. An important debate within health geography is that of whether the environment has compositional or contextual effects on health. That is, the issue of whether individual or area effects on health predominate. Accordingly, the compositional school of thought is that individuals have risks of ill health, therefore an area's ill health is reflective of that of the individuals who live (or work, as appropriate) there. For example, do obese people congregate in similar locations? Conversely, the contextual theory is that living (or working) in an area imposes ill health on that area's residents. For example, do certain attributes of places cause its inhabitants to become obese? This study seeks to address the question of fast food and obesity from both contextual (the physical environment of fast food outlets) and compositional (individual consumption) perspectives

Objectives and hypothesis of project

To describe, measure and map obesity, fast food outlet density and fast food consumption in Bristol, UK(and Leeds,UK)

To investigate the relationship between density of outlets and area measures of deprivation, and to examine whether there is a dose-response relationship between deprivation and outlet density (e.g. as deprivation rises, outlet density rises).

To assess the relationship between obesity and energy dense, low nutrient ("fast") foods: availability and consumption.

To consider the policy implications of the results.

Hypothesis: Fast food outlet density and consumption will be related to the patterns of obesity. Families with the lowest incomes will be more influenced by or susceptible to these factors than higher income households.

Methods

Stage 1 - Describing and mapping obesity

The first stage in the study of spatial variations in obesity will be to build a geographic information system (GIS). The GIS will contain all the census data for Bristol/Avon Region at 'output area' (the new small area census unit), as well as the Index of Deprivation (Communities and Local Government, 2004) at 'super output area' level. This will allow us to identify areas of both low and high social deprivation. The GIS will also contain obesity data from the ALSPAC dataset.

Spatial microsimulation modelling will then be used for estimating or predicting small area levels of obesity. Specifically, we will build on the SimObesity model (a deterministic, re-weighting, spatial microsimulation model developed in the School of Geography, University of Leeds), which combines individual micro-data from national level surveys, such as the Health Survey for England (HSE), which only have location data at the scale of large areas, with census statistics for lower Super Output Areas (SOAs) to create synthetic micro-data estimates for SOAs in Leeds. The new, synthesised, micro dataset includes all the attributes from both the survey and the census datasets. The key benefits of using spatial microsimulation are: to add more attributes to the population under analysis by adding census data to the survey data thereby creating a richer dataset; to get data to a smaller geographical scale in order to identify 'hot spots' of problem areas; and it is cheaper and quicker than commissioning a survey of the local area. There is significant experience of spatial microsimulation modelling in the University of Leeds (Clarke, 1996; Ballas et al, 2005; Procter, 2007b).

Significant clusters of obesity (from both children and adults) will be identified using Spatial Scan Statistics (such as SaTScan, Kulldorff, 1997). Temporal (serial cross sectional) as well as geographic analysis will be undertaken.

Stage 2 - Relationship between fast food and obesity

Once the patterns and spatial/temporal variations of obesity have been understood, the next exercise is to consider the relationship with fast food consumption. There are two components to this analysis.

Firstly we wish to examine the relationship between fast food outlet density and obesity. This stage of the project will involve sourcing (e.g. from the council or yellow pages) the location of fast food restaurants in the study area and transposing this data into a GIS. Ground truthing of the data will also be required (i.e. physically checking the locations exist). Then fast food outlet density for output areas can be calculated and the relationship with obesity considered. The association with an area measure of deprivation will also be considered.

The next stage is to take this further, and to consider the relationship between actual fast food consumption and obesity. This Fast Food consumption data regarding diet is available from the ALSPAC cohort and will be added to the previously described GIS model. This data may be used to simulate fastfood consumption in children in other geographical areas using spatial microsimulation modelling.

Analysis of these data will involve the use of multi-level modelling and geographically weighted regression, in order that the special properties of spatial data (e.g. spatial autocorrelation) can be accounted for.

Stage 3 - Policy implications

The final stage in the project will be to consider how the results from the previous two stages can be utilised to influence policy and to help slow down the rising rates of obesity. The spatial microsimulation model will be used to undertake "what if" scenario analysis to theoretically evaluate the potential impact of policy/intervention suggestions on the prevalence of obesity in say 5, 10 or 20 years time, which is cheaper and much quicker than running a pilot study. Further, we will work with key local stakeholders (from both public and private sector, as both have a responsibility to endorse public health (Stafford et al, 2007)) to enable suggestions to be rated for validity, relevance and potential for change. This is important, as small individual programmes are unlikely to make a difference to the obesity epidemic. For maximum benefit an obesity prevention policy needs to take a coordinated, multi-component, multi-sectoral public health approach and overall policy unity and coherence is required, with buy-in of all stakeholders.

How will the research be useful and to whom

This research will be useful to the PCTs for health planning as it will identify any hot spots of problem areas of obesity. It will also elucidate further on the impact of aspects of the environment and diet on obesity and whether these issues can be addressed using public health policies, and if so, the likely future impact of such changes.

Interdisciplinary nature of the research

This project is clearly interdisciplinary. It combines the quantitative geographic techniques (such as spatial microsimulation, GIS) with those from medical research, using data from local studies as well as national cross sectional surveys. Training will be given for use of the spatial techniques (e.g. SimObesity, ArcGIS, geographically weighted regression). There is extensive experience of using these techniques within the University of Leeds, plus some external courses are available.

Timetable

Year 1/2: Develop literature review; gather information for mapping; learn techniques; carry out stage 1

Year 3/4: Carry out stage 2

Year 5/6: Carry out stage 3 and write up thesis

References

Andrieu, E., N. Darmon, et al. (2006). "Low-cost diets: more energy, fewer nutrients." European Journal of Clinical Nutrition 60(3): 434-6.

Black D, Morris J, Smith C, Townsend P (1980). Inequalities in health: report of a Research Working Group. London: Department of Health and Social Security

Bowman SA, Gortmaker SL, Ebbeling CB, Pereira MA, Ludwig DS (2004). Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. Pediatrics, 113: 112-118

Communities and Local Government. The English Indices of Deprivation 2004. http://www.communities.gov.uk/index.asp?id=1128449 Accessed September 2007

Darmon, N., A. Briend, et al. (2004). "Energy-dense diets are associated with lower diet costs: a community study of French adults." Public Health Nutrition 7(1): 21-7.

Drewnowski, A. (2003). "The role of energy density." Lipids 38(2): 109-15.

Drewnowski, A., P. Monsivais, et al. (2007). "Low-energy-density diets are associated with higher diet quality and higher diet costs in French adults." Journal of the American Dietetic Association 107(6): 1028-32.

Ebbeling CB, Sinclair KB, Pereira MA, Garcia-Lago E, Feldman HA, Ludwig DS (2004). Compensation for energy intake from fast food among overweight and lean adolescents. The Journal of the American Medical Association, 291 (23): 2828-2833

Foresight Report (2007). Tackling Obesities: future choices http://www.foresight.gov.uk/Obesity/obesity_final/20.pdf Accessed October 2007

Hardy R, Wadsworth M, Kuh D (2000). The influence of childhood weight and socioeconomic status on change in adult body mass index in a British national birth cohort. International Journal of Obesity, 24 (6): 725-34

Kinra S, Nelder RP, Lewendon GJ (2000). Deprivation and childhood obesity: a cross sectional study of 20,973 children in Plymouth, United Kingdom. Journal of Epidemiology & Community Health, 54 (6): 456-460

Kinra S, Baumer JH, Davey Smith G (2005). Early growth and childhood obesity: a historical cohort study. Archives of Disease in Childhood, 90 (11): 1122-1127

Kulldorff M (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26: 1481-1496

Macintyre S, Ellaway A, Cummins S (2002). Place effects on health: how can we conceptualise, operationalise and measure them? Social Science and Medicine, 55: 125-39

Mendoza, J. A., A. Drewnowski, et al. (2006). "Dietary energy density is associated with selected predictors of obesity in U.S. Children." Journal of Nutrition 136(5): 1318-22.

Mendoza, J. A., A. Drewnowski, et al. (2007). "Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults." Diabetes Care 30(4): 974-9.

Mohan J, Twigg L, Barnard S, Jones K (2005). Social capital, geography and health: a small-area analysis for England. Social Science and Medicine, 60: 1267-1283

Monden CWS, van Lenthe FJ, Mackenbach JP (2006). A simultaneous analysis of neighbourhood and childhood socio-economic environment with self-assessed health and health-related behaviours. Health and Place, 12(4): 394-403

Okasha M, McCarron P, McEwen J, Durnin J, Davey Smith G (2003). Childhood social class and adulthood obesity: findings from the Glasgow Alumni Cohort. Journal of Epidemiology and Community Health, 57: 508-9

Parsons TJ, Power C, Logan S, Summerbell CD (1999). Childhood predictors of adult obesity: a systematic review. International Journal of Obesity, 23 (Suppl. 8): S1-107

Prentice AM & Jebb SA (2003). Fast foods, energy density and obesity: a possible mechanistic link. Obesity Reviews, 4 (4): 187-194

Reidpath DD, Burns C, Garrard J, Mahoney M, Townsend M (2002). An ecological study of the relationship between social and environmental determinants of obesity. Health and Place, 8: 141-145

Rudolf MCJ, Cole TJ, Krom AJ, Sahota, P, Walker J (1999). Growth of primary school children: a validation of the 1990 standards and their use in growth monitoring. Archives Disease in Childhood 83: 298-301

Rudolf MCJ, Levine R, Feltbower RG, Connor A, Robinson M (2006). The Trends project: development of a methodology to reliably monitor the obesity epidemic in childhood. Archives of Disease in Childhood; 91: 309-311

Select Committee on Health - Third Report (Obesity) (2004). Health Committee Publications, http://www.parliament.thestationeryoffice.co.uk/pa/cm200304/cmselect/cmh... Accessed December 2005

Speiser PW, Rudolf MC, Anhalt H, Camacho-Hubner C, Chiarelli F, Eliakim A, Freemark M, Gruters A, Hershkovitz E, Iughetti L, Krude H, Latzer Y, Lustig RH, Pescovitz OH, Pinhas-Hamiel O, Rogol AD, Shalitin S, Sultan C, Stein D, Vardi P, Werther GA, Zadik Z, Zuckerman-Levin N, Hochberg Z; Obesity Consensus Working Group (2005). Childhood Obesity, The Journal of Clinical Endocrinology and Metabolism, 90 (3):1871-87

Stamatakis E, Primatesta P, Chinn S, Rona R, Falascheti E (2005). Overweight and obesity trends from 1974 to 2003 in English children: what is the role of socioeconomic factors? Archives of Disease in Childhood, 90: 999-1004

St-Onge MP, Keller KL, Heymsfield SB (2003). Changes in childhood food consumption patterns: a cause for concern in light of increasing body weights. The American Journal of Clinical Nutrition, 78 (6): 1068-1073

Strauss RS & Knight J (1999). Influence of the home environment on the development of obesity in children. Pediatrics, 103 (6): e85

World Health Organisation (2004). Report of a WHO Consultation on Obesity: preventing and managing the global epidemic. WHO Technical Report Series; 894. Geneva. http://www.who.int/nutrition/publications/obesity/en/index.html (accessed Oct 2007).