Various aspects of the relationship between parent and child influence the child's social and emotional development [1, 2] and there is increasing evidence that specific aspects of the parent-child relationship are also associated with general physical health both in childhood and in adulthood with more negative relationships being associated with poorer general health outcomes [3-6]. Relationships between parenting and more specific conditions such as obesity and oral health are also reported. An increased risk of obesity has been shown to be associated with parenting styles which might be considered authoritarian ie low warmth and high control or "demandingness" [7, 8] and there is some emerging work on similar associations between parenting and oral health in childhood [9-11]. There is also evidence parenting is socially patterned and that parenting changes over time as a function of changes in sociodemographic factors eg changes in the mother's health, the availability of social support and also (to a lesser extent) changes in financial circumstance; deterioration in these sociodemographic factors is associated with a reduced (less warm and supportive) parenting score over time whereas improvements in maternal health specifically are associated with an increased (more warm and supportive) parenting score. These changing relationships may in part explain some of the social patterning which can be observed in inequalities in child health.

Parent-child relationships have been classified and measured in a variety of ways but key aspects which are proving to be robust are the mother's (or main carer's) sensitivity towards and attunement to the needs of the child [12], different parenting styles [13], discipline [14] and also specific aspects of the parent-child relationship such as maternal warmth and support and hostility towards / rejection of the child [15]. As well as the provision of sustenance and stimulation, children also need their parents or carers to provide support, structure and supervision if they are to develop optimally [16]. Research has shown that parent-child relationships which are warm and supportive are associated with positive child social, emotional and physical outcomes [15, 16] whereas cold, neglecting relationships between parent and child increase the risk of problematic outcomes [3].

The aim of this proposal is to examine aspects of the parent-child relationship which have been measured over time in the ALSPAC cohort and to investigate their associations with and ability to predict child health outcomes both generally [6] and with regard to specific outcomes such as obesity and oral health. We are also interested to determine further the role of sociodemographic factors on parenting and child health outcomes. In particular we plan to investigate whether the warmth, support and control between parent and child are associated with specific conditions such as obesity and oral health but we would also like to investigate whether we can replicate a study undertaken on the NICHD Study of Early Child Care to determine the extent to which parenting mediates the detectable effects of socioeconomic risk on health in childhood [4].

Methods

Study Population: The Avon Longitudinal Study of Parents and Children (ALSPAC: see www.alspac.bris.ac.uk) cohort is ideally placed to investigate these relationships as it has various measures of child health and also measures of the parent-child relationship and associated socio-demographic factors over time. ALSPAC is a population-based study which has been described in detail elsewhere ([17].

Measures of Health: Measures of health include maternal assessment of the child's general health (annually), number of physical symptoms in the last 12 months (annually) and also more specific measures such as DXA estimates of fat and muscle which are available at several timepoints between 9 and 17 years years. Oral health is measured via maternal report throughout childhood with questions on brushing teeth, visits to the dentist etc and via child report at 8 and 10 years. Actual observations of child dentition are also available for a subsample of the cohort in early childhood and a questionnaire about oral health is currently being administered to the cohort (age 17). Measures of the parent-child relationship include measures of attachment which have been collected between 18 months and 6 years, measures of warmth and support as well as of control, hostility and resentment (available in early childhood at eight and 33 months) and also measures of physical discipline (shouting, smacking etc) which have been collected regularly between 18 months and 6 years.

Potential confounders: A report by Waylen and Stewart-Brown [18] has shown that the sociodemographic factors associated with parenting include maternal age and education level, maternal physical and mental health (measured annually), social support and financial circumstance. Variables such as the presence of the mother's romantic partner (measured at each time point) and also ethnic group should also be accounted for.

Other possible confounders include child gender, age and birth weight / length and also gestational age; a second measure of infant weight and length collected at eight months in order to develop a measure of early rapid weight gain (models of growth have also been developed using study data); food frequency questionnaires at various ages, dietary intake of different food types including sweets and chocolate measured from 6 months onwards by maternal report and also child self-report at 10 years and diet diaries completed at 7, 10 and 13 years; the mother's occupation and that of her partner, maternal height and pre-pregnancy (used to calculate maternal BMI), maternal tobacco use both during and after pregnancy; the amount of time the child spends watching TV / playing outside or with other children (from early childhood onwards).

References

[1] Maccoby EE, Martin JA, Hetherington EM. Socialization in the context of the family: parent-child interaction. In: Anonymous, ed. Mussen Manual of Child Psychology. New York: Wiley 1983:1--102.

[2] Patterson G, DeBaryshe B, al e. A developmental perspective on antisocial behaviour. American Journal of Psychology. 1989;44:329-35.

[3] Repetti RL, Taylor SE, Seeman TE. Risky families: family social environments and the mental and physical health of offspring. Psychological Bulletin. 2002;128(2):330-66.

[4] Belsky J, Bell B, Bradley RH, Stallard N, Stewart-Brown SL. Socioeconomic risk, parenting during the preschool years and child health age 6 years. Eur J Public Health. 2007 January 12, 2007:ckl261.

[5] Stewart-Brown S, Fletcher L, Wadsworth MEJ. Parent-child relationships and health problems in adulthood in three UK national birth cohorts. European Journal of Public Health. 2005;15(6):640-6.

[6] Waylen A, Stallard N, Stewart-Brown S. Parenting and health in mid-childhood: a longitudinal study. Eur J Public Health. 2008;18(3):300-5.

[7] Rhee KE, Lumeng JC, Appugliese DP, Kaciroti N, Bradley RH. Parenting styles and overweight status in first grade. Pediatrics. 2006;117(6):2047-54.

[8] Ambrosini G, Oddy W, Robinson M, O'Sullivan T, Hands B, de Klerk N, et al. Adolescent dietary patterns are associated with lifestyle and family psycho-social factors. Public Health Nutrition. 2009;12(10):1807-15.

[9] Law CS. The impact of changing parenting styles on the advancement of pediatric oral health. J Calif Dent Assoc. 2007;35(3):192-7.

[10] Amin M, Harrison R. Understanding parents' oral health behaviors for their young children. Qualitative Health Research. 2008;19(1):116-27.

[11] Astrom A. Parental influences on adolescents' oral health behavior: two year follow-up of the Norwegian Longitudinal Health Behavior Study participants. European Journal of Dental Sciences. 1998;106:922-30.

[12] Ainsworth MDS, Bell SM, Stayton DJ, Schaffer H. Individual differences in strange-situation behaviour of one-year olds. In: Anonymous, ed. The origins of human social relations. New York: Academic Press 1971.

[13] Baumrind D. Current patterns of parental authority. Developmental Psychology. 1971;4(2):1-103.

[14] Gershoff ET. Corporal punishment by parents and associated child behaviors and experiences: A meta-analytic and theoretical review. Psychological Bulletin. 2002 Jul;128(4):539-79.

[15] Barber BK, Stolz HE, Olsen JA. Parental support, psychological control and behavioural control: assessing relevance across time, culture and method. Report. Oxford, UK: Society for Research in Child Development; 2005.

[16] Bradley R, Caldwell BM. Caregiving and the regulation of child growth and development: describing proximal aspects of the caregiving system. Developmental Review. 1995;15:38-85.

[17] Golding J, Pembrey M, Jones R. ALSPAC--the Avon Longitudinal Study of Parents and Children. I. Study methodology. Paediatr Perinat Epidemiol. 2001;15(1):74-87.

[18] Waylen A, Stewart-Brown S. Parenting in ordinary families: diversity, complexity and change. York: Joseph Rowntree Foundation; 2008.

Common variant (rs7927894) has a reasonably strong effect on atopic dermatitis [1]. We propose two

analyses: 1) to look at the association of this genetic variant with atopic dermatitis in ALSPAC and 2) to look at the association of this genetic variant with asthma, given the effect of filaggrin on asthma.

References

[1] Gordillo et al. A common variant on chromosome 11q13 is associated with atopic dermatitis Nat Genet. 2009 May;41(5):596-601.

The children within ALSPAC with diagnoses of ASD and ADHD have been identified from 3 independent sources: (a) the clinical records of all children in the cohort investigated for a suspected developmental disorder by a multi-disciplinary assessment (b) the national educational database in England (PLASC) which identified all children in state schools (over 90% of children) who were needing special educational provision in 2003 and c) the Development and Well Being assessment (DAWBA), applied in the ALSPAC 9 yr research clinic. A total of 86 children were identified with ASD and 175 with ADHD by 11 years of age.

ALSPAC has repeatedly asked questions about children's sleep patterns from birth through to adolescence. A multi disciplinary ALSPAC sleep interest group (Alan Emond, Peter Fleming, Pete Blair, Esther Crawley, Raghu Lingam, Paul Gringras, Nicola Scott and John Henderson) have been overseeing the data. Now that the 16 year old data is available, the dataset is complete through childhood and can be used to answer four crucial sleep-related health questions:

1. What are 'normal' sleep patterns of children in the UK?

We will describe sleep patterns from birth to 16, using latent class analyses and trajectory modelling, and then explore the impact of factors such as co-sleeping, social adversity, parental smoking and alcohol use. This will cover areas including:

Are poor sleepers in infancy, still poor sleepers in adolescence? What impact does social adversity have on sleep patterns?

2. What is the association between short-sleepers and ADHD?

The association between decreased sleep time in infancy and later diagnoses of ADHD is unclear.(1) ALSPAC data allows us to define all the short sleepers in infancy including those that drop their daytime nap early, and then follow-up those children that eventually receive a diagnosis of ADHD. This 'chicken and egg' question could help to provide an early marker of ADHD, help understand causes of ADHD and offer a potential target for early treatment.

3. Is there an association between long-term iron stores, sleep disturbance and ADHD?

Low iron stores, as measured by peripheral ferritin levels have been associated with both restless sleep, and symptoms of ADHD.( 2) The extent of this relationship is still unknown. If it is real, then one would expect increased numbers of children with ADHD to have lower levels of ferritin. Sleep, ferritin levels and ADHD have already been measured within ALSPAC offering a chance to explore another potentially treatable cause of ADHD.

4. What is the relationship between sleep disturbance, breast-feeding and autistic spectrum disorders?

Exciting new genetic research has suggested physiological reasons why sleep problems are so much more common in children with autism than in the general population. (3) Dr Gringras has proposed that due to certain hormones related to early brain development, theoretically, it is non-breast fed infants that should be at increased risk for sleep disorders and autism. ALSPAC, through its questions on early feeding, sleep and later diagnoses of autism has already gathered the data necessary to explore this fascinating question with potentially huge public health implications.

Variables required

In addition to the sleep variables to which we already have access , we are requesting questionnaire data:

1.developmental diagnoses from the IDI database-11 yrs

2. DAWBA data- 7,10 yrs

3.SDQ data-4,6,8, 9, 11yrs

4. SCDC data- 4.5, 6.5, 7.5 yrs

5. IQ data- 8 yrs

6. lab data: ferritins from CIF- 8months,18 months

Project team

The analyses will be supervised by Prof Alan Emond and Dr Paul Gringras, supported by the ALSPAC sleep interest group.

The ALSPAC database is huge, and we a will need a considerable amount of statistical support as this will be the first time all ALSPAC sleep data will have been divided into latent classes, to track each individual child's sleep history and find common patterns. Dr Pete Blair, a senior research statistician at Bristol University, has already worked with aspects of the ALSPAC data and been part of many key publications. His familiarity with the data is a huge advantage, as is his interest in childhood sleep. Pete will be undertaking the analyses, assisted by academic F2 trainee Dr Joanna Humphries.

Whilst Prof Emond, Dr Gringras and the rest of the sleep interest group already have enough ring-fenced academic time to carry out the above work without funding, none of them have Dr Blair's statistical expertise or familiarity with the ALSPAC dataset. We are requesting funding from The Guys Hospital Charitble Trust for Dr Blair's salary for 6 months and a small 'data buddy' fee to gain access to all the cleaned ALSPAC variables required. The only non-pay resources requested are the travel costs associated with meetings of the researchers in London and Bristol. No overheads are included.

The interpretation and writing up of the results will be undertaken by the ALSPAC sleep interest group within existing academic sessions.

REFERENCES

1.Gringras P. Sleep disorders and ADHD. In Taylor, E editor. ADHD. Mackeith Press 2007

2.Konofal E, Lecendreux M, Deron J, Marchand M, Cortese S,Zaim M, Mouren MC, Arnulf I. Effects of iron supplementation on attention deficit hyperactivity disorder in children.

Pediatr Neurol 2008;38:20-26.

3.J Melke, H Goubran Botros, P Chaste, C Betancur, G Nygren, H Anckarsa , M Rastam,O Stahlberg, IC Gillberg, R Delorme, N Chabane, M-C Mouren m,Simeoni, F Fauchereau, CM Durand, F Chevalier, Drouot, C Collet,J-M Launay M Leboyer C Gillberg T Bourgeron and the PARIS study. Abnormal melatonin synthesis in autism spectrum Disorders. Molecular Psychiatry (2008) 13, 90-98

The beneficial effect of pet ownership on the physical, social and psychological health of people has been well documented (Friedmann, 1995; Headey, 2003; Katcher, 1981; Katcher & Friedmann, 1982; McNicholas et al., 2005). Contact with companion animals during childhood is likely to have effects on child development , and therefore ownership of pets (and the different types) is also likely to be of significance to child development.

Most studies of health benefits of pets are cross-sectional. Consequently it is difficult to determine whether these health benefits result from pet ownership or whether it is healthier people who choose to own pets. The relatively few longitudinal studies indicate the direction of causality is from pet to owner (Serpell 1991; Headey & Grabka 2007). Longitudinal national representative surveys in Germany and Australia show that pet owners make about 15% fewer annual doctor visits than non-owners (Headey & Grabka 2007). The relationship remains statistically significant after controlling for gender, age, marital status, income and other variables associated with health.

Pet ownership data was collected from the ALSPAC cohort up to age 10 years. This was using the carer questionnaire. At gestation, and 8 months, the number of cats, dogs, rabbits, rodents, birds and other pets were collected. This was then expanded to include categories fish, tortoises and turtles, and any remaining pet types as 'other pets', for the rest of the data collection. These data were collected at 21, 33, 47, 85, 97 and 122 months.

Our team at Liverpool University are currently using the ALSPAC data to explore if there is any association between pet ownership, and obesity, using measures of BMI, fatness and physical activity. This project is funded by WALTHAM(registered trademark) . The data is currently limited to up to 10 years, but it is plausible to consider that there may also be interesting effects beyond this time. In fact, the teenage years may be particularily important in relation to pet ownership, as this is a time when children become more independent and responsible, and for example may walk their dog on their own. For this reason we propose that questions on pet ownership be re-introduced to ALSPAC data collection. The data will be of use not only to researchers interested in obesity and physical activity, but those interested in the effects of the human-companion animal bond in relation to many different aspects of physical and psychological health and development.

To our knowledge, there have been no detailed longitudinal studies decribing pet ownership trends during childhood and teenage years, despite the knowledge that pets, in particular dogs, are more likely to be found in households with school age children that those without (Westgarth et.al., 2007). It is thought that pet ownership is associated with factors such as household type and socioeconomic status, and so there is high potential for confounding in studies of health-related factors. In addition, the factors associated seem to vary with specific pet type, and so the effects of, for example, cat, dog, and small animal ownership need to be investigated separately (Westgarth et.al., In Press). For these reasons it is well worth fully characterising aspects of pet ownership in childhood years using a large and detailed resource such as ALSPAC.

Our proposal to re-introduce pet ownership questions back into the ALSPAC questionnaire at 18 years can be done in a number of formats.

1) Firstly, we would like to re-introduce the basic questions relating to pet ownership, namely - Do you own a pet? And how many cats, dogs, rabbits, rodents, birds, fish, tortoises and terrapins, and other pets, do you own?

2) Because there is a data gap from age 10 to 18, we also propose attempting to inform this gap by asking children to recall whether they owned pets at age 14, and which types. We do not imagine recall bias to be a significant problem, because we imagine that people can reliably remember whether or not they owned a type of pet. They may not be able to recall 'numbers of' a particular pet as accurately though, and therefore a simple indication of pet type is probably most appropriate.

3) Because we have reliable pet ownership information up to 10 years, it would be possible to test the validity of asking respondents to recall pet ownership, by asking them what types of pet they owned up to 10 years old, and then checking this against the data already collected. Surveys of pet ownership are often cross-sectional and ask respondents to recall previous pet ownership, and so a measure of the validity of such an approach would be extremely useful in the field of Human-Animal Interaction research. It would also inform the results from 2) above.

4) The data on pet ownership in the ALSPAC resourse is limited, albeit of large numbers. This would be an opportunity to further explore particular aspects and attitudes relating to pet ownership, that may further elucidate any impact that pet ownership has on health.

In particular:

How much the child is involved in the day-to-day responsibilities and care of the pet.

For example, if they own a dog, do they walk it and how often. This can be compared with the reported frequency of activities such as walking from all children, in particular those not owning a dog.

If they own a dog, what breed, and size of dog.

Measures of attachment to the pet (measured using a published and validated pet attachment scale), as this may affect the impact of owning a pet on a particular individual.

WALTHAM(registered trademark) is a leading scientific authority in pet nutrition and well-being. For almost 50 years they have researched the nutritional and behavioural needs of companion animals and their benefits to humans. In 2008, WALTHAM(registered trademark) formed a public-private partnership with the National Institutes of Health (NIH) in the USA to encourage research in the field of HAI and to help grow the field. In particular they acknowledged the uses of birth cohort studies such as ALSPAC. Sandra McCune directs the Human-Animal Interaction research programme at the WALTHAM(registered trademark) Centre for Pet Nutrition where she manages a large portfolio of HAI research projects. WALTHAM(registered trademark) has identified the role of pets in physical activity and obesity as a research priority for the programme and are keen to support this study.

References

Friedmann, E. (1995). The role of pets in enhancing human well-being: physiological effects. In The Waltham Book of Human-Animal Interaction: Benefits and Responsibilities of Pet Ownership, pp. 33-53. Edited by I. Robinson. Oxford: Elsevier Science Ltd.

Headey, B. (2003). Pet ownership: good for health? Medical Journal Of Australia 179, 460-461.

Headey, B & Grabka, M.W. (2007) Pets and human health in Germany and Australia: National Longitudinal results. Social Indicators Research (2007) 80: 297-311.

Katcher, A. H. (1981). Interactions between people and their pets: form and function. In Interrelations between people and pets, pp. 41-67. Edited by B. Fogle. Springfield: Charles C. Thomas.

Katcher, A. H. & Friedmann, E. (1982). Potential health value of pet ownership. California Veterinarian 36, 9-13.

McNicholas, J., Gilbey, A., Rennie, A., Ahmedzai, S., Dono, J.-A. & Ormerod, E. (2005). Pet ownership and human health: a brief review of evidence and issues. BMJ 331, 1252-1254.

Serpell,J. (1991) Beneficial effects of pet ownership on some aspects of human health and behaviour Journal of the Royal Society of Medicine Volume 84 December: 717-720

Westgarth, C., Pinchbeck, G. L., Bradshaw, J. W., Dawson, S., Gaskell, R. M. & Christley, R. M. (2007). Factors associated with dog ownership and contact with dogs in a UK community. BMC Vet Res 3, 5.

Westgarth, C., Pinchbeck, G. L., Bradshaw, J. W., Dawson, S., Gaskell, R. M. & Christley, R. M. (In Press). Factors associated with cat ownership in a UK community. Vet Record.

Dietary information has been collected repeatedly from a 10% sub-sample of the ALSPAC cohort (around 1000 children) at the following ages: 4 months, 8 months, 1 1/2 years, 3 1/2 years, 5 years and 7 years. Information was collected by a one-day unweighed food record at 4 months, and by a three-day unweighed food record at all other ages. All the dietary data have been coded and prepared and are ready for use. Information on the use of salt at the table and in cooking is available at the following ages: 3 1/2 years, 5 years, and 7 years.

Information is available on a range of socio-demographic and lifestyle factors, including the following: parental education, social class and age, housing tenure, financial difficulties and some questionnaire information about physical activities (e.g. time spent watching TV, walking, cycling etc.).

Table 1 shows mean non-discretionary sodium intakes in ALSPAC from 4 months to 3 1/2 years, in comparison with target average sodium intakes suggested by the Scientific Advisory Committee on Nutrition to the Food Standards Agency.

Table 1. Non-discretionary intakes of sodium (mg) from 4 months to 3 1/2 years and the proportion of children consuming at or below target intakes

____________Percentiles____________ % below

Age Mean (sd) 2.5 25 50 75 97.5 Target target

4 months 180 (59) 92 140 171 210 324 less than 400 99.4

8 months 612 (324) 216 374 546 772 1402 400 28.4

18 months 1432 (439) 706 1140 1384 1668 2472 800 4.9

43 months 1823 (470) 1006 1516 1789 2091 2883 800 0.6

It can be seen that from the age of 8 months the great majority of children are consuming in excess of SACN target intakes for sodium from non-discretionary sodium intake. These figures do not include an estimation of salt added at the table or added to vegetables during cooking. However, the proportion of sodium derived from non-discretionary sources is likely to be lower than it is in adults (15-20%), as most parents do not usually allow their children to add salt at the table, and many report avoiding adding salt when cooking for children. (At age 5 only 15.6% regularly added salt to their children's food in cooking, 47.9% reported never adding it, while only 1.4% allowed their child to add salt at table regularly and 66.1% never did this).

Data from the diaries collected at 4 months suggests that breastfed infants had a lower sodium intake than formula fed infants at this age. Other work has shown a difference in blood pressure at age 7 years between breastfed and formula fed infants.

We will use the data collected in ALSPAC to answer the following research questions:

1. What are the main dietary sources of salt at each age (4m, 8m, 1 1/2y, 2y, 3 1/2y, 5y, 7y)?

2. What food groups distinguish most between children with high and low sodium intakes at each age?

3. How does salt intake relate to intakes of other foods and nutrients relevant to cardiovascular health, and to sociodemographic and lifestyle factors?

4. How well does salt intake "track" throughout early childhood?

5. Do breastfed infants subsequently go on to have lower salt intakes than formula-fed infants.

Aim of the project

The aim of the project is to investigate the relationship between the infants born "near term" (32 to 36 weeks gestation) and IQ.

Background

The long term effects of prematurity are well recorded in infants born before 32 weeks corrected gestation. Extreme prematurity is associated with increased risks of cerebral palsy, speech[1], educational[1], and executive functioning[2] deficits as well as reduction in more global measures of cognition. The long term outcome of infants born at only moderately premature gestations however is less clear. Previous studies have suggested increased rates of educational concerns in infants born before 37 weeks gestation[3, 4] but little evidence exists on specific measures of cognition, particularly IQ, and other measures of neuro-development. At present these infants little specific educational input, and indeed recent recommendations have suggested that that only infants below 32 weeks of gestational age should be offered formal neuro-developmental follow-up.

Experimental design and methods to be used in investigating this problem

Infants born above 31 weeks gestational age will be identified from the ALSPAC cohort. The exposure of interest will be gestational age, categorized as near term (less than 37 weeks corrected gestation) or term (37 weeks or more). The primary outcome will be IQ (as measured by the WISC-II at 8 years). In addition measures of school performance, manual dexterity, attention, memory and language skills will also be assessed.

Covariates

Measures of neonatal wellbeing will be obtained from the STORK dataset (available for all infants born in St Michaels orSouthmeadHospital). Measures used in the analysis will be:

• Antenatal factors: Gender, maternal parity and maternal hypertension.
• Intrapartum factors: Birth weight, length and head circumference (corrected for gestation), mode of birth (i.e. spontaneous cephalic, emergency or elective caesarian section, instrumental or breech) and maternal pyrexia.
• Social factors: Maternal age, socioeconomic group and educational achievements, car ownership, housing tenure, crowding and ethnicity.

Any infants who are admitted to the neonatal unit will have further data on presence or not of neurological signs recorded from the Neonatal Data. Covariates will be added to the model in the blocks above.

Analysis Strategy

Exposure groups will be defined by completed gestational age (32-36 weeks). A reference groups will be defined of infants born at term (37 weeks or more completed weeks) who did not require admission to a neonatal unit.

Outcomes will be both the difference in mean value in the outcome variable (if normally distributed) and the proportion of infants in the lowest 10%. An IQ of less than 80 will be considered 'low'. Linear and logistic regression models will be used to assess the association between the exposure and the outcomes as appropriate.

A secondary analysis will be performed with the results stratified by the completed week of gestation and including all infants born at term (including those admitted to the neonatal units).

Chained Equations may be used to impute the value of any missing covariate data. All analysis will be performed using STATA 10 software (Stata Corp, TX,USA).

Study power

Assuming that around 5% of infants will be born moderately preterm and that there is IQ data available on 6000 infants within the cohort, we would have a power of greater than 99% to find a clinically important difference of 3.25 IQ points (a quarter of one standard deviation) with an alpha of 5%.

References

1. Wolke D, Samara M, Bracewell M, Marlow N, Group EPS: Specific language difficulties and school achievement in children born at 25 weeks of gestation or less.[see comment]. Journal of Pediatrics 2008;152:256-262.

2. Marlow N, Hennessy EM, Bracewell MA, Wolke D, Group EPS: Motor and executive function at 6 years of age after extremely preterm birth. Pediatrics 2007;120:793-804.

3. Chyi LJ, Lee HC, Hintz SR, Gould JB, Sutcliffe TL: School outcomes of late preterm infants: special needs and challenges for infants born at 32 to 36 weeks gestation. J Pediatr 2008;153:25-31.

Background: The role of vitamin D in calcium metabolism and bone health is well documented. Vitamin D enables normal bone mineralization and prevents hypocalcemic tetany. It is also necessary for bone growth and bone remodeling by osteoblasts and osteoclasts. Vitamin D deficiency is common and typically arises from inadequate intake, inadequate sunlight exposure, and limited absorption or conversion into active metabolites (often due to liver or kidney disorders). Recent studies suggest that vitamin D influences various health conditions (such as diabetes, heart disease and hypertension) and mortality. Accumulating evidence also suggests previously unsuspected roles for vitamin D in brain development and neuroprotection. Recent population-based studies link vitamin D deficiency in old age with cognitive function cross-sectionally (Buell, et al., 2009; Lee, et al., 2009; Llewellyn, et al., 2009) and prospectively (Llewellyn, et al., in review). Further research is needed to establish whether vitamin D deficiency influences cognitive development in children.

The mechanisms underlying these associations are unclear, and residual confounding is possible, particularly in cross-sectional studies. Vitamin D receptors are present in a wide variety of cells, including neurons and glial cells, and genes encoding the enzymes involved in the metabolism of vitamin D are also expressed in the brain (McCann & Ames, 2008). In a recent review, Buell and Dawson-Hughes (2008) emphasize that Vitamin D may be neuroprotective through antioxidative mechanisms, immunomodulation, neuronal calcium regulation, detoxification mechanisms and enhanced nerve conduction. Vitamin D may play a role in brain detoxification pathways by reducing cellular calcium, inhibiting the synthesis of inducible nitric oxide synthase and increasing levels of the antioxidant glutathione (Garcion, 2002). Vitamin D stimulates neurogenesis and regulates the synthesis of neurotrophic factors which are important for cell differentiation and survival (Brown, et al., 2003). Vitamin D is also an immunosuppressor and may inhibit autoimmune damage to the nervous system (Garcion, 2002). Calcitriol stimulates amyloid-beta phagocytosis and clearance while protecting against apoptosis (Masoumi, et al., 2009). Low levels of vitamin D may also be associated with an increased risk of neurological diseases such as multiple sclerosis (Munger, et al., 2006), and Parkinson's (Newmark & Newmark, 2007).

The majority of brain development occurs in utero and during childhood. Optimal cognitive development in early-life may confer resistance to late-life cognitive decline and dementia in late life through 'cognitive reserve' and higher levels of midlife functioning. To our knowledge no prospective study has examined the association between in utero or early childhood vitamin D levels and childhood cognition or educational attainment. We also intend to examine whether genetic variants associated with vitamin D levels are also associated with cognitive function in childhood using an instrumental variables approach.

We would envisage the following series of papers to result from this work:

1) Association of childhood vitamin D with childhood intelligence.

2) Association of childhood vitamin D with educational attainment (key stage and GCSE) and the possible mediation by intelligence.

3) Association of maternal vitamin D during pregnancy with childhood intelligence and educational attainment (taking into account childhood vitamin D levels).

4) Possible neuroimaging studies (in collaboration with Tomas Paus and others) to further explore the above.

This work fits very well with Debbie Lawlor's MRC funded vitamin D grant which had as a main focus vascular and metabolic outcomes but noted that we would also examine relevant associations with a wide range of other health outcomes for which causal claims for vitamin D have been made. In this respect bone and respiratory/atopy phenotypes were specifically mentioned (in the grant and original proposal to exec.) but IQ and educational outcomes were not and therefore we are submitting this request here.

Methods

Existing data and planned (funded) vitamin D assays will be used. Analyses will be completed by Anna Tolpeinan with supervison by Debbie Lawlor and input (including development of analysis plan) from other applicants. Our preliminary analysis plan is to use multivariable regression models with adjustment for potential confounders. Potential mediators will be added as additional terms to fully adjusted models. Debbie Lawlor has access to all necessary data except educational attainment. We are aware that these data have been funded by the ESRC large grant and would be very keen to collaborate with the key person involved in the funding and collection of those data.

A building body of research evidence suggests that intrauterine and early life exposures contribute to chronic disease risk in later life. While recent evidence supports the role of childhood adversities on reproductive lifespan,1-4 yet there is a limited understanding of mechanisms through which these adversities impact health. Researchers have established an association between psychosocial stressors in childhood and disruption of pubertal timing. The strongest literature is among girls, and using age at menarche (AAM) as a proxy measure for pubertal timing. Earlier age at menarche has been linked to all cause mortality,5 risk for breast cancer,6-8 cardiovascular disease,9 depression,10 metabolic syndrome11 and associated features, including overweight/obesity,12,13 insulin resistance,14 and polycystic ovarian syndrome,10 while later age at menarche is associated with depression,15,16 fractures,17-19 and lower bone mineral density.20-22 To date there has been limited exploration of psychosocial predictors of pubertal onset and the tempo of pubertal development among both girls and boys.

Psychosocial antecedents of age of menarche include: familial conflict,23 alterations in family structure,24 stressful home circumstances,25-27 paternal absence in childhood,28 and quality of familial relationships.29,30 3,25,31-34 A growing number of studies have demonstrated an association between child abuse and age at menarche.3,35-39 In the five studies to date sexual abuse has demonstrated a stronger and more consistent association with early menarche while physical abuse has had a weakly positive association in some studies.3,35-39 A shared limitation of these studies is the failure to account for additional childhood hardships, such as poverty, that are also associated with pubertal development.

The intent of this study is to investigate the independent and cumulative impact of multiple types of childhood adversities (including child abuse, neglect, family conflict, and socioeconomic status) on timing of puberty in this prospective birth cohort. We hypothesized that children exposed to higher cumulative adversities will be more likely to have altered timing of menarche-earlier or later age at onset. We proposed to investigate the relation between exposure to early life adversities and the tempo of pubertal development and timing of puberty. Specifically, we are interested in exploring the impact of exposure to physical abuse, sexual abuse, emotional abuse, neglect, and/or family conflict and the timing and tempo of pubertal development. We hypothesize that childhood adversities may impact pubertal development through influences on rate of change in BMI over time, lifestyles and health behaviours, including dietary habits, physical activity, and sedentary behaviours. Existing studies are limited by retrospective exposure assessment, and failure to consider multiple types of adversities.

Thus, a proposed mechanism linking exposure to child abuse and sexual maturation is through dysregulation of neuroendocrine systems that respond to environmental challenges (such as the hypothalamic-pituitary-adrenal (HPA), hypothalamic-pituitary-gonadal (HPG) axes. Child maltreatment is a significant psychosocial stressor and therefore may influence responsiveness of the HPA and HPG axes, neuroendocrine functioning, hormonal milieu and ultimately influence timing of sexual maturation. In addition, child abuse may be associated with nutritional factors, energy expenditure, level of activity, and health behaviors in early life that impact AAM. Compelling evidence from several recent studies suggest a relation between childhood adversities and body mass index (BMI) in adulthood.40,41 Child maltreatment has been linked to both excessive weight control and obesity,40-47 and disordered eating.48-53

A second area of interest is the relation between pubertal development and disordered eating patterns and body dissatisfaction. Puberty is one commonly cited risk factor for disordered eating, body dissatisfaction, and eating disorders.10,54 Past studies which have investigated the timing of puberty in relation to the development of eating disorders, and body dissatisfaction have produced mixed findings.54-58 A common limitation shared by previous studies includes the use of cross-sectional methodology, clinical samples, or small community samples. The advantage of the AVON study is that it is robust and comprehensive in the measurement of disordered eating and puberty, the longitudinal methodology, sample size, and sample population. This secondary analysis will focus on the impact of characteristics of pubertal development, particularly timing and tempo, on patterns of disordered eating, eating disorders, and degree of body dissatisfaction.

Please see the attached Excel spreadsheet which outlines the concepts, measures, source, and time points requested (as outlined in application item #8).

References

1. Mishra GD, Cooper R, Tom SE, Kuh D. Early life circumstances and their impact on menarche and menopause. Womens Health (Lond Engl) 2009;5(2):175-90.

2. Parent AS, Teilmann G, Juul A, Skakkebaek NE, Toppari J, Bourguignon JP. The timing of normal puberty and the age limits of sexual precocity: variations around the world, secular trends, and changes after migration. Endocr Rev 2003;24(5):668-93.

3. Romans SE, Martin JM, Gendall K, Herbison GP. Age of menarche: the role of some psychosocial factors. Psychol Med 2003;33(5):933-9.

4. Slyper AH. The pubertal timing controversy in the USA, and a review of possible causative factors for the advance in timing of onset of puberty. Clin Endocrinol (Oxf) 2006;65(1):1-8.

5. Jacobsen BK, Heuch I, Kvale G. Association of low age at menarche with increased all-cause mortality: a 37-year follow-up of 61,319 Norwegian women. Am J Epidemiol 2007;166(12):1431-7.

6. Hamilton AS, Mack TM. Puberty and genetic susceptibility to breast cancer in a case-control study in twins. N Engl J Med 2003;348(23):2313-22.

7. Bernstein L. Epidemiology of endocrine-related risk factors for breast cancer. J Mammary Gland Biol Neoplasia 2002;7(1):3-15.

8. Romundstad PR, Vatten LJ, Nilsen TI, Holmen TL, Hsieh CC, Trichopoulos D, Stuver SO. Birth size in relation to age at menarche and adolescent body size: implications for breast cancer risk. Int J Cancer 2003;105(3):400-3.

9. Remsberg KE, Demerath EW, Schubert CM, Chumlea WC, Sun SS, Siervogel RM. Early menarche and the development of cardiovascular disease risk factors in adolescent girls: the Fels Longitudinal Study. J Clin Endocrinol Metab 2005;90(5):2718-24.

10. Stice E, Presnell K, Bearman SK. Relation of early menarche to depression, eating disorders, substance abuse, and comorbid psychopathology among adolescent girls. Dev Psychol 2001;37(5):608-19.

11. Frontini MG, Srinivasan SR, Berenson GS. Longitudinal changes in risk variables underlying metabolic Syndrome X from childhood to young adulthood in female subjects with a history of early menarche: the Bogalusa Heart Study.Int J Obes Relat Metab Disord 2003;27(11):1398-404.

12. Kaplowitz PB, Slora EJ, Wasserman RC, Pedlow SE, Herman-Giddens ME.Earlier onset of puberty in girls: relation to increased body mass index and race. Pediatrics 2001;108(2):347-53.

13. Adair LS, Gordon-Larsen P. Maturational timing and overweight prevalence in US adolescent girls. Am J Public Health 2001;91(4):642-4.

14. Ibanez L, Potau N, de Zegher F. Recognition of a new association: reduced fetal growth, precocious pubarche, hyperinsulinism and ovarian dysfunction. Ann Endocrinol (Paris) 2000;61(2):141-2.

15. Bisaga K, Petkova E, Cheng J, Davies M, Feldman JF, Whitaker AH. Menstrual functioning and psychopathology in a county-wide population of high school girls. J Am Acad Child Adolesc Psychiatry 2002;41(10):1197-204.

16. Herva A, Jokelainen J, Pouta A, Veijola J, Timonen M, Karvonen JT, Joukamaa M. Age at menarche and depression at the age of 31 years: findings from the Northern Finland 1966 Birth Cohort Study. J Psychosom Res 2004;57(4):359-62.

17. Fujiwara S, Kasagi F, Yamada M, Kodama K. Risk factors for hip fracture in a Japanese cohort. J Bone Miner Res 1997;12(7):998-1004.

18. Roy DK, O'Neill TW, Finn JD, Lunt M, Silman AJ, Felsenberg D, Armbrecht G, Banzer D, Benevolenskaya LI, Bhalla A, Bruges Armas J, Cannata JB, Cooper C, Dequeker J, Diaz MN, Eastell R, Yershova OB, Felsch B, Gowin W, Havelka S, Hoszowski K, Ismail AA, Jajic I, Janott I, Johnell O, Kanis JA, Kragl G, Lopez Vaz A, Lorenc R, Lyritis G, Masaryk P, Matthis C, Miazgowski T, Gennari C, Pols HA, Poor G, Raspe HH, Reid DM, Reisinger W, Scheidt-Nave C, Stepan JJ, Todd CJ, Weber K, Woolf AD, Reeve J. Determinants of incident vertebral fracture in men and women: results from the European Prospective Osteoporosis Study (EPOS). Osteoporos Int 2003;14(1):19-26.

19. Silman AJ. Risk factors for Colles' fracture in men and women: results from the European Prospective Osteoporosis Study. Osteoporos Int 2003;14(3):213-8.

20. Rosenthal DI, Mayo-Smith W, Hayes CW, Khurana JS, Biller BM, Neer RM, Klibanski A. Age and bone mass in premenopausal women. J Bone Miner Res 1989;4(4):533-8.

21. Varenna M, Binelli L, Zucchi F, Ghiringhelli D, Gallazzi M, Sinigaglia L. Prevalence of osteoporosis by educational level in a cohort of postmenopausal women. Osteoporos Int 1999;9(3):236-41.

22. Eastell R. Role of oestrogen in the regulation of bone turnover at the menarche. J Endocrinol 2005;185(2):223-34.

23. Moffitt TE, Caspi A, Belsky J, Silva PA. Childhood experience and the onset of menarche: a test of a sociobiological model. Child Dev 1992;63(1):47-58.

24. Pesonen AK, Raikkonen K, Heinonen K, Kajantie E, Forsen T, Eriksson JG. Reproductive traits following a parent-child separation trauma during childhood: a natural experiment during World War II. Am J Hum Biol 2008;20(3):345-51.

25. Wierson M, Long PJ, Forehand RL. Toward a new understanding of early menarche: the role of environmental stress in pubertal timing. Adolescence 1993;28(112):913-24.

26. Ellis BJ, Essex MJ. Family environments, adrenarche, and sexual maturation: a longitudinal test of a life history model. Child Dev 2007;78(6):1799-817.

27. Bogaert AF. Age at puberty and father absence in a national probability sample. J Adolesc 2005;28(4):541-6.

28. Belsky J, Steinberg L, Draper P. Childhood experience, interpersonal development, and reproductive strategy: and evolutionary theory of socialization. Child Dev 1991;62(4):647-70.

29. Ellis BJ, Garber J. Psychosocial antecedents of variation in girls' pubertal timing: maternal depression, stepfather presence, and marital and family stress. Child Dev 2000;71(2):485-501.

30. Ellis BJ, McFadyen-Ketchum S, Dodge KA, Pettit GS, Bates JE. Quality of early family relationships and individual differences in the timing of pubertal maturation in girls: a longitudinal test of an evolutionary model. J Pers Soc Psychol 1999;77(2):387-401.

31. Maestripieri D, Roney JR, DeBias N, Durante KM, Spaepen GM. Father absence, menarche and interest in infants among adolescent girls. Dev Sci 2004;7(5):560-6.

32. Saxbe DE, Repetti RL. Brief report: Fathers' and mothers' marital relationship predicts daughters' pubertal development two years later. J Adolesc 2008.

33. Tither JM, Ellis BJ. Impact of fathers on daughters' age at menarche: a genetically and environmentally controlled sibling study. Dev Psychol 2008;44(5):1409-20.

34. Mendle J, Turkheimer E, D'Onofrio BM, Lynch SK, Emery RE, Slutske WS, Martin NG. Family structure and age at menarche: a children-of-twins approach. Dev Psychol 2006;42(3):533-42.

35. Brown J, Cohen P, Chen H, Smailes E, Johnson JG. Sexual trajectories of abused and neglected youths. J Dev Behav Pediatr 2004;25(2):77-82.

36. Foster H, Hagan J, Brooks-Gunn J. Growing up fast: stress exposure and subjective "weathering" in emerging adulthood. J Health Soc Behav 2008;49(2):162-77.

37. Vigil JM, Geary DC, Byrd-Craven J. A life history assessment of early childhood sexual abuse in women. Dev Psychol 2005;41(3):553-61.

38. Wise LA, Palmer JR, Rothman EF, Rosenberg L. Childhood Abuse and Early Menarche: Findings From the Black Women's Health Study. Am J Public Health 2009.

39. Zabin LS, Emerson MR, Rowland DL. Childhood sexual abuse and early menarche: the direction of their relationship and its implications. J Adolesc Health 2005;36(5):393-400.

40. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, Marks JS. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 1998;14(4):245-58.

41. Williamson DF, Thompson TJ, Anda RF, Dietz WH, Felitti V. Body weight and obesity in adults and self-reported abuse in childhood.Int J Obes Relat Metab Disord 2002;26(8):1075-82.

42. Lissau I, Sorensen TI.Parental neglect during childhood and increased risk of obesity in young adulthood. Lancet 1994;343(8893):324-7.

43. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, Marks JS. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 1998;14(4):245-58.

44. Gunstad J, Paul RH, Spitznagel MB, Cohen RA, Williams LM, Kohn M, Gordon E. Exposure to early life trauma is associated with adult obesity. Psychiatry Res 2006;142(1):31-7.

45. Lissau I, Sorensen TI. Parental neglect during childhood and increased risk of obesity in young adulthood. Lancet 1994;343(8893):324-7.

46. Noll JG, Zeller MH, Trickett PK, Putnam FW. Obesity risk for female victims of childhood sexual abuse: a prospective study. Pediatrics 2007;120(1):e61-7.

47. Williamson DF, Thompson TJ, Anda RF, Dietz WH, Felitti V. Body weight and obesity in adults and self-reported abuse in childhood. Int J Obes Relat Metab Disord 2002;26(8):1075-82.

48. Johnson J, Cohen P, Kasen S, Brook JS. Childhood adversities associated with risk for eating disorders or weight problems during adolescence or early adulthood. American Journal of Psychiatry 2002;159(3):394-400.

49. Wonderlich SA, Wilsnack RW, Wilsnack SC, Harris TR. Childhood sexual abuse and bulimic behavior in a nationally representative sample. Am J Public Health 1996;86:1082-1086.

50. Everill JT, Waller G. Reported sexual abuse and eating psychopathology: a review of the evidence for a causal link. Int J Eat Disord. 1995;18:1-11.

51. Welch SL, Fairburn CG. Childhood sexual and physical abuse as risk factors for the development of bulimia nervosa. Child Abuse Negl. 1996;20:633-642.

52. Ackard DM, Neumark-Sztainer D, Hannan PJ, French S, Story M. Binge and purge behavior among adolescents: associations with sexual and physical abuse in a nationally representative sample: the Commonwealth Fund survey. Child Abuse Negl 2001;25(6):771-85.

53. Fonseca H, Ireland M, Resnick MD. Familial correlates of extreme weight control behaviors among adolescents. Int J Eat Disord 2002;32(4):441-8.

54. Patton GC, Viner R. Pubertal transitions in health. Lancet 2007;369(9567):1130-9.

55. Ackard DM, Peterson CB. Association between puberty and disordered eating, body image, and other psychological variables. Int J Eat Disord 2001;29(2):187-94.

56. Kaltiala-Heino R, Rimpela M, Rissanen A, Rantanen P. Early puberty and early sexual activity are associated with bulimic-type eating pathology in middle adolescence. J Adolesc Health 2001;28(4):346-52.

57. Killen JD, Hayward C, Litt I, Hammer LD, Wilson DM, Miner B, Taylor CB, Varady A, Shisslak C. Is puberty a risk factor for eating disorders? Am J Dis Child 1992;146(3):323-5.

58. Mangweth-Matzek B, Rupp CI, Hausmann A, Kemmler G, Biebl W. Menarche, puberty, and first sexual activities in eating-disordered patients as compared with a psychiatric and a nonpsychiatric control group. Int J Eat Disord 2007;40(8):705-10.

Recent genome-wide analysis identified a genetic variant on 5p14.1 (rs4307059), which is associated with risk for autism spectrum disorder (ASD). We recently investigated up to 7313 ALSPAC children and studied association between rs4307059 and potential broader autism traits including early vocabulary and communication (MacArthur-Communicative-Development-Inventory/MacArthur-Toddler-Communication-Questionnaire; Denver-Developmental-Screening-Test), social behaviour and behavioural difficulties (Emotionality-Activity-Sociability-Temperament-Survey; Revised-Rutter-Parent-Scale for Preschool Children; Standard-Assessment-Tests of social skills; Strengths-and-Difficulties-Questionnaire, Cambridge Hormones-and-Moods-Project-Friendship-Questionnaire; Special-Educational-Needs assessment), social communication (Social-and-Communications-Disorders-Checklist; Children's-Communication-Checklist) and verbal intelligence (Wechsler-Intelligence-Scale-for-Children-III) assessed between 15-months and 12-years. Our results suggested that common variation in rs4307059 is associated with ASD-related phenotypes and support the role of rs4307059 as QTL for ASD. We aim to extend this approach and investigate the association between the SNP and ASD-related traits assessed in mothers. We therefore propose to genotype rs4307059 in the ALSPAC maternal sample.