Substance use though typically initiated in adolescence is heterogeneous. There are likely to be different patterns or trajectories of substance use which have implications for research on aetiology, prevention, and later outcomes. Specific trajectories of substance use may have different aetiological pathways and maybe more likely to lead to poor adolescent and adult outcomes, which maybe missed if average "values" of substance use are used. Research, therefore, into this area needs to identify the common types of substance use trajectory in order then to test and explore specific hypotheses on aetiology, or on who may benefit or needs to be targeted by prevention activities and the type of intervention that maybe effective, and then to carry these forward into determining their impact on adult substance dependence and other outcomes.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is the largest birth cohort with detailed biological and behavioural data from before birth through till late adolescence in the world. ALSPAC is organised as a resource for the whole scientific community and provides the most detailed phenotypic, genotypic and environmental assessments of parents and their children across the life course of any comparable study available to scientists studying early life influences on health (www.alspac.bris.ac.uk). Approximately 14,000 children along with their parents were recruited to ALSPAC between 1991 and 1992. Since this time these participants have been assessed regularly, mainly through questionnaires and clinics. Since the age of ten years these assessments have included measurement of self-reported substance use (tobacco, alcohol, cannabis and other illicit drugs) and since the age of 15 years these self-reports have been augmented by hair-based toxicology. The current assessment sweep at age 17 years is due to be completed in 2010. Approximately 10,000 ALSPAC participants have DNA stored to allow genotyping, and for 2,000 information from a full genome wide scan is available. Existing ALSPAC support will allow the collection and preliminary processing of data on adolescent substance use at 17, and the drug testing of hair specimens collected at 15 and the future clinic at 17. The current application is for the statistical support necessary to consolidate these data and create an accessible research resource for future secondary analyses by researchers with an interest in drug use and dependence. The value of this resource will be demonstrated through the analysis and identification of trajectories of substance use to age 17, a preliminary genome wide association study in relation to these trajectories, and the identification of key risk and protective factors collected in ALSPAC that are relevant to different hypotheses and their bivariate association with the drug use trajectories. The project has the following aims:

1.) To identify different trajectories of adolescent substance use - both drug specific (i.e. alcohol, tobacco, and cannabis) and an overarching analysis of any adolescent substance use.

2.) To compare and validate self-reported data on selected substances with toxicological evidence based on hair analysis (cannabis, amphetamines, ecstasy, opiates, benzodiazepines, ketamine, methaphetamine) and urinalysis (cotinine)

3.) To create an accessible database of self-reported substance use amongst ALSPAC participants from ages ten to 17 years, including specific trajectories of substance use, and hair analysis.

4.) To compare case participants exhibiting the above phenotypes with study controls in a subset of approximately 2000 study members in whom whole genome scan data are available in order to identify genetic loci of possible influence on trajectories of adolescent substance use in order to inform more detailed genetic investigations.

5.) To identify, collate, and make available information on key predictors, mediating factors, and confounders of substance use collected in ALSPAC including measures of:- social position, family adversity, parental and prenatal substance use, impulsivity (conduct problems and antisocial behaviour), subjective response, peer substance use, depression and anxiety, educational performance, life events, sexual maturation, and neighbourhood.

6.) To provide bivariate analyses of these factors on substance use trajectories in order to support researchers and research clusters test specific hypotheses on causal pathways determining adolescent substance use.

Many studies have reported an association between body mass index (BMI) and asthma that is stronger in girls than boys. However, it is difficult to prove the direction of causality. Genomewide association studies have identified a common variant in the FTO gene chromosome 16 that is associated with BMI and, in particular, fat mass. We used a Mendelian Randomization approach to assess evidence for a causal effect of BMI or fat mass on asthma.

A total of 4690 children enrolled in the ALSPAC birth cohort had measures of FTO genotype, BMI at age 7 years, asthma at age 7.5 (defined as a physician diagnosis together with a report of either wheezing or asthma treatment in the last year), and confounding variables. Fat and lean mass were measured at 9 years by dual energy x-ray absorptiometry (DEXA), in 4145 of these children. Associations with fat and lean mass were adjusted for height, height2 and gender. Fat mass was rescaled so that its standard deviation was similar to that for BMI.

There was clear evidence of associations of BMI (OR per kg/m2 1.07 (95% CI 1.02-1.11) and fat mass (OR per 500g 1.08 (1.04-1.13)) with asthma. The association with lean mass was weaker (OR per kg 1.05 (0.99-1.11)). Associations with BMI and lean mass appeared stronger in girls than boys, but the association with fat mass did not differ. BMI and fat mass, but not lean mass, were strongly associated with FTO genotype (Pless than 0.001). However, there was little evidence for an association of FTO with asthma (ORs 0.94 (0.78-1.14) for A:T and 1.19 (0.92,1.53) for A:A compared with T:T).

A Mendelian Randomization test did not support a causal link between asthma and fat mass or BMI. However, given that Mendelian Randomization tests have low power, these results do not exclude a causal effect. The discovery of further genes related to BMI and fat mass may increase the power of the Mendelian Randomization approach.

We therefore would like to investigate nine more BMI related SNPs that are now available in ALSPAC (see details of SNPs below).

Public health interest often centres around the causal effect of an exposure on a particular outcome. For ethical, practical, or financial reasons, randomized controlled trials (RCTs) to investigate such an effect may not be possible and inferences must be drawn from observational data. These may be distorted by reverse causation, measurement errors, or the presence of socioeconomic and behavioural confounding factors that are difficult to measure even if known. Mendelian randomization is an instrumental variable (IV) method that permits estimation of the exposure-outcome causal effect in the presence of confounding. The assumptions required, i.e. all variables are continuous and all relationships between variables are linear, are often violated in epidemiological applications where the outcome (e.g. disease status) is typically binary and hence cannot have a linear relationship with the exposure. This is especially relevant to case-control data where the exposure-outcome relationship is usually logistic. The aim of this project is to develop theoretically sound methods for dealing with non-linear models and to investigate the practical implications of weakening, or even violating, some of the more restrictive conditions, given the type of data that we expect to arise in these settings. Theory must inform practice but applications should drive the theoretical investigation. This is not always the case and we view this as one of the main strengths of our proposal.

IV methods for binary outcomes are often used in econometrics to estimate causal effects when the exposure is subject to measurement error. We will investigate the adaptability of these approaches to epidemiological settings. Relevant methods also exist for RCT data when the effect for a particular subgroup is of interest. We will clarify when these are useful in Mendelian randomization applications and what conditions are required. The causal literature has shown that bounds on the average causal effect can be calculated without making restrictive distributional or relational assumptions. We will extend these to measures of casual effect that are more likely in epidemiological settings (e.g. causal odds ratios) and identify the data conditions under which they will be useful in practice. In order to get more precise estimates, data are often combined from different epidemiological studies. This pooling of data raises all the classic issues associated with meta-analysis and existing meta-analytic methods will be extended to take account of these. Finally, models that allow for more complex biological pathways will be developed to cater for multiple exposure and disease endpoints.

Introduction

Relations between cognition and substance abuse in adolescence

A number of studies have indicated that specific aspects of cognitive function may play a role in risk of substance abuse in young people1. For example, studies have reported that children at high risk for substance abuse, who have not yet started using substances (for example, children of substance dependent parents) may have specific cognitive deficits, including lower academic achievement, lower verbal and visuospatial ability, and reduced attention span and executive function. These findings have been extended in adoption studies, in which cognitive impairments were found in children of substance abusing parents, even though these children were adopted at birth and raised in an environment without substance abuse. These findings suggest that potential cognitive impairments that may predispose to later substance abuse may have a biological component and can not be entirely explained by rearing circumstances. Tarter et al. (2004) have hypothesized that cognitive tasks tapping into prefrontal cortex (PFC) dysfunction in particular may contribute to the liability to substance dependence2. However, there is a lack of studies that examine the relationships between cognitive functioning and the development of substance involvement over time, and take account of the influences of other potentially important factors, such as a youngster's personality, behavioural problems and family-related factors.

Personality, behavioural problems and family-related factors

Certain aspects of personality, including impulsivity, sensation-seeking motivation and low locus of control, have been related to increased risk of substance abuse in adolescence. However, few studies have examined how personality and cognition may work in conjunction to increase adolescents' risk of substance abuse. One cross-sectional study, which was limited to girls, reported that personality (i.e., temperament) acted as a mediator in the relation between executive function and substance use in adolescence3. It is however, also conceivable that cognition can act as a mediator in the relationship between personality and risk of substance abuse. In further disentangling these relationships, a longitudinal study design would provide major advantages in resolving the temporal relationships between cognition, personality and the development of substance abuse. Another study found that specific combinations of cognitive impairment and personality (i.e., disinhibition) posed the greatest risk for the development of substance abuse4. It would be important to replicate these findings and extend them by also examining other personality traits, such as sensation seeking and low locus of control. Relationships could first be explored for each trait individually and subsequently for combinations of personality traits. Furthermore, it would be important to take into account other aspects of a youngster's behaviour such as symptoms of conduct disorder and attention-deficit-hyperactivity disorder.

Family-related factors can also play an important role in adolescent substance abuse. For example, it is relatively well-established that young people who have a poor relationship with their parents or are poorly monitored by their parents have increased risk of substance abuse5. Young people's family background can also influence cognitive function. For example, Silveri et al. (2004) reported that parental substance abuse has a pronounced impact on young people's emotional and cognitive development6. Yet, few studies have examined to what extent the relationship between substance abuse and cognitive function in adolescence is influenced by family factors.

Genetic influences

A large literature of family, adoption and twin studies has indicated that genetic influences play a role in substance use and abuse as well as in cognition. Specific genetic variants have been studied in relation to cognitive function and substance abuse. A considerable literature indicates that the neurotransmitter dopamine plays a role in many if not all addictive phenotypes7 as well as in cognitive processes mediated by the PFC8. There has been a flurry of studies examining the role of the COMT gene in executive function. COMT encodes the enzyme catechol-o-methyltransferase (COMT) and is critical for dopamine metabolism in the PFC. For example, a study based on children participating in the ALSPAC study found that the COMT Val158Met polymorphism was associated with IQ as well as executive function (particularly cognitive tasks tapping into PFC function)9, although the effects may be stronger for IQ than for certain specific cognitive tasks10. Studies to date, however, have not tended to take into account potential differences between tasks tapping into affective (or 'hot') versus non-emotional (or 'cold') cognitive processes11. Furthermore, findings to date have largely been limited to the COMT Val158Met polymorphism, while there is evidence that the study of COMT haplotypes may be more promising in accounting for phenotypic variability12;13.

COMT has also been associated with substance abuse, although there are negative findings as well14. Some of the discrepancy may be related to personality, for example, in a sample of polysubstance abusers, the Val158Met polymorphism was found to be associated with addiction through impulsivity or dyscontrol15. When explaining the relations between cognition and substance abuse it will be important to include genetic information. This will contribute towards clarification of these relations and may also provide some insight into the reasons for the underlying discrepancies in genetic studies of substance abuse.

Aims of the proposed study:

We propose to study the relations between cognition and substance use/abuse in a large and representative UK-based sample of adolescents. The specific aims are to:

1. Examine the relations between aspects of cognition, specifically IQ and PFC functioning, and the development of substance involvement over time;

2. Examine the influences of personality, problem behaviour and family-related factors (including relations with parents, parental monitoring and parental substance abuse) on these relationships;

3. Investigate whether genes that have been related to both cognition and substance abuse (e.g., COMT) moderate the association between both phenotypes.

Sample and statistical analysis

The Avon Longitudinal Study of Parents and Children (ALSPAC) is an ongoing UK-based longitudinal study, comprising 14,541 mothers recruited in 1991 who have been followed with their offspring from before birth up to age 16 (www.alspac.ac.uk). DNA samples are available for ~10,000 children and COMT has already been typed in this cohort. Typing of other genes that may play a role in the relationship between cognition and substance abuse is currently taking place.

We would be interested in studying the following variables:

Concept Person Source Timepoints

Substances

Alcohol use Child Child/ parent Childhood/ adolescence

Cigarette use Child Child/ parent Childhood/ adolescence

Cannabis use Child Child/ parent Childhood/ adolescence

Cognition

IQ Child Child Childhood/ adolescence

Verbal ability Child Child Childhood/ adolescence

Working memory Child Child Childhood

Attention Child Clinic Childhood/ adolescence

Executive function Child Clinic Childhood/ adolescence

Inhibition/ impulsivity Child Clinic Childhood/ adolescence

Mediating/ moderating factors

Personality

Temperament Child Parent Childhood

Sensation seeking Child Child Adolescence

Locus of control Child Child Childhood/ adolescence

Family-related variables

Parental substance abuse Parent Parent Childhood/ adolescence

Parent-child relations Child/ Parent Parent/ child Childhood/ adolescence

Parental monitoring Parent Parent/ child Childhood/ adolescence

Child problem behaviour

Conduct problems Child Parent/ child Childhood/ adolescence

Attention-Deficit- Child Parent/ child Childhood/ adolescence

Hyparactivity Problems

Genetic influences

COMT Child

Relations between variables will be evaluated using linear/ logistic regression analysis and path analysis. Latent class analyses will be used to define clusters of cognitive and other variables with different predictive relationships with substance abuse. Latent growth curve modelling will allow the study of predictors of change and individual trajectories over time.

References

1. N. Z. Weinberg, J Am Acad Child Adolesc Psychiatry 36, 1177-86 (1997).

2. R. E. Tarter, L. Kirisci, M. Habeych, M. Reynolds, M. Vanyukov, Drug Alcohol Depend 73, 121-32 (2004).

3. P. R. Giancola and A. C. Mezzich, J Child Psychol Psychiatry 44, 857-66 (2003).

4. L. Kirisci, R. E. Tarter, M. Vanyukov, M. Reynolds, M. Habeych, Drug Alcohol Depend 76, 125-33 (2004).

5. K. H. Shelton et al., (In Press (J Youth Adolesc)).

6. M. M. Silveri , G. K. Tzilos, P. J. Pimentel, D. A. Yurgelun-Todd, Ann N Y Acad Sci 1021, 363-70 (2004).

7. A. I. Leshner and G. F. Koob, Proc Assoc Am Physicians 111, 99-108 (1999).

8. E. M. Tunbridge, P. J. Harrison, D. R. Weinberger, Biol Psychiatry 60, 141-51 (2006).

9. J. H. Barnett et al., Am J Psychiatry 164, 142-9 (2007).

10. J. H. Barnett, L. Scoriels, M. R. Munafo, Biol Psychiatry 64, 137-44 (2008).

11. A. Kerr and P. D. Zelazo, Brain Cogn 55, 148-57 (2004).

12. S. Shifman et al., Am J Hum Genet 71, (2002).

13. A. G. Nackley et al., Science 314, 1930-3 (2006).

14. M. B. van den Bree, Curr Psychiatry Rep 7, 125-32 (2005).

15. D. J. Vandenbergh, L. A. Rodriguez, I. T. Miller, G. R. Uhl, H. M. Lachman, Am J Med Genet 74, 439-42 (1997).

The aim of this proposal is to perform follow-up genotyping of the top associated signals associated with early developmental traits within the remaining ALSPAC sample in order to replicate findings from the original ALSPAC genome-wide analysis. The signal at each respective locus will be represented through 1-2 common SNPs identified from the original ALSPAC genomewide association testing in conjunction with bioinformatics analysis. The selected SNPs will also be subjected to further analysis in other large birth cohorts.

Background

Hypertensive disorders of pregnancy (Hypertension identified at least twice after the 20th week of gestation with proteinuria present at least twice after the 20th week of gestation (pre-eclampsia) or without proteinuria (gestational hypertension)) are by far the commonest complication of pregnancy and are associated with increased risk of maternal and perinatal mortality, small for gestational age and preterm birth in the short time and with increased risk of cardiovascular disease in mother and offspring in the long term. Hypertensive disorders of pregnancy (HDP) affect 10-20% of pregnancies in developed countries.

Despite the common occurrence of HDP, their important impact on maternal and child health in the short and long-term and the many decades of research into this condition, two recent reviews noted that the pathophysiology of these disorders, how to predict women who are at high risk and how to prevent HDP remained unclear. They note that in part this reflects the failure to appreciated the complex nature of the syndrome which is likely to be affected by a number of pathophysiological pathways; the limited ability in studies to date to explore possible differences in pathophysiology and consequences for potentially different sub-types and the lack of large studies with adequate maternal and fetal/offspring data. With respect to exploring pathophysiology and genetic pathways it has been noted that too few studies have examined maternal and fetal genotype together when taking a candidate gene approach and that no studies to date have examined within one study population a wide array of variants that relate to all of the plausible pathophysiolgogical pathways that are likely to be involved in these conditions. It was noted that studies that restrict samples to first pregnancies are problematic, because although pre-eclampsia is more common in nulliparous women, different subtypes may be more common in women who have experienced previous pregnancies and it is important to try to understand the whole spectrum of this condition.

Classical twin studies have reported heritabilities of around 20% for HDP and have lead some to conclude that the genetic contribution to HDP was smaller than hitherto believed. However, examining levels of association in twin mothers fails to recognise the potential importance of paternal genes expressed in the fetus. Two large studies using record linkage between Norwegian population registers and containing over 1 million participants examined a wide range of familial relationships and concluded that heritability was 30% with fetal paternal genotype. The plausible pathophysiological mechanisms for HDP, including maternal-fetal immunological maladaptation, maternal endothelial dysfunction, oxidative stress, inflammatory response and glucose-insulin metabolism mechanisms, have high heritabilities (greater than 50%).

A number of genome wide association studies (GWAS) of pre-eclampsia or HDP are currently underway. Only a minority of these include genetic information on fetus/offspring as well as mother. Other studies with genome wide data are potentially able to complete GWAS for pre-eclampsia with some having fetal genome wide data only and others maternal data only.

The 50K Cardiochip has densely typed loci for all pathways that are plausibly linked to HDP and cardiovascular disease and therefore provides an efficient way of establishing the many genetic variants that are likely to be associated with HDP. This includes loci that have to date been identified as relating to HDP. Brendan Keating (co-applicant) produced this chip and is responsible for updates/revisions to later versions.

Our aim is to submit a grant proposal to BHF in March 2009 that will have the following objectives:

1. Identify and collate all GWAS of HDP and also potential additional studies with genome wide data and the possibility to define cases of HDP. To combine data from all such studies in order to identify key loci in mothers and fetus associated with HDP.

2. Add any new loci from (1) to an update version of the cardiochip

3. Use the newly updated cardiochip on all ALSPAC mothers and offspring in order to determine the association of genetic variants (in mothers or fetus) associated with HDP, cardiovascular disease and plausible mechanisms for HDP, with patterns of change in blood pressure during pregnancy in ALSPAC mothers and with later blood pressure in ALSPAC offspring ages 7-17 and mothers at the 17 year postnatal clinic.

Methods

PILOT

Before submitting the grant application we would like to complete a small pilot study in ALSPAC. This would involve using the current version of the cardiochip on 1000 ALSPAC samples (500 mothers and their 500 offspring). Debbie Lawlor is able to identify 500 mothers who have had obstetric data abstracted and for whom there is extracted DNA on both mother and offspring. From this 'eligible' sample she will identify 300 mothers-offspring pairs where mother had severe HDP (pre-eclampsia and/or systolic BPgreater than 200 on at least 2 occasions after 20 weeks gestation) and a random sample of 200 mother-offspring pairs where there is no evidence of HDP or existing hypertension in mother. We would like permission to ship DNA from these mother-offspring pairs (1000 samples in total: 500 mothers/500 offspring) to the University of Pennsylvania ASAP (DAL can provide IDs immediately) where Brendan Keating will provide genotyping with the cardiochip. The cardiochip will be provided for free for this pilot and MRC CAiTE will cover shiping costs. Analyses will be completed by DAL, Brendan Keating & Nic Timpson.

Brendan Keating requires 1ug total of DNA (50 or 100ng/ul) not from cell-lines for this work.

FULL GRANT PROPOSAL

The aims of the full grant proposal are detailed above.

With respect to ALSPAC we would request funds to complete genotyping using the cardiochip on all ALSPAC mothers and offspring with DNA. Our plan would be for this genotyping on the complete sample to be completed at University of Pennsylvania with a newly updated cardiochip. Funds would be requested for shipping samples; completion of the genotyping; merging of large amount of new genetic data with the existing ALSPAC data; a statistician to complete analyses under supervision of applicants. If funded for this complete project we would again require 1ug of DNA (50 or 100ng/ul) from whole blood samples and not cell lines for each sample from mothers and offspring.

This proposal has been discussed with Sue Ring who feels that is is feasible.

(No outline received).

Scoliosis is defined as a lateral curvature of the spine greater than 10 degrees[1]. It most commonly starts between aged 10 years and skeletal maturity[2] (adolescent idiopathic socliosis, AIS), and has an incidence ranging between 1-3% depending on populations studied[3, 4], although this incidence does appear to be increasing[5]. There is an equal gender prevalence for minor scoliotic curves (10 degrees), but far more girls have curves greater than 30 degrees compared with boys[6]. For unknown reasons, 80-90 percent of thoracic and thoracolumbar curves are to the right[7] suggested to be linked with handedness[8], but 70% of single lumbar curves are to the left[7]. Again, for reasons that are not clearly understood, some scoliotic curves progress: estimates vary from 3.5%[9] to 15%[10], while others remain static.

Scoliosis is not always a benign structural abnormality, although the mortality rate for individuals with AIS is comparable to that of the general population[11]. There is a direct correlation between the magnitude of the thoracic scoliotic curve and pulmonary function[12]; back pain is a complaint in 80% of scoliotic patients in long-term follow-up studies[13]; and the psychological and social effects of scoliosis are real but variable and hard to quantify. Nevertheless, studies that have looked into this area consistently find mild social dysfunction such as an unwillingness to wear snug clothing or bathing suits[12], but also high rates of work disability[14,15] and reduced rate of marriage[15], although no studies that we could find assess the psychological and social effects in children and young adults.

Current knowledge about the causes of the initiation or induction of the scoliotic curve is scarce. Some work has been carried out on the determinants of the initiation or induction of the scoliotic curve, but very little using a longitudinal prospective study design. Some work has also been done on the determinants of curve progression, but contradictory results have been found for the association with bone density, anthropometry, and hypermobility, probably because of small sample sizes. No coherent picture on the genetic determinants of either induction or curve progression has been found, probably because scoliosis is due to a complex interplay between environmental and genetic influences. This also suggests that scoliosis may be a heterogeneous group: more accurate phenotyping according not only to curve structure, but associated clinical features, may therefore allow more accurate classification into subgroups with different prognosis and different genetic determinants.

Therefore the aims of this project are

1. to quantify the incidence of scoliosis in the UK population from aged 7 to 17 years, and to describe the percentage progression and regression (i.e. natural history), using a large population based birth cohort.

2. to assess the association between the standard Adams forward bending test with Cobb angle measured by DXA analysis in children

3. to assess the impact of scoliosis induction and progression in children (in terms of time off school, pain, sleep, physical activity, fractures)

4. to accurately phenotype and therefore classify into relevant subgroups, children with scoliosis in terms of curve structure and associated clinical features such as hypermobility, bone density, muscle function, family history

5. to prospectively investigate the risk factors for scoliotic curve induction or initiation, focussing on early life events, bone and muscle quality and growth

6. to prospectively investigate the risk factors for scoliotic curve progression or regression, focussing on growth, anthropometry, puberty, bone density and hypermobility

7. to investigate the genetic determinants of scoliosis using a large population based birth cohort to carry out genome wide association studies, with replication and further study to be carried out in a secondary care disease cohort

Methods

The Adams forward bending test was performed at research clinics when the offspring were aged 7, 9, 10, 11 and 13 by trained research assistants. We are requesting funding for this measure to be repeated at aged 17. The results of the Adams forward bending test will be used as the main outcome of interest, with children being categorised into having or not having scoliosis on the basis of an ATI greater or less than 7 degrees. Analyses will be repeated using the ATI as a continuous outcome to assess trends. In addition, total body DXA scans have been performed with full spinal DXA results. Cobb angles (traditional method of assessing scoliosis on plain X-rays) will be measured on these scans.

Exposures of interest

(1) demographics and early life events, (2) growth, (3) bone and muscle parameters, (4) genetics

Concept: Specific measure: Person: Source: Time point:

demographics gender child Q birth

ethnicity child Q birth

SES mother Q preg, birth, aged 4

early life event gestational age child Q birth

birth weight and length child assessment birth

growth anthropometry child assessment 7, 9,10,11,13,15,17

puberty child Q 9, 11, 13, 15

R/L handedness child Q

arachnodactyly child assessment 12

bone and muscle parameters total body DXA child assessment 9,12,15

grip strength child assessment 13

hypermobility child assessment 13,17

genetics GWAS data child biological

Impact of scoliosis

(1) chronic pain, (2) sleep, (3) physical activity, (4) time off school, (5) psychological, (6) lung function (7) fractures

Concept: Specific measure: Person: Source: Time point:

chronic pain chronic pain child Q 17

sleep disrupted/poor sleep child Q 13,14,16

physical activity reported child Q 9

accelerometer child assessment 12, 17

time off school time off school child Q 14,16

psychological DAWBA child Q 7,9,10,11,13,15

CIS-R child Q 7,9,10,11,13,15

lung function methacholine challenge child assessment 8

spirometry child assessment 9

NO, salbutamol challenge child assessment 15

fractures fractures child Q 12, 15

[1] Kane W (1997) Clin Orthop 126:43-46

[2] Dobbs MB et al (1999) Orthop Clin N Am 30:331-341

[3] Wang Y et al (1996) Chinese J Epi 17:160-162

[4] Grivas et al (2002) Studies Health Tech Info 91:76-80

[5] Wong HK et al (2005) Spine 30:1188-1196

[6] Roach JW (1999) Orthop Clin N Am 30:353-365

[7] Rinsky LA et al (1988) West J Med 148:182-191

[8] Burwell RG (2003) Develop Neurorehab 6:137-170

[9] Robitaille YVON et al (1984) Int J Epi 13:319-323

[10] Dickson RA et al (1980) BMJ 281:165-167

[11] Weinstein SL et al (1983) J Bone Joint Surg Am 65:447

[12] Aaro S et al (1984) Spine 9:220

[13] Weinstein SL et al (1981) J Bone Joint Surg Am 63:702

[14] Fowles JV et al (1978) Clin Orthop 134:212-217

[15] Nachemson A (1968) Acta orthop Scand 39:466-476

Life course epidemiology is concerned with understanding how risk factors occurring during development and degenerative periods combine to affect disease risk.(1) Central to this approach is an understanding that many physiological processes reach a peak level (their highest functional level) early in life, remain relatively stable through mid-life, and then decline during older age. For example, there is evidence that greatest lung volume is usually attained in late adolescence / early adulthood in humans, that it then remains constant into the 6th/7th decade of life in healthy non-smokers and then begins to decline.(2) Similarly, peak bone mass, though achieved somewhat earlier in females compared to males, is achieved by the end of the second decade in both genders, remains constant through middle-age, before declining, particularly post-menopause in women.(3) Peak vascular and metabolic health appears to be reached by late adolescence and begins to decline in healthy individuals during their 60s/70s.(4, 5) Disease risk, related to sub-optimal levels of physiological function, may result from attaining lower than average peak levels of function (most likely due to risk factors early in life during developmental periods), earlier decline from the peak level or more rapid decline in older age.(2) The importance of the developmental period in determining the level of the peak function has been emphasised in relation to bone health by describing the actual value of peak bone mass achieved by late 20s as the "bone bank for the remainder of life".(3)

This underlying theory suggests that ideal life course studies will have repeat measurements of key physiological functions across life in large cohorts of individuals and will be able to examine the relative importance of level of peak function, age at decline and speed of decline in relation to future health risk as well as key risk factors for these different aspects of functional trajectories. To our knowledge, no existing study has such repeat life course measurements from early age into late adulthood and it will be some decades before existing birth cohorts that do have repeat infant and childhood measurements reach older age. One potential alternative to examining life course trajectories in markers of physiological function in a single cohort study is to use multiple cohorts that cover different life course stages. The aim of this study is to examine the extent to which this might be possible.

We have chosen blood pressure as our measurement of an important marker of physiological function since it is an important risk factor for cardiovascular disease and is likely to have been measured in most cohort studies. Textbook descriptions of age related changes in blood pressure are based on series of cross-sectional studies of different age groups.(6) These show marked increases in systolic blood pressure from birth to age 1 (from on average 70mmHg to 95mmHg), followed by steady increases of 1-2mmHg per year to late 20s after which the rise is more marked to age 80 when it begins to level off, but never appears to decline with increasing age.(6) A very different pattern is observed for diastolic blood pressure, which appears to have a more gentle increase in early life, rising by just 2mmHg in the first year of life and then 0.5-1mmHg per year to age 40, where they remain relatively stable until the 6th decade and then show a very gradual decline.(6) On top of these average age-related changes are gender differences with little evidence of a gender difference until adolescence, at which time increases in both systolic and diastolic blood pressure become more gentle in females compared to males, so that mean blood pressure is greater in males than females by age 18.(6) On average systolic blood pressure is 10mmHg in males than females at age 18.6 However, in middle-age the increase in systolic blood pressure is greater in females than males so that by age 70 year systolic blood pressure is on average 10mmHg higher in females than males.(6) Whether these age-related differences in blood pressure from cross-sectional studies are found in prospective studies of the same individuals with repeat measurements is unclear.

The specific objectives of this study are:

1. To develop statistical methods to model the average population age related trajectory of blood pressure by combining information from cohorts of different ages with repeat measures of blood pressure.

2. To apply these models to MRC funded cohorts in order to investigate the sociodemographic differences in such trajectories.

3. To apply latent class analysis to individual cohort to identify groups of individuals with different trajectories and to investigate the risk factors for such groups.

Methods

Cohort selection

Cohorts are selected on the basis of (i) receiving core funds from the UK Medical Research Council (the first objective of our grant was to identify such cohorts and because funding is from MRC public health network the focus is primarily on MRC funded cohorts (though we hope to extend this if possible in the future); (ii) general population cohort studies; (iii) have at least three examinations on study participants where blood pressure was assessed.

To date 5 cohorts fulfiling these criteria have been identified of which ALSPAC is one

Statistical methods

Analyses will be undertaken within each individual cohort using Bayesian models and latent class analyses as appropriate.

Variables required from ALSPAC

Gender

Month & Year of birth

Whether singleton/twin/triplet

Mothers education

Fathers education

Head of household social class

Age at each clinic measurement 7, 8, 9, 10, 11, 13, 15

Systolic blood pressure at each clinic measurement 7, 8, 9, 10, 11, 13, 15

Diastolic blood pressure at each clinic measurement 7, 8, 9, 10, 11, 13, 15

History of blood pressure medication

Height and weight at age clinic measurement 7, 8, 9, 10, 11, 13, 15

Birth weight

Gestational age

References

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(2) Strachan DP, Sheikh A. A life course approach to respiratory and allergic diseases. In: Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology. 2nd Edition. 2 ed. Oxford: Oxford University Press; 2004. p. 240-59.

(3) Mora S, Gilsanz V. Establishment of peak bone mass. Endocrinol Metab Clin North Am 2003 March;32(1):39-63.

(4) McGill HC, Jr., McMahan CA, Herderick EE, Malcom GT, Tracy RE, Strong JP. Origin of atherosclerosis in childhood and adolescence. Am J Clin Nutr 2000 November;72(5 Suppl):1370S-15S.

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(6) Bazzano LA, He J, Whelton PK. Blood pressure in westernized and isolated populations. In: Lip GYH, Hall JE, editors. Comprehensive Hypertension.Philadelphia: Mosby, Elsevier; 2007. p. 21-30.