Proposal summaries
B1444 - Dietary patterns during pregnancy and gestational weight gain - 11/10/2012
Aim: We wish to examine whether dietary patterns derived during pregnancy are associated with gestational weight gain (GWG).
Background: It has recently been shown in ALSPAC that increased GWG is associated with adverse outcomes in child outcomes (Fraser et al, 2010). Little is known about what actually drives GWG; one potentially modifiable factor is dietary intake, whereby increased energy intake will lead to greater weight gain.
Exposure variables: Dietary patterns have already been derived using Principal Components Analaysis (Northstone et al, 2008)
Outcome variables: Pre-pregnancy weight combined with weight gain (kg/wk) in each trimester as determined by multi-level models (Fraser et al. 2010).
Confounding variables: Parity, maternal age and education level, smoking before/during pregnancy. In addition we will adjust for energy intake estimated from teh pregnancy FFQ.
We hypothesise that higher scores on the 'processed' component will lead to greater weight gain overall during pregnancy, while those mums scoring highly on the 'healthy' and 'traditional' patterns will gain less. Of particular interest is determining whether rates of weight gain differ by trimester according to dietary pattern scores.
References
K Northstone, P Emmett, I Rogers. Dietary patterns in pregnancy and associations with socio-demographic and lifestyle factors. Eur J Clin Nutr 2008; 62: 471-479.
Fraser A, Tilling K, Brion M-J, Ness A, Deanfield J, Hingorani A, Nelson, SM, DaveySmith G, Lawlor DA. Association of maternal weight gain in pregnancy with offspring obesity, metabolic and vascular traits in childhood: the Avon Longitudinal Study of Parents and Children. Circulation; 2010;121:2557-2564
B1442 - Early growth and childhood asthma a meta-analysis - 11/10/2012
Aim
To examine the association of early growth with the risk of childhood asthma using data from European cohort studies.
Hypothesis
Previously, it has been shown that low birth weight is associated with increased risks of asthma, chronic obstructive airway disease, and impaired lung function (FEV1, FVC) in adults. In children, low birth weight is associated with increased risks of respiratory symptoms. The developmental plasticity hypothesis suggests that the associations between low birth weight and asthma are explained by early adaptive mechanisms in response to various exposures in fetal and early postnatal life. These adaptive mechanisms might be beneficial for short term survival but might also lead to increased susceptibility of development of asthma in childhood and, perhaps, adulthood. Developmental adaptations might include impaired lung development leading to lifelong smaller airways and impaired lung function. Low birth weight is not likely to be causal for asthma per se. The same birth weight might be the result of various growth patterns. Information about growth characteristics in early life enables identification of critical periods for specific exposures and development of asthma. Children with a low birth weight tend to have a postnatal catch up growth, which leads to increased risks of childhood obesity. Both catch up growth and obesity have been suggested to be associated with pulmonary functioning, respiratory symptoms and childhood asthma. However, studies so far showed inconsistent results, which might partly be due to methodological issues including differences in definitions of catch up growth, obesity and asthma outcomes and the adjustment for gestational age and other confounders.
Methods
Cohorts were identified through birth cohort registries. Studies were eligible if they included children from 1989 onwards, had information on at least preterm birth or birth weight and preschool wheezing (1-4 years) or school age asthma (5-10 years), and allowed to use original data. Pooled odds ratios from random effect models were calculated from the per cohort logistic regression analysis to assess the association between birth weight and asthma, and gestational age and asthma. Secondly a individual patient data meta-analysis will be performed to assess the interaction between gestational age and birth weight.
Exposure
Growth characteristics at birth (weight, gestational age) and at the age of 1 year (range 6-18 months; weight) will be used.
Outcomes
Information on preschool wheezing (no, yes) at the ages of 1-4 years, and school age wheezing (no, yes) and physician diagnosed asthma (no, yes) at the ages of 5-10 years will be collected. Cohorts that defined wheezing and doctor-diagnosed asthma with use of questions from the International Study on Asthma and Allergy in Childhood (ISAAC) (14) are preferred but not obligatory.
Covariates
Information about a large number of potential covariates will be collected including e.g. maternal age, BMI, education, history of asthma or atopy (eczema, hay fever or house dust mite allergy), smoking habits during pregnancy and in the first year after birth, parity or number of siblings at time of birth, gestational hypertension, gestational diabetes, children's sex, gestational age, ethnicity, breastfeeding status, daycare attendance in the first year of life and pet keeping.
B1464 - A statistical framework to identify associations between diet and disease a formal comparison of competing approaches - 28/09/2012
The overall aim of this project is to evaluate existing techniques and develop new ones for investigating associations between diet and disease from nutritional data. Furthermore, potential pathways of diet with chronic diseases such as asthma will be explored.
Specific Objectives:
1. To evaluate existing data-driven methods for empirically derived dietary patterns in nutritional epidemiology.
2. To investigate and develop new statistical methods for analyzing nutritional data derived from other fields of research.
3. To explore causal pathways of diet with chronic diseases such as asthma and obesity.
Objective 1-2 will asess the performance of existing data-driven techniques in nutritional epidemiology and newly developed ones with the use of simulation studies. In order to do that, we will create a hypothetical population in which food intake derived from the ALSPAC food frequency questionnaire could be related to disease under the conditions that we control for and in a way that we could specify in terms of relative risks.
Objective 3 will use the ALSPAC data for identifying potential pathways between diet and asthma, allergy with the use of Causal models.
Exposure Variables: Food Frequency Data
Outcome Variables: Self-reported data on asthma and allergy , Lung function measurements
Confounding variables: energy intake, smoking status, infections, antibiotics and paracetamol use; educational level, housing tenure, financial difficulties, pre-pregnancy body mass index (BMI), ethnicity, age, parity, history of asthma, eczema; sex of child, gestational age, breast fed in first 6 months, day care at 8 months, multiple pregnancy, pets in infancy, damp/condensation/mould, child exposed to environmental tobacco smoke at weekends, season of birth, season of FFQ completion, birth weight, head circumference, birth length
B1407 - Acne Adolescent Mental Health and Educational Attainment - 27/09/2012
There appears to be a consensus in the psychological and dermatological literature that acne has adverse effects on mental health. Likewise, there is a consensus among economists and educationalists that mental health has a meaningful impact on educational attainment. However, the magnitude of this impact has been difficult to analyse because of reverse causality, educational attainment many worsen mental health as much as worsening mental health might cause poor educational attainment.
B1463 - Longitudinal modelling of high dimensional DNA methylation data - 27/09/2012
Aims
We are interested in developing statistical methods for the analysis of epigenetic data. Specific methods of interest include the use of independent component analysis (ICA) to identify key modes of variation in epigenetic changes through time that might then be correlated with subject exposures. We plan to explore the application of existing ICA algorithms that would operate solely on the epigenetic data and also on new methods that try to use the exposure data to help inform the identification of the latent factors within a joint model. We would also like to consider models that also use genome-wide SNP data to infer latent factors. At the same time the application of these methods may allow artifacts to be identified within the data which could be subsequently removed . This would produce a clean version of the data that could be used by others in any subsequent analysis of this data. Once methods have been developed and tested on simulated data we would apply them to the real data and search for asociations between uncovered latent factors and exposure levels.
B1441 - Analysis of the risk profiles of ALSPAC adolescent communities and individuals within a friendship network context - 27/09/2012
Social network analysis provides an insight on the complex influences that relationships between individuals may have on their health and social wellbeing throughout the lifecourse. ALSPAC has a unique data resource in this respect; a mapped friendship network of adolescents linked to detailed records of behavioural traits and health/social outcomes.This will allow the investigation of risk taking behaviour in adolescents within friendship 'communities' and how the clustering of these communities relate to the location of the friends residence and school. The investigation will be conducted as a 'Complexity Project' run by Uiniveristy of Bristol Computational Sciences Department. The complexity student will be supervised by Computational Science and School of Social and Community Medicine Staff.
This project has two components, a methodological one - to apply novel computational methods to analyse the ALSPAC friendship dataset, and an analytical one - to analyse the friendship data in this way to provide additional useful information to identify / target high risk groups within the population, which may otherwise not be obvious based only on individual risk profiles/standard epidemiological techniques.
Specifically the project aims to look at the following hypothesees:
Hypothesis 1: Can community detection algorithms (CDA), a novel computational method of network data analysis, be used to identify groups of closely connected individuals within a population?
Required Variables: CCXB friendship matrix data and friendship attribute variables.
Hypothesis 2: Within these groups of connected individuals is disease transmission (in this instance Chlamydia infection) more likely than between different "communities"? Note: community in this sense is defined purely by the computer algorithm rather than in the real world e.g. according to location of residence.
Required Variables: Chlamydia infection status from 17 year clinic.
An important element of the 'complexity study' project is to simplify complex data. In this project the ALSPAC participants propensity for risk taking behaviour (e.g. substance use, alcohol use, anti-social behaviour) will be categorised using statistical methods (e.g. principal component analysis) to define an ALSPAC risk profile. This profile can be used to look at "diffusion of behaviour", or preferential mixing of individuals based on behaviour - over and above sexual risk.
Hypothesis 3: Do the individuals within a "community" detected in this way share other characteristics such as risk behaviours?
Required Variables: Risk taking measures from the 15 and 17 year clinics and the 16 year questionnaire. Attainment data from Key Stage 4 (Linkage).
The risk profile data will be used to investigate the extent to which like with like mixing (homophily, e.g. if two individuals who smoke are more likely to become friends due to their shared smoking habit) drives the formation of friendship groups detected through CDAs. We will further use these data, together with computer simulation techniques to analyse whether interventions aimed at communities could be more effective than interventions at reducing disease transmission. The analysts will look at ways to visualise these data in a more compelling way than a list of densities/closeness measures using specialist network
ALSPAC Research Proposal Form page 7 of 8 December 2010
drawing software.
Hypothesis 4: Does the clustering of friendship communities within schools/geographical areas influences homophily of friendship networks.
Required Variables: Pseudonymised school codes and Lower Super Output Area of home residence.
This strand investigates the clustering of network community effects within the key georgaphical spaces for adolescents; schools and residential areas. We will consider if the geographical clustering appears to 'validate' the network communities and whether individuals with links outside of these shared geographical/network communities have differing risk profiles.
The team:
Katy Turner, Steve Gregory, John Macleod, Andy Boyd, Matthew Hickman, Rhiannon Pinney. Rhiannon is a Bristol Complexity Sciences student with Maths background, who will be supported by the rest of the team, Katy and Steve to be her main supervisors. 3 month project, leading to mini-dissertation write up, ideally peer review paper and potentially expansion/development to full PhD project proposal.
B1440 - Core Renewal Bid 2014-2019 - 20/09/2012
Not available
B1438 - Fussy eating in early life Does it matter - 06/09/2012
Infant feeding can be a source of considerable stress for parents. Children who refuse to eat the 'right' foods or appear particularly fussy can give particular cause for concern. Health visitors and other early year's practitioners have little solid advice to pass on to these worried parents despite often being the first port of call, as the evidence surrounding any potential detrimental effects of being a fussy or 'faddy' eater as an infant or toddler is limited and conflicting.
There is relatively little evidence about whether parents' perceptions of their child being a 'fussy eater' really reflect differences in diet compared with other children. Some studies suggest that children who are faddy in their eating tend to eat less and more slowly, potentially suggesting lower energy intake. Carruth et al (1998), reported that "picky eaters" had lower dietary variety and diversity scores, the only study we are aware of examining such an association. The largest study published to date (Dubois et al, 2007a) based on 2103 Canadian children aged 2.5 to 4.5 years showed that 'picky' eaters consumed less energy, fat and protein compared with children whose parents reported no concerns about fussy eating. Parents are likely to worry that a fussy eating child will not grow sufficiently. However, supplementing a fussy child's diet with energy-dense foods could result in fussy eaters being more likely to develop obesity. Only a handful of studies have been performed to date examining the associations between faddy eating and growth. These primarily cross-sectional studies provide conflicting evidence , have not looked at height growth, suffer from small sample sizes and only one has been performed in the UK (Wright et al, 2007).
The only UK study was based on the Millenium cohort (Wright et al, 2007), reporting a prevalence of "faddy" eating (defined by a single question) of 8% among 445 children aged 30 months. These children were found to have gained less weight compared to those who weren't "faddy" eaters. Picky eaters in an Israeli study (Ekstein, 2010) were more likely to be underweight compared to a control group (total n=34). In a study of 240 Portugese children aged 3-13, Viana et al (2008) reported a small negative association with "food fussiness" (defined by combining multiple questions) and BMI. Finally, Gregory et al (2010) found no association between child food fussiness and BMI in their sample of 156 Australian 2-4 year olds.
In addition, the definition of a faddy eater varies across these studies and has been ascertained at different ages making comparisons difficult. Designating a child as a faddy eater has previously been based on one simple question (Wright et al, 2007; Mascola et al 2010) through to combining responses to at least 6 questions (Carruth et al, 1998; Gregory et al, 2010; Viana et al, 2008). It is not clear what the impact of using different definitions is on the associations between picky eating, diet, and growth. Furthermore, no studies (to our knowledge) have explored whether picky eaters in early childhood are likely to remain picky eaters throughout childhood.
The aim of the current study is to use a large UK-based prospective cohort study to fill the gaps in the literature by a) examining different ways of defining faddy eaters; b) assessing the persistence of faddy eating throughout childhood; c) determining whether faddy eaters (particularly during toddlerhood when it causes the most concern) consume a balanced diet or not by examining associations with dietary intake and in particular with the Variety index; d ) determine any associations between being a faddy eater in infancy/toddlerhood and 1) height, weight and BMI growth throughout childhood and 2) early adult body composition, BP and other cardiovascular risk factors.
References
BR Carruth, J Skinner, K Houck, J Moran, F ColettaD Ott. The phenomenon of "Picky eater": A behavioural marker in eating patterns of toddlers. J Am Coll Nutr 1998; 17: 180-186.
TM Dovey, PA Staples, EL Gibson, JCG Halford. Food neophobia and 'picky/fussy' eating in children: A review. Appetite 2008: 50; 181-193.
L Dubois, AP Farmer, M Girard, K Peterson. Preschool children's eating behaviours are related to dietary adequacy and body weight. Eur J Clin Nutr 2007a; 61: 846-855.
L Dubois, A Farmer, M Girard, K Peterson, F Tatone-Tokuda. Problem eating behaviours related to social factors and body weight in preschool children: A longitudinal study. Int J Behav Nutr and Phys Act 2007b; 4: 9.
S Ekstein, D Laniado, B Glick. Does picky eating affect weight-for-length measurements in young children? Clin Pediatr 2010; 49: 217-220.
JE Gregory, SJ Paxton, AM Brozovic. Maternal feeding practises, child eating behaviour and body mass index in preschool-aged children: a prospective analysis. Int J Behav Nutr and Phys Act 2010; 7: 55.
AJ Mascola, W Bryson, WS Agras. Picky eating during childhood: A longitudinal study to age 11 years. Eating Behaviours 2010; 11: 253-257.
V Viana, S Sinde, JC Saxton. Children's eating behaviour questionnaire: associations with BMI in Portugese children. Br J Nutr 2008; 100:445-450.
CM Wright, KN Parkinson, D Shipton, RF Drewett. How do toddler eating problems relate to their eating behaviour, food preferences and growth? Pediatrics 2007; 120: e1069.
B1430 - Is variation in genes encoding methylarginine metabolising enzymes associated with endothelial dysfunction - 30/08/2012
Circulating concentrations of asymmetric dimethylarginine (ADMA) strongly predict CV outcomes [1] however causal relationships have been difficult to establish. Local regulation of ADMA levels is achieved through metabolism by three enzymes dependent on the tissue and cell type: the two isoforms of dimethylarginine dimethylaminohydrolase (DDAH) and the poorly characterised enzyme alanine-glyoxylate aminotransferase-2 (AGXT2, which is mainly restricted to kidney).
Recent reports demonstrate a low-frequency coding polymorphism (V140I) in the AGXT2 gene alters urinary metabolite levels [2]. I have now demonstrated that AGXT2 plays a physiological role in ADMA metabolism by phenotyping AGXT2 null mice and quantifying the association between kidney tissue AGXT2 expression and circulating methylarginines in human transplant recipients (accounting for ~20% of ADMA variation). Furthermore I have shown disruption of the AGXT2 gene leads to hypertension in mice and, by association with V140I, diastolic blood pressure in humans [3]. The increased circulating ADMA observed with AGXT2 disruption may lead to hypertension by inhibiting production of endothelial-derived nitric oxide (NO) with consequent endothelial dysfunction.However local NO production has also been shown to inhibit renal sodium reabsorption along the nephron [4] and this is an alternative mechanism by which AGXT2 disruption might mediate hypertension.
As part of an intermediate fellowship application, which includes a number of epidemiological, human physiological and animal studies, I plan to examine the mechanisms underlying AGXT2 mediated hypertension. Specifically I hope to examine in collaboration with Professor John Deanfield the association between V140I and flow-mediated dilatation in humans to investigate if AGXT2 disruption leads to hypertension through endothelial dysfunction in humans.
Hypothesis: Functional variants in methylarginine metabolising enzymes are associated with impaired endothelial function
Exposure: Common variation in the genes encoding AGXT2 (and DDAH1) at loci previously reported to associate with an intermediate phenotype e.g. rs37369 (AGXT2 - V140I).
Outcomes: Per cent flow mediated dilatation (%FMD).
Adjustment for confunding by ethnicity (reported ethnic origin).
Power calculations: There will be 90% (alpha=0.05)power to detect an absolute difference of 0.5% in percent FMD (mean FMD: 8%; SD:4%) in those carrying the minor alle (assuming a MAF 0.1) at rs37369.
The opportunity to investigate the mechanism underlying the ADMA-CVD relationship using genetic tools will help us to establish the causal relationships in this area. Results of these studies may lead to the development of biomarkers for risk prediction as well as the potential to develop strategies to target this pathway.
1. Aucella, F., et al., Methylarginines and mortality in patients with end stage renal disease: A prospective cohort study. Atherosclerosis, 2009. 207(2): p. 541-545.
2. Suhre, K., et al., A genome-wide association study of metabolic traits in human urine. Nature Genetics, 2011. 43(6): p. 565-9.
3. Caplin, B., et al., Alaninie-Glyoxylate Aminotransferase-2 Metabolises Endogenous Methylarginines, Regulates Nitric Oxide and Controls Blood Pressure. . Arterioscler Thromb Vasc Biol, In Revision.
4. Garvin, J.L., M. Herrera, and P.A. Ortiz, Regulation of renal NaCl transport by nitric oxide, endothelin, and ATP: clinical implications. Annual review of physiology, 2011. 73: p. 359-76.
B1428 - Relationship between primary tooth development and adolescent bone outcomes - 30/08/2012
Not much is known about the relationship between teeth and other growth and developmental processes. To date research has looked into the relationships beween teeth, skeletal development and height. A close associated was found to exist between the classification stages of mandibular cainines and skeletal maturity(1). Other studies have also looked into the correlation between dental development calculated from the number of permanent teeth and other measures of other somatic maturation including age of menarche (r=0.59) (2). Furthermore research by Filipson et al has looked at the relationhsip between dental age and growth trajectories. Results indicated an increase in the difference in time between sexual and dental maturation signifies a greater remaining height growth(2).
Many developmental processes share common pathways and teeth are no different. Prior to eruption process mononuclear cells move to the dental follicle and fuse to produce osteoclasts. Osteoclasts resorb alveolar bone and form an eruption pathway (3). Molecules involved in this process and signalling cascades have been studied using rodent models. Three molecules play a pivotal role in osteoclast formation; Receptor activator of nuclear factor-6B ligand (RANKL), osteoprotegerin (OPG) and CSF-1. RANKL promotes formation of osteoclasts (3). OPG inhibits the function of RANKL and osteoclast differentiation. OPG is expressed in the dental follicle in rats'; however expression of this molecule is reduced when incubated with CSF-1. This inhibition and the cell-cell signalling of RANKL from the alveolar bone enable osteoclast formation leading to an eruption process (3). Genes involved in this eruption process (RANKL/OPG) as also closely associated with bone turnover and bone mineral density.
3. Hypothsis
There is a relationship between tooth development and other aspects of development and bone phenotypes.
4. Aims:
We aim to investigate the relationship between tooth phenotypes and bone outcomes. In doing so we need to take into account any confounders and mediators. We then aim to use results from a previous genome-wide-association of tooth development to conduct some instrumental variable analysis using previosly identified genetic predictors of tooth development and bone outcomes.
Exposure variables:
Number of teeth (15 and 24 months)
Age first tooth at (15 months)
Any milk teeth fallen out (64 months)
Number of milk teeth fallen (64 and 78 months)
Genotype for 'Age at first tooth' (from previous GWAS to use as an instrumental variable)
Genotype for 'number of teeth''(from previous GWAS to use as an instrumental variable)
Outcome variables:
HipDXA (17 years): Standardised hip outcomes individually error corrected:
Femoral neck BMD
Total hip BMD
Minimum neck width
Cross-sectional area
Cross-sectional moment of inertia
Cortical Thickness
Minimum Section Modulus
Maximum buckling ratio
pQCT (17 years): Standardised outcomes individually error corrected:
Cortical area
Cortical content
Cortical density
Periosteal Circumference
Cortical thickness
Endosteal circumference
Confounding variables:
Height
Weight
Total body fat mass
Total body lean mass
BMI
Insulin
Pubic Hair
References:
1. Chertkow S, Tooth mineralization as an indicator os the pubertal growth spurt. American Journal of Orthodontics 7, 79-91 (1980)
2. Filipsson R and Hall K, Correlation between dental maturity, height development and sexual maturation in normal girls. Annals of Human Biology 3, 205-210 (1976)
3. Wise,G.E., Frazier-Bowers,S., & R.N.D'Souza Cellular, Molecular, and Genetic Determinants of Tooth Eruption. Critical Reviews in Oral Biology & Medicine 13, 323-335 (2002).
B1427 - The association between inter-pregnancy interval gestational weight gain and later cardiovascular health - 30/08/2012
Background:
Pregnancy represents a metabolic challenge to women - in a normal pregnancy, a woman will become relatively insulin resistant, hyperlipidaemic, and have an up-regulation of coagulation factors and the inflammatory cascade (Sattar) in order to support the pregnancy. Most (Green et al.; Ness et al.; Parikh), but not all (Steenland) studies have found a positive association between parity and CVD in later life, suggesting that the more pregnancies a women has in her lifetime, the greater her risk of cardiovascular disease. However the mechanisms underlying this association are poorly understood. One possibility is that the stress test of pregnancy has long term effects and indeed there is some evidence to support this.
To our knowledge, no study has examined the association of inter-pregnancy interval and later cardiovascular health. It is possible that exposure to multiple pregnancies within a short time frame may be a risk factor for later cardiovascular health, since the woman's metabolic system would have less time to recover to its usual state between pregnancies.
Shorter inter-pregnancy interval may also be associated with greater gestational weight gain, which in ALSPAC has been shown to be associated with adverse outcomes in both the mother and offspring (Fraser 2010; Fraser 2011).
Aims:
To examine the association of inter-pregnancy interval with i) gestational weight gain, and ii) ii) cIMT, arterial stiffness and cardiovascular risk factors measured approximately 17 years postpartum in the mothers of the Avon Longitudinal Study of Parents and Children (ALSPAC).
Exposure variable:
Inter-pregnancy interval (analysis restricted to women who have 2 offspring in order to remove confounding by parity)
Outcome variables:
1. Gestational weight gain - as modelled using linear spline multilevel models by Kate Tilling and used in previous publications (Fraser 2010; Fraser 2011). Since these data are only available for the index child of ALSPAC, these analysis will be restricted to women where the ALSPAC index child is their second child, and they have no further children. The association between inter-pregnancy interval and gestational weight gain will be assessed by fitting an interaction between a categorised indicator of inter-pregnancy interval (short, medium (reference), long) and gestational age in the multilevel model of gestational weight gain
2. ii) cIMT, arterial stiffness and Cardiovascular risk factors measured at the 'focus on mothers' clinic 18 years postpartum. Detailed measures to be examined are the calculated 10-year risk of CVD based on the Framingham risk score, cIMT, arterial stiffness, BMI, waist circumference, systolic and diastolic blood pressure, glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, insulin, pro-insulin, triglycerides, and C-reactive protein. Linear or logistic regression will be used to assess the association between interpregnancy interval and each outcome.
Confounding variables:
Maternal age at delivery and parity were obtained from obstetric records. Information on further pregnancies was obtained from various questionnaires. Information on prepregnancy weight and height, maternal smoking in pregnancy, maternal education, and household social class was based on questionnaire responses. Maternal education was categorized as below or above university level. The highest parental occupation was used to allocate the children to family social class groups (classes I [professional/managerial] to V [unskilled manual workers], according to the 1991 British Office of Population and Census Statistics classification). Maternal smoking in pregnancy was categorized as follows: never smoked; smoked before pregnancy or in the first trimester and then stopped; and smoked throughout pregnancy.
Additional relevant variables:
Information on diabetes mellitus and CVD diagnosed during follow-up was collected by a questionnaire completed 18 years after the index pregnancy. Women reported having been told they had a heart attack, heart failure, angina, and/or stroke.
References:
Fraser A, Tilling K, Macdonald-Wallis C, Sattar N, Brion MJ, Benfield L, Ness A, Deanfield J, Hingorani A, Nelson SM, Smith GD, Lawlor DA (2010). Association of maternal weight gain in pregnancy with offspring obesity and metabolic and vascular traits in childhood. Circulation;121:2557-64
Fraser A, Tilling K, Macdonald-Wallis C, Hughes R, Sattar N, Nelson SM, Lawlor DA. (2011) Associations of gestational weight gain with maternal body mass index, waist circumference, and blood pressure measured 16 y after pregnancy: the Avon Longitudinal Study of Parents and Children (ALSPAC); Am J Clin Nutr. 93: 1285-92
Green A, Beral V, Moser K (1988) Mortality in women in relation to their childbearing history. BMJ; 297: 391-395.
Ness RB, Harris T, Cobb J, Flegal KM, Kelsey JL, et al. (1993) Number of pregnancies and the subsequent risk of cardiovascular disease. N Engl J Med; 328: 1528-1533.
Parikh NI, Cnattingius S, Dickman PW, Mittleman MA, Ludvigsson JF, et al. (2010) Parity and risk of later-life maternal cardiovascular disease. American Heart Journal; 159: 215-221
Sattar, N. (2004) Do pregnancy complications and CVD share common antecedents? Atherosclerosis Supplements; 5: 3-7
Steenland K, Lally C, Thun M (1996) Parity and coronary heart disease among women in the American Cancer Society CPS II population. Epidemiology; 7: 641-643.
B1426 - Predictors of chronic fatigue in children age 18-19 years - 30/08/2012
Aims: Our aim is to investigate whether fatigue in 18 year-old children in the ALSPAC cohort is predicted by a range of factors, including fatigue at younger ages, exercise, and obesity. We also aim to investigate the relationship between fatigue, pain and depression in young adulthood, to explore groups of symptoms in fatigued children, and to investigate the relationship between fatigue and disability.
Hypotheses: Childhood fatigue and lifestyle factors may be associated with chronic disabling fatigue at age 18-19 years.
Exposure variables: body mass index; physical activity (questionnaire and accelerometer data); chronic fatigue at at 13-14 years (defined as fatigue that had prevented school attendance or participation in activities and that was not due to sport, possible sleep apnoea or probable depression [Crawley E, Hughes R, Northstone K, Tilling K, Emond A, Sterne JA. Pediatrics. 2012;130(1):e71-9]) and 15-16 years (defined using an appropriate threshold on the Chalder Fatigue Scale).
Outcome variables: We will identify ALSPAC 18 year-olds with high levels of fatigue using responses to fatigue and energy levels questions asked in the CIS-R questionnaire which was included in TF4. We will investigate the relationship between fatigue and disability (physical function) using responses to the SF-36 inventory which formed part of the "Your Changing Life" questionnaire and between fatigue, disability and depression (using responses to the MFQ questionnaire).
Confounding variables: family adversity.
B1425 - Predictors of chronic fatigue in children age 16-17 years - 30/08/2012
Aims: Few studies have investigated the aetiology of chronic disabling fatigue in children. Our aim is to investigate whether chronic disabling fatigue in 16 year-old children in the ALSPAC cohort is predicted by a range of factors, including maternal psychopathology, childhood psychological problems, exercise, and obesity.
Hypotheses: Maternal and childhood psychological, environmental and lifestyle factors may be associated with chronic disabling fatigue at age 16-17 years.
Exposure variables: episodes of maternal anxiety (Crown-Crisp Experiential Index anxiety subscale score greater than 8) and depression (Edinburgh Depression Scale score greater than 12) occurring at up to multiple time points since pregnancy; childhood psychological problems (derived from the 'Development and Well-Being Assessment' (DAWBA) questionnaire) at age 15 years; body mass index; and physical activity (questionnaire and accelerometer data); chronic fatigue at at 13-14 years (defined as fatigue that had prevented school attendance or participation in activities and that was not due to sport, possible sleep apnoea or probable depression [Crawley E, Hughes R, Northstone K, Tilling K, Emond A, Sterne JA. Pediatrics. 2012;130(1):e71-9]).
Outcome variables: We will identify ALSPAC 16 year-olds with high levels of fatigue using an appropriate threshold on the Chalder Fatigue Scale. This questionnaire was included in TF3. The threshold will be defined by looking at Chalder Fatigue scores in children diagnosed with CFS/ME according to National Insititute for Health & Clinical Excellence (NICE) diagnostic criteria using data from a cohort of paediatric patients which is maintained in the Centre for Child & Adolescent Health.
Confounding variables: family adversity
B1424 - Predictors of chronic fatigue in children age 13-14 years - 30/08/2012
Aims: Few studies have investigated the aetiology of chronic disabling fatigue in children. Our aim is to investigate whether chronic disabling fatigue in 13 year-old children in the ALSPAC cohort is predicted by a range of factors, including maternal psychopathology, childhood psychological problems, adverse life events, behavioural problems, exercise, obesity, and sleep patterns.
Hypotheses: Maternal and childhood psychological, environmental and lifestyle factors may be associated with chronic disabling fatigue at age 13-14 years.
Exposure variables: episodes of maternal anxiety (Crown-Crisp Experiential Index anxiety subscale score greater than 8) and depression (Edinburgh Depression Scale score greater than 12) occurring at up to 10 time points between pregnancy and child age 9-10 years; childhood psychological problems (derived from the 'Development and Well-Being Assessment' (DAWBA) questionnaire), upsetting events, and behavioural problems (from the 'Strengths and Difficulties Questionnaire' (SDQ)) occurring at age 7-9 years; sleep patterns at 8 timepoints; body mass index; diet (food frequency questionnaire and dietary diaries); and physical activity (questionnaire and accelerometer data).
Outcome variables: We have identified 117 & 53 ALSPAC 13 year-olds with disabling fatigue of >=3 and >=6 months duration, respectively, defined as fatigue that had prevented school attendance or participation in activities and that was not due to sport, possible sleep apnoea or probable depression [Crawley E, Hughes R, Northstone K, Tilling K, Emond A, Sterne JA. Pediatrics. 2012;130(1):e71-9].
Confounding variables: family adversity.
B1437 - Association of polymorphisms in 1L1RL1 pathway and asthma in children - 30/08/2012
Our aim is to replicate the analyses of the PIAMA birth cohort in the ALSPAC birth cohort. We hypothesise that the association of the IL1RL1 pathway with asthma, the number of eosinophils and the wheezing phenotypes can be replicated in the ALSPAC birth cohort. Therefore, we would like to study the association of SNPs of the IR1RL1 pathway (genes IL33, IL1RL1, IL1RAcP, MYD88, TIRAP, IRAK1, IRAK4 and TRAF6) with a doctor diagnosis of asthma in childhood, with the number of eosinophils in childhood with wheezing phenotypes in childhood in the ALSPAC birth cohort. Furthermore, we would like to study 10 selected interactions of SNPs in the IL1RL1 pathway and the presence of a doctor diagnosis of asthma in childhood.
B1435 - Febrile seizures GWA study - 30/08/2012
Aims: To identify genetic variants associated with febrile seizures in early childhood.
Hypothesis: Febrile seizures in childhood is linked to specific genetic polymorphisms
A discovery GWAS will be performed on genetic data from 8 different birth cohort studies, all paticipants of the EAGLE consortium. Results from this will be used in a meta-analysis association study.
The study is in line with several recent meta-GWAS studies that also included ALSPAC data.
Due to limited ressources in the ALSPAC group usually working on meta-GWAS projects, the applicants will assist in the data preparation and calculations on site.
Variables: Data from the already performed GWAS will be linked to questionaire data. The outcome variable will be occurrence of febrile seizures from age 3 months to 5 years as a dichotomous variable. This phenotype will be validated in details with the help from Prof. Jean Golding and Prof. Christopher Verity from ALSPAC. Additional information on sex of the child will be included in the analysis.
B1434 - Describing habitual levels of physical activity PA in older people in terms of impacts and how this relates to bone and other systems DUPLICATE OF B1372 - 30/08/2012
Hip fracture is a major cause of morbidity in older people, leading to loss of independence, and a huge burden economically in terms of healthcare costs. Osteoporosis, defined as reduction in bone mineral density (BMD) and bone strength, is strongly related to hip fracture risk. Several mechanisms contribute to the age-related decline in BMD and bone strength, including an age-related decline in the intensity and amount of PA, leading to a reduction in the quality and quantity of mechanical strains (defined as deformation of bone per unit length in relation to loading). For example, less than 30% of 65-74 year-olds and 15% of adults aged 75 and over report any moderate intensity PA lasting at least ten minutes in the previous four weeks (Craig R et al, 2009). These low levels of PA have numerous adverse health outcomes, reflecting the importance of physical activity for a range of systems including bone, leading to initiatives to co-ordinate existing activity promotion schemes (http://www.ageuk.org.uk).
B1433 - Cannabis misuse and adverse outcomes for young people - 30/08/2012
Aims: Understanding the range of risk factors and outcomes associated with cannabis use by young people, specifically young teenagers.
Research questions:
(1) What are the health, educational and social harms associated with cannabis use amongst under 18 year olds? To what extent can causality be established?
(2) What are the long-term health and social harms associated with cannabis use amongst young people?
(3) What is the relationship between young people's cannabis use and subsequent drug use and dependency?
(4) Does the amount of cannabis use (frequency or quantity) increase the harms or risk of harm experienced?
(5) Does the age of onset of use increase the harms or risk of harm experienced?
(6) What are the main risk factors for cannabis use and heavy cannabis use amongst young people?
(7) What are the typical trajectories of cannabis use as young people move out of adolescence?
The analysis will use univariate and multivariate methods. The key challenge has been to identify longitudinal datasets that will deliver robust evidence in terms of the scope of the data and the number of cases available.
A literature review and analysis of the Offending, Crime and Justice survey (OCJS) have already been completed. This has provided context and evidence of antecendents and outcomes for three groups: non-canabis users, early cannabis users (first tried cannabis aged 15 or younger) or late cannabis users (first tried cannabis aged 16 or older). Analysing the ALSPAC data is the final strand of the project. It is intended that ALSPAC will provide a more detailed insight into the first two of the three groups listed, more specifically educational, social adjustment and detailed information on cannabis and other substance use (including parental).
B1432 - Genetic determinants of white matter disease in preterm infants and impact on neurodevelopmental outcome - 30/08/2012
Aims: To clarify the genetic component of white matter disease and outcome in preterm infants, by exploring genetic variation and correlation with imaging endophenotype and neurodevelopmental phenotype.
Hypothesis: Genetic variation in preterm infants correlates with white matter damage and developmental outcome at age 2.
Exposure variable: Preterm birth less than 37 weeks Gestational Age
Outcome variables:
1. Whole organism - Bayley III neurodevelopmental assessment at age 2 years
2. Endophenotype - White matter damage assessed by quantitative MRI at term equivalent age (Tract Based Spatial Statistics TBSS and Deformation Based Morphometry DBM)
Confounding variables: Sex, birth weight, IUGR, days of assisted ventilation, antenatal steroids, culture positive sepsis, chronic lung disease
Background: More than 1 in 10 babies worldwide are born prematurely, and 40-50% of children with birthweight less than 1000g have neurodevelopmental problems (Marlow et al. 2005). Rates of prematurity are increasing worldwide (WHO 2012) and the predominant type of pathology now consists of diffuse white matter injury rather than florid parenchymal lesions such as cystic periventricular leukomalacia.
Infection, inflammation, chronic lung disease, gender and intra-uterine growth restriction have all been shown to impact brain structure and outcome but this does not fully account for the range of neurological outcomes for preterm babies with similar clinical features in similar environments. Individual susceptibility to injury might have a role to play, and be modulated by genetic factors. The most severe functional end-point of the various brain parenchyma pathologies remains cerebral palsy, a multi-factorial heterogeneous phenotype with a stable incidence despite changes in obstetric and neonatal practices (Stanley et al 2000). A genetic component for cerebral palsy has been suggested by previous case-control studies (Wu et al 2009, Hollegaard et al 2012) but studies tend to report conflicting results and have to contend with small sample sizes and other methodological issues.
The ALSPAC dataset will allow us to overcome some of these hurdles by starting with a large cohort and further increasing power by focusing on biological pathways within those data rather than searching for individually significant SNPs. The documented clinical variables and outcome measures in the database provide the opportunity to account for confounders and assess the clinical significance of resulting gene candidates. Using our bioinformatics expertise we hope to apply novel pathway analysis methods to genome-wide data in order to uncover biological patterns. We aim to combine prior knowledge from three sources in order to select a final group of gene candidates and focus validation:
1. GWAS data (ALSPAC)
2. Biologically driven (neonatal mouse subplate co-expression patterns)
3. Literature evidence
MRI is a safe and non-invasive method of obtaining an intermediate measure of the brain substrate, and our perinatal neuroimaging group has extensive experience in this field. The data obtained by advanced neuroinformatics methods such as TBSS and DBM are quantitative endophenotypes that are known to relate to outcome in the preterm population (Boardman et al 2010, van Kooij et al 2012). We will use advanced bioinformatics strategies to integrate the genetic and imaging datasets, both large amounts of data that can be more informative in conjunction than separately. This will then guide validation in our cohort by correlation of gene variation and linked quantitative imaging, thus opening a window on genetic effects on brain structure and function.
References
Marlow N, Wolke D, Bracewell MA, Samara M, N Engl J Med, 2005;352(1):9-19
Born Too Soon: The Global Action Report on Preterm Birth, WHO 2012-08-13
Stanley F, Blair E, Alberman E. MacKeith Press; 2000
Wu D, Yan-Feng Z, Xiao-Yan X, Li Y, Gong-Chun Z, Xi-Song B, Jiu-Lai T, Dev Med Child Neurol, 2011;53: 217-225
Hollegaard MV, Skogstrand K, Thorsen P, Norgaard-Pedersen B, Hougaard DM, Grove J, Hum Mutat, 2012; Epub ahead of print
van Kooij BJ, de Vries LS, Ball G, van Haastert, Benders MJ, Groenendaal F, Counsell SJ, AJNR Am J Neuroradiol, 2012; 33(1):188-94
Boardman JP, Craven C, Valappil S, Counsell SJ, Dyet LE, Rueckert D, Aljabar P, Rutherford MA, Chew AT, Allsop JM, Cowan F, Edwards AD, Neuroimage, 2010; 52(2): 409-14
B1431 - Detecting and modelling selection in developmental lifecourse and ageing-related genes - 30/08/2012
Aims:
To execute a thorough analysis on evidence of selection acting on a suite of developmental, lifecourse and ageing-related genes.
Hypotheses:
This is an exploratory analysis and we are not testing any pre-defined hypotheses per se.
Methods:
We would like to use ALSPAC GWAS and imputed data as part of a project investigating the evolution of certain genes linked with development (LIN28B and KCNJ2) and ageing diseases (APOE), as well as other genes of interest in TLD's thesis (CHRNA5, SERPINA1). We will use various established approaches to conduct a rounded analysis for each genic region concerning whether there is evidence that selection has acted. We then plan to use the information gained from the selection detection phase, in addition to data from the 1000 Genomes Project, to model the action of selection via simulation approaches and tailor-made, informed, hypothetical selection models, to gain further insight into the values of key parameters. The selection detection stage will include calculation of haplotype-based statistics (e.g. iHS (Voight et al. (2006))) and the use of likelihood-based methods (Nielsen et al.(2009)), in addition to comparative population statistics (using 1000 Genomes data). Other selection detection methods may be incorporated as the analysis evolves. We request all genetic data for each named gene within 5mb of the gene start position, all genetic data between the gene start and stop positions, and all data within 5mb of the gene end position. (These surrounding regions may also harbour interesting signatures of selection). It is important to understand the mechanisms of selection acting on a locus. For example, in the case of APOE this has been studied in depth in the literature for the E2/3/4 haplotype (Fullerton et al.(2000), Drenos and Kirkwood (2010)). As yet, there is not a definitive conclusion for the evolutionary history of these alleles. We hope that by combining all available methods from the literature and by implementing these methods on a combination of different data sets, we will reach a more concrete conclusion for the APOE gene. In addition, we would like to use the information produced from the selection detection phase to run different models of selection for these genes and use Approximate Bayesian Computation methodology (Itan et al. (2009)) to calibrate the models with parameter estimations.
We would like to run this analysis (selection detection + model selection) on a suite of different genes which have been associated with developmental phenotypes (LIN28B - age at menarche (Perry et al(2009)), KCNJ2 - age at first tooth eruption (Pillas et al.(2010))) and ageing phenotypes (APOE - Alzheimer Disease), in addition to two genes with implications across the lifecourse (CHRNA5 - nicotine dependence, SERPINA1 - alpha 1-antitrypsin deficiency). The Proximal 14q32.1 SERPIN subcluster, for example, has been studied for evidence of selection (Seixas et al.(2007)) previously. The authors found evidence to suggest that a deletion in SERPINA2 has been positively selected in Africans. Despite having been studied in the past, we would still like to consider selection in and around SERPINA1 as our proposed work could benefit from larger sample sizes and the incorporation of other selection detection techniques, in addition to the benefits of using 1000 Genomes datasets. Additionally, we would like to computationally model selection signals to gain further insight. We would eventually like to execute a high level comparison of positive selection acting on early-acting versus late-acting traits (e.g. LIN28B/KCNJ2 vs APOE).
Note: exposure/outcome/confounding variables are not defined in this project
References
Stephanie M. Fullerton, et al., Apolipoprotein E Variation at the Sequence Haplotype Level: Implications for the Origin and Maintenance of a Major Human Polymorphism. American journal of human genetics,2000. 67(4): p. 881-900
Fotios Drenos and Thomas B. L. Kirkwood, Selection on Alleles Affecting Human Longevity and Late-Life Disease: The Example of Apolipoprotein E. PLoS ONE, 2010. 5(4): p. e10022.
Benjamin F. Voight, et al., A Map of Recent Positive Selection in the Human Genome. PLoS Biol, 2006. 4(3): p. e72.
Rasmus Nielsen, et al., Genomic scans for selective sweeps using SNP data. Genome Research, 2005. 15(11): p. 1566-1575.
Yuval Itan, et al., The Origins of Lactase Persistence in Europe. PLoS Comput Biol, 2009. 5(8): p. e1000491.
John R. B. Perry, et al., Meta-analysis of genome-wide association data identifies two loci influencing age at menarche. Nat Genet, 2009. 41(6): p. 648-650.John R. B. Perry, et al., Meta-analysis of genome-wide association data identifies two loci influencing age at menarche. Nat Genet, 2009. 41(6): p. 648-650.
Demetris Pillas, et al., Genome-Wide Association Study Reveals Multiple Loci Associated with Primary Tooth Development during Infancy. PLoS Genet, 2010. 6(2): p. e1000856
Susana Seixas, et al., Sequence Diversity at the Proximal 14q32.1 SERPIN Subcluster: Evidence for Natural Selection Favoring the Pseudogenization of SERPINA2. Molecular Biology and Evolution, 2007. 24(2): p. 587-598.