Biology subjects

Howe, L. D.

Publications and source records attributed to Howe, L. D..

12 recordsLinked to original sources

bioRxiv2020

Exploring the causal effects of genetic liability to ADHD and Autism on Alzheimer's disease

BackgroundThere are very few studies investigating possible links between Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and Alzheimers disease and these have been limited by small sample sizes, diagnostic and recall bias. However, neurocognitive deficits affecting educational attainment in individuals with ADHD could be risk factors for Alzheimers later in life while hyper plasticity of the brain in ASD and strong positive genetic correlations of ASD with IQ and educational attainment could be protective against Alzheimers. MethodsWe estimated the bidirectional total causal effects of genetic liability to ADHD and ASD on Alzheimers disease through two-sample Mendelian randomization. We investigated their direct effects, independent of educational attainment and IQ, through Multivariable Mendelian randomization. ResultsThere was limited evidence to suggest that genetic liability to ADHD (OR=1.00, 95% CI: 0.98 to 1.02, p=0.39) or ASD (OR=0.99, 95% CI: 0.97 to 1.01, p=0.70) was associated with risk of Alzheimers disease. Similar causal effect estimates were identified when the direct effects, independent of educational attainment (ADHD: OR=1.00, 95% CI: 0.99 to 1.01, p=0.07; ASD: OR=0.99, 95% CI: 0.98 to 1.00, p=0.28) and IQ (ADHD: OR=1.00, 95% CI: 0.99 to 1.02. p=0.29; ASD: OR=0.99, 95% CI: 0.98 to 1.01, p=0.99), were assessed. Finally, genetic liability to Alzheimers disease was not found to have a causal effect on risk of ADHD or ASD (ADHD: OR=1.12, 95% CI: 0.86 to 1.44, p=0.37; ASD: OR=1.19, 95% CI: 0.94 to 1.51, p=0.14). ConclusionsIn the first study to date investigating the causal associations between genetic liability to ADHD, ASD and Alzheimers, within an MR framework, we found limited evidence to suggest a causal effect. It is important to encourage future research using ADHD and ASD specific subtype data, as well as longitudinal data in order to further elucidate any associations between these conditions.

genetics↗

bioRxiv2020

Is genetic liability to ADHD and ASD causally linked to educational attainment?

BackgroundIndividuals with Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD) are at risk of poor educational outcomes. Parental educational attainment has also been associated with risk of ADHD/ASD in the offspring. Despite evidence that ADHD and ASD show genetic links to educational attainment, less is known on the causal nature of the associations and the possible role of IQ. MethodsWe assessed the total causal effects of genetic liability to ADHD/ASD on educational attainment using two-sample Mendelian randomization (MR). We assessed the possible contribution of IQ to the identified causal effects by estimating the "direct" effects of ADHD/ASD on educational attainment, independent of IQ, using Multivariable MR (MVMR). Reverse direction analyses were performed. The latest GWAS meta-analyses of ADHD, ASD, educational attainment and IQ were used. Causal effect estimates were generated using inverse variance weighted models (IVW). Sensitivity analyses were performed to assess the robustness of the estimates and the presence of pleiotropy. ResultsGenetic liability to ADHD had a total (MRIVW:-3.3 months per doubling of liability to ADHD; 95%CI: -4.8 to -1.9; pval= 5*10-6) and direct negative causal effect on educational attainment (MVMRIVW:-1.6 months per doubling of liability to ADHD; 95%CI: -2.5 to -0.6; pval= 4*10-4). There was little evidence of a total causal effect of genetic liability to ASD on educational attainment (MRIVW: 4 days, per doubling of liability to ASD; 95%CI: -4.9 months to 5.6 months; pval= 0.9) but some evidence of a direct effect not via IQ (MVMRIVW:29 days per doubling the genetic liability to ASD; 95%CI: 2 to 48; pval= 0.03). Reverse direction analyses suggested that genetic liability to higher educational attainment was associated with lower risk of ADHD (MRIVWOR: 0.3 per standard deviation (SD) increase; 95%CI: 0.26 to 0.36; pval= 6*10-51), even after IQ was entered in the models (MVMRIVWOR: 0.33 per SD increase; 95%CI: 0.26 to 0.43; pval= 6*10-17). On the contrary, there was evidence consistent with a positive causal effect of genetic liability to higher educational attainment on risk of ASD (MRIVWOR: 1.51 per SD increase; 95%CI: 1.29 to 1.77; pval= 4*10-7), which was found to be largely explained by IQ (MVMRIVWOR per SD increase: 1.24; 95%CI: 0.96 to 1.60; pval= 0.09). ConclusionsOur findings suggest that despite the genetic and phenotypic overlap between ADHD and ASD, they present highly differentiated causal associations with educational attainment. This highlights the necessity for specialized educational interventions for children with ADHD and ASD. Further research is needed in order to decipher whether the identified causal effects reflect parentally transmitted effects, diagnostic masking, or selection bias.

genetics↗

bioRxiv2019

Mendelian randomisation for mediation analysis: current methods and challenges for implementation

Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Mediation analysis experiences a number of methodological difficulties, including bias due to confounding and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable Mendelian randomisation (MVMR) and two-step Mendelian randomisation. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although Mendelian randomisation relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our examples demonstrate that it is unlikely to be affected by confounders of the exposure or mediator and the outcome, reverse causality and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR, and can improve causal inference in mediation analysis.

genetics↗

bioRxiv2019

Age at period cessation and trajectories of cardiovascular risk factors across mid and later life: a prospective cohort study

What is already known about this topicAge at period cessation is associated with cardiovascular disease. Whether age at period cessation adversely affects change in conventional cardiovascular intermediates from mid to later life is not well understood.\n\nWhat this study addsWomen and clinicians concerned about the impact of type and timing of period cessation on conventional cardiovascular intermediates from midlife should be reassured that the impacts over the long term are small.\n\nAbstractO_ST_ABSObjectiveC_ST_ABSTo examine the association between age at period cessation (by type of period cessation) and trajectories of anthropometry, blood pressure, lipids and HBA1c from midlife.\n\nDesignProspective cohort study of women recruited to the Medical Research Council National Survey of Health and Development (NSHD).\n\nSettingPopulation based prospective cohort study.\n\nParticipantsWomen participating in NSHD with a known date of period cessation and at least one measure of each intermediate cardiovascular risk factor.\n\nExposuresAge at period cessation and type of period cessation (hysterectomy compared with natural menopause).\n\nOutcomesRepeated measures of systolic blood pressure, diastolic blood pressure, body mass index (BMI) and waist circumference (WC) from 36 to 69 years and repeated measures of triglyceride, low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and glycated haemoglobin (HBA1c) from 53 to 69 years.\n\nResultsWe found no evidence that age at period cessation was associated with trajectories of log triglyceride, LDL-c and HDL-c from 53 to 69 years and trajectories of blood pressure from 36 to 69 years, regardless of whether period cessation occurred naturally or due to hysterectomy. While we found some evidence of associations of age at period cessation with log BMI, log WC and log HBA1C, patterns were not consistent and differences were small at age 69 years, with confidence intervals that spanned the null. For instance, the difference in log WC at age 69 per year increase in age at natural menopause was 0.003 (95% confidence interval, -0.0002, 0.01) while the difference per year increase in age at hysterectomy was -0.002 (95% CI, -0.005, 0.001).\n\nConclusionHow and when women experience period cessation is unlikely to adversely affect conventional cardiovascular risk factors across mid and later life. Women and clinicians concerned about the impact of type and timing of period cessation on conventional cardiovascular intermediates from midlife should be reassured that the impacts over the long term are small.

epidemiology↗

bioRxiv2019

Is disrupted sleep a risk factor for Alzheimers disease? Evidence from a two-sample Mendelian randomization analysis

INTRODUCTIONIt is established that Alzheimers disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD.\n\nMETHODSMendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness) on AD risk.\n\nRESULTSOverall, there was little evidence that sleep traits affect the risk of AD. There was some evidence to suggest that self-reported daytime napping was associated with lower AD risk (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.50 to 0.99). Some other sleep traits (accelerometer-measured eveningness and sleep duration, and self-reported daytime sleepiness) had ORs for AD risk of a similar magnitude to daytime napping, but were less precisely estimated.\n\nDISCUSSONOur findings provide tentative evidence that daytime napping may reduce AD risk. However, findings should be replicated using independent samples.

epidemiology↗

bioRxiv2019

Epigenetic gestational age and trajectories of weight and height during childhood: a prospective cohort study

BackgroundDifferences between an individuals estimated epigenetic gestational age (EGA) and their actual gestational age (GA) are defined as gestational age acceleration (GAA). GAA is associated with increased birthweight and birth length. Whether these associations persist through childhood is yet to be investigated.\n\nMethodsWe examined the association between GAA and trajectories of height and weight from birth to 10 years (n=785) in a British birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). EGA of participants was estimated using DNA methylation data from cord blood using a recently-developed prediction model. GA of participants was gathered in ALSPAC from clinical records and was measured from last menstrual period (LMP) for most participants. GAA of participants, measured in weeks, was calculated as the residuals from a regression model of EGA on actual GA. Height and weight were obtained from several sources including birth records, research clinics, routine child health clinics, links to health visitor records and parent-reported measures from questionnaires. Analyses were performed using linear spline multilevel models and adjusted for maternal age, maternal pre-pregnancy BMI, maternal smoking during pregnancy and maternal education.\n\nResultsIn adjusted analyses, offspring with a one-week greater GAA were born on average 0.14 kg heavier (95% Confidence Interval (CI) 0.09, 0.19) and 0.55 cm taller (95% CI 0.33, 0.78) at birth. These differences in weight persisted up to approximately age 9 months but thereafter began to attenuate and reduce in magnitude. From age 5 years onwards, the association between GAA and weight reversed such that GAA was associated with lower weight and this association strengthened with age (mean difference at age 10 years -0.60 kg (95% CI, -1.19, -0.01)). Differences in height persisted only up to age 9 months (mean difference at 9 months 0.15 cm, (95% CI -0.09, 0.39)). From age 9 months to age 10 years, offspring with a one-week greater GAA were of comparable height to those with no GAA (mean difference at age 10 years -0.07 cm, (95% CI -0.64, 0.50)).\n\nConclusionsGestational age acceleration is associated with increased birth weight and length and these differences persist to age 9 months. From 5 years onwards, the association of GAA and weight reverses such that by age 10 years greater GAA is associated with lower childhood weight. Further work is required to examine whether the weight effects of GAA strengthen further through adolescence and into early adulthood.

genomics↗

bioRxiv2019

Puberty timing and adiposity change across childhood and adolescence: disentangling cause and consequence

ObjectiveTo better understand if earlier puberty is more likely a result of adiposity gain in childhood than a cause of adiposity gain in adulthood.\n\nDesignProspective birth cohort study.\n\nSettingPopulation based study of children born in 1991/1992 in Bristol UK (Avon Longitudinal Study of Parents and Children (ALSPAC)).\n\nParticipants4,186 participants (2,176 female and 1,990 male) of predominantly White ethnicity with 18,232 repeated measures throughout follow-up.\n\nExposures & outcomesRepeated measures of height from 5y to 20y to identify puberty timing (age at peak height velocity) and repeated measures of dual-energy X-ray absorptiometry-derived fat mass from age 9y to 18y, modelled separately in females and males using models based on chronological age and time before and after puberty onset.\n\nResultsMean age at peak height velocity was 11.7y (standard deviation (SD)=0.8y) for females and 13.6y (SD=0.9y) for males. In adjusted models of fat mass by chronological age, a one-year later age at peak height velocity was associated with 20.4% (95% Confidence Interval (CI): 18.5% to 22.3%) and 22.8% (95% (CI): 20.7% to 24.8%) lower fat mass in females and males respectively at 9y. These differences were smaller at age 18y: 7.8% (95% (CI):5.9% to 9.6%) and 11.9% (95% (CI): 9.1%, to 14.7%) lower fat mass in females and males respectively per year later age at peak height velocity. Trajectories of fat mass by time before and after puberty onset provided strong evidence for an association of pre-pubertal fat mass with puberty timing, and little evidence of an association of puberty timing with post-pubertal changes in fat mass in females. In males, findings were less clear before puberty though there was some evidence for an association of earlier puberty timing with great post-pubertal gain in fat mass.\n\nConclusionsEarlier puberty is more likely a result of adiposity gain in childhood than a cause of adiposity gain in adulthood in females. In males early to puberty, differences in fat mass after puberty are driven partially by tracking of adiposity from early childhood but also greater gains in post-pubertal adiposity. Reducing levels of childhood adiposity may help prevent both earlier puberty, later life adiposity and their associated adverse social, mental and physical health sequelae.

epidemiology↗

bioRxiv2019

Effects of body mass index on relationship status, social contact, and socioeconomic position: Mendelian Randomization study in UK Biobank

ObjectiveTo assess whether body mass index (BMI) has a causal effect on social and socioeconomic factors, including whether both high and low BMI can be detrimental.\n\nDesignMendelian Randomization, using genetic variants for BMI to obtain unconfounded estimates, and non-linear Mendelian Randomization.\n\nSettingUK Biobank.\n\nParticipants378,244 men and women of European ancestry, mean age 57 (SD 8 years).\n\nMain outcome measuresTownsend deprivation index, income, age completed full time education, degree level education, job class, employment status, cohabiting relationship status, participation in leisure and social activities, visits from friends and family, and having someone to confide in.\n\nResultsHigher BMI was causally associated with higher deprivation, lower income, fewer years of education, lower odds of degree-level education and skilled employment. For example, a 1 SD higher genetically-determined BMI (4.8kg/m2 in UK Biobank) was associated with {pound}1,660 less income per annum [95%CI: {pound}950, {pound}2,380]. Non-linear Mendelian Randomization provided evidence that both low BMI (bottom decile, <22kg/m2) and high BMI (top seven deciles, >24.6kg/m2) can increase deprivation and reduce income. In men only, higher BMI was related to lower participation in leisure and social activities. There was no evidence of causal effects of BMI on visits from friends and family or in having someone to confide in. Non-linear Mendelian Randomization analysis showed that low BMI (bottom three deciles, <23.5kg/m2) reduces the odds of cohabiting with a partner or spouse for men, whereas high BMI (top two deciles, >30.7kg/m2) reduces the odds of cohabitation with a partner or spouse for women.\n\nConclusionsBMI affects social and socioeconomic outcomes, with both high and low BMI being detrimental for some measures of SEP. This suggests that in addition to health benefits, maintaining healthy ranges of BMI across the population could have benefits both for individuals and society.

epidemiology↗

bioRxiv2018

What explains the effect of education on cardiovascular disease? Applying Mendelian randomization to identify the consequences of education inequality

Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the role of body mass index, systolic blood pressure and smoking in mediating the effect of education on cardiovascular disease risk?\n\nFindingWe find consistent evidence that body mass index, systolic blood pressure and smoking mediate the effect of education, explaining up to 18%, 27% and 33% respectively. Including all three risk factors in a model together explains around 40% of the effect of education.\n\nMeaningIntervening on body mass index, systolic blood pressure and smoking would lead to reductions in cases of CVD attributable to lower levels of education. Over half of the effect of education on risk of cardiovascular disease is not mediated through these risk factors.\n\nImportanceLower levels of education are causally related to higher cardiovascular risk, but the extent to which this is driven by modifiable risk factors also associated with education is unknown.\n\nObjectiveTo investigate the role of body mass index, systolic blood pressure and smoking in explaining the effect of education on risk of cardiovascular disease outcomes.\n\nDesignMultivariable regression analysis of observational data and Mendelian randomization (MR) analysis of genetic data.\n\nSettingUK Biobank and international genome-wide association study consortia.\n\nParticipantsPredominantly individuals of European ancestry.\n\nMain outcomes and measuresThe effects of education (per 1-standard deviation increase, equivalent to 3.6 years) on coronary heart disease, cardiovascular disease (all subtypes), myocardial infarction and stroke risk (all measured in odds ratio, OR), and the degree to which this is mediated through body mass index, systolic blood pressure and smoking.\n\nResultsEach additional standard deviation of education associated with 13% lower risk of coronary heart disease (OR 0.87, 95% confidence interval [CI] 0.84 to 0.89) in observational analysis and 37% lower risk (OR 0.63, 95% CI 0.60 to 0.67) in Mendelian randomization analysis. As a proportion of the total risk reduction, body mass index mediated 15% (95% CI 13% to 17%) and 18% (95% CI 14% to 23%) in the observational and Mendelian randomization estimates, respectively. Corresponding estimates for systolic blood pressure were 11% (95% CI 9% to 13%) and 21% (95% CI 15% to 27%), and for smoking, 19% (15% to 22%) and 33% (95% CI 17% to 49%). All three risk factors combined mediated 42% (95% CI 36% to 48%) and 36% (95 % CI 16% to 63%) of the effect of education on coronary heart disease in observational and Mendelian randomization respectively. Similar results were obtained when investigating risk of stroke, myocardial infarction and all-cause cardiovascular disease.\n\nConclusions and relevanceBMI, SBP and smoking mediate a substantial proportion of the protective effect of education on risk of cardiovascular outcomes and intervening on these would lead to reductions in cases of CVD attributable to lower levels of education. However, more than half of the protective effect of education remains unexplained and requires further investigation.

epidemiology↗

bioRxiv2018

Education, intelligence and Alzheimer’s disease: Evidence from a multivariable two-sample Mendelian randomization study

ObjectivesTo examine whether educational attainment and intelligence have causal effects on risk of Alzheimers disease (AD), independently of each other.\n\nDesignTwo-sample univariable and multivariable Mendelian Randomization (MR) to estimate the causal effects of education on intelligence and vice versa, and the total and independent causal effects of both education and intelligence on risk of AD.\n\nParticipants17,008 AD cases and 37,154 controls from the International Genomics of Alzheimers Project (IGAP) consortium\n\nMain outcome measureOdds ratio of AD per standardised deviation increase in years of schooling and intelligence\n\nResultsThere was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions. Similar overall effects were observed for both educational attainment and intelligence on AD risk in the univariable MR analysis; with each SD increase in years of schooling and intelligence, odds of AD were, on average, 37% (95% CI: 23% to 49%) and 35% (95% CI: 25% to 43%) lower, respectively. There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account, but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis.\n\nConclusionsThere is robust evidence for an independent, causal effect of intelligence in lowering AD risk, potentially supporting a role for cognitive training interventions to improve aspects of intelligence. However, given the observed causal effect of educational attainment on intelligence, there may also be support for policies aimed at increasing length of schooling to lower incidence of AD.

genetics↗

bioRxiv2017

The causal effect of educational attainment on Alzheimer’s disease: A two-sample Mendelian randomization study

BackgroundObservational evidence suggests that higher educational attainment is protective for Alzheimers disease (AD). It is unclear whether this association is causal or confounded by demographic and socioeconomic characteristics. We examined the causal effect of educational attainment on AD in a two-sample MR framework.\n\nMethodsWe extracted all available effect estimates of the 74 single nucleotide polymorphisms (SNPs) associated with years of schooling from the largest genome-wide association study (GWAS) of educational attainment (N=293,723) and the GWAS of AD conducted by the International Genomics of Alzheimers Project (n=17,008 AD cases and 37,154 controls). SNP-exposure and SNP-outcome coefficients were combined using an inverse variance weighted approach, providing an estimate of the causal effect of each SD increase in years of schooling on AD. We also performed appropriate sensitivity analyses examining the robustness of causal effect estimates to the various assumptions and conducted simulation analyses to examine potential survival bias of MR analyses.\n\nFindingsWith each SD increase in years of schooling (3.51 years), the odds of AD were, on average, reduced by approximately one third (odds ratio= 0.63, 95% confidence interval [CI]: 0.48 to 0.83, p<0.001). Causal effect estimates were consistent when using causal methods with varying MR assumptions or different sets of SNPs for educational attainment, lending confidence to the magnitude and direction of effect in our main findings. There was also no evidence of survival bias in our study.\n\nInterpretationOur findings support a causal role of educational attainment on AD, whereby an additional [~]3.5 years of schooling reduces the odds of AD by approximately one third.

epidemiology↗

bioRxiv2017

Assessing the causal role of body mass index on cardiovascular health in young adults: Mendelian randomization and recall-by-genotype analyses

BackgroundMendelian randomization (MR) studies of body mass index (BMI) and cardiovascular health in mid-to-late life suggest causal relationships, but the nature of these has not been explored systematically at younger ages. Using complementary MR and recall-by-genotype (RbG) methodologies, our objective was to estimate the causal effect of BMI on detailed measures of cardiovascular health in a population of young healthy adults.\n\nMethods and FindingsData from the Avon Longitudinal Study of Parents and Children were used. For MR analyses, a genetic risk score (GRS) comprising 97 independent single nucleotide polymorphisms (SNPs) and constructed using external weighting was used as an instrument to test the causal effect of each unit increase in BMI (kg/m2) on selected cardiovascular phenotypes measured at age 17 (N=7909). An independent enriched sample from the same cohort participated in a RbG study at age 21, which enabled more detailed cardiovascular phenotyping (N=418; 191/227 from the lower/upper [~]30% of a genome-wide GRS distribution predicting variation in BMI). The causal effect of BMI on the additional cardiovascular phenotypes was assessed by comparing the two recalled groups. Difference in mean BMI between RbG groups was 3.85kg/m2 (95% CI: 2.53, 4.63; P=6.09x1011). In both MR and RbG analyses, results indicated that higher BMI causes higher blood pressure (BP) and left ventricular mass (indexed to height2.7, LVMI) in young adults (e.g. difference in LVMI per kg/m2 using MR: 1.07g/m2.7; 95% CI: 0.62, 1.52; P=3.87x10-06 and per 3.58kg/m2 using RbG: 1.65g/m2.7 95% CI: 0.83, 2.47; P=0.0001). Additionally, RbG results indicated a causal role of higher BMI on higher stroke volume (SV; difference per 3.58kg/m2: 1.49ml/m2.04; 95% CI: 0.62, 2.35; P=0.001) and cardiac output (CO; difference per 3.58kg/m2: 0.11l /min/m1.83; 95% CI: 0.03, 0.19; P=0.01). Neither analysis supported a causal role of higher BMI on heart rate.\n\nConclusionsComplementary MR and RbG causal methodologies, together with a range of appropriate sensitivity analyses, showed that higher BMI is likely to cause worse cardiovascular health, specifically higher BP and LVMI, even in youth. These consistent results support efforts to prevent or reverse obesity in the young.

epidemiology↗