scholarly journals The Causal Effects of Health Conditions and Risk Factors on Social and Socioeconomic Outcomes: Mendelian Randomization in UK Biobank

2019 ◽  
Author(s):  
Sean Harrison ◽  
Alisha R Davies ◽  
Matt Dickson ◽  
Jessica Tyrrell ◽  
Michael J Green ◽  
...  
Keyword(s):  
Risk Factors ◽  
Household Income ◽  
Alcohol Intake ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Health Conditions ◽  
List Type ◽  
Uk Biobank ◽  
Socioeconomic Outcomes ◽  
University Degree

AbstractObjectivesTo estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy-makers and decision-makers allocate resources efficiently and effectively.DesignWe used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors (alcohol intake, body mass index [BMI], cholesterol, systolic blood pressure, smoking) on 19 social and socioeconomic outcomes.SettingUK Biobank.Participants337,009 men and women of white British ancestry, aged between 39 and 72 years.Main outcome measuresAnnual household income, employment, deprivation (measured by the Townsend deprivation index [TDI]), degree level education, happiness, loneliness, and 13 other social and socioeconomic outcomes.ResultsResults suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income (mean difference = −£24,394, 95% confidence interval (CI): −£33,403 to −£15,384), the chance of owning accommodation (absolute percentage change [APC] = −21.5%, 95% CI: −29.3% to −13.6%), being satisfied with health (APC = −32.4%, 95% CI: −48.9% to −15.8%), and of obtaining a university degree (APC = −73.8%, 95% CI: −90.7% to −56.9%), while also increasing deprivation (mean difference in TDI = 1.89, 95% CI: 1.13 to 2.64, approximately 236% of a decile of TDI). There was evidence that asthma increased deprivation and decreased both household income and the chance of obtaining a university degree, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null.ConclusionsHigher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.What is known?Studies have shown associations between poor health and adverse social (e.g. wellbeing, social contact) and socioeconomic (e.g. educational attainment, income, employment) outcomes, but there is also strong evidence that social and socioeconomic factors influence health.These bidirectional relationships make it difficult to establish whether health conditions and health risk factors have causal effects on social and socioeconomic outcomes.Mendelian randomization is a technique that uses genetic variants robustly related to an exposure of interest (here, health conditions and risk factors for poor health) as a proxy for the exposure.Since genetic variants are randomly allocated at conception, they tend to be unrelated to the factors that typically confound observational studies, and are less likely to suffer from reverse causality, making causal inference from Mendelian randomization analyses more plausible.What this study addsThis study suggests causal effects of higher BMI, smoking and alcohol use on a range of social and socioeconomic outcomes, implying that population-level improvements in these risk factors may, in addition to the well-known health benefits, have social and socioeconomic benefits for individuals and society.There was evidence that asthma increased deprivation, decreased household income and the chance of having a university degree, migraine reduced the chance of having a weekly leisure or social activity, especially in men, and depression increased loneliness and decreased happiness.There was little evidence for causal effects of cholesterol, systolic blood pressure or breast cancer on social and socioeconomic outcomes.

scholarly journals The causal effects of health conditions and risk factors on social and socioeconomic outcomes: Mendelian randomization in UK Biobank

2020 ◽  
Vol 49 (5) ◽  
pp. 1661-1681 ◽  
Author(s):  
Sean Harrison ◽  
Alisha R Davies ◽  
Matt Dickson ◽  
Jessica Tyrrell ◽  
Michael J Green ◽  
...  
Keyword(s):  
Risk Factors ◽  
Household Income ◽  
Alcohol Intake ◽  
Mendelian Randomization ◽  
Mean Difference ◽  
Causal Effects ◽  
Health Conditions ◽  
Uk Biobank ◽  
Socioeconomic Outcomes ◽  
University Degree

Abstract Background We aimed to estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy makers and decision makers allocate resources efficiently and effectively. Methods We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors [alcohol intake, body mass index (BMI), cholesterol, systolic blood pressure, smoking] on 19 social and socioeconomic outcomes in 336 997 men and women of White British ancestry in UK Biobank, aged between 39 and 72 years. Outcomes included annual household income, employment, deprivation [measured by the Townsend deprivation index (TDI)], degree-level education, happiness, loneliness and 13 other social and socioeconomic outcomes. Results Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income [mean difference = -£22 838, 95% confidence interval (CI): -£31 354 to -£14 321] and the chance of owning accommodation [absolute percentage change (APC) = -20.8%, 95% CI: -28.2% to -13.4%], of being satisfied with health (APC = -35.4%, 95% CI: -51.2% to -19.5%) and of obtaining a university degree (APC = -65.9%, 95% CI: -81.4% to -50.4%), while also increasing deprivation (mean difference in TDI = 1.73, 95% CI: 1.02 to 2.44, approximately 216% of a decile of TDI). There was evidence that asthma decreased household income, the chance of obtaining a university degree and the chance of cohabiting, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.

scholarly journals Estimated effects of health conditions and risk factors on social and socioeconomic outcomes: mendelian randomisation of UK Biobank data

The Lancet ◽  
2019 ◽  
Vol 394 ◽  
pp. S49
Author(s):  
Sean Harrison ◽  
Alisha R Davies ◽  
Matt Dickson ◽  
Jessica Tyrrell ◽  
Michael J Green ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Conditions ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Socioeconomic Outcomes

scholarly journals MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

2018 ◽  
Author(s):  
Louise A C Millard ◽  
Marcus R Munafò ◽  
Kate Tilling ◽  
Robyn E Wootton ◽  
George Davey Smith
Keyword(s):  
Lung Function ◽  
Detrimental Effect ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Uk Biobank ◽  
Facial Aging ◽  
Heavy Smoking ◽  
Never Smokers

AbstractMendelian randomization (MR) is an established approach for estimating the causal effect of an environmental exposure on a downstream outcome. The gene x environment (GxE) study design can be used within an MR framework to determine whether MR estimates may be biased if the genetic instrument affects the outcome through pathways other than via the exposure of interest (known as horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and a recently published tool (PHESANT) means that it is now possible to do this comprehensively, across thousands of traits in UK Biobank. In this study, we introduce the GxE MR-pheWAS approach, and search for the causal effects of smoking heaviness – stratifying on smoking status (ever versus never) – as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. If this effect is entirely mediated by tobacco intake, we would expect to see an effect in ever smokers but not never smokers. We used PHESANT to search for the causal effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by: 1) strength of effect of rs16969968 among ever smokers, and 2) strength of interaction between ever and never smokers. We replicated previously established causal effects of smoking heaviness, including a detrimental effect on lung function and pulse rate. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify causal effects of an exposure, while simultaneously assessing the extent that results may be biased by horizontal pleiotropy.Author summaryMendelian randomization uses genetic variants associated with an exposure to investigate causality. For instance, a genetic variant that relates to how heavily a person smokes has been used to test whether smoking causally affects health outcomes. Mendelian randomization is biased if the genetic variant also affects the outcome via other pathways. We exploit additional information – that the effect of heavy smoking only occurs in people who actually smoke – to overcome this problem. By testing associations in ever and never smokers separately we can assess whether the genetic variant affects an outcome via smoking or another pathway. If the effect is entirely via smoking heaviness, we would expect to see an effect in ever but not never smokers, and this would suggest that smoking causally influences the outcome. Previous Mendelian randomization studies of smoking heaviness focused on specific outcomes – here we searched for the causal effects of smoking heaviness across over 18,000 traits. We identified previously established effects (e.g. a detrimental effect on lung function) and novel results including a detrimental effect of heavier smoking on facial aging. Our approach can be used to search for the causal effects of other exposures, where the exposure only occurs in known subsets of the population.

scholarly journals CAUSES OF FRAILTY: GENETIC ANALYSIS IN 164,000 UK BIOBANK PARTICIPANTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S222-S222
Author(s):  
Janice Atkins ◽  
Luke C Pilling ◽  
Dylan Williams ◽  
Juulia Jylhävä ◽  
David Melzer
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Mendelian Randomization ◽  
Frailty Index ◽  
Geriatric Syndrome ◽  
Uk Biobank ◽  
Genomewide Association Study ◽  
European Descent ◽  
Ongoing Work

Abstract Frailty is an important and common geriatric syndrome, yet the mechanisms are multifactorial and not well understood. The Frailty Index (FI) is based on the Rockwood Index for accumulation of deficits, and we aimed to use genetics to gain mechanistic insights for interventions to prevent and delay frailty in older people. We performed a genomewide association study in 60 to 70 year old UK Biobank participants of European descent (n=164,610). We identified 26 independent genetic signals at 24 loci associated with the FI. Several of these signals have previously been associated with traits such as cardiovascular disease, intelligence, and educational attainment, but 6 of the signals appear to be novel. We will also present the results of ongoing work both to identify causal risk factors for FI using Mendelian randomization methods, and replication and functional follow-up in the TwinGene cohort, at the meeting.

scholarly journals Factorial Mendelian randomization: using genetic variants to assess interactions

2019 ◽  
Vol 49 (4) ◽  
pp. 1147-1158 ◽  
Author(s):  
Jessica M B Rees ◽  
Christopher N Foley ◽  
Stephen Burgess
Keyword(s):  
Risk Factors ◽  
Instrumental Variables ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Real Data ◽  
Uk Biobank ◽  
Pharmacological Interventions ◽  
Natural Break ◽  
Using Data ◽  
Randomization Analysis

Abstract Background Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial. Methods We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure. Results Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups. Conclusions Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

scholarly journals Appraising causal relationships of dietary, nutritional and physical-activity exposures with overall and aggressive prostate cancer: two-sample Mendelian-randomization study based on 79 148 prostate-cancer cases and 61 106 controls

2019 ◽  
Vol 49 (2) ◽  
pp. 587-596 ◽  
Author(s):  
Nabila Kazmi ◽  
Philip Haycock ◽  
Konstantinos Tsilidis ◽  
Brigid M Lynch ◽  
Therese Truong ◽  
...  
Keyword(s):  
Physical Activity ◽  
Prostate Cancer ◽  
Risk Factors ◽  
Cancer Risk ◽  
Serum Iron ◽  
Mendelian Randomization ◽  
Prostate Cancer Risk ◽  
Uk Biobank ◽  
Monounsaturated Fat ◽  
Aggressive Prostate Cancer

Abstract Background Prostate cancer is the second most common male cancer worldwide, but there is substantial geographical variation, suggesting a potential role for modifiable risk factors in prostate carcinogenesis. Methods We identified previously reported prostate cancer risk factors from the World Cancer Research Fund (WCRF)’s systematic appraisal of the global evidence (2018). We assessed whether each identified risk factor was causally associated with risk of overall (79 148 cases and 61 106 controls) or aggressive (15 167 cases and 58 308 controls) prostate cancer using Mendelian randomization (MR) based on genome-wide association-study summary statistics from the PRACTICAL and GAME-ON/ELLIPSE consortia. We assessed evidence for replication in UK Biobank (7844 prostate-cancer cases and 204 001 controls). Results WCRF identified 57 potential risk factors, of which 22 could be instrumented for MR analyses using single nucleotide polymorphisms. For overall prostate cancer, we identified evidence compatible with causality for the following risk factors (odds ratio [OR] per standard deviation increase; 95% confidence interval): accelerometer-measured physical activity, OR = 0.49 (0.33–0.72; P = 0.0003); serum iron, OR = 0.92 (0.86–0.98; P = 0.007); body mass index (BMI), OR = 0.90 (0.84–0.97; P = 0.003); and monounsaturated fat, OR = 1.11 (1.02–1.20; P = 0.02). Findings in our replication analyses in UK Biobank were compatible with our main analyses (albeit with wide confidence intervals). In MR analysis, height was positively associated with aggressive-prostate-cancer risk: OR = 1.07 (1.01–1.15; P = 0.03). Conclusions The results for physical activity, serum iron, BMI, monounsaturated fat and height are compatible with causality for prostate cancer. The results suggest that interventions aimed at increasing physical activity may reduce prostate-cancer risk, although interventions to change other risk factors may have negative consequences on other diseases.

scholarly journals Causal effects of cardiovascular risk factors on onset of major age-related diseases: A time-to-event Mendelian randomization study

2018 ◽  
Vol 107 ◽  
pp. 74-86 ◽  
Author(s):  
Liang He ◽  
Irina Culminskaya ◽  
Yury Loika ◽  
Konstantin G. Arbeev ◽  
Olivia Bagley ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Time To Event ◽  
Age Related

scholarly journals Common health conditions in childhood and adolescence, school absence, and educational attainment: Mendelian randomization study

2019 ◽  
Author(s):  
Amanda Hughes ◽  
Kaitlin H Wade ◽  
Frances Rice ◽  
Matt Dickson ◽  
Alisha Davies ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Secondary School ◽  
Educational Attainment ◽  
Mendelian Randomization ◽  
Negative Influence ◽  
Autism Spectrum ◽  
Health Conditions ◽  
Childhood And Adolescence ◽  
List Type ◽  
School Absence

ABSTRACTObjectivesTo assess the causal relationship of different health conditions in childhood and adolescence with educational attainment and school absence.DesignLongitudinal observational study and Mendelian randomization (MR) analyses.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), a population sample of children from South-West England born in 1991-1992.Participants6113 unrelated children with available GCSE records and genetic data (50% female).ExposuresSix common health conditions with known genetic markers measured at age 10 (primary school) and 13 (mid-secondary school). These were: symptoms of Attention-Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), depression, asthma, migraines and BMI. Genetic liability for these conditions and BMI was indexed by polygenic scores.Main outcome measuresEducational attainment at age 16 (total GCSE and equivalents points score), school absence at age 14-16.ResultsIn multivariate-adjusted observational models, all health conditions except asthma and migraines were associated with poorer educational attainment and greater school absence. Substantial mediation by school absence was seen for BMI (e.g. 35.6% for BMI at 13) and migraines (67% at age 10), with more modest mediation for behavioural and neurodevelopmental measures of health. In genetic models, a unit increase in genetically instrumented BMI z-score at age 10 predicted a 0.19 S.D. decrease (95% CI: −0.28 to −0.11) in attainment at 16, equivalent to around a 1/3 grade difference in each subject. It also predicted 8.6% more school absence (95% CI:1.3%, 16.5%). Similar associations were seen for BMI at age 13. Consistent with previous work, genetic liability for ADHD predicted lower educational attainment, but did not clearly increase school absence.ConclusionsTriangulation across multiple approaches supported a causal, negative influence of higher BMI on educational attainment and school absence. Further research is required to understand the mechanisms linking higher BMI with school absence and attainment.What is already known on this topicOn average, children with common health conditions have worse educational attainmentIt is unclear whether all health-attainment and health-absenteeism associations are causal, or reflect confounding by social and economic circumstancesWe do not know how much health-related school absenteeism contributes to these associationsWhat this study addsResults support a negative influence of high BMI in secondary school on educational attainment (GCSEs) and absenteeismAbsenteeism substantially mediated BMI-GCSE associations, suggesting a target for interventionThere was less evidence for causal effects of Autism Spectrum Disorder, depressive symptoms, asthma or migraines on attainment and absenteeismContribution of absenteeism to ADHD-GCSE associations was modest, suggesting interventions should target other mechanisms

scholarly journals Appraising causal relationships of dietary, nutritional and physical-activity exposures with overall and aggressive prostate cancer: two-sample Mendelian randomization study based on 79,148 prostate cancer cases and 61,106 controls

2019 ◽  
Author(s):  
Nabila Kazmi ◽  
Philip Haycock ◽  
Konstantinos Tsilidis ◽  
Brigid M. Lynch ◽  
Therese Truong ◽  
...  
Keyword(s):  
Physical Activity ◽  
Prostate Cancer ◽  
Risk Factors ◽  
Cancer Research ◽  
Cancer Risk ◽  
Serum Iron ◽  
Mendelian Randomization ◽  
Prostate Cancer Risk ◽  
Uk Biobank ◽  
Unsaturated Fat

SummaryBackgroundProstate cancer is the second most common male cancer worldwide, but there is substantial geographical variation suggesting a potential role for modifiable risk factors in prostate carcinogenesis.MethodsWe identified previously reported prostate cancer risk factors from the World Cancer Research Fund’s (WCRF) systematic appraisal of the global evidence (2018). We assessed whether each identified risk factor was causally associated with risk of overall (79,148 cases and 61,106 controls) or aggressive (15,167 cases and 58,308 controls) prostate cancer using Mendelian randomization (MR) based on genome wide association study (GWAS) summary statistics from the PRACTICAL and GAME-ON/ELLIPSE consortia. We assessed evidence for replication in UK Biobank (7,844 prostate cancer cases and 204,001 controls).FindingsWCRF identified 57 potential risk factors, of which 22 could be instrumented for MR analyses using single nucleotide polymorphisms (SNPs). In MR analyses for overall prostate cancer, we identified evidence compatible with causality for the following risk factors (odds ratio [OR] per standard deviation increase; 95% confidence interval): accelerometer-measured physical-activity, OR=0.49 (0.33-0.72; p=0.0003); serum iron, OR=0.92 (0.86-0.98; p=0.007); body mass index (BMI), OR=0.90 (0.84-0.97; p=0.003); and mono-unsaturated fat, OR=1.11 (1.02-1.20; p=0.02). Findings in our replication analyses in UK Biobank were compatible with our main analyses (albeit with wide confidence intervals). In MR analysis, height was positively associated with aggressive prostate cancer risk: OR=1.07 (1.01-1.15; p=0.03).InterpretationThe results for physical-activity, serum iron, BMI, mono-unsaturated fat and height are compatible with causality for prostate cancer but more research is needed to rule out violations of MR assumptions for some risk factors. The results suggest that interventions aimed at increasing physical activity may reduce prostate cancer risk, but the direction of effects of BMI, and iron are at odds with their effects on other diseases, so the overall public health impact of intervening on these need to be considered.FundingWorld Cancer Research Fund International (2015/1421), Cancer Research UK program grant (C18281/A19169), National Institute for Health Research, Bristol Biomedical Research Centre, and Victorian Cancer Agency (MCRF18005).

scholarly journals Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study

2019 ◽  
Vol 48 (5) ◽  
pp. 1425-1434 ◽  
Author(s):  
Xiangrui Meng ◽  
Xue Li ◽  
Maria N Timofeeva ◽  
Yazhou He ◽  
Athina Spiliopoulou ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Vitamin D ◽  
Health Outcomes ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Disease Outcomes ◽  
Multiple Health

Abstract Background Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. Methods We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization–Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. Results The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. Conclusions We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

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