Effects of increased body mass index on employment status: a Mendelian randomisation study

Abstract Background The obesity epidemic may have substantial implications for the global workforce, including causal effects on employment, but clear evidence is lacking. Obesity may prevent people from being in paid work through poor health or through social discrimination. We studied genetic variants robustly associated with body mass index (BMI) to investigate its causal effects on employment. Dataset/methods White UK ethnicity participants of working age (men 40–64 years, women 40–59 years), with suitable genetic data were selected in the UK Biobank study (N = 230,791). Employment status was categorised in two ways: first, contrasting being in paid employment with any other status; and second, contrasting being in paid employment with sickness/disability, unemployment, early retirement and caring for home/family. Socioeconomic indicators also investigated were hours worked, household income, educational attainment and Townsend deprivation index (TDI). We conducted observational and two-sample Mendelian randomisation (MR) analyses to investigate the effect of increased BMI on employment-related outcomes. Results Regressions showed BMI associated with all the employment-related outcomes investigated. MR analyses provided evidence for higher BMI causing increased risk of sickness/disability (OR 1.08, 95% CI 1.04, 1.11, per 1 Kg/m2 BMI increase) and decreased caring for home/family (OR 0.96, 95% CI 0.93, 0.99), higher TDI (Beta 0.038, 95% CI 0.018, 0.059), and lower household income (OR 0.98, 95% CI 0.96, 0.99). In contrast, MR provided evidence for no causal effect of BMI on unemployment, early retirement, non-employment, hours worked or educational attainment. There was little evidence for causal effects differing by sex or age. Robustness tests yielded consistent results. Discussion BMI appears to exert a causal effect on employment status, largely by affecting an individual’s health rather than through increased unemployment arising from social discrimination. The obesity epidemic may be contributing to increased worklessness and therefore could impose a substantial societal burden.

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Causal Returns to Education

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2006-0103 ◽

2006 ◽

Vol 226 (1) ◽

Author(s):

Anton L. Flossmann ◽

Winfried Pohlmeier

Keyword(s):

Educational Attainment ◽

Educational Policy ◽

Empirical Evidence ◽

Returns To Education ◽

Causal Effect ◽

Causal Effects ◽

Continuous Treatment ◽

Treatment Variable ◽

Secondary Schooling ◽

Upper Secondary

SummaryThis paper surveys the empirical evidence on causal effects of education on earnings for Germany and compares alternative studies in the light of their underlying identifying assumptions. We work out the different assumptions taken by various studies, which lead to rather different interpretations of the estimated causal effect. In particular, we are interested in the question to what extend causal return estimates are informative regarding educational policy advice. Despite the substantial methodological differences, we have to conclude that the empirical findings for Germany are quite robust and do not deviate substantially from each other. This also holds for the few studies which rely on ignorability conditions, regardless of whether they use educational attainment as a continuous treatment variable or as a discrete treatment indicator. Own estimates based on the matching approach indicate that the selection into upper secondary schooling is suboptimal

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

10.1101/401042 ◽

2018 ◽

Cited By ~ 13

Author(s):

Emma L Anderson ◽

Laura D Howe ◽

Kaitlin H Wade ◽

Yoav Ben-Shlomo ◽

W. David Hill ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Educational Attainment ◽

Mendelian Randomization ◽

Causal Effect ◽

Univariate Analysis ◽

Causal Effects ◽

Training Interventions ◽

Bidirectional Relationship ◽

Years Of Schooling

AbstractObjectivesTo examine whether educational attainment and intelligence have causal effects on risk of Alzheimer’s disease (AD), independently of each other.DesignTwo-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.Participants17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP) consortiumMain outcome measureOdds ratio of AD per standardised deviation increase in years of schooling and intelligenceResultsThere 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.ConclusionsThere 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.

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11. Identification and Estimation of Causal Effects with Time-Varying Treatments and Time-Varying Outcomes

Sociological Methodology ◽

10.1111/j.1467-9531.2007.00185.x ◽

2007 ◽

Vol 37 (1) ◽

pp. 393-434 ◽

Cited By ~ 19

Author(s):

Jennie E. Brand ◽

Yu Xie

Keyword(s):

Employment Status ◽

Causal Effect ◽

Causal Effects ◽

Potential Outcomes ◽

Time Varying ◽

Potential Outcome ◽

Units Of Analysis ◽

Wisconsin Longitudinal Study ◽

Time Periods ◽

Causal Variable

We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.

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

10.1101/2020.04.15.043380 ◽

2020 ◽

Author(s):

Panagiota Pagoni ◽

Christina Dardani ◽

Beate Leppert ◽

Roxanna Korologou-Linden ◽

George Davey Smith ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Educational Attainment ◽

Mendelian Randomization ◽

Causal Effect ◽

Autism Spectrum ◽

Causal Effects ◽

Direct Effects ◽

Limited Evidence ◽

Genetic Liability

ABSTRACTBackgroundThere are very few studies investigating possible links between Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s.MethodsWe estimated the bidirectional total causal effects of genetic liability to ADHD and ASD on Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s, 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.

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Estimation of causal effects of a time-varying exposure at multiple time points through Multivariable Mendelian randomization

10.1101/2022.01.04.22268740 ◽

2022 ◽

Author(s):

Eleanor Sanderson ◽

Tom G Richardson ◽

Tim T Morris ◽

Kate Tilling ◽

George Davey Smith

Keyword(s):

Direct Effect ◽

Genetic Variants ◽

Causal Effect ◽

Smoking Behaviour ◽

Causal Effects ◽

Mendelian Randomisation ◽

Multiple Time ◽

Time Points ◽

Time Periods ◽

Multiple Time Points

Mendelian Randomisation (MR) is a powerful tool in epidemiology to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effects obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of many exposures are thought to vary throughout an individual's lifetime and there may be periods during which an exposure has more of an effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all of the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies, however this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. We illustrate the method through estimation of the causal effects of childhood and adult BMI on smoking behaviour.

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Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

10.1101/2021.12.08.471751 ◽

2021 ◽

Author(s):

Jonathan Sulc ◽

Jenny Sjaarda ◽

Zoltan Kutalik

Keyword(s):

Causal Inference ◽

Large Scale ◽

Causal Effect ◽

Causal Effects ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Individual Level ◽

Glucose Levels ◽

Causal Function ◽

The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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

International Journal of Epidemiology ◽

10.1093/ije/dyz280 ◽

2020 ◽

Vol 49 (4) ◽

pp. 1163-1172 ◽

Cited By ~ 3

Author(s):

Emma L Anderson ◽

Laura D Howe ◽

Kaitlin H Wade ◽

Yoav Ben-Shlomo ◽

W David Hill ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Educational Attainment ◽

Mendelian Randomization ◽

Causal Effect ◽

Univariate Analysis ◽

Causal Effects ◽

Bidirectional Relationship ◽

Robust Evidence ◽

Years Of Schooling

Abstract Objectives To examine whether educational attainment and intelligence have causal effects on risk of Alzheimer’s disease (AD), independently of each other. Design Two-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 AD risk. Participants 17 008 AD cases and 37 154 controls from the International Genomics of Alzheimer’s Project (IGAP) consortium. Main outcome measure Odds ratio (OR) of AD per standardized deviation increase in years of schooling (SD = 3.6 years) and intelligence (SD = 15 points on intelligence test). Results There was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions [OR for intelligence on education = 0.51 SD units, 95% confidence interval (CI): 0.49, 0.54; OR for education on intelligence = 0.57 SD units, 95% CI: 0.48, 0.66]. 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: 23–49%) and 35% (95% CI: 25–43%) lower, respectively. There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account (OR = 1.15, 95% CI: 0.68–1.93), but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis (OR = 0.69, 95% CI: 0.44–0.88). Conclusions There is robust evidence for an independent, causal effect of intelligence in lowering AD risk. The causal effect of educational attainment on AD risk is likely to be mediated by intelligence.

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Does smoking cause lower educational attainment and general cognitive ability? Triangulation of causal evidence using multiple study designs

Psychological Medicine ◽

10.1017/s0033291720003402 ◽

2020 ◽

pp. 1-9

Author(s):

Suzanne H. Gage ◽

Hannah M. Sallis ◽

Glenda Lassi ◽

Robyn E. Wootton ◽

Claire Mokrysz ◽

...

Keyword(s):

Educational Attainment ◽

Cognitive Ability ◽

Fluid Intelligence ◽

Causal Effect ◽

Smoking Initiation ◽

Sensitivity Analyses ◽

Mendelian Randomisation ◽

General Cognitive Ability ◽

Inverse Variance ◽

Lower Educational Attainment

Abstract Background Observational studies have found associations between smoking and both poorer cognitive ability and lower educational attainment; however, evaluating causality is challenging. We used two complementary methods to explore this. Methods We conducted observational analyses of up to 12 004 participants in a cohort study (Study One) and Mendelian randomisation (MR) analyses using summary and cohort data (Study Two). Outcome measures were cognitive ability at age 15 and educational attainment at age 16 (Study One), and educational attainment and fluid intelligence (Study Two). Results Study One: heaviness of smoking at age 15 was associated with lower cognitive ability at age 15 and lower educational attainment at age 16. Adjustment for potential confounders partially attenuated findings (e.g. fully adjusted cognitive ability β −0.736, 95% CI −1.238 to −0.233, p = 0.004; fully adjusted educational attainment β −1.254, 95% CI −1.597 to −0.911, p < 0.001). Study Two: MR indicated that both smoking initiation and lifetime smoking predict lower educational attainment (e.g. smoking initiation to educational attainment inverse-variance weighted MR β −0.197, 95% CI −0.223 to −0.171, p = 1.78 × 10−49). Educational attainment results were robust to sensitivity analyses, while analyses of general cognitive ability were less so. Conclusion We find some evidence of a causal effect of smoking on lower educational attainment, but not cognitive ability. Triangulation of evidence across observational and MR methods is a strength, but the genetic variants associated with smoking initiation may be pleiotropic, suggesting caution in interpreting these results. The nature of this pleiotropy warrants further study.

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Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Nature Communications ◽

10.1038/s41467-021-26970-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Liza Darrous ◽

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Complex Traits ◽

Causal Effect ◽

Hdl Cholesterol ◽

Disease Diagnosis ◽

Causal Effects ◽

Mendelian Randomisation ◽

Simultaneous Estimation ◽

Summary Statistics ◽

High Systolic Blood Pressure ◽

Wide Range

AbstractMendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.

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Causal Effects of Body Mass Index and Maternal Age on Oocyte Maturation in Assisted Reproductive Technology: Model-Average Causal Effect and Bayesian LASSO Method

Iranian Journal of Public Health ◽

10.18502/ijph.v49i11.4734 ◽

2020 ◽

Author(s):

Ahad ALIZADEH ◽

Reza OMANI-SAMANI ◽

Mohammad Ali MANSOURNIA ◽

Azadeh AKBARI SENE ◽

Abbas RAHIMI FOROUSHANI

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Maternal Age ◽

Causal Effect ◽

Univariate Analysis ◽

Model Averaging ◽

Causal Effects ◽

Historical Cohort ◽

Bayesian Lasso ◽

Model Average

Background: Body Mass Index (BMI) and maternal age are related to various disorders of the female reproductive system. This study aimed to estimate the causal effects of BMI and maternal age on the rate of metaphase II oocytes (MII) using a new statistical method based on Bayesian LASSO and model averaging. Methods: This investigation was a historical cohort study and data were collected from women who underwent assisted reproductive treatments in Tehran, Iran during 2015 to 2018. Exclusion criteria were gestational surrogacy and donor oocyte. We used a new method based on Bayesian LASSO and model average to capture important confounders. Results: Overall, 536 cycles of 398 women were evaluated. BMI and Age had inverse relationships with the number of MII based on univariate analysis, but after adjusting the effects of other variables, there was just a significant association between age and the number of MII (adjusted incidence rate ratio (aIRR) of age =0.989, 95% CI: [0.979, 0.998], P=0.02). The results of causal inference based on the new presented method showed that the overall effects of age and BMI of all patients were significantly and inversely associated with the number of MII (both P<0.001). Therefore the expected number of MII decreased by 0.99 for an increase of 1 year (95% CI: [-1.00,-0.97]) and decreased by 0.99 for each 1-unit increase in BMI (95% CI: [-1.01,-0.98]). Conclusion: Maternal age and BMI have significant adverse casual effects on the rate of MII in patients undergoing ART when the effects of important confounders were adjusted.

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