scholarly journals 454Relationships between sleep traits and metabolic profiles: evidence from multivariable regression and Mendelian randomization analyses

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Neil Goulding ◽  
Maxime Bos ◽  
Diana van Heemst ◽  
Raymond Noordam ◽  
Deborah Lawlor
Keyword(s):  
Sleep Duration ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Inflammatory Marker ◽  
Genome Wide Association Studies ◽  
Inverse Variance ◽  
Multivariable Regression ◽  
Multivariable Analyses ◽  
Insomnia Symptoms

Abstract Background Sleep traits are associated with cardiometabolic disease. The aim of this study was to explore the causal effect of sleep traits (duration and insomnia) on multiple metabolic traits. Methods We used age, sex and BMI adjusted multivariable regression (N = 17,370) and two-sample summary statistic Mendelian randomization (MR) to examine effects of sleep duration and insomnia symptoms on ∼150 NMR metabolites. Multivariable analyses were conducted on data from nine European cohorts and meta-analysed. MR analyses utilised summary statistics from published genome-wide association studies (GWAS) of self-reported sleep traits (sample 1; N = 446,118 to 1,331,010) and from GWAS on NMR serum metabolites (sample 2; N = 38,618). We used inverse-variance weighted (IVW) for the main MR analyses and weighed median (WM) and MR-Egger to explore bias due to pleiotropy. Results MR IVW and multivariable analyses both suggest a positive effect of insomnia symptoms on glycoprotein acetyls (MR: 0.06 s.d. increase in mean concentration comparing any symptoms to none; p = 5.9e-4) and between total sleep duration and creatinine (MR: 0.16 s.d. increase per additional hour; p = 0.03). WM and MR-Egger analyses show consistent results. There was evidence for thirteen and eight effects of insomnia and duration in multivariable only and three and one, respectively, in MR only. Conclusions Insomnia symptoms lead to higher levels of an inflammatory marker (glycoprotein acetyls) and longer sleep duration leads to higher creatinine levels. Key messages We found no evidence of widespread metabolic disruption by sleep traits.

scholarly journals Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Maxime M. Bos ◽  
Neil J. Goulding ◽  
Matthew A. Lee ◽  
Amy Hofman ◽  
Mariska Bot ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Multivariable Regression ◽  
Artery Disease ◽  
Insomnia Symptoms

Abstract Background Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. Methods We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. Results We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (− 0.08 standard deviation (SD)[95% confidence interval (CI) − 0.12, − 0.03] in AMV and − 0.03SD [− 0.07, − 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (− 0.04SD [− 0.08, 0.00] in AMV and − 0.05SD [− 0.09, − 0.02] in MR), and lower phospholipids in very large HDL particles (− 0.04SD [− 0.08, 0.002] in AMV and − 0.05SD [− 0.08, − 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. Conclusions Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.

scholarly journals Investigating the relationships between unfavorable sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses

2020 ◽  
Author(s):  
Maxime M Bos ◽  
Neil J Goulding ◽  
Matthew A Lee ◽  
Amy Hofman ◽  
Mariska Bot ◽  
...  
Keyword(s):  
Sleep Duration ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Multivariable Regression ◽  
Artery Disease ◽  
Insomnia Symptoms

Background: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. Methods: We used AMV (N=17,370) combined with two-sample MR (N=38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. Results: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (-0.08 standard deviation (SD)[95% confidence interval (CI): -0.12, -0.03] in AMV and -0.03SD [-0.07, -0.003] in MR), higher glycoprotein acetyls (0.08SD [95%CI: 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (-0.04SD [-0.08, 0.00] in AMV and -0.05SD [-0.09, -0.02] in MR) and lower phospholipids in very large HDL particles (-0.04SD [-0.08, 0.002] in AMV and -0.05SD [-0.08, -0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1-hour [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. Conclusions: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.

Daytime sleepiness, night sleep changes and ALS: a Mendelian randomization study

2020 ◽  
Author(s):  
Gan Zhang ◽  
Linjing Zhang ◽  
Tao Huang ◽  
Dongsheng Fan
Keyword(s):  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sleep Efficiency ◽  
Genome Wide Association Studies ◽  
Night Sleep ◽  
Inverse Variance ◽  
Point Increase ◽  
Increased Risk

Abstract Background Observational studies have indicated that there is a high prevalence of daytime sleepiness and night sleep changes in amyotrophic lateral sclerosis (ALS). However, the actual relation between these symptoms and ALS remains unclear. We aimed to determine whether daytime sleepiness and night sleep changes have an effect on ALS. Methods We used 2-sample mendelian randomization to estimate the effects of daytime sleepiness, sleep efficiency, number of sleep episodes and sleep duration on ALS. Summary statistics we used was from resent and large genome-wide association studies on the traits we chosen (n = 85,670–452,071) and ALS (cases n = 20,806, controls n = 59,804). Inverse variance weighted method was used as the main method for assessing causality. Results A genetically predicted 1-point increase in the assessment of daytime sleepiness was significantly associated with an increased risk of ALS (inverse-variance-weighted (IVW) odds ratio = 2.70, 95% confidence interval (CI): 1.27–5.76; P = 0.010). ALS was not associated with a genetically predicted 1-SD increase in sleep efficiency (IVW 1.01, 0.64–1.58; P = 0.973), Number of sleep episodes (IVW 1.02, 0.80–1.30; P = 0.859) or sleep duration (IVW 1.00, 1.00–1.01; P = 0.250). Conclusions Our results provide novel evidence that daytime sleepiness causes an increase in the risk of ALS and indicate that daytime sleepiness may be inherent in preclinical and clinical ALS patients, rather than simply affected by potential influencing factors.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

scholarly journals A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuquan Wang ◽  
Tingting Li ◽  
Liwan Fu ◽  
Siqian Yang ◽  
Yue-Qing Hu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Random Effect ◽  
Pleiotropic Effect ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Novel Method

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

scholarly journals Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

2017 ◽  
Author(s):  
Jorien L. Treur ◽  
Mark Gibson ◽  
Amy E Taylor ◽  
Peter J Rogers ◽  
Marcus R Munafò
Keyword(s):  
Genetic Correlation ◽  
Sleep Duration ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genetic Correlations ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Caffeine Consumption ◽  
Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

scholarly journals bGWAS: an R package to perform Bayesian genome wide association studies

2020 ◽  
Vol 36 (15) ◽  
pp. 4374-4376
Author(s):  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fetal Origins of Mental Disorders? An Answer Based on Mendelian Randomization

2018 ◽  
Vol 21 (6) ◽  
pp. 485-494 ◽  
Author(s):  
Subhi Arafat ◽  
Camelia C. Minică
Keyword(s):  
Mental Disorders ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Fetal Origins ◽  
Barker Hypothesis ◽  
Main Criticism

The Barker hypothesis states that low birth weight (BW) is associated with higher risk of adult onset diseases, including mental disorders like schizophrenia, major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD). The main criticism of this hypothesis is that evidence for it comes from observational studies. Specifically, observational evidence does not suffice for inferring causality, because the associations might reflect the effects of confounders. Mendelian randomization (MR) — a novel method that tests causality on the basis of genetic data — creates the unprecedented opportunity to probe the causality in the association between BW and mental disorders in observation studies. We used MR and summary statistics from recent large genome-wide association studies to test whether the association between BW and MDD, schizophrenia and ADHD is causal. We employed the inverse variance weighted (IVW) method in conjunction with several other approaches that are robust to possible assumption violations. MR-Egger was used to rule out horizontal pleiotropy. IVW showed that the association between BW and MDD, schizophrenia and ADHD is not causal (all p > .05). The results of all the other MR methods were similar and highly consistent. MR-Egger provided no evidence for pleiotropic effects biasing the estimates of the effects of BW on MDD (intercept = -0.004, SE = 0.005, p = .372), schizophrenia (intercept = 0.003, SE = 0.01, p = .769), or ADHD (intercept = 0.009, SE = 0.01, p = .357). Based on the current evidence, we refute the Barker hypothesis concerning the fetal origins of adult mental disorders. The discrepancy between our results and the results from observational studies may be explained by the effects of confounders in the observational studies, or by the existence of a small causal effect not detected in our study due to weak instruments. Our power analyses suggested that the upper bound for a potential causal effect of BW on mental disorders would likely not exceed an odds ratio of 1.2.

scholarly journals Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses

2018 ◽  
Vol 48 (3) ◽  
pp. 684-690 ◽  
Author(s):  
Wes Spiller ◽  
Neil M Davies ◽  
Tom M Palmer
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Software Application ◽  
Genome Wide ◽  
Inverse Variance ◽  
Randomization Analysis ◽  
Summary Data ◽  
Weighted Estimation

Abstract Motivation In recent years, Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods. Implementation mrrobust is freely available as a Stata package. General features The package includes inverse variance weighted estimation, as well as a range of median, modal and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualizing each estimate either individually or simultaneously. The package also provides statistics such as IGX2, which are useful in assessing attenuation bias in causal estimates. Availability The software is freely available from GitHub [https://raw.github.com/remlapmot/mrrobust/master/].

scholarly journals Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

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