To examine the effects of sleep traits on glycated hemoglobin (HbA1c).This study triangulated evidence across multivariable regression (MVR) and one- (1SMR) and two-sample Mendelian randomization (2SMR) including sensitivity analyses on the effects of five self-reported sleep traits (i.e., insomnia symptoms [difficulty initiating or maintaining sleep], sleep duration, daytime sleepiness, napping, and chronotype) on HbA1c (in SD units) in adults of European ancestry from the UK Biobank (for MVR and 1SMR analyses) (n = 336,999; mean [SD] age 57 [8] years; 54% female) and in the genome-wide association studies from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) (for 2SMR analysis) (n = 46,368; 53 [11] years; 52% female).Across MVR, 1SMR, 2SMR, and their sensitivity analyses, we found a higher frequency of insomnia symptoms (usually vs. sometimes or rarely/never) was associated with higher HbA1c (MVR 0.05 SD units [95% CI 0.04-0.06]; 1SMR 0.52 [0.42-0.63]; 2SMR 0.24 [0.11-0.36]). Associations remained, but point estimates were somewhat attenuated after excluding participants with diabetes. For other sleep traits, there was less consistency across methods, with some but not all providing evidence of an effect.Our results suggest that frequent insomnia symptoms cause higher HbA1c levels and, by implication, that insomnia has a causal role in type 2 diabetes. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycemia and prevent diabetes. 

/pdf/assessing-the-causal-role-of-sleep-traits-on-glycated-36adsxyt.pdf

Assessing the Causal Role of Sleep Traits on Glycated Hemoglobin: A Mendelian Randomization Study

Abstract Study Objectives Sleep and circadian phenotypes are associated with several diseases. The present study aimed to investigate whether sleep and circadian phenotypes were causally linked with coronavirus disease 2019 (COVID-19)-related outcomes. Methods Habitual sleep duration, insomnia, excessive daytime sleepiness, daytime napping, and chronotype were selected as exposures. Key outcomes included positivity and hospitalization for COVID-19. In the observation cohort study, multivariable risk ratios (RRs) and their 95% confidence intervals (CIs) were calculated. Two-sample Mendelian randomization (MR) analyses were conducted to estimate the causal effects of the significant findings in the observation analyses. Beta values and the corresponding 95% CIs were calculated and compared using the inverse variance weighting, weighted median, and MR-Egger methods. Results In the UK Biobank cohort study, both often excessive daytime sleepiness and sometimes daytime napping were associated with hospitalized COVID-19 (excessive daytime sleepiness [often vs. never]: RR=1.24, 95% CI=1.02-1.5; daytime napping [sometimes vs. never]: RR=1.12, 95% CI=1.02-1.22). In addition, sometimes daytime napping was also associated with an increased risk of COVID-19 susceptibility (sometimes vs. never: RR= 1.04, 95% CI=1.01-1.28). In the MR analyses, excessive daytime sleepiness was found to increase the risk of hospitalized COVID-19 (MR IVW method: OR = 4.53, 95% CI = 1.04-19.82), whereas little evidence supported a causal link between daytime napping and COVID-19 outcomes. Conclusions Observational and genetic evidence supports a potential causal link between excessive daytime sleepiness and an increased risk of COVID-19 hospitalization, suggesting that interventions targeting excessive daytime sleepiness symptoms might decrease severe COVID-19 rate.

/pdf/associations-of-sleep-and-circadian-phenotypes-with-covid-19-ubj63qn7.pdf

Associations of sleep and circadian phenotypes with COVID-19 susceptibility and hospitalization: an observational cohort study based on the UK Biobank and a two-sample Mendelian randomization study

Importance
Although hypothesis-driven research has identified several factors associated with psychosis, this one-exposure-to-one-outcome approach fails to embrace the multiplicity of exposures. Systematic approaches, similar to agnostic genome-wide analyses, are needed to identify genuine signals.


Objective
To systematically investigate nongenetic correlates of psychotic experiences through data-driven agnostic analyses and genetically informed approaches to evaluate associations.


Design, Setting, Participants
This cohort study analyzed data from the UK Biobank Mental Health Survey from January 1 to June 1, 2021. An exposome-wide association study was performed in 2 equal-sized split discovery and replication data sets. Variables associated with psychotic experiences in the exposome-wide analysis were tested in a multivariable model. For the variables associated with psychotic experiences in the final multivariable model, the single-nucleotide variant-based heritability and genetic overlap with psychotic experiences using linkage disequilibrium score regression were estimated, and mendelian randomization (MR) approaches were applied to test potential causality. The significant associations observed in 1-sample MR analyses were further tested in multiple sensitivity tests, including collider-correction MR, 2-sample MR, and multivariable MR analyses.


Exposures
After quality control based on a priori criteria, 247 environmental, lifestyle, behavioral, and economic variables.


Main Outcomes and Measures
Psychotic experiences.


Results
The study included 155 247 participants (87 896 [57%] female; mean [SD] age, 55.94 [7.74] years). In the discovery data set, 162 variables (66%) were associated with psychotic experiences. Of these, 148 (91%) were replicated. The multivariable analysis identified 36 variables that were associated with psychotic experiences. Of these, 28 had significant genetic overlap with psychotic experiences. One-sample MR analyses revealed forward associations with 3 variables and reverse associations with 3. Forward associations with ever having experienced sexual assault and pleiotropy of risk-taking behavior and reverse associations without pleiotropy of experiencing a physically violent crime as well as cannabis use and the reverse association with pleiotropy of worrying too long after embarrassment were confirmed in sensitivity tests. Thus, associations with psychotic experiences were found with both well-studied and unexplored multiple correlated variables. For several variables, the direction of the association was reversed in the final multivariable and MR analyses.


Conclusions and Relevance
The findings of this study underscore the need for systematic approaches and triangulation of evidence to build a knowledge base from ever-growing observational data to guide population-level prevention strategies for psychosis.

/pdf/nongenetic-factors-associated-with-psychotic-experiences-2jaejl6i.pdf

Nongenetic Factors Associated With Psychotic Experiences Among UK Biobank Participants: Exposome-Wide Analysis and Mendelian Randomization Analysis.

/pdf/smoking-and-infertility-multivariable-regression-and-cz12n21j.pdf

Smoking and infertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study

BackgroundHigher body mass index (BMI) is associated with subfertility in women and men. This relationship is further substantiated by a few small randomized-controlled trials of weight reduction and success of assisted reproduction. The aim of the current study was to expand the current evidence-base by investigating the association between BMI and subfertility in men and women using multivariable regression and Mendelian randomization. Methods and findingsWe studied 34,157 women (average age 30, average BMI 23.1 kg/m2) and 31,496 men (average age 33, average BMI 25.4 kg/m2) who were genotyped and are participating in the Norwegian Mother, Father and Child Cohort Study. Self-reported information was available on time-to-pregnancy and BMI. A total of 10% of couples were subfertile (time-to-pregnancy [≥]12 months). Our findings support a J-shaped association between BMI and subfertility in both sexes using multivariable logistic regression models. Non-linear Mendelian randomization validated this relationship. A 1 kg/m2 greater genetically predicted BMI was linked to 15% greater odds of subfertility (95% confidence interval 4% to 28%) in obese women ([≥]30.0 kg/m2) and 14% lower odds of subfertility (-25% to -3%) in women with BMI <20.0 kg/m2. A 1 kg/m2 higher genetically predicted BMI was linked to 23% greater odds of subfertility (6% to 43%) among obese men and 36% decreased odds (-62% to 7%) among men BMI <20.0 kg/m2. A genetically predicted BMI of 23 and 25 kg/m2 was linked to the lowest subfertility risk in women and men, respectively. The main limitations of our study were that we did not know whether the subfertility was driven by the woman, man, or both; the exclusive consideration of individuals of northern European ancestry; and the limited amount of participants with obesity or BMI values <20.0 kg/m2. ConclusionsWe observed a J-shaped relationship between BMI and subfertility in both sexes, when using both a standard multivariable regression and Mendelian randomization analysis, further supporting a potential causal role of BMI on subfertility. AUTHOR SUMMARYO_ST_ABSWHY WAS THIS STUDY DONE?C_ST_ABSO_LIHigher body mass index (BMI) in both women and men is associated with subfertility in in observational studies. A few small randomized-controlled trials of weight reduction have reported an increased success of assisted reproduction in women. In addition, women with BMI <18.5 kg/m2 have lower conception rates with assisted reproduction technologies. A non-linear relationship between BMI and subfertility is suggested. C_LIO_LIWe aimed to investigate the association between BMI and subfertility using both a standard multivariable regression and a Mendelian randomization approach. C_LI WHAT DID THE RESEARCHERS DO AND FIND?O_LIWe examined the relationship between BMI and subfertility (time-to-pregnancy [≥]12 months) among all men and women in the Norwegian Mother, Father and Child Cohort Study with available genotype information and anthropometric data (34,157 women, 31,496 men). C_LIO_LIWe observed a J-shaped relationship between BMI and subfertility in both sexes, when using both a standard multivariable regression and Mendelian randomization analysis. C_LI WHAT DO THESE FINDINGS MEAN?O_LITogether with previous observational and trial evidence, findings support a causal effect of overweight/obesity on subfertility in women and men. C_LIO_LIOur findings expand the current evidence by also indicating that individuals at the lower end of the BMI distribution (<20 kg/m2) may have an increased risk of subfertility. C_LIO_LICurrent advice and support for overweight and obese subfertile couples to lose weight should continue. Additionally, clinicians should consider appropriate advice to those with low BMI on whether they should increase weight to obtain a normal BMI. C_LI

https://www.medrxiv.org/content/medrxiv/early/2021/06/25/2021.06.21.21259264.full.pdf

Body mass index and subfertility: multivariable regression and Mendelian randomization analyses in the Norwegian Mother, Father and Child Cohort Study.

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method's performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

/pdf/exploiting-collider-bias-to-apply-two-sample-summary-data-3lflasfggv.pdf

Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

Over the last decade the availability of SNP-trait associations from genome-wide association studies data has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. A weighted sum of these estimates is then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach is closely related to the work of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our paper serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

https://medrxiv.org/content/10.1101/2020.10.20.20216358v1.full.pdf

Objective To examine the effects of sleep traits on glycated haemoglobin (HbA1c). Design Observational multivariable regression (MVR), one-sample Mendelian randomization (1SMR), and two-sample summary data Mendelian randomization (2SMR). Setting UK Biobank (UKB) prospective cohort study and genome-wide association studies from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). Participants In MVR and 1SMR, participants were adults (mean (SD) age 57 (8) years; 54% female) from the UKB (n=336,999); in 2SMR, participants were adults (53 (11) years; 52% female) from MAGIC (n=46,368). All participants were adults of European ancestry. Exposures Self-reported insomnia frequency (usually vs sometimes or rarely/never); sleep duration: 24-hour sleep duration (hours/day); short sleep (≤6 hours vs 7-8 hours) and long sleep (≥9 hours vs 7-8 hours); daytime sleepiness and daytime napping (each consisting of 3 categories: never/rarely, sometimes, usually); chronotype (5 categories from definite morning to definite evening preference). Main outcome measure HbA1c in standard deviation (SD) units. Results Across MV, 1SMR, 2SMR, and their sensitivity analyses we found a higher frequency of insomnia (usually vs sometimes or rarely/never) was associated with higher HbA1c (MVR: 0.053 SD units, 95% confidence interval (0.046 to 0.061), 1SMR: 0.52, (0.42 to 0.63), 2SMR: 0.22, (0.10 to 0.35)). Results remained significant but point estimates were somewhat attenuated after excluding people with diagnosed diabetes. For other sleep traits, there was less consistency with significant associations when using some, but not all methods. Conclusions This study suggests that insomnia increases HbA1c levels. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycaemia and prevent diabetes. SUMMARY BOX What is already known on this topic In observational data, insomnia, short sleep duration, and evening preference are associated with higher risk for type 2 diabetes. Mendelian randomization (MR) studies have not found evidence of a causal effect of short sleep on type 2 diabetes or glycaemic traits but have indicated an effect of insomnia on type 2 diabetes. It is unclear whether insomnia influences HbA1c levels, a marker of long-term hyperglycaemia, in the general population. Recently identified genetic variants robustly associated with insomnia, sleep duration, daytime sleepiness, napping, and chronotype can be used in MR studies to explore causal effects of these sleep traits on HbA1c levels. What this study adds This study suggests that a higher frequency of insomnia increases HbA1c levels in the general population and after excluding people with diabetes. We found no robust evidence for causal effects of other sleep traits on HbA1c levels. These findings improve our understanding of the impact of sleep traits on HbA1c levels and have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycaemia and prevent diabetes.

/pdf/assessing-the-causal-role-of-sleep-traits-on-glycated-1gi6cfv09p.pdf

Junxi Liu

Papers

Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

Assessing the causal role of sleep traits on glycated haemoglobin: a Mendelian randomization study