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Genetically Predicted Insomnia in Relation to 14 Cardiovascular Conditions and 17 Cardiometabolic Risk Factors: A Mendelian Randomization Study

Background This Mendelian randomization study aims to investigate causal associations between genetically predicted insomnia and 14 cardiovascular diseases (CVDs) as well as the potential mediator role of 17 cardiometabolic risk factors. Methods and Results Using genetic association estimates from large genome‐wide association studies and UK Biobank, we performed a 2‐sample Mendelian randomization analysis to estimate the associations of insomnia with 14 CVD conditions in the primary analysis. Then mediation analysis was conducted to explore the potential mediator role of 17 cardiometabolic risk factors using a network Mendelian randomization design. After correcting for multiple testing, genetically predicted insomnia was consistent significantly positively associated with 9 of 14 CVDs, those odds ratios ranged from 1.13 (95% CI, 1.08–1.18) for atrial fibrillation to 1.24 (95% CI, 1.16–1.32) for heart failure. Moreover, genetically predicted insomnia was consistently associated with higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol, each of which may act as a mediator in the causal pathway from insomnia to several CVD outcomes. Additionally, we found very little evidence to support a causal link between insomnia with abdominal aortic aneurysm, thoracic aortic aneurysm, total cholesterol, low‐density lipoprotein cholesterol, glycemic traits, renal function, and heart rate increase during exercise. Finally, we found no evidence of causal associations of genetically predicted body mass index, high‐density lipoprotein cholesterol, or triglycerides on insomnia. Conclusions This study provides evidence that insomnia is associated with 9 of 14 CVD outcomes, some of which may be partially mediated by 1 or more of higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol.

In the pre-processing step of primary analysis, the independent instrument SNPs of insomnia were identified using a common statistical approach: i) associated with insomnia at genome-wide significance (two-sided P-value from the meta-analysis of the GWAS results of insomnia: P<5×10 -8 ), ii) r 2 between SNPs <0.1 and distinct genomic loci are >250kb apart. 14 As indicated by Swerdlow et al. (2016) 96 , in a MR study with a fixed sample size, the P-value for the SNP-biomarker association provides an indirect measure of the effect size, and these specific metrics of effect size can be used to inform the selection of SNPs as instruments in an MR analysis. Statistical analyses in GWAS set stringent significance thresholds (typically P-value < 5×10 -8 ) in order to reduce the number of falsepositive associations arising from the vast number of statistical tests performed. 96 Provided an association is identified robustly (P<5×10 -8 ), the size of the genetic effect gains importance when prioritizing SNPs for use as MR instruments, with SNPs of larger effect preferred because they increase statistical power provided the minor allele frequency is sufficiently high. 20 In addition, we reported the R 2 statistic and the related F statistic. The R 2 statistic measures the variance in the exposure explained by those selected SNPs. Using SNPs with a large R 2 can avoid that the instrument is weak and weak instrument bias. The F statistic is a measure of instrument strength and can be used to judge the extent of weak instrument bias. 97 In this study, we calculated F statistics through the formula denotes the sample size and k denotes the number of instruments. In terms of the rule of thumb 25 , F statistic greater than 10 means that assumption (a) is satisfied and avoids the bias caused by weak instrument. 97,98

Genetic variant instruments for BMI
The summary data of genetic associations with BMI were obtained from large GWAS of BMI 29 (after imputation, 2 554 637 variants in 339 224 individuals of European descent) in GIANT (Genetic Investigation of Anthropometric Traits) consortium (http://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium). Totally 78 independent SNPs that were associated with BMI at genome-wide significance (P<5×10 -8 ) were clumped by Noyce et al. 99 , and together this explained 2.2% of the variance in BMI (F statistics = 97.81). Since four of 78 SNPs (rs16951275 in chr2, rs29941 in chr14, rs1528435 in chr15, and rs7141420 in chr19) were unavailable in UK Biobank, the leaving 74 SNPs were utilized as genetic instrumental variables for BMI in mediation analysis. Data on major and minor alleles for each instrument SNP, along with allele frequencies, beta coefficients for allele dose and 5-kg/m 2 change in BMI (i.e., the change in BMI on a 5-kg/m 2 scale per effect allele), P-values, and standard errors (SEs) were extracted. SNPs were aligned to the same effect allele across the data sources before analysis, and we checked the effect allele frequencies for concordance (The details of those SNPs were provided in Table S6).

Genetic variant instruments for HDL-C and TG
The summary data of genetic associations with HDL-C and TG were obtained from publicly available data through the Global Lipids Genetics Consortium, which included 188 577 individuals of primarily European ancestry. 30 Totally 86 independent SNPs that were associated with HDL-C at genome-wide significance (P<5×10 -8 ) and a total of 51 independent SNPs that were associated with TG at genome-wide significance (P<5×10 -8 ) were clumped by Hindy et al. (2018) 77 , these SNPs explained 5.9% and 4.6% of the variance in HDL-C and TG, respectively (F statistics = 137.42 and 178.24, respectively). Since one of 86 SNPs of HDL-C (rs7422339 in chr2) was unavailable in UK Biobank, the leaving 85 SNPs were utilized as genetic instrumental variables for HDL-C in mediation analysis. Besides, all 51 SNPs of TG were available in UK Biobank and were utilized as genetic instrumental variables for TG in mediation analysis. Data on major and minor alleles for each instrument SNP, along with allele frequencies, beta coefficients for allele dose and 1 SD change in each lipid trait (i.e., the change in HDL-C or TG on a 1 SD scale per effect allele), Pvalues, and standard errors (SEs) were extracted. SNPs were aligned to the same effect allele across the data sources before analysis, and we checked the effect allele frequencies for concordance (The details of those SNPs were provided in Table S7 and S8).

Data S3: Estimate the proportion mediated by each mediator
The extent to which the association of insomnia with each CVD outcome selected in Step a of mediation analysis was mediated by BMI, TC, or HDL-C was tested in a post hoc analysis after BMI, TC, and HDL-C were identified as the potential mediators, using a similar approach as Zhan et al. (2017) 100 did.
Taking insomnia as exposure, BMI as a mediator, CAD as an outcome as an example, the total effect (odds ratio: OR) per genetically predicted 1-unit-higher log-odds of liability to insomnia on We first consider the variance of indirect effect XY IE → , i.e., the variance of product MX YM

Data S5: Multivariable mendelian randomization (MVMR)
To consider the role of multiple mediators (BMI, TG, and HDL-C) simultaneously and to investigate the independent causal effects (direct causal effect) for insomnia not mediated by these three mediators for each CVD outcome, we additionally performed MVMR using summary data estimates of the association between SNP-exposure, SNP-mediators and SNP-outcome. To be noted, we also included LDL-C in the MVMR analysis to adjusts for potential pleiotropic effects, since relevant genetic variants are likely to be associated with multiple lipid traits.
To minimize the sample-overlap between the samples used to estimate the SNP-exposure associations and SNP-outcome associations, we used summary statistics for the SNPs-insomnia associations from GWAS meta-analysis in both UK Biobank and 23andMe, but this data was only available for 248 independent SNPs (see Table S1 and S2) associated with insomnia at genome-wide significance (P<5×10 -8 ). 14 In addition, since much more summary statistics for the associations between these SNPs and risk factors are available in ENGAGE 1000 Genome Consortium 59,60 (Table S10) than the GIANT 29 and GLGC consortium 30 , we used summary statistics of BMI, TG, HDL-C, and LDL-C obtained from the ENGAGE 1000 Genome Consortium in this study. Summary statistics for the associations between these SNPs and CVD outcomes were calculated from the identified white British individuals in UK Biobank. We excluded SNPs whose associations with any of BMI, TG, HDL-C, LDL-C, and CVD outcomes was unavailable from their respective publicly available data. Totally 239 independent SNPs were finally included as instrumental variables in MVMR analysis. We also performed replication analysis using summary data of IS, CAD, AF, and HF from previous published GWAS studies (Table S9). 55-58 The multivariable inverse-variance weighted (MVMR-IVW) method was applied to the data to investigate the direct causal effect of insomnia, BMI, TG, and HDL-C on each CVD outcome, respectively. We evaluated instrument strength using two sample conditional F-statistic and tests for horizontal pleiotropy using R package MVMR. 103 The results of MVMR analysis were shown in Table S34. After adjusting for BMI, HDL-C, TG, and LDL-C, the main analysis of MVMR-IVW suggested that insomnia was independent causally associated with a higher risk of all 9 CVD outcomes selected in the primary analysis, the ORs ranged from 1.11 (95% CI: 1.06-1.16) for atrial fibrillation (AF) to 1.22 (95% CI: 1.14-1.3) for heart failure (HF). Except for ischemic stroke (IS), the estimated direct effects of insomnia on the other 8 CVD outcomes were all attenuated compared to the total effects (primary analysis). The replication analysis showed similar results, except for AF. The results of replication analysis did not support that a genetically predicted insomnia was significant directly associated with a higher risk of AF (OR = 1.03, 95% CI: 1-1.06, P = 0.045) under a Bonferroni-corrected threshold of P < 0.0125 (α = 0.05/4 outcomes). Theoretically speaking, this MVMR analysis was unable to calculate conditional F-statistics to assess the strength of our multi-variable instruments: the pairwise covariance between a SNP estimated association with any two exposures will equal to zero only when the effects of the SNPs on each exposure were estimated from separate samples; when the samples are overlapping, the requisite pairwise covariance are determinable only using individual-level data. 103 If we directly assumed that the pairwise covariances between SNP associations are zero, the conditional F-statistics for insomnia, BMI, HDL-C, TG, and LDL-C equals 11.01, 1.65, 0.94, 0.75, and 2.19, respectively. This suggested that conditional F-statistics for BMI, HDL-C, TG, and LDL-C were likely to be small, and the effect estimates were likely to subject to weak instrument bias. The horizontal pleiotropy statistic for this model is 262.76, the critical value at a 5% level of significance for a chi-squared distribution with 233 degrees of freedom is 269.61 (P = 0.09), which indicates no potential pleiotropy. 103

Data S6: The potential mediator role of blood pressure traits
In our main study, arterial hypertension was considered as a CVD outcome. Since blood pressure traits (including systolic blood pressure (SBP), diastolic blood pressure (DBP)) are also important cardiometabolic risk factors, we additionally explored whether SBP and DBP mediate the causal pathway from insomnia to other 13 CVD outcomes using a network MR design.

Data sources
The genetic association estimates with 2 blood pressure traits were taken from Evangelou et al. 104 , however, this GWAS analysis was adjusted for body mass index [denoted these two traits as Systolic blood pressure adjusted body mass index (SBPadjBMI) and diastolic blood pressure adjusted body mass index (DBPadjBMI), respectively]. 104 In addition, we used genetic association estimates with SBP and DBP calculated from 424 811 white British participants in the UK Biobank (see Figure S1 for the flow chart of individual selection). The blood pressure traits were recorded automatically at the baseline assessment center for all participants, we used the second reading of the automated blood pressure, where missing data were replaced with the first measure, as did by Carter et al. (2019) 52 . For each blood pressure trait, we additional excluded individuals with this trait missing or individuals without genetic data from our total analysis dataset (N=424 811) that passed our quality control, then the genetic associations with each blood pressure trait were obtained from the individuals in UK Biobank using linear regression controlling for 10 principal components, which can further control for population stratification. The basic characters of these summary data were presented in the Table S35.

Methods
Then we explored whether SBPadjBMI, DBPadjBMI, SBP and DBP mediate the causal pathway from insomnia to CVD outcome using a network MR design. For each CVD outcome, this design consists of 3 different MR analyses (Step a-c) 22 Step a: the estimation of causal effect of genetically determined insomnia on this CVD outcome was obtained, which was in accordant with our primary analysis; Step b: 248 independent SNPs associated with insomnia at genome-wide significance from Jansen et al. (2019) 14 were utilized as instrumental variables to estimate the causal effects of genetically determined insomnia on each blood pressure traits, using the respective GWAS summary statistics described in Supplemental Section 6.1 and Table S35. This step was conducted using IVW method, in addition, complementary approaches including weighted median method, mode-based estimate, MR-Egger regression method, MR-PRESSO method were used to examine causal effect. Moreover, leave-one-out sensitivity analysis was also performed to assessing the reliance of the MR results on a particular variant. All the estimated effects were unit change in a blood pressure trait expressed per genetically predicted 1-unit-higher log-odds of liability to insomnia (per 2.72-fold multiplicative increase in the odds of insomnia). The Bonferroni-corrected threshold ( ) 0.0125 0.05 4 P  was used in this step, 0.05 P  but above the Bonferroni corrected significance threshold was considered as suggestive association. The sample overlap between insomnia GWAS study and two consortia of blood pressure traits (ICBP and UK Biobank) are 31% and 29%, respectively.
Step c: for blood pressure traits that causal association is observed in Step b (DBP), we estimated the causal effect of each mediator on this CVD outcomes, respectively, using conventional one-sample MR analysis using individual data from UK Biobank. Totally 98 independent SNPs that were associated with DBP at genome-wide significance (P<5×10 -8 ) were clumped by Sun et al. 105 (F statistics = 32.8). Since 1 of 98 SNPs (rs687621 in chr5) was unavailable in UK Biobank, the leaving 97 SNPs were utilized as genetic instrumental variables for DBP in mediation analysis. The causal effect estimate of DBP on this CVD outcome was obtained using the two-stage least-squares (2SLS) method: in the first stage, the exposure is regressed on the genetic variants and 10 principal components in a multivariate linear regression; in the second stage the outcome is regressed on the predicted values of the exposure from the first regression and 10 principal components in a logistic regression. The results were converted to ORs expressed per genetically predicted 1 mm Hg increased of the blood pressure trait, and the Bonferroni-corrected threshold ( ) 0.0063 0.05 8 P  was used in this step.
If causal associations were observed in all three steps, the conclusion can be drawn that the specific blood pressure traits are mediators in the pathway of insomnia to this CVD outcome. The indirect effect of insomnia on this CVD outcome mediated through each mediator and the proportion mediated by each mediator were calculated (see Data S3 for details of mediation analysis and Data S4 for the calculation of 95% confidence intervals).
Finally, to examine the existence of bidirectional causality between selected mediator and insomnia, we performed a bidirectional MR analysis to examine whether the selected mediator can casually affect insomnia by using mediator-associated independent SNPs as IV (97 independent SNPs selected by Sun et al. 105 ), the summary statistics of DBP and insomnia were obtained from UK Biobank (Table S35) and Jansen et al. (2019) 14 , respectively.

Data S7: Replication analysis using UK Biobank individual-level data
Insomnia complaints in UK Biobank were defined according to Table S1. Insomnia was available in 424516 individuals of 424811 identified white British individuals, with the prevalence equals to 28.63% [N cases/(cases+controls) = 121526/424516]. We randomly divided this sample into two equally sized groups. For each of 247 SNPs, we calculated its effect on insomnia (on a log OR scale) using the first sample using logistic regression, adjusted for age, sex, and 10 genetic principal components. And for each CVD outcome, individuals suffering from any other 13 CVD outcomes were further excluded from the analysis's dataset from the second sample (as the primary analysis did). Then the genetic associations with each CVD outcome (on a log OR scale) were obtained using the same way as the primary analysis. Finally, the overall causal estimate of insomnia on each CVD outcome was obtained using an inverse variance weighted (IVW) method performed using a multiplicative random-effects model.  [2006][2007][2008][2009][2010]. Insomnia complaints were assessed by asking: "Do you have trouble falling asleep at night or do you wake up in the middle of the night?" Participants were instructed to answer this question in relation to the previous four weeks when in doubt. The participants were able to choose one of the following four answers: "never/rarely", "sometimes", "usually", or "prefer not to answer". Insomnia cases were defined as participants who answered this question with "usually", while participants answering "never/rarely" or "sometimes" were defined as controls. 23andMe 944477 Participants completed one or more questions related to seven phenotypic concepts concerning sleep. Insomnia cases affirmed at least one of the following questions: "Have you ever been diagnosed with, or treated for: Insomnia?"; "Have you ever been diagnosed with, or treated for, any of the following conditions: Insomnia but not Narcolepsy, Sleep apnea or Restless leg syndrome"; "Has a doctor ever told you that you have any of 242 these conditions: Insomnia (difficulty getting to sleep or staying asleep)?"; "Have you ever been diagnosed by a doctor with any of the following neurological conditions: Sleep disturbance"; "Do you routinely have trouble getting to sleep at night?"; "What sleep disorders have you been diagnosed with? Please select all that apply: Insomnia, trouble falling or staying asleep"; "Have you ever taken these medications? Prescription sleep aids"; "In the last 2 years, have you taken any of these medications? Prescription sleep aids". Participants were classified as controls if they did not provide a positive or uncertain ("I don't know": "I am not sure") to any of the questions listed above, nor to any of the following questions: "Have you ever been diagnosed with, or treated for Insomnia, Narcolepsy, Sleep apnea, Restless leg syndrome?", "Have you ever been diagnosed with or treated for any of the following conditions? Post-traumatic stress disorder (PTSD); Autism; Asperger's; Sleep disorder", "Have you ever been diagnosed with or treated for a sleep disorder?".  401.X I10 6150, 6177 CVDs: cardiovascular diseases; ICD: international classification of disease; OPCS: office of population censuses and surveys classification of surgical operations and procedures; 6150: data code used in UK Biobank represents health condition diagnosed by doctor (self-reported from touchscreen); 6177: data code used in UK Biobank represents medication for health condition (selfreported from touchscreen); 20002: data code used in UK Biobank represents non-cancer illness code (self-reported from interview with nurse).             Results were obtained from multiplicative random-effects inverse-variance weighted method. a The sample size denotes the total number of individuals (case + control) in the second sample for each CVD outcome (for each CVD outcome, individuals suffering from any other 13 CVD outcomes were further excluded from the control group). b Estimate represent odds ratios (OR) expressed per genetically predicted 1-unit-higher log-odds of liability to insomnia (per 2.72-fold increase in the prevalence of the insomnia); c I 2 statistic quantifies the amount of heterogeneity among estimates based on individual SNPs.   Table S18. Sensitivity analysis of association between genetically predicted insomnia and 17 cardiometabolic risk factors in step b of mediation analysis.    Table S20. Leave-one-out analysis of association between genetically predicted insomnia and 6 cardiometabolic risk factors in replication analysis of step b of mediation analysis.  Table S21. Leave-one-out analysis of the associations between genetically predicted body mass index and 9 cardiovascular diseases in step c of mediation analysis.                the minimum value and maximum value of inverse variance weighted estimate; b the minimum value and maximum value of P value; SBP: systolic blood pressure; DBP: diastolic blood pressure; SBPadjBMI: systolic blood pressure adjusted body mass index; DBPadjBMI: diastolic blood pressure adjusted body mass index; IVW: inverse variance weighted. 12.9% (5.82% ,19.97%) TEXY: total effect of the exposure on the outcome expressed in odds ratios (OR) scale; NIEXY: natural indirect effect of exposure on the outcome in log OR scale; Proportion: the proportion of the total effect of exposure on outcome that mediator accounts for; CI: confidence interval; DBP: diastolic blood pressure. Figure S1. Flowchart of UK Biobank individual selection.