Insomnia and Coronary Artery Diseases: A Mendelian Randomisation Study : Cardiology Discovery

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Original Articles

Insomnia and Coronary Artery Diseases: A Mendelian Randomisation Study

Zhang, Wenjuan1; Zha, Lingfeng2; Dong, Jiangtao3; Chen, Qianwen4; Wu, Jianfei5; Tang, Tingting2; Xia, Ni2; Zhang, Min2; Jiao, Jiao2; Xie, Tian2; Xu, Chengqi5; Tu, Xin5,∗; Nie, Shaofang2,∗

Editor(s): Fu, Xiaoxia; Xu., Tianyu

Author Information
Cardiology Discovery 1(3):p 154-162, September 2021. | DOI: 10.1097/CD9.0000000000000019
  • Open

Abstract

Clinical Perspective

WHAT IS NEW

  • Observational studies indicate that insomnia may increase the risk of developing and/or dying from cardiovascular diseases, especially coronary artery disease (CAD). However, the underlying causal relationship between insomnia and CAD is not clear yet.
  • A two-sample Mendelian Randomization study was carried out to explore the exact causal relationship between insomnia and CAD using public genome-wide association studies summary data.
  • Some valuable variants might involve in the development of CAD by leading the insomnia, which might be a causal factor for CAD.

WHAT ARE THE CLINICAL IMPLICATIONS?

  • Although epidemiological studies suggested that insomnia symptoms are independently associated with the incidence of cardiovascular diseases, whether it is causative or not is still unknown.
  • A mendelian randomization analyses found that abnormal sleep is an important signal of increased CAD risk.
  • Improving the quality of sleep might be a new way for populations with insomnia to prevent CAD.

Introduction

Insomnia, which is categorized by insomnia early, insomnia late, and insomnia middle, is highly prevalent in adults, especially in the population older than 65 years old.[1,2] The prevalence of insomnia symptoms is approximately 30% to 35% worldwide, and the effects can be devastating for both patients and society.[3] Anecdotal evidence suggests that insomnia is related to a range of other disorders, such as anxiety disorders, alcohol abuse, and major depression.[4,5] Earlier evidence suggests an apparent association between insomnia symptoms and cardiovascular diseases: under the prospective studies, researchers observed that insomnia increased the risk of adverse cardiovascular outcomes[6]; some researchers reported that insomnia increased the risk of myocardial infarction (MI) and heart failure[7]; some researchers reported that insomnia is significantly associated with the incidence of hypertension,[8–11] while others reported that insomnia moderately influenced the incidence of acute MI.[12] Recently, a large-scale cohort study from the China Kadoorie Biobank indicated that the number of insomnia symptoms was positively correlated with the overall risk of total cardiovascular diseases.[13] These results highlighted the importance of high-quality sleep for the prevention of cardiovascular diseases. However, the exact causality between insomnia and cardiovascular diseases remains unexplored.

Although many epidemiological investigations show that multiple biomarkers are associated with complex diseases, the ability of traditional observational studies in determining whether these factors are causal is limited. Mendelian randomization (MR) is an emerging method to evaluate the causal relationship between exposures and outcomes by using genetic variants.[14] Because genetic variants are obtained before birth, genetic markers are good risk factors due to their independence from confounding factors. Over the past decade, MR studies have provided important new insights into disease etiology, especially in cardiovascular diseases. Many related studies have focused on the causal relationships between the biomarkers and coronary artery disease (CAD): C reactive protein (CRP) was only a noncausal inflammation biomarker for CAD, and low-density lipoprotein cholesterol (LDL-C) was a causal risk factor for CAD.[15–18] Formerly, MR analyses were called single-sample MR or one-sample MR because they were generally carried out using instruments, exposure, and outcome that were assessed in the same sample; however, it is often either too difficult or too expensive to measure both the exposure and outcome in the same sample. Researchers later termed the MR analyses as two-sample MR because they were carried out by measuring the exposure and outcome in different samples. Since then, the scope of MR analysis has been expanded, and MR studies continue to gain popularity.[19] Until now, genome-wide association studies (GWASs) achieved great success in discovering the genetic risk loci for complex diseases. With increasing GWAS data being readily available and free, a two-sample MR study can be carried out on publicly available GWAS summary data, which is of great value to researchers.[20,21] Interestingly, recent large-sample GWASs from the UK Biobank have reported some genetic susceptibility loci for both insomnia symptoms and CAD. Although epidemiological studies suggested that insomnia symptoms are independently associated with the incidence of cardiovascular diseases, whether it is causative or not is still unknown. Here, we carried out a two-sample MR study to explore the exact causal relationship between insomnia and CAD using public GWAS summary data.

Methods

Study design

A two-sample bidirectional MR analysis was carried out using publicly available GWAS summary data [Figure 1], to reveal the causal relationship between insomnia and CAD.[22,23] The two-sample bidirectional MR analysis is an emerging and effective method to test the causal assumption in epidemiology, and researchers could profit from its tremendous sample sizes. Single nucleotide polymorphisms (SNPs) that were identified to be susceptible to insomnia in previous GWASs (P < 5.0 × 10−8) were selected as instrumental variables to evaluate the causal influence of insomnia on CAD risk [Figure 1]. Cases and non-cases were diagnosed by the physician according to the relevant guidelines with the clinical data. To evaluate the potential pleiotropy of the selected insomnia-related SNPs that may exert on CAD via other CAD risk factors, we also analyzed data on the association of selected insomnia SNPs with a range of CAD risk factors: fasting insulin (FI), fasting glucose (FG), diastolic blood pressure (DBP), systolic blood pressure (SBP), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), LDL-C, type 2 diabetes mellitus (T2D), body mass index (BMI) and BMI-adjusted waist-to-hip ratio (WHR). Considering that the genetic risk for CAD events may also be a causal factor for insomnia symptoms, we performed an opposite direction MR analysis.

F1
Figure 1:
Study design for Mendelian randomization (MR). MR was used to test our assumption that exposure (insomnia) could cause outcomes (CAD). The 3 assumptions of MR are as follows: ① SNPs must be associated with insomnia, ② SNPs must not be associated with confounders, and ③ SNPs must affect CAD through insomnia and not in other ways. CAD: Coronary artery disease; SNP: Single nucleotide polymorphism.

Data sources

The association data were obtained from publicly available GWAS summary data [Figure 2]: insomnia from http://ctg.cncr.nl/software/summary_statistics[24]; CAD from CARDIoGRAMplusC4D Consortium[25]; glycohemoglobin and FI from Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC)[26]; T2D from DIAGRAM consortium[27]; HDL-C, LDL-C, TG and TC from Global Lipids Genetics Consortium (GLGC)[28]; BMI[29] and BMI-adjusted WHR from Genetic Investigation of Anthropometric Traits (GIANT)[30]; and SBP and DBP from the International Consortium for Blood Pressure (ICBP).[31]

F2
Figure 2:
Data sources and instrumental variables selection. BMI: Body mass index; CAD: Coronary artery disease; DBP: Diastolic blood pressure; DIAGRAM: Consortium for T2D; FG: Fasting glucose; FI: Fasting insulin; GIANT: Genetic Investigation of Anthropometric Traits; GLGC: Global Lipids Genetics Consortium; GWAS: Genome-wide association study; HDL-C: High-density lipoprotein cholesterol; ICBP: International Consortium for Blood Pressure; LDL-C: Low-density lipoprotein cholesterol; MAGIC: Meta-Analyses of Glucose and Insulin-Related Traits Consortium; MR: Mendelian randomization; r 2: Degree of linkage disequilibrium; SBP: Systolic blood pressure; SNPs: Single nucleotide polymorphisms; T2D: Type 2 diabetes mellitus; TC: Total cholesterol; TG: Triglycerides; WHR: Waist-to-hip ratio.

Instrumental variables

We acquired SNPs associated with insomnia from publicly available GWAS summary data. GWAS and genome-wide gene-based association studies indicated that 12 independent SNPs [Table 1] were strongly (P < 5.0 × 10−8; linkage disequilibrium r2 < 0.1) associated with insomnia based on 113,006 individuals in the UK Biobank Study.[24] We then retrieved the summary level data of these 12 SNPs in the CAD GWAS dataset obtained from the CARDIoGRAMplusC4D Consortium and downloaded them from the open data websites (www.cardiogramplusc4d.org). Eight of the 12 SNPs associated with insomnia [Table 2] were included in the CAD GWAS dataset and selected for our main instrumental variable analyses [Figure 2]. We performed the opposite direction MR analysis using 64 SNPs associated with CAD, as the genetic risk of CAD events may also help predict insomnia.

Table 1 - Insomnia-associated SNPs in GWAS
SNP Gene EA NA EAF OR BETA SE P
rs375216017 MEIS1 GT G 0.105057 1.08602 0.089574 0.01572 1.21 × 10−8
rs62144051 MEIS1 G A 0.094156 1.09558 0.096151 0.016318 3.81 × 10−9
rs62144053 MEIS1 A G 0.094736 1.09807 0.098931 0.016272 1.20 × 10−9
rs62144054 MEIS1 A G 0.093791 1.09813 0.097846 0.016237 1.68 × 10−9
rs113851554 MEIS1 T G 0.055851 1.18877 0.178239 0.020371 2.14 × 10−18
rs182588061 MEIS1 T G 0.020027 1.20601 0.228449 0.03632 3.18 × 10−10
rs139775539 MEIS1 A AC 0.047642 1.19029 0.186994 0.022403 7.00 × 10−17
rs11679120 MEIS1 A G 0.047453 1.19076 0.187813 0.022461 6.18 × 10−17
rs115087496 MEIS1 C G 0.047391 1.17852 0.177042 0.022576 4.43 × 10−15
rs549771308 MEIS1 C CT 0.119845 1.07652 0.090546 0.015777 9.51 × 10−9
rs11693221 MEIS1 T C 0.047889 1.17001 0.170821 0.022572 3.79 × 10−14
rs574753165 SCFD2 G A 0.005252 1.39765 0.39474 0.067503 4.98 × 10−9
BETA: Effect size; EA: Effect allele; EAF: Effect allele frequency; GWAS: Genome wide association studies; NA: No effect allele; OR: Odds ratio; P: P values indicate genome-wide significance in GWAS; SE: Standard error of the effect size; SNP: Single nucleotide polymorphism.

Table 2 - Characteristics of the studied insomnia-associated SNPs in both insomnia and CAD GWAS
Insomnia results CAD results


SNP Gene EA NA OR BETA SE P logOR SE_gc P value_gc
rs62144051 MEIS1 G A 1.09558 0.096151 0.016318 3.81 × 10−9 0.0134 0.01544 0.385361
rs62144053 MEIS1 A G 1.09807 0.098931 0.016272 1.20 × 10−9 0.0119 0.01553 0.44368
rs62144054 MEIS1 A G 1.09813 0.097846 0.016237 1.68 × 10−9 0.01254 0.01552 0.419132
rs113851554 MEIS1 T G 1.18877 0.178239 0.020371 2.14 × 10−18 0.02204 0.0196 0.260858
rs182588061 MEIS1 T G 1.20601 0.228449 0.03632 3.18 × 10−10 0.02437 0.02394 0.308773
rs139775539 MEIS1 A AC 1.19029 0.186994 0.022403 7.00 × 10−17 0.02766 0.02069 0.181329
rs11679120 MEIS1 A G 1.19076 0.187813 0.022461 6.18 × 10−17 0.02543 0.02108 0.227643
rs11693221 MEIS1 T C 1.17001 0.170821 0.022572 3.79 × 10−14 0.03329 0.0215 0.121583
BETA: Effect size; CAD: Coronary artery disease; EA: Effect allele; gc: Correction of GWAS, obtained from the CARDIoGRAMplusC4D Consortium of CAD GWAS dataset; GWAS: Genome-wide association studies; NA: No effect allele; OR: Odds ratio; P: P values indicate genome-wide significance in GWAS; SE: Standard error of the effect size; SNP: Single nucleotide polymorphism.

Statistical analysis

SNPs from different data sources were matched and assigned to the same effect allele. The inverse-variance weighted meta-analysis method, as well as the weighted median and MR-Egger regression methods, were used to analyze the association of the reference variants with CAD and MI.[32]P < 0.008 ((P < 0.05)/8 SNPs) was the cut-off of statistical significance for the analyses of 8 SNPs based on the Bonferroni test; statistical tests for the MR analyses of CAD and MI were thought to be statistically significant at P < 0.025 ((P < 0.05)/2 outcome measures). R (R Foundation, version 3.2.5) was used to conduct all our analyses.

Patient involvement

No patients were involved in this study. This study was carried out based on the STREGA reporting guidelines.[33]

Results

Causal effect of insomnia on CAD

60,801 cases and 123,504 non-cases from case-control studies were included in our analysis. Eight independent SNPs were significantly associated with insomnia in 113,006 individuals in the UK Biobank Study, whereas none of these 8 SNPs achieved the significance level of the Bonferroni test (P < 0.006) for their association with CAD. Furthermore, none of the 8 SNPs were associated with cardiovascular risk factors (P > 0.05 or no data available in GWASs) [Table 3].

Table 3 - GWAS data of the insomnia-associated SNPs in potential confounders
GLGC MAGIC DIAGRAM ICBP GIANT




SNP EA NA HDL-C LDL-C TG TC FG FI T2D SBP DBP BMI WHR_adj_BMI
rs62144051 G A
rs62144053 A G
rs62144054 A G
rs113851554 T G
rs182588061 T G
rs139775539 A AC
rs11679120 A G
rs11693221 T C 0.4871 0.675 0.2904 0.3416 0.8863 0.2476 0.45 0.83 0.826 0.71 0.37
–: No data; The significance P value was more than 0.006 (0.05/0.8); adj: Adjust; BMI: Body mass index; DBP: Diastolic blood pressure; DIAGRAM: Consortium for T2D; EA: Effect allele; FG: Fasting glucose; FI: Fasting insulin; GIANT: Genetic Investigation of Anthropometric Traits; GLGC: Global Lipids Genetics Consortium; GWAS: Genome-wide association studies; HDL-C: High-density lipoprotein cholesterol; ICBP: International Consortium for Blood Pressure; LDL-C: Low-density lipoprotein cholesterol; MAGIC: Meta-Analyses of Glucose and Insulin-Related Traits Consortium; NA: No effect allele; SBP: Systolic blood pressure; SNP: Single nucleotide polymorphism; T2D: Type 2 diabetes mellitus; TC: Total cholesterol; TG: Triglycerides; WHR: Waist-to-hip ratio.

The results showed that the effects of insomnia on CAD were consistent with the effects of 8 SNPs on insomnia and CAD, indicating a significant correlation between insomnia and CAD [Figure 3]. Later, using the inverse-variance weighted method, we found a causal association between insomnia symptoms and the prevalence of CAD (P = 0.002; odds ratio (OR) = 1.15; 95% CI: 1.05–1.25) [Figure 4]. Furthermore, consistent results regarding the causal association between insomnia symptoms were obtained in the complementary analyses with a weighted median method (P = 0.015; OR = 1.14; 95% CI: 1.03–1.27) and MR-Egger regression with a broader CI range (OR = 1.15; 95% CI: 0.87–1.50). No evidence showed pleiotropy (MR-Egger intercept; P = 0.997) or heterogeneity between the MR assessments obtained for disparate SNPs (P = 0.9993 for heterogeneity).

F3
Figure 3:
Comparison plot of the association between insomnia-related SNPs associated with insomnia and CAD. Each dot represents the effect of the SNP on insomnia and CAD. The slope of the line represents the causal association. CAD: Coronary artery disease; SNPs: Single nucleotide polymorphisms.
F4
Figure 4:
Forest plot shows the estimate of the effect of genetically increased insomnia risk of CAD. CAD: Coronary artery disease; CI: Confidence interval; EA: Effect allele; NA: No effect allele; OR: Odds ratio; SNP: Single nucleotide polymorphisms.

Causal effect of CAD on insomnia

To identify whether genetic risk for CAD events is also a causal factor for insomnia symptoms, we performed an opposite direction MR using 64 CAD susceptibility SNPs. However, little evidence was found for the assumption that the genetic risk of CAD events is associated with insomnia symptoms (P = 0.990; OR = 1.00; 95% CI: 0.96–1.04) [Figures 5 and 6]. The results were unchanged after analysis with the weighted median method (P = 0.111; OR = 0.95; 95% CI: 0.90–1.01) and MR-Egger method (P = 0.224; OR = 0.94; 95% CI: 0.86–1.04) [Figure 6]. This evidence indicated no reverse causality between CAD events and insomnia symptoms.

F5
Figure 5:
Comparison plot of the relationship between CAD-related SNPs associated with insomnia and CAD. Each dot represents the effect of the SNP on insomnia and CAD. The slope of the line represents the causal association. CAD: Coronary artery disease; SNPs: Single nucleotide polymorphisms.
F6
Figure 6:
Association of genetic liability to CAD (exposure) on insomnia symptom (outcome). CAD: Coronary artery disease; CI: Confidence interval; IVW: Inverse variance weighted approach; MR: Mendelian randomization; OR: Odds ratio. Sixty-four CAD associated SNPs were included in the analysis.

Discussion

This two-sample bidirectional MR analysis shows that the genetic susceptibility to insomnia is associated with an increased risk of CAD and MI. The findings of our study corroborate the results of several observational studies that indicated a significantly positive correlation between insomnia and CAD risk.[34,35]

Comparison with previous studies

Various observational and epidemiological investigations have indicated an association between insomnia and CAD and highlighted the importance of high-quality sleep for the prevention of CAD. In 2014, a meta-analysis including 311,260 participants from 17 cohort studies showed that insomnia symptoms prominently increased the risk of different kinds of cardiovascular diseases, such as MI, CAD, and stroke, and raised the mortality of CAD (hazard ratio (HR) = 1.33; 95% CI: 1.13–1.57).[36] In 2017, Javaheri and Redline[37] reviewed the past studies and reported that insomnia might increase the risk of hypertension, CAD, acute coronary syndrome, and heart failure. Lüscher[38] recently pointed out that insomnia is a novel cardiovascular risk factor. However, the exact causal relation between insomnia and CAD remains unexplored. Our study used the latest research methods to thoroughly investigate the possibility of causality between insomnia and CAD.

Potential mechanisms

The exact mechanisms involved in the associations between insomnia and CAD have not been fully clarified. Numerous observational, prospective, and experimental studies have demonstrated that insomnia can trigger inflammation in several ways, including increasing systemic inflammation; activating the nuclear factor (NF)-κB transcription control pathway; activating proinflammatory cytokines produced by monocytes; and increasing markers of systemic inflammation, such as CRP and IL-6, which are decreased after standard therapy for insomnia.[39,40] Immune activation in response to insomnia can elicit profound inflammatory diseases, such as asthma, inflammatory bowel disease, and cardiovascular disease. Inflammation is a key feature of atherosclerosis and represents a well-known pathogenic mechanism that underlies both coronary plaque progression and stability and adverse events following stent implantation.[41,42] Anti-inflammatory treatment for cardiovascular disease has become a promising direction, and anti-inflammatory drugs such as canakinumab have been shown to significantly reduce cardiovascular events.[43] Studies have shown that statins, as important drugs for cardiovascular disease, can regulate inflammatory nuclear transcription factors, and eventually reduce the expression of adhesion molecules and inflammatory factors, and decrease the level of proinflammatory factors.[44,45] In addition, a significant reduction in CRP has been found to be predictive of CAD. IL-6 participates in myocardial damage during ischemia-reperfusion and leads to the instability of atherosclerotic plaques. Clinical trials indicated that tocilizumab, the IL-6 receptor antagonist, attenuated the inflammatory response, reduced CRP levels, and then decreased the release of troponin T (TnT) in non ST-segment elevation myocardial infarction (NSTEMI), which has become a potential therapeutic target in MI.[46] Based on a large number of previous studies, if insomnia is correlated with cardiovascular diseases and inflammation, a signal of the onset of cardiovascular diseases, then a credible assumption is that treating insomnia may alleviate inflammation and reduce the risk of cardiovascular disease. Treatment strategies targeting inflammatory biotype and/or the insomniac symptoms will be a possible development prospect for the prevention of cardiovascular disease. Emerging evidence will support this possibility.

When investigating the temporal relationship between sleep and adverse outcomes, the potential effects of cardiovascular risk factors must be considered. A study indicated that sleep disturbances increased the risk of later-onset T2D.[47] Moreover, it remains ambiguous whether abnormal sleep is the marker of undetermined prevalent CAD or of early/latent disease that is subsequently reflected in adverse cardiovascular outcomes and death. Many researchers have tried to minimize the influences of baseline prevalent CAD by ruling out individuals with these conditions, whereas other have adjusted for these factors. Although exclusion criteria and adjustments were implemented in our sensitivity analysis, there may not be residual confounders because there may be unidentified or potential disease or risk factors, as many population-based studies assess these variables at baseline instead of based on self-reporting. Another important concern that needs to be accounted for is whether there is a direct correlation between the increased incidences of cardiovascular risk and/or abnormal sleep patterns that lead to poor health behaviors and increased cardiovascular risk, therefore leading to an increase in cardiovascular events. In this study, our results showed that the effects of 8 insomnia-associated SNPs on insomnia and CAD were consistent, indicating a significant causal relationship between insomnia and CAD. Interestingly, the 8 insomnia-related SNPs were in MEIS1, which was previously associated with clinically diagnosed restless legs syndrome (RLS), a common neurological disorder characterized by nocturnal dysesthesias and an urge to move.[48,49] Recently, Mahmoud et al[50] identified MEIS1 as a key regulatory factor for the cardiomyocyte cell cycle and suggested it may be a promising therapeutic target for cardiac regeneration. In mouse cardiomyocytes, when MEIS1 was knocked out, it was adequate to amplify the proliferative window of postnatal cardiomyocytes; however, reactivating the mitosis of cardiomyocytes in the adult heart caused no adverse influence on cardiac function. In contrast, when MEIS1 is overexpressed in cardiomyocytes, neonatal myocyte proliferation is reduced, and neonatal cardiac regeneration is suppressed.[51] To date, there is little evidence that MEIS1 is directly involved in CAD or affects CAD risk factors, and our results suggest that there is no inverse causal relationship between insomnia and CAD. We speculate that MEIS1 affects insomnia through a complex mechanism, which then increases the risk of CAD in a certain way.

Advantages and limitations

This study has some crucial advantages. Here, we explored the causal relation between insomnia and CAD using a two-sample bidirectional MR analysis dependent on publicly available summary data from GWASs. The two-sample bidirectional MR analysis is an emerging and effective method to test the causal assumption in epidemiology, and researchers could profit from its tremendous sample sizes. Our study also investigated the potential pleiotropy of our genetic variants to reduce bias from confounding, confirming the causal relationship between insomnia and CAD.

This study also has several limitations. First, the partial overlap may exist among the studies involved in the GWAS meta-analysis of insomnia and the CARDIoGRAMplusC4D consortium. This may have resulted in model overfitting when the SNP-insomnia associations were assessed in the study involved in the CARDIoGRAMplusC4D consortium. This limitation could be solved or weekend by strong instruments, such as the F statistic.[50] Second, some clinical data are missing from this study, such as age and sex, so the underlying non-linear correlation between insomnia and CAD could not be assessed. Future studies with the dataset of age and sex could fill in the vacancy. Third, a larger sample size necessary for the replication of our results were unavailable. Future studies with a larger sample size might solve this limitation. The dependability of MR analysis findings relies on 3 main hypotheses that may be affected by pleiotropy, stratified population, and linkage disequilibrium. The influences of population stratification on the results cannot be excluded thoroughly, as the CARDIoGRAMplusC4D consortium contained populations from distinct ancestries, which possibly have distinct allele frequencies. However, the analyses in the CARDIoGRAMplusC4D consortium were performed independently and then integrated; furthermore, we carried out the genetic principal component analysis for adjustment to reduce the possible influence of the stratified population.

Conclusions

In conclusion, our MR analyses found a significant association between insomnia and CAD. Our study reminds us that abnormal sleep is an important signal of increased CAD risk, and more consideration should be given to explore these risks, as well as the duration and quality of sleep, during patient consultations.

Acknowledgments

We thank everyone who participated in and supported this research. We are also grateful to the GWAS consortia for the open and free access to the summarized data we used in our MR study. We would like to thank Editage (www.editage.cn) for English language editing.

Funding

This study was funded by grants from the National Natural Science Foundation of China (No. 81500186 to S. Nie; No. 81670361 to T. Tang).

Author Contributions

Wenjuan Zhang, Lingfeng Zha and Jiangtao Dong participated in the performance of the research, in data analysis and in the writing of the paper; Qianwen Chen, Jianfei Wu, Tingting Tang, Min Zhang, Jiao Jiao, Ni Xia, Tian Xie and Chengqi Xu contributed new reagents or analytic tools and participated in the performance of the research; Shaofang Nie, Xin Tu and Xiang Cheng participated in research design and data analysis and in the writing of the paper. All authors reviewed the paper.

Conflicts of Interest

None.

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Keywords:

Coronary artery diseases; Genetic variants; Insomnia; Mendelian randomization

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