Author + information
- Received March 14, 2019
- Revision received June 7, 2019
- Accepted July 2, 2019
- Published online September 2, 2019.
- Iyas Daghlas, BSa,b,
- Hassan S. Dashti, PhD, RDa,b,
- Jacqueline Lane, PhDa,b,c,
- Krishna G. Aragam, MD, MSa,b,d,
- Martin K. Rutter, MDe,f,
- Richa Saxena, PhDa,b,c and
- Céline Vetter, PhDa,g,∗ (, )@iyas_daghlas@DrCelineVetter
- aBroad Institute of MIT and Harvard, Cambridge, Massachusetts
- bCenter for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- cAnesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- dCardiology Division, Massachusetts General Hospital, Boston, Massachusetts
- eDivision of Endocrinology, Diabetes and Gastroenterology, Faculty of Biology, Medicine and Health, School of Medical Sciences, University of Manchester, Manchester, United Kingdom
- fManchester Diabetes Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
- gDepartment of Integrative Physiology, University of Colorado at Boulder, Boulder, Colorado
- ↵∗Address for correspondence:
Dr. Céline Vetter, Department of Integrative Physiology, University of Colorado at Boulder, 1725 Pleasant Street, Ramaley N368, 354 UCB, Boulder, Colorado 80309-0354.
Background Observational studies suggest associations between extremes of sleep duration and myocardial infarction (MI), but the causal contribution of sleep to MI and its potential to mitigate genetic predisposition to coronary disease is unclear.
Objectives This study sought to investigate associations between sleep duration and incident MI, accounting for joint effects with other sleep traits and genetic risk of coronary artery disease, and to assess causality using Mendelian randomization (MR).
Methods In 461,347 UK Biobank (UKB) participants free of relevant cardiovascular disease, the authors estimated multivariable adjusted hazard ratios (HR) for MI (5,128 incident cases) across habitual self-reported short (<6 h) and long (>9 h) sleep duration, and examined joint effects with sleep disturbance traits and a coronary artery disease genetic risk score. The authors conducted 2-sample MR for short (24 single nucleotide polymorphisms) and continuous (71 single nucleotide polymorphisms) sleep duration with MI (n = 43,676 cases/128,199 controls), and replicated results in UKB (n = 12,111/325,421).
Results Compared with sleeping 6 to 9 h/night, short sleepers had a 20% higher multivariable-adjusted risk of incident MI (HR: 1.20; 95% confidence interval [CI]: 1.07 to 1.33), and long sleepers had a 34% higher risk (HR: 1.34; 95% CI: 1.13 to 1.58); associations were independent of other sleep traits. Healthy sleep duration mitigated MI risk even among individuals with high genetic liability (HR: 0.82; 95% CI: 0.68 to 0.998). MR was consistent with a causal effect of short sleep duration on MI in CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus Coronary Artery Disease Genetics Consortium) (HR: 1.19; 95% CI: 1.09 to 1.29) and UKB (HR: 1.21; 95% CI: 1.08 to 1.37).
Conclusions Prospective observational and MR analyses support short sleep duration as a potentially causal risk factor for MI. Investigation of sleep extension to prevent MI may be warranted.
- coronary artery disease
- genetic risk score
- Mendelian randomization
- myocardial infarction
- sleep duration
- UK Biobank
Insufficient sleep has been identified as a public health epidemic (1), emphasizing the need to understand risks associated with unfavorable sleep habits. Both short (<7 h) and long sleep duration (>8 h) are associated with greater risk of myocardial infarction (MI) (2–4). Potential mediators of this association include cardiometabolic risk factors (5,6), unhealthy lifestyle behaviors (7), inflammation (8), and endothelial dysfunction (9). Given the global burden of heart disease, it is critical to understand the impact of modifiable risk factors such as sleep duration.
Previous studies have predominantly focused on sleep duration as an isolated risk factor for cardiovascular disease (2). However, sleep is multidimensional (10), such that studies of both the independent and joint effects of sleep duration with other sleep traits such as sleep quality (11), sleep timing (12), insomnia (13), and daytime napping (14) on cardiovascular outcomes are warranted. Furthermore, although a healthy lifestyle appears to reduce coronary artery disease (CAD) risk across strata of genetic liability (15), no work has investigated this finding with regard to sleep health.
Observational studies are susceptible to reverse causality and residual confounding, which limit causal inference. These limitations may be overcome by use of genetic variants (or single nucleotide polymorphisms [SNPs]) as proxies for lifetime exposure to longer or shorter sleep in Mendelian randomization (MR). MR leverages the random assignment of genetic variants at gametogenesis, independently of environmental confounders, to obtain causal estimates of exposure risks that are substantially less confounded and not susceptible to reverse causality (16). Genome-wide association studies (GWAS) have identified suitable genetic variants as proxies for sleep duration, allowing for a test for the hypothesis that sleep duration is a causal risk factor for MI. Establishing causality between sleep duration and coronary disease could have important implications for sleep-targeted interventions to reduce cardiovascular risk.
We tested whether short and long sleep durations are associated with higher MI risk in the UK Biobank (UKB). We investigated whether sleep traits (insomnia symptoms, difficulty getting up, napping, or late sleep timing) or genetic predisposition for CAD modified the association between sleep duration and MI. MR analysis using genetic data from UKB and from the largest, publicly available CAD GWAS (17) was used to assess evidence for causality.
The UKB is an ongoing prospective population-based cohort study that enrolled >500,000 volunteers 40 to 69 years of age from 2006 to 2010 (18). Participants were recruited from across the United Kingdom and enrolled at 1 of several assessment centers. Of the 9 million individuals invited to participate, 5.5% were ultimately enrolled. At baseline recruitment, each participant completed a standardized questionnaire and a standardized interview with a study nurse, and had anthropometric and physiological measurements taken (18). Blood, saliva, and urine were collected from each participant.
Ascertainment of exposure
Sleep duration was self-reported with a standardized question: “About how many hours sleep do you get in every 24 hours? (please include naps),” with responses in hourly increments. We excluded individuals with missing sleep duration and sleep durations <4 or >11 h (to minimize implausible sleep durations and possible confounding by poor health). Questions to assess other sleep traits are listed in the Online Appendix.
Ascertainment of outcomes and covariates
Incident MI was the primary outcome for observational analyses, comprising fatal and nonfatal ST-segment elevation and non–ST-segment elevation MIs. Cases were ascertained through a UKB algorithm combining data from linked hospital admissions and death registries (Online Appendix), with all other participants presumed to be free of myocardial infarction. The last recorded MI was on February 21, 2016, which was used as the censoring date for other participants if no death or outcome had been recorded. This resulted in a median follow-up of 7.04 years. Individuals with baseline self-reported coronary revascularization, ischemic stroke, MI, lung cancer, breast cancer, prostate cancer, and colorectal cancer were excluded; stroke and MI exclusions were supplemented by electronic health record data gathered as part of the UKB outcomes adjudication. Secondary analyses included incident coronary revascularization as an outcome (based on hospital episode database codes K40-46, K49-50, and K75) (19). Unless otherwise specified, all 32 covariates used in multivariable models were ascertained at baseline through self-report or nurse interview (Online Appendix). Sleep medications used for adjustment are listed in Online Table 1.
Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident MI across hours of habitual sleep duration, with 7 to 8 h initially serving as a referent group (additional details in Online Appendix Section 4). We report models that are: 1) unadjusted; 2) adjusted for age and sex; 3) additionally adjusted for body mass index (BMI) and waist-hip ratio; and 4) fully adjusted, including all covariates with a p value <0.10 in the multivariable models. Statistical significance of sleep duration after multivariable adjustment guided creation of bins of habitual sleep duration that aggregated individuals with similar risk attributed to sleep duration (<6, 6 to 9, and >9 h). These bins were used in subsequent models with multivariable adjustment and are the primary reported results. To estimate the independent effect of sleep duration, the main model adjusted for insomnia symptoms. We then adjusted for difficulty getting up, sleep timing, and napping to evaluate whether the association of sleep duration with MI is independent of other dimensions of sleep. Covariate modeling and missing data handling is detailed in the supplement (Online Appendix Section 3).
Analyses involving interactions of sleep duration with the CAD genetic risk score (GRS) were restricted to unrelated participants of White British ancestry (n = 310,917) who passed genomic quality control procedures (see the Genetic Analysis section). The CAD GRS included 68 SNPs achieving genome-wide significance in prior CAD GWAS excluding the UKB (Online Table 2) (20). Each UKB participant’s GRS was calculated as described later in the text for 1-sample MR analyses. We first tested multivariable-adjusted interactions of the GRS with sleep duration. We then stratified participant groups by genetic risk and unfavorable sleep duration (<6 h or >9 h, to maximize power), and tested associations with incident MI; we report estimates for favorable sleep duration within each stratum of genetic CAD risk (1st quartile for low risk; 2nd and 3rd quartile for medium risk; 4th quartile for high risk). To assess reverse causality of coronary disease on sleep duration, we regressed the CAD GRS on long and short sleep duration in logistic regressions adjusted for sex, age, genotyping array, and 10 principal components of ancestry. In addition to genetic interaction analyses, we assessed interactions of sleep duration with sex, insomnia symptoms, difficulty getting up, sleep timing, napping, depression, obesity (using ethnicity-specific cutoffs ), hypertension, and type 2 diabetes (Online Appendix Section 4).
Secondary analyses added incident coronary revascularization to the MI outcome (19). We additionally adjusted for the following diseases self-reported at baseline: hypo- and hyperthyroidism, migraines, rheumatoid arthritis, osteoarthritis, deep vein thrombosis, and chronic obstructive pulmonary disease. To determine whether undiagnosed sleep apnea may be a confounder, we also created and adjusted for a modified STOP-BANG (22) risk scale for sleep apnea (missing the question “Has anyone observed you stop breathing during sleep?” and replacing neck circumference with waist circumference dichotomized to the threshold for metabolic syndrome ). In sensitivity analyses, we excluded participants with baseline CAD risk factors (hypertension, diabetes, high cholesterol, use of aspirin, angina, and smoking) and excluded the first year of follow-up to address concerns of reverse causality (11). We estimated associations without removing participants with extreme (<4 or >11 h) sleep durations. Finally, we used Fine–Gray models to assess whether accounting for the competing risk of death influenced results (24).
Generation of genetic instruments for sleep duration
Genotyping, quality control, and imputation procedures in the UKB are described elsewhere (25). GWAS in individuals of European ancestry in the UKB have identified 78 SNPs associated with continuous sleep duration (n = 446,118), 27 SNPs associated with short sleep duration (<7 h; n = 106,192 cases/305,742 controls), and 8 SNPs associated with long sleep duration (>8 h; n = 34,184 cases/305,742 controls) (Online Tables 3 and 4) (26). We refer to a set of SNPs that proxy sleep duration as “genetic instruments.” These genetic instruments are strongly associated with objectively measured, 7-day accelerometry (n = 85,499) sleep duration estimates in UKB (26).
To minimize bias in effect estimates induced by correlation between SNPs, we restricted our genetic instrument to independent SNPs not in linkage disequilibrium (R2 < 0.1). Analogous to our observational analysis, we started with broad definitions of short (<7 h) and long sleep duration (>8 h). We did not test long sleep in MR given the limited number of SNPs.
This MR study can be conceptualized as a natural experiment whereby, at gametogenesis, study participants are randomly allocated genetic variants that either increase or decrease lifelong exposure to longer or shorter sleep duration. We combine these genetic variants into a multi-SNP genetic instrument that robustly and reliably associates with sleep duration. We then regress the SNP–MI associations against the SNP–sleep associations, and meta-analyze across all SNPs in the genetic instrument. For valid causal estimates, MR makes 3 assumptions: 1) the genetic instrument is strongly associated with the exposure of interest (in this case sleep duration); 2) the genetic instrument does not share common causes with the outcome of interest (e.g., the SNP–sleep estimates being confounded by hypercholesterolemia); and 3) the genetic instrument influences the outcome only through the exposure of interest (no horizontal pleiotropy, e.g., variants influencing the outcome through higher blood pressure) (26). A clinically oriented summary of MR is available elsewhere (27).
Our primary MR analysis used a 2-sample design, where exposures and outcomes are measured in nonoverlapping datasets, which minimizes the false-positive rate (27). We used beta-weighted sleep duration genetic instruments as exposures, and outcome data from a MI GWAS with no participant overlap with UKB (CARDIoGRAMplusC4D; n = 43,676 cases/128,199 controls) (Online Appendix Section 5) (17). Participants in the GWAS were predominantly of European ancestry. To harmonize effects with observational analyses, MI was the primary outcome in MR. We also estimated 2-sample MR associations of sleep duration with CAD in CARDioGRAMplusC4D (n = 60,801 cases/123,504 controls), which included the following outcomes: MI, acute coronary syndrome, chronic stable angina, and coronary stenosis >50%. Fixed-effects inverse-variance weighted (IVW) was our main MR approach. Estimates for the continuous sleep duration trait were scaled to hours by multiplying per-minute betas and SEs by 60. Estimates for the short sleep duration trait were scaled to the increase in odds of MI per doubling in the odds of short sleep duration by multiplying log-odds ratios by 0.693 as previously described (28).
For replication, we used individual-level data of unrelated UKB participants of White British ancestry in 1-sample MR. Here, MI included self-reported heart attack and ICD codes for MI (as used in the phenotypic analyses for incident MI) (Online Appendix Section 2), and CAD included MI and/or revascularization as reported in a previous UKB GWAS (29) (Online Appendix Section 6). We used this combined incident and prevalent GWAS definition of MI and CAD in MR because, unlike in observational studies, there is no concern for disease occurrence influencing the exposure. MR thus represents an opportunity to use genetic instruments independently of the timing of the outcome and baseline assessment. The sum of sleep duration risk alleles multiplied by GWAS effect sizes was regressed against the MI and CAD outcomes, adjusting for the top 10 principal components of ancestry, genotyping array, age, and sex. Effect estimates were scaled as stated in the preceding text.
Sensitivity analyses for pleiotropy, outliers, and confounding
We undertook several analyses to test the second MR assumption that the genetic instrument is not influenced by confounding. First, we tested the association of the genetic instrument with key coronary risk factors. We then created genetic instruments from GWAS (26) adjusted for either BMI, insomnia, or a composite of clinically relevant variables (BMI, naps, Townsend deprivation index, smoking status, alcohol consumption, menopause status, employment status, and sleep apnea). We also used SNP estimates from GWAS that excluded participants participating in shift work or who reported a range of baseline prevalent disease (including coronary disease and ischemic stroke).
We undertook several analyses to test the third MR assumption that the genetic instrument influences the outcome only through sleep duration rather than through pleiotropic pathways. In 2-sample MR, we used random-effects IVW, weighted median (30), MR Egger (31), MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) (32), MR-RAPS (unpublished: Zhao, Wang, Hemani, et al., arXiv 2019), and manual pruning of pleiotropic SNPs associated with cardiometabolic risk factors as sensitivity analyses for genetic confounding through pleiotropy (Online Appendix Section 5). Given the reduced power in these MR sensitivity analyses, in cases where the MR sensitivity analysis results differed between the MI and CAD outcomes, we deferred to the results from the CAD GWAS, because it included ∼17,000 more cases than the MI GWAS (Online Appendix Section 5). We examined leave-one-out plots to identify outlier SNPs. For 1-sample MR sensitivity analyses, we used an unweighted GRS and the control function estimator (Online Appendix Section 5).
A 2-tailed significance threshold of 0.05/2 = 0.025 was used for all analyses except interaction analyses; for these, we used a Bonferroni-adjusted alpha of 0.05/11 = 0.0045 to account for multiple comparisons. Analyses were conducted in R software version 3.3 (R Foundation for Statistical Computing, Vienna, Austria), and the TwoSampleMR package was used for MR analyses (33).
Sleep duration and incident MI
The analytic sample for the prospective cohort analysis consisted of 461,347 participants, with 5,218 incident MIs over a median follow-up of 7.04 years (interquartile range: 6.33 to 7.74 years) (Figure 1). Baseline characteristics across hours of sleep duration are shown in Online Table 5. Participants regularly sleeping 7 to 8 h were more likely to be employed and report excellent self-reported health, and were less likely to report a history of smoking, depression, high cholesterol, or hypertension. Online Table 6 compares characteristics of excluded participants to included participants.
In age- and sex-adjusted analyses, participants sleeping <7 h or >8 h had a significantly higher risk of incident MI, and effect sizes across strata of sleep duration were consistent with a dose-dependent association (Table 1). After multivariable adjustment sleep durations of 4, 5, 10, and 11 h remained independently associated with incident MI (Table 1). We thus binned 6, 7, 8, and 9 h into the referent group for subsequent observational analyses. Short and long sleep were consistently associated with incident MI after full multivariable adjusted HR (MVHR) (MVHR<6h = 1.20; 95% CI: 1.07 to 1.33; p = 0.001; MVHR>9h = 1.34; 95% CI: 1.13 to 1.58; p = 0.0006) (Table 2). We found no evidence for effect modification of the association of sleep duration with MI (Online Table 7). Results were robust in sensitivity and secondary analyses (Table 2), were similar when including coronary revascularization in the outcome (MVHR<6h = 1.12; 95% CI: 1.03 to 1.23; MVHR>9h = 1.25; 95% CI: 1.08 to 1.44) (Online Table 8), and were unchanged in Fine–Gray models treating death as a competing risk. Associations were similar in analyses not imposing a sleep duration cutoff (Online Tables 9 and 10). The sleep duration effects persisted with control for other sleep traits and disturbances, and were not influenced by control for a modified STOP-BANG risk scale for sleep apnea (Online Table 11). Compared with individuals without insomnia sleeping 6 to 9 h, concomitant short sleep duration and frequent insomnia symptoms were associated with a 30% higher risk of incident MI (MVHR: 1.30; 95% CI: 1.15 to 1.47). Relative to those with favorable (6 to 9 h) sleep duration and the least difficulty getting up, those with unfavorable sleep duration (<6 h or >9 h) and who reported getting up to be “not at all easy” had an 81% higher risk of incident MI (MVHR: 1.81; 95% CI: 1.42 to 2.31).
Interplay between genetic risk of CAD and sleep duration on risk of incident MI
The CAD GRS was associated with increased risk of incident MI (n = 310,917, cases = 3,513; adjusted HR for 1 SD increase = 1.31; 95% CI: 1.27 to 1.35; Q4 vs. Q1 HR: 1.91; 95% CI: 1.74 to 2.10). There was no evidence of interaction between short or long habitual sleep duration with the GRS, suggesting independent contributions of genetic predisposition and sleep duration to MI risk (p = 0.13 and 0.14, respectively). Compared with those with 6 to 9 h of sleep and low genetic risk (lowest 25% genetic risk), having unfavorable sleep duration (<6 or >9 h) and high genetic risk (top 25% genetic risk) was associated with a 130% higher risk of MI (MVHR: 2.30; 95% CI: 1.88 to 2.82) (Figure 2). Point estimates were consistent with a cardioprotective association of favorable sleep duration at high genetic CAD risk (MVHRfavorable sleep duration: 0.82; 95% CI: 0.68 to 0.998; p = 0.048) (Online Table 12). There was no association between the CAD GRS and short (p = 0.21) or long (p = 0.95) sleep duration.
MR of sleep duration against MI
IVW estimates in 2-sample MR were consistent with a causal effect of short sleep duration on MI (odds ratio [OR]per additional h of sleep: 0.80; 95% CI: 0.67 to 0.95; p = 0.013; ORshort sleep: 1.19; 95% CI: 1.09 to 1.29; p = 4.2e-04) (Figure 3). Similar results were seen with CAD in 2-sample MR (ORper additional h of sleep: 0.79; 95% CI: 0.68 to 0.92; p = 3.20e-03; ORshort sleep: 1.24; 95% CI: 1.11 to 1.38; p = 1.79e-06) (Online Table 13).
One-sample MR analyses were restricted to 337,532 unrelated UKB participants of White British ancestry (n = 17,157 cases/320,375 controls). We observed similar causal effects for shorter sleep duration on MI (ORper additional h of sleep: 0.86; 95% CI: 0.70 to 1.06; p = 0.17; ORshort sleep: 1.21; 95% CI: 1.08 to 1.37; p = 1.47e-03) (Figure 3). The overlap of the confidence intervals for the continuous sleep duration genetic instrument estimates in UKB was likely driven by low power, as the estimates for CAD (sample size n = 17,157 cases/320,375 controls) did not overlap with the null (OR: 0.81; 95% CI: 0.68 to 0.97; p = 0.02) (Online Figure 1). Results from unweighted analyses and the control function estimator (ORper additional h of sleep: 0.84; 95% CI: 0.67 to 1.04) were similar.
MR sensitivity analyses
The genetic instrument was associated with BMI (Online Table 14), consistent with either a confounding or mediating role for BMI. Using a genetic instrument adjusted for BMI in 2-sample MR did not influence results (ORper additional h of sleep: 0.81; 95% CI: 0.65 to 0.99) (Online Table 15). Similar results were obtained using an instrument controlling for insomnia and a range of lifestyle traits (Online Table 15). To further address confounding of SNP–sleep associations by occupation or prevalent disease, we used a genetic instrument from GWAS excluding shift-workers or participants with a range of prevalent disease, including stroke and coronary disease; this also yielded similar effect estimates (Online Table 15).
Sensitivity analyses testing violations of the third MR assumption were consistent with the primary analysis, indicating that pleiotropy was likely not driving results (Online Table 13). The MR Egger intercept test for horizontal pleiotropy was not significant (pcontinuous sleep = 0.23; pshort sleep = 0.22). A single variant considerably distorted the weighted median and MR Egger estimates, and effects were more concordant with IVW estimates when this outlier was pruned (Online Table 13). Leave-one-out-analysis did not reveal outlier SNPs driving IVW associations (Online Figures 2 to 5).
In this first MR analysis of sleep duration and coronary disease, we identified a potentially causal effect of short sleep duration on MI. Prospective observational analysis identified a dose-dependent contribution of short and long habitual sleep duration to the risk of incident MI independent of numerous confounders and sleep traits. Concomitant insomnia symptoms and difficulty getting up exacerbated this risk. Favorable sleep duration protected against MI independently of individual genetic predisposition to coronary disease. Altogether, our results highlight sleep as a modifiable and potentially causal risk factor for MI regardless of inherited risk and other sleep traits. These findings are summarized in the Central Illustration.
MR analyses were consistent with a causal link between shorter sleep duration and MI, and were robust to numerous sensitivity analyses for confounding, horizontal pleiotropy, and reverse causality. Given that MR is a priori less susceptible to confounding and reverse causality, these results provide high-quality evidence supporting sleep duration as a potentially causal risk factor for MI. These findings, triangulated (34) with our robust prospective observational findings, are supported by a strong mechanistic basis, with pathways including metabolic disease (5), deranged sympathetic function (5), and impaired endothelial function (9). Direct comparison of genetic with observational estimates is limited given that inherited genetic variation influences exposures over the life course, whereas observational associations capture phenotypes at 1 point in life. This potentially explains why MR demonstrated an effect for <7 h sleep duration per night, whereas observational analyses showed an association for <6 h/night. This causal evidence is timely, because recent work has demonstrated that sleep extension for short sleepers is a feasible intervention (35). However, randomized trials of sleep extension will be the most rigorous test of causality.
The second key contribution is the finding that a healthy sleep duration mitigates risk of MI even among those with high genetic liability. This is in line with previous work showing that a healthy lifestyle may mitigate inherited risk for CAD (15), with our results extending this finding to sleep health. Finally, we showed that the association of sleep duration with MI was independent of all other sleep traits and additively increased risk when comorbid with sleep disturbances. There was no evidence of interaction between sleep traits, implying that effects of individual sleep traits on coronary risk are unchanged by the presence or absence of other sleep traits. This is overall consistent with prior work (13), however, we did not replicate a previously reported interaction between sleep quality and sleep duration (11).
Residual confounding and reverse causality potentially explain observational associations, but have a smaller effect on MR analyses. For instance, whereas sleep apnea is a risk factor for CAD, its prevalence in the UKB is lower than in previous studies and is likely incompletely assessed (36). However, adjusting for a modified STOP-BANG sleep apnea risk scale did not influence results. As evidence against reverse causality, the CAD GRS was not associated with sleep duration, and results from lag-time analyses were largely unchanged. Results from MR sensitivity analyses using sleep duration GWAS with exclusions for baseline comorbidities also indicated that our causal estimates were unlikely to be biased by confounding or reverse causality. Other limitations include the use of self-reported rather than objective sleep duration assessment (37), and selection of relatively healthy participants into UKB, which might induce collider bias (38). We did not have information on whether participants were lost to follow-up (e.g., emigration), which may have led to misclassification of cases as controls. If nondifferential, this measurement error would likely bias results towards the null. Finally, generalizability of genetic analyses is limited to individuals of European ancestry.
Altogether, triangulation of MR and observational analyses support short sleep duration as a potentially causal risk factor for MI, and a healthy sleep duration may mitigate MI risk among those at high genetic risk.
COMPETENCY IN MEDICAL KNOWLEDGE: Short duration of sleep increases the risk of developing acute myocardial infarction, and healthy sleep duration may be cardioprotective for people with a genetic predisposition to coronary disease.
TRANSLATIONAL OUTLOOK: Future research should investigate whether interventions to lengthen sleep can help prevent coronary events in short sleepers with cardiac risk factors.
This research has been conducted using the UK Biobank Resource (application 6818). The authors thank the staff and participants of the UK Biobank and the UK Biobank Sleep and Chronotype Genetics team. They also thank the following groups who made summary statistics publicly available for analysis: C4D (Coronary Artery Disease Genetics Consortium) and CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis).
This work was supported by the National Institutes of Health, NIH/NIDDK grant R01DK105072 (Dr. Saxena) and R01DK107859 (Dr. Saxena), and the Phyllis and Jerome Lyle Rappaport MGH Research Scholar Award (Dr. Saxena). Dr. Rutter is supported by The University of Manchester Research Infrastructure Fund. The funders had no role in the study design; data collection; data analysis and interpretation; writing of the report; or the decision to submit for publication. Dr. Rutter has been a consultant and Advisory Board member for GlaxoSmithKline, Novo Nordisk, Roche, and Merck Sharp & Dohme; has received lecture fees from Merck Sharp & Dohme; and has received grant support from Novo Nordisk, Merck Sharp & Dohme, and GlaxoSmithKline. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.
- Abbreviations and Acronyms
- body mass index
- coronary artery disease
- confidence interval
- genetic risk score
- genome-wide association study
- hazard ratio
- inverse-variance weighted
- myocardial infarction
- Mendelian randomization
- multivariable adjusted hazard ratio
- odds ratio
- single nucleotide polymorphism
- UK Biobank
- Received March 14, 2019.
- Revision received June 7, 2019.
- Accepted July 2, 2019.
- 2019 American College of Cardiology Foundation
- Centers for Disease Control and Prevention
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