
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-07481
00001
10.1097/MD.0000000000039741
3
3400
Research Article
Observational Study
Sleep duration and heart failure risk: Insights from a Mendelian Randomization Study
Zeng Lianlin MD lianlin2020@outlook.com
a
Fu Shasha MD 1753029961@qq.com
a
Xu Hailan MD 1172159208@qq.com
a
Zhu Lutao MD 1258088791@qq.com
a
Li Xiaomei BD 3066243866@qq.com
a
Cheng Kang BD 406559531@qq.com
a
Li Yangan BD 3066243866@qq.com
a
https://orcid.org/0009-0007-4219-7031
Hu Kehui PhD a*
a Department of Rehabilitation Medicine, Suining Central Hospital.
* Correspondence: Kehui Hu, Department of Rehabilitation Medicine, Suining Central Hospital (e-mail: 1452673713@qq.com).
13 9 2024
13 9 2024
103 37 e3974129 8 2023
23 8 2024
27 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

To investigate the causal relationship between sleep duration and heart failure (HF) in a European population. We focused on the continuous sleep duration of 460,099 European individuals as our primary exposure. Genome-wide significant single nucleotide polymorphisms (SNPs, n = 9851,867) linked to continuous sleep duration were adopted as instrumental variables. The outcome of interest was based on HF events in a European cohort (n = 977,323; with 930,014 controls and 47,309 cases). We employed a two-sample Mendelian randomization (MR) approach to infer causality between sleep duration and the incidence of HF. For validation purposes, an additional cohort of 336,965 European individuals diagnosed with insomnia was selected as a secondary exposure group. Using its SNPs, a subsequent two-sample MR analysis was conducted with the HF cohort to further corroborate our initial findings. Employing the MR methodology, we selected 57 SNPs that are associated with sleep duration, and 24 SNPs that are associated with insomnia as instrumental variables. We discerned a substantial association between genetically inferred sleep duration and HF risk (odds ratio: 0.61; 95% confidence interval: 0.47–0.78, P < .0001). Our subsequent analysis highlighted a pronounced increased HF risk associated with insomnia (odds ratio: 1.54; 95% confidence interval: 1.08–2.17, P < .02). These conclusions were further bolstered by consistent results from sensitivity analyses. Our study suggests a causal linkage between sleep duration and the onset risk of HF in the European population. Notably, shorter sleep durations were associated with a heightened risk of HF.

Causal Relationship
Heart failure
Mendelian randomization
single nucleotide polymorphisms
Sleep duration
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

Heart failure (HF) is a complex clinical syndrome resulting from changes in cardiac structure and function due to various heart diseases, leading to impaired ventricular filling or ejection capabilities.[1] Currently, the global number of HF patients has reached a staggering 22.5 million, increasing at a rate of 10% annually.[2] Owing to its progressive nature, HF results in high rates of disability and mortality, imposing a substantial economic burden on society and families.[3] Despite ongoing research into the mechanisms and therapeutic interventions of HF, significant challenges remain. The pathological cornerstone of HF lies in aberrant cardiac remodeling.[4] During compensatory phases, myocardial tissues subjected to chronic overload hypertrophy in response to neurohumoral factors and other pro-growth agents, concomitant with myocardial cell apoptosis, leading to a progressive diminution of effective contractile units.[5] To counterbalance this functional decrement, the residual myocardial cells undergo adaptive hypertrophy, coupled with extracellular matrix deposition and reactive interstitial fibrosis.[6] Advancement of myocardial hypertrophy frequently precipitates myocardial ischemia, hypoxia, exacerbated oxidative stress, autophagy of myocardial cells, and apoptosis, detrimentally impinging upon cardiac functionality.[7] This cascade ultimately ushers in the decompensated phase of HF. Present therapeutic modalities fall short of efficaciously reversing pathological cardiac remodeling, thereby inadequately mitigating the morbidity and mortality associated with HF. Hence, reducing the incidence of HF by improving lifestyle is the most cost-effective method.

Sleep profoundly affects brain activity,[8] muscle responsiveness,[9] blood pressure regulation,[10] circulation,[11] hormonal equilibrium,[12] respiratory processes,[13] and metabolic functions.[14] Sleep can be differentiated based on various attributes such as morning or evening preferences, sleep duration, and symptoms of insomnia.[15] In contemporary society, a misalignment between biological rhythms and daily routines has led to widespread sleep disturbances.[16] Observational research indicates that prolonged or reduced sleep durations and the presence of insomnia are associated with cardiovascular health risks.[17] Cohort studies have indicated that various sleep patterns, including sleep duration, nighttime sleep types, and insomnia, can elevate the risk of cardiovascular diseases.[18] However, the validity of these conclusions is questionable due to the influence of confounding factors and the occurrence of selection bias.

Mendelian randomization (MR) is a statistical method that uses genetic variations as IVs to evaluate the causal relationship between exposure factors and diseases or other outcomes.[19] Due to the random allocation of SNPs, they are not affected by confounding factors and are less susceptible to measurement errors.[20] This allows MR to reduce the influence of confounding factors and selection bias often encountered in observational studies. Given the profound implications of the causal relationship between sleep and HF for public health, it is imperative to ascertain a reliable linkage between them. In this research, we delve into the genome-wide data pertaining to sleep duration and insomnia from the IEU open GWAS database. By employing a two-sample MR approach in relation to HF, we aim to furnish robust evidence to bolster the preventative strategies against HF.

2. Materials and methods

2.1. Study design

Figure 1 depicts the overall design of our study. To obtain more convincing results, we opted for the MR method to conduct two analyses: one on sleep duration and HF and another on insomnia and HF. In our study, we aimed to satisfy the three assumptions adhered to in MR research. First, the IVs exhibit a strong correlation with sleep duration and insomnia. Second, IVs operate independently of other confounding factors. Last, apart from the exposure, IVs do not influence the outcome through any other pathways. Given that the GWAS summary statistics we utilized are sourced from the ieu open GWAS database (https://gwas.mrcieu.ac.uk/), no additional ethical approval was required.

Figure 1. The design plan of the study. MR = Mendelian randomization.

2.2. Data sources

The summary statistics for sleep duration, insomnia, and HF in this study were sourced from the IEU open GWAS database (https://gwas.mrcieu.ac.uk/). The respective GWAS IDs for these datasets are sleep duration: ukb-b-4424; insomnia: ukb-a-13; and HF: ebi-a-GCST009541.

2.3. Selection of IVs

To select SNPs that are strongly associated with the exposure variables to be used as IVs, we applied a filter criterion of P < 5 × 10−8. To mitigate errors caused by linkage disequilibrium, we established conditions for SNP relationships as r2 < 0.001 and a distance of 10,000 kb. Weak instrumental variables were filtered out using an F-test with a threshold of F > 10. Finally, we utilized phenoScanner v2 to mitigate the effects of confounding factors. Following these criteria, we identified 57 SNPs associated with sleep duration and 32 SNPs related to insomnia for inclusion in the study.

2.4. Statistical analysis

We employed various methods, including MR Egger, weighted median (WM), inverse variance weighted (IVW), simple mode, and weighted mode, to assess the association between sleep duration and HF, as well as the risk association between insomnia and HF. Four statistical methods: MR Egger is a method used to explore the presence of directional bias in MR, weigh the influence of different IVs on the results, and estimate causal effects using regression analysis. WM provides a consistent estimate of the causal effect even when up to 50% of IVs are invalid, and its estimate is the median of the effect estimates of all single SNPs sorted by weight. IVW provides a reasonable estimate of the causal effect by weighted summing the effect of all SNPs on the outcome. Weighted mode provides an estimate of the causal effect by estimating the most common effect size between the exposure and the outcome variable. We expressed the estimated causal relationship between sleep duration and HF as an odds ratio (OR) and its 95% confidence interval. Differences were considered statistically significant when the P < .05. To validate the robustness of our results, we performed a heterogeneity test on MR Egger and IVW using Cochran’s Q test. Concurrently, the MR Egger intercept test was used to detect pleiotropy of SNPs, and a leave-one-out analysis was conducted to determine if the overall estimates were driven by a single SNP. All MR analyses were carried out in R software (R 4.3.0) using the packages “TwoSampleMR,” “MendelianRandomization,” and “MRPRESSO.”

3. Results

3.1. Associations between exposure and IVs

To select SNPs that are strongly associated with sleep duration and insomnia, we applied a filter criterion of P < 5 × 10−8 to the GWAS data. Subsequently, using R software, we generated a linear Manhattan plot as well as a circular plot (Fig. 2). After accounting for and eliminating factors such as linkage disequilibrium, weak instrumental variables and confounding influences, we identified 57 SNPs associated with sleep duration (Table 1) and 24 SNPs associated with insomnia (Table 2).

Table 1 Fifty-seven SNPs associated with sleep duration.

SNP	Effect_allele	Other_allele	Beta	Eaf.exposure	SE.exposure	
rs10510128	A	G	0.0114031	0.207951	0.00197431	
rs11039216	T	C	−0.0102766	0.532944	0.00160373	
rs112100783	A	G	−0.0252948	0.033439	0.00454852	
rs113113059	C	T	−0.0111278	0.219834	0.00193332	
rs11621908	T	C	−0.019985	0.082807	0.00294327	
rs11650677	A	G	0.0111701	0.339185	0.00168963	
rs11982852	T	C	−0.0117257	0.243858	0.00186248	
rs12518468	C	T	−0.0106435	0.328685	0.00170288	
rs12567114	A	G	0.0123378	0.276368	0.00179427	
rs13107325	T	C	−0.0242704	0.074905	0.00303933	
 rs1348047	T	G	−0.012642	0.267253	0.00182049	
 rs1463053	A	G	0.0092696	0.639694	0.00166133	
rs151014368	A	G	0.0113794	0.20734	0.00198822	
 rs1517572	C	A	0.0116589	0.581256	0.00162182	
 rs1553132	G	A	0.0105257	0.258638	0.0018253	
rs17391944	G	T	0.0218515	0.04986	0.00372396	
 rs174564	G	A	0.00974534	0.348602	0.001678	
 rs1939455	T	G	−0.0158002	0.120291	0.0025174	
 rs1972712	C	T	0.011796	0.249455	0.00184758	
 rs2072727	C	T	−0.00926905	0.56459	0.00161401	
 rs2192528	G	A	−0.00980706	0.522493	0.00160143	
 rs2236295	T	G	−0.00907587	0.403043	0.00163637	
 rs2734831	G	T	−0.00979722	0.606881	0.00163853	
 rs2748809	C	T	−0.00925348	0.429258	0.00164549	
 rs2839753	C	T	−0.0106369	0.265347	0.00181248	
 rs2863957	A	C	0.0289035	0.220537	0.00192922	
rs34354917	A	C	−0.0100207	0.288625	0.00176809	
rs34556183	G	A	−0.0133534	0.279888	0.00178155	
rs34786000	T	G	0.0109576	0.553361	0.00162787	
rs35126035	C	A	−0.00919552	0.55833	0.00164378	
 rs365663	G	A	−0.00928337	0.45495	0.00161004	
 rs374153	T	C	−0.0130999	0.842567	0.00219716	
 rs4767550	G	A	0.0108732	0.413171	0.00163264	
rs55658675	T	C	−0.00969462	0.352906	0.00167463	
rs56367859	G	A	0.0116218	0.397585	0.00163585	
rs56372231	T	C	0.0117703	0.332564	0.00169729	
rs62444917	C	A	0.0129633	0.222315	0.00192625	
 rs6681755	A	G	0.0115272	0.199783	0.00200498	
 rs6783516	T	G	−0.00983781	0.583817	0.00163116	
 rs7016314	C	T	0.0100011	0.655927	0.00168824	
 rs7115856	C	A	0.010819	0.461273	0.00160279	
rs72771082	G	A	0.0109737	0.217832	0.00193583	
 rs7517981	C	T	−0.0099657	0.601498	0.00163399	
rs75539574	C	A	0.0236649	0.085774	0.00287424	
rs76258078	G	A	−0.0216874	0.049994	0.00368137	
 rs7644809	C	T	−0.0101517	0.576033	0.00162466	
 rs7711696	T	G	−0.00986657	0.304987	0.00173522	
 rs7740402	G	T	−0.00951481	0.3061	0.00173455	
 rs7740559	G	A	0.0091983	0.578213	0.00162416	
 rs7831557	A	G	−0.0105656	0.517438	0.00160135	
 rs8038326	G	A	−0.0133838	0.273169	0.00179311	
 rs8047587	T	G	−0.0110198	0.439514	0.00161283	
 rs9302680	A	G	0.0120438	0.439272	0.00161069	
 rs9382445	C	T	−0.00948383	0.375168	0.00164901	
 rs9611007	T	C	−0.0135938	0.141673	0.00229721	
 rs9810474	T	C	−0.0111549	0.232184	0.00189404	
 rs9903898	T	C	−0.00944768	0.488883	0.00160094	
beta = effect sizes for each SNP, eaf = effect allele frequency, se = standard error.

Table 2 Twenty-four SNPs associated with insomnia.

Exposure	SNP	Beta	Eaf.exposure	P value	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs10087341	−0.32626	0.740887	0.659671	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs10156602	−1.22711	0.79517	0.12278	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs11152363	1.048152	0.712743	0.141403	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs113851554	0.736758	0.3621	0.041883	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs11635495	−0.09249	0.811826	0.909298	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs11804386	−0.3736	0.816014	0.647074	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs13186678	−0.04526	0.905125	0.960122	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs1547630	0.651247	0.794905	0.412628	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs17879819	1.707268	0.741594	0.021326	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs2132083	1.462537	0.820207	0.074565	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs224071	0.277601	0.812241	0.732522	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs2863957	0.862089	0.731236	0.238419	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs2956278	−1.35183	0.831248	0.103894	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs324017	−0.81932	0.742511	0.269833	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs3808937	0.915578	0.719882	0.203428	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs3817576	1.156069	0.761079	0.128766	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs4688760	0.860421	0.738745	0.244138	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs4886140	0.975011	0.758342	0.198543	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs4943439	1.406577	0.765484	0.066136	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs6690017	−0.16237	0.80168	0.839501	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs6744461	−1.13938	0.76265	0.135182	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs71373536	1.36805	0.669155	0.04091	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs7572387	−1.06129	0.731924	0.147059	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs9815484	0.767023	0.759428	0.312495	
Sleeplessness/insomnia ‖ id:ukb-a-13	rs9878792	1.059989	0.741617	0.15292	

Figure 2. Manhattan plot from a GWAS related to sleep, as well as a circos (or circular) plot indicating chromosomal translocations. P < 5 × 10−8. GWAS = genome-wide association study. (A) Circos plot (B) Manhattan plot.

3.2. MR results

In the MR study between sleep duration and HF, the IVW method indicates a causal relationship between sleep duration and HF (beta = −0.502, SE = 0.131, P = .0001) and the results from the WM method (beta = −0.387, SE = 0.163, P = .017) indicate a causal relationship between sleep duration and HF (Table 3, Figure 3A). The intercept reflects the overall genetic average pleiotropic variation (that is, the average direct effect of the variant on the outcome). A non-zero intercept (MR Egger test) indicates the presence of directional pleiotropy. However, the regression analysis by MR Egger suggests that there is a low likelihood of result bias caused by directional pleiotropy (intercept = −0.001; SE = 0.006; P = .822).

Table 3 Statistical results from a Mendelian randomization analysis of sleep duration and risk of heart failure.

Method	SNPs	Beta	SE	P value	OR	95% CI	
MR Egger	57	−0.3972	0.482228	0.413678	0.672198	0.261226–1.729729	
Weighted median	57	−0.38716	0.162596	0.017261	0.678986	0.493692–0.933823	
Inverse variance weighted	57	−0.50217	0.130609	0.000121	0.605214	0.468524–0.781783	
Simple mode	57	−0.2496	0.354392	0.484169	0.779116	0.388989–1.56051	
Weighted mode	57	−0.32076	0.282182	0.260498	0.725597	0.417347–1.261519	
OR = odds ratio, SNP = single nucleotide polymorphism.

Figure 3. (A) Scatter plots of genetic associations with sleep duration against the genetic associations with heart failure. (B) Scatter plots of genetic associations with insomnia against the genetic associations with heart failure. The slopes of each line represent the causal association for each method. The blue line represents the inverse‐variance weighted estimate, the green line represents the weighted median estimate, and the dark blue line represents the Mendelian randomization‐Egger estimate. MR = Mendelian randomization, SNP = single nucleotide polymorphism.

In the MR study between insomnia and HF, the WM method indicated a prominent causal correlation between the variables (beta = 0.481, SE = 0.209, P = .022). Similarly, the Weighted mode approach (beta = 0.713, SE = 0.333, P = .040) further validated this causal association (Table 4, Figure 3B). The MR Egger test suggests there is no pleiotropy influencing the outcomes (intercept = −0.01; SE = 0.006; P = .107).

Table 4 Statistical results from an MR analysis of insomnia and risk of heart failure.

Method	SNPs	Beta	SE	P value	OR	95% CI	
MR Egger	25	1.204	0.4929	0.02272	3.333424	1.268603–8.75902	
Weighted median	25	0.743	0.2193	0.000704	2.102233	1.367757–3.231117	
Inverse variance weighted	25	0.4286	0.1771	0.01553	1.535107	1.084896–2.172147	
Weighted mode	25	0.8994	0.3053	0.007052	2.458128	1.351226–4.471787	
MR = Mendelian randomization, OR = odds ratio, SNP = single nucleotide polymorphism.

In the MR analysis, the statistical results regarding the relationship between sleep duration and the risk of HF occurrence, as well as insomnia and the risk of HF occurrence, are presented in Figure 4.

Figure 4. (A) Forest plot of various statistical methods for the risk of heart failure associated with sleep duration. (B) Forest plot of various statistical methods for the risk of heart failure associated with insomnia. OR = odds ratio.

3.3. Heterogeneity and sensitivity test

Based on the Cochran’s Q test, we did not detect significant heterogeneity, implying minor differences between the IV estimates of different SNPs. Heterogeneity typically refers to variability in causal relationship estimates. Each SNP offers insight into the overall causal relationship. Through the “leave-one-out” analysis, we established that no single SNP disproportionately influenced the overall IVW estimation (Fig. 5). While the asymmetry in the funnel plot might suggest the presence of directional pleiotropy, potentially introducing bias into the MR approach, both our funnel plot and subsequent MR Egger regression analysis did not exhibit evident asymmetry (Fig. 6).

Figure 5. Funnel plot to assess heterogeneity. (A) Heterogeneity of the relationship between Sleep Duration and HF (B) Heterogeneity of Insomnia and HF relationships. The blue line represents the inverse‐variance weighted estimate, and the dark blue line represents the Mendelian randomization‐Egger estimate.

Figure 6. Forest plot of the causal effects of single nucleotide polymorphisms associated with sleep duration on heart failure and insomnia and heart failure. The significance of red lines are MR results of MR Egger test and IVW method. IVW = inverse variance weighted, MR = Mendelian randomization.

4. Discussion

Our MR analysis provided compelling evidence of a causal link between sleep duration and HF susceptibility. Our data demonstrated an inverse relationship between sleep duration and HF risk, implying that reduced sleep durations are associated with increased odds of HF. Furthermore, we observed a direct association between the severity of insomnia and heightened risk of HF, underscoring the potential adverse health impacts of sleep disturbances.

HF is characterized as a clinical syndrome in which the cardiac output is insufficient to meet the body’s metabolic needs.[21] Clinically, patients with HF commonly present with persistent or exacerbated dyspnea, fatigue, and signs of fluid retention, including peripheral edema and pulmonary congestion. These manifestations can be attributed to anomalies in either ventricular diastolic or systolic function.[22] While traditional risk factors such as age, smoking, obesity, and hypertension have been well established, emerging evidence underscores the potential association between sleep duration and an increased risk of developing HF. A plethora of literature studies indicate that adequate sleep is a pivotal component of cardiovascular health.[23] Chronic sleep deprivation or interruptions can lead to elevated blood pressure, increased inflammatory responses, heightened stress hormone levels, and metabolic disturbances, all of which are critical risk factors for cardiovascular diseases.[24] Additionally, prolonged lack of sleep may also result in obesity, aberrant cholesterol levels, and diabetes, further amplifying the risk for cardiovascular conditions.[25] Beyond mere sleep duration, distinct sleep attributes critically modulate cardiovascular outcomes. Obstructive sleep apnea, delineated by recurrent respiratory pauses accompanied by nocturnal oxyhemoglobin desaturations, is demonstrably linked with elevated predispositions to hypertension, cardiac arrhythmias, and HF.[26] Circadian rhythm perturbations, often manifested among shift workers, potentiate metabolic dysregulations, thereby augmenting cardiovascular risk.[27] Equally imperative is the consideration that sleep quality, depth, and synchronization with endogenous biological clocks significantly influence cardiovascular system integrity and function.

The intricate interplay between sleep and cardiovascular health manifests through a series of sophisticated physiological and molecular cascades. Chronic inadequacies in sleep duration or disturbances in sleep quality have been shown to instigate inflammatory pathways, resulting in elevated levels of pro-inflammatory mediators, notably tumor necrosis factor-alpha (TNF-α)[28] and interleukin-6 (IL-6).[29] Such sustained inflammatory states can potentiate endothelial dysfunction and hasten the course of atherosclerotic processes.[30] Moreover, sleep-related breathing disorders, typified by obstructive sleep apnea (OSA), have been implicated in intermittent hypoxia episodes, which, in turn, might exacerbate mitochondrial perturbations and accentuate oxidative stress, culminating in heightened cardiomyocyte vulnerability and apoptosis.[31] Long-term sleep perturbations might further modulate the neuroendocrine axis, perturbing the homeostasis of catecholamines, cortisol, and other stress hormones, thereby jeopardizing myocardial contractility and vascular homeostasis.[32] However, our statistics do not provide a clear definition of long-duration sleep and extended-duration sleep, which is quite confusing. There is literature research, using linear MR analysis, and consistent with existing literature, they unveiled a causal relationship between an additional 1 hour of genetically-predicted sleep and a significant 13% reduction in the incidence of metabolic syndrome. More importantly, reducing sleep duration is also associated with increased risks of several other metabolic-related diseases, such as central obesity, dyslipidemia, hypertriglyceridemia, and hyperglycemia, demonstrating a clear causal link.[33]

In the context of MR, our findings elucidate a discernible negative association between sleep duration and the propensity for HF within European populations. Distinctively, as the severity of insomnia intensifies, so does the susceptibility to HF. Drawing from these preliminary insights, there emerges a pressing need for expansive multicenter studies that encompass varied ethnic and demographic segments. Such investigative pursuits hold promise for illuminating the nuanced molecular mechanisms and cellular pathways that anchor this observed relationship.

5. Conclusions

In summary, our findings underscore that, within the European population, sleep duration emerges as a pivotal risk factor for the onset of HF. There is a conspicuous trend wherein a reduction in sleep duration correlates with an augmented risk of HF, and notably, the severity of insomnia directly escalates the susceptibility to HF.

Author contributions

Data curation: Lianlin Zeng.

Formal analysis: Lianlin Zeng.

Investigation: Lianlin Zeng.

Writing – original draft: Lianlin Zeng.

Methodology: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Project administration: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Resources: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Software: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Supervision: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Validation: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Visualization: Shasha Fu, Hailan Xu, Lutao Zhu, Xiaomei Li, Yangan Li.

Conceptualization: Kang Cheng, Kehui Hu.

Writing – review and editing: Yangan Li, Kehui Hu.

Abbreviations:

CI confidence interval

HF heart failure

IV instrumental variable

IVW inverse variance weighted

MR Mendelian randomization

OR odds ratio

SNP single nucleotide polymorphism

WM weighted median

The data of this study were all obtained from the IEU OPEN GWAS database, and therefore ethics committee approval was not required.

The authors declare no conflict of interest, financial or otherwise. We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest in any product, service, and/or company that could be construed as influencing the position presented in the manuscript entitled.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Zeng L, Fu S, Xu H, Zhu L, Li X, Cheng K, Li Y, Hu K. Sleep duration and heart failure risk: Insights from a Mendelian Randomization Study. Medicine 2024;103:37(e39741).
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