Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2026 Oct 16;105(42):e49302. doi: 10.1097/MD.0000000000049302

Gut microbiota, sleep and coronary artery disease

A Mendelian randomization study

Ye-Dan Li a, Hong Meng a,*, Ran Qu a, Shan-Ri Yang a, Hao Wang a
PMCID: PMC13680943  PMID: 42888607

Abstract

Inadequate and poor-quality sleep is increasingly recognized as a risk factor for coronary artery disease (CAD). Gut microbiota may play a role in sleep regulation. However, the associations between gut microbiota, sleep, and CAD risk, and the causal nature of these associations, remain unclear. We performed a 2-step Mendelian randomization analysis using summary-level data to assess the association between gut microbiota and CAD and the potential mediating roles of sleep-related traits (i.e., chronotype, insomnia, sleep duration, short sleep, and long sleep) herein. The inverse-variance weighted method served as the primary analysis, with several other complementary methods as sensitivity analyses. Our findings revealed that 2 gut microbial taxa were negatively associated with genetically predicted CAD risk, while 4 taxa, including species Veillonella unclassified (odds ratio: 1.054, 95% confidence interval: 1.010–1.101), showed positive associations. Regarding sleep-related traits, we found that insomnia (odds ratio: 1.553, 95% confidence interval: 1.081–2.230) and short sleep duration causally increased CAD risk, whereas longer sleep duration causally reduced CAD risk. The mediation Mendelian randomization analysis demonstrated that insomnia partially mediated the causal, predisposing effect of species Veillonella unclassified on increased CAD risk, with a proportion mediated of 5.7%. This study provides evidence for a causal relationship between specific gut microbial taxa and CAD risk, as well as the mediating role of insomnia in this relationship. These findings offer novel insights into potential strategies for CAD prevention through modulation of the gut microbiome and improvement of sleep quality.

Keywords: coronary artery disease, gut microbiome, Mendelian randomization, prevention, sleep

1. Introduction

The increasing prevalence of cardiovascular disease (CVD) and related morbidity have caused tremendous global health burden. CVD remains the leading cause of death worldwide, accounting for a third of all deaths globally, with an estimated number of 17.9 million persons per year.[1] Therefore, it is crucial to explore the risk factors and develop effective preventive measures to this disease.

Poor and unhealthy sleep have emergently been proved to play a role in the development of CVD.[2] Both insufficient sleep duration and poor sleep quality contribute to the increased CVD risk in observational studies.[3–5] Additionally, the application of the Mendelian Randomization (MR) method, which can help infer causality,[6] further provides evidence for the causal effect of sleep on CVD risk.[7–9]

Intervention at the level of sleep in order to treat and prevent the onset of CVD requires a better understanding of the factors that characterize poor sleep quality and quantity. Emerging evidence suggests that gut microbiota may play a crucial role. Both epidemiological and MR studies have indicated that alterations in gut microbiota composition are associated with various sleep disorders and conditions characterized by comorbid sleep disturbances.[10–12] There is growing evidence supporting a strong link between gut microbiota and the development of CVD.[13]

Based on this, we hypothesize that gut microbiota may causally contribute to poor sleep-related traits, thereby increasing the risk of coronary artery disease (CAD). In this study, we employed the mediation MR design to evaluate the causal relationship between gut microbiota (exposure) and CAD (outcome), and to investigate the potential mediation role of sleep herein (mediators).

2. Materials and methods

2.1. Study design

This is a 2-step MR study consisting of 3 main components as outlined in Figure 1: analysis of causal effects of 211 gut microbiota on CAD (step 1); analysis of causal effects of 5 sleep-related traits on CAD (step 2); and mediation analysis of sleep-related traits in the pathway from gut microbiota to CAD (step 3).

Figure 1.

Figure 1.

Open in a new tab

Study overview. Step 1 represents the causal effects of gut microbiota on CAD. Step 2 represents the causal effects of sleep-related traits on CAD. Step 3 represents the mediation analysis of sleep-related traits in the pathway from the gut microbiota to CAD: path a was the causal effect of gut microbiota on sleep-related traits; path b was the causal effect of sleep-related traits on CAD; path c was the total effect of gut microbiota on CAD. CAD = coronary artery disease.

2.2. Data sources

The summary statistics for gut microbiota, sleep-related traits, and CAD were derived from previous genome-wide association studies (GWAS), as listed in Table 1. Patient consents were obtained by corresponding studies.

Table 1.

Detailed information for GWAS involved in the present MR study.

Phenotype Studies Sample size Ancestry Consortium or cohort
Gut microbiota Lopera et al[14] 7738 European Dutch Microbiome Project
Sleep-related traits Chronotype (binary, morning person/evening person) Jones et al[15] 697,828 European UKB and 23andMe
Insomnia (binary, yes/no) Lane et al[16] 129,270 cases and 237,627 controls European UKB
Sleep duration (continuous) Dashti et al[17] 446,118 European UKB
Short sleep (binary, yes/no) Dashti et al[17] 106,192 cases and 305,742 controls European UKB
Long sleep (binary, yes/no) Dashti et al[17] 34,184 cases and 305,742 controls European UKB
CAD (binary, yes/no) Nikpay et al[18] 60,801 cases and 123,504 controls European and Asian CARDIoGRAMplusC4D
Open in a new tab

CAD = coronary artery disease, CARDIoGRAMplusC4D = coronary artery disease genome-wide replication and meta-analysis plus the coronary artery disease genetics, GWAS = genome-wide association study, MR = Mendelian randomization, UKB = United Kingdom Biobank.

2.2.1. Gut microbiota

Data for bacterial taxa were obtained from a GWAS of 7738 individuals of European ancestry from the Dutch Microbiome Project.[14] This is currently the largest database with species-level gut microbiota data, and the participants are relatively homogeneous. The gut microbiome was measured by shotgun metagenomic sequencing from stool samples, consisting of 207 taxonomic units which are categorized into 5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species.

2.2.2. Sleep-related traits

We investigated sleep using 5 sleep-related traits, i.e., chronotype, insomnia, sleep duration, short sleep and long sleep.

GWAS data for self-reported chronotype were derived from meta-analysis of United Kingdom Biobank (UKB) and the 23andMe cohort.[15] In the UKB, chronotype was determined based on the question, “Do you consider yourself to be?” Participants who identified themselves as “definitely a morning person” or “more a morning than evening person” were classified as morning persons (n = 252,287), whereas those who considered themselves as “definitely an evening person” or “more an evening than a morning person” were as the reference group for evening persons (n = 150,908). In the 23andMe cohort, participants responded to 2 identical questions “Are you naturally a night person or a morning person?.” Based on their responses, individuals who regarded themselves as either “morning person” or “night person” at least once were categorized accordingly. This resulted in 120,478 morning persons and 127,622 night persons in the 23andMe cohort.

We obtained the genetic associations of insomnia from the GWAS among 237,627 individuals of European ancestry from the UKB.[16] Insomnia refers to the difficulty in falling asleep or maintaining sleep and was measured by the self-reported question, “Do you have trouble falling asleep at night,” or “do you wake up in the middle of the night?.” Individuals who reported “usually” were considered to have frequent insomnia (n = 129,270), and those who reported “never/rarely” were considered controls (n = 237,627).

Genetic associations of self-reported sleep duration were obtained from a GWAS among 446,118 individuals of White European ancestry from the UKB.[17] Self-reported sleep duration was a continuous variable which refers to the number of hours of sleep an individual get in every 24 hours (including naps). Participants responded to the question ‘About how many hours sleep do you get in every 24 hours? (please include naps)’ with hour increments. Extreme responses of < 3 hours or > 18 hours were not included in the GWAS analysis. Based on this continuous measure of sleep duration, 2 binary variables were generated, that is, short sleep and long sleep. Short sleep was defined as a sleep duration < 7 hours, while long sleep was defined as a sleep duration ≥ 9 hours according to previous prospective studies.[19] The GWAS on short sleep compared the short sleep group (n = 106,192) to the reference group (≥ 7 and < 9 hours of sleep, n = 305,742). Accordingly, the GWAS on long sleep compared the long sleep group (n = 34,184) to the same reference group as in the GWAS on short sleep.

2.2.3. CAD

GWAS dataset for CAD was retrieved from the CAD genome-wide replication and meta-analysis plus the CAD consortium. This cohort assembled 60,801 cases and 123,504 controls from 48 studies, of which 77% of the participants were of European ancestry.[18] CAD cases were defined as an inclusive diagnosis of myocardial infarction, acute coronary syndrome, chronic stable angina, or coronary stenosis > 50% (Table 1).[18]

2.3. Selection of instrumental variable (IV)s

Genetic variants of microbiota taxon with the P value < 1 × 10−5 and effect allele frequency > 0.01 were selected as the IVs for gut microbiota. The threshold of P value < 1 * 10−5 was determined by maximizing the genetic variance explained by genetic predictors, as used in previous gut microbiome MR studies.[20] For the IVs of sleep-related traits and CAD, the GWAS significance level with the P value < 5 * 10−8 was applied. Single nucleotide polymorphism (SNPs) were excluded if they were in linkage disequilibrium (R2 > 0.1, the SNP with the largest absolute effect estimate was retained), or were palindromic (with a minor allele frequency > 0.42). The strength of the selected genetic instrument was assessed using F statistics, with a mean F statistic < 10 regarded as a weak set of IVs.[21]

2.4. Statistical analysis

To estimate the causal effects of gut microbiota and sleep-related traits on CAD, we performed 2-sample MR analysis, respectively (steps 1 and 2 in Fig. 1). The inverse-variance weighted analysis with a random-effect model was used as the primary analysis. Cochran Q statistic was calculated to quantify heterogeneity.

A 2-sided P value that passed the Bonferroni correction was defined as statistically significant, which is .0002 (.05/207) for gut microbiota and .01 (.05/5) for sleep-related traits, and P < .05 was considered to have a suggestively significant association. In addition, we used MR-Egger, simple median, and penalized weighted median analyses as sensitivity analyses.[22,23] Steiger-filtering analysis was also adopted to identify and exclude genetic variants that have a stronger association with the outcome than with the exposure, suggestive of reverse causality.[24] We further applied the MR pleiotropy residual sum and outlier (MR-PRESSO) method (NbDistribution = 1500) to detect possible outliers and calculate causal estimates after removing the identified outliers.[25]

Based on the 2-sample MR analyses above, the gut microbiota and sleep-related traits with significant or suggestively significant causal effects on CAD were included in the mediation analysis. First, we performed the MR analysis to examine the causal effect of gut microbiota on sleep-related traits. If so, in the second step, we would calculate the indirect effect (a_* b). The proportion of the total effect mediated by sleep-related traits was estimated by dividing the indirect effect (a_* b) by the total effect (c), with a representing the effect of gut microbiota on sleep-related traits (step 3, path a, Fig. 1), b representing the effect of sleep-related traits on CAD (step 3, path b, Fig. 1), and c representing the total effect of gut microbiota on CAD (step 3, path c, Fig. 1). Standard errors were derived using the delta method, and effect estimates were obtained from 2-sample MR analysis.

All analyses were performed using the R statistical software version 4.0.1 with the TwoSampleMR, MedelianRandomization and RMediation packages.[26–28]

2.5. Ethical approval

Not applicable.

3. Results

3.1. Association between gut microbiota and CAD

Initially, 13,535 gut microbiota-related SNPs were identified with a significance level of P value < 1 * 10−5, of which 12,930 were also identified in the CAD dataset. After removing SNPs in linkage disequilibrium (10,738) and palindromic SNPs (n = 269), 1923 SNPs were finally used to examine the causal effect of 203 gut microbiota (five microbiotas were excluded due to no eligible SNPs) on CAD (Table S1, Supplemental Digital Content 1).

A total of 6 genetically predicted gut microbiotas (including 1 family, 2 genera, and 3 species) were suggestively associated with CAD (Table S2, Supplemental Digital Content 2 and Fig. 2A, P < .05). Among them, 4 were positively and 2 were negatively associated with CAD risk. Genetically predicted genus Alistipes and family Rikenellaceae increased the CAD risk most (both with odds ratio [OR]: 1.093, 95% confidence interval [CI]: 1.013–1.180). Conversely, genetically predicted species Paraprevotella unclassified reduced the CAD risk most (OR: 0.931, 95% CI: 0.880–0.986). The F statistic of the IVs for these 6 gut microbiotas were all > 10.

Figure 2.

Figure 2.

Open in a new tab

Association of genetically predicted (A) microbiota and (B) sleep-related traits with risk of CAD, analyzed with the IVW method. CAD = coronary artery disease, CI = confidence interval, IVW = inverse-variance weighted, nSNP = number of single nucleotide polymorphism, OR = odds ratio, SNP = single nucleotide polymorphism.

MR-Egger regression analyses showed nonsignificant intercepts for all traits (Table S2, Supplemental Digital Content 2). The MR-PRESSO method did not identify any outliers, and the Steiger-filtering method did not identify any genetic variants that explained significantly more of the variance in the outcome than any of the exposure traits.

3.2. Association between sleep-related traits and CAD

The detailed information for the SNPs used in the association between 5 sleep-related traits and CAD was provided in Table S3–S8, Supplemental Digital Content 3. Inverse-variance weighted MR analysis with a random-effect model showed that short sleep was significantly associated with an increased CAD risk (OR: 4.789, 95% CI: 2.198–10.437; P = 8.1 × 10-5. Figure 2B and Table S9, Supplemental Digital Content 4). Individuals who sleep < 7 hours had an almost 4-fold higher CAD risk compared with those who sleep for 7 to 9 hours. Insomnia was suggestively associated with an increased CAD risk (OR: 1.553, 95% CI: 1.081–2.230. Figure 2B and Table S9, Supplemental Digital Content 4), whereas sleep duration was suggestively associated with a decreased CAD risk (OR: 0.996, 95% CI: 0.993–0.999. Figure 2B and Table S9, Supplemental Digital Content 4). The IVs for the 5 sleep-related traits were all with an F statistic > 10.

The MR-Egger regression analyses revealed nonsignificant intercepts for all results (Table S9, Supplemental Digital Content 4). The MR-PRESSO method identified 1 outlier in both the associations between insomnia and sleep duration with CAD. However, excluding these outliers did not substantially affect the results (OR: 1.450, 95% CI: 1.403–1.496 for the association of insomnia and CAD; OR: 0.995, 95% CI: 0.994–0.996 for the association of sleep duration and CAD). Additionally, the Steiger-filtering method did not identify any genetic variants that explained significantly more variance in the outcome than in the exposure traits.

3.3. Mediation effect of sleep-related traits in the causal association between gut microbiota and CAD

When analyzing the causal effect of gut microbiota on sleep-related traits, we found that only the species Veillonella unclassified had a causal, predisposing effect on the risk of insomnia (OR: 1.007, 95% CI: 1.001–1.012; Table S10, Supplemental Digital Content 5), which subsequently increased the risk of CAD (OR: 1.553, 95% CI: 1.081–2.230; Fig. 2B and Table S9, Supplemental Digital Content 4). The indirect effect of insomnia was 0.003 (95% CI: 1.391 × 10−5 to 0.007), mediating 5.7% of the total effect of species Veillonella unclassified on CAD risk (Fig. 3).

Figure 3.

Figure 3.

Open in a new tab

Insomnia mediated the causal effect of species Veillonella unclassified on CAD risk. The results were derived from the IVW method. CAD = coronary artery disease, IVW = inverse-variance weighted.

4. Discussion

In the present MR study, we identified that 6 microbial taxa, including the species Veillonella unclassified, played a causal role in the pathogenesis of CAD. Mediation analysis further revealed that insomnia mediated the effect of species Veillonella unclassified on CAD risk, accounting for 5.7% of the total effect.

Veillonella species are known to be part of the normal oral and intestinal microbiota,[29] but their specific role in cardiovascular health has now gradually unveiled. Koren et al reported that the genus Veillonella is identified in most atherosclerotic plaque samples, implying that Veillonella might play a crucial (possibly potential pathogenic) role in the development of atherosclerosis and CVD.[30] Our study was consistent with and further extended this study. First, with the use of the MR method, we were able to elucidate that the association between Veillonella species and CAD risk was of a causal nature, while Koren et al only reached their conclusion from observational studies, in which the results are affected by residual confounding.[31] Second, rather than simply investigating the association between gut microbiota and CAD, we focused on exploring the underlying causal pathways mediating this association, giving insight into potential targeted interventions.

The gut-brain axis modulation may explain this association. On the one hand, Veillonella species, including Veillonella unclassified, are known for their distinctive metabolic capability to ferment lactate into short-chain fatty acids, particularly propionate.[32,33] Short-chain fatty acids exert widespread effects on neurological and behavioral processes via the gut-brain axis. By crossing the blood–brain barrier, they have been shown to increase neurogenesis, improve neuronal homeostasis and function, and contribute to serotonin biosynthesis.[34] Serotonin is a precursor to melatonin[35] and has been proved to be involved in the regulation of circadian rhythms and sleep-wake behaviors.[36,37] On the other hand, disrupted circadian rhythms can affect various physiological processes such as blood pressure regulation and heart rate variability, potentially increasing CVD risk.[38,39]

The implications of our study are several-fold. First, our findings have significant implications for both clinical practice and public health strategies. The identification of specific microbial taxa as causal factors in CAD pathogenesis suggests that microbiome profiling could potentially be used as a tool for CAD risk assessment. Furthermore, interventions aimed at modulating the abundance or activity of these taxa, particularly Veillonella species, might offer novel approaches to CAD prevention and treatment. Second, the link between Veillonella species, insomnia, and CAD underscores the importance of considering sleep health in cardiovascular risk management. Clinicians should be aware of this connection and consider addressing sleep issues as a novel approach to cardiovascular health. Third, it is important to note that while the mediation effect of insomnia accounted for 5.7% of the total effect, a substantial portion of the influence of Veillonella species on CAD risk remains unexplained. This suggests the existence of other, yet unidentified pathways through which this microbial species affects cardiovascular health, such as dietary factors.[40] Future studies should aim to uncover these additional mechanisms to provide a more comprehensive understanding of the pathophysiology of CAD.

This was the first MR study to establish a causal link between gut microbiota, sleep, and CAD risk, providing novel insights into the risk assessment and prevention strategy of CAD. However, several limitations of our study should be acknowledged. First, the sleep-related traits included in this analysis were primarily derived from self-reported questionnaire data, which may be subject to recall bias or misclassification. Second, the GWAS datasets used in this MR study were largely restricted to individuals of European ancestry. This study’s primary limitation is its relatively small sample size in the gut microbiome GWAS, which may constrain the detection of potential causal relationships. However, it remains the largest GWAS of the gut microbiome to date, providing species-level data for a more precise classification of gut microbiota taxa. Additionally, the MR effect estimates reflect lifelong genetic exposures, which might not fully capture the benefits of shorter-term microbiome changes. Nonetheless, the anticipated causal direction of effects can inform potential efficacy, guiding further investigations through animal experiments and clinical trials. Finally, by using summary-level data, we were not able to perform subgroup analyses, for example, stratified by sex or ethnicity.

5. Conclusions

In conclusion, our study provides compelling evidence for the causal role of specific microbial taxa, particularly species Veillonella unclassified, in CAD pathogenesis, and reveals a novel pathway involving insomnia. These findings not only enhance our understanding of the complex etiology of CAD but also open up new possibilities for innovative preventive and therapeutic strategies targeted at sleep quality and gut microbiota. Future research should focus on elucidating pathways other than insomnia.

Acknowledgements

All authors have made substantial contributions to this work, reviewed and approved the final manuscript, and agreed to its submission and authorship.

Author contributions

Conceptualization: Ye-Dan Li, Hao Wang, Hong Meng.

Data curation: Ye-Dan Li, Ran Qu, Shan-Ri Yang, Hao Wang.

Formal analysis: Ye-Dan Li, Ran Qu, Hong Meng.

Resources: Ran Qu, Shan-Ri Yang.

Software: Shan-Ri Yang.

Writing – original draft: Ye-Dan Li, Hong Meng.

Writing – review & editing: Ye-Dan Li, Ran Qu, Shan-Ri Yang, Hao Wang, Hong Meng.

medi-105-e49302-s001.xlsx (240.5KB, xlsx)
medi-105-e49302-s002.xlsx (12.2KB, xlsx)
medi-105-e49302-s003.xlsx (9.5KB, xlsx)
medi-105-e49302-s004.xlsx (11.9KB, xlsx)
medi-105-e49302-s005.xlsx (11.2KB, xlsx)
medi-105-e49302-s006.xlsx (38.6KB, xlsx)
medi-105-e49302-s007.xlsx (14.6KB, xlsx)
medi-105-e49302-s008.xlsx (15.8KB, xlsx)
medi-105-e49302-s009.xlsx (11.6KB, xlsx)
medi-105-e49302-s010.xlsx (10.2KB, xlsx)

Abbreviations:

CAD
coronary artery disease
CI
confidence interval
CVD
cardiovascular disease
GWAS
genome-wide association study
IV
instrumental variable
MR
Mendelian randomization
MR-PRESSO
Mendelian Randomization Pleiotropy RESidual Sum and Outlier/MR Pleiotropy RESidual Sum and Outlier
OR
odds ratio
SNP
single nucleotide polymorphism
UKB
United Kingdom Biobank

The present Mendelian randomization study was based exclusively on publicly available, anonymized summary statistics from previously published genome-wide association studies. All original studies had received approval from the relevant institutional ethics committees and obtained informed consent from participants. Consequently, no further ethical approval was required for this study.

The authors have no funding and conflicts of interest to declare.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000049302).

How to cite this article: Li Y-D, Meng H, Qu R, Yang S-R, Wang H. Gut microbiota, sleep and coronary artery disease: A Mendelian randomization study. Medicine 2026;105:42(e49302).

Contributor Information

Ye-Dan Li, Email: liyedan7980177@163.com.

Shan-Ri Yang, Email: quzhengqu01@163.com.

Hao Wang, Email: 1830866899@qq.com.

References

  • [1].Bakhit M, Fien S, Abukmail E, et al. Cardiovascular disease risk communication and prevention: a meta-analysis. Eur Heart J. 2024;45:998–1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Miller MA, Howarth NE. Sleep and cardiovascular disease. Emerg Top Life Sci. 2023;7:457–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [3].Wu TT, Zou YL, Xu KD, et al. Insomnia and multiple health outcomes: umbrella review of meta-analyses of prospective cohort studies. Public Health. 2023;215:66–74. [DOI] [PubMed] [Google Scholar]
  • [4].Sabanayagam C, Shankar A. Sleep duration and cardiovascular disease: results from the national health interview survey. Sleep. 2010;33:1037–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Clark AJ, Salo P, Lange T, et al. Onset of impaired sleep and cardiovascular disease risk factors: a longitudinal study. Sleep. 2016;39:1709–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol. 2016;27:3253–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Ai S, Zhang J, Zhao G, et al. Causal associations of short and long sleep durations with 12 cardiovascular diseases: linear and nonlinear Mendelian randomization analyses in UK Biobank. Eur Heart J. 2021;42:3349–57. [DOI] [PubMed] [Google Scholar]
  • [8].Yang Y, Fan J, Shi X, et al. Causal associations between sleep traits and four cardiac diseases: a Mendelian randomization study. ESC Heart Fail. 2022;9:3160–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Jia Y, Guo D, Sun L, et al. Self-reported daytime napping, daytime sleepiness, and other sleep phenotypes in the development of cardiometabolic diseases: a Mendelian randomization study. Eur J Prevent Cardiol. 2022;29:1982–91. [DOI] [PubMed] [Google Scholar]
  • [10].Sen P, Molinero-Perez A, O’Riordan KJ, McCafferty CP, O’Halloran KD, Cryan JF. Microbiota and sleep: awakening the gut feeling. Trends Mol Med. 2021;27:935–45. [DOI] [PubMed] [Google Scholar]
  • [11].Yan W, Zhuang Z, Gao Y, Wang Y, He D. A Mendelian randomization investigation of the causal association between the gut microbiota and sleep disorders. Front Microbiol. 2024;15:1372827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Yue M, Jin C, Jiang X, et al. Causal effects of gut microbiota on sleep-related phenotypes: a two-sample mendelian randomization study. Clocks Sleep. 2023;5:566–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Tang WH, Kitai T, Hazen SL. Gut microbiota in cardiovascular health and disease. Circ Res. 2017;120:1183–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Lopera-Maya EA, Kurilshikov A, van der Graaf A, et al. Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project. Nat Genet. 2022;54:143–51. [DOI] [PubMed] [Google Scholar]
  • [15].Jones SE, Lane JM, Wood AR, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10:343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Lane JM, Jones SE, Dashti HS, et al. Biological and clinical insights from genetics of insomnia symptoms. Nat Genet. 2019;51:387–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Dashti HS, Jones SE, Wood AR, et al. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nat Commun. 2019;10:1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Nikpay M, Goel A, Won HH, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47:1121–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Cappuccio FP, D’Elia L, Strazzullo P, Miller MA. Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. Sleep. 2010;33:585–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51:600–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Bowden J, Smith GD, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28:30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Hemani G, Tilling K, Smith GD. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13:e1007081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46:1734–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Tofighi D, MacKinnon DP. RMediation: an R package for mediation analysis confidence intervals. Behav Res Methods. 2011;43:692–700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Kolenbrander PE. Multispecies communities: interspecies interactions influence growth on saliva as sole nutritional source. Int J Oral Sci. 2011;3:49–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Koren O, Spor A, Felin J, et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc Natl Acad Sci U S A. 2011;108(Suppl 1):4592–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR statement. JAMA. 2021;326:1614–21. [DOI] [PubMed] [Google Scholar]
  • [32].Zhang S-M, Huang S-L. The commensal anaerobe veillonella dispar reprograms its lactate metabolism and short-chain fatty acid production during the stationary phase. Microbiol Spectr. 2023;11:e0355822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Hung JH, Zhang SM, Huang SL. Nitrate promotes the growth and the production of short-chain fatty acids and tryptophan from commensal anaerobe Veillonella dispar in the lactate-deficient environment by facilitating the catabolism of glutamate and aspartate. Appl Environ Microbiol. 2024;90:e01148–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Silva YP, Bernardi A, Frozza RL. The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front Endocrinol (Lausanne). 2020;11:25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Gershon MD, Tack J. The serotonin signaling system: from basic understanding to drug development for functional GI disorders. Gastroenterology. 2007;132:397–414. [DOI] [PubMed] [Google Scholar]
  • [36].Monti JM. Serotonin control of sleep-wake behavior. Sleep Med Rev. 2011;15:269–81. [DOI] [PubMed] [Google Scholar]
  • [37].Shehata RSA, Nour ZAM, Badr AMA, Khalifa EM. Serotonin variations and sleep disorders among shift workers. A cross-sectional study. Toxicol Ind Health. 2021;37:603–9. [DOI] [PubMed] [Google Scholar]
  • [38].Hermida RC, Ayala DE, Fernández JR, Portaluppi F, Fabbian F, Smolensky MH. Circadian rhythms in blood pressure regulation and optimization of hypertension treatment with ACE inhibitor and ARB medications. Am J Hypertens. 2011;24:383–91. [DOI] [PubMed] [Google Scholar]
  • [39].Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep Med Rev. 2012;16:47–66. [DOI] [PubMed] [Google Scholar]
  • [40].Zhang X, Gérard P. Diet-gut microbiota interactions on cardiovascular disease. Comput Struct Biotechnol J. 2022;20:1528–40. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-105-e49302-s001.xlsx (240.5KB, xlsx)
medi-105-e49302-s002.xlsx (12.2KB, xlsx)
medi-105-e49302-s003.xlsx (9.5KB, xlsx)
medi-105-e49302-s004.xlsx (11.9KB, xlsx)
medi-105-e49302-s005.xlsx (11.2KB, xlsx)
medi-105-e49302-s006.xlsx (38.6KB, xlsx)
medi-105-e49302-s007.xlsx (14.6KB, xlsx)
medi-105-e49302-s008.xlsx (15.8KB, xlsx)
medi-105-e49302-s009.xlsx (11.6KB, xlsx)
medi-105-e49302-s010.xlsx (10.2KB, xlsx)

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES