Causal relationship between gut microbiota and brain microbleeds: a comprehensive Mendelian randomization study

doi:10.21203/rs.3.rs-3578619/v1

Download PDF

Research Article

Causal relationship between gut microbiota and brain microbleeds: a comprehensive Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-3578619/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Considerable evidence has been reported that alterations in gut microbiota composition could cause cerebrovascular diseases. The microbiota-gut-brain axis also hinted at a possible contribution of the gut microbiota to cerebrovascular diseases. However, the causal association between the gut microbiome and the risk of brain microbleeds (BMB) is unclear.

Methods

We performed two-sample bidirectional Mendelian randomization (MR) utilizing the summary-level data of respective genome-wide association study (GWAS) for 211 gut microbial taxa and two BMB phenotypes to reveal the causal association between gut microbiota and BMB.

Results

We identified 7 causal relationships between genetic liability in the gut microbiome and any BMB, including those involving the genus Lachnospiraceae. We found 13 associations between genetic liability in the gut microbiome and lobar BMB. Moreover, we found 6 associations between genetic liability in the gut microbiome and deep infratentorial BMB. The bidirectional, heterogeneity, and pleiotropy analyses confirmed the robustness of MR results.

Conclusion

Our MR analysis revealed that the gut microbiota was causally associated with BMB and may be helpful in providing new insights for further mechanistic and clinical studies of microbiota-mediated cerebrovascular diseases.

Gut microbiota

brain microbleeds

Mendelian randomization

Genetics

SNPs

Brain microbleeds (BMB), also referred to as cerebral microbleeds or cerebral microhemorrhages correspond to hemosiderin deposits as a result of microscopic hemorrhages that are visible on MRI sequences.¹ In many prospective and epidemiological studies, BMB is highly prevalent in people with clinically manifest cerebrovascular disease including both ischemic stroke and intracerebral hemorrhage, and has been shown to increase the risk of stroke recurrence. ^{2, 3} Once BMB is detected, the impact that they have on the outcomes of patients with them is of great interest, as they may represent a potential biomarker for an underlying cerebrovascular disease state. Therefore, there is an urgent need to explore the etiology of BMB to find rapid and effective treatment methods and prevent cerebrovascular diseases that may occur later. This has been an important and challenging problem, particularly at the microscopic and molecular levels.

"The gut microbiota" refers to all the microorganisms in the human gastrointestinal tract, with a population reaching trillions.⁴ Due to the intricate symbiotic relationship between the gut microbiota and the host, it is closely associated not only with gastrointestinal diseases but also with overall human health.^5–7 Recently, the complex relationship between the gut microbiota and the occurrence of cerebrovascular diseases through the gut-brain axis has sparked a controversy. So far, an increasing amount of indirect evidence logically supports this hypothesis. Firstly, gut dysbiosis can directly contribute to risk factors for stroke, including hypertension, diabetes, aging, dyslipidemia, obesity, etc.^8–10 Furthermore, numerous studies have indicated an association between alterations in gut microbiota composition and the risk of cerebrovascular diseases. For example, enrichment of Enterococcus and a reduction of Prevotella have been associated with an increased risk of intracerebral hemorrhage (ICH).^{11, 12} However, it is still unclear whether there is any association between gut microbiota and BMB, and there is a lack of population-level research with a higher level of causal evidence.

Animal model-based or clinical trials examining the onset of BMB following the intervention of gut microbiome are absent and too challenging to accomplish. Disentangling this causality is of great clinical importance, which could help bridge these gaps from traditional epidemiological studies. A better approach to overcome these limitations is Mendelian randomization (MR). MR analysis utilizes the inherent characteristics of common genetic variations to modify the interested environmental exposure and has become a widely used method to explore potential causal relationships between environmental exposures and diseases.^13,14 Moreover, MR results are less likely to be influenced by environmental confounding that could bias causal estimates. Depending on single-nucleotide polymorphisms (SNP) as an instrumental variable, we used MR analyses to infer the causal association between the gut microbiome and BMB.

Study overview.

Using summary statistics of genome-wide association studies (GWAS), we conducted a comprehensive MR study to explore the causal association of the gut microbiome with BMB, including strictly lobar BMB and deep infratentorial BMB. The overall flow chart of this study is shown in Fig. 1. This MR study strictly followed the three assumptions: (1) genetic variations as SNPs must be significantly associated with gut microbiome (as exposure); (2) SNPs were not related to any confounder factors; (3) outcome can be affected by SNPs only through exposure. Our results were in accordance with the STROBE-MR guidelines.¹⁵

GWAS Summary Data for gut microbiota.

The international consortium MiBioGen was established to study the influence of human genetics on the gut microbiome. The summary human gut microbiome GWAS data that coordinated 16S ribosomal RNA gene sequencing profiles and genotyping data from 18,340 participants from 24 cohorts from the USA, Canada, Israel, South Korea, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland, and the UK to explore the association between autosomal human genetic variants and the gut microbiome.¹⁶ After removing 15 unidentified bacterial taxa, the GWAS data we obtained finally covered a total of 196 taxa (ordered by taxonomy): 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera.¹⁶ Since only a few gut microbiomes had three or more independent SNPs at genome-wide significance levels (P < 5 × 10^− 8), a higher cut-off (P < 1 × 10^− 5) was used for obtaining SNPs to obtain more comprehensive results. We also assess the association using P < 5 × 10^− 8 in supplementary. We clumped these SNPs for linkage disequilibrium (LD) to r² < 0.001 based on the European 1,000 Genomes panel. Then, we standardized the SNP allele frequencies across studies and eliminated palindromic SNPs (0.42–0.58) with unclear allele frequencies. Finally, to evaluate the strength of weak instrumental bias, we computed the F-statistic of SNPs. It was considered that no bias was caused by weak SNPs if the F-statistic was more than 10.¹⁷

GWAS Summary Data for BMB.

Summary data for BMB were obtained from a meta-analysis of GWASs in 11 population-based cohort studies and three case-control or case-only stroke cohorts.¹⁸ BMB were detected in 3,556 of the 25,862 participants (97.1% European ancestry individuals), of which 2,179 were strictly lobar and 1,293 were deep infratentorial BMB. Age and sex were adjusted in each included cohort.

Statistical analysis.

Principal analyses were conducted using the inverse-variance weighted (IVW) MR method. This method assumes that all genetic variants are valid instrumental variables and produces the most accurate estimates.¹⁹ For gut microbiome with only one SNP available, we used the Wald ratio method, as other MR methods require at least 2 SNPs. As robustness validation analyses, the weighted median method and MR-Egger regression were used, and their findings complemented those calculated by the IVW approach.^{20, 21} Odds ratios (OR) and 95% confidence intervals (CI) were used to present the findings of causal connections. The significance cutoff was established at p < 0.05.

The potential heterogeneity was measured and tested using Cochran's Q statistics, and the horizontal pleiotropy was estimated using the MR-Egger intercept test. For significant MR estimates, the MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test was used to identify outliers for horizontal pleiotropy and heterogeneity. The leave-one-SNP-out analysis was conducted to determine if the aggregated estimate was biased by a high-influence point. Finally, we performed bi-directional MR analyses and MR Steiger directionality test to determine the direction of causation.

All analyses in this study were performed based on R software (version 4.2.3). In our MR study, the "TwoSampleMR" R package (version 0.5.6) and the "MRPRESSO" R package were utilized.

The selection of single nucleotide polymorphisms.

First, we identified 2047 SNPs that were associated with both gut microbiota and lobar BMB at a significance level of p < 1 × 10^− 5. To further investigate the causal effects, we used more strict significance level of p < 5 × 10^− 8 to select SNPs and identified 22 SNPs that fit this criteria. Then, we screened for SNPs associated with deep infratentorial BMB in the same way. 1783 (genome-wide statistical significance threshold, p < 1 × 10^− 5) and 22 (genome-wide statistical significance threshold, p < 5 × 10^− 8) SNPs were selected as SNPs. The F statistics of the SNPs were all largely > 10 (Table S1), indicating no evidence of weak instrument bias.

Causal effects of the gut microbiome on the lobar brain microbleeds.

Through the IVW, in the set of SNPs (p < 1 × 10^− 5), the majority of gut microbiomes were not related to lobar BMB (Table S2). Meanwhile, we found that the genus Lachnospiraceae NC2004 group, class Negativicutes, order Selenomonadales, family Victivallaceae, and phylum Cyanobacteria were the protective factors against lobar BMB (genus Lachnospiraceae NC2004 group: OR [95% CI]: 0.59 [0.44, 0.80]; class Negativicutes: OR [95% CI]: 0.49 [0.30, 0.81]; order Selenomonadales: OR [95% CI]: 0.49 [0.30, 0.81]; family Victivallaceae: OR [95% CI]: 0.77 [0.62, 0.96] ; phylum Cyanobacteria: OR [95% CI]: 0.69 [0.48, 0.98]). The order Bacillales, as well as the genus Terrisporobacter, phylum Firmicutes, genus Coprobacter, and genus Ruminococcaceae UCG009, were identified as risk factors for lobar BMB (order Bacillales: OR [95% CI]: 1.33 [1.06, 1.67]; genus Terrisporobacter: OR [95% CI]: 1.61 [1.07, 2.40]; phylum Firmicutes: OR [95% CI]: 1.69 [1.08, 2.65]; genus Coprobacter: OR [95% CI]: 1.33 [1.01, 1.75] ; genus Ruminococcaceae UCG009: OR [95% CI]: 1.38 [1.00, 1.90]) (Fig. 2, Table S6). We identified the SNPs under a more stringent condition (p < 5 × 10^− 8), and we found that family Streptococcaceae, genus Streptococcus, and genus Intestinibacter were causally associated with lobar BMB (wald ratio, family Streptococcaceae: OR [95% CI]: 3.87 [1.07, 13.94]; genus Streptococcus: OR [95% CI]: 3.61 [1.07, 12.20]; genus Intestinibacter: OR [95% CI]: 2.99 [1.11, 8.07]) (Table S4).

The same association between the gut microbiome and lobar BMB was still noted in the MR-egger and weighted median analyses (Table S6). MR-Egger and MR-PRESSO tests showed that there is no horizontal pleiotropy or outliers (Fig. 2). In addition, the leave-one-out method revealed that no SNP was significantly associated with the outcome. Finally, bi-directional MR analyses showed that there is no evidence that lobar BMB affects gut microbiota (Table S6). The result of the MR Steiger directionality test also supports above conclusion (Table S6).

Causal effects of the gut microbiome on the deep infratentorial brain microbleeds.

In the set of SNPs (p < 1 × 10^− 5), as shown in Fig. 3, IVW analyses show that phylum Proteobacteria, genus Oscillospira, order Desulfovibrionales and family Desulfovibrionaceae were causally related to a increased risk of deep infratentorial BMB (phylum Proteobacteria: OR [95% CI]: 2.28 [1.10, 4.72]; genus Oscillospira: OR [95% CI]: 2.21 [1.02, 4.80]; order Desulfovibrionales: OR [95% CI]: 1.90 [1.01, 3.58]; family Desulfovibrionaceae: OR [95% CI]: 2.04 [1.01, 4.13]). In contrast, class Bacilli was causally related to a decreased risk of deep infratentorial BMB (OR [95% CI]: 0.40 [0.20, 0.78]). No significant correlation was found between other gut microbiomes and deep infratentorial BMB (Table S3). In the set of SNPs (p < 5 × 10^− 8), we found that order Gastranaerophilales was causally associated with deep infratentorial BMB (wald ratio, OR [95% CI]: 0.30 [0.10, 0.88]) (Table S5).

The results of the MR-Egger intercept test and MR-PRESSO global test showed that there were no horizontal pleiotropic effects and The Cochran Q test did not provide any evidence of heterogeneity (Fig. 3). Results from the weighted median method and MR-egger method also supported principal analyses (IVW), which was not significant (Table S7). And the results of the leave-one-out analysis showed that no matter which SNP was removed, it would not have a fundamental impact on the results (Figure S2). Finally, through the bi-directional MR analyses and MR Steiger directionality test, we found that there was no reverse causal relationship between the deep infratentorial BMB and gut microbiota associated with it (Table S7).

As far as we know, this is the first MR investigation to evaluate whether gut microbiota is causally connected with BMB. In the biggest GWAS of the gut microbiota, robustly related gene variations were discovered. We explored genetic vulnerability to some gut bacteria that is causally connected with BMB using extensive genomic data from more than 3556 European persons.²² Interestingly, the genetic susceptibility to the genus Intestinibacter, genus Streptococcus, and family Streptococcaceae was found to causally promote lobar BMB, while order Gastranaerophilales can protect deep infratentorial BMB (p < 1×10^− 8). We also found certain gut microbes that could be BMB risk factors (p < 10^− 5). These findings may have ramifications for public health efforts focused on lowering BMB risk.

The reasons why the effect of bacteria features on BMB founding in our study should be discussed. According to locations, BMB could be sorted as lobar BMB mainly caused by cerebral amyloidosis, and deep infratentorial BMB primarily induced by hypertension. The effect of the gut microbiome on BMB is indirect, such as hypertension, metabolite, and pro-inflammatory factors.^{23, 24} Hypertension is an important inducing factor for BMB, particularly for deep infratentorial BMB. Based on emerging evidence, the progression and pathogenesis of hypertension are closely related to the gut microbiome. Corresponding to the negative causal effect of Streptococcus on deep infratentorial BMB in MR, this bacterial trait was also found to be particularly lower in hypertension patients,²⁵ but Streptococcus mutans Harboring the cnm Gene have been proved to be related to increase the evidence of BMB.²⁶ This interesting outcome may indicate whether Streptococcus plays an essential role in BMB with hypertension or not. In another study, Firmicutes and Proteobacteria were found to be both significantly changed in ICH patients.²⁷

Through pro-inflammatory factors produced by gut bacteria entering the bloodstream, the perturbation of gut microbiota could lead to a higher incidence of autoimmunity and allergy in the whole body.²⁸ The pro-inflammatory factors include inflammatory markers (such as lipopolysaccharides, c-reactive protein, and cytokines. eg), gut microbes, and their derivatives. Especially when the blood-brain barrier is breached, these factors could constantly interact with pattern recognition receptors in intestinal epithelial cells, immune cells in peripheral blood, and even cells in the central neural system.^{29, 30} Proteobacteria, with highly immune-stimulatory lipopolysaccharides in prevous studies, was calculated to be a risk factor for BMB in our study³¹.

Previous researches have shown that gut microbial metabolites such as trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), and equol are associated with various cerebral vascular diseases. Although no clear relationship between TMAO and BMB has been reported, it is of paramount importance as representative research to elucidate the influence of microbial metabolites and to achieve the proof of concept study, "microbial-targeted non-lethal therapeutics".^32,33 SCFAs may influence the microbiome-brain-gut axis through direct and indirect pathways.^{33, 34} Recently, Haak BW et al. showed that the reduction of butyrate-producing bacteria in the gut microbiome was associated with hemorrhagic stroke using 16S ribosomal RNA amplicon sequencing.³⁵

BMB is not fatal but increases the risk of stroke by about 2–4 times and the risk of ICH by about 5 times.^{36, 37} Treatment with antithrombotic drugs may lead to an increase in BMB, and the use of warfarin may increase the risk of ICH. The recent AHA/ASA publication provides advice on the risk of ICH in patients with BMB.³⁸ Therefore, there is an urgent need to explore novel ways to stop the development and clinical disease resulting from BMB.³⁹ The incomplete understanding of the pathogenesis is a major reason for the lack of more specific preventive and therapeutic strategies for BMB. Encouragingly, accumulating evidence supports the role of gut microbiota in the etiology of BMB.⁴⁰ Such advances in the understanding of BMB have provided targets for potential BMB therapies.

There were several limitations to MR selecting the gut microbiome as an exposure. First, the abundance of the gut microbiome may also be affected by diet, gender, medication, time of sampling, etc., which may reduce the variance explained by genetics. Second, the 16S rRNA gene sequencing from most GWAS typically allows resolution at the genus level, so we cannot estimate whether particular strains or species are correlated with results. Third, the use of different cohorts means that non-linear associations cannot be tested, especially when the gut microbiome may vary between cohorts, and subgroup analysis is not possible. However, causal effects should be generally consistent across cohorts.

GWAS: large-scale genome-wide association studies

MR, Mendelian randomization

BMB: brain microbleeds

ICH: intracerebral hemorrhage

SNP: single-nucleotide polymorphism

LD: linkage disequilibrium

OR: Odds ratios

MR-PRESSO: MR Pleiotropy Residual Sum and Outlier

CI : confidence intervals

IVW: inverse-variance weighted

TMAO: trimethylamine N-oxide

SCFAs: short-chain fatty acids

Acknowledgements

Not applicable.

Funding

This study was supported by the Shanghai Changhai Hospital (Grant No. GH145-05).

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. All data generated or analysed during this study are included in this published article and its supplementary information files.

Author contributions

Tianxiang Gao: participated in study design, analyzed data, prepared figures, wrote manuscript. Hanchen Liu: participated in study design, analyzed data, wrote manuscript. Congyan Wu: wrote manuscript. Jianmin Liu and Xiaoxi Zhang: participated in study design, interpreted data, wrote manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Ethics approval and consent to participate

Not applicable.

Consent for Publication

Not applicable.

Competing interests

The authors declare no competing interests.

Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12:822–38.
Wilson D, Ambler G, Lee KJ, Lim JS, Shiozawa M, Koga M, et al. Cerebral microbleeds and stroke risk after ischaemic stroke or transient ischaemic attack: A pooled analysis of individual patient data from cohort studies. Lancet Neurol. 2019;18:653–65.
Debette S, Schilling S, Duperron MG, Larsson SC, Markus HS. Clinical significance of magnetic resonance imaging markers of vascular brain injury a systematic review and meta-analysis. Jama Neurol. 2019;76:81–94.
Thursby E, Juge N. Introduction to the human gut microbiota. Biochem J. 2017;474:1823–36.
Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018;361:k2179.
Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2021;19:55–71.
Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N Engl J Med. 2016;375:2369–79.
Durgan DJ, Lee J, McCullough LD, Bryan RM. Examining the role of the microbiota-gut-brain axis in stroke. Stroke. 2019;50:2270–7.
Iatcu CO, Steen A, Covasa M. Gut microbiota and complications of type-2 diabetes. Nutrients. 2021;14.
Honarpisheh P, Bryan RM, McCullough LD. Aging microbiota-gut-brain axis in stroke risk and outcome. Circ Res. 2022;130:1112–44.
Xiao L, Zheng H, Li J, Zeng M, He D, Liang J, et al. Targeting nlrp3 inflammasome modulates gut microbiota, attenuates corticospinal tract injury and ameliorates neurobehavioral deficits after intracerebral hemorrhage in mice. Biomed Pharmacother. 2022;149:112797.
Yu X, Zhou G, Shao B, Zhou H, Xu C, Yan F, et al. Gut microbiota dysbiosis induced by intracerebral hemorrhage aggravates neuroinflammation in mice. Front Microbiol. 2021;12:647304.
Davey Smith G, Hemani G. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89–R98.
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.
Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: The strobe-mr statement. Jama-Journal of the American Medical Association. 2021;326:1614–21.
Kurilshikov A, Medina-Gomez C, Bacigalupe R, Radjabzadeh D, Wang J, Demirkan A, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet. 2021;53:156–.
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.
Knol MJ, Lu DW, Traylor M, Adams HHH, Romero JRJ, Smith AV, et al. Association of common genetic variants with brain microbleeds a genome-wide association study. Neurology. 2020;95:E3331–43.
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.
Bowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through egger regression. Int J Epidemiol. 2015;44:512–25.
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.
Knol MJ, Lu D, Traylor M, Adams HHH, Romero JRJ, Smith AV, et al. Association of common genetic variants with brain microbleeds: A genome-wide association study. Neurology. 2020;95:e3331–43.
Zhu S, Jiang Y, Xu K, Cui M, Ye W, Zhao G, et al. The progress of gut microbiome research related to brain disorders. J Neuroinflamm. 2020;17:25.
Wan S, Dandu C, Han G, Guo Y, Ding Y, Song H, et al. Plasma inflammatory biomarkers in cerebral small vessel disease: A review. CNS Neurosci Ther. 2023;29:498–515.
Yan Q, Gu Y, Li X, Yang W, Jia L, Chen C, et al. Alterations of the gut microbiome in hypertension. Front Cell Infect Microbiol. 2017;7:381.
Hosoki S, Saito S, Tonomura S, Ishiyama H, Yoshimoto T, Ikeda S, et al. Oral carriage of streptococcus mutans harboring the cnm gene relates to an increased incidence of cerebral microbleeds. Stroke. 2020;51:3632–9.
Li W, Wu LX, Huang BS, Yang LJ, Huang JQ, Li ZS, et al. A pilot study: Gut microbiota, metabolism and inflammation in hypertensive intracerebral haemorrhage. J Appl Microbiol. 2022;133:972–86.
Okada H, Kuhn C, Feillet H, Bach JF. The 'hygiene hypothesis' for autoimmune and allergic diseases: An update. Clin Exp Immunol. 2010;160:1–9.
König J, Wells J, Cani PD, García-Ródenas CL, MacDonald T, Mercenier A, et al. Human intestinal barrier function in health and disease. Clin translational Gastroenterol. 2016;7:e196.
Wells JM, Brummer RJ, Derrien M, MacDonald TT, Troost F, Cani PD, et al. Homeostasis of the gut barrier and potential biomarkers. Am J Physiol Gastrointest Liver Physiol. 2017;312:G171–g193.
Al Bander Z, Nitert MD, Mousa A, Naderpoor N. The gut microbiota and inflammation: An overview. Int J Environ Res Public Health. 2020;17.
Roberts AB, Gu X, Buffa JA, Hurd AG, Wang Z, Zhu W, et al. Development of a gut microbe-targeted nonlethal therapeutic to inhibit thrombosis potential. Nat Med. 2018;24:1407–17.
Lee J, d'Aigle J, Atadja L, Quaicoe V, Honarpisheh P, Ganesh BP, et al. Gut microbiota-derived short-chain fatty acids promote poststroke recovery in aged mice. Circul Res. 2020;127:453–65.
Dalile B, Van Oudenhove L, Vervliet B, Verbeke K. The role of short-chain fatty acids in microbiota-gut-brain communication. Nat Rev Gastroenterol Hepatol. 2019;16:461–78.
Haak BW, Westendorp WF, van Engelen TSR, Brands X, Brouwer MC, Vermeij JD, et al. Disruptions of anaerobic gut bacteria are associated with stroke and post-stroke infection: A prospective case-control study. Translational stroke research. 2021;12:581–92.
Akoudad S, Portegies ML, Koudstaal PJ, Hofman A, van der Lugt A, Ikram MA, et al. Cerebral microbleeds are associated with an increased risk of stroke: The rotterdam study. Circulation. 2015;132:509–16.
Wilson D, Ambler G, Lee KJ, Lim JS, Shiozawa M, Koga M, et al. Cerebral microbleeds and stroke risk after ischaemic stroke or transient ischaemic attack: A pooled analysis of individual patient data from cohort studies. Lancet Neurol. 2019;18:653–65.
Whelton PK, Carey RM, Aronow WS, Casey DE Jr., Collins KJ, Dennison Himmelfarb C et al. 2017 acc/aha/aapa/abc/acpm/ags/apha/ash/aspc/nma/pcna guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: A report of the american college of cardiology/american heart association task force on clinical practice guidelines. Circulation. 2018;138:e484-e594.
Gao Y, Li D, Lin J, Thomas AM, Miao J, Chen D, et al. Cerebral small vessel disease: Pathological mechanisms and potential therapeutic targets. Front Aging Neurosci. 2022;14:961661.
Tonomura S, Gyanwali B. Cerebral microbleeds in vascular dementia from clinical aspects to host-microbial interaction. Neurochem Int. 2021;148:105073.

No competing interests reported.

supplement.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Causal relationship between gut microbiota and brain microbleeds: a comprehensive Mendelian randomization study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

INTRODUCTION

METHODS

Statistical analysis.

RESULTS

DISCUSSION

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1