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Indian Journal of Ophthalmology logoLink to Indian Journal of Ophthalmology
. 2024 May 13;72(Suppl 5):S801–S808. doi: 10.4103/IJO.IJO_3304_23

Association between gut microbiota and central retinal artery occlusion: A two-sample Mendelian randomization study

Jin Chen 1,#, Xinghua Wang 1,#, Junjie Yang 1, Jiahui Huang 1, Meng Xie 1, Zixuan Su 1, Fagang Jiang 1,✉
PMCID: PMC11670838  PMID: 38736244

Abstract

Purpose:

The gut microbiota might be closely related to central retinal artery occlusion (CRAO), but the causality has not been well defined. Two-sample Mendelian randomization (MR) study was used to reveal the potential causal effect between the gut microbiota and CRAO.

Methods:

Data for gut microbiota were obtained from the genome-wide association studies of the Dutch Microbiome Project (DMP) (n = 7738) and the MiBioGen consortium (n = 18,340), and data on CRAO were obtained from samples of FinnGen project (546 cases and 344,569 controls). Causalities of exposures and outcomes were explored mainly using the inverse variance weighted method. In addition, multiple sensitivity analyses including MR-Egger, weighted median (WM), simple mode, weighted mode, and MR Pleiotropy RESidual Sum and Outlier were simultaneously applied to validate the final results.

Results:

We identified three microbial pathways (two risk factors/one protective factor) and seven microbial taxa (two risk factors/five protective factors) associated with CRAO in the DMP study. Based on the data from the MiBioGen consortium, we identified seven microbial taxa (two risk factors/five protective factors) associated with CRAO, including the Eubacterium genus, which was consistently identified as a risk factor in both the DMP and the MiBioGen consortium MR analyses.

Conclusion:

Our study implicates the potential causal effects of specific microbial taxa and pathways on CRAO, potentially providing new insights into the prevention and treatment of CRAO through specific gut microbial taxa and pathway. Since our conclusion is a hypothesis derived from secondary genome-wide association studies (GWAS) data analysis, further research is needed for confirmation.

Keywords: Causal relationship, central retinal artery occlusion, gut microbiota, Mendelian randomization, microbial pathway

Central retinal artery occlusion (CRAO) is a type of acute ischemic stroke that results in significant visual impairment. It can also serve as a warning sign for potential future cerebrovascular and cardiovascular events.[1] The incidence rate of CRAO is estimated to be around 1.9 cases per 100,000 person-years.[2] The risk of developing CRAO is positively associated with advancing age and the presence of vascular risk factors, such as hypertension, hyperlipidemia, diabetes, and smoking.[3] Despite extensive research, the underlying pathophysiology of CRAO is still poorly understood. Current hypotheses suggest it may be associated primarily with atherosclerosis, embolism, inflammatory vascular disease, or hypercoagulability.[4]

With the advancement of metagenomic sequencing technology, there is growing recognition of the influence of gut microbiota on human health, autoimmunity, and the occurrence of diseases. This emerging understanding offers a fresh perspective on the human microbiome.[5] Recent research also highlights the presence of a gut–eye connection, whereby gut dysbiosis may have a significant impact on the onset and development of various ocular diseases such as glaucoma, macular degeneration, uveitis, and dry eye.[6,7]

Recent studies have found that patients with retinal artery occlusion (RAO) exhibit distinctive alterations in microbial composition and function, suggesting an association between RAO and the gut microbiota.[8] However, it is essential to highlight that the current evidence linking the gut microbiota with CRAO is only correlational and not causal. More research is needed to fully understand the potential causal links and mechanisms underlying this relationship.

Mendelian randomization is a method of analysis that utilizes genetic variants associated with a proposed risk factor as indicators to infer causal relationships between that exposure and an outcome of interest. This method emulates the randomization process utilized in randomized controlled trials, which is considered the benchmark for establishing causal relationships.[9] Recently, there has been a growing use of Mendelian randomization (MR) to explore the causal relationships between diseases, such as the relationship between the gut microbiota and ocular diseases.[10,11]

In this study, a two-sample MR analysis was conducted using genome-wide association studies (GWAS) datasets obtained from the Dutch Microbiome Project (DMP)[12] and MiBioGen consortium[13] to explore the potential causal relationships between the gut microbiota and CRAO.

Methods

Study design and the assumption of MR

The study design as depicted in Fig. 1, followed by the guidelines outlined in the strengthening the reporting of observational studies in epidemiology using mendelian randomization (STROBE-MR) checklist.[14] A two-sample MR analysis was conducted to explore the causal relationship between microbial taxa as well as the pathways and CRAO.

Figure 1.

Figure 1

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Flow chart for the MR study. CRAO=central retinal artery occlusion, DMP=Dutch Microbiome Project, IV=instrumental variable, MR=mendelian randomization, SNP=single nucleotide polymorphism

MR analysis relies on three fundamental assumptions: relevance, independence, and exclusion restriction[15]: (1) relevance: the genetic variants selected for the analysis are assumed to be associated with the risk factor; (2) independence: the selected genetic variants are assumed not to be associated with any confounders that could affect the association between the risk factor and the outcome; and (3) exclusion restriction: the selected genetic variants are assumed to affect the outcome only through their effect on the risk factor and not through other pathways. These assumptions are crucial for the validity of MR analysis.

Data sources

The genetic data for gut microbiota were sourced from a GWAS conducted on 7738 individuals of European descent, as part of the DMP. The gut microbiota composition was identified through shotgun metagenomic sequencing of stool samples. A total of 205 microbial pathways and 207 taxa were included in this study.[12] Another source of GWAS data regarding exposure is the MiBioGen consortium, which included 18,340 individuals from 24 cohorts. This dataset comprised 211 gut microbial taxa, of which 15 unknown families or genera were excluded, resulting in 196 microbial taxa available for MR analysis. The GWAS summary data for CRAO were obtained from the FinnGen project, which includes 546 cases and 344,569 controls. These sequencing data have undergone rigorous quality control.

Instrumental variable (IV) selection

To get more comprehensive results, we selected IVs that achieved locus-wide significance with a P value of less than 1 × 10−5. The linkage disequilibrium threshold was set at 0.001, and a clumping window of 10,000 kb was used. For palindromic single-nucleotide polymorphisms (SNPs), forward strand alleles were determined using allele frequency information. If the SNPs were not present in the outcome GWAS summary data, proxies with r2 ≥0.8 were used as substitutes. To minimize SNP–confounder correlation, we used PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/) to identify the SNPs associated with confounding factors such as body mass index, hypertension, hyperlipidemia, diabetes, venous thrombosis, and atherosclerosis. Any SNPs found to be associated with these confounders were excluded from our analysis. To avoid potential weak instrumental bias, the F-statistic (F = beta2/se2) was calculated. If F >10, the correlation between IV and the exposure was considered strong enough to protect the MR analysis from weak instrumental bias.[16]

Statistical analysis

In this study, the primary method used for two-sample MR analysis was inverse variance weighting (IVW),[17] IVW provides a robust estimate of the causal effect by combining the genetic instrumental variable estimates from multiple genetic variants. In addition, two other methods, MR-Egger and weighted median (WM), were employed as sensitivity analyses.[18] MR-Egger method is known to provide causal estimates with larger standard errors and lower effect sizes compared to IVW.[19] However, it is useful for investigating potential pleiotropy bias and obtaining an intercept test for unbiased estimation of the causal effect.[20,21] The results of the analysis were reported as odds ratios (ORs) with 95% confidence intervals (CIs) to illustrate the causality between exposures and outcomes.[22] The sensitivity analyses using weighted median and MR-Egger were conducted to assess the robustness of the findings under different assumptions and to account for potential biases.

The weighted median analysis in MR involves calculating the median of the MR association estimates, taking into consideration the accuracy of each instrumental variable. This approach yields consistent estimates even in scenarios where more than half of the instruments may be invalid.[18] It is a valuable method that accounts for potential biases and ensures reliable causal estimates are obtained.[18]

Various sensitivity tests were performed, including Cochran's Q-statistic to estimate heterogeneity and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods to evaluate and rectify horizontal pleiotropy.[23] Compared to IVW and MR-Egger, MR-PRESSO was able to detect outliers and test for differences in results before and after eliminating outliers.[24]

Leave-one-out analysis was conducted to assess the impact of a single SNP on the MR results. In the end, the online web tool (http://glimmer.rstudio.com/kn3in/mRnd/) was used to calculate statistical power.[25]

To strengthen the assessment of causality, the Bonferroni method[26] was employed to establish significance thresholds for multiple testing. These thresholds were determined based on the number of traits being analyzed. Specifically, the significance thresholds were set at 2.43 × 104 (0.05/205) for microbial pathways from DMP, 2.42 × 104 (0.05/207) for microbial taxa from DMP, and 2.55 × 104 (0.05/196) for microbial taxa from the MiBioGen consortium. P values reaching nominal significance (P < 0.05) were considered to suggest nominal potential causal effects. In this study, the analyses were conducted using R software (version 4.1.0; The R Foundation for Statistical Computing, Vienna, Austria). The primary R packages utilized in our manuscript comprised TwoSampleMR, MRPRESSO, and MendelianRandomization (GitHub repository: https://github.com/1527311/20221004).

Results

Selection of instrumental variables

After a series of IV screening steps, we included 2186 SNPs from 205 microbial pathways identified in the DMP study, 1961 SNPs from 207 microbial taxa identified in the DMP study and 2712 SNPs from 196 microbial taxa identified in the MiBioGen consortium analysis. The F-statistic for each SNP exceeded 10, indicating the absence of weak instrument bias.

Causal effects of gut microbial pathway on CRAO

Based on data from Dutch Microbiome Project, the study identified three gut microbial pathways that have potential relationship with CRAO using the IVW method [Fig. 2]. Fig. 3a shows the impact of changes in 205 microbial pathways on CRAO. From outside to inside, the -log10(P) values of IVW, MR-Egger, and WM are represented, respectively. The outer circle displays exposures with IVW results with P < 0.05. Red color represents the risk factors, while green color represents the protective factors. The inner circle displays OR with the IVW method, with a dashed line indicating the coordinate value of 1. Red color indicates an OR >1, while green color indicates an OR <1.

Figure 2.

Figure 2

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The forest plot of the causal estimates between the gut microbiota and CRAO. The OR and 95% CI were obtained using the IVW method. The prefixes f, g, and s in the taxa column refer to families, genus, and species, respectively. CI = confidence interval, CRAO = central retinal artery occlusion, IVW = inverse variance weighted, OR = odds ratio

Figure 3.

Figure 3

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Causal analysis of gut microbiota microbial pathways and CRAO based on MR analyses. (a) Causal effects of 205 gut microbial pathways on CRAO. (b) Causal effects of 207 gut microbial taxa from the Dutch Microbiome Project on CRAO. (c) Causal effects of 196 gut microbial taxa from the MiBioGen consortium on CRAO. CRAO = central retinal artery occlusion, MR = Mendelian randomization

The pathway pyruvate fermentation to acetate and lactate II [OR = 1.538 (1.016–2.327), P = 0.0419] and the pathway pyrimidine deoxyribonucleosides salvage [OR = 3.087 (1.341–7.105), P = 0.0080] were also found to be associated with an increased risk of CRAO. However, the pathway l-histidine degradation I [OR = 0.577 (0.356–0.936), P = 0.0258] was identified as a protective factor against CRAO.

A scatterplot illustrating the causal effect of the gut microbial pathways on CRAO is shown in Fig. 4.

Figure 4.

Figure 4

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Scatterplot of the effect of gut microbial pathways on CRAO (a–c). CRAO = central retinal artery occlusion

Causal effects of gut microbial taxa on CRAO

Based on data from the Dutch Microbiome Project, the study identified seven gut microbial taxa that have potential relationship with CRAO using the IVW method [Fig. 2]. One of the microbial taxa with an IVW analysis P value less than 0.05 was excluded due to the limited number of SNPs (only two). This exclusion was based on the concern that the small SNP count might compromise the robustness of the results. Fig. 3b shows the impact of changes in 207 microbial taxa on CRAO.

The family Eubacteriaceae [OR = 2.225 (1.178–4.202), P = 0.0137] and the genus Eubacterium [OR = 2.225 (1.178–4.203), P = 0.0137] were identified as risk factors for CRAO. Conversely, the family Bacteroidaceae [OR = 0.529 (0.335–0.834), P = 0.0061], the family Burkholderiales_noname [OR = 0.682 (0.513–0.907), P = 0.0084], the genus Bacteroides [OR = 0.535 (0.343–0.832), P = 0.0056], Burkholderiales_noname [OR = 0.682 (0.513–0.906), P = 0.0084], and the species Burkholderiales_bacterium_1_1_47 [OR = 0.682 (0.513–0.906), P = 0.0084] were identified as protective factors against CRAO. A scatterplot illustrating the causal effect of the gut microbial taxa on CRAO is shown in Fig. 5.

Figure 5.

Figure 5

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Scatterplot of the effect of gut microbial taxa from the Dutch Microbiome Project on CRAO (a–g). CRAO = central retinal artery occlusion

Based on data from MiBioGen consortium, the study identified seven gut microbial taxa that have potential relationship with CRAO using the IVW method [Fig. 2]. Fig. 3c shows the impact of changes in 196 microbial taxa on CRAO.

The family Oxalobacteraceae [OR = 1.477 (1.061–2.058), P = 0.0210] and the genus Eubacterium fissicatena group [OR = 1.486 (1.004–2.198), P = 0.0476] were identified as risk factors for CRAO. In addition, the genus Butyricimonas [OR = 0.490 (0.258–0.931), P = 0.0293], the genus Christensenellaceae R 7 group [OR = 0.348 (0.130–0.927), P = 0.0347], the genus Coprococcus1 [OR = 0.451 (0.239–0.852), P = 0.0141], the genus Fusicatenibacter [OR = 0.527 (0.300–0.926), P = 0.0258], and the genus Howardella [OR = 0.635 (0.429–0.940), P = 0.0232] were identified as protective factors against CRAO. A scatterplot illustrating the causal effect of the gut microbial taxa on CRAO is shown in Fig. 6.

Figure 6.

Figure 6

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Scatterplot of the effect of gut microbial taxa from the MiBioGen consortium on CRAO (a–g). CRAO = central retinal artery occlusion

Sensitivity analysis

No evidence of heterogeneity was found in the associations between the gut microbiota and CRAO, as indicated by Cochrane's Q-statistic. In addition, the MR-PRESSO method identified no outlier SNPs and the MR-Egger intercept did not demonstrate any evidence of directional pleiotropy effects. The leave-one-out analysis, as demonstrated in Supplementary Figs S1 (877.8KB, tif) –S3 (2.1MB, tif) , indicated that there was no single SNP that was primarily responsible for the association between the gut microbiota and CRAO.

Discussion

Through MR analyses of gut microbiota and CRAO, we identified potential causal associations between the gut microbiota (three pathways and 14 taxa) and CRAO (values of P < 0.05 in IVW methods). These findings provide new insights into the potential role of gut bacteria in the prevention and treatment of CRAO.

The gut microbiota, a complex community of microorganisms residing in the intestines, play a pivotal role in various physiologic processes, such as metabolism, immune regulation, and inflammation.[27] CRAO is primarily caused by emboli, which are often derived from atherosclerotic plaques. These emboli block the blood vessels in the eye, leading to CRAO. There are also CRAOs caused by small vessel disease (microangiopathic).[28] Recent research has started to uncover the potential links between the gut microbiota and CRAO.[8] Zysset-Burri et al.[8] conducted a study and observed that certain bacterial groups were associated with RAO. They found that the class Actinobacteria was more prevalent among RAO patients, while the family Lachnospiraceae and the genera Odoribacter and Parasutterella were more abundant in the control groups. This suggests a potential association between the gut microbiota and CRAO, providing a basis for further exploration of the mechanisms involved.

The association between the gut microbiome and CRAO may be explained by risk factors such as atherosclerosis, diabetes, hypertension, and others, which partially overlap between the different entities.[29] Studies have shown that these atherosclerotic plaques contain microbial DNA that is shared with the gut microbiota, such as Actinobacteria.[30] Changes in the gut microbiome composition were discovered in people with symptomatic atherosclerosis. Patients with atherosclerosis had an increased presence of Collinsella bacteria, while Eubacterium and Roseburia were less prevalent than in healthy individuals.[31] In a study on the gut microbiome of diabetic patients, it was observed that Escherichia coli, Clostridium species, Bacteroides caccae, and Eggerthella lenta were more abundant in patients with diabetes compared to controls. However, Eubacterium rectale and others were found to be less abundant in diabetic patients.[32]

In addition, the gut microbiota produce various metabolites, some of which possess signaling functions. Trimethylamine N-oxide (TMAO) is a metabolite produced by the gut microbiota that regulates various metabolic pathways, including cholesterol and steroid metabolism. By altering the expression of cholesterol transport proteins, TMAO may inhibit the reverse transport of cholesterol. The collective action of these metabolites enables the modulation of host inflammation and metabolism, thereby impacting disease development. Compared to healthy individuals, patients with RAO exhibit elevated levels of TMAO. The levels of TMAO are positively correlated with the abundance of Akkermansia, but negatively correlated with the abundance of Parasutterella and Lachnospiraceae.[8]

The pathway pyruvate fermentation to acetate and lactate II and the pathway pyrimidine deoxyribonucleosides salvage were identified as risk factors for CRAO in our study. These pathways are involved in various metabolic processes, such as alcohol and carbohydrate degradation, energy production, and pyrimidine nucleotide salvage.[33] The pathway l-histidine degradation I was identified as a protective factor against CRAO. Research has found enrichment of the mevalonate and methylerythritol phosphate pathways, both involved in cholesterol metabolism, in patients with RAO. In addition, enrichment of the mevalonate pathway, which is a target of statin drugs, is an important pathway in the development of atherosclerosis, suggesting that certain specific metabolic pathways of the gut microbiota may mediate CRAO by influencing the overall vascular state.[8] Consequently, these pathways could be potential targets for interventions aimed at reducing CRAO risk.

Our analysis revealed a causal association between the risk of CRAO and the family as well as the genus Eubacteriaceae. This finding aligns with the results from the MiBioGen consortium, where the family Oxalobacteraceae and the genus E. fissicatena group were identified as risk factors for CRAO. It is worth noting that within the Eubacterium, five bacterial genus have been identified as protective factors against CRAO, while two genus have been identified as risk factors for CRAO. Eubacterium, a core component of the human gut microbiome, is recognized by its phylogenetic and often phenotypic diversity, but taxonomic changes have complicated the assessment of the role of various taxa in causing infections. Notably, several members of this genus produce butyrate, a compound that is essential for maintaining energy balance, regulating colonic motility and immunity, and suppressing intestinal inflammation.[34] Despite the general perception of Eubacterium as beneficial bacteria, the effects of specific species and strains on human health may vary. The potential mechanisms of specific Eubacterium species and strains in the development of CRAO need to be further investigated. Oxalobacteraceae is known for its ability to break down oxalate salts, and its deficiency has been identified as a risk factor for renal stone formation.[35] Indeed, there could be other pathways or factors contributing to the development of CRAO in individuals.

Overall, nine microbial taxa were identified as protective factors against CRAO in our study. Bacteroides, potential colonizers of the colon, constitute a significant portion of the gut microbial community.[36] Research by Yoshida et al.[37] has revealed that in patients with coronary artery disease, 16S rRNA sequencing of human feces shows a significant decrease in the abundance of Bacteroides vulgatus and Bacteroides dorei. Notably, gavage with these two species can mitigate the formation of atherosclerotic lesions in atherosclerosis-prone mice, significantly improve endotoxemia, reduce the production of gut microbial lipopolysaccharides, and effectively suppress proinflammatory immune responses. This suggests a potential protective effect of the Bacteroides genus against CRAO, although further research is needed to confirm this hypothesis. Another relevant genus is Burkholderia, which has been found to play an indispensable role in the effectiveness of immunotherapy. Research has shown that the use of anti-Cytotoxic T-Lymphocyte-Associated Protein 4 (anti-CTLA-4) monoclonal antibodies promotes the growth of fragile Bacteroides fragilis and other bacteria in the intestinal mucosa. These bacteria produce polysaccharides that stimulate immune cells located in the lamina propria. As a result, this enhances interleukin-12–dependent Th1 immune responses in tumor-draining lymph nodes.[38] Based on the above mechanism, we speculate that Burkholderia could potentially help prevent CRAO by regulating immune responses. The diverse roles of these bacteria underscore the complex interactions between the gut microbiome and human health and the need for further research to unravel these relationships.

Nutrition based on the microbiome has begun to be used to predict variable clinical phenotypes or guide personalized treatment for diseases such as metabolic syndrome and gastrointestinal disorders. In recent years, successful attempts in developing personalized diets for regulating blood sugar levels have offered hope for further progress in disease control and treatment. The gut microbiome, which is highly individualized and influenced by diet, plays a crucial role in human health. Therefore, understanding and manipulating it could provide new avenues for disease prevention and treatment.[39] While the treatment of CRAO primarily involves vasodilation and thrombolysis,[40] the therapeutic effect of adjusting the gut microbiota may be limited. However, it is possible to use the gut microbiota as a basis to predict the risk of CRAO and to intervene in the gut microbiota of high-risk individuals to reduce the risk of CRAO.

It is crucial to take into account the limitations of our study when interpreting the results. Firstly, due to the limited number of SNPs with P < 5 × 10−8, we selected SNPs with P < 1 × 10−5 as IVs, which may have affected the reliability of IVs. To address this, we took several steps to screen IVs, such as excluding weak IVs with F < 10 to avoid confounding effects. Secondly, the MR analysis performed in this study is a correlation analysis and does not provide a mechanistic explanation of the observed associations between the gut microbiota and CRAO. Thirdly, potential pleiotropy may have affected the MR analysis. However, as all exposures in our MR analysis had at least three IVs, the impact of potential pleiotropy may have been mitigated to some extent.[41] Fourthly, the study participants were mostly of European ancestries, and the limited number of intestinal microbiome data from other ethnicities may limit the generalizability of our findings to other populations. Because the databases did not provide enough information about the participants, our study may have a possible bias due to our inability to match the ages of the two samples in our analysis. Since our conclusion is a hypothesis derived from secondary GWAS data analysis, further research is needed for confirmation.

Conclusions

In conclusion, our study provides evidence suggesting a potential causal relationship between specific gut microbiota and CRAO. These findings open up new avenues for the prevention and treatment of CRAO through modulating the gut microbiota. However, it is important to note that the interactions between the gut microbiota and host metabolism and immunity are complex and not yet fully understood. Further research is necessary to gain a deeper understanding of these interactions, which can help develop more effective strategies for managing CRAO.

Conflicts of interest

There are no conflicts of interest.

Figure S1

Leave-one-out analysis for gut microbial pathways on CRAO (a-c). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl1.tif (877.8KB, tif)
Figure S2

Leave-one-out analysis for gut microbial taxa from Dutch Microbiome Project on CRAO (a-g). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl2.tif (2.1MB, tif)
Figure S3

Leave-one-out analysis for gut microbial taxa from MiBioGen consortium on CRAO (a-g). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl3.tif (2.1MB, tif)

Acknowledgements

We would like to acknowledge the participants and investigators of the Dutch Microbiome Project, MiBioGen consortium, and FinnGen project for sharing the GWAS summary statistics. We thank Figma (www.figma.com) for expert assistance in Fig. 1.

Funding Statement

This work was supported by the National Natural Science Foundation of China, grant number 81900912.

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Associated Data

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

Supplementary Materials

Figure S1

Leave-one-out analysis for gut microbial pathways on CRAO (a-c). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl1.tif (877.8KB, tif)
Figure S2

Leave-one-out analysis for gut microbial taxa from Dutch Microbiome Project on CRAO (a-g). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl2.tif (2.1MB, tif)
Figure S3

Leave-one-out analysis for gut microbial taxa from MiBioGen consortium on CRAO (a-g). CRAO = central retinal artery occlusion, MR=mendelian randomization

IJO-72-801_Suppl3.tif (2.1MB, tif)

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