Association between gut microbiota and anxiety disorders: a bidirectional two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-3795111/v1

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Research Article

Association between gut microbiota and anxiety disorders: a bidirectional two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-3795111/v1

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Background

There are many articles reporting that the component of intestinal microbiota implies a link to anxiety disorders (AD), and the brain-gut axis is also a hot topic in current research. However, the specific relevance between gut microbiota and AD is uncertain. We aimed to investigate causal relationship between gut microbiota and AD by using bidirectional Mendelian randomization (MR).

Methods

Genetic instrumental variable (IV) for the gut microbiota were obtained from a genome-wide association study (GWAS) involving 18,340 participants. Summary data for AD were derived from the GWAS and included 158,565 cases and 300,995 controls. We applied the inverse variance weighting (IVW) method as the main analysis. To ensure the robustness of the results, we further executed weighted median method and MR-Egger regression to evaluate potential horizontal pleiotropy.

Result

We discovered 16 potential connections between bacterial traits and AD. Utilizing the IVW method, we identified 7 species that exhibited a direct correlation with the risk of AD: class Negativicutes, genus Eubacteriumbrachygroup, genus Coprococcus3, genus Enterorhabdus, genus Oxalobacter, genus Ruminiclostridium6, order Selenomonadales. Additionally, we found 9 species that exhibited a negative association with AD: class Methanobacteria, family Methanobacteriaceae, family Porphyromonadaceae, genus Blautia, genus Butyricicoccus, genus Erysipelotrichaceae-UCG003, genus Parasutterella, order Methanobacteriales, phylum Proteobacteria.

Conclusion

Our study found a causal relation between parts of the gut microbiota and AD. Further randomized controlled trials are crucial to elucidate the protective effects of probiotics on AD and their particular protection systems.

Anxiety disorders

Gut microbiota

Mendelian randomization

Single nucleotide polymorphism

Anxiety disorders (AD), being the prevailing mental disorders, have a substantial impact on individuals and society alike^[1]. The core features of AD contain indiscriminate anxiety and fear or elusion of persistent and debilitating threats, resulting in substantial medical costs and a burdensome morbidity burden^{[1, 2]}. As one of the most popular mental illnesses among young individuals, AD are also the earliest-onset mental disorders^[3]. Amidst the COVID-19 pandemic, there has been a significant surge in the occurrence of AD among children, adolescents, and young adults globally^[4]. First-line treatments for AD include medication and psychotherapy^[5]. However, medication treatments carry certain side effects and risks, such as dependence, cognitive impairment, and an increased risk of heart disease^[6]. The majority of individuals suffering from AD lack access to efficacious treatment options, leaving them vulnerable to relapse^{[7, 8]}.

Many studies have shown that the occurrence of AD is related to changes in intestinal flora^{[9, 10]}. In social anxiety disorder (SAD), there was an increase in the relative abundance of Anaeromassillibacillus and Gordonibacter genera, whereas healthy controls exhibited an enrichment of Parasuterella^[11]. Another article found a reduction in Eubacterium rectale and Fecalibacterium, as well as an increase in Escherichia, Shigella, Fusobacterium, and Ruminococcus in patients with generalized anxiety disorder (GAD)^[12]. In addition, there are numerous documents demonstrating an association between the gut microbiota and mental illness, and the modulation of the gut microbiota on the gut-brain axis has garnered significant attention, such as an elevation of Enterobacteriaceae and Desulfovibrio, and a reduction of Faecalibacterium in patients with AD^{[10, 13–17]}. In the aforementioned section, it was observed that the evidence exhibits complexities and disparities, as well as some contradictory results, potentially stemming from various confounding factors among different studies.

The previous studies examining the connection between gut microbiota and AD have predominantly relied on cross-sectional designs, which limits the ability to establish a causal relationship between these associations. Therefore, unraveling the causal mechanisms behind gut microbiota-derived AD not only enhances our understanding of their pathogenesis but also provides valuable guidance for implementing microbiota-directed interventions in clinical settings to address AD. In light of this, it is imperative to unravel the causal link between the gut microbiota and AD.

MR is a statistical approach that infers a causal relationship with exposure to a result. It leverages genetic variations linked to the exposure as a proxy for the exposure itself, enabling the assessment of the association between the exposure and the outcome^[18]. Due to the highly effective findings of large-scale genome-wide association study (GWAS) at the gut microbiota and disease level, MR analysis has been abroad used in many scenarios, such as between the oral microbiome and AD, relations between genetically determined metabolites and anxiety symptoms^{[19, 20]}. However, there are no specific studies on the causal relationship between gut microbiota and AD. In this research, we applied a bidirectional two-sample MR method to investigate causal relationship between the gut microbiota and AD.

2.1 The assumptions and study design of MR

MR is a methodology employed to assess causal associations between variables. In order to ensure the validity of MR analysis, three fundamental assumptions must be met: (i) the instrumental variable (IV) exhibits a strong link to the exposure factor, (ii) the IV remains unaffected by potential confounding factors., and (iii) the IV influences the result factor solely via the exposure factor^[21]. By applying strict selection criteria, appropriate SNPs were selected as IV for conducting MR analysis on two independent samples. The main aim was to examine the causal relationship between gut microbiota and AD. Furthermore, this study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) framework^[22]. (Fig. 1.)

2.2 Data sources

The data on gut microbiota GWAS used in this study were obtained from an overall meta-analysis conducted by the MiBioGen consortium. The meta-analysis comprised a total of 18,340 individuals from 24 different groups. The main aim was to examine the microbial composition, specifically targeting the variable regions V4, V3-V4, and V1-V2 of the 16s rRNA gene for analysis^[23].

As for the data on genetic variants linked to AD, they were sourced from the MRC-IEU consortium. The cases were defined as individuals who had sought medical attention for symptoms of nervousness, anxiety, or depression. The study population consisted of individuals of European descent, comprising both males and females, and the data were sourced from the year 2018. The dataset included a total of 158,565 cases and 300,995 controls. Detailed information regarding the data origins for this MR study can be found in Table 1.

Table 1

Information regarding the GWAS and datasets employed in our analyses is provided in detail.
Exposure or outcome		Sample size	Ancestry	Links for data download	PMID
Human gut microbiome	18,340 participants		Mixed	https://mibiogen.gcc.rug.nl	33462485
Anxiety	158,565cases; 300,995 controls		European	Trait: Seen doctor (GP) for nerves, anxiety, tension or depression - IEU OpenGWAS project (mrcieu.ac.uk)

2.3 Selection of IV

To fulfill the demands of MR studies, our initial step involved the SNPs that exhibited an intense association with the exposure factors. However, when employing a stringent threshold of (p < 5×10^− 8), we obtained a limited number of IVs. Consequently, we adjusted the threshold to (p < 1×10^− 5) to ensure the inclusion of more IVs, thereby enabling robust and reliable results. For the selection of IVs associated with IgAV in the reverse MR analysis, a heightened level of stringency was implemented by applying a p-value threshold of p < 5 x 10^− 8.

We utilized the F-statistic to further evaluate the instrument strength. The F-statistic was determined using the formula: F = β² / SE². This statistic provided an assessment of the overall instrument strength^[24]. (Fig. 2.) An F-statistic exceeding 10 was considered indicative of an intense conjunction between the IV and the exposure. This criterion was utilized to ensure that the selected IVs met the requirement of being strongly correlated with the exposure factor.

2.4 Statistical analysis

The primary methodology employed in MR analysis is the inverse variance weighting (IVW) method. This approach utilizes a meta-analysis technique to combine the Wald estimates connected to individual single nucleotide polymorphisms (SNPs), providing comprehensive estimate of the collective impact of gut microbiota on AD. A crucial assumption in MR is the absence of horizontal pleiotropy, where the IV has a direct impact on the outcome variable solely through the exposure factor, without any influence from through alternative pathways. When this assumption is satisfied, the IVW method can provide estimates that are consistent and estimates^[25]. In cases where a causal relationship (p < 0.05) is established by the IVW method, two alternative approaches, namely MR-Egger and the weighted median approach, are utilized to supplement an enrich the IVW results. The MR-Egger method relaxes the assumption of a zero intercept, and it can estimate causal effects, even pleiotropy was presented in IVs. The intercept in the MR-Egger method can indicate the extent of horizontal pleiotropy^[25]. These additional methods provide valuable insights and strengthen the overall analysis by considering potential biases and alternative causal pathways.

The weighted median method can return unbiased causal estimate when only 50% of SNPs are valid ^[26]. In this study, we employed a significance threshold of p < 0.05 to determine statistical significance, and the assessment of causality was expressed through odds ratios (OR) and 95% confidence intervals (CI). In instances where causal relationships were established, unidentified taxa were excluded, and additional sensitivity analyses were performed to guarantee the stability of the consequences.

All of the aforementioned analyses were performed utilizing the TwoSampleMR package (version 0.5.7) in R (version 4.3.0), providing a robust and standardized approach to MR analysis.

According to the criteria for IV selection, a total of 2,818 SNPs were identified and selected as IV associated with gut microbiota. The F-statistics for these IVs all exceed 10, suggesting that the estimated coefficients are improbable to be influenced by the bias caused by weak instruments. Supplementary Table 1 and Supplementary Table 2 provides detailed information about the selected IVs.

The majority of gut microbiota showed no significant correlation with AD. However, using the IVW method, we identified 16 bacterial features that were significantly associated with the risk of AD (Supplementary Table 3).

We used three methods, IVW, weighted median and MR-Egger, and defined p < 0.05 for IVW method screening as a positive result. Therefore, a total of 16 bacterial genera were identified to have a statistically significant causal relationship with AD based on IVW method (Figs. 3 and 4). Nine of the bacteria are negatively correlated with AD, indicating that they may have a protective effect on AD. They are: class Methanobacteria (OR = 0.9933, 95% CI, 0.9880–0.9987, p = 0.0150), family Methanobacteriaceae (OR = 0.9933, 95% CI, 0.9880–0.9987, p = 0.0150), family Porphyromonadaceae (OR = 0.9789, 95% CI, 0.9616–0.9966, p = 0.0196), genus Blautia (OR = 0.9838, 95% CI, 0.9725–0.9952, p = 0.0056), genus Butyricicoccus (OR = 0.9859, 95% CI, 0.9739–0.9981, p = 0.0233), genus ErysipelotrichaceaeUCG003 (OR = 0.9914, 95% CI, 0.9833–0.9995, p = 0.0381), genus Parasutterella (OR = 0.9911, 95% CI, 0.9823–0.9999, p = 0.0478), order Methanobacteriales (OR = 0.9933, 95% CI, 0.9880–0.9987, p = 0.0150), phylum Proteobacteria (OR = 0.9867, 95% CI, 0.9759–0.9977, p = 0.0176). Supplementary Table 4 shows the completed data. In sensitivity analysis, MR-Egger, weighted median demonstrated consistent results, except for family Porphyromonadaceae and genus Erysipelotrichaceae UCG003, where the MR-Egger trend was in the contrary direction compared to IVW and weighted median.

Another seven species showed a positive correlation with AD, class Negativicutes(OR = 1.0131, 95% CI, 1.0022–1.0241, p = 0.0183), genus Eubacteriumbrachygroup (OR = 1.0068, 95% CI, 1.0010–1.0127, p = 0.0225), genus Coprococcus3 (OR = 1.0164, 95% CI, 1.0046–1.0285, p = 0.0065), genus Enterorhabdus (OR = 1.0117, 95% CI, 1.0027–1.0208, p = 0.0108), genus Oxalobacter (OR = 1.0067, 95% CI, 1.0009–1.0125, p = 0.0231), genus Ruminiclostridium6 (OR = 1.0129, 95% CI, 1.0048–1.0212, p = 0.0019), order Selenomonadales (OR = 1.0131, 95% CI, 1.0022–1.0241, p = 0.0183) (Supplementary Table 4). In the MR-Egger method, the trends of class Negativicutes, genus Eubacteriumbrachygroup and order Selenomonadales are different from those of the IVW and WM methods.

In horizontal pleiotropy analysis, we used the MR-Egger method and found p value of the MR-intercept were all greater than 0.05. In addition, further MR PRESSO analysis was conducted, ruling out the existence of horizontal pleiotropy (P > 0.05) (Supplementary Table 5 and Supplementary Table 6). To assess the heterogeneity of gut microbiome IVs, we employed Cochran's Q test statistics, which revealed no heterogeneity among the gut microbiome IVs (P > 0.05) (Supplementary Table 7).

Reverse MR analyses were conducted to examine the links between 16 bacterial traits and AD. No significant statistical relationship was observed using the IVW method: class Methanobacteria (OR = 0.7314, 95% CI, 0.2217–2.4124, p = 0.6075), class Negativicutes (OR = 0.7112, 95% CI, 0.4186–1.2085, p = 0.2077), family Methanobacteriaceae (OR = 0.7309, 95% CI, 0.2216–2.4106, p = 0.0150), family Porphyromonadaceae (OR = 0.8716, 95% CI, 0.4908–1.5480, p = 0.6392), genus Eubacteriumbrachygroup (OR = 1.4058, 95% CI, 0.4060–4.8674, p = 0.5909), genus Blautia (OR = 0.9453, 95% CI, 0.5572–1.6038, p = 0.8348), genus Butyricicoccus (OR = 0.9834, 95% CI, 0.5704–1.6952, p = 0.9518), genus Coprococcus3 (OR = 0.8886, 95% CI, 0.5040–1.5667, p = 0.6831), genus Enterorhabdus (OR = 1.0383, 95% CI, 0.4168–2.5868, p = 0.9356), genus Erysipelotrichaceae-UCG003 (OR = 0.6593, 95% CI, 0.3556–1.2221, p = 0.1858), genus Oxalobacter (OR = 1.2849, 95% CI, 0.4021–4.1051, p = 0.6724), genus Parasutterella (OR = 0.7245, 95% CI, 0.3713–1.4136, p = 0.3447), genus Ruminiclostridium6 (OR = 0.7095, 95% CI, 0.3825–1.3162, p = 0.2764), order Methanobacteriales (OR = 0.7314, 95% CI, 0.2217–2.4124, p = 0.6075), order Selenomonadales (OR = 0.7112, 95% CI, 0.4186–1.2085, p = 0.2077), phylum Proteobacteria (OR = 0.8970, 95% CI, 0.5284–1.5226, p = 0.6871) (Supplementary Table 8 and Supplementary Table 9).

In the context of this study, we used two-sample MR studies to discover the link between AD and gut microbiota. Among the 16 species of bacteria we found, 9 species were negatively correlated with AD and may have a protective effect on AD, and the other 7 species were positively correlated with the occurrence of AD and may promote the development of AD.

Regarding the gut microbiome that is inversely related to AD, it has been reported that untreated patients with IBS had a decreased abundance of Methanobacteria^[27], while the prevalence of depression and anxiety in active ulcerative colitis patients exceeds 50%^[28]. Methanobacteria is a taxonomic class that encompasses multiple genera, Methanobacteriales being one of its orders, and Methanobacteriaceae being a family within the order Methanobacteriales. In an MR study of gut microbiota and psychiatric disorders, Methanobacteriaceae have adverse effects on obsessive-compulsive disorder (OCD) and suiside attmpt (SA)^[29]. In patients with irritable bowel syndrome (IBS), there is a higher level of anxiety and a deficiency of Methanobacteriales^[30]. This indicates a negative correlation between Methanobacteria, from the class to the family level, and anxiety.

Porphyromonadaceae was also found to be inversely related to stress and the 42-item Depression, Anxiety, and Stress Scale (DASS-42) in another study^[31]. Blautia stercoris MRx0006 has been shown to alleviate social dysfunctions, monotonous behaviors, and anxiety-like behaviors relevant to autism disorders in a mouse model^[32]. Butyricicoccus was also inversely associated with AD in a cross-sectional study, which is consistent with our findings^[12]. Approximately 70% of individuals with autism spectrum disorder (ASD) exhibit comorbid symptoms of anxiety, and the findings from a published article confirming the decreased relative abundance of ErysipelotrichaceaeUCG003 in ASD patients further support our research results indicating a negative correlation between ErysipelotrichaceaeUCG003 and AD^[33]. In a study examining social anxiety disorder, the control group exhibited higher levels of the beneficial bacteria Parasutterella compared to the anxiety group. The term "psychobiotics" has been coined to refer to these microbes that are associated with improved mood^[11]. According to reports, the inhibition of Proteobacteria by Lactobacilli has been found to alleviate symptoms of anxiety and depression^[34].

Seven bacteria positively linked to anxiety may indicate that they exacerbate anxiety, but they were less reported. Long-term traditional Tibetan Buddhist meditation can reduce anxiety, with Negativicutes declining in the meditation group contrasted with the control group^[35]. Another study demonstrated that the administration of Lactobacillus plantarum DR7 for a duration of 12 weeks resulted in a decrease in stress and anxiety levels among individuals experiencing stress, with the abundance of Negativicutes increasing after DR7 supplementation^[36]. In a study in which consuming prebiotics altered the microbiota of healthy adults, the prebiotics reduced Eubacteriumbrachygroup but did not significantly change biomarkers of stress or mental health symptoms^[37]. No literature evidence has been found linking Selenomonadales to AD. However, it may be related to major depressive disorder (MDD) and Alzheimer's disease^{[38, 39]}. In previous studies on anxiety disorder cases, it has been found that individuals with AD have lower levels of Coprococcus^[40]. However, in our study, we observed an increasing trend in Coprococcus3, despite belonging to the same genus. This suggests that even within the same genus, the impact of different species may vary. Therefore, our research emphasizes the need to study the gut microbiota at the species standard for a more specific understanding.

Nevertheless, it is crucial to acknowledge that our study has certain limitations. First, the findings of this analysis were limited to the European population and may not be generalizable to other populations. Secondly, the diagnostic criteria for AD are different, and the sources of GWAS data are also different. The results of MR analysis may also be different. If we want to further establish the detailed and reliable relationship between AD and intestinal microbes, more GWAS data are needed for analysis. Finally, the partially different trends in our IVW and MR-Egger results may be due to the different sensitivities of the two methods to hypothesis testing or the presence of specific hypothesis testing biases. Therefore, further examination and validation are needed, and consequents should be interpreted and reported with discretion and in conjunction with other available evidence and sensitivity analyzes to assess the reliability and consistency of causal effects.

In summary, utilizing large-scale GWAS analysis, MR studies have disclosed a causal relationship between gut microbiota and AD. Among these, nine species exhibited a negative correlation, while seven species showed a positive correlation with AD. However, further exploration of the mechanisms linking gut microbiota to AD requires the establishment of larger GWAS databases. Several gut bacteria have been identified to reduce the occurrence of anxiety, offering promising prospects for the treatment and precaution of AD. Subsequent research should prioritize the exploration of the underlying mechanisms and the development of targeted interventions based on these findings.

Anxiety disorders

Mendelian randomization

IV(s)

Instrumental variable(s)

GWAS

Genome-wide association study

IVW

inverse variance weighting

SAD

social anxiety disorder

GAD

generalized anxiety disorder

STROBE-MR

Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization

SNP(s)

single nucleotide polymorphism(s)

odds ratios

confidence intervals

OCD

obsessive-compulsive disorder

suiside attmpt

IBS

irritable bowel syndrome

DASS-42

the 42-item Depression, Anxiety, and Stress Scale

ASD

autism spectrum disorder

MDD

major depressive disorder

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All the data generated or analyzed during the course of this study have been included in the published article [and its supplementary information files].

Competing interests

The authors declare no competing interests.

Fundings

Guangzhou Science and Technology Plan Project (No.202102010115)

Guangdong Yiyang Health Sciences Foundation (No. JZ2022001-3)

Author information

^aDepartment of Psychiatry, Guangdong Second Provincial General Hospital, Guangzhou, 510317, PR. China.

^bSchool of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, China

*Corresponding Authors: Caiqin Feng, Department of Psychiatry, Guangdong Second Provincial General Hospital, Guangzhou, 510317, PR. China. E-mail: [email protected]

Author’s contribution

CQF designed the research framework. JBL determined the data and analysis methods. CHF assisted in conducting the literature review. JQW was responsible for manuscript writing. BLT and JFC performed the data statistical analysis. XZH and XZ were responsible for critical revisions.

Acknowledgements

We express our gratitude to the hospital action teams, staff, and participants from the participating hospitals for their valuable support in data collection. Additionally, we extend our appreciation to our collaborators for their assistance throughout the process.

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No competing interests reported.

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Association between gut microbiota and anxiety disorders: a bidirectional two-sample Mendelian randomization study

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Abstract

Background

Methods

Result

Conclusion

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1 Introduction

2 Materials and methods

2.1 The assumptions and study design of MR

2.2 Data sources

2.3 Selection of IV

2.4 Statistical analysis

3 Results

4 Discussion

5 Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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