Gut microbiota and autoimmune diseases: Insights from Mendelian randomization

Abstract

In recent years, the scientific community has shown interest in the role of gut microbiota in the development of autoimmune diseases (AID). Although observational studies have revealed significant associations between gut microbiota and AID like rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis, these connections do not necessarily imply causality. Mendelian randomization (MR) approach has been extensively employed to investigate the causal relationship. Relevant MR study findings indicate that a reduction in beneficial microbial populations, particularly Bifidobacterium and Lactobacillus, and an increase in potential pathogenic microbes, is correlated with an elevated AID risk. Given the innovative potential of MR in unraveling the etiopathogenesis of AIDs, this article offers an overview of this methodological approach and its recent applications in AID research.

Gut microbiota is significantly linked to autoimmune diseases (AID) like systemic lupus erythematosus (SLE), multiple sclerosis (MS), and rheumatoid arthritis (RA). These associations include changes in gut microbial diversity and the abundance of specific gut microbiota, but they do not necessarily imply causation. Given the innovative potential of Mendelian randomization in uncovering the etiology of AID, this review outlines the methodology and its recent applications in studying SLE, MS, and RA.

1. INTRODUCTION

The human body hosts extensive symbiotic microbes, primarily bacteria, residing in varied niches including the gut, skin, vagina, and oral cavity. ¹ These microbes present inter‐ and intra‐individual variations in diversity and abundance across different organs. ² Factors such as dietary habits, environmental exposures, and host genetics have been attributed to the broad microbial variance. ³ Notably, the microbiota and its metabolites may regulate the development and function of the host's immune system. ⁴ These microbiota also influence other physiological activities in mammals, including metabolism and behavior. ⁵ , ⁶ Recent scientific research suggests an important role of commensal bacteria in the pathogenesis of various diseases, particularly autoimmune diseases (AID). ⁷

Mendelian randomization (MR) offers a new approach to analyze the intricate interactions between the gut microbiota and AID. ⁸ , ⁹ The aim of this review was to synthesize existing research and enhance our understanding of the microbiota's role in AID pathogenesis. We also aimed to evaluate the potential use of MR in this area.

2. THE PATHOPHYSIOLOGY OF AID

AID occur when the immune system mistakenly attacks the body's own tissues, leading to tissue damage and systemic disturbances. ¹⁰ AID include a range of diseases, from rheumatoid arthritis (RA) to systemic lupus erythematosus (SLE). ¹¹ , ¹² Distinctly marked by the immune system's heightened reactivity to self‐antigens, AID often result in inflammation and damage in various tissues. ¹³ The exact etiology behind these diseases is multifactorial, often rooted in a combination of genetic susceptibilities, environmental triggers, and at times, hormonal influences. ¹⁴ , ¹⁵ , ¹⁶ Of note is the prevalence of AID, which stands at approximately 4.5%, with a marked gender disparity: females exhibit a 6.4% prevalence rate compared to 2.7% in males, underlining potential gender‐based disparities in the immune response. ¹⁷

2.1. Immune cells

The pathogenesis of AID may be intricately linked with immunocyte dysfunction. ¹⁸ Within the T‐cell subgroups, regulatory T‐cells (Tregs) play an indispensable role in maintaining immune tolerance. ¹⁹ Compromised function of Tregs may pave the way for effector T‐cells to target self‐tissues. ²⁰ In addition, T‐helper 17 (Th17) cells primarily work in autoimmune pathologies via interleukin‐17 (IL‐17) secretion, which leads to recruitment of neutrophils, activation of innate immune cells, enhancement of B‐cell functionality, and the induction of pro‐inflammatory cytokines. ¹⁸ Th1 cells are typical cellular actors in cell‐mediated inflammation and delayed‐type hypersensitivity reactions. They are deemed pivotal in immunity against intracellular pathogens. Moreover, interferon‐γ (IFN‐γ), a signature cytokine of Th1 subset, has been historically related to the pathophysiology of several AID, including type 1 diabetes (T1D), multiple sclerosis (MS), and RA. ¹⁸ Th2 cells were initially characterized as anti‐inflammatory, based on their capacity to counteract cell‐mediated or Th1 disease models. Genain's group reported that in marmosets with experiment allergic encephalomyelitis, cytokine production was shifted from a Th1 to Th2 pattern, and titers of autoantibodies to myelin oligodendrocyte glycoprotein were increased. They hypothesized that Th2 response might exacerbate autoimmunity by amplifying the production of pathogenic autoantibodies. ²¹

Additionally, while B‐cells are the primary source of antibodies, anomalies in certain subgroups may related to AID pathogenesis. ²² Excessive immune responses can lead to an overabundance of self‐antibodies, ultimately resulting in autoimmune disorders. For example, neutrophils may pose an exaggerated response against self‐tissues particularly in conditions like SLE. ²³ Neutrophil extracellular traps can expose self‐antigens, activating T‐cells, B‐cells, and macrophages. This instigates autoimmune reactions and results in tissue or organ damage. ²⁴ , ²⁵ Overactive macrophages may stimulate protease secretion, leading to a significant rise in soluble CD163 levels in peripheral blood, ultimately causing tissue damage and accelerating disease progression. ²⁶ , ²⁷ , ²⁸

2.2. Gut microbiota

The gut microbiota, consisting of diverse microbes, holds a critical position in human health. They not only facilitate nutrient absorption and metabolism but also greatly influence our immune functions. Human gut microbiota enriched of an estimated 1000 unique species with gradient concentrations vary from a mere 10^2–3 bacteria per gram in the stomach to 10^11–12 bacteria per gram in the large intestine. ²⁹

Changes in the composition and metabolic functions of the gut microbiota are correlated with numerous pathological conditions. Dysbiosis, or the imbalance in the gut microbial ecosystem, can trigger inflammatory responses and is suspected to promote autoimmune conditions like RA and inflammatory bowel disease. ³⁰ In addition, the metabolic byproducts of these gut microbiota can regulate the function and proliferation of immune cells. For example, short‐chain fatty acids can promote the proliferation of Tregs. ³¹ Meanwhile, beneficial microbes, primarily Bifidobacterium and Lactobacillus, also play crucial roles in promoting Tregs and maintaining immunological tolerance. ³² , ³³ Furthermore, these beneficial microbes bolster the integrity of intestinal mucosal barrier, protecting bloodstream from harmful agents, and pathogens infiltration. ³⁴ , ³⁵ The dynamic relationship between the gut microbiota and the human immune system is fundamental for maintaining immunologic balance and preventing AID.

Recent studies have shed light on the relationship between gut microbiota and AID. ³⁶ Dysbiosis has been consistently linked to conditions such as RA and SLE. ³⁰ , ³⁷ In particular, individuals with AID have demonstrated a marked reduction in the abundance of beneficial bacteria and an increase in certain microbial species. ³⁸ Furthermore, therapeutic strategies like fecal microbiota transplantation have shown promise in improving dysbiosis and AID‐associated symptoms. ³⁹ Nevertheless, comprehensive and multifaceted studies are still required to understand the complex interactions between the gut microbiota and AID.

3. MR ANALYSIS

3.1. Basic concepts and methodological features

MR is a genetic‐based instrumental analytical tool for evaluating causal relationships in epidemiological studies. This statistical method uses genetic variants, primarily single nucleotide polymorphisms (SNPs), as instrumental variables (IVs) to discern the causal effect of exposures on outcomes. ⁴⁰

3.1.1. Basic concepts

Genetic IVs: IVs refer to genetic variants that are associated with the exposures but not with confounders. ⁴¹

Assumptions: MR analysis has three core assumptions. (1) The genetic variant should be robustly associated with the exposure. (2) The variant should affect the outcome only through exposure. (3) The variant should be independent of any confounder of the exposure‐outcome relationship. ⁴²

3.1.2. Methodological characteristics

Thanks to the random assortment of alleles during gamete formation and the Mendelian law of segregation, individuals inherit genetic variants independent of confounding factors. This genetic lottery offers a sort of “natural experiment”, allowing MR to sidestep many biases observed in traditional observational studies. ⁴³ Subsequently, reverse causation is a main challenge in epidemiology research. By deploying genetic variants fixed at conception, MR can effectively avoid this issue, since these variants precede the onset of disease or outcome. ⁴⁴ Moreover, although MR may be biased to pleiotropy, advanced MR techniques, such as MR‐Egger regression and weighted median methods, have been developed to address pleiotropic effects and provide more valid causal estimates. ⁴⁵ While MR provides a powerful platform for causal inference, it's not without limitations. For instance, the method depends on the availability of robust genetic instruments, and the results can be skewed if the aforementioned assumptions are violated. Furthermore, MR conclusions are population‐specific, owing to genetic differences across populations. ⁴⁶ In conclusion, MR offers a unique window into causal relationship investigation, drawing strength from the principles of genetics. As with all statistical methods, while it has several advantages, it also demands a judicious understanding of its assumptions and potential pitfalls.

4. RESEARCH PROGRESS ON GUT MICROBIOTA AND SLE

The term “lupus” was created by physicians in the 19th century to describe cutaneous manifestations, and it took nearly a century to realize that the disease is systemic and caused by abnormal autoimmune reactions. ⁴⁷ Currently, lupus or SLE is defined as a medical condition where the immune system mistakenly attacks and damages normal cells and tissues across the entire body. ⁴⁸ It primarily impacts females in their reproductive years and manifests in multiple organs, encompassing the skin, joints, and kidneys. ⁴⁹

Globally, the estimated adult SLE prevalence ranges from 30 to 150 per 100,000 people, with annual incidence rates between 2.2% and 23.1% per 100,000 people. ⁵⁰ SLE is characterized by abnormal immune system activation, resulting in exaggerated response of B and T cells and the loss of immune tolerance to self‐antigens. SLE clinical symptoms include mild fatigue and articular pain, as well as severe life‐threatening organ injury. ⁴⁸

Possible etiologies of SLE involve genetic, environmental, hormonal, and immunological factors. ⁴⁷ Genome‐wide association studies (GWAS) have identified over 60 SLE susceptibility risk loci. ⁵¹ Various environmental factors, including ultraviolet exposure, silica exposure, smoking, as well as viral and bacterial infections, all contribute to the development of SLE. ⁵² , ⁵³ , ⁵⁴ , ⁵⁵ , ⁵⁶ The treatment options currently available for SLE consist of glucocorticoids, immunosuppressive drugs, and antimalarial medications; however, their application is restricted due to the occurrence of severe adverse effects. ⁴⁸ , ⁵⁷ Recently, increased research has revealed that the gut microbiota, as an environmental factor, plays a role in the advance of SLE, ⁵⁸ and adjusting the gut microbiota seems like an available therapeutic approach. ⁵⁹

4.1. Gut microbiota changes and SLE

The gut microbiota is significantly dysregulated in subjects with SLE. In 2014, Hevia et al. first reported the limited gut microbiota diversity in lupus patients compared to healthy individuals. ⁶⁰ Similarly, Azzouz et al. also found that disease activity in lupus was negatively correlated with gut microbiota biodiversity. Moreover, in SLE patients with higher disease activity, the abundance of the anaerobic genus Ruminococcus gnavus was five times higher than normal levels on average. ³⁷ Additionally, a previous research indicated the changed composition of the gut microbiota in SLE patients, and seven enriched microbes experienced reduction after treatment. ⁵⁸

The Bacteroidetes and Firmicutes are the dominant bacteria groups in human body. ⁶¹ In SLE patients, however, there is an increase in Bacteroidetes and a decrease in Firmicutes, leading to a significant decline in the Firmicutes/Bacteroidetes (F/B) ratio. ⁶² Widhani et al. also reported that the F/B ratio was lower in lupus subjects with moderate or high disease activity compared to those with mild disease activity. ⁶³ Moreover, studies have demonstrated a negative correlation between Firmicutes and the SLE disease activity index, ⁶⁴ suggesting that Firmicutes may delay lupus progression. Therefore, a decreased F/B ratio is an important indicator of gut microbiota in SLE patients.

Furthermore, research indicated that certain bacterial genera were enriched in SLE patients, such as Rhodococcus, Eggerthella, Klebsiella, Prevotella, Actinomyces, and Flavonifractor, while Dialister and Pseudobutyrivibrio were significantly reduced. ⁶⁰ , ⁶² In a systematic review including SLE patients, there was an abundance decline of Ruminococcaceae, a rise in Enterobacteriaceae and Enterococcaceae, and no significant changes in Spirochaetaceae and Bacteroidaceae. ⁶⁵

In summary, SLE patients exhibit significant changes in gut microbiota, including microbial diversity, abundance of specific microbes, and F/B ratio. These changes may be related to the development of SLE.

4.2. The role of gut microbiota in SLE

Although observational correlation between the gut microbiota and SLE has been extensively studied, whether a causal relationship exists remains unclear.

An MR study selected SNPs related to the human gut microbiota from GWAS involving 18,473 individuals and 122,110 variant loci. ⁶⁶ A total of 25 population‐based cohorts from various countries participated in this extensive multi‐ethnic GWAS. The primary objective was to investigate the relationship between ordinary genetic variations on autosomes and the gut microbiota. To extract the effect estimates of relevant SNPs, data from a substantial SLE GWAS comprising 7219 cases and 15,991 controls of European ancestry were utilized. ⁶⁷ This study indicated negative correlations between Bacteroides, Faecalibacterium, Ruminococcus, and Actinobacteria and the risk of SLE. Bacilli, Lactobacillus, and Eggerthella were identified as potential risk factors for SLE, supporting a potentially beneficial or detrimental cause and effect from the gut microbiota components to SLE risk. ⁹

In addition, Xu et al. carried out another study to verify the relationship between the gut microbiota and AID. ⁸ Data for gut microbiota were derived from GWAS meta‐analysis with 340,240 individuals from 18 cohorts. ⁶⁶ Exploration of microbial composition was conducted using three distinct variable regions within the 16S rRNA gene. To account for variations in sequencing depth, all datasets were standardized to include 10,000 reads per sample. A comprehensive set of 211 taxa were incorporated. Summary statistics pertaining to SLE were derived from publicly accessible GWAS that encompassed 7219 cases and 15,991 controls. ⁶⁷ The results indicated that higher genetic predicted Bifidobacterium were related to a lower risk of SLE, while higher levels of Ruminococcus were positively correlated with SLE risk. ⁸

In conclusion, MR analyses support a potential causality between components of the gut microbiota and SLE risk.

5. RESEARCH PROGRESS ON THE GUT MICROBIOTA AND MS

MS is a progressive neuro‐inflammatory and neurodegenerative disease, which typically begins in early adulthood. ⁶⁸ It has a female tendency, with an estimated prevalence of 450.1 per 100,000 for women and 159.7 for men. ⁶⁹ Immune activation plays a key role in MS pathological mechanism, involving T cells, B cells, and microglia. T cell subsets like CD8⁺ T cells, CD4⁺ Th1 cells, and Th17 cells are linked to MS. ⁷⁰ Autoreactive T cells also produce cytokines contributing to MS, including interferon‐gamma, IL‐17, and granulocyte‐macrophage colony‐stimulating factor. ⁷¹ Besides, B‐cell depletion in MS can attenuate the pro‐inflammatory activity of CD4⁺ and CD8⁺ T cells. ⁷² , ⁷³ Moreover, in early active MS lesions, approximately 40% of phagocytic cells are pro‐inflammatory microglia, and their activation can result in axonal injury, demyelination, and blood–brain barrier disruption, among others. ⁷⁴

The etiology of MS remains unclear, with potential risk factors including genetics, ⁷⁵ low vitamin D levels, ⁷⁶ Epstein–Barr virus exposure, ⁷⁷ smoking, ⁷⁸ and the gut microbiota. ⁷⁹ Several studies have linked low vitamin D levels with an increased risk of MS. ⁸⁰ , ⁸¹ Across numerous meta‐analyses, Epstein–Barr virus antibody seropositivity and infectious mononucleosis have consistently been associated with an increased risk of MS. ⁸² Of note, the gut microbiota is essential for the onset and maturation of the immune system. ⁸³ It connects the immune system to certain environmental factors and helps it tolerate harmless external and self‐antigens. ⁸⁴

5.1. Gut microbiota changes and MS

Previous studies have reported changes in the gut microbiota composition of MS patients. Galluzzo et al. found that MS patients had a lower abundance of Firmicutes and a higher abundance of Bacteroidetes. ⁸⁵ In MS‐discordant monozygotic twins, the untreated MS twin had a higher level of Akkermansia than the healthy twin. ⁸⁶ MS patients also had lower levels of Prevotella, Faecalibacterium prausnitzii, and Bacteroides, and higher levels of Akkermansia muciniphila. ⁸⁷ , ⁸⁸ Moreover, Cantarel et al. found less Faecalibacterium in fecal samples from seven MS patients compared with eight healthy controls in the USA. ⁸⁹ Chen et al. reported increased Blautia, Mycoplasma, and Dorea, along with decreased Prevotella and Adlercreutzia in relapsing–remitting MS patients. ⁹⁰ Furthermore, a case–control study found that patients with MS had higher levels of Methanobrevibacter and Akkermansia, while Butyricimonas was less abundant. ⁸⁷

5.2. Relationship between gut microbiota and MS by MR

The association between the gut microbiota and MS has been widely researched, but a causal link is still uncertain.

We have searched one MR study of gut microbiota and MS. Xu et al. used a two‐sample MR to reveal a causality between the gut microbiota and AID including MS. ⁸ They used summary statistics of gut microbiota from a GWAS meta‐analysis, including 340,240 individuals from 18 cohorts. ⁶⁶ Meanwhile, summary‐level data for MS were downloaded from the most recent GWAS meta‐analysis by the International MS Genetics Consortium, which included 14,802 MS cases and 26,703 controls of European ancestry. ⁹¹ SNPs associated with gut bacterial taxa with p < 5.0 × 10⁻⁸ were selected as potential IVs. Finally, the analysis results indicated a causal association between the Bifidobacterium and MS, with higher Bifidobacterium abundance being associated with a higher MS risk. ⁸

6. RESEARCH PROGRESS ON GUT MICROBIOTA AND RA

RA is a chronic systemic AID primarily affecting the joints, with symmetric peripheral joint inflammation (such as wrist and metacarpophalangeal joints) as its main characteristic. The affected joint structures undergo progressive damage and are often accompanied by systemic symptoms. ⁹² , ⁹³ RA affects 0.5% to 1% of the global population, with a female incidence rate 2–3 times higher than that of males. ⁹³ In China, the prevalence is estimated to be 0.42%, with a total affected population of approximately 5 million, and a male‐to‐female ratio of about 1:4. ⁹⁴ RA not only impairs the physical function, social participation of patients and quality of life but also imposes a significant economic burden on families and society. ⁹⁵ , ⁹⁶ The pathogenesis of RA is still unclear. Current research suggests that its development involves a complex interaction between genetic, environmental, and immune factors. ⁹⁷ Genetic factors determine 60% to 70% of the risk of RA, and those with a family history of RA have a 3‐ to 9‐fold increased risk of developing the disease. ⁹⁸ , ⁹⁹ Environmental factors like smoking, silica exposure, and infections are also involved in the development of RA by disrupting immune tolerance to post‐translationally modified proteins. ¹⁰⁰ , ¹⁰¹ Dendritic cells (DCs) subsequently present these proteins to T‐cells, which then activate B‐cells to undergo plasma cell differentiation and thus secrete autoantibodies. ¹⁰² , ¹⁰³ These autoantibodies form immune complexes to activate immune cells, which further attract other inflammatory cells into the joints, resulting in local damage. Additionally, DCs promote the differentiation of Th17 cells and inhibit the differentiation of Treg cells, shifting the balance of T cells toward inflammation. Subsequently, activated T cells stimulate effector cells and their effector molecules, including macrophages, fibroblasts, osteoclasts, and chondrocytes, leading to cartilage and bone destruction. ⁹² Furthermore, the connection between dietary risk factors and the pathogenesis of RA is still being explored. Increasing evidence suggests that a healthy diet can prevent the development of RA. ¹⁰⁴ Research indicates that the influence of dietary factors may be mediated by the gut microbiota, intestinal permeability, or local immune system. ¹⁰⁵ Recent epidemiological studies also suggest a link between gastrointestinal and urinary tract infections and a reduced risk of RA, indicating that disruption of gut microbiota may play a role in this disease. ¹⁰⁶ Unfavorable alterations in the composition of the gut microbiota also referred to as dysbiosis, can impact the autoimmune response and disease outcomes in RA. ¹⁰⁷ , ¹⁰⁸ , ¹⁰⁹ For instance, these alterations can promote the growth of potential pathogenic microorganisms and a reduction in the beneficial bacteria. ¹¹⁰ , ¹¹¹

6.1. Gut microbiota changes and RA

Data from some arthritis animal models suggest gut microbiota plays a critical role in the development of the disease. ¹¹² In a preclinical model in RA mice, a significant enrichment of Prevotellaceae, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae, particularly Prevotella copri, has been observed. ³⁸ , ¹¹³ , ¹¹⁴ , ¹¹⁵ In RA rat models, an increase in Prevotella abundance and a decrease in the relative abundance of Lactobacillus hominis, Lactobacillus reuteri, and Lactobacillus vaginalis have been found. ¹¹⁶ , ¹¹⁷

Most bacteria in the human gut belong to the Firmicutes and Bacteroidetes phylum. Bacteroides and Prevotella genera, as dominant members of Bacteroidetes, play a pivotal role in maintaining the balance of the gut microbiota. Prevotellaceae is a major bacterial family linked to dysbiosis. The abundance of Prevotella is consistently increased in RA patients, both before and after clinical diagnosis. ¹¹³ , ¹¹⁸ , ¹¹⁹ It has been found that in new‐onset untreated RA patients, the increase in Prevotella abundance is associated with a decrease in Bacteroides and the loss of beneficial microorganisms. ³⁰ Apart from Prevotella, preliminary evidence suggests that dysbiosis of other microorganisms is also linked to the onset of RA. Chen et al. confirmed that RA patients had a higher abundance of Collinsella and Eggerthella compared to first‐degree relatives of patients with RA and healthy controls, accompanied by an overall decrease in gut diversity. ¹²⁰ Muñiz Pedrogo et al. found there was an increase in the numbers of Clostridiaceae and Epsilonproteobacteria in RA patients. ¹²¹ Furthermore, Zhang et al. observed an increase in Lactobacillus salivarius and a decrease in Haemophilus species in the gut of Chinese patients with RA ¹⁰⁸ . Meanwhile, a reduced abundance of Faecalibacterium prausnitzii has been demonstrated to help maintain gut barrier function, modulate the Th17/Treg balance, and exhibit significant anti‐inflammatory effects. ¹²² In addition, other studies have shown increased abundance of Klebsiella, Enterococcaceae, Escherichia‐shigella, Comamonadaceae, Moraxellaceae, Akkermansia, Eisenbergiella, Streptococcus, Lactobacillus, and Flavobacterium in RA patients, while the abundance of genera Bacteroides fragilis, Fusicatenibacter, Megamon, Bifidobacterium, Clostridium, Sarcina, and Enterococcus was reduced. ¹²³ , ¹²⁴ , ¹²⁵ , ¹²⁶ , ¹²⁷ , ¹²⁸ , ¹²⁹

Besides, an elevated abundance of Ascomycota and a reduced abundance of Basidiomycota have been observed in synovial fluid samples from RA patients at the phylum level. ¹³⁰ The fecal samples from Chinese RA patients demonstrated an increased abundance of Wallemia and Candida species and a decreased abundance of Scedosporium, Pholiota, and Trichosporon. ¹³¹

6.2. Relationship between gut microbiota and RA by MR

Studies have indicated that changes in gut microbiota are associated with RA. ¹¹⁸ , ¹³² However, due to potential confounding factors and other limitations, it remains difficult to assess a causal association between the two through case–control studies. Therefore, the existence of a causal relationship between them remains uncertain.

Xu et al. extracted summary GWAS data for RA, including 14,361 European ancestry RA cases and 43,923 controls from 18 studies, and identified 17 independent SNPs linked to 12 genera of RA ⁸ . These MR studies revealed no causal relationship between these gut microbiota taxa and RA. Similarly, an MR study by Inamo included a total of 19,234 RA cases and 61,565 controls from both Asian and European populations. This study obtained 26 SNPs associated with reduced bacterial taxa from gut microbiota GWAS. The results showed that association p values from three methods, inverse variance weighting (IVW), weighted median (WM), and MR‐Egger were all non‐significant (all p > 0.05), with no evidence of heterogeneity (heterogeneity p > 0.166), suggesting null causal relationship between gut microbiota and RA risk. ¹³³

However, Lee conducted two‐sample MR analyses using the same data as Inamo's MR analysis and demonstrated a significant correlation between gut microbiota and the risk of RA. A total of 32 SNPs were selected. The results showed that the MR estimates determined by IVW and MR‐Egger regression analyses supported a causal relationship between the gut microbiota and RA (IVW: β = −0.024, p = 0.0006; MR‐Egger: β = −0.027, p = 0.005), while the WM approach yielded no evidence of a causal relationship (β = −0.005, p = 0.144). ¹³⁴ Of note, Inamo only extracted SNPs related to reduced bacterial taxa in the gut microbiota as IVs, which accounts for the different SNPs set in their studies. On the other hand, although Lee found a significant association through IVW and MR‐Egger analyses, the β coefficient was negative. If dysbiosis had a causal effect on the occurrence of RA, the β coefficient might be expected to be positive. ¹³⁵

Overall, the results of MR analyses may support a relationship between gut microbiota and RA, but further research is needed to investigate the extent to which gut microbiota influences the development of RA. In addition, longitudinal studies are needed to demonstrate that specific microbial dysbiosis occurs prior to the development of RA, and relevant in vivo studies and microbiome‐centered intervention trials are needed to further validate this view. ¹³⁵ , ¹³⁶

7. CONCLUSION

We have reviewed the possible involvement of gut microbiota in causing AID. The composition of gut microbiota is altered in patients with AID, which suggests it could be used as a biomarker for diagnosis, prevention, and treatment of these diseases.

MR analysis results suggest that gut microbiota, specifically microbes such as Bifidobacterium, Faecalibacterium, and Ruminococcus, may have a causal relationship with AID. Of note, the feasibility of MR studies depends on the availability of robust genetic variations in the diseases. We thus need more suitable GWAS datasets that reflect a relevant measure of dysbiosis in AID. By conducting more relevant MR studies, we can improve our understanding of the role of gut microbiota in AID and provide better guidance to patients. Besides, we should not be limited to changes in individual bacteria but the bacterial networks based on functionality, metabolites, and how these networks affect the diseases.

In summary, we have gained a clearer understanding of the relationship between gut microbiota and AID. Future research can use diverse population samples, advanced molecular biology techniques, and animal models to explore gut microbiota‐related interventions and therapeutics for AID.

AUTHOR CONTRIBUTIONS

Fangxiang Mu conceived and designed the research, and wrote the original draft; Gusbakti Rusip and Florenly reviewed and edited the draft. All authors were involved in drafting and revising the manuscript.

FUNDING INFORMATION

This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Mu F, Rusip G, Florenly F. Gut microbiota and autoimmune diseases: Insights from Mendelian randomization. FASEB BioAdvances. 2024;6:467‐476. doi: 10.1096/fba.2024-00037

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.