FormalPara Key Summary Points

This study is the first Mendelian randomization study to focus on gut microbiota and psoriatic arthritis

In this study, Mendelian randomization, KEGG and GO analyses were used to analyze the genetic association between gut microbiota and psoriatic arthritis

This study provides new insights and ideas for the clinical prevention and treatment of psoriatic arthritis

Introduction

Psoriatic arthritis (PsA) is a common chronic inflammatory immune-mediated disease associated with psoriasis, which can affect the peripheral and axial skeleton, leading to musculoskeletal dysfunction [1]. Recent studies based on Classification Criteria for the Study of Psoriatic Arthritis study (CASPAR) criteria have indicated that PsA occurred in up to 30% of patients with psoriasis [2]. PsA has various impacts on the physical and mental health of patients as well as their social and economic status. Studies have found that PsA patients have an increased risk of cardiovascular diseases, respiratory system diseases and malignancy [3]. Interestingly, the risk of PSA for gastrointestinal diseases is also gradually being clarified [4, 5]. Moreover, PsA patients experience higher rates of work absenteeism and lower work productivity [6]. Although the etiology of PsA is still unclear, it is widely accepted that multiple environmental, immune and genetic factors contribute to its development and progression [7]. For example, among the genetic factors, human leukocyte antigen (HLA) alleles have been shown to play a crucial role in conferring susceptibility and severity to PsA[8]. With the advancement of understanding of PsA's pathogenesis, the treatment goal has shifted from pain relief to joint disease remission. Therefore, early diagnosis and prevention of PsA and the development of novel therapies are essential for preventing structural damage, disability and associated economic and social consequences [9].

Gut microbiota, which resides on the surface of the largest human mucous membrane (gastrointestinal tract), plays a huge role in maintaining human health [10]. Increasing evidence suggests that the gut microbiota is involved in various diseases. The proposed microbial-gut-brain axis provides new insights for preventing and treating mental disorders [11]. Moreover, the mechanism by which the “host-gut microbiota” axis regulates the local homeostasis of the gut and affects autoimmune diseases such as rheumatoid arthritis [12, 13] and systemic lupus erythematosus [14] is also gradually being elucidated. Surprisingly, with the continuous advancement of high-throughput sequencing technology, the role of the “gut microbiota-joint” axis has been gradually revealed [15], but the underlying mechanism remains to be further explored. Gut microbiota plays an important role in many types of inflammatory arthritis, including PsA [16,17,18]. Much evidence, both directly and indirectly, suggests a link between PsA and gut microbiota. Epidemiological evidence and Mendelian randomization analysis indicate that inflammatory bowel disease (IBD), one of the risk factors for PsA development, is associated with joint symptoms in about one-third of patients during the disease course [19, 20] and is characterized by dramatic changes in gut microbiota [21]. Notably, some studies have found that the gut microbiota plays a role in psoriatic bone remodeling, even in the absence of arthritis symptoms [22]. Moreover, although PsA patients have no intestinal symptoms, microscopic examination reveals mucosal lesions in their intestines [23]. These findings reveal the pathogenic link among the skin, joints and intestines of patients with PsA. However, previous observational studies have been confounded by various factors such as age, lifestyle, dietary habits, etc. [24], limiting the causal inference between PsA and gut microbiota.

To explore the causal relationship between risk factors and disease, Mendelian randomization analysis is a method of using genetic variation as an instrumental variable [25, 26]. According to the law of segregation and independent assortment of genotypic inheritance, the relationship between genetic variation and exposure or outcome is not confounded by confounding factors [27]. With the increasing number of genetic loci related to the abundance of gut microbiota, it provides a new possibility to explore the relationship between gut microbiota and PsA from the genetic level [28]. We used the genome-wide association study (GWAS) summary statistics from the MiBioGen and FinnGen consortia to conduct a two-sample bidirectional MR to elucidate the role of gut microbiota in PsA development and provide a new perspective for the clinical prevention and treatment of PsA.

Methods

Study Design

Based on the GWAS summary data of gut microbiota and psoriatic arthritis, a two-sample bidirectional MR design was used to investigate the potential causal relationship between gut microbiota and psoriatic arthritis (Fig. 1). The instrumental variables for MR strictly followed the three assumptions: (1) the genetic variations used as IVs are strongly associated with exposure of interest; (2) the IVs are not strongly correlated to potential confounders; (3) IVs can influence outcomes only through exposure and not via other pathways[29].

Fig. 1
figure 1

Bidirectional two-sample Mendelian randomization process

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Data Sources

The data used in this secondary study were obtained from a public database, therefore no separate ethical approval was required for this study. Summary statistics on gut microbiota were derived from the MiBioGen consortium (https://mibiogen.gcc.rug.nl/, accessed on 22 July 2023), which comprised 18,340 individuals from 24 population-based cohorts, most of whom were of European ancestry, thus minimizing the confounding effect of race on the analysis [30]. In this study, the gut microbiota was classified into 211 taxa (131 genera, 35 families, 20 orders, 16 classes and 9 phyla), and a total of 5,717,754 SNPs were analyzed.

Summary-level data for PsA were extracted directly from the FinnGen consortium (https://www.r9.finngen.fi/, accessed on 22 July 2023), which comprised only individuals of European ancestry, thus reducing the potential bias due to population stratification. The dataset consists of 3186 PsA patients and 24,086 controls and included 20,165,225 SNPs. The FinnGen Project (Finnish Database) is a large-scale genetic research program that aims to explore the association between genomic information and health outcomes of the Finnish population, as detailed on the FinnGen website (https://finngen.gitbook.io/documentation/).

Selection of Genetic Instrumental Variables

To investigate the potential causal relationship between gut microbiota and PsA, we selected the appropriate IVs using the following quality control procedure, based on Mendel's three basic assumptions, to ensure the robustness and reliability of MR analysis. Frist, we selected SNPs associated with these traits at genome-wide significance (P < 1E − 5) as instrumental variables.[18, 31]. Second, we excluded SNPs with linkage disequilibrium (R2 < 0.01 and clumping window size = 10,000 kb) and removed echo sequences from the analysis. Third, according to the third assumption of MR, we excluded the IVs that were strongly associated with the outcomes (P < 5E-5)[32]. Fourth, we excluded weak IVs by ensuring the strength of genetic instruments for exposures using the F statistic. The F statistic was computed using the following formula: F = R2 × [(N − 1 − K)/K] × (1 − R2), where R2 and N refer to the cumulative explained variance by the selected SNPs and sample size, respectively, and R2 was calculated using the formula: R2 = 2 × MAF × (1 − MAF) × β2, (MAF = minor allele frequency; N = sample size; K = number of IVs) [33]. When the F statistic is > 10, the problem of weak instrument bias in the two-sample model can be avoided [34].

Statistical Analysis

All data analyses in this study were performed using the Two Sample MR and MRPRESSO packages in R 4.3.0. When P < 0.05 was considered significant evidence of a potential causal relationship, we applied the random-effects IVW method as the primary method to assess the potential causal relationship between gut microbiota and PsA. The IVW method can provide accurate estimates when invalid IVs and potential pleiotropic effects are excluded, which are obtained by calculating the Wald estimates using the effect estimates of each SNP on exposure and outcome risk [35]. We also used the weighted median (WM) method [36], MR-Egger regression [37] and weighted model [38] as supplementary methods for IVW. To examine heterogeneity, we adopted The Cochran’s Q statistic (MR-IVW) and Rucker's Q statistic (MR Egger), and P > 0.05 indicated that there was no heterogeneity. To control for confounding factors, we used the following methods for sensitivity analysis: First, we performed the intercept test of MR-Egger and assumed there was horizontal pleiotropy when the intercept of the MR-Egger analysis deviated from zero (P > 0.05) [39]. Second, we applied the MR-PRESSO method to assess overall horizontal pleiotropy. We sorted SNPs in ascending order by their MR-PRESSO Outlier test P-values and removed outliers one by one. After the outliers were removed, we were asked to repeat the MR-PRESSO Global test until the P-value of the Global test was > 0.05. We used the remaining SNPs after removing pleiotropic ones for subsequent MR analysis [40]. Third, we used the leave-one-out sensitivity test to detect the pleiotropy caused by each SNP, which estimated the effects of the remaining SNPs after excluding individual SNPs one by one. Furthermore, we harmonized all alleles to the forward strand in our analysis. We performed bidirectional Mendelian randomization using the SNPs that passed the quality control criteria.

GO and KEGG Enrichment Analysis

To explore the biological role of gut microbiota in the pathogenesis and progression of PsA, we performed GO and KEGG enrichment analysis based on the selected genes near the lead SNPs on gut microbiota screened by MR analysis. We conducted the GO function (cellular component, molecular function and biological process) analysis and KEGG pathway enrichment of the lead SNPs using the BINGO plug-in and the David database (https://david.ncifcrf.gov/). We considered P < 0.05 as significant difference [41].

Results

To investigate the potential causal relationship between gut microbiota and psoriatic arthritis, we selected 274 SNPs as instrumental variables based on the criteria described above. Specific information about the selected SNPs is provided in Supplementary Table 1–2.

As shown in Fig. 2, when PsA is the outcome, eight bacterial taxa, specifically, Methanobacteria (class), Methanobacteriales (order), Methanobacteriaceae (family), Rikenellaceae (family), Eubacterium fissicatena (genus), Butyricicoccus (genus), Methanobrevibacter (genus) and Ruminococcaceae UCG011 (genus), were identified to be associated with PsA mainly by IVW method, with F statistics ranging from 18.922 to 28.778. The IVW estimate indicated that the genetic prediction of Methanobacteria (class) [0.841 (0.718–0.985), P = 0.032], Methanobacteriales (order) [0.841 (0.718–0.985), P = 0.032], Methanobacteriaceae (family) [0.841 (0.718–0.985), P = 0.032], Eubacterium fissicatena group (genus) [0.782 (0.648–0.944), P = 0.010] and Methanobrevibacter (genus) [0.804 (0.659–0.981), P = 0.031] had a protective effect on PsA, while Rikenellaceae (family) [1.314 (1.024–1.686), P = 0.032], Butyricicoccus (genus) [1.602 (1.019–2.519), P = 0.041] and Ruminococcaceae UCG011 (genus) [1.212 (1.039–1.413), P = 0.014] were associated with an increased risk of PsA.

Fig. 2
figure 2

MR analysis results for the association between gut microbiota and PsA

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To evaluate the robustness of the findings, we performed sensitivity analysis to assess the causal effects of gut microbes on PsA. We observed no heterogeneity for Methanobacteria (class), Methanobacteriales (order), Methanobacteriaceae (family), Rikenellaceae (family), Eubacterium fissicatena group (genus), Methanobrevibacter (genus) and Ruminococcaceae UCG011 (genus) by MR-IVW and MR-Egger methods (P > 0.05).

Furthermore, there was no indication of significant directional horizontal pleiotropy according to the MR-Egger intercept test results. We also applied MR-PRESSO to test for horizontal pleiotropy and found no significant evidence of deviation from the null hypothesis (P > 0.05) (Table 1). However, we detected heterogeneity for Butyricicoccus (genus) on PsA using MR-IVW (P < 0.05) and the MR-Egger intercept test suggested the presence of directional horizontal pleiotropy. In contrast, MR-PRESSO showed no horizontal pleiotropy or outliers for Butyricicoccus (genus) (Table 1). The leave-one-out test plot, scatter plot, Forest plot and funnel plot of eight selected IVs are provided in Supplementary Figs. 1–4.

Table 1 Sensitivity analysis of the MR analysis results of gut microbes on PsA
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For the GO term enrichment analysis, we identified eight GO biological processes that were associated with PsA, such as the regulation of plasma membrane, regulation of RNA metabolism and processing and positive regulation of axonogenesis (Fig. 3). However, the KEGG pathway enrichment analysis did not reveal any significant pathway for the genes enriched by gut microbes in psoriatic arthritis.

Fig. 3
figure 3

GO terms enrichment analysis of gut microbes on PsA

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Reverse MR Analysis

Based on the reverse MR analysis, we detected a suggestive association between PsA and six taxa, including family Clostridiaceae1, family Defluviitaleaceae, genus Butyrivibrio, genus Defluviitaleaceae UCG011, genus Clostridium sensu stricto 1 and genus Ruminococcaceae UCG011 (Table 2). Specifically, PsA was associated with a decrease in the genus Butyrivibrio and an increase in the rest of the family Clostridiaceae1, family Defluviitaleaceae, genus Defluviitaleaceae UCG011, genus Clostridium sensu stricto1 and genus Ruminococcaceae UCG011. Sensitivity analysis showed no heterogeneity and pleiotropy (Supplementary Table 3). GO term enrichment analysis revealed 20 GO biological processes that were related to six taxa (e.g., positive regulation of interferon-gamma production, positive regulation of T cell proliferation and positive regulation of T cell-mediated cytotoxicity) (Fig. 4). KEGG enrichment analysis identified interleukin (IL)-17A-mediated signaling pathway, Th1 and Th2 cell differentiation and IL-17 signaling pathway (Fig. 5). Figure 6 shows a summary network that illustrates the relationship between gut microbiota and PsA.

Table 2 Reverse gut microbiota and PsA Mendelian analysis results
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Fig. 4
figure 4

GO enrichment analysis Scatter plots of PsA on gut microbes

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Fig. 5
figure 5

KEGG enrichment analysis Scatter plots of PsA on gut microbes

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Fig. 6
figure 6

Causal relationships between gut microbiota and PSA by Mendelian randomization analysis

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Discussion

In this study, we used the summary statistics of gut microbiota from the largest GWAS meta-analysis by the MiBioGen consortium and the summary statistics of PsA from the FinnGen consortium R9 release data to conduct a two-sample bidirectional MR analysis, which effectively avoided the confounding effects of confounding factors, to demonstrate the important role of genetically predicted specific gut microbiota and PsA. We found that Methanobacteria, Methanobacteriales, Methanobacteriaceae, Eubacterium fissicatena group and Methanobrevibacter had a protective effect on PsA, and we also found that Rikenellaceae and Ruminococcaceae UCG011 might be a potential risk factor for PsA. Due to the pleiotropy and heterogeneity of Butyricicoccus, we exclude it from our elucidating interpretation. Reverse MR analysis confirmed and strengthened the impact of PsA on gut microbiota. Among them, Ruminococcaceae UCG011 showed a clear bidirectional causal relationship. These findings provide a new idea for the clinical prevention and treatment of patients with PsA.

Short-chain fatty acids (SCFAs) are a class of organic acids that are mainly composed of acetate, propionate and butyrate. They are produced by the gut microbiota through the fermentation of dietary fiber and play an important role in maintaining the metabolism, nervous and immune systems [42]. Recent studies have shown that SCFAs are effective modulators of osteoclast metabolism and bone homeostasis [43], which provide new insights into the role of SCFAs in PsA. In our study, we found that the butyrate-producing Eubacterium fissicatena group, which is associated with butyric acid production, may relieve the occurrence and progression of PsA by producing SCFAs and balancing intestinal homeostasis. This finding is consistent with the previous study by Paine et al., which found that the butyrate levels in psoriasis patients who were at risk for PsA were significantly decreased [44], suggesting that reduced levels of butyric acid produced by the gut microbiota are associated with PsA. The pathogenesis of PsA involves various immune cells, and many studies have shown that SCFAs can act on different immune cells to alleviate inflammation. For example, Singh et al. found butyric acid activates GPR109a on the surface of macrophages and dendritic cells (DCs), thereby inhibiting inflammation and inducing the differentiation of regulatory T (Treg) cells and interleukin-10 (IL-10)-producing T cells [45]. Cholan et al. confirmed that using butyrate significantly reduced the recruitment of M1-type pro-inflammatory macrophages and revealed its anti-inflammatory activity [46]. Tyagi et al. clarified that Treg cells regulate the production of Wnt10b by interacting with BM CD8 T cells, which act on stromal cells and osteoblasts to promote bone formation [47]. Therefore, we propose that the imbalance of Eubacterium fissicatena group plays an important role in the occurrence and development of PsA.

Methanogens are a group of archaea that can convert various inorganic or organic substrates into methane and carbon dioxide under anaerobic conditions [48]. Methanogens have been detected in various human body sites, such as the gastrointestinal tract, oral cavity and vagina, and their potential role in human health and disease has aroused increasing interest [49]. In this study, we provide novel evidence for the beneficial effects of methanogens on human health via the gastrointestinal tract. We found that the abundance of methanogens, especially Methanobacteria, Methanobacteriales and Methanobacteriaceae, was negatively correlated with the risk of PsA, which is indirectly consistent with previous studies. As mentioned earlier, IBD is a high risk factor for PsA. Previous studies have found that only 24% to 30% of IBD patients are methane producers compared to about 48% of healthy people [50]. Pimentel et al. speculated that the cause of the decrease in intestinal methanogens was due to diarrhea removing the communities from the intestinal tract—effectively “colonic purging” of methanogens [51]. The low abundance of methanogens in the gut of IBD patients provides a new idea for the occurrence of PsA, but the mechanism needs further study, and our results can only provide a reference. Our study provides new insights into clinical probiotic supplementation, and it is promising to use live formulations of methanogens as dietary supplements to influence methanogens to prevent various diseases, including PsA.

This study investigated the pathogenic mechanism of PsA regulated by the gut microbiota through a potential genetic pathway, based on previous (mainly indirect) evidence. We identified two common bacteria Rikenellaceae and Ruminococcaceae UCG011 associated with increased PsA risk. As a common intestinal gram-negative bacteria, Rikenellaceae has been detected in differences in various diseases. Several studies have found that a high-fat diet can lead to an increase in intestinal Rikenellaceae and aggravate obesity and inflammation in mice [52,53,54,55]. Wang et al. and Johnson et al. observed that higher abundance of Rikenellaceae family was associated with more severe SLE-like disease in SNF1 mice and MRL/lpr mice [56, 57]. This is consistent with our findings, but interestingly, previous studies have found that HLA-B27 is the major genetic factor leading to inflammatory spondyloarthritis represented by PsA. However, Phoebe Lin et al. found that in the gut of transgenic HLA-B27 and human β2-microglobulin (hβ2m) Lewis rats, the relative abundance of Rikenellaceae was significantly reduced compared with wild-type Lewis rats[58]. This result contradicts previous experimental studies and our research findings, suggesting that the effect of HLA-B27 on the gut microbiota may vary among species or involve other unknown factors. Further randomized controlled trials and experimental studies are needed to reveal the underlying mechanism.

Ruminococcaceae UCG011, which has bidirectional causal relationship with PsA, belongs to Ruminococcaceae. Previous studies have supported that Ruminococcaceae were more abundant in psoriasis patients [59, 60]. Shi et al. showed that a high-sugar and medium-fat diet and systemic IL-23 expression in mice exacerbated psoriasis-like skin and joint inflammation and increased the abundance of Ruminococcaceae in their gut [61]. Another animal study also demonstrated that a high-fat diet led to the accumulation of Ruminococcaceae in mice and increased inflammation [54]. Our results further support the pathogenic role of Ruminococcaceae in patients with PsA. Reverse MR analysis showed that PsA could reduce the abundance of Butyrivibrio and lead to the enrichment of Clostridiaceae1, Defluviitaleaceae, Defluviitaleaceae UCG011, Clostridium sensu stricto1 and Ruminococcaceae UCG011. Butyrivibrio, as a producer of SCFAs, plays an important role in maintaining gut homeostasis. Our research found that PsA was significantly negatively correlated with the abundance of Butyrivibrio in the gut. In addition, Clostridiaceae1 and Clostridium sensu stricto1 were identified as PsA-associated gut flora for the first time in our study. A previous cross-sectional study reported that both IBD-A and rheumatoid arthritis patients had higher abundance of Clostridiaceae than healthy controls, suggesting a potential common microbial link for inflammatory arthritis [62]. Moreover, our study showed that PsA was associated with a higher abundance of Defluviitaleaceae and Defluviitaleaceae UCG011. However, contradictory findings about these two taxa in different diseases exist. For example, in contrast to healthy controls (HCs), RA patients and individuals at high-risk for RA showed a reduction in the abundance of genus Defluviitaleaceae_UCG-011 in their saliva [63]. Interestingly, the abundance of Defluviitaleaceae_incertae_sedis (belongs to Defluviitaleaceae) was significantly higher in immunoglobulin A nephropathy than in HCs [64]. The reason for this discrepancy may be due to differences in the type of tissue examined, although they are essentially diseases of the immune system. Our study provides strong evidence that PsA is associated with an increased abundance of these two taxa in the gut.

Furthermore, GO enrichment analysis revealed that a large number of GO biological processes were involved in the relationship between gut microbiota and PsA. Although the KEGG pathway enrichment analysis did not identify the pathways through which the gut microbiota affected PsA, it did identify the pathways through which PsA altered the gut microbiota. These findings not only corroborated previous studies but also provided a new way to explore the gut microbiota of PsA. For instance, lymphocytes represented by T cells were the most frequent immune infiltrates in PsA [1]. Our study found that the positive regulation of T-helper 17 type immune response, IL-17 production, IL-17A-mediated signaling pathway and other biological functions as well as Th17 cell differentiation and IL-17 signaling pathways participated in the effect of PsA on gut microbiota. Previous studies had found that Th17 cells, detected in peripheral blood and synovial fluid of patients with PsA, exhibited increased numbers and an increased functional phenotype [65]. Siba P Raychaudhuri et al. confirmed that blockade of IL-17RA with an anti-IL-17RA antibody inhibited the production of IL-6, IL-8 and MMP-3 [66]. Although the experimental evidence supporting the role of IL-17 in PsA was far less than that for RA or psoriasis [67], our study provided new evidence to support the role of IL-17 in PsA.

In summary, our study explored the potential relationship between gut microbiota and PsA and found a variety of probiotics and harmful bacteria for PsA, which played a certain role in the clinical care and prevention of patients with PsA.

Limitations

However, there are still the following problems in our study. First, the relatively small sample size of gut microbiota data may have introduced some bias to our analysis. Second, our GWAS data on gut microbiota only covered the taxonomic levels from phylum to genus, which may have missed some specific bacteria that had causal effects on PsA at a finer resolution such as the species or strain level. Third, although most participants in the MiBioGen consortium for gut microbiota data had European ancestry, there may still be some confounding from population structure, and the generalizability of our findings to other ethnic groups may be limited [68].

Conclusions

We used a two-sample bidirectional Mendelian randomization analysis to reveal the causal relationship between gut microbiota and PsA. Our findings suggest that alterations in the gut microbiota may influence the risk and severity of PsA and that PsA may affect the composition and diversity of the gut microbiota. These results provide new insights into the potential mechanisms underlying the interactions between gut microbiota and PsA and offer evidence for the clinical implications of modulating the gut microbiota for PsA prevention and treatment, such as through probiotic intake, fecal microbiota transplantation and other strategies.