We checked the NHGRI-EBI GWASs catalogue (https:/www.ebi.ac.uk/gwas/), which is a systematic catalogue of documented associations of reported genome-wide association studies (GWAS) [11]. We used intestinal microbiota as an exposure and OA as an outcome. Publicly accessible summary statistics datasets of intestinal microbiota GWAS (total 3,326 individuals) of European ancestors were used as an exposure [12,13]. As an outcome, summary data were used from 3,498 patients with OA of the knee and hip and 11,009 controls of European descent from the United Kingdom from the arcOGEN analysis [14]. The OA patients were unrelated and of European origin, and all had primary OA of the hip or knee of radiographic Kellgren–Lawrence grade ≥2, or clinical evidence of disease to a level requiring total joint replacement. Women were 60.8% of the patients and joint replacement was performed in 78.6% [14]. We conducted a two-sample MR analysis utilizing genetic variants correlated with intestinal microbiota as IVs to enhance inference. The ‘top hits’ from a GWAS can be used to define genetic instruments for exposures in MR analyses. The selection criteria for IVs were as follows; 1) Association p-values of single-nucleotide polymorphisms (SNPs) with exposure should be less than a threshold of p<1×10⁻⁵ in GWAS dataset of exposure. 2) The SNPs from exposure dataset also should be present in GWAS dataset of outcome. Summary statistics (beta coefficients and standard errors [SEs]) were collected for 29 different SNPs correlated with intestinal microbiota as IVs (Figure 1). And we used the OA GWAS summary results for the 29 SNPs as an outcome [14].

Study of MR demands that genetic variants is linked to, but not to, possible confounders of, exposure [15]. Therefore, first, we evaluated the independent association of SNPs with gut microbiota. Second, we looked at the relation between each SNP and the occurrence of OA. Third, we merged these results to evaluate the causal relationship between intestinal microbiota and OA occurrence using an MR analysis. Two-sample MR, a tool used to measure the causal effects of exposure (gut microbiota) on outcomes (OA) utilizing summary statistics from various GWASs [16], was used to determine the causal association between intestinal microbiome and occurrence of OA utilizing summary results from GWASs linked to intestinal microbiome and OA with 29 SNPs as IVs (Table 1). The inverse variance weighting (IVW) method utilizes a meta-analysis technique to incorporate the Wald ratio calculation of the causal impact derived from various SNPs and to provide an estimate of the causal impact of the exposure on the outcome, whenever any of the genetic variants satisfies the assumptions of the IVs [16]. While the inclusion of several variants in the MR study results in improved statistical accuracy, it has the ability to incorporate pleiotropic genetic variants that are not appropriate for IVs [17]. For the discovery and adjustment of pleiotropy, i.e., the correlation of genetic variations with more than one component, the weighted median and MR-Egger regression methods have been used. MR-Egger regression is resilient to invalid instruments, checks and accounts for the existence of imbalanced pleiotropy by introducing a parameter for this bias by integrating summary data estimates of causal effects from multiple individual variants [18,19]. MR-Egger regression was used to assess the potential for violations of MR assumptions and to improve the reliability of causal inference. The weighted median estimator offers an accurate estimate of the causal effect, even though up to 50% of the data that contributes to the study is derived from genetic variants that are invalid IVs [20]. The benefit of the weighted median estimator is that it maintains better consistency in the results relative to the MR-Egger analysis [20]. Analyzes were found statistically relevant at p< 0.05. Both MR analyzes were conducted on the MR Base Platform (App version: 1.2.2 3a435d [31 January 2019], R version: 3.5.1) [21].

Heterogeneity between SNPs was tested using Cochran Q-statistics and funnel plots [22]. We have carried out a “leave one out” study to explore the likelihood of a causal relationship being powered by a single SNP. We conducted a subgroup study on the anatomical location (hip/knee) for the sensitivity examination.

A total of 29 SNPs from GWAS on intestinal microbiota were chosen as IVs (Table 1). All of them were correlated with intestinal microbiota of genome-wide significance, except for rs1230666 and rs2294239 (Table 1). Rs11724031 was strongly correlated with OA, while some were not substantially linked to OA (Figure 2).

The IVW approach finds no evidence to prove a causal relationship between intestinal microbiota and OA (beta=−0.001, SE=0.004, p=0.748) (Table 2, Figures 2 and 3). The regression of MR-Egger showed that directional pleiotropy was unlikely to result in a bias (intercept=0.002, SE=0.007, p=0.697). The MR-Egger study showed no causal relation between intestinal microbiota and OA (beta=−0.002, SE=0.005, p=0.630) (Table 2, Figure 3). The weighted median analysis also did not have evidence of a causal relationship between intestinal microbiota and OA (beta=−0.002, SE=0.005, p=0.630) (Table 2, Figure 3). The MR estimates calculated using IVW, the median weighted and the MR-Egger regression approaches were consistent. Therefore, the findings of the MR analysis did not support a causal association between intestinal microbiome and OA risk.

The Cochran Q test and the funnel test displayed no signs of heterogeneity between the IV estimates dependent on the individual variants, and the MR-Egger regression indicated no signs of directional pleiotropy (Table 2, Figure 3). Results from the “leave one out” study revealed that the IVW point estimate was not guided by a single SNP, and the MR results stayed non-significant after excluding rs1230666 and rs2294239 (Figure 4). When the MR study was stratified by anatomical location (hip/knee), the findings were not relevant in both the knee and hip OA.