To investigate the potential causal relationship between immune cells and AS, we conducted a univariable MR analysis in our study ( Figure 1A ). All genetic variations used as IVs adhere to the three fundamental assumptions: 1) the chosen IVs had a clear association with the exposure, 2) the selected IVs had no relationship with any confounding factors, and 3) the IVs could influence the outcomes solely through their impact on the exposure (12). To minimize the potential for reverse connection, we conducted a bidirectional MR analysis to explore the relationship between immune cells and AS ( Figure 1B ). Recognizing that various immune cells may interact and affect each other due to genetic pleiotropy, potentially introducing confounding effects, we subsequently carried out a multivariable MR analysis to assess the direct influence of immune cells on AS ( Figure 1C ).

The genome-wide association study (GWAS) data on immune cells were acquired from the latest study involving 3,757 individuals of Sardinian descent within the European population (13). This study included 3,757 cases and 3,027 controls, with a gender distribution of 43% male and 57% female. The ages of the participants ranged from 18 to 102 years (13). A total of 731 immunophenotypes were included, including 118 absolute cell (AC) counts, 389 median fluorescence intensities (MFIs) reflecting surface antigen levels, 32 morphological parameters (MPs), and 192 relative cell (RC) counts (13). The MFI, AC, and RC features encompass B cells, CDCs, mature stages of T cells, monocytes, myeloid cells, TBNK (T cells, B cells, and natural killer cells), and Treg panels, while the MP feature encompasses CDC and TBNK panels (13). This study measured 22 million genetic variations (13).

The data related to AS were sourced from the FinnGen database (https://www.finngen.fi/fi). This dataset encompasses 1,462 cases, 164,682 controls, and 16,380,022 single-nucleotide polymorphisms (SNPs). Diagnoses of AS were made in accordance with the International Classification of Diseases, specifically ICD-10 (M45), ICD-9 (7200), and ICD-8 (7124) coding standards. All populations included in the database were of European origin. Additional relevant information can be obtained from the FinnGen website (Risteys · Home (finregistry.fi)).

We conducted various strict quality controls to choose IVs to satisfy the three core assumptions of MR analysis and ensure the robustness and reliability of MR analysis. First, we selected the SNPs of immune cells at a genome-wide significance threshold (p < 5E−08) (10). Second, we addressed the issue of linkage disequilibrium (LD) between SNPs by removing strongly linked variants (r² = 0.001, clumping distance of 10,000 kb). This step aimed to mitigate any potential bias in the results caused by LD (10). Then, we utilized the PhenoScanner database to exclude the effect of confounder factors. Finally, we computed the F-statistics for all the selected SNPs. We excluded SNPs with F-statistics less than 10 to ensure that all remaining SNPs were strongly associated with the exposure (14). We calculated F-statistics using the formula F = beta²/standard error²(SE) (15, 16) ( Supplementary Table 1 ).

In our MR analysis, we employed the inverse-variance weighted (IVW) method as our primary analytical approach. We established statistical significance by setting the threshold for p-values at 6.84E−05, which was adjusted using the Bonferroni method (0.05/731). Additionally, we considered p-values falling between 6.84E−05 and 0.05 suggestive of significance. Since the IVW method assumes the absence of an intercept term, we conducted the MR-Egger test to assess the presence of the intercept (17). Additionally, to enhance the robustness of our results, we employed the MR-Egger, weighted median, weighted mode, and simple mode methods.

pWe performed Cochran’s Q test and MR-Egger intercept analysis to ensure the absence of heterogeneity and pleiotropy. Heterogeneity was deemed present if the Q–p-value was less than 0.05, leading to the utilization of a random effects model for the analysis (18). MR-Egger intercept p-value exceeding 0.05 indicated the absence of pleiotropy (19). Finally, we employed leave-one-out analysis to investigate the potential impact of single SNP points on the causal relationship between immune cells and AS.

We conducted all statistical analyses using the “TwoSampleMR” (version 0.5.7) packages within the R statistical software (version 4.3.1).

In our study, we investigated the association between 731 immune cells and AS ( Supplementary Table 2 ). Our analysis revealed that 39 of these immune cells were significantly associated with AS, comprising 22 risk factors and 17 protective factors ( Supplementary Table 3 ). However, we excluded 31 factors with fewer than three SNPs each due to limitations in the number of available SNPs. Consequently, we identified four potential pathogenic factors and four potential protective factors. We calculated terminally differentiated CD8⁺ T cell %CD8⁺ T cell, HLA DR⁺ CD8⁺ T cell absolute count, and HLA DR⁺ CD8⁺ T cell %lymphocyte using the fixed effects model ( Table 1 ). For HLA DR on CD14⁻ CD16⁺ monocyte, CD8 on effector memory CD8⁺ T cell, CD4 on CD39⁺ activated CD4 regulatory T cell, HLA DR on CD33^dim HLA DR⁺ CD11b⁺, and HLA DR on CD33^dim HLA DR⁺ CD11b⁻, we employed a random effects model for calculation ( Table 1 ).

Following the application of the Bonferroni test, we identified one risk factor and one protective factor associated with AS ( Table 1 ; Figures 2 , 3 ). CD8 on effector memory CD8⁺ T cell could significantly increase the risk of AS (p: 1.2302E−05, OR: 2.9871, 95%CI: 1.8289–4.8786). HLA DR on CD33^dim HLA DR⁺ CD11b⁺ could significantly decrease the risk of AS (p: 1.2301E−06, OR: 0.5446, 95%CI: 0.4260–0.6962).

A bidirectional MR analysis was performed, and evidence of a reverse relationship was identified, particularly between CD4 on CD39⁺ activated CD4 regulatory T cells and AS. The bidirectional MR was analyzed by the random effects model. It was observed that AS could lead to a decrease in CD4 on CD39⁺ activated CD4 regulatory T cell (p: 0.0124, OR: 0.9570, 95%CI: 0.9245–0.9905) ( Table 2 ; Figures 4 , 5A ). The potential causal relationship between them appears to be bidirectional.

The multivariable MR analysis included HLA DR on CD33^dim HLA DR⁺ CD11b⁺ and CD8 on effector memory CD8⁺ T cell. HLA DR on CD33^dim HLA DR⁺ CD11b⁺ could decrease the risk of AS (p: 2.113E−06, OR: 0.0.5423, 95%CI: 0.4210–0.6983) ( Table 3 ; Figure 5B ).

Our findings indicated that HLA-DR exhibits both protective and promotive effects on AS across various immune cells. It is hypothesized that the presence of different HLA-DR subtypes on various immune cells determines their respective roles. Kchir reported elevated expression levels of HLA-DRB1*11 in AS patients, and HLA-DRB1*11 did not have a direct effect on AS but showed dependence on HLA-B27 (20). Meanwhile, their findings also indicated that HLA-DRB1*13 played a protective role in AS (20). These findings support our results on the role of HLA-DR in AS.

Our study demonstrated that CD8⁺ T cells can contribute to the development of AS. Terminally differentiated CD8⁺ T cell %CD8⁺ T cell, HLA DR⁺ CD8⁺ T cell absolute count, HLA DR⁺ CD8⁺ T cell %lymphocyte, and CD8 on effector memory CD8⁺ T cell could increase the risk of AS. Previous studies have shown a significant increase in IL-6 levels in CD8⁺ T cells in the peripheral blood of patients with AS (9). IL-6, a pro-inflammatory cytokine, may enhance the body’s inflammatory response and promote the progression of AS (9, 21). The role of CD8⁺ cells in the pathogenesis of AS is dependent on the presentation of antigens through HLA-B27, and it is widely acknowledged that HLA-B27 plays a significant role in promoting the development of AS (22–25). Moreover, cytotoxic cells restricted by HLA-B27 can be found in the synovial fluids of AS patients (26).

In this study, we also found that HLA DR on CD14⁻ CD16⁺ monocytes was associated with a reduced risk of AS. Wright proposed that CD14⁻ CD16⁺ mononuclear cells may contribute to AS by promoting T helper 17 cells (Th17) responses, which can produce the IL-17 to accelerate the pathogenesis of AS (24, 27–29). However, our findings differed from those reported by Wright. It is proposed that the protective influence exerted by HLA-DR offsets the promotional impact of CD14⁻ CD16⁺ on AS. The protective influence of HLA-DR has been reported by a previous study (20).

CD4 on CD39⁺ activated CD4 regulatory T cell has the capacity to decrease the risk of AS. Conversely, AS can diminish the population of CD4 on CD39⁺ activated CD4 regulatory T cells. However, CD4 cell activation leads to increased secretion of IL-10, which exerts an anti-inflammatory effect and diminishes the risk of AS (9, 21). However, the body experiences immune dysregulation in AS patients, impairing immune suppression function and potentially resulting in reduced IL-10 secretion by CD4 cells, thereby inhibiting their function (9). The collaborative action of CD39 and CD73 results in the conversion of ATP to ADP and cAMP, ultimately generating adenosine (30, 31). Adenosine can interact with multiple receptors, including A1, A2A, A2B, and A3, leading to various immune responses (31, 32). Adenosine stimulates immune responses through A1 and A3 receptors, while it exerts immunosuppressive effects when engaging with A2A and A2B receptors (32–34). In the context of AS pathogenesis, we hypothesize that CD39 utilizes the latter pathway to participate in AS.

First, we identified potential causative factors of AS through univariable MR. The use of the Bonferroni test reduced the probability of a type I error and increased the stability of our results. Furthermore, to eliminate mutual confounding between immune cells, we employed multivariate MR. These methods have significantly reduced the influence of confounding factors and greatly increased result confidence. It has been confirmed that the development of AS can be inhibited by HLA-DR on CD33^dim HLA DR⁺ CD11b⁺.

It is hypothesized that HLA-DR reduces the risk of AS through its subtype HLA-DRB113. Previous studies have indicated decreased expression of HLA-DRB113 in AS patients (20). Further investigations are necessary to determine if other isoforms may also contribute. Furthermore, AS patients with low activity were found to have decreased CD11b expression compared to normal controls (35). This may be due to CD11b’s ability to inhibit the inflammatory response, thereby reducing the occurrence of AS. In mice, CD11b promotes neutrophil apoptosis to inhibit inflammation (36). It is suspected that a similar effect of reducing inflammation may occur in the human body. Additionally, CD33 inhibits the expression of pro-inflammatory cytokines, including IL-1β and TNF-α (37, 38). Consequently, CD33 may decrease the incidence of AS by mitigating the body’s inflammatory response. However, further research is required to elucidate the specific mechanisms.

Our MR analysis has several strengths. First, we used a comprehensive approach, including univariable, bidirectional, and multivariable MR, to address potential confounding factors and reverse causality. Second, we conducted multiple sensitivity analyses to validate our hypotheses and minimize bias. Third, our research was limited to the European population to reduce population bias. Finally, we used the Bonferroni test to confirm the causative role of AS.

However, it is important to note that our study has limitations. The conclusions drawn from our data could not be immediately generalized to other populations, as all of our data originated from European sources. Additionally, the relatively small sample size may introduce bias, highlighting the need for larger samples to ensure more robust results.