Causal role of immune cells in male infertility: a Mendelian randomization study

Introduction

Infertility is a global sexual health problem that is estimated to affect 8% to 12% of couples of childbearing age worldwide. In infertile couples, male factors are identified as causing or contributing to at least about 50% of cases (1). In addition to the profound psychological and social stress on patients, male infertility imposes a significant economic burden on the health care system. The causes of male fertility disorder are various, but its pathological mechanism has not been fully understood (2). Possible causes include varicocele, orchitis, prostatitis, oligospermia, asthenozoospermia, azoospermia and other male disorders (3,4). Despite extensive conventional diagnosis, the specific cause of male infertility remains undiagnosed in more than half of patients, posing challenges in selecting appropriate treatments and predicting outcomes (5).

Recent epidemiological studies have revealed a link between the immune inflammatory response and subsequent male infertility. Bacteria and viruses interact with host immune and inflammatory systems through their unique structures, resulting in significant changes in the number of immune cell subsets such as T cells, B cells, phagocytes, thus affecting the occurrence of male infertility (6-8). In addition to the virus, similar results have been found in patients with varicocele, inflammatory mediators secreted by immune cells, such as interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), IL-1b, IL-17, IL-23 cause testicular damage and promote spermatogenic cell apoptosis by disrupting the immunosuppressive microenvironment (3). This is consistent with the conclusion that chronic inflammation and varicocele are risk factors for male infertility. The presence of antisperm antibodies (ASA) in blood and semen may contribute to immune infertility (9), which further supports the critical role of immune responses in the pathogenesis of male infertility.

Current research suggests that autoimmune and chronic inflammatory diseases may adversely affect male reproductive health and fertility, and these diseases are thought to lead to decreased semen quality and decreased libido. However, scientific work in this area faces several challenges and limitations. These include sample size limitations, flaws in study design, confounding factors that cannot be adequately accounted for in existing studies, and significantly increased study costs, especially when exploring the potential causal relationship between immune cells and male infertility. In addition, the results of clinical trials have not met expectations, indicating that further in-depth and accurate research is needed in this area.

Mendelian randomization (MR) is an analytical approach utilizing genetic variation as an instrumental variable (IV). It is employed in epidemiology to deduce the fundamental causes of diseases (10). This method is based on Mendel’s law of independence assortment and uses genetic variation closely associated with a particular exposure factor to assess the potential causal relationship between that exposure factor and the outcome. This method has found extensive application in establishing causal associations in autoimmune diseases, and its efficacy has been confirmed through numerous studies (11,12). MR reduces the influence of confounding variables and avoids the problem of reverse causal confounding compared to traditional observational studies. Wang et al. (13) and Gu et al. (14) explored the causal role of immune cells in schizophrenia (SCZ) and systemic lupus erythematosus, respectively, but there was no study on male infertility.

Following previous observational studies that identified multiple associations between immune cell traits and male infertility, our study employed two-sample MR to explore the potential causal relationship between 731 immune traits and male infertility risk. Without prior assumptions, this study aims to systematically reveal the causal links between immune cells and male infertility, particularly focusing on less-researched immune traits. Through this approach, we seek to expand our understanding of the potential role of immune cells in male infertility and provide a scientific foundation for more targeted diagnosis and treatment strategies for infertility patients. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2024-657/rc).

Methods

Study design

Our study aimed to investigate the potential causal relationship between 731 immune cell traits (categorized into seven groups) and male infertility using a two-sample MR approach. Ensuring the reliability of inference demands that valid IVs adhere to three crucial assumptions: (I) genetic variation should have a direct association with the relationship influenced by exposure; (II) genetic variation should not be linked to confounding factors that may exist between exposure and outcome; (III) genetic variation should not influence the outcome through pathways other than exposure (Figure 1A) (15). We initially selected 731 immune phenotypes as exposures and male infertility as the outcome to identify immune cells that may prevent or promote male infertility. Those immune cells with a potential causal relationship were defined as infertility-associated immune cells. To explore potential reverse causation between these infertility-associated immune cells and male infertility, we conducted a second-step reverse MR study. In causal inference, genetic variation serves as a surrogate for risk factors (Figure 1B). We obtained data from public databases and did not necessitate ethical review for this research. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Principle (A) and analytical workflow (B) of MR analysis. The MR analysis in this study comprises two steps. Each step involves three assumptions (assumptions 1, 2, and 3) and five major components of MR analysis (the names of the components are shown in the left boxes, and their specific contents are shown in the right boxes) to investigate the potential causal relationship between exposure and outcome. Step 1: 731 immune cell traits are considered as exposures, and male infertility as the outcome. SNPs derived from immune cell traits are regarded as IVs, and the causal effects of immune cells on male infertility are estimated through MR analysis. Step 2: male infertility is used as the exposure, and immune cells identified as associated with male infertility in Step 1 are considered outcomes. SNPs derived from male infertility are used as IVs to test for reverse causation via MR analysis. IV, instrumental variable; IVW, inverse-variance weighted; MR, Mendelian randomization; SNP, single nucleotide polymorphism.

Genome-wide association study (GWAS) data sources for male infertility

Our study involved GWAS data on male infertility, which was obtained through FinnGen Consortium version R9 (16). In our study, we used the “male infertility” phenotype, which included 1,271 patients and 119,297 controls. Men with genital cancer were excluded from the control group. Male infertility status was determined by a physician based on diagnostic information from ICD-10 code N46, ICD-9 and ICD-8 code 606. Such diagnostic information may include azoospermia, oligospermia, infertility due to extratesticular causes, and unspecified male infertility (17).

Immunity-wide GWAS data sources

To further analyze the association between immune traits and genetic background, we extracted a GWAS pooled dataset of 731 different immune traits (GCST0001391 to GCST0002121) from the GWAS Catalog (18). The study also revealed 122 significant independent association signals at a total of 70 genetic loci, including 53 previously unreported new signals. These correlations have aided in identifying the molecules and mechanisms regulating 459 cellular characteristics. To comprehensively evaluate cell phenotype, the study employed flow cytometry (FACS) to analyze a variety of cell parameters, encompassing 118 absolute cell counts (AC), 389 median fluorescence intensities (MFI) reflecting surface antigen levels, 32 morphological parameters (MP), and 192 relative cell counts (RC). Specifically, MFI, AC and RC cell parameters cover seven groups: B cells, conventional dendritic cells (cDC), mature T cells, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells) and regulatory T cells (Treg). MP cell parameters were used exclusively to assess the characteristics of cDC and TBNK groups. It is worth noting that the initial GWAS analysis was generated based on data from 3,757 individuals of European ancestry to ensure that there was no cohort overlap between samples (19).

Selection of IVs

In order to ensure the authenticity and reliability of our research results, we have implemented a number of strict control measures. For each immune trait, significance thresholds were strictly set at P<1×10⁻⁵ to screen out reliable IVs (20). To obtain single nucleotide polymorphisms (SNPs) that were independent of each other, we trimmed these instruments within a window size of 10,000 kb to reduce the impact of r²<0.001 in linkage disequilibrium. At the same time, all palindrome sequences were eliminated to eliminate interference caused by possible duplication of gene sequences. For male infertility, we also maintained the significance threshold at P<1×10⁻⁵. We calculated the F-statistic for each IV to assess its strength and exclude the risk of bias from weak instrument variables. Only IVs with an F statistic greater than 10 were retained to ensure that the IV used for subsequent analysis was robust to male infertility studies.

Statistical analysis

We employed the inverse variance weighting (IVW) method to estimate the primary MR effect, which was reported as an odds ratio (OR) with a 95% confidence interval (CI). Additionally, we employed the weighted median method and MR-Egger regression to estimate the causal effects. These five methods are widely acknowledged and commonly employed for robust analysis of MR study results. When employing the weighted median method, a minimum of 50% of the SNPs must adhere to the assumption of being valid IVs. The MR-Egger regression adjustment enables the identification of deviations from standard IV assumptions and yields an effect estimate unaffected by such violations. Sensitivity analyses are essential for evaluating potential biases in MR studies. These analyses consider two factors: heterogeneity testing and pleiotropy testing. Heterogeneity was assessed using the Cochran’s Q test in the IVW method, and the presence of pleiotropy was indicated by the intercept in MR-Egger regression (an intercept with a P value less than 0.05 was considered indicative of pleiotropy). Additionally, we employed the MR-PRESSO and Radial MR methods to eliminate SNPs with pleiotropic outliers (P<0.05). In cases where potential outliers were identified, they were excluded, and the IVW estimation was repeated to evaluate the robustness of our findings.

The MR analysis was performed using R software (version 4.3.1), TwoSample MR package (version 0.5.7), along with Storm Statistical Platform (www.medsta.cn/software).

Results

To investigate the causal effect of male infertility on immunophenotype, we performed two sample MR analysis and identified 23 suggestive immunophenotypes, 17 with elevated and 6 with decreased immunocyte levels, that were associated with male infertility risk under the premise that IVW significance was less than 0.05 and beta values of the main method were consistent. Nine of them were in the B cell group, two in the cDC group, seven Treg and one maturation stage of T cells were classified into the T cell group, and three TBNK and one myeloid cell were classified into the other groups (Figure 2). IVW method P values of all 731 immune cells are shown in https://cdn.amegroups.cn/static/public/tau-2024-657-1.xlsx. The statistical results of the 26 positive results are detailed in https://cdn.amegroups.cn/static/public/tau-2024-657-2.xlsx.

Figure 2 Forest plot showing the potential causal relationships between immune cell traits and male infertility, identified using MR analyses where (PIVW<0.05) and the OR directions were consistent across five major MR methods. AC, absolute cell counts; cDC, conventional dendritic cells; CI, confidence interval; DC, dendritic cell; HLA DR, Human leukocyte antigen-type DR; IgD, immunoglobulin D; IVW, inverse-variance weighted; MR, Mendelian randomization; nsnp, number of SNPs; OR, odds ratio; SNPs, single nucleotide polymorphisms; TBNK, T cells, B cells, natural killer cells; T/B, T cell to B cell ratio; Treg, regulatory T cell.

In the B cell group, analysis of OR values using IVW method found that naive-mature B cell %lymphocyte (OR =0.777, 95% CI: 0.662–0.911, P<0.01), naive-mature B cell AC (OR =0.881, 95% CI: 0.813–0.954, P<0.01), CD25 on immunoglobulin D (IgD)⁻ CD38⁻ (OR =0.924, 95% CI: 0.856–0.997, P=0.04) play a protective role in male infertility. On the other hand, the remaining six immunophenotypes, including CD19 on IgD⁺ CD38⁻ naive, CD19 on naive-mature B cell, CD19 on IgD⁺ CD38dim, CD19 on IgD⁺ CD24⁻, CD20 on unswitched memory, IgD-CD38 dim %lymphocyte, are identified as risk factors.

In the T cell group, all immunophenotypes including seven Tregs (CD28 on CD39⁺ secreting Treg, CD39⁺ CD8br AC, CD28⁻CD8dim %T cell, CD39⁺ CD8br%T cell, CD28⁻CD8br %T cell, CD25⁺ CD8br%CD8br, CD45RA⁺ CD28⁻ CD8br %CD8br) and one maturation stage of T cell (naive CD4⁺ %T cell) were found to be risk factors for male infertility using IVW.

The OR value of IVW method in cDC cell group showed that the increase of CD62L on CD62L⁺ plasmacytoid DC (pDC) (OR =0.872, 95% CI: 0.763–0.998, P=0.047) and FSC-A on pDC (OR =0.873, 95% CI: 0.819–0.930, P<0.01) had protective effect on male infertility.

In other groups, except double-positive (DP) (CD4⁺CD8⁺) %T cell (OR =0.724, 95% CI: 0.542–0.968, P=0.03), Human leukocyte antigen-type DR (HLA DR) on CD33br HLA DR⁺ CD14⁻, T/B, TCRgd AC cell were risk factors of male infertility. Furthermore, the outcomes from the other five methodologies and sensitivity analyses underscore the resilience of the identified potential causal relationships. Notably, the intercept of MR-Egger and the global test of MR-PRESSO dismiss the likelihood of horizontal pleiotropy. Scatter plots and funnel plots further affirm the stability of the results. In addition, male infertility had no causal effect on all 23 immune phenotypes (P<0.05) by reverse MR, with IVW significance less than 0.05 effect value direction consistency as the screening criterion (Figure 3). See https://cdn.amegroups.cn/static/public/tau-2024-657-3.xlsx for detailed statistical results of reverse MR.

Figure 3 The circular heatmap of the results from five Mendelian randomization experiments examining male infertility and 23 male infertility-associated immune cell traits (four groups: B cells, T cells, cDC, other). AC, activated cell; cDC, conventional dendritic cells; IVW, inverse variance weighted; MR, Mendelian randomization; OR, odds ratio.

Discussion

Under long-term scientific observation, testis is considered to be an organ with immune privilege, and human seminal plasma shows a wide range of immunosuppressive activity (21). Combined with previous literature reports and our research results, immune cells and inflammatory mediators secreted by immune cells may affect testicular function and sperm quality, among which B cells, T cells, cDC cells and other immune cells play an important role in different stages (3). These findings provide new insights into how the immune system regulates testicular function and sperm production.

Our study found that among the various B cell subtypes, naive-mature B cell %lymphocyte, naive-mature B cell AC, and CD25 on IgD⁻ CD38⁻ B cell were protective factors in male reproductive health. In contrast, other B cell subtypes were risk factors for male infertility. Naive-mature B cells, as a common phenotype of the first two types of cells, describe B cells that mature but have not encountered antigen and are expected to differentiate into antibody-producing effector cells and memory B cells after encountering antigen, thus exerting protective effects (22); this is consistent with our results. Furthermore, references in the literature that CD25 on IgD⁻ CD38⁻ reduces the risk of autoimmune thyroiditis (23), a disease known to be a risk factor for male infertility, further support the possibility that CD25 on IgD⁻CD38⁻ plays a protective role in male infertility (24). Among the seven immunophenotypes identified as risk factors, CD19, CD38, CD20 and IgD were the most common. Ma et al. (25) found that CD20⁺ B cells, CD38⁺ (activated B cells) and CD138⁺ (plasma cells) present in interstitial compartments by immunohistochemical staining analysis of testes from coronavirus disease 2019 (COVID-19) patients and proposed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may trigger a B cell-mediated secondary autoimmune response, which may be a key pathway for the primary pathological mechanism of viral orchitis and subsequent testicular damage and ultimately affect sperm production and male reproductive ability. These findings are not only partially consistent with our findings, but also provide further scientific evidence for the association between B cells and male infertility.

In our study, we identified eight T cell subtypes associated with male infertility, seven of which were Treg immunophenotypes and one maturation stage of T cell. Most of these Tregs were unreported subsets of CD8⁺ Tregs that were not well understood until now. Naive CD4⁺ T cells may differentiate into a subset of Tregs expressing transcription regulator Foxp3 to function (26). To date, CD8⁺ T cells widely recognized in research include naive CD8⁺ CD28⁻, inducible CD8⁺ CD28⁻, CD8⁺ CD25⁺, and CD8⁺ CD122⁺ T cells (27). However, only CD8⁺ CD25⁺ T cells were consistently over-expressed by Jacobo et al. (28) in the chronic phase of the autoimmune orchitis (AO) model, suggesting its potential function in regulating chronic inflammation. Although numerous studies have been devoted to investigating the function and differentiation of Tregs, there is relatively limited information on the role of Tregs in testis and their association with male fertility. Tregs, as key immune regulatory cells that control inflammatory processes, are known to have immune tolerance, tendency to chronic inflammatory responses, and immunosuppressive effects (29). A study has shown that destruction of CD8⁺ Treg-dependent recognition restricted autoantigens can lead to immune response disorders, tissue damage, autoimmune diseases (30). Based on our findings, we hypothesize that CD8⁺ Tregs may play a crucial role in the pathological mechanism leading to infertility by naturally inhibiting organ-specific autoimmune responses and exacerbating the chronicity of inflammatory reactions, resulting in sustained damage to testicular tissues. However, further foundational research is needed to confirm these assumptions.

Dendritic cells (DC) are the main antigen presenting cells, which can be divided into three categories: conventional DC 1 (cDC 1), conventional DC 2 (cDC 2) and pDC (31). In our study, we found that two pDC-associated phenotypes (CD62L on CD62L⁺ pDC, FSC-A on pDC) were protective factors for male infertility. Eduardo et al. revealed that the number of pDC increased significantly after 10 days when rhesus monkeys were infected with Brazilian Strain of Zika Virus. pDC secrets high amounts of type I interferon (IFN) after sensing virus entry, which may play a protective role by inhibiting virus replication (32). In addition, Duan et al. (33) also showed that pDC exists in chronic epididymitis, confirming that pDC may also play a protective role in inhibiting chronic inflammatory response, which is consistent with our findings. Our study further reinforces the importance of pDCs in maintaining male reproductive health and suggests that they may play a key role in suppressing reproductive infections and inflammation.

TBNK cells, as a potential lymphocyte subset, of which its biological characteristics and functions have not been fully clarified, may have the characteristics of T cells, B cells and natural killer (NK) cells (18). In our study, with the exception of DP (CD4⁺ CD8⁺) %T cells, all of the cell subtypes that we identified were identified as risk factors for male infertility. DP (CD4⁺ CD8⁺) cells play a key role in T cell development and are essential for mature T cells to correctly recognize antigens and maintain self-tolerance (34). This finding suggests that an increase in the proportion of DP cells may have a substantial role in safeguarding testicular immune privilege. These DP cells have an irreplaceable role in acquiring antigen specificity and in preventing self-attack. At the same time, other immune cell subtypes have not been documented for their role in male fertility. Therefore, our study provides a preliminary scientific basis for further exploring the influence of immune cell subtypes on male infertility, and points out the direction for basic research in related fields in the future.

Our results suggest a potential causal role for immune cells in male infertility, specifying for the first time that increased numbers of 17 immune cells and decreased numbers of six immune cells are associated with an increased risk of male infertility. These findings suggest a new direction for basic research on immune-induced male infertility and may provide targets for new drug development. However, there are certain limitations in this study. Firstly, although we used a GWAS pooled dataset with a large sample size for immune traits and male infertility, the male infertility data is currently only available from the FinnGen database, with a lack of data from other ethnic groups, which may introduce ethnic bias and limit the generalizability of our findings to other populations. Secondly, the lack of individual-level information prevented us from conducting more detailed stratified analyses, such as subgroup analyses based on gender or the different causes of male infertility. Thirdly, while we employed a more lenient threshold for result evaluation, which may increase the risk of false positives, it also allowed us to more comprehensively explore the potential associations between immune traits and male infertility. Therefore, future studies may require larger sample sizes or meta-analyses to validate these findings and identify additional relevant factors. Fourthly, despite using MR-PRESSO and MR-Egger regression tests to account for pleiotropic effects, the inclusion of pleiotropic SNPs in our study still carries the risk of introducing bias. We also emphasize that, while MR effectively reduces confounding biases in traditional observational studies and controls for reverse causality through genomic data, these causal inferences remain statistical associations rather than definitive proof of causality. In conclusion, although our study has enhanced the understanding of the immune mechanisms involved in male infertility, further experimental and population-based studies are needed to validate these preliminary findings and confirm their generalizability.

Conclusions

Our research proves that there is a potential causal relationship between some immune cell subtypes and male infertility at the genetic level, which provides a new direction for basic research and new drug development in this field, but the specific influence mechanism of these immune cell subtypes in male infertility needs to be verified by experiments.

Acknowledgments

We express our gratitude to the investigators who provided the valuable genetic summary statistics that made this study possible.

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2024-657/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2024-657/prf

Funding: This work was supported by the research grants from the Shaanxi Province Innovation Capability Supporting Program (No. 2024CX-GXPT-39), the Clinical Research Project of AFMU (No. 2023LC2301), the National Natural Science Foundation of China (No. 82070973), the Military Family Planning Special Project (No. 24JSZ06), and Key Specialized Project of Air Force Aviation Medicine (No. HKYXZDZKKT01).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2024-657/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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