Causal relationship between serum uric acid levels and male infertility: a bidirectional two-sample Mendelian randomization study

Introduction

Uric acid is the final product of purine metabolism, and hyperuricemia arises when uric acid synthesis exceeds its excretion. At physiological pH, uric acid predominantly exists as the monovalent urate anion, which is neutralized by sodium cations (1). In recent decades, the prevalence of hyperuricemia has steadily increased due to lifestyle changes and demographic shifts, rendering it a significant public health concern (2).

Infertility is commonly defined as the inability of a couple to conceive after 12 months of regular, unprotected intercourse (3). According to the World Health Organization, approximately 15% of couples worldwide experience fertility issues, with male factors contributing to 50% of these cases (4). The etiology of male infertility is diverse, encompassing testicular dysfunction, endocrine disorders, lifestyle factors, congenital anatomical abnormalities, exposure to gonadotoxic agents, and aging (5). Sperm quality is the primary determinant of male fertility, and among infertility cases, approximately half of the affected men exhibit some form of sperm quality defect (6).

Current research presents inconsistent conclusions regarding the causal relationship between serum uric acid levels and male infertility. Srivastava et al., through an analysis of seminal plasma biochemical indicators, found that serum uric acid levels were significantly higher in the fertile group than in the infertile group, suggesting potential benefits of uric acid for sperm quality (7). Conversely, a multicenter clinical study in China found a negative correlation between serum uric acid levels and both semen volume and total sperm count, after investigating associations with semen volume, total sperm count, sperm concentration, and motility (8). These inconsistencies may be influenced by variations in sample size, study populations, and potential confounding variables. Thus, further rigorous research is necessary to clarify this relationship.

Mendelian randomization (MR) is an innovative method that employs single nucleotide polymorphisms (SNPs) as genetic variants to mimic the randomization process in randomized controlled trials. This approach can infer causal relationships between exposure factors and disease outcomes. Genotype randomization occurs at conception, rendering it less susceptible to postnatal confounding factors and less likely to be influenced by confounders in observational analyses (9). The unique advantages of MR analysis include natural randomization, and avoidance of reverse causation and confounding factors (10).

To our knowledge, no previous studies have employed MR analysis to investigate the causal relationship between serum uric acid levels and male infertility. Therefore, we designed the bidirectional two-sample MR study to explore this potential causal link. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-365/rc).

Methods

Study design

This study utilized a bidirectional, two-sample MR approach, employing summary statistics from genome-wide association studies (GWAS) to examine the association between serum uric acid levels and male infertility (Figure 1). In MR analysis, genetic variants must meet the instrumental variable (IV) assumptions: (I) SNPs must be associated with the risk factor (relevance assumption); (II) SNPs must not be influenced by confounders of the risk factor-outcome association (independence assumption); (III) SNPs must affect the outcome only through the risk factor (exclusion restriction assumption).

Figure 1 Study design overview. IVs, instrumental variables; MR, Mendelian randomization; SNP, single nucleotide polymorphism.

In the forward MR analysis, serum uric acid levels were considered the exposure variable, while male infertility was the outcome. Conversely, in the reverse MR analysis, these roles were reversed.

Data source

The serum uric acid levels datasets were sourced from the UK Biobank. The summary statistics used in this study involved 343,836 individuals of European ancestry and included 19,041,286 SNPs. This research was part of a meta-analysis of GWAS, investigating the genetic associations across 220 human phenotypes and identifying nearly 5,000 novel loci. Serum uric acid levels were measured from blood samples collected between 2006 and 2010 at 22 study centers across the UK from participants aged 40 to 69 years. The analysis was conducted using a Beckman Coulter AU5800 instrument, with a detection range of 89–1,785 µmol/L.

The male infertility dataset is sourced from the FinnGen research project, which includes 73,479 samples of European ancestry and a total of 16,377,329 SNPs. This project integrates genotype data from Finnish biobanks with digital health records from Finnish health registries. Male infertility diagnoses were identified based on physician-diagnosed information using International Classification of Diseases (ICD)-10 code N46, and ICD-9 and ICD-8 codes 606 (encompassing azoospermia, oligospermia, infertility due to extra testicular causes, and unspecified male infertility) derived from digital health records. Men with cancers of the reproductive organs were excluded from the control group. The male infertility data from the FinnGen project does not overlap with the data from UK Biobank.

Details of the dataset are shown in Table 1. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013), and all data were sourced from publicly available GWAS databases.

Selection of IVs

In the forward MR analysis, SNPs associated with serum uric acid levels at the genome-wide significance level (P<5×10⁻⁸) were chosen as IVs. Conversely, in the reverse MR analysis, SNPs associated with male infertility were chosen with a significance threshold of P<5×10⁻⁶. To ensure SNPs independence, we pruned them using r²<0.001 and a distance of 10,000 kb. To avoid violating the independence assumption in MR, we used the R package “FastTraitR” to assess whether the retained SNPs were associated with confounding factors (11). The F-statistic was calculated as F = R² × (N−2)/(1−R²), where an F-statistic greater than 10 indicates no weak instrument bias (12). Before conducting the MR analysis, outlier IVs were removed using MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) to address potential pleiotropy (12).

Statistical analysis

We performed univariable MR analysis to estimate the causal relationship between serum uric acid levels and the risk of male infertility. Inverse variance weighted (IVW) was used as the determinant method of the causal estimate of serum uric acid levels on the risk of male infertility (13). In addition, the other four MR methods, including MR-Egger, weighted median simple mode, and weighted mode, are powerful supplements to the IVW method, providing more reliable estimates under broader conditions (14-16).

Additionally, we conducted multivariable MR (MVMR) analysis, an extension of univariable MR, which allows for the joint estimation of the causal effects of multiple risk factors (17).

To assess the causal relationship between male infertility and serum uric acid levels, we carried out bidirectional MR. Specifically, we first conducted MR analysis from serum uric acid levels to male infertility, and then reversed the direction.

Sensitivity analyses

To assess heterogeneity, we calculated Cochran’s Q statistic using the MR-Egger and IVW methods. The P value greater than 0.05 suggested no significant heterogeneity. In the presence of heterogeneity, we employed the random effects IVW method to estimate causal relationships; otherwise, we used the fixed effects model (18).

We performed a leave-one-out analysis to evaluate the influence of individual SNPs on the causal effect of the exposure on the outcome (19). Pleiotropy was assessed using the MR-Egger regression intercept value and the MR-PRESSO global test, with P>0.05 indicating no potential pleiotropy (12).

All MR analyses were performed using the “TwoSampleMR” (version 0.5.7), “forestploter” (version 1.1.1) and “MRPRESSO” (version 1.0) packages in R (version 4.1.3). P<0.05 was deemed statistically significant.

Results

Genetic instruments

In the forward MR analysis, we identified 174 SNPs that were strongly associated with serum uric acid levels (Table S1). Subsequently, we identified 10 SNPs for reverse MR analysis to investigate the potential causal relationship between male infertility and serum uric acid levels (Table S2). Additionally, F-statistic values were greater than 10, indicating no weak instrument bias.

Univariable MR analyses

The forward MR analysis indicated no causal relationship between serum uric acid levels and male infertility. The results were consistent across five models, including the IVW model [odds ratio (OR) 0.924, 95% confidence interval (CI): 0.614–1.390, P=0.70]. Similarly, in the reverse MR analysis, the IVW results provided evidence of no significant causal relationship between male infertility and serum uric acid levels (OR 1.000, 95% CI: 0.997–1.003; P=0.96). Comprehensive details are presented in Figure 2.

Figure 2 Association of serum uric acid levels with the risk of male infertility. MR, Mendelian randomization; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; MR-PRESSO, Mendelian randomization pleiotropy residual sum and outlier.

The relevant figures of genetic association between serum uric acid levels and male infertility can be found in supporting information (Figures S1-S8).

MVMR analyses

We employed MVMR to estimate the effect of serum uric acid levels on male infertility. After adjusting for phenotypes such as body mass index (BMI), type 2 diabetes, alcohol consumption, and smoking, serum uric acid levels showed no significant causal association with male infertility (OR 0.839, 95% CI: 0.613–1.148, P=0.27). Reverse MVMR similarly indicated no significant causal relationship between male infertility and serum uric acid levels (OR 1.003, 95% CI: 0.994–1.012, P=0.49) (Table 2).

Sensitivity analysis

In the MR analysis, the Cochran’s Q test showed that the P values of those outcomes were over 0.05. Furthermore, no outlier IVs were detected in our MR-PRESSO results, and MR-Egger regression analysis showed no obvious evidence of directional pleiotropy (Figure 2). The leave-one-out analysis confirmed that the causal association evaluation was not influenced by the exclusion of any single SNP (Figures S3,S7).

Discussion

The causes of male infertility are diverse, including genetic mutations, lifestyle choices, medications, and diseases. Male infertility can impact self-esteem, mental health, and marital status (20), leading to significant psychological stress and emerging as a serious health issue. Identifying risk factors and vulnerable populations for male infertility is therefore essential.

In the forward MR analysis, our results did not support a causal relationship between genetically predicted serum uric acid levels and male infertility (IVW OR 0.924, 95% CI: 0.614–1.390, P=0.70). In the reverse MR analysis, our results indicated no reverse causation between serum uric acid levels and male infertility (IVW OR 1.000, 95% CI: 0.997–1.003, P=0.96). Further MVMR analysis found no causal relationship between serum uric acid levels and male infertility after adjusting for confounding factors such as BMI, type 2 diabetes, alcohol consumption, and smoking, nor evidence of reverse causation. These findings provide genetic evidence suggesting that serum uric acid levels are unlikely to be a risk factor for male infertility.

The gold standard for assessing male reproductive potential is sperm quality, which includes sperm concentration, motility, and morphology (21). To some extent, the relationship between uric acid and sperm quality may reflect its association with male infertility. Previous studies on this association have yielded inconclusive results. A multicenter clinical study, involving 654 males aged 20 to 45 years, found a significant negative correlation between serum uric acid levels and both semen volume and total sperm count, suggesting that higher uric acid levels might negatively affect semen quality (8).

However, a retrospective study involving 433 fertile men indicated no significant correlation between high uric acid levels and semen volume, sperm concentration, total sperm count, or normal sperm morphology rate (22). Furthermore, a prospective cohort study found that uric acid-lowering drugs (allopurinol, febuxostat) had no significant impact on sperm quality in men with gout (23).

Typically, semen contains non-protein nitrogen compounds, such as uric acid. Studies indicate that the concentration ratio of serum uric acid to seminal plasma uric acid is approximately one (24), suggesting that serum uric acid levels can partially reflect uric acid levels in semen. Interestingly, normal males have higher seminal plasma uric acid concentrations compared to infertile males (7). Another study similarly found that seminal plasma uric acid concentrations were significantly higher in the normal fertility group compared to the infertile group, suggesting potential benefits of uric acid for sperm quality and male fertility.

Mitochondria are essential to sperm function, particularly in supplying energy for sperm motility and managing reactive oxygen species (ROS). Mitochondrial dysfunction disrupts the balance between ROS and antioxidant defenses, leading to oxidative stress and impairing male fertility (25). Notably, studies indicate that uric acid elevates lipid peroxidation products (26,27), disrupts mitochondrial respiratory chain enzymes, damages mitochondria, induces sperm morphological defects, and decreases sperm motility (8,25). This suggests that mitochondrial dysfunction and oxidative stress could mediate the association between serum uric acid levels and sperm quality. Furthermore, excessive ROS in semen can cause sperm DNA fragmentation, leading to abnormal sperm morphology (28-30). A meta-analysis by Santi et al. showed that sperm DNA fragmentation levels are a predictor of male infertility, with a sensitivity of 79% and a specificity of 86% (31). Elevated urate concentrations (12 mg/dL for 24 hours) reduce calmodulin binding to endothelial nitric oxide synthase, decreasing nitric oxide production and thus reducing sperm motility (32,33). Uric acid may further affect sperm function by altering reproductive hormone levels and influencing epididymal secretion (8).

Notably, elevated uric acid levels in semen are abnormal and occur only under specific conditions, such as retrograde ejaculation, direct contact of sperm with uric acid in urine, or extremely high blood uric acid levels. As a crucial free radical scavenger, uric acid also exhibits antioxidant stress properties. Studies have shown that hydroxyl radicals (•OH) and superoxide anions (O2•−) can reduce sperm motility, vitality, and morphology (34), while uric acid can eliminate these radicals and prevent oxidative damage (35), thereby enhancing sperm function and fertilization capacity. Uric acid acts as a coenzyme for cyclooxygenase, preventing its oxidative inactivation, thereby participating in sperm function and fertilization processes (36).

The impact of uric acid on male infertility, particularly its relationship with sperm quality, remains inconsistent in current research and necessitates further exploration. The clinical significance of our MR study lies in its novel investigation of the relationship between serum uric acid levels and male infertility, providing complementary information for clinical practice in the absence of randomized controlled trials.

There are several strengths in this study. Firstly, the principal advantage is the MR design, which employs genetic variants as IVs to investigate the causal relationship between exposure and outcome, thereby reducing potential confounding and reverse causation biases. Secondly, our study benefits from a large sample size and utilizes multiple sensitivity analysis methods to mitigate potential biases. Thirdly, our comprehensive definition of male infertility—which includes azoospermia, oligospermia, extratesticular causes, and unspecified male infertility—ensures the generalizability of our results.

There are several limitations in this study. Firstly, since all participants were of European descent, the findings may not be generalizable to other ethnic groups. Secondly, the GWAS for uric acid lacked sex-stratified data, may introduce potential bias. Thirdly, the absence of detailed semen analysis parameters in the FinnGen study prevents an exploration of the relationship between uric acid levels and specific semen quality metrics, potentially introducing bias. Additional GWAS studies are necessary to validate our conclusions.

Conclusions

Our study provides genetic evidence indicating no significant causal relationship between serum uric acid levels and male infertility in the general population, suggesting that serum uric acid is not a potential risk factor for male infertility. This conclusion necessitates further validation through large-scale, multicenter randomized controlled trials.

Acknowledgments

We want to acknowledge the participants and investigators of UK Biobank, and FinnGen study.

Funding: This study was supported by the following grants: Luzhou Science and Technology Bureau (No. 15197) and the Affiliated Hospital of Southwest Medical University (Nos. 19078 and 16024).

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-365/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-365/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-365/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 (as revised in 2013).

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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