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. 2024 Mar 8;103(10):e37346. doi: 10.1097/MD.0000000000037346

The association between COVID-19 and infertility: Mendelian randomization analysis

Mei Zhang a,b, Tingyuan Wen a,b, Dejing Wang a,b,*
PMCID: PMC10919494  PMID: 38457599

Abstract

Since December 2019, COVID-19 has triggered a global pandemic. The association of COVID-19 with the long-term reproductive situation of women and males is not clear. Thus, our aim was to assess the causal association between COVID-19 and infertility using Mendelian randomization (MR) analysis based on the OpenGWAS database. Two-sample MR analysis was conducted using one genome-wide association study (GWAS) on COVID-19 and infertility in individuals of European ancestry. The summary data of genetic variation come from the GWAS in European populations. We applied several MR methods, including MR Egger, weighted median, inverse variance weighted, simple mode, weighted mode, to test causal relationships. After observing the statistical analysis results of MR, we conducted sensitivity analysis to test robustness. After gene prediction, it was found that there was no clear causal relationship between COVID-19 and male infertility in MR analysis [OR 0.4702 (95% CI, 0.1569–1.4093), P = .178]. Moreover, COVID-19 was not associated with female infertility [OR 0.9981 (95% CI, 0.763–1.544), P = .646]. Sensitivity analysis showed that the MR results were robust [level pleiotropy, male: (MR–Egger, intercept = 0.1967434; se = 0.1186876; P = .2392406); female: (MR–Egger, intercept = −0.05902506; se = 0.05362049; P = .3211367)]. To further validate the impact of COVID-19 on infertility, we added a covariate (sex hormone binding global levels, abortion) to the MR analysis, which is a multivariate MR analysis. According to univariate and multivariate MR analyses, the evidence does not support that COVID-19 is a causal risk factor for infertility in European population. This information can provide information for doctors in reproductive centers when managing infertility patients.

Keywords: COVID-19, female, infertility, male, Mendelian randomization

1. Introduction

On March 11, 2020, COVID-19 was declared a pandemic by the World Health Organization.[1] As of August 2021, millions of deaths have been confirmed worldwide, which has attracted attention in the field of epidemiology.[2–4] Despite the active efforts of governments around the world, the emergence of several virus variants and deficiencies in obtaining effective vaccines have prevented the epidemic from being urgently controlled worldwide.[5] It is known that COVID-19 is related to neurodegenerative diseases,[6] hypothyroidism,[7] cardiovascular diseases,[8,9] obesity and diabetes.[10] However, whether COVID-19 will affect human fertility is still controversial.

In 2021, Andrology published a viewpoint article proposing the possibility that targets on sperm support the binding and integration of COVID-19, such as all receptors (AT1R, AT2R, MAS) and ligand processing enzymes (ACE1 and ACE2) required for the angiotensin signaling cascade reaction.[11] Clinical studies have confirmed a decrease in circulating testosterone levels after infection.[12] Temiz et al detected deterioration in sperm morphology after infection in COVID rehabilitation patients, but semen quality standards are still within the WHO definition of normality.[13] Li et al found that 39% of COVID rehabilitation patients suffer from oligospermia, and their semen spectrum is typically characterized by elevated levels of white blood cell infiltration.[14] Some studies have not only confirmed the significant impact of infection on various aspects of semen quality but also reported a gradual return to normal after infection, which may largely depend on the severity of the disease.[15,16]

Infertility refers to a disease in which clinical pregnancy cannot be determined after 12 months of unprotected sexual activity, or due to impaired reproductive ability of a person as an individual or their partner.[17] Phelan et al reported a shortened or disrupted menstrual cycle and an increase in menstrual volume after COVID-19.[18] They found that menstrual changes were more common in systemic complications caused by COVID-19 in COVID-19 patients.[19] Inappropriately high concentrations of follicle-stimulating hormone and luteinizing hormone were observed in COVID-19 patients.[20] Similar to men, there are also ACE receptors on female follicles that are affected by COVID-19.[21] The data from clinical traits have sample limitations, and most of them are small-scale studies. Moreover, given the differences in research results among countries with different economic levels and populations, it is crucial to establish strict evidence regarding the link between COVID-19 and infertility. Therefore, we hope to determine the causal relationship between COVID-19 and infertility.

Randomized controlled clinical trials are the gold standard for determining causal relationships. However, due to medical ethics and experimental considerations, RCTs are difficult to conduct. MR is an emerging epidemiological research method that uses genetic variations (single nucleotide polymorphisms [SNPs]) as instrumental variables (IVs) for causal inference.[22] Due to the Mendelian genetic rules followed by gametes, MR can overcome the shortcomings of traditional observational epidemiological studies: unknown confounding factors and reverse causal relationships. In this study, we performed a 2-sample MR analysis to further explore the causal relationship between COVID-19 and infertility. This was a study only used published or publicly available data. The Zunyi Medical University Research Ethics Committee confirmed that no ethical approval was needed.

2. Materials & methods

2.1. Data sources for MR analysis

Data sources for a 2-sample Mendelian randomization study was conducted using the genome-wide association study (GWAS) database. Summary data on COVID-19 include 1683768 cases (38984 infected cases and 1644784 uninfected individuals as the control group). The GWASs data for COVID-19 comes from the COVID-19 Host Genetics Initiative Round 5.[23] This data is adjusted based on patient age, gender, etc. In all cases, a definitive clinical diagnosis was passed. The genetic variants associated with infertility were derived from 2 previous GWAS meta-analyses including 72779 (male infertility) and 75450 (female infertility) individuals of FinnGen participants (https://www.finngen.fi/en). The dataset includes 680 and 72799 male infertility controls, 6481 and 68969 female infertility controls, respectively, but does not include individuals with gender uncertainty, high genotype defense. The diagnosis of infertility is based on WHO standards.[24] Since all 3 data sources are from Europe, we speculate that there may be more overlap between the 2. Table 1 gives detailed information about the exposure and outcome.

Table 1.

Summary of exposure and outcome.

Exposure/Outcome GWASID Yr Sample size (samples/controls) Number of SNPs Source
COVID-19 ebi-a-GCST011073 2020 1683768 (38984/1644784) 8660,177 PubMed ID 7220587
Male infertility finn-b-N14_MALEINFERT 2021 73479 (680/72799) 16,377,329 https://www.fnngen.f/en
Female infertility finn-b-N14_FEMALEINFERT 2021 75450 (6481/68969) 16,377,038 https://www.fnngen.f/en
Miscarrage finn-b-O15_COMPLIC_ABORT_ECTOP_MOLAR 2021 (627/89340) 16,378,822 https://www.fnngen.f/en
Sex hormone-binding globulin levels ebi-a-GCST90025958 2021 397043 4217,370 PubMed ID 34226706
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GWAS = genome-wide association study, SNP = single nucleotide polymorphism.

2.2. IV Selection

Throughout the process, 3 core assumptions should be fully considered (Fig. 1). We carefully selected the IV related to COVID-19 from multiple perspectives using strict screening criteria. Firstly, P≤1 × 10-5 (relaxation significance threshold) is used to identify SNPs that may be associated with COVID-19. Secondly, we removed linkage imbalanced variations within the range of LD, R2≥0.001 and 10000kb and performed SNPs clustering.[25,26] Thirdly, we excluded confounding factors such as abortion, sex hormone binding global levels. Fourthly, if the f-statistic exceeds 10, using the formula F = R2 (N-K-1)/[K (1-R2)], the selected IV is selected, indicating strong predictive potential.[27] Finally, we extract SNPs related to COVID-19 from the result dataset and discard SNPs related to infertility (P≤1 × 10-5). In addition, the SNP information in the resulting data is shown in Tables 2 and 3.

Figure 1.

Figure 1.

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A brief illustration of 2-sample MR for COVID-19 and infertility. MR = Mendelian randomization.

Table 2.

Data for 5 SNPs as instrumental variables in COVID-19 and male infertility.

SNPs for IV COVID-19 (exposure) Male Infertility
Chr Position SNP ALT REF EAF Beta SE P F EAF Beta SE P
9 136142355 rs643434 A G 0.0997 0.092084 0.016154 1.20E-08 28.77597 0.0885 -0.025 0.0972 .7972
12 113406945 rs757405 A T 0.1181 0.15634 0.015084 3.61E-25 75.57452 0.0575 -0.3232 0.1228 .0084
1 155105882 rs4971066 G T 0.1777 −0.07676 0.0134 1.02E-08 36.66381 0.2099 -0.0292 0.0682 .6692
3 45838013 rs2271616 T G 0.371 0.1013 0.010114 1.29E-23 13.85436 0.456 -0.0209 0.0551 .7042
3 45847241 rs17078348 G A 0.7092 0.068926 0.010783 1.64E-10 13.23086 0.7913 0.0418 0.068 .5382
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ALT = alternative allele, Beta = effect size, Chr = chromosome, EAF = effect allele frequency, IV = instrumental variable, REF = reference allele, SE = standard error.

Table 3.

Data for 4 SNPs as instrumental variables in COVID-19 and female infertility.

SNPs for IV COVID-19 (exposure) Female Infertility
Chr Position SNP ALT REF EAF beta SE P F EAF beta SE P
21 34611571 rs12482060 G C 0.0997 0.09208 0.0161 1.20E-08 87.58190 0.0873 -0.0459 0.0334 .1692
1 155105882 rs4971066 G T 0.1181 0.15634 0.0150 3.61E-25 45.83050 0.058 0.0133 0.0409 .74469
3 45838013 rs2271616 T G 0.1777 -0.0767 0.0134 1.02E-08 17.31823 0.2121 -0.0544 0.023 .01814
3 45847241 rs17078348 G A 0.371 0.1013 0.0101 1.29E-23 16.53884 0.4542 0.0464 0.0189 .01393
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ALT = alternative allele, Beta = effect size, Chr = chromosome, EAF = effect allele frequency, IV = instrumental variable, REF = reference allele, SNP = single nucleotide polymorphism.

2.3. Data analysis

In the MR study, statistical analysis was conducted using both R 4.1.3 software and the R software package, “Two Sample MR.”[28] We coordinated the exposure data and result data into a dataset for the analysis of univariate MR results (Table 4). Several MR methods for estimating causal effects were used, such as MR Egger, MR-PRESSO, weighted median, inverse variance weighted, simple mode, weighted mode. MR Egger analysis showed that MR Egger analysis showed that MR Egger analysis showed that MR did not indicate the presence of horizontal pleiotropy issues (Supplementary Table 1, http://links.lww.com/MD/L822). MR-PRESSO showed that the original Global Test P value was <0.05, and after removing outliers, the MR result (disturbance test P value < .05) showed a significant difference from the original result. The inverse variance weighted (IVW) is used as the gold standard for MR results, with other methods as auxiliary methods.[29]

Table 4.

Causal effect between COVID-19 and infertility by different MR analysis methods.

Exposure Outcome method nSNP OR 95%CI P value
COVID-19 Male infertility MR Egger 5 0.02295907 (0.0013, 0.4040) .08181688
Weighted median 5 0.81183155 (0.3449, 1.9111) .63318482
Inverse variance weighted 5 0.67774417 (0.2839, 1.6179) .38091895
Simple mode 5 1.11422449 (0.3006, 4.1299) .87929653
Weighted mode 5 0.9660127 (0.3492, 2.6724) .63318482
Female infertility MR Egger 4 0.05162215 (0.0033, 0.8122) .1695710
Weighted median 4 0.63197989 (0.1947, 2.0510) .4448511
Inverse variance weighted 4 0.4701966 (0.1569, 1.4093) .1778628
Simple mode 4 0.7760239 (0.1402, 4.2965) .7904206
Weighted mode 4 0.79211034 (0.1116, 5.6243) .8307282
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2.4. Sensitivity analysis and visualization

After obtaining the statistical analysis results of MR, we conducted sensitivity analysis to test robustness, including heterogeneity test, leave-one-out test and pleiotropy test.[30] We adopted various visualization methods (leave-one-out plots, funnel plots, and forest plots) to present the results in different dimensions. To further validate the impact of COVID-19 on infertility, we added a covariate (abortion, sex hormone binding global levels) to the MR analysis, which is a multivariate MR analysis (Table 5).

Table 5.

Associations of COVID-19, sex hormone-binding globulin levels, abortion with male infertility and female infertility in Univariable Mendelian randomization analysis

Outcome Exposure nSNP OR OR (95%CI) P value
Male infertility COVID-19 3 0.9717989 (0.5888793–1.603712) .9108793
Sex hormone-binding globulin levels 348 1.1062261 (0.8896622–1.375507) .3637652
Female infertility COVID-19 3 1.0439332 (0.8818097–1.235864) .617559552
Sex hormone-binding globulin levels 348 0.8545664 (0.7938996–0.919869) 2.87487E-05
abortion 0 1.0307522 (0.9969797–1.065669) .074743859
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3. Results

IVW was selected as the gold standard to control this heterogeneity among SNPs when MR analyzing, with other means as auxiliary means. The results of the univariate MR analysis on the causal relationship between COVID-19 and infertility are shown in Table 4. No association between COVID-19 and male infertility was shown according to the IVW method [OR 0.4702 (95% CI, 0.1569–1.4093), P = .178]. No significant causal relationship between COVID-19 and female infertility during genetic prevention [OR 1.085 (95% CI, 0.764–1.544), P = .646]. Multivariate MR analysis showed that COVID-19, sex hormone binding global levels, and abortion were not directly associated with infertility (Table 5). Other methods results are similar to the IVW, where the OR value is approaching 1 and P value is not notable. Therefore, COVID-19 does not seem to have led to an increase in the infertility rate, according to the analysis results of MR with nonhierarchical data in the databases.

On the basis of the analysis test, no substantial bias were found in the estimated causal effects when removing single SNP and repeating the test (Fig. 2). According to these results, our findings are robust, and a single SNP departure did not affect the total causal calculation effect. The contribution of each single SNP on infertility risk was calculated using the Wald ratio method and visualizing in the forest map (Fig. 3). Based on the leave-one-out analysis experiment, although rs2271616 in male infertility and rs4971066 and rs643434 in female infertility seem to have a direct influence on the results, no increase in COVID-19 infection was found to increase the risk of infertility. Our analysis provides suggestive evidence that COVID-19 is not a risk factor for infertility by Mendelian randomization study method analysis. Due to the lack of a clear causal relationship, 2 scatter plots were placed in Supplementary Figure 1, http://links.lww.com/MD/L827 and Supplementary Figure 2, http://links.lww.com/MD/L826

Figure 2.

Figure 2.

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Leave-one-out sensitivity analysis plot of (A) COVID-19 on the risk of male infertility and (B) female infertility.

Figure 3.

Figure 3.

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Forest plots showing the causal effect of each single SNP on the risk of (A) male infertility and (B) female infertility. SNP = single nucleotide polymorphism.

4. Discussion

To the best of our knowledge, this 2-sample MR study is the first to use an open gene database to examine the causal relationship between COVID-19 and infertility. These results are usually reliable in sensitivity analysis. We found no suggestive association between COVID-19 predicted by genes and infertility.

COVID-19 is a viral infectious disease caused by COVID-19, with variability in infection patterns among individuals.[31] Many studies have focused on the impact of COVID-19 infection on the lungs, but little is known about whether this virus affects male fertility.[32] However, there is rapidly accumulating evidence that COVID-19 infects male urogenital tissue.[33,34] A meta-analysis proved that mild/asymptomatic COVID-19 has a certain impact on male semen in the short term, especially within 70 days after infection.[35] Its mechanism may be associated with the presence and activation of ACE2 receptors, as well as the cleavage of viral spike proteins through TMPRSS2.[12,36] Therefore, cells expressing high levels of ACE2 seem to be highly susceptible to virus invasion. The main receptor ACE2 of the virus is also expressed in adult supporting cells, stromal cells and spermatogonia, and the virus was consistently detected in sperm samples.[37] In conclusion, viral proteins seem to interact with human protein targets and play a role at any time throughout the male reproductive cycle.

It was reported that after COVID-19, the menstrual cycle was shortened or interrupted, and menstrual volume increased.[18] Others found that COVID-19 patients more commonly experienced systemic complications caused by COVID-19.[19] COVID-19 patients have low concentrations of follicle stimulating hormone and luteinizing hormone.[20] Similarly, female follicles also have ACE receptors, which are influenced by COVID-19.[21,38] The data from clinical features have sample limitations and are mostly small-scale studies. Furthermore, it is crucial to establish rigorous evidence on the link between COVID-19 and infertility. Therefore, we hope to determine the causal relationship between COVID-19 and infertility.

However, our MR analysis showed that there was no association between COVID-19 and infertility. Perhaps because of the popularity of the COVID-19 vaccine and the rapid development of drugs, the immune level in the human body has increased, which reduces the risk of COVID-19 vaccine infection. However, the existing GWAS database does not exclude the data on taking drugs and taking vaccines. Second, it may also be due to the mutation of the virus itself that our current MR analysis is not comprehensive enough. Furthermore, there are also studies indicating that sperm quality may not be related to the severity of COVID-19 infection[39,40] and that COVID-19 is not associated with female infertility.[41]

Our research has the following advantages: using MR to evaluate the etiology of diseases effectively avoids unknown confounding factors and reverse causal relationships; the data on risk factors come from the largest and latest GWAS; and the data are limited to major European lineage cohorts to reduce confusion caused by population stratification. More importantly, we studied the correlation between COVID-19 and infertility for the first time so far. But then, our research also has some limitations. We did not explore the relationship between the severity of COVID-19 infection and infertility. As a result, the severity of COVID-19 infection and larger samples study are needful for affirming and upgrading the research.

5. Conclusions

In conclusion, our analysis provides suggestive evidence that COVID-19 is not a risk factor for infertility by Mendelian randomization study method analysis. Both univariate and multivariate MR analyses showed no direct association between COVID-19 and infertility. Due to current clinical sample and hierarchical analysis of COVID-19 severity limitations, further research is needed in the future to contributed to more accurate conclusions.

Acknowledgments

We thank all the researchers and participants who participated in the GWAS data used in this study.

Author contributions

Supervision: Tingyuan Wen, Dejing Wang.

Validation and Visualization: Mei Zhang.

Writing – original draft: Mei Zhang.

Writing – review & editing: Mei Zhang, Dejing Wang.

Supplementary Material

medi-103-e37346-s001.docx (13.2KB, docx)

graphic file with name medi-103-e37346-s002.jpg

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graphic file with name medi-103-e37346-s003.jpg

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Abbreviations:

ALT
alternative allele
GWAS
genome-wide association study
IV
instrumental variable
IVW
inverse variance weighted
MR
Mendelian randomization
REF
reference allele
SE
standard error
SNP
single nucleotide polymorphism

The datasets generated during and/or analyzed during the current study are publicly available.

This was an study only used published or publicly available data. The Zunyi Medical University Research Ethics Committee confirmed that no ethical approval was needed.

This work was supported by grants from the Joint Fund of Guizhou Provincial Science and Technology Department (20177104); Project of Guizhou Administration of Traditional Chinese Medicine (2018072); Science and Technology Achievement Application and Industrialization Project of Guizhou Provincial Science and Technology Department (20194447); Science and Technology Fund of Guizhou Health Commission (2022146); Doctor Fund of Zunyi Medical University (201704); and Zunyi Medical University Innovation Project (202210661276).

The authors have no conflicts of interest to disclose.

Supplemental Digital Content is available for this article.

How to cite this article: Zhang M, Wen T, Wang D. The association between COVID-19 and infertility: Mendelian randomization analysis. Medicine 2024;103:10(e37346).

Contributor Information

Mei Zhang, Email: 1552281143@qq.com.

Tingyuan Wen, Email: 781138835@qq.com.

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