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. 2025 Nov 3;16:2005. doi: 10.1007/s12672-025-03738-y

Mendelian randomization analysis: causal link between hyperthyroidism/thyrotoxicosis and ovarian cancer

Hong Wang 1,✉, Caijuan Liang 2, Yanqiu Yu 3, Jing Lin 1, Yanan Dong 1, Xiaolan Hong 1, Huikai Huang 1
PMCID: PMC12583247  PMID: 41182475

Abstract

Background

The purpose of this study was to use a two-sample Mendelian randomization (MR) approach to explore the causal link between hyperthyroidism/thyrotoxicosis and ovarian cancer (OC).

Methods

By leveraging genome-wide association study (GWAS) data from publicly available databases, we identified single nucleotide polymorphisms (SNPs) representing genetic variants for both hyperthyroidism/thyrotoxicosis and OC. We employed the inverse variance weighted method (IVW) to assess the association between hyperthyroidism/thyrotoxicosis and the risk of OC. Further, we evaluated SNP heterogeneity using the Cochran’s Q test and detected the presence of outlier SNPs using the MR-PRESSO test. To examine the pleiotropy of SNPs, we employed the MR-Egger intercept test. Additionally, a “leave-one-out” sensitivity analysis was conducted to assess the influence of individual SNPs on the MR results. Finally, we performed a reverse MR analysis to explore the possibility of reverse causality.

Results

The results of the IVW analysis indicated a causal relationship between hyperthyroidism/thyrotoxicosis and OC, with hyperthyroidism/thyrotoxicosis increasing the incidence of OC (p < 0.05). According to the MR-Egger intercept and MR-PRESSO global tests, no horizontal pleiotropy was observed in this study (P > 0.05). Sensitivity analysis using “leave-one-out” method indicated the stability of these genetic variants.

Conclusions

Our systematic analyses provide evidence supporting a potential causal relationship between hyperthyroidism/thyrotoxicosis and the risk of OC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03738-y.

Keywords: Ovarian cancer, Hyperthyroidism/thyrotoxicosis, Thyroid dysfunction, Mendelian randomization, Causal relationship

Introduction

Ovarian cancer (OC) is the seventh most common malignant tumor in women, with the highest mortality rate among gynecological tumors [1]. The five-year survival rate ranges from 30% to 50%. It is important to note that the survival rate is significantly higher, close to 90%, for patients diagnosed in stage I, while it drops to only 20% for patients diagnosed in stage IV [2]. The latest statistics project approximately 12,810 °C deaths in the United States in 2022 [3]. Despite increased awareness of OC, curative and survival trends have not seen significant improvement. Therefore, it is crucial to identify the underlying factors more effectively in order to enhance disease prevention and screening efforts. Hyperthyroidism/thyrotoxicosis, is a prevalent endocrine disorder associated with increased morbidity and mortality [4]. Tetraiodothyronine (T4) and triiodothyronine (T3) serve as critical indicators for the clinical assessment of thyroid dysfunction, exhibiting high sensitivity and strong specificity. Elevated levels of T3 and T4 are indicative of hyperthyroidism, whereas decreased levels suggest hypothyroidism [5, 6]. Some research indicates that thyroid hormones (T3 and T4) play a crucial role in regulating the differentiation, growth, and metabolism of various tissues, including the ovaries and endometrium [7–9]. Studies have indicated that T4 weakened the growth of OC by inhibiting the tumor suppressor genes GDF-15 and IGFBP-6 [10]. T3 and T4 also played a role in the αvβ3-mediated epithelial to mesenchymal transition (EMT) of OC cells [11]. This suggests a potential link between thyroid hormones and OC metastasis. Additionally, elevated levels of estrogen, which were associated with elevated levels of thyroid hormone, might increase the risk of OC due to the activation of estrogen receptor (ER) mRNA expression. Consequently, elevated thyroid hormone levels could potentially elevate the risk of OC [12]. In conclusion, thyroid function resistance is closely related to OC.

A large amount of observational clinical data indicate that thyroid dysfunction is associated with the risk of death from OC. In a population-based case-control study, the researchers found that a history of hyperthyroidism was associated with an increased risk of OC (OR 1.8) [13]. Another study conducted by Kang et al. examined the association between thyroid dysfunction and OC, using data from the Nurses’ Health Study (NHS) [14]. Additionally, in this large international study of women with OC using the OC Association Consortium (OCAC) database, recent hyperthyroidism was associated with an increased risk of death (HR = 1.94; 95% CI = 1.19–3.18). However, a history of hypothyroidism (n = 624 cases) was less associated with mortality (HR = 1.16; 95% CI = 1.03–1.31) [15]. The above genetic epidemiological data only show patients in the United States, but do not involve information on the European population. The results of observational studies are often controversial due to potential confounders, reverse causation, and other issues. Therefore, it is uncertain to what extent the associations between hyperthyroidism/thyrotoxicosis and OC, are confounded or affected by reverse causation.

A well-acknowledged method to support causal inference in observational data is Mendelian randomization (MR) [16]. The theoretical premise of MR is that the alleles of gametes are randomly assigned during meiosis (Mendel’s second Law). This means that genetic variation is randomly distributed in the population, and individuals naturally carry or do not carry the genetic variation associated with risk factors at birth. Importantly, this genetic variation is not associated with other confounding factors. Therefore, MR research can effectively avoid bias from reverse causality and confounding, allowing for a more accurate clarification of the causal relationship between variables [17]. In this study, a two-sample MR analysis was conducted to explore the causal relationship between hyperthyroidism/thyrotoxicosis and OC risk.

Materials and methods

Study design and data sources

The database used in this study is IEU Open GWAS (https://gwas.mrcieu.ac.uk/), namely hyperthyroidism or thyrotoxicosis (GWAS ID: ebi-a-GCST90038636; ukb-b-20289; finn-b-THYROTOXICOSIS) and OC (GWAS ID: ebi-a-GCST90018888; ieu-b-4963). The GWAS data of exposure and outcome were shown in Table 1.

Table 1.

Data sources of summary GWAS datasets

ID Trait ID Definitions N case Year Consortium Author Sex Population Nsnp Sample_size Build N control Category Subcategory
Exposure
ebi-a-GCST90038636 Hyperthyroidism/Thyrotoxicosis SNP effect on Hyperthyroidism/Thyrotoxicosis 3,731 2021 NA NA NA NA 9,587,836 484,598 HG19/GRCh37 480,867 NA NA
ukb-b-20,289 Hyperthyroidism/Thyrotoxicosis SNP effect on Hyperthyroidism/Thyrotoxicosis 3,545 2018 MRC-IEU Ben Elsworth Males and Females European 9,851,867 462,933 HG19/GRCh37 459,388 Binary NA
finn-b-THYROTOXICOSIS Thyrotoxicosis SNP effect on Thyrotoxicosis 4,142 2021 NA NA Males and Females European 16,380,464 217,835 HG19/GRCh37 213,693 Binary NA
Outcome
ebi-a-GCST90018888 OC SNP effect on OC 1,588 2021 NA Sakaue S NA European 24,137,758 246,520 HG19/GRCh37 244,932 NA NA
ieu-b-4963 OC SNP effect on OC 1,218 2021 UK Biobank Burrows Males and Females European 9,822,229 199,741 HG19/GRCh37 199,741 Disease Cancer
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OC ovarian cancer; GWAS genome-wide association study; nsnp number of single nucleotide polymorphism; NA: not applicable

The bidirectional two-sample MR method was employed to investigate the causal relationship between hyperthyroidism/thyrotoxicosis and OC. The selection of instrumental variables (IVs) in this study is guided by three fundamental principles. First, IVs must exhibit a significant relationship with the exposure. Second, IVs should not be associated with confounding factors. Finally, IVs must influence outcomes exclusively through the exposure [18, 19]. To satisfy the three assumptions of IVs in MR research, the chosen IVs must adhere to the following screening criteria: locus-wide significance with a p-value < 5 × 10− 8; a linkage disequilibrium clumping procedure with r2 < 0.001 and a window size of 10,000 kb; harmonization of data while removing palindromic single nucleotide polymorphisms (SNPs); and a minor allele frequency >0.01 [20]. SNPs that successfully pass through this rigorous screening process will be utilized for the final causal analysis.

A positive MR analysis was conducted using three SNPs associated with thyroid dysfunction as IVs, with OC as the study outcome. In reverse MR analysis, two SNPs associated with OC were used as IVs, while thyroid dysfunction was considered as the outcome. All data extracted for analysis are shown in Table S1. The assumptions and design of the MR study were depicted in Fig. 1. STROBE-MR guidelines [21, 22] were used to guide the design of this study (Supplementary Table SS1).

Fig. 1.

Fig. 1

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Overview of the technical route and framework of this MR research design. IVS instrumental variables; SNP single nucleotide polymorphism; LD linkage disequilibrium; MR mendelian randomization

Two-sample MR analysis

We conducted a two-sample MR analysis using summary association data for SNPs associated with thyroid (Table 2). We used inverse variance weighting (IVW) as the main analysis method [23], weighted median, MR-Egger and weighted model as supplementary analysis methods to evaluate the causal effect, and the heterogeneity and pleipotency tests were carried out. The IVW MR approach combines individual MR estimates from SNPs to calculate an overall weighted estimate of the potential causal effect. To evaluate the stability of the results, we performed a leave-one-out analysis [23–25]. Heterogeneity was assessed using Cochran’s Q test, and publication bias was examined by analyzing the symmetry of the funnel plot [26].

Table 2.

Pleiotropic evaluation of Hyperthyroidism/Thyrotoxicosist and OC

ID. exposure ID.outcome outcome nsnp egger_intercept se_intercept pval_intercept
ebi-a-GCST90038636 ieu-b-4963 OC 17 -0.00020371 0.000130978 0.140716683
ukb-b-20,289 ebi-a-GCST90018888 OC 12 -0.033232523 0.019886295 0.125648726
finn-b-THYROTOXICOSIS ebi-a-GCST90018888 OC 11 -0.063730964 0.033462253 0.089234538
finn-b-THYROTOXICOSIS ieu-b-4963 OC 9 -0.000598493 0.000239409 0.04099947
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OC ovarian cancer; nsnp number of single nucleotide polymorphism

Statistical analysis

The analysis was performed using the R programming language (version 4.1.3). MR analysis was carried out based on the ‘TwoSampleMR’ software package, which applies MR-PRESSO analysis to identify abnormal SNPs [27]. A total of 25 hypothesis tests were conducted in this study, requiring the use of the false discovery rate (FDR) to manage false positive results in repeated comparisons [28]. Statistical significance was declared at an FDR-adjusted P value (PFDR) ≤ 0.05. Associations with raw P values less than 0.05, but with a PFDR above the criteria of 0.05, were considered to provide suggestive evidence for the reported association [29]. The primary method used for conducting the MR study was the IVW approach with random effect [30, 31]. The IVW method is widely used in MR studies due to its robust testing effectiveness. Additionally, four supplementary approaches for causal inference were employed: Weighted median, Simple mode, Weighted mode, and MR-Egger [32, 33]. Each technique has distinct assumptions on the efficacy of IVs, with the IVW method being considered the most reliable [34]. The IVW approach integrates cumulative causal estimates of the Wald ratio for each instrumental variable. On the other hand, MR-PRESSO can automatically identify and eliminate outliers in the IVW linear regression, providing refined MR estimates [35]. The ratio value of the causal effect of exposure on outcome for each SNP is determined by dividing the effect of the SNP on the result by the effect of the SNP on the exposure. All statistical tests were bilateral tests with a test level of P < 0.05 was considered significant.

Sensitivity analysis

Sensitivity analysis consists of two components: the pleiotropic activity test and the heterogeneity test [36]. The pleiotropic test involves calculating the IVW result after removing each SNP and determining the sum of the residual squares of the SNP’s influence and the IVW result. The calculation involves determining the distance between the SNP and the regression line after eliminating the SNP. The total sum of squares is then calculated by adding all the distances calculated from each SNP. As the value increases, the level of pleiotropy becomes more significant. The MR-Egger intercept test is used to detect pleiotropic data and assess the robustness of the results [23]. P < 0.05 suggests the presence of pleiotropy in the data. To evaluate the reliability of the IVW method, a heterogeneity analysis was conducted using the MR heterogeneity method. This analysis takes into account potential differences in IVs arising from various analysis platforms, experiments, populations, etc. A p >0.05 suggests no significant heterogeneity among the included IVs, allowing the results to be interpreted without considering the effects caused by heterogeneity.

Results

Biological associations between hyperthyroidism/thyrotoxicosis with OC via forward MR

According to the MR-Egger regression evaluation, horizontal pleiotropy was observed for finn-b-THYROTOXICOSIS as the exposure and ieu-b-4963 as the outcome, while no horizontal pleiotropy was found for the other results (Table 2).

Figure 2 presents a scatter plot illustrating the causal relationship between hyperthyroidism/thyrotoxicosis and OC, employing various MR methods. The results of the IVW analysis aligned with those obtained from the other three analytical methods. The IVW analysis revealed a potential significant causal effect between hyperthyroidism/thyrotoxicosis and OC. By converting relative risk ratios, we demonstrated that genetically predicted hyperthyroidism/thyrotoxicosis increases the risk of OC (ID: ebi-a-GCST90038636 ID: ieu-b-4963; OR = 1.0943, 95% CI = 1.0285–1.1643, P = 0.0044; ID: ukb-b-20289 ID: ebi-a-GCST90018888; OR = 49675, 95% CI = 1.99-1.24E + 10, P = 0.0363; and ID: finn-b-THYROTOXICOSIS ID: ebi-a-GCST90018888; OR = 1.1364, 95% CI = 1.0231–1.2623, P = 0.0170; respectively). However, when only finn-b-THYROTOXICOSIS was used as the exposure and ieu-b-4963 was used as the outcome, the IVW result was not significant (OR = 1.0007, 95% CI = 0.9998–1.0016, P = 0.1068). This result was not reliable due to the presence of horizontal pleiotropy. The results of the MR analysis were presented in Table S2 and Fig. 3.

Fig. 2.

Fig. 2

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The scatter plots for the causal effects of Hyperthyroidism/thyrotoxicosis on OC. Inverse variance weighted (light blue), MR-Egger (deep blue), weighted median (light green), and weighted mode (deep green) slopes represent results from these regression analyses. SNP single nucleotide polymorphism; OC ovarian cancer; MR mendelian randomization

Fig. 3.

Fig. 3

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The forest plot shows the OR and 95% CI for the effect of Hyperthyroidism/thyrotoxicosis on OC using the two-sample MR. OR odds ratio; CI confidence interval; OC ovarian cancer; MR mendelian randomization; * statistical significance; Red risk factors

Sensitivity analysis was conducted to test the reliability of the MR analysis results. The heterogeneity test showed no significant heterogeneity among the SNPs, as indicated by a P-value greater than 0.05 for Cochran’s Q statistic (Table S3). The MR-PRESSO evaluation also did not reveal any obvious signs of horizontal pleiotropy (Table S4). A heterogeneity test was conducted using both the IVW and MR-Egger test methods, with the results depicted in Fig. 4 indicating no significant heterogeneity. Subsequently, a sensitivity analysis was performed utilizing the leave-one-out method. Each point in the forest plot represents the MR analysis after excluding a single SNP, demonstrating the overall effect of the analysis. The findings were illustrated in Fig. 5, where all IVs were positioned to the right of zero. This indicated that the removal of any SNP did not impact the results. Furthermore, none of the IVs identified as outliers significantly influence the causal effect estimate of hyperthyroidism or thyrotoxicosis on OC, suggesting that the MR results were robust.

Fig. 4.

Fig. 4

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The forest plots of susceptibility on the risk of Hyperthyroidism/thyrotoxicosis. The red points show the combined causal estimate using all SNPs together in a single instrument, using two different methods (Inverse variance weighted and MR-Egger). SNP single nucleotide polymorphism; MR mendelian randomization

Fig. 5.

Fig. 5

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The leave-one-out sensitivity analysis of the estimations for Hyperthyroidism/thyrotoxicosis and OC. Points correspond to MR estimates without specific SNPs. The red line marks the significance threshold for this analysis. SNP single nucleotide polymorphism; OC ovarian cancer; MR mendelian randomization

Biological associations between OC with hyperthyroidism/thyrotoxicosis via reverse MR

Locus-wide significance (p < 5 × 10− 6) was used as the screening criterion, and the results showed no evidence of horizontal pleiotropy in all reverse MR analysis results (Table 3).

Table 3.

Pleiotropic evaluation of OC and Hyperthyroidism/Thyrotoxicosis

ID. exposure ID.outcome outcome nsnp egger_intercept se_intercept pval_intercept
ieu-b-4963 ebi-a-GCST90038636 OC 20 0.00024722 0.000185301 0.19878772
ebi-a-GCST90018888 ukb-b-20,289 OC 9 0.000256054 0.00057421 0.669117957
ebi-a-GCST90018888 finn-b-THYROTOXICOSIS OC 18 -0.008306835 0.015736561 0.604834837
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OC ovarian cancer; nsnp number of single nucleotide polymorphism

Additionally, the IVW method showed that there was no support for OC being a risk or protective factor for hyperthyroidism/thyrotoxicosis, with odds ratios (OR) close to 1 and non-significant p-values (ID: ebi-a-GCST90018888 ID: finn-b-THYROTOXICOSIS; OR = 1.0073, 95% CI = 0.9449–1.0739, P = 0.8227; ID: ebi-a-GCST90018888 ID: ukb-b-20289; OR = 0.9996, 95% CI = 0.9987–1.0004, P = 0.3001; and ID: ieu-b-4963 ID: ebi-a-GCST90038636; OR = 1.0078, 95% CI = 0.9455–1.0741, P = 0.8121; respectively) (Table S2).

Sensitivity analysis using MR-Egger, IVW and MR-PRESSO showed no susceptibility to horizontal pleiotropy and heterogeneity in this study (P > 0.05). The sensitivity analysis results were shown in Table S3 and Table S4.

Discussion

Most previous epidemiological studies have demonstrated a close association between hyperthyroidism and the incidence of colorectal cancer, liver cancer, breast cancer, lung cancer, and other types of cancer [37–39]. In a comprehensive study investigating OC in women, it was observed that a recent history of hyperthyroidism and hypothyroidism is linked to a decline in 5-year survival rates [15]. However, there is currently a lack of research exploring the causal relationship between thyroid dysfunction and OC using the MR method.

This study utilized aggregated GWAS data and employed a two-way, two-sample MR method to comprehensively evaluate the potential causal relationship between thyroid dysfunction and the incidence of OC. Eventually, we found that hyperthyroidism or thyrotoxicosis was associated with an increased risk of OC. OC did not change the risk of hyperthyroidism/thyrotoxicosis. These findings supported the results of a previous observational study. In a clinical investigation, T4 levels were elevated in OC patients (P < 0.05), indicating an association between hyperthyroidism and OC [40]. Minlikeeva et al. [15] obtained data from the OC Society and found that hyperthyroidism was associated with an increased risk of cancer death based on meta-analysis (HR = 1.94; 95% CI = 1.19–3.18). During a median follow-up of 28 years, women with hyperthyroidism had a higher risk of death from OC, although the number of cases was limited [41]. Our findings provided a new perspective on the etiology of OC, suggesting that hyperthyroidism/thyrotoxicosis might contribute to its development.

It should be noted that this is an assumption regarding the causality between hyperthyroidism/thyrotoxicosis and OC. Ovarian surface epithelial (OSE) cells express multiple nuclear hormone receptor genes, including those encoding thyroid hormone and estrogen receptors (TR and ER, respectively). OC was hormone-dependent. Epidemiological evidence indicated that hyperthyroidism was associated with an increased risk of ovarian surface inflammation, OC [12]. Furthermore, thyroid hormones directly impact the ovaries and indirectly affect SHBG, PRL, GnRH secretion, and coagulation factors, thereby influencing the menstrual pattern. Treating thyroid dysfunction can reverse menstrual abnormalities and improve fertility [42].

Our study has several advantages. Firstly, a two-sample MR study based on SNP as an IV can effectively avoid unknown confounding factors. Secondly, the data included in this study is a GWAS database with a large sample size, which makes our results less susceptible to demographic differences. This study presents a novel finding, demonstrating a causal relationship between hyperthyroidism/thyrotoxicosis and OC. This discovery holds promise for the development of new treatment strategies for OC.

However, there are certain limitations in this study that need to be acknowledged. Firstly, the research focused only on hyperthyroidism/thyrotoxicosis, and the data for other thyroid disease were not included due to research constraints. Therefore, the potential causal effects of other types of thyroid disease on OC has not been explored. Secondly, the present study did not investigate the relationship between hyperthyroidism/thyrotoxicosis and the risk of developing OC of different subtypes and stages, and subsequent refinement of the Meta-analyses of MR and subgrouping by different subtypes and stages of OC could address this limitation. Thirdly, several MR studies have included participants from European populations; however, genetic effects may vary across different races or populations, a limitation commonly observed in genetic research [43, 44]. Therefore, validation in diverse populations is essential. Although this study primarily relies on data from a European cohort, it provides robust evidence for understanding genetic associations within this demographic. We encourage future studies involving other populations and groups to verify the broader applicability of these findings. Subsequent research will focus on multi-ethnic groups to assess the consistency of the results across various populations, thereby enhancing the generalizability and practical significance of the conclusions.

Conclusions

By the two-sample MR method, we established a positive causal relationship between hyperthyroidism/thyrotoxicosis and OC, indicating an increased risk of OC associated with these conditions. This knowledge can help analyze the risk of developing OC in patients with hyperthyroidism/thyrotoxicosis and provide valuable insights for early prevention and intervention. However, the impact of OC on thyroid dysfunction was not observed in this MR study.

Supplementary Information

Below is the link to the electronic supplementary material.

12672_2025_3738_MOESM1_ESM.xlsx (34.4KB, xlsx)

Supplementary Material 1. Table S1 The GWAS summary data of hyperthyroidism/thyrotoxicosis and OC.

12672_2025_3738_MOESM2_ESM.xlsx (14.9KB, xlsx)

Supplementary Material 2. Table S2 The MR analysis of the causal relationship between hyperthyroidism/thyrotoxicosis and OC.

12672_2025_3738_MOESM3_ESM.xlsx (11.1KB, xlsx)

Supplementary Material 3. Table S3 The P of Cochran’s Q statistic was used to analyze the heterogeneity between SNPs.

12672_2025_3738_MOESM4_ESM.xlsx (10.8KB, xlsx)

Supplementary Material 4. Table S4 MR-PRESSO evaluated the horizontal pleiotropy of MR results.

12672_2025_3738_MOESM5_ESM.docx (34.5KB, docx)

Supplementary Material 5. Table S5 STROBE-MR checklist.

Acknowledgements

Not applicable.

Author contributions

H.W. contributed to the study conception and design. H.W., C.L., Y.Y., J.L., Y.D., X.H. and H.H. collected the data and performed the data analysis. H.W., C.L., Y.Y., J.L., Y.D., X.H. and H.H. contributed to the interpretation of the data and the completion of figures and tables. H.W., C.L., Y.Y., J.L., Y.D., X.H. and H.H. contributed to the drafting of the article and final approval of the submitted version.

Funding

Not applicable.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12672_2025_3738_MOESM1_ESM.xlsx (34.4KB, xlsx)

Supplementary Material 1. Table S1 The GWAS summary data of hyperthyroidism/thyrotoxicosis and OC.

12672_2025_3738_MOESM2_ESM.xlsx (14.9KB, xlsx)

Supplementary Material 2. Table S2 The MR analysis of the causal relationship between hyperthyroidism/thyrotoxicosis and OC.

12672_2025_3738_MOESM3_ESM.xlsx (11.1KB, xlsx)

Supplementary Material 3. Table S3 The P of Cochran’s Q statistic was used to analyze the heterogeneity between SNPs.

12672_2025_3738_MOESM4_ESM.xlsx (10.8KB, xlsx)

Supplementary Material 4. Table S4 MR-PRESSO evaluated the horizontal pleiotropy of MR results.

12672_2025_3738_MOESM5_ESM.docx (34.5KB, docx)

Supplementary Material 5. Table S5 STROBE-MR checklist.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Articles from Discover Oncology are provided here courtesy of Springer

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