The role of preeclampsia in breast cancer risk: insights from Mendelian randomization study

Abstract

Background

The relationship between preeclampsia and breast cancer risk is still debated, with observational studies yielding inconsistent results. This research aims to clarify the causal link between preeclampsia and breast cancer using Mendelian randomization (MR) methods.

Methods

We utilized genome-wide association study (GWAS) data to identify single nucleotide polymorphisms (SNPs) that are significantly associated with preeclampsia, which served as genetic instruments. A two-sample Mendelian randomization (TSMR) approach was applied, primarily using inverse variance weighting (IVW) to assess the causal impact of preeclampsia on breast cancer risk. To strengthen our findings, a meta-analysis of IVW estimates from both discovery and validation cohorts was performed, complemented by sensitivity analyses to investigate heterogeneity and potential horizontal pleiotropy.

Results

In the discovery cohort, IVW analysis revealed a potential inverse relationship between preeclampsia and breast cancer risk (OR, 0.971; 95% CI, 0.947–0.996; P = 0.022). However, the validation cohort did not demonstrate a significant causal association (OR, 0.992; 95% CI, 0.975–1.008; P = 0.327). The combined meta-analysis indicated that preeclampsia might be linked to a lower risk of breast cancer (OR, 0.986; 95% CI, 0.972–0.999; P = 0.041). In subgroup analysis, preeclampsia was found to have a potential association only with estrogen receptor (ER)-negative breast cancer (OR, 0.956; 95% CI, 0.916–0.999; P = 0.043), while no significant link was observed with estrogen receptor (ER)-positive breast cancer (OR, 0.972; 95% CI, 0.945–1.000; P = 0.051). Sensitivity analyses indicated no significant heterogeneity or evidence of horizontal pleiotropy (P > 0.05).

Conclusion

This MR study, supported by a robust meta-analysis, suggests that preeclampsia may have a protective effect against breast cancer, especially ER-negative breast cancer. However, to firmly establish this relationship, additional prospective studies with larger populations are warranted. Moreover, further exploration of the biological mechanisms underlying this potential association is needed through both in vitro and in vivo research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-02248-1.

Introduction

Preeclampsia, a pregnancy-related hypertensive disorder [1–3], affects approximately 8–10% of pregnancies worldwide and is characterized by high blood pressure and organ dysfunction [4–6]. This condition poses significant risks to both maternal and fetal health, including complications such as preterm birth, fetal growth restriction, and increased maternal morbidity [7, 8]. Beyond immediate pregnancy outcomes, recent research has emphasized the long-term health implications of preeclampsia. Women who experience this condition are at a heightened risk for cardiovascular diseases, metabolic disorders, benign and malignant tumors, and various other chronic health issues later in life [9, 10].

Despite substantial evidence linking preeclampsia to various long-term health risks, its potential association with breast cancer remains underexplored. Breast cancer, one of the most prevalent malignancies in women, arises from a complex interplay of genetic, hormonal, and environmental factors [11, 12]. Investigating whether pregnancy-related complications like preeclampsia contribute to breast cancer risk is crucial for advancing targeted prevention strategies and improving overall women’s health outcomes.

Observational studies examining the relationship between preeclampsia and breast cancer have yielded inconsistent results [13, 14]. Some research suggests that women with a history of preeclampsia may have an altered risk of developing breast cancer [15, 16], potentially due to hormonal changes or differences in breast tissue composition. However, these findings are often confounded by various factors, including socioeconomic status, lifestyle choices, and genetic predispositions, making it difficult to establish a definitive causal relationship.

Mendelian randomization (MR) offers a promising methodological framework to clarify these associations [17, 18]. By utilizing genetic variants associated with preeclampsia as instrumental variables (IVs), MR could provide causal estimates that are less susceptible to confounding and reverse causation biases that are common in traditional observational studies [19, 20]. Currently, MR is being increasingly applied in the field of reproductive health to explore causal relationships between various exposures and outcomes. This growing interest highlights the method’s potential to address key questions in reproductive medicine, such as the impact of genetic, environmental, and lifestyle factors on fertility, pregnancy complications, and reproductive disorders [21–24]. This approach allows for a more robust examination of the causal pathways linking preeclampsia to breast cancer, potentially revealing important insights into the etiology of both conditions.

This study aims to investigate the causal relationship between preeclampsia and breast cancer using MR analysis. By leveraging genetic data and summary statistics from large-scale genome-wide association studies (GWAS), we seek to elucidate whether preeclampsia is a risk factor for breast cancer and to explore the implications for clinical practice and public health. Understanding this relationship could not only enhance our knowledge of breast cancer etiology but also inform preventive measures for women who have experienced preeclampsia.

Study design

The framework of this study relies on the three key assumptions of MR research [25]. Firstly, it is essential that the single nucleotide polymorphisms (SNPs) chosen as IVs demonstrate a strong association with preeclampsia. Secondly, these IVs must not have significant links to any confounding factors. Finally, the impact of the IVs on breast cancer risk should occur solely through preeclampsia, without engaging any alternative pathways (Fig. 1).

Fig. 1.

Schematic illustration of the three key assumptions of MR study. SNP single nucleotide polymorphism; ER estrogen receptor

Data sources

All the large GWAS summary data used in this study are sourced from Integrative Epidemiology Unit (IEU) (https://gwas-api.mrcieu.ac.uk/), which focuses on the integration of genetic data with epidemiological research to better understand health outcomes. It provides a range of resources including GWAS summary data, enabling researchers to explore causal relationships between genetic variants and various diseases. The GWAS ID for preeclampsia is ebi-a-GCST90018906, with a sample size of 267,242 individuals. The GWAS IDs for the discovery group and the validation group for breast cancer are “ieu-a-1129” (sample size = 106,776) and “ebi-a-GCST004988” (sample size = 139,274), respectively. The GWAS IDs for breast cancer subtypes are sourced from the Breast Cancer Association Consortium (BCAC) [estrogen receptor (ER)-negative breast cancer: ieu-a-1135 (sample size = 55,149); estrogen receptor (ER)-positive: ieu-a-1132 (sample size = 83,691)] [26]. To avoid differences in genetic backgrounds across ethnicities, all GWAS datasets included in this study were restricted to European populations. Detailed information regarding the aforementioned GWAS is summarized in Table 1.

Table 1.

Description of the GWAS datasets used in this MR study

Trait (GWAS ID)	Sample size	Consortium/Reference	Population
Preeclampsia (ebi-a-GCST90018906)	267,242	Sakaue S et al. (2021)	European
Breast cancer (ieu-a-1129)	106,776	BCAC (2017)	European
Breast cancer (ebi-a-GCST004988)	139,274	Michailidou K et al. (2017)	European
ER-negative breast cancer (ieu-a-1135)	55,149	BCAC (2017)	European
ER-positive breast cancer (ieu-a-1132)	83,691	BCAC (2017)	European

GWAS Genome-wide association study; BCAC Breast Cancer Association; Consortium; ER Estrogen receptor

Selection of IVs

First, considering the first key assumption of MR [27], that single nucleotide polymorphisms (SNPs) must be strongly associated with preeclampsia and independent of one another, SNPs significantly associated with preeclampsia (P < 1 × 10^–5, R² < 0.001, window = 10,000 kb) were screened at the genome-wide level [28–30]. Subsequently, to satisfy the independence assumption of MR [31], which states that SNPs should not be associated with known confounding factors related to the “exposure-outcome” relationship, SNPs were checked in the Phenoscanner database to ensure that the selected SNPs were not associated with known confounders [32]. Additionally, to assess whether the selected IVs were subject to weak instrument bias, F-statistics were calculated. The calculation formula is $F=\frac{{\beta }^2}{S{E}^2}$. If F > 10, it indicates a low likelihood of weak instrument bias [33]. The remaining SNPs are utilized as genetic proxies for preeclampsia to investigate the causal relationship with breast cancer and its subtypes (Fig. 2).

Fig. 2.

Flowchart of this MR study. GWAS genome-wide association study; ER estrogen receptor; IVs instrumental variables; SNP single nucleotide polymorphism; LD linkage disequilibrium; IVW inverse variance weighted; MR-PRESSO Mendelian randomization pleiotropy residual sum and outlier

MR and sensitivity analysis

This study employed the inverse variance weighted (IVW) method [34], weighted median method [35], weighted mode, and MR-Egger regression [36] for MR analysis. When all genetic variants met the IV assumptions, the IVW method provided an appropriate estimate of causal effects and served as the primary analysis result. However, due to certain pleiotropic biases associated with the IVW method, sensitivity analyses were necessary to validate the results. The analysis using the weighted median method was deemed reliable if the contribution weight of the IVs to genetic variation exceeded 50%. The MR-Egger method acted as a robust evaluation tool when the assumption of instrument strength independent of direct effect (InSIDE) was satisfied [37]; a non-zero intercept from this method was utilized to assess pleiotropy. The Cochran Q test, leave-one-out analysis, and funnel plots were employed to evaluate heterogeneity among IVs. If the IVW method yields significant results (P < 0.05), and no pleiotropy or heterogeneity is detected, the outcome can still be considered positive as long as the β values from other methods align in the same direction, even if their results are not statistically significant [38]. The MR-Egger intercept and MR pleiotropy residual sum and outlier (MR-PRESSO) global test were used to assess horizontal pleiotropy. Meta-analysis was conducted to evaluate the combined effects of the IVW results from both the discovery and validation cohorts. All analyses were executed using R software (version 4.3.2), with statistical significance defined as a P-value < 0.05.

Result

SNPs as IVs in this MR study

A total of 27 SNPs were selected as IVs to analyze the causal relationship between preeclampsia and breast cancer in the discovery cohort, as well as its subtypes. Additionally, 28 SNPs were used for the analysis of the causal relationship between preeclampsia and breast cancer in the validation cohort. All IVs underwent the rigorous screening process mentioned above. Detailed information about IVs is summarized in Supplementary Table 1.

Genetic associations between preeclampsia and breast cancer

The discovery cohort indicated that preeclampsia may reduce breast cancer risk by approximately 3% [odds ratio (OR), 0.971; 95% confidence interval (CI), 0.947–0.996; P = 0.022]. Although the validation cohort showed a similar trend, it did not reach statistical significance (OR, 0.992; 95% CI, 0.975–1.008; P = 0.327) (Fig. 3). To minimize selection bias arising from different GWAS, a meta-analysis was conducted to combine the IVW effect estimates. The meta-analysis indicated that preeclampsia may serve as a potential protective factor against breast cancer (OR, 0.986; 95% CI, 0.972–0.999; P = 0.041) (Fig. 4).

Fig. 3.

Forest plot illustrating the causal effect of preeclampsia on breast cancer. OR odds ratio; CI confidence interval

Fig. 4.

Meta-analysis of IVW effect sizes from discovery and validation cohorts. OR odds ratio; CI confidence interval

Genetic associations between preeclampsia and breast cancer subtypes

Further subgroup analysis suggests that preeclampsia may have a closer relationship with ER-negative breast cancer (OR, 0.956; 95% CI, 0.916–0.999; P = 0.043), rather than with ER-positive breast cancer (OR, 0.972; 95% CI, 0.945–1.000; P = 0.051) (Fig. 5).

Fig. 5.

Forest plot illustrating the causal effect of preeclampsia on breast cancer subtypes. ER estrogen receptor; OR odds ratio; CI confidence interval

Analysis of heterogeneity and horizontal pleiotropy

Cochran’s Q test results indicated no heterogeneity among the selected IVs, with all P > 0.05 (Table 2). Additionally, the funnel plot for the chosen SNPs demonstrated that the distribution of causal effects in this study was largely symmetrical, suggesting no bias (Fig. 6). The leave-one-out analysis showed that the MR results were minimally influenced by any single SNP (Supplementary Fig. 1). The MR-Egger intercept and MR-PRESSO global test jointly indicated an absence of horizontal pleiotropy (Table 2).

Table 2.

Outcomes of the sensitivity analysis conducted in this MR study

Exposure	Cochran’s Q test (IVW) P-value	Egger intercept test P-value	MR-PRESSO global test P-value
Breast cancer (ieu-a-1129)	0.147	0.660	0.132
Breast cancer (ebi-a-GCST004988)	0.250	0.713	0.230
ER-negative breast cancer (ieu-a-1135)	0.228	0.733	0.242
ER-positive breast cancer (ieu-a-1132)	0.133	0.684	0.129

MR-PRESSO Mendelian randomization pleiotropy residual sum and outlier; ER estrogen receptor

Fig. 6.

Funnel plots illustrating the heterogeneity among SNPs in this MR study. a Funnel plot illustrating the heterogeneity among SNPs in this MR study exploring the causal effect of preeclampsia on breast cancer in the discovery cohort. b Funnel plot illustrating the heterogeneity among SNPs in this MR study exploring the causal effect of preeclampsia on breast cancer in the validation cohort. c Funnel plot illustrating the heterogeneity among SNPs in this MR study exploring the causal effect of preeclampsia on ER-negative breast cancer. d Funnel plot illustrating the heterogeneity among SNPs in this MR study exploring the causal effect of preeclampsia on ER-positive breast cancer

Discussion

Preeclampsia, a pregnancy complication marked by hypertension and proteinuria, has drawn significant attention for its potential link to various cancer risks [39]. Growing evidence highlights a possible connection between preeclampsia and cancer, particularly breast cancer [40]. Investigating this relationship is crucial, as it could provide valuable insights into cancer prevention and uncover the biological mechanisms bridging pregnancy complications with long-term health outcomes.

Current research on preeclampsia and breast cancer has produced mixed results. Some studies indicate that women with a history of preeclampsia may have a reduced risk of breast cancer [40], while others have failed to establish a consistent association [41]. This inconsistency may arise from variations in study design, sample size, and population characteristics. Moreover, the mechanisms through which preeclampsia might influence breast cancer risk remain largely unexplored. One proposed mechanism is the hormonal changes that occur during preeclampsia, which may affect breast tissue and influence cancer susceptibility. Increased levels of hormones such as placental growth factor and vascular endothelial growth factor, alongside altered estrogen metabolism, could play significant roles in modulating breast cancer risk [42–45]. These hormonal alterations may create an environment less conducive to cancer development, particularly in hormone-sensitive subtypes. Vascular endothelial growth factor (VEGF) and placental growth factor (PlGF) play important roles in the development of breast cancer. VEGF is a key angiogenic factor involved in the formation of tumor blood vessels, and angiogenesis is a necessary process for tumor growth and metastasis. Studies have shown that high VEGF expression is associated with the invasiveness, metastatic potential, and poor prognosis of breast cancer [46, 47]. Notably, research indicates that in premenopausal patients, the proportion of VEGF-positive tumor cells is higher, and VEGF expression is more pronounced in ER-negative tumors, which suggests that VEGF may be more closely associated with the risk and prognosis of ER-negative breast cancer [48]. PlGF, a member of the VEGF family, plays an important role in tumor angiogenesis and the regulation of the tumor microenvironment. Although research on PlGF is relatively limited, existing studies suggest that overexpression of PlGF is closely associated with the progression of breast cancer [49, 50]. During pregnancy, changes in pregnancy hormones may regulate PlGF expression, thereby influencing angiogenesis and cell proliferation in breast tissue, mechanisms that may be closely related to the development of breast cancer. Studies have shown that PlGF could serve as a biomarker for breast cancer and, in future research, may be a potential target for targeted therapy [51]. However, the specific mechanisms underlying this are still unclear. Future research could further explore how these hormones influence the expression of VEGF and PlGF through molecular pathways, thereby revealing their potential mechanisms in breast cancer and providing new directions for clinical intervention. Another potential mechanism involves the inflammatory processes associated with preeclampsia. Chronic inflammation is a recognized risk factor for various cancers, including breast cancer. However, the inflammatory response during preeclampsia is distinct, characterized by a complex interplay of immune factors. This heightened inflammatory state may paradoxically exert protective effects against the development of certain cancers, including breast cancer, by altering immune responses and tumor microenvironments. Further exploration of these mechanisms through in vitro and in vivo studies is crucial to understanding the biological underpinnings of the association between preeclampsia and breast cancer risk.

To the best of our knowledge, this is the first study to explore the relationship between preeclampsia and breast cancer risk using MR analysis. Findings from the discovery cohort suggest a potential reduction in breast cancer risk associated with preeclampsia, with an estimated 3% decrease in odds. The meta-analysis, combining data from two independent cohorts, further supports the hypothesis that preeclampsia may serve as a protective factor against breast cancer. Intriguingly, this protective effect appears to be more pronounced in ER-negative breast cancer. MR has proven to be a powerful method for investigating causal relationships in epidemiology, particularly for conditions like preeclampsia and breast cancer. By utilizing genetic variants as IVs, MR enables the inference of causal effects while mitigating the confounding and reverse causation biases inherent in observational studies. This robust framework allows for a deeper exploration of the potential protective role of preeclampsia against breast cancer, offering novel insights into the interplay between genetic predisposition to preeclampsia and breast cancer risk.

However, while MR offers significant advantages, it is not without limitations [52]. One major concern is the assumption that the selected genetic variants are directly associated with the exposure (preeclampsia) and not related to other pathways influencing the outcome (breast cancer). This assumption, known as the “exclusion restriction,” can be difficult to establish. Moreover, horizontal pleiotropy, where genetic variants influence the outcome through pathways unrelated to the exposure, can bias MR estimates. Sensitivity analyses, including the MR-Egger and MR-PRESSO tests, are essential to assess the validity of the findings and to detect potential pleiotropic effects. Additionally, the choice of SNPs as IVs poses challenges, as the strength of these instruments can vary. Weak instruments can lead to biased estimates and reduced statistical power, which may obscure the true relationship between preeclampsia and breast cancer risk. Furthermore, genetic variants associated with preeclampsia might not fully capture the complexity of the condition, which involves a multitude of environmental and lifestyle factors. In addition, it is important to highlight that this study relied on data from the publicly accessible IEU OpenGWAS database. While the database provides valuable and large-scale genetic data, it should be noted that certain datasets within it have not undergone rigorous peer-reviewed validation. As such, this may introduce potential biases that could affect the reliability and generalizability of the results. Although these datasets offer a useful starting point for exploration, the lack of peer-reviewed validation could limit the precision of the conclusions drawn from this study. Future research should consider corroborating these findings with validated datasets to further assess their robustness. Finally, our MR analysis only included European populations, which may limit the generalizability of the findings to other ethnic groups with different genetic backgrounds. Genetic variations across ethnicities can influence the relationship between exposures and outcomes, meaning that the causal associations identified in our study may not be applicable to non-European populations. Future studies incorporating diverse ethnic groups are necessary to validate our findings and explore potential differences in causal relationships across populations.

In conclusion, the relationship between preeclampsia and breast cancer risk, particularly regarding ER-negative subtypes, warrants further investigation. Current research suggests a potential protective effect, but the mechanisms underlying this association remain largely unexplored. The application of MR provides a promising avenue for elucidating these relationships while addressing some of the limitations inherent in observational studies. Future research should focus on validating these findings through larger, well-designed studies and exploring the biological pathways involved. A comprehensive understanding of how preeclampsia may influence breast cancer risk could have profound implications for prevention strategies and women’s health, ultimately contributing to better outcomes for women affected by both conditions.

Conclusion

This study employed MR method to investigate the causal relationship between preeclampsia and breast cancer risk. The findings suggest that preeclampsia may serve as a protective factor against breast cancer, with a notable potential link to ER- subtypes. Future studies with larger sample sizes and refined methodologies are essential to validate these findings and explore the underlying mechanisms by which preeclampsia influences breast cancer risk.

Author contributions

Yitao Guo designed the study. Chenfei Qian, Ganwei Xiong and Linzhe Miao performed data analysis. Yitao Guo, Ganwei Xiong and Shihao Hong drafted the manuscript. Chenfei Qian and Ganwei Xiong contributed equally.

Funding

None.

Data availability

Upon request, the corresponding author can supply the data supporting the findings of this study.

Declarations

Ethics approval and consent to participate

All data used in this study were obtained from publicly available databases, so additional ethical approval was not required.

Competing interests

The authors declare no competing interests.