Uterine Fibroids-Associated GWAS Loci and the Risk of Arterial Hypertension: A Pilot Study

Abstract

Background:

Uterine fibroids (UFs) are the most common benign tumors in women of reproductive age and are frequently associated with impaired fertility, reproductive dysfunction, and pregnancy complications. Arterial hypertension (AH) is another prevalent chronic condition in women, while increasing epidemiological evidence demonstrates the existence of a bidirectional relationship between UFs and AH. However, the genetic mechanisms underlying this association remain unclear. We hypothesized that UF-associated loci identified in genome-wide association studies (GWAS) may contribute to AH susceptibility.

Methods:

Genomic DNA from 606 hospitalized patients with UFs (n = 178 with comorbid AH; n = 428 AH-free) underwent allele-specific PCR amplification targeting 17 common GWAS-derived polymorphisms.

Results:

The rs1812266 (LOC105375949) locus was associated with a reduced risk of AH (odds ratio (OR) = 0.74; p = 0.028). Model-based multivariate dimensionality reduction (MB-MDR) analysis revealed significant gene–gene interactions (p_perm ≤ 0.05) involving UF loci and AH risk, including five key variants (rs66998222, LOC102723323; rs2456181, ZNF346; rs1812266, LOC105375949; rs10929757, GREB1; rs7986407, FOXO1) appearing in multiple models. Notably, rs66998222 was observed in five models, suggesting this residue possesses a central role. For gene–environment interactions, five variants, rs66998222, LOC102723323; rs1812266, LOC105375949; rs10929757, GREB1; rs2456181, ZNF346; rs2553772, LOC105376626, appeared in multiple models, with the smoking × rs66998222 interaction being central to five models. These six risk variants subsequently underwent systematic functional annotation to characterize the potential associated biological roles. Bioinformatics analysis indicated that single nucleotide polymorphisms (SNPs) associated with oxidative stress, renin–angiotensin–aldosterone system (RAAS) function, tissue fibrosis, angiogenesis, and smooth muscle cell remodeling are common mechanisms in both UFs and AH. Cis-eQTL genes and transcription factor (TF)-linked biological processes mediate these mechanisms. Validation using the Cardiovascular Disease Knowledge Portal confirmed the relevance of several SNPs to blood pressure traits.

Conclusions:

To our knowledge, this is the first study to explore the genetic overlap between UFs and AH, providing novel molecular evidence for shared pathophysiological pathways. Our findings support the concept of a common genetic predisposition underlying both conditions and may inform new directions for integrated reproductive and cardiovascular health strategies.

Graphical abstract

Keywords

uterine fibroids / hypertension / genome wide association studies / single nucleotide polymorphism

1. Introduction

Uterine fibroids (UF) and arterial hypertension (AH) are highly prevalent conditions that exert a considerable impact on the health of women during both reproductive and postmenopausal periods [1]. UF is recognized as one of the most common benign tumors affecting women of reproductive age. According to epidemiological data, the prevalence of uterine fibroids reaches approximately 70–80%, with clinically significant symptoms observed in about 20–50% of affected individuals [2, 3]. This condition is associated with a wide range of adverse outcomes, including abnormal uterine bleeding, anemia, chronic pelvic pain, reproductive dysfunction, infertility, and pregnancy-related complications [4, 5]. Similarly, arterial hypertension remains one of the most widespread chronic disorders, affecting an estimated 28.5–31.1% of the global adult population [6].

AH has been frequently identified as a potential risk factor for the development of UF [7]. Moreover, growing evidence suggests a possible bidirectional relationship, indicating that UF itself may contribute to an increased risk of hypertensive disorders. Several studies have shown that the presence of UF is associated with a 1.44- to 1.88-fold higher risk of developing hypertension [8, 9], as well as hypertensive complications during pregnancy, such as preeclampsia [10, 11].

Despite the accumulating clinical and epidemiological data supporting an association between these conditions, the genetic underpinnings of this relationship remain largely unexplored. In recent decades, genome-wide association studies (GWAS) have emerged as a powerful approach for identifying genetic loci associated with susceptibility to various complex diseases [12]. In the context of UF, GWAS have successfully identified multiple genetic variants linked to increased disease risk [13, 14, 15, 16]. Similarly, GWAS have also revealed numerous loci implicated in the pathogenesis of AH [17, 18].

However, to date, no studies have specifically addressed whether genetic loci associated with UF risk may also influence the development of AH.

2. Materials and Methods

2.1 Study Participants

The study cohort consisted of 606 unrelated UF patients from Central Russia, including 178 hospitalized individuals with comorbid arterial hypertension and 428 normotensive controls. The Ethical Review Committee of Kursk State Medical University approved the study protocol (protocol No 5 from May 11, 2021), and all participants provided written informed consent. The study was carried out in accordance with the guidelines of the Declaration of Helsinki. The inclusion criteria for the study required participants to have self-declared Russian ancestry and to have been born in Central Russia. Table 1 provides the baseline and clinical characteristics of the study cohort.

The patients were enrolled in the study with ultrasound-verified UF from 2021 to 2023 at two tertiary care facilities (Perinatal Centre and Kursk City Maternity Hospital).

Patients with AH were selected from those with clinically confirmed systolic blood pressure \ge140 mmHg and/or diastolic blood pressure \ge90 mmHg, as well as patients taking antihypertensive drugs [19] (Table 1).

2.2 Identification and Inclusion of Environmentally Modifiable AH Risk Factors

Cigarette smoking has been conclusively linked to hypertension pathogenesis through its dual-phase impact on endothelial function and vascular tone regulation. Nicotine induces sympathetic nervous system activation, leading to vasoconstriction and increased blood pressure, while long-term exposure contributes to endothelial dysfunction and arterial stiffness [6, 18].

2.3 Selection of Genes and Polymorphisms

The selection of genetic variants for this study was performed in two consecutive stages. In the initial stage, candidate single nucleotide polymorphisms (SNPs) were identified from the database - GWAS Catalog (https://www.ebi.ac.uk/gwas/; accessed March 14, 2024), which at that time contained 238 SNPs across 169 loci associated with uterine fibroid susceptibility based on 22 genome-wide association studies. Priority was given to SNPs that showed consistent associations with uterine fibroid risk in at least two independent studies of European populations. We excluded variants with minor allele frequencies below 0.05 and those that presented technical challenges for TaqMan probe design due to high GC content, GC clamps, or extended homopolymeric sequences. This initial screening process yielded seven candidate SNPs: rs72709458 (TERT), rs58415480 (SYNE1), rs7907606 (STN1/SLK), rs547025 (SIRT3), rs117245733 (LINC00598), rs7986407 (FOXO1) and rs2456181 (ZNF346). The second stage of SNP selection utilized the Reproductive System Knowledge Portal (https://reproductive.hugeamp.org/; accessed October 10, 2024), which provides comprehensive GWAS meta-analysis data for reproductive disorders. Using the search term “uterine fibroids”, we identified additional candidate SNPs while applying the same exclusion criteria regarding minor allele frequency and technical feasibility of genotyping using fluorescent probes. This secondary analysis contributed ten additional SNPs to our study: rs66998222 (LOC102723323), rs59760198 (DNM3), rs10929757 (GREB1), rs9419958 (STN1), rs1812266 (LOC105375949), rs1986649 (FOXO1), rs641760 (PITPNM2), rs2235529 (WNT4), rs2553772 (LOC105376626), and rs11031731 (THEM7P/WT1). The combined selection process resulted in a final set of 17 SNPs that met our criteria for both established association with uterine fibroids and technical suitability for genotyping analysis. This two-phase approach allowed us to comprehensively capture genetic variants of potential importance in uterine fibroid pathogenesis while ensuring methodological robustness in our subsequent analyses.

2.4 Genetic Analysis

Genotyping procedures were conducted at the Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University (Kursk, Russia). Venous blood samples (up to 5 mL) were collected from the cubital vein of each participant and stored in EDTA-coated tubes at –20 °C until processing. Genomic DNA was extracted using standard protocols, including phenol/chloroform extraction and ethanol precipitation, and its purity, quality, and concentration were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

Allele-specific real-time PCR assays, developed in-house, were used for genotyping. Primer and probe sequences were designed using Primer3web software (version 4.1.0, ELIXIR, Cambridge, MA, USA) [20]. PCR amplification was carried out in 25 µL reaction mixtures containing 1.5 units of Hot Start Taq DNA polymerase (Biolabmix, Novosibirsk, Russia), approximately 10 ng of DNA, and the following reagent concentrations: 0.25 µM of each primer; 0.1 µM of each probe; 250 µM of each dNTP; and varying MgCl₂ concentrations: 4 mM for rs59760198, 3 mM for rs117245733, rs547025, rs10929757, rs9419958, rs1812266, rs1986649, rs2553772 and rs11031731, 3.5 mM for rs2456181, rs7907606, rs641760 and rs2235529, 2.5 mM for rs7986407, rs58415480 and rs72709458, and 1.5 mM for rs66998222. The PCR buffer (1\times concentration) contained 67 mM Tris-HCl (pH 8.8), 16.6 mM (NH₄)₂SO₄, and 0.01% Tween-20. The thermal cycling protocol consisted of an initial denaturation at 95 °C for 10 minutes, followed by 39 cycles of 92 °C for 30 seconds and annealing at various temperatures: 64 °C (rs117245733 LINC00598, rs1986649 FOXO1, rs2553772 LOC105376626, rs11031731 THEM7P/WT1, rs2235529 WNT4, rs59760198 DNM3), 65 °C (rs547025 SIRT3, rs10929757 GREB1, rs641760 PITPNM2), 63 °C (rs2456181 ZNF346), 62 °C (rs7907606 STN1/SLK), 60 °C (rs58415480 SYNE1, rs1812266 LOC105375949), 59 °C (rs7986407 FOXO1, rs72709458 TERT), 61 °C (rs9419958 STN1), and 66 °C (rs66998222 LOC102723323). For quality control, 10% of samples underwent blinded duplicate genotyping, demonstrating >99% concordance. SNPs rs9419958 (STN1) and rs7907606 (STN1, SLK), which deviated from Hardy-Weinberg equilibrium in controls, were re-genotyped and showed 100% concordance with initial results, confirming data reliability.

2.5 Statistical Analysis

The analysis of statistical data was performed using the STATISTICA software (version 13.3, StatSoft, Santa Clara, CA, USA). To assess the normality of the data distribution, the Shapiro-Wilk test was utilized. As most quantitative variables did not follow a normal distribution, the results were expressed as medians (Me) along with their corresponding interquartile ranges [Q1–Q3]. For comparing quantitative variables between two independent groups, the Mann-Whitney U test was applied. Differences in categorical variables were evaluated using Pearson’s chi-squared test, with Yates’ correction for continuity when appropriate.

The Hardy-Weinberg equilibrium for genotype distributions was tested using Fisher’s exact test. The SNPStats web-based platform (https://www.snpstats.net/start.htm, accessed on February 7, 2025) was employed to perform logistic regression analyses examining potential correlations between genotype distributions and AH susceptibility. This analysis followed an additive genetic model and was adjusted for confounding factors, including age, body mass index (BMI) and age of UF diagnosis. To account for the influence of risk factors on genetic marker associations, separate analyses were performed for individuals with and without exposure to these factors.

The AH patient cohort and control group met the required power criteria of 80%, as calculated using the online GAS Power Calculator [21]. Analyses employed a multiplicative model with a significance level of 0.05. Given an AH prevalence of 0.08 in Central Russia and minor allele frequencies ranging from 0.07 to 0.49, the sample sizes of AH patients (n = 180) and controls (n = 430) provided sufficient power to detect intergroup differences in minor allele frequencies at OR between 1.39 and 1.8.

The analysis utilizing the model-based multivariate dimensionality reduction (MB-MDR) method investigated combinations of genotypes at two, three, and four levels, assessing both gene-gene (G\timesG) interactions and interactions between genotypes and AH risk factor (smoking) (gene-environment, G\timesE) (method was described in details in our previous studies [22]). To ensure the robustness of the results, empirical p-values (p_perm) were calculated for each model using permutation testing with 1000 iterations, which is the standard approach for evaluating all potential interactions of a given complexity. Associations demonstrating permutation-adjusted significance (p_perm 0.05) were considered statistically reliable [23]. All regression models incorporated adjustments for three key confounders: age, BMI and age of UF diagnosis to control for potential confounders.

All statistical analyses were performed using R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). For every interaction tier, we retained the top three to four models exhibiting maximal Wald statistics and minimal p-values for subsequent evaluation. The MB-MDR method also enabled the identification of specific genotype combinations significantly associated with the studied phenotypes (p 0.05). These calculations were performed using the MB-MDR program compatible with R (version 3.6.3).

To explore the functional implications of the studied SNPs, several bioinformatics tools described in details in our previews studies [24] were employed:

• The GTEx Portal (http://www.gtexportal.org/, accessed on February 13, 2025) was used to analyze SNP associations with expression quantitative trait loci (eQTLs) in various tissues, including heart, vessels and blood [25].

• eQTLGen (https://www.eqtlgen.org/, accessed on February 13, 2025) provided additional data on SNP-eQTL relationships, particularly in peripheral blood samples.

• HaploReg v4.2 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php, accessed on February 13, 2025) was utilized to examine SNP locations within regulatory elements, such as DNase hypersensitive regions, and their links to histone modifications.

• The atSNP Function Prediction tool (http://atsnp.biostat.wisc.edu/search, accessed on February 13, 2025) assessed how SNP variations affected transcription factor binding affinity based on reference and alternative alleles [26].

• Gene Ontology (http://geneontology.org/, accessed on February 13, 2025) was applied to identify biological processes enriched among transcription factors associated with the studied SNPs, connecting these processes to AH pathogenesis [27].

• The Cardiovascular Disease Knowledge Portal (CVDKP) (https://cvd.hugeamp.org/, accessed on February 13, 2025) integrated genetic association data, offering insights into SNP relationships with AH and related phenotypes, such as isolated increased systolic or diastolic blood pressure [28].

The integration of these bioinformatics tools provided a comprehensive understanding of the functional roles of the SNPs, their interactions with environmental risk factors, and their contributions to the molecular mechanisms underlying AH pathogenesis. This approach combined genetic, environmental, and computational analyses to uncover intricate patterns of disease susceptibility.

3. Results

3.1 Association of UF GWAS-Loci With Risk of AH in Russian Women

Significant inverse association with AH risk was observed for rs1812266 effect allele C (LOC105375949) in the combined analysis (OR = 0.74, 95% CI = 0.57–0.97, p = 0.028) (Table 2).

3.2 Gene-Gene Interactions Analysis (MB-MDR, MDR Modeling)

The MB-MDR analysis identified seven statistically significant intergenic interaction models (p_perm \le 0.05) involving UF GWAS loci and AH risk: two two-locus, three three-locus, and two four-locus interactions (Table 3). These robust gene-gene networks incorporated nine polymorphic loci, with five key variants—rs66998222 (LOC102723323), rs2456181 (ZNF346), rs1812266 (LOC105375949), rs10929757 (GREB1), and rs7986407 (FOXO1)—participating in multiple interaction models, suggesting their central role in the genetic architecture of AH susceptibility among UF patients. Notably, SNP rs66998222 (LOC102723323) appeared in five out of the seven most statistically significant interaction models, suggesting its potential central role in the genetic architecture linking UF susceptibility loci to AH risk.

The MDR method revealed several key findings (Fig. 1). First, the genetic variants included in the most significant gene-gene interaction models were predominantly characterized by antagonistic and additive effects, with the exception of SNPs rs2456181 ZNF346 and rs10929757 GREB1, which demonstrated pronounced synergistic interactions. Second, the most notable individual effect was observed for rs66998222 LOC102723323, contributing 1.01% to the trait entropy associated with AH. Third, the individual (main) effects of the genetic variants involved in the top gene-gene interaction models (ranging from 0.38% to 1.01% of AH entropy contribution) were comparable to the effects of gene-gene interactions (0.06% to 0.64% of AH entropy contribution).

Fourth, the strongest associations with AH were identified for the following genotype combinations of the polymorphic gene variants: rs66998222 LOC102723323 (A/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.231; p = 0.0005); rs1812266 LOC105375949 (G/G) \times rs66998222 LOC102723323 (A/G) (Beta = 0.1403; p = 0.017); rs11031731 THEM7P, WT1 (G/G) \times rs1812266 LOC105375949 (G/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.2095; p = 0.007); rs2553772 LOC105376626 (T/G) \times rs10929757 GREB1 (C/C) \times rs7986407 FOXO1 (G/G) (Beta = 0.405; p = 0.002); rs10929757 GREB1 (C/C) \times rs66998222 LOC102723323 (A/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.33; p = 0.0007); rs1986649 FOXO1 (C/C) \times rs1812266 LOC105375949 (C/G) \times rs66998222 LOC102723323 (A/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.384; p = 0.0009); rs1812266 LOC105375949 (G/G) \times rs59760198 DNM3 (T/T) \times rs66998222 LOC102723323 (A/G) \times rs7986407 FOXO1 (A/A) (Beta = 0.499; p = 0.002) (Supplementary Table 1).

3.3 Gene–Environment Interactions of UF-GWAS-significant Loci Associated With AH Risk (MB-MDR and MDR Modeling)

Using the MB-MDR approach, seven most significant gene–environment interaction models associated with AH were identified, including one two-level, three three-level, and three four-level interaction models (Table 4). In total, six genetic variants were involved in the top SNP–smoking interaction models, with five of them—rs66998222 (LOC102723323), rs1812266 (LOC105375949), rs10929757 (GREB1), rs2456181 (ZNF346), and rs2553772 (LOC105376626)—appearing in two or more of the most significant G\timesE models. Notably, the interaction between smoking \times rs66998222 (LOC102723323) served as a central component in five out of the seven most significant genotype–environment interactions associated with AH.

In the next stage of the analysis, interactions between these genetic variants and risk factors were further evaluated using Multifactor Dimensionality Reduction (MDR) modeling (Fig. 2). First, MDR analysis demonstrated that smoking, as an environmental risk factor, exhibited the weakest individual effect, contributing only 0.15% to AH-related entropy, which is considerably lower compared to the individual effects of SNPs (ranging from 0.52% to 1.01%). Second, smoking displayed heterogeneous interaction patterns with the genetic variants involved in the top gene–environment interaction models: a moderate synergistic effect in interaction with rs10929757 (GREB1), a moderate antagonistic effect with rs2456181 (ZNF346), and additive effects with rs66998222 (LOC102723323) and rs1812266 (LOC105375949).

Third, the strongest associations with AH were observed for the following gene–environment interaction combinations: no smoking \times rs66998222 LOC102723323 (A/G) (Beta = 0.135; p = 0.002); no smoking \times rs1812266 LOC105375949 (G/G) \times rs66998222 LOC102723323 (A/G) (Beta = 0.192; p = 0.005); no smoking \times rs10929757 GREB1 (C/C) \times rs2456181 ZNF346 (C/C) (Beta = 0.18; p = 0.018); no smoking \times rs2553772 LOC105376626 (T/G) \times rs66998222 LOC102723323 (A/G) (Beta = 0.159; p = 0.006); no smoking \times rs1812266 LOC105375949 (C/G) \times rs66998222 LOC102723323 (A/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.485; p = 0.00001); no smoking \times rs10929757 GREB1 (C/C) \times rs66998222 LOC102723323 (A/G) \times rs2456181 ZNF346 (C/C) (Beta = 0.385; p = 0.0004); smoking \times rs2553772 LOC105376626 (T/T) \times rs10929757 GREB1 (C/A) \times rs7986407 FOXO1 (A/A) (Beta = 0.46; p = 0.044) (Supplementary Table 2).

3.4 Bioinformatic Annotation of AH-linked Polymorphisms

Our integrated analysis of genetic variants, epistatic interactions, and gene-environment effects revealed significant associations between arterial hypertension and six UF-associated GWAS loci: rs2456181 ZNF346, rs7986407 FOXO1, rs66998222 LOC102723323, rs10929757 GREB1, rs1812266 LOC105375949 and rs2553772 LOC105376626. These risk variants subsequently underwent systematic functional annotation to characterize their potential biological roles.

3.4.1 QTL-Effects

The results of the cis-eQTL analysis (Table 5), shed light on the impact of specific genetic variants on gene expression. According to the GTEx Portal, rs2456181 ZNF346 increases expression of FGFR4 and UIMC1 in blood and arteries, and also increases UIMC1 in heart. In the same time rs2456181 ZNF346 increases expression itself in arteries. SNP rs7986407 FOXO1 increases expression of ENSG00000287837 in blood, arteries and heart and SNP decreases expression of SLC25A15 in arteries and heart. And, lastly, SNP rs2553772 LOC105376626 increases expression of CD44 and ENSG00000255521 in arteries; and SNP decreases ENSG00000289526 in blood and APIP in heart.

The eQTLGen Browser data further demonstrated significant expression quantitative trait loci associations in blood samples. The rs2456181 variant in ZNF346 was associated with decreased expression of ZNF346-IT1 alongside increased expression levels of UIMC1, FGFR4, and HK3. Similarly, rs7986407 in FOXO1 showed correlations with reduced MRPS31, SLC25A15, and KBTBD7 expression, while exhibiting elevated expression of WBP4 and FOXO1 itself. Finally, the rs2553772 polymorphism in LOC105376626 displayed positive associations with CD44 expression but negative correlations with RP1-68D18.4 levels (Table 5).

3.4.2 Transcription Factors

The analysis of transcription factors revealed that the SNP allele G rs2456181 ZNF346 creates DNA binding sites for 45 TFs, co-controlling response to interleukin-9-mediated signaling pathway (GO:0038113; false discovery rate (FDR) = 1.04 \times 10^-2); growth hormone receptor signaling pathway via Janus kinase/signal transducer and activator of transcription (JAK-STAT) (GO:0060397; FDR = 1.72 \times 10^-2); cellular response to interleukin-17 (GO:0097398; FDR = 2.91 \times 10^-2); positive regulation of vascular endothelial growth factor production (GO:0010575; FDR = 2.42 \times 10^-3); cell surface receptor signaling pathway via JAK-STAT (GO:00007259; FDR = 1.55 \times 10^-2); positive regulation of angiogenesis (GO:0045766; FDR = 1.57 \times 10^-2) (Supplementary Table 3).

The ref allele A rs7986407 FOXO1 creates DNA binding sites for 37 TFs, co-controlling response to peroxisome proliferator activated receptor signaling pathway (GO:0035357; FDR = 1.06 \times 10^-2); cardiac muscle cell myoblast differentiation (GO:0060379; FDR = 1.26 \times 10^-2); retinoic acid receptor signaling pathway (GO:0048384; FDR = 5.67 \times 10^-4); positive regulation of apoptotic process (GO:0043065; FDR = 4.95 \times 10^-2) (Supplementary Table 4).

The analysis of transcription factors revealed that the SNP allele A rs66998222 LOC102723323 creates DNA binding sites for 28 TFs, co-controlling cellular response to cytokine stimulus (GO:0071345; FDR = 1.07 \times 10^-2). Ref allele G rs66998222 LOC102723323 creates DNA binding sites for 24 TFs, co-controlling regulation of interleukin-1 beta production (GO:0032651; FDR = 2.29 \times 10^-2), response to hypoxia (GO:0001666; FDR = 3.02 \times 10^-2) and positive regulation of cytokine production (GO:0001819; FDR = 1.90 \times 10^-2) (Supplementary Table 5).

SNP allele C rs10929757 GREB1 creates DNA binding sites for 44 TFs, co-controlling response to cardiac chamber formation (GO:0003207; FDR = 2.21 \times 10^-2); atrioventricular canal development (GO:0036302; FDR = 2.95 \times 10^-2); interleukin-6-mediated signaling pathway (GO:0070102; FDR = 3.31 \times 10^-2); heart valve formation (GO:0003188; FDR = 3.59 \times 10^-2); cardiac left ventricle morphogenesis (GO:0003214; FDR = 3.89 \times 10^-2); regulation of cell proliferation involved in heart morphogenesis (GO:2000136; FDR = 4.30 \times 10^-2); atrioventricular valve development (GO:0003171; FDR = 3.87 \times 10^-3); regulation of cardiac muscle tissue growth (GO:0055021 FDR = 1.54 \times 10^-2); ventricular septum development (GO:0003281; FDR = 3.33 \times 10^-2); cardiocyte differentiation (GO:0035051; FDR = 8.37 \times 10^-3); cardiac muscle tissue development (GO:0048738; FDR = 3.67 \times 10^-2); response to hypoxia (GO:0001666; FDR = 1.79 \times 10^-2); muscle structure development (GO:0061061; FDR = 4.47 \times 10^-3); regulation of cytokine production (GO:0001817; FDR = 7.00 \times 10^-3). Reference allele A rs10929757 GREB1 creates DNA binding sites for 13 TFs, co-controlling positive regulation of apoptotic process (GO:0043065; FDR = 3.13 \times 10^-2) (Supplementary Table 6).

The SNP allele C rs1812266 LOC105375949 creates DNA binding sites for 29 TFs, co-controlling response to interleukin-4-mediated signaling pathway (GO:0035771; FDR = 5.94 \times 10^-3); growth hormone receptor signaling pathway via JAK-STAT (GO:0060397; FDR = 1.29 \times 10^-2); SMAD protein signal transduction (GO:0060395; FDR = 7.02 \times 10^-4); cell surface receptor signaling pathway via JAK-STAT (GO:0007259; FDR = 9.49 \times 10^-3). The Ref allele G rs1812266 LOC105375949 creates DNA binding sites for 41 TFs, co-controlling response to cardiac muscle cell myoblast differentiation (GO:0060379; FDR = 1.19 \times 10^-2); cardiac chamber formation (GO:0003207; FDR = 1.18 \times 10^-2); cardiac left ventricle morphogenesis (GO:0003214; FDR = 2.11 \times 10^-2); positive regulation of cardiac muscle cell proliferation (GO:0060045; FDR = 4.20 \times 10^-2); cardiac muscle cell proliferation (GO:0060038; FDR = 4.51 \times 10^-2); regulation of cardiocyte differentiation (GO:1905207; FDR = 4.49 \times 10^-2); regulation of interleukin-2 production (GO:0032663; FDR = 1.16 \times 10^-2); vasculogenesis (GO:0001570; FDR = 1.46 \times 10^-2) (Supplementary Table 7).

SNP allele G rs2553772 LOC105376626 creates DNA binding sites for 20 TFs, jointly involved in positive regulation of cholesterol biosynthetic process (GO:0045542; FDR = 3.67 \times 10^-3); SREBP signaling pathway (GO:0032933; FDR = 4.45 \times 10^-3), cellular response to transforming growth factor beta stimulus (GO:0071560; FDR = 3.01 \times 10^-2). Reference allele T rs2553772 LOC105376626 creates DNA binding sites for 42 TFs, regulating the following biological processes: cardiac muscle tissue regeneration (GO:0061026; FDR = 1.37 \times 10^-3); atrioventricular node development (GO:0003162; FDR = 6.08 \times 10^-3); cardiac right ventricle morphogenesis (GO:0003215; FDR = 3.46 \times 10^-4); aortic valve morphogenesis (GO:0003180; FDR = 2.36 \times 10^-3); negative regulation of cardiac muscle hypertrophy (GO:0010614; FDR = 3.62 \times 10^-2); positive regulation of vascular endothelial growth factor production (GO:0010575; FDR = 4.19 \times 10^-2); cardiac muscle hypertrophy (GO:0003300; FDR = 4.65 \times 10^-2); regulation of cardiac muscle tissue growth (GO:0055021; FDR = 4.90 \times 10^-3); cardiocyte differentiation (GO:0035051; FDR = 3.40 \times 10^-2); positive regulation of angiogenesis (GO:0045766; FDR = 6.25 \times 10^-3); muscle organ development (GO:0007517; FDR = 4.36 \times 10^-2) (Supplementary Table 8).

3.4.3 Histone Modification Patterns at Identified Risk Loci

Epigenetic profiling using HaploReg v4.2 revealed characteristic histone modification patterns at AH-associated risk loci. The SNPs rs2456181 (ZNF346), rs7986407 (FOXO1), rs10929757 (GREB1), and rs2553772 (LOC105376626) consistently showed active chromatin marks in blood and cardiac tissue, including H3K4me1 (histone H3 lysine 4 mono-methylation), H3K27ac (lysine 27 acetylation), and H3K9ac (lysine 9 acetylation). Notably, rs2456181, rs7986407, and rs2553772 additionally demonstrated similar regulatory histone signatures in vascular tissues (Supplementary Table 9).

3.4.4 Bioinformatic Exploration of AH-associated Variants and Phenotypic Correlations

The Cardiovascular Disease Knowledge Portal confirms that two GWAS-significant variants identified in our study—rs2456181 ZNF346 and rs1812266 LOC105375949—demonstrate established associations with elevated arterial hypertension risk in prior research, corroborating our findings. Moreover, rs2456181 ZNF346 increases and rs66998222 LOC102723323 decreases systolic blood pressure. Also, rs7986407 FOXO1 increases and rs66998222 LOC102723323 decreases diastolic blood pressure (Supplementary Table 10).

4. Discussion

Studies have demonstrated that UF is associated with a 1.44- to 1.88-fold increased risk of AH [8, 9], as well as with a higher likelihood of hypertensive pregnancy disorders, including preeclampsia [10, 11]. Conversely, an elevated risk of UF has also been reported among individuals with AH [7].

Both conditions share common pathophysiological features involving alterations in smooth muscle cells (SMCs). In UF, changes occur in both the myometrial tissue and vascular SMCs, whereas AH is also characterized by profound structural and functional changes in vascular SMCs [29]. According to recent findings, patients with UF exhibit alterations in arteriolar architecture, including mitochondrial and endoplasmic reticulum stress, as well as myocyte migration, which may potentially influence blood pressure regulation [30].

It is well established that components involved in blood pressure regulation, such as the renin-angiotensin-aldosterone system (RAAS) [31], may influence the risk of UF development through mechanisms associated with inflammation, cellular proliferation, angiogenesis, and fibrosis [32, 33]. These processes represent common pathogenic pathways linking UF and AH [34, 35].

Moreover, alterations in estrogen levels, which play a critical role in the pathogenesis of UF [36, 37, 38, 39], are also involved in the regulation of postmenopausal vasomotor symptoms, including blood pressure modulation [40].

Given these shared pathophysiological mechanisms and accumulating clinical evidence supporting the association between UF and AH, it is plausible to assume that these two conditions are not only interrelated but may also mutually exacerbate each other.

In our study, we first evaluated the association between 17 confirmed GWAS loci associated with UF and the risk of AH. We found that rs1812266 (LOC105375949 G/C) was associated with a decreased risk of AH (OR = 0.74, 95% CI = 0.57–0.97, p = 0.028). Our findings are supported by bioinformatics data from the Cardiovascular Disease Knowledge Portal (CVDKP), which aggregates data from meta-analyses of GWAS studies worldwide. According to CVDKP, six studies showed an association between rs1812266 (LOC105375949 G/C) and a decreased risk of AH (Supplementary Table 11).

Bioinformatics analysis revealed that the C allele of the SNP, which showed protective effects regarding AH risk in our study, generates binding sites for transcription factors (TFs) involved in the regulation of processes such as «interleukin-4-mediated signaling pathway», which plays a key role in suppressing pro-inflammatory responses, reducing vascular damage, and improving vascular function [41]. Additionally, the C allele is involved in the JAK-STAT pathway, activated by growth hormone or cytokine receptors (e.g., IL-6, Angiotensin II). The pathway regulates cardiomyocyte hypertrophy and inflammatory processes, suggesting their role in cardiovascular pathology risk, including AH [42, 43]. Moreover, the C allele of this SNP regulates “SMAD protein signal transduction” which reduces fibrotic damage in vascular SMCs [44].

Furthermore, our study demonstrated that UF-GWAS significant loci, such as rs2456181 (ZNF346), rs7986407 (FOXO1), rs66998222 (LOC102723323), rs10929757 (GREB1), and rs2553772 (LOC105376626), were involved in the most significant gene–gene interactions associated with the development of AH.

Functional annotation revealed that rs2456181 ZNF346, through cis-eQTL effects, influences the expression of several genes involved in the regulation of cardiovascular system function. Specifically, rs2456181 increases the expression of genes such as FGFR4, UIMC1, and HK3 in blood, arteries, and the heart, while concurrently reducing the expression of ZNF346-IT1. The FGFR4 gene (fibroblast growth factor receptor 4) is involved in cell proliferation and angiogenesis, and its increased expression can contribute to vascular dysfunction and AH [45, 46]. The UIMC1 gene (ubiquitin interaction motif containing 1) plays a role in the DNA damage response and cell cycle regulation, and is closely associated with menopause [47]. This could impact endothelial function, vascular remodeling, and, ultimately, increase the risk of AH [48]. The HK3 gene (hexokinase 3) is involved in glucose metabolism [49], and its dysregulation may be associated with metabolic disorders, which are often linked to AH [50]. The G allele (major in our case) of rs2456181 forms binding sites for transcription factors that regulate processes are likely connected to the common pathophysiological pathways of UF and AH, such as inflammation, endothelial dysfunction, and vascular remodeling [29, 30]. For instance, IL-9 and IL-17 are pro-inflammatory cytokines that may amplify inflammation and promote the development of AH [51]. Data from the CVDKP confirm that the G allele of rs2456181 is associated with an increased risk of AH and an isolated increase in systolic blood pressure.

The FOXO1 gene (forkhead box O1) regulates the cell cycle, apoptosis, oxidative stress, and vascular homeostasis [52] and may contribute to heart hypertrophy and vascular remodeling, linking it to AH [53]. Functional analysis revealed that rs7986407 (FOXO1), involved in the most significant gene-gene interactions, affects the expression of several genes potentially involved in the development of AH. According to cis-eQTL analysis, rs7986407 reduces the expression of SLC25A15 (mitochondrial ornithine transporter) in arteries and the heart. The SLC25A15 gene plays a role in nitrogen metabolism and mitochondrial function [54, 55], and its reduced expression may be associated with disturbances in energy metabolism and oxidative stress, which are characteristic of AH [56, 57, 58, 59]. Furthermore, the locus correlates with decreased expression of MRPS31 (mitochondrial ribosomal protein) and KBTBD7 (a protein involved in ubiquitination), as well as increased expression of WBP4 (Wnt signaling pathway-binding protein). The MRPS31 gene is involved in mitochondrial ribosome biogenesis, and its dysregulation may affect the energy balance of cells [60]. WBP4 gene dysfunction has been linked to the development of neurodevelopmental syndrome with hypotonia as a symptom [61]. KBTBD7 gene has been shown in mice experiments to influence inflammation and dysfunction in the myocardium [62]. According to the CVDKP, the G allele of rs7986407 (FOXO1) is associated with an increase in diastolic blood pressure, confirming its potential role in regulating vascular tone and the risk of AH.

The LOC102723323 gene is a long non-coding RNA (lncRNA) whose function is not fully understood. However, it is known that lncRNAs can play a significant role in regulating gene expression, including genes associated with the cardiovascular system [63]. Functional analysis revealed its potential influence on cytokine pathways and the hypoxia response. The minor SNP allele A (rs66998222 LOC102723323), involved in forming the best gene-gene interaction models, creates binding sites for transcription factors that regulate inflammatory processes, which play a key role in the pathogenesis of AH [51, 64]. According to data from the Cardiovascular Disease Knowledge Portal, the A allele of rs66998222 is associated with a reduction in both systolic and diastolic blood pressure, highlighting its potential protective role in regulating vascular tone.

The GREB1 gene (growth regulation by estrogen in breast cancer 1) is known for its role in regulating cell proliferation and apoptosis, as well as being a target of estrogen-dependent signaling, influencing blood pressure [65]. Functional analysis revealed that the A allele (rs10929757 GREB1), involved in the most significant gene-gene interactions, forms binding sites for transcription factor “positive regulation of apoptotic process”, a key factor in vascular wall remodeling [30]. The C allele (SNP rs10929757), on the other hand, creates binding sites for transcription factors that control the development of heart compartments, regulation of cytokine production and response to hypoxia. IL-6 is a key pro-inflammatory cytokine that can contribute to endothelial dysfunction and vascular inflammation, characteristic of AH [66]. The response to hypoxia also plays a crucial role in regulating blood pressure through the activation of HIF-1\alpha (hypoxia-inducible factor) and related pathways [67].

According to the cis-eQTL analysis, rs2553772 LOC105376626, also involved in the most significant gene-gene interactions, increases the expression of CD44 in arteries and blood, and decreases the expression of APIP in the heart. The CD44 gene encodes the hyaluronan receptor and may contribute to the pathogenesis of AH through endothelial-mesenchymal transition and inflammatory processes [68, 69]. The APIP gene (adenosine deaminase acting on RNA) is associated with cardioprotective function [70]. The reference (minor in our case) allele T (rs2553772) forms binding sites for transcription factors that regulate crucial processes for maintaining cardiovascular homeostasis and may be linked to the pathogenesis of AH [51]. The G allele (rs2553772) creates binding sites for transcription factors controlling SREBP and TGF-\beta signalling pathways and play significant roles in regulating lipid metabolism and fibrosis, which may influence the development of vascular stiffness and AH [71, 72].

Summarizing the findings above, we conclude that SNPs influence the risk of hypertension by affecting inflammation, response to oxidative stress, RAAS function, tissue fibrosis, angiogenesis, and SMCs remodeling. These effects are mediated through cis-eQTL interactions and biological processes related to transcription factors (Fig. 3).

5. Conclusions

For the first time, our team analyzed the relationship between GWAS loci of UF and the risk of AH, which allows for a fundamental confirmation of previously identified clinical correlations [7, 8, 9]. The obtained results may be of significant importance for developing new strategies for the prevention and treatment of both UF and AH [29], as well as for understanding the common molecular mechanisms underlying these diseases.

6. Study Limitations

This study has several limitations. First, the number of analyzed SNP markers was restricted, which may have limited the scope of genetic associations identified. Second, the use of TaqMan probe-based genotyping imposed methodological constraints, resulting in the exclusion of certain SNPs due to difficulties in probe design. Third, certain clinical variables were either missing for some participants or characterized by low prevalence, which limited our ability to explore gene–environment interactions and the potential influence of SNPs on specific clinical features of the disease. Finally, the principal limitations of this research involve the modest cohort size and the necessity for validation across different ethnic populations.

Availability of Data and Materials

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

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