Introduction
Polycystic ovary syndrome (PCOS) is an endocrine disorder commonly seen in women of reproductive age, characterized by reproductive, metabolic and psychological dysfunction that affects all aspects of life.1,2 The global prevalence of PCOS is estimated to be between 5% and 18%.3,4 Women with PCOS typically exhibit menstrual irregularities, higher androgen levels, enlarged ovaries, and metabolic disturbances associated with obesity and insulin resistance.4 While much has been explored regarding the clinical manifestations and long-term complications, such as type 2 diabetes, cardiovascular disease, infertility, and immune disorders the pathophysiological mechanisms underlying PCOS remain poorly understood.5–9 Immune dysregulation is thought to play a critical role in the pathophysiology of PCOS, yet its precise mechanisms and impact on disease progression are still not fully elucidated.
Recent studies have increasingly concerned the role of inflammation and immune cells in the pathophysiology of PCOS,10 it has been observed that patients with PCOS were often in a state of chronic low-grade inflammation, which induces oxidative stress in the ovaries and disrupts follicular development and oocyte quality.11 Studies have shown that chronic low-grade inflammation in patients with PCOS was associated with unusual activation of a variety of immune cells.12 For example, TLR4 gene polymorphisms in PCOS patients may be associated with their chronic inflammatory state.13 In addition, macrophages accumulate in the ovary and secrete proinflammatory factors, which may affect the normal function of the ovary.14 A Mendelian Randomization (MR) study revealed that levels of Memory B cell AC, CD39+ CD4+ %CD4+, CD20 on CD20- CD38-, and HLA DR on CD14- CD16+ monocyte were significantly associated with PCOS risk.15 However, the precise mechanisms by which immune cells influence the development of PCOS require further investigation.
In recent years, progress in metabolomics technology allows us to explore the role of metabolites in disease more comprehensively. Metabolites are products of cellular metabolic processes that reflect the physiological and pathological state of the organism and can affect the process of disease and be targeted for therapeutic intervention.16,17 Plasma metabolite abnormalities have been shown to be frequently associated with disturbances in their hormone metabolism and abnormal ovarian morphology in patients with PCOS, and some of the abnormal metabolites were associated with chronic inflammatory states.18,19 We hypothesized a causal relationship between immune cells, metabolites and PCOS, and immune cells play an important role in the pathogenesis of PCOS by possibly through these metabolites.
To deeply investigate this hypothesis, this study proposed the MR method. MR analysis has become a useful tool for causal inference using single nucleotide polymorphisms (SNP) as instrumental variables (IVs) to effectively control mixtures in traditional observational studies and avoid bias from causality.20,21 The MR analysis minimizes residual confusion due to the random organization of genetic material during in vitro fertilization, independent of environmental and lifestyle factors. We conducted an MR study and two mediation analyses using summary data from immune cells, blood metabolites, and genome-wide association statistics (GWAS) for PCOS to explore the mediating role of immune cells through blood metabolites, offering new insights for its prevention and treatment.
Materials and Methods
Study Design
We first screened for PCOS-associated immune cells primarily by inverse variance weighting (IVW) using 731 immune cells as the exposure factor and PCOS as the outcome factor. Next, we used 1400 plasma metabolites as mediators and proceeded to employ two-step RM (TSMR) and multivariate MR (MVMR) methods further. Our study attempted to elucidate the important mediating role that plasma metabolites may play in the cause-effect pathway between immune cells and PCOS, providing ideas for mechanisms of disease onset and therapeutic targets (Figure 1).
Figure 1 Schematic diagrams illustrating the study design. |
Data Sources
Genetic information related to PCOS was obtained from the GWAS summary database (https://gwas.mrcieu.ac.uk/) with the dataset ebi-a-GCST90044902, which includes information on genetic variants from 797 PCOS cases and 140,558 controls, totaling 22,981,890 SNPs. The PCOS cases were identified from national registers by ICD codes (ICD-10 E28.2, ICD-9256.4, or ICD-8256.90), and all remaining women were considered controls. These criteria require the presence of at least two of the following three criteria: chronic anovulation, hyperandrogenism, and polycystic ovaries visualized by ultrasonography.22
The 731 immune cell characteristics were categorized into four main types, including absolute count (AC) (118), median fluorescence intensities (MFI) (389), morphological parameters (MP) (32) and relative cell (RC) (n=192). Among them, MP mainly included CDC and TBNK panels, while AC, MFI and RC mainly included B cells, CDC, maturation stages of T cells, myeloid cells, monocytes, TBNK and Treg panels (Table S1). GWAS summary data for immunity cells traits were available from the GWAS catalog (accession numbers from ebi-a-GCST90001391 to ebi-a-GCST90002121), which includes 3575 Sardinians, 22 million SNPs.23 The 1400 plasma metabolites consisted primarily of 1091 metabolites and 309 metabolite ratios, and the data used in this study were derived from a series of large GWASs by Chen et al that included 8299 individuals from the Canadian Longitudinal Study of Aging (CLSA) cohort,17 which is available from the GWAS catalog (https://www.ebi.ac.uk/gwas, accession numbers from GCST90199621 to GCST90201020) (Table S2).
Instrumental Variable Selection
In MR analysis, the selection of IVs had to fulfill three assumptions: 1. IVs should have a high correlation with the exposure factor of interest; 2. IVs must not be associated with any confounding factors that could affect the results; 3. IVs affect the results only through the exposure factor and do not have any direct effect on the results.24 We used P-values less than 1×10−5 to filter 731 immune cells and 1400 metabolites strongly associated SNPs as IVs.25,26 We then excluded SNPs in linkage disequilibrium (LD) using r2 = 0.001 and kb = 10,000 as criteria. Then, weak IV bias was also evaluated by calculating the F-statistic and weak IVs are excluded by selecting SNPs with an F-test value exceeding 10 for further analysis.27 Finally, to control for the impact of confounding factors on instrumental variables, this study employed the Phenoscanner and R software packages, setting a P < 1 × 10−5 to eliminate palindromic SNPs and incompatible SNPs. This process excluded factors closely associated with the occurrence of PCOS, such as BMI, smoking, diabetes, depression, anxiety, and other related conditions,28 ensuring that the resulting instrumental variables were appropriate for subsequent analysis.
Sensitivity Analysis
Significant heterogeneity was detected using Cochran’s Q test, with a P-value less than 0.05 indicating statistical significance. Pleiotropy was evaluated using the MR pleiotropy residual sum and outlier (MR-PRESSO) test and the MR-Egger regression intercept, with P-values greater than 0.05 suggesting the absence of pleiotropy. The robustness of our findings was further validated by a leave-one-out analysis, which examined the influence of individual SNPs on the MR results.
Statistical Analysis
Multiple MIR methods, including MR Egger,29 weighted median,30 IVW, simple mode, and weighted mode, were used to estimate causal effects, with IVW selected as the primary analytical method, with a P < 0.05 considered to be causal.31 To visualize the MR results, we plotted funnel plots and forest plots, where the funnel facilitates the assessment of potential publication bias, and the forest plots show the causal estimates and their confidence intervals, demonstrating a more comprehensive MR result.
We calculated the total effect (βall) of immune cells on PCOS, the effect of immune cells on metabolites β1, and the effect of metabolites on PCOS β2 by TSMR analysis, which led to the calculation of the indirect effect (β1*β2), the direct effect (β′) as βall - β1*β2, and the proportion of mediating effects as β1*β2/βall.32 All analyses were performed with R (version 4.3.0) and version 0.5.8 of the TwoSampleMR package.
Results
Causal Effect of Immune Cells andPCOS
Through correlation analysis, LD removal, and calculation of F-value, we screened a total of 18,621 SNPs as IVs of immune cells (Table S3). Analyzed by five MR methods, we mainly focused on the IVW method, and based on its P value of less than 0.05. Furthermore, the odds ratios (ORs) for the remaining four methods were consistent with the direction of the initial analysis (Supplementary Figure S1A). We obtained a total of 33 immune cells that were causally associated with PCOS (P<0.05). Among them, 20 immune cells were risk factors for PCOS, including Myeloid DC %DC, CD4 Treg %T cell, Lymphocyte AC, DN (CD4-CD8-) AC, BAFF-R on CD24+ CD27+, BAFF-R on IgD+ CD24+, BAFF-R on IgD+ CD38- unsw mem, BAFF-R on IgD- CD24-, BAFF-R on IgD- CD27-, BAFF-R on IgD- CD38-, BAFF-R on IgD- CD38br, BAFF-R on memory B cell, BAFF-R on sw mem, CD19 on IgD+ CD38- unsw mem, CD20 on CD20- CD38-, CD20 on IgD- CD38dim, CD24 on IgD+ CD38-, CD25 on memory B cell, CD28 on CD39+ resting Treg, and SSC-A on HLA DR+ T cell, while 13 immune cells were protective factors for PCOS, including CD24+ CD27+ AC, CD11c+ monocyte %monocyte, CD25hi CD45RA+ CD4 not Treg %T cell, CM DN (CD4-CD8-) AC, CD8dim %T cell, NKT AC, CD27 on CD24+ CD27+, CD27 on IgD+ CD38- unsw mem, CD38 on naive-mature B cell, CD16 on CD14- CD16+ monocyte, CD64 on CD14- CD16+ monocyte, CD14 on Mo MDSC, and HLA DR on CD33dim HLA DR+ CD11b+ (Figure 2 and Table 1). In addition, heterogeneity and pleiotropy tests indicated no significant issues (P > 0.05, Table 1). The leave-one-out analysis further assessed the stability of the results (Supplementary Figure S1B), and in addition, the results of the funnel plot demonstrated that our results were not biased (Supplementary Figure S1C). Then we performed reverse MR analysis with PCOS as the exposure factor and these 33 immune cells as the outcome factor, we found that there was no causal relationship between PCOS and these immune cells (P>0.05, Table 1).
Table 1 MR Analysis of Immune Cells and PCOS |
Figure 2 Mendelian randomized analysis of immune cells and PCOS. (A) Circle plot of five MR methods; (B) Forest plot of causal relationship between 33 immune cells and PCOS. |
Causal Effect of Metabolites andPCOS
By correlation analysis, LD removal, and F-value screening calculations, we identified 34,843 SNPs as plasma metabolites of IVs (Table S4). Analyzed by five MR methods, we mainly focused on the IVW method, and based on its P value of less than 0.05. Furthermore, the ORs for the remaining four methods were consistent with the direction of the initial analysis (Supplementary Figure S2A). Consequently, we identified 51 metabolites (including two unknown metabolites) that have a causal relationship with PCOS (P<0.05). Of these, 28 metabolites increased the risk of PCOS, including methionine sulfoxide levels, hexanoylcarnitine levels (Biocrates platform) and epiandrosterone sulfate levels. In contrast, 23 metabolites decreased the risk of PCOS, such as N-acetylglycine levels, EDTA levels, and tauro-beta-muricholate levels (Figure 3 and Table 2). Furthermore, there was no heterogeneity or pleiotropy (P>0.05, Table 2), and the stability of the results was illustrated by leave-one-out analysis (Supplementary Figure S2B) and funnel plots (Supplementary Figure S2C).
Table 2 MR Analysis of Metabolites and PCOS |
Figure 3 Mendelian randomized analysis of plasma metabolites and PCOS. (A) Circle plot of five MR methods; (B) Forest plot of causal relationship between 51 plasma metabolites and PCOS. |
Causal Effect of Immune Cells and Metabolites
Based on the prior identification of immune cells and plasma metabolites, we used a TSMR method to extend our exploration of their mediating role. Firstly, we performed MR analysis from immune cell phenotypes to plasma metabolites using 33 immune cells as exposure factors and 51 plasma metabolites as outcome indicators. The results showed a causal relationship between 20 immune cells and 32 plasma metabolites, which was further calculated to derive an effect value β1 from immune cells to metabolites. Interestingly, we found that an immune cell phenotype may be causally associated with multiple metabolites. For example, CM DN (CD4-CD8-) AC, CD25 on memory B cell and HLA DR on CD33dim HLA DR+ CD11b+ were causally associated with more than four metabolites. Among them, HLA DR on CD33dim HLA DR+ CD11b+ was positively associated with EDTA levels, tauro-beta-muricholate levels, tetradecanedioate (C14-DC) levels and 2-hydroxyarachidate levels but negatively correlated with Alpha-ketoglutarate to ornithine ratio (Tables 3 and Figure 4). Subsequently, we performed MR analysis and MR-PRESSO tests (P > 0.05) with 31 plasma metabolites as exposure factors and PCOS as an outcome, which showed no pleiotropic and unbiased SNPs (Tables 4). With these tests, we determined an effect value of β2 from metabolites to PCOS and calculated an overall effect βall from immune cells to PCOS.
Table 3 MR Analysis of Immune Cells and Metabolites |
Table 4 MR-PRESSO Tests of Metabolites and PCOS |
Figure 4 Mendelian randomized analysis of immune cells and plasma metabolites. (A) Forest plot of causal relationship between 20 immune cells and 32 plasma metabolites; (B) Scatter plots showed that HLA DR on CD33dim HLA DR+ CD11b+ was positively associated with EDTA levels, Tauro-beta-muricholate levels, Tetradecanedioate (C14-DC) levels and 2-hydroxyarachidate levels, and negatively correlated with Alpha-ketoglutarate to ornithine ratio. |
Mediation Analysis
To illuminate plasma metabolites mediating the causal relationship between immune cells and PCOS through mediation, we performed a mediation analysis. We identified 17 plasma metabolites that mediated the relationship between 12 immune cells and PCOS (P < 0.05). Notably, immune cells do not necessarily exert their effects on PCOS through a single plasma metabolite. Specifically, the effects of CD25 on memory B cells and HLA DR on CD33dim HLA DR+ CD11b+ on PCOS were mediated by three distinct plasma metabolites. Similarly, the effects of Myeloid DC %DC, NKT AC, and CD20 on CD20- CD38- on PCOS were mediated by two different plasma metabolites.
Additionally, both metabolites, Histidine betaine (hercynine) levels and Alpha-ketoglutarate to ornithine ratio, mediated more than one type of causal relationship between immune cells and PCOS. Moreover, 17 plasma metabolites played a mediating role, and the top five plasma metabolites with the highest mediating ratios were as follows: X-23782 levels had the highest mediating ratio of 26.4% (P=0.007), followed by ornithine levels with a mediating ratio of 22.0% (P=0.013), tetradecanedioate (C14-DC) levels with a mediating ratio of 20.2% (P=0.025), EDTA levels with a mediating ratio of 17.3% (P=0.001), and the creatine-to-carnitine ratio with a mediating ratio of 16.3% (P=0.002) (Figure 5 and Table 5).
Table 5 MR Analysis of the Causal Effects Between Immune Cells, Plasma Metabolites and PCOS |
Figure 5 Mendelian randomized analysis of the causal effects between immune cells, plasma metabolites and PCOS. (A) Forest plots of immune cells, plasma metabolites, and PCOS; (B) Plasma metabolites mediated the causal relationship between immune cells and PCOS (risk factors in red, protective factors in green). |
Discussion
To investigate the relationship between immune cells, plasma metabolites, and PCOS, we conducted the first multiple bidirectional two-sample MR studies and mediation studies to investigate the causal relationship between immune cells and PCOS via plasma metabolites. Our study highlights 12 immune cells impacting PCOS through 17 metabolites, advancing the understanding of immune mechanisms in PCOS risk and suggesting potential therapeutic approaches targeting immune modulation. To our knowledge, this is the first large-scale genetic correlation analysis of immune cells, plasma metabolites, and PCOS. Because we used GWAS genetic data, the results are not susceptible to environmental confounders.
Our MR analysis identified a total of 33 immune cells causally associated with PCOS, with more than half belonging to the B cell panel, including 10 protective factors and 8 risk factors. The importance of B cells in the progression of PCOS has been emphasized in recent studies and our finding not only highlights the important role of B cells in the development of PCOS but also underscores the complexity of their involvement in PCOS pathogenesis.33 CD20, a specific antigen located on the surface of B lymphocytes, is likely crucial for regulating B cell proliferation, differentiation, and signal transduction processes.34 Based on this, a recent MR analysis suggested that CD20- CD38- B cells may act as protective factors for PCOS, potentially by safeguarding normal ovarian morphology through the preservation of normal immune system pathways.35 However, our study found that CD20 on CD20- CD38- and CD20 on IgD- CD38dim B cells were associated with an increased risk of PCOS. This suggests that greater attention should be given to the characteristic of memory B cells rich in autoantibodies, as this trait is related to chronic infections, autoimmune diseases, and disease accumulation.35 Additionally, observational studies have consistently confirmed the dysregulation of immune cell levels in PCOS, which aligns with our observation of the risk factors for PCOS involve distinct immune cell phenotypes, including Myeloid DC %DC, CD4 Treg %T cells, Lymphocyte AC, DN (CD4-CD8-) AC, and various B and T cell subsets.36 These findings support the notion that immune dysfunction, particularly an overactive immune response, plays a significant role in PCOS.37 Such overactivation could lead to chronic inflammation, which may further promote the development of insulin resistance and hormonal imbalances-hallmarks of PCOS pathogenesis.38
On the other hand, 13 immune cells were identified as protective factors for PCOS. These immune cells, such as CD24+ CD27+ AC, CD11c+ monocyte %monocyte, CD25hi CD45RA+ CD4 not Treg %T cell, and various other immune cell types like NKT AC, CD27 on CD24+ CD27+, and HLA DR on CD33dim HLA DR+ CD11b+, were found to be associated with a decreased risk of developing PCOS. We hypothesize that these immune cells, particularly those involved in the maturation stages of T cells, monocytes, and myeloid cells, may play a role in modulating the inflammatory state through their corresponding immune functions. By maintaining a balanced immune response, these protective immune cells could potentially reduce the risk of PCOS. This may involve suppression of excessive inflammation, modulation of insulin sensitivity, or regulation of hormonal pathways, ultimately contributing to a protective effect against the disease.39,40 Overall, these findings further support the hypothesis that immune cell-mediated inflammation plays a crucial role in PCOS, with certain immune cell types acting as risk factors and others serving as protective factors. Understanding the roles of these immune cells in PCOS pathogenesis could offer novel therapeutic targets for managing or preventing the disease.
Metabolites have a vital role in recognizing high-risk populations at an early stage and in preventing disease, with potential clinical applications.41 An MR analysis identified metabolites that may be causally involved in the development of PCOS, which was also identified in our study as having impact on the risk of PCOS.42 An increasing number of studies highlight the role of both innate and adaptive immunity in responding to changes in metabolic conditions.43 The emerging field of immunometabolism is built upon evidence of immune-derived signals being activated in metabolically significant tissues, and it explores how immune cells assist tissues in adapting to environmental challenges.44 Adipose tissue is one of the most explored tissues in the field, and the expression and activation of various immune cell types and anti-inflammatory cytokines have been studied in both lean and obese states.45 In addition, the immune profile of adipose tissue, generating chronic low-grade inflammation, gradually becomes systemic and leads to insulin resistance and metabolic diseases.46 The results of this study also confirmed causal relationships between 20 immune cell types and 32 plasma metabolites. Therefore, although the specific molecular mechanisms underlying the interactions between immune cells and metabolites during the pathogenesis of PCOS remain unclear, it could be speculated that research from the perspectives of immunometabolism may provide new insights for the comprehensive understanding, diagnosis, and treatment of PCOS.
We found a causal relationship between CD25 on memory B cell and PCOS through three mediators, X-23782 levels, tetradecanedioate (C14-DC) levels, and 1-oleoyl-GPI (18:1) levels. CD25 on memory B cell is positively correlated with these three metabolites, which in turn are positively correlated with PCOS. Tetradecanedioate (C14-DC) is considered a biomarker of transporter function, a metabolite of fatty acid ω-oxidation.47,48 As it turns out, its role in the metabolism associated with pregnancy is significant, and we hypothesize that it may indirectly affect ovarian function and reproductive health.49 1-oleoyl-GPI (18:1) is a phospholipid compound that has been shown to be upregulated in activated platelets, but its association with ovarian disease requires further study.50
In addition, we found that HLA DR on CD33dim HLA DR+ CD11b reduced the risk of PCOS through three mediators, Alpha-ketoglutarate to ornithine ratio, tauro-beta-muricholate levels, and EDTA levels. HLA DR on CD33dim HLA DR+ CD11b+ was negatively correlated with Alpha-ketoglutarate to ornithine ratio, while Alpha-ketoglutarate to ornithine ratio was positively correlated with PCOS. HLA DR on CD33dim HLA DR+ CD11b+ was positively correlated with both tauro-beta-muricholate levels and EDTA levels, while both two were negatively correlated with PCOS. Alpha-ketoglutarate is a key intermediate in the Krebs cycle, shown to promote oocyte meiosis and embryonic development and reduce oxidative stress.51,52 Ornithine is an amino acid metabolite mainly involved in the urea cycle. Macrophages metabolize arginine through different pathways to produce ornithine, which affects the immune response and inflammatory processes.53 When Alpha-ketoglutarate combines with ornithine to form ornithine-alpha-ketoglutarate (OKG), some studies have found that it may be capable of enhancing immune function and reducing oxidative stress and inflammation in patients with severe illness.54,55 These effects on immune and reproductive function Alpha-ketoglutarate to ornithine ratio affect PCOS and provide indirect evidence. Interestingly, we found that Ornithine levels and Alpha-ketoglutarate to ornithine ratio again play a mediating role in CD20 on CD20- CD38- increased PCOS risk. CD20 on CD20- CD38- was negatively correlated with Ornithine levels, which were negatively correlated with PCOS, while CD20 on CD20- CD38- was positively correlated with Alpha-ketoglutarate to ornithine ratio.
Our study also discovered that Myeloid DC %DC may affect PCOS through two mediators, Choline phosphate to choline ratio and Gamma-glutamylglutamine levels. Choline metabolism has been shown to have a role in ovarian function, and one study found that choline supplementation promoted ovarian follicular development and increased the number of corpus luteum in the ovary.56 Gamma-glutamylglutamine is a metabolite produced through the action of gamma-glutamyltransferase (GGT), and it has been shown that GGT levels correlate with the prognosis of ovarian cancer.57 Therefore, we believe that these two metabolites are relevant to PCOS. In addition, our results suggested that NKT AC affected the development of PCOS through two mediators, cysteinylglycine disulfide levels and cys-gly, oxidized levels. These two are both products of glutathione metabolism, which are related to oxidative stress and can reflect the state of intracellular redox balance,58 and oxidative stress is a factor in PCOS. Notably, histidine betaine (hercynine) levels acted as a mediator not only in DN (CD4-CD8-) AC with PCOS, but also in BAFF-R on CD24+ CD27+ and PCOS. Histidine betaine (hercynine) is a metabolite derived from histidine, and as Histamine has been reported to induce ovulation in rats,59 which may be indirect evidence for its regulation of PCOS.
In recent years, there has been growing attention on the identification of immune cells and plasma metabolites, as diagnostic biomarkers for polycystic ovary syndrome. The decreased transcript levels of HDDC3 and SDC2 were validated in granulosa cells from women with PCOS and indicated that HDDC3 and SDC2 might serve as candidate biomarkers for PCOS in clinical practice.10 As for plasma metabolites, Murri et al published a valuable review where they compared few studies based on the analyses of plasma obtained from PCOS women and healthy controls and as can be observed metabolites connected with lipid, amino acid, as well as energy metabolism such as citric acid cycle seem to as the most characteristic for PCOS.60,61 Our findings open new avenues for understanding the pathogenesis of PCOS and highlight the key role of blood metabolites in mediating the relationship between immune cells and PCOS, rather than focusing solely on one or the other. This not only deepens our understanding of the pathological processes of PCOS but also lays the theoretical foundation for the development of new immune therapies and metabolite-targeted interventions.
Our Mendelian mediator analysis provides new insights into how immune cells affect PCOS via plasma metabolites. The relationship between the three is illustrated from a genetic perspective, highlighting potential biomarkers and therapeutic targets that can be used in clinical practice. While the use of a large amount of GWAS pooled data increased the robustness and reliability of our findings, this study still has some limitations. First, MR is not a substitute for objective clinical trials, as it is only a method to analyze the causal relationship between exposure and outcome. Causality is elucidated from a genetic perspective and cannot be determined from environmental factors. Second, the data in this study were primarily from a European population, which may limit its generalizability to other ethnic groups. Future studies should focus on validating these findings in different populations, and functional studies are needed to elucidate the specific biological mechanisms of how the identified immune cells and metabolites affect PCOS. Investigating the therapeutic potential of these immunometabolic pathways may open new avenues for the treatment of PCOS.
Conclusions
In this study, we systematically evaluated the causal relationship between immune cells and plasma metabolites and PCOS and found that 12 immune cells were causally linked to PCOS through 17 metabolites. The results reveal the important roles of immune cells and metabolites in the pathogenesis of PCOS and advance PCOS treatment strategies, emphasizing the necessity of further research to explore these complex biological interactions and their potential therapeutic application.
Data Sharing Statement
The datasets presented in the study are available in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Materials.
Ethics Approval and Informed Consent
The study was approved by the Medical Ethical Review Board of Quanzhou Medical College [Ethical Review No. (2025003), Quanzhou Medical College]. In addition, all participants were exempted from providing informed consent, as the databases used in this study all report obtaining informed consent from the patients.
Consent for Publication
We confirm that we have reviewed the article and consent to the publication of any details, images, videos, recordings, or other materials included within it. We understand that this content will be made publicly available as part of the publication process. We further confirm that I have been provided with the final version of the article and am fully aware of the materials being published.
Acknowledgments
We thank all the participants and investigators involved in the GWAS, as well as all the authors for their contributions to this article.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This study was funded by Special fund project of the Second Affiliated Hospital of Fujian Medical University (grant No.2022BD1002).
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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