Causal Associations between Prostate Cancer and Various Hematological Parameters: Insights from Multivariable and Bidirectional Mendelian Randomization Analyses

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Methods We applied genome-wide association studies (GWAS) to conduct both forward and reverse Mendelian randomization (MR) analyses within a two-sample framework. We further undertook multivariable MR analyses to investigate the associations between diverse hematological parameters and PCa. To validate the robustness of our results, we implemented several sensitivity analysis techniques. Results In our univariable MR analysis, genetically predicted mean corpuscular volume (Odds Ratio [OR]: 0.942, 95% Confidence Interval [CI]: 0.891–0.996, P=0.035) and mean corpuscular hemoglobin (OR: 0.934, 95% CI 0.882–0.988, P=0.018) in red blood cells were associated with a decreased risk of PCa. Moreover, MR analysis revealed that genetically predicted increases in eosinophil count (OR: 1.081, 95% CI 1.005–1.163, P=0.036) and basophil count (OR: 1.235, 95% CI 1.006–1.516, P=0.044) were linked to an elevated risk of PCa (all P<0.05). In the multivariable analysis, we found that basophil count was associated with an increased incidence of PCa (OR:1.432, 95% CI 1.028–1.996, P=0.034). In the reverse MR analysis, we observed that genetically predicted PCa was associated with an increase in neutrophil count (OR: 1.012, 95% CI: 1.001–1.023, P=0.031) and red blood cell count (OR: 1.008, 95% CI: 1.000–1.016, P=0.042). Conclusion In conclusion, our investigation elucidates the causal associations between specific hematological parameters and PCa. These insights contribute significantly to our understanding of the genetic determinants of PCa and their potential interactions with various hematological parameters. Prostate cancer Mendelian randomization hematological parameters Single nucleotide polymorphisms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction PCa is the second most common malignancy and a leading cause of cancer-related death among men globally. In the United States, an estimated 288,300 new cases were reported in 2023, underscoring its significant impact on male health through substantial morbidity and mortality rates(Siegel et al. 2023). Clinical assessment of PCa typically entails the measurement of serum prostate-specific antigen (PSA) levels, imaging studies, and digital rectal examination to detect induration. Definitive diagnosis, however, hinges on the histopathological evaluation of samples obtained via transrectal ultrasound-guided prostate biopsy(Sekhoacha et al. 2022; Schaeffer et al. 2023). Ongoing research endeavors are directed towards augmenting early detection and diagnostic modalities for PCa, with an increasing emphasis on the identification of serum biomarkers(Wei et al. 2023; Cittadini et al. 2022). A prospective study leveraging data from the UK Biobank has elucidated the correlation between various hematologic parameters and the risk and mortality associated with PCa. Notably, elevated levels of red blood cell (RBC) and platelet counts have been identified as risk enhancers for PCa, whereas mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) levels are inversely related to PCa risk(Watts et al. 2020). Additionally, the width of red blood cell distribution has been recognized as an independent prognostic factor for PCa(Huang et al. 2021). Analysis of data from 16,266 patients has uncovered a linkage between an elevated neutrophil-to-lymphocyte ratio and adverse prognosis in PCa(Gu et al. 2016), Moreover, neutrophil counts have been independently correlated with the five-year survival rates in localized PCa(Bahig et al. 2015), and baseline basophil counts, as well as the basophil-to-lymphocyte ratio, have been associated with poorer clinical outcomes in metastatic hormone-sensitive PCa(Hadadi et al. 2022). These clinical observations underpin our research, aiming to explore the potential links between various hematological markers and the development and progression of PCa. Our research further seeks to clarify the genetic underpinnings of the associations between these hematologic indicators and PCa. Observational epidemiological studies are frequently subject to biases including reverse causation, misclassification, and unmeasured confounding, which challenge their capacity to definitively establish causal links between PCa and various hematological markers(Grosman and Scott 2022). To address these limitations, MR approaches have been increasingly utilized(Lin et al. 2021). MR leverages genetic variants as instrumental variables for exposures, thereby enhancing the rigor of causal inference(Birney 2022). This methodology significantly reduces the influence of residual confounding, given that genetic variations are allocated randomly at conception and are independent of environmental and lifestyle variables. Furthermore, the MR framework is inherently protected against reverse causation, as the genetic determinants of exposure precede the onset and progression of disease. In this investigation, we applied both univariable and multivariable MR analyses across two independent cohorts to explore the potential causal associations between specific hematological parameters and PCa. The results of this study hold substantial implications for the development of more refined follow-up and monitoring protocols for individuals diagnosed with cancer, potentially informing targeted intervention strategies and improving patient outcomes. Methods Figure 1 of our investigation delineates the methodology and foundational principles of MR employed in our study. Utilizing summary-level data from GWAS, we conducted both univariate (forward and reverse MR) and multivariate MR analyses within a two-sample MR framework. As a genetic instrumental approach, MR leverages genetic variants that are randomly inherited at conception to infer causal relationships between exposures and outcomes. This technique is predicated on three critical assumptions: (1) the instrumental variables, represented by genetic variants, must have a strong association with the exposure of interest; (2) these genetic variants should not be associated with any confounders; and (3) the genetic variants should not have a direct effect on the outcome, ensuring that the observed association is mediated through the exposure. Our study exclusively leveraged publicly available summary-level data from extensive GWAS and consortia, obviating the need for ethical approval. It is essential to emphasize that all primary studies included in our analysis had already obtained ethical approval from appropriate academic ethics committees, and informed consent was secured from all participants. Our research rigorously adhered to the Strengthening the Reporting of Observational Studies in Epidemiology for Mendelian Randomization (STROBE-MR) guidelines(Burgess et al. 2023, Skrivankova et al. 2021). GWAS data sources for hematological parameters Supplementary Table 1 meticulously catalogues essential demographic and phenotypic data pertinent to our study's cohort. This table delineates a range of variables, including the GWAS identification number, publication year, sample size, number of controls, and the demographic specifics of each phenotype examined. Our analysis predominantly utilized data from the UK Biobank consortium, ensuring that the information for each phenotype originated from a consistent population base and temporal context. Specifically, the PCa dataset (GWAS ID: ebi-a-GCST90018905, 2021) comprised 11,599 cases and 199,628 controls, all of whom were of European descent. The selection of this dataset was strategically based on its congruence in initiation year with the majority of hematological parameters assessed. Similarly, for hematological traits, data procurement was also through the UK Biobank consortium to maintain uniformity across studies. An example includes the dataset for White Blood Cell Count (GWAS ID: ebi-a-GCST90018978, 2021, PMID: 34594039), which included 350,470 participants and analysis of 19,060,103 Single Nucleotide Polymorphisms (SNPs). For comprehensive insights, detailed enumerations of all utilized datasets are available in Supplementary Table 1. Instrumental variables selection To enhance the integrity of our dataset, we applied rigorous filtering criteria, excluding SNPs with high Linkage Disequilibrium (LD) (r² > 0.001 and a clumping window < 10,000 kb), thereby ensuring the inclusion of only SNPs with a P-value less than 5×10 − 8 . Harmonization efforts involved aligning the coded and reference alleles between exposure and outcome datasets and the exclusion of palindromic SNPs with ambiguous allele frequencies. This scrupulous approach facilitated the compilation of a refined dataset, significantly reducing duplication and improving data fidelity. The influence of missing SNPs was deemed negligible, obviating the need for proxy SNPs as replacements for missing instrumental variables (IVs) in the outcome dataset. The robustness of genetic instruments was evaluated using the F-statistic, with an F > 10 indicating satisfactory instrument strength. For an exhaustive depiction of our study's methodology, Fig. 2 provides a detailed schematic. To mitigate the influence of potential confounders or mediators such as Body Mass Index (BMI), Type II diabetes, alcohol consumption frequency, LDL cholesterol levels, current and past smoking habits, and diabetes diagnosed by a physician, we leveraged the PhenoScanner database V2. This resource was instrumental in identifying and excluding SNPs associated with confounding phenotypes, thereby enhancing the accuracy and reliability of our causal inferences. Statistical analysis Bidirectional univariable MR analyses were conducted to investigate potential causal relationships between hematological parameters and PCa. SNPs associated with the exposures were meticulously selected from GWAS to ensure the robustness of our findings. To ensure accurate allele harmonization, the SNPs were carefully aligned, ensuring that the effect alleles for both the exposure and outcome corresponded to the same allele. This rigorous approach significantly enhances the precision and reliability of our analysis, laying a robust foundation for elucidating the causal dynamics between hematological parameters and PCa. We applied three distinct MR methodologies—Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median—to investigate the causal links between hematological parameters and PCa. The IVW method, our primary analytic strategy, presumes that the chosen genetic variants are legitimate instrumental variables. It aggregates the variant effects to deduce the overall causal impact of the exposure on the outcome, offering unbiased estimations in the absence of horizontal pleiotropy. The MR-Egger approach facilitates the detection and adjustment for horizontal pleiotropy, thus permitting the identification and correction of directional pleiotropy influences. Despite potentially reduced statistical power, MR-Egger introduces a mechanism to mitigate potential pleiotropy-induced biases in instrumental variable analyses, particularly through examination of its regression intercept. The Weighted Median method, by deriving estimates from over half of the data weighted by valid instrumental variables, provides a resilient estimate of the overall causal effect. This method prioritizes genetic variants according to their association strength with the exposure, using a weighted median of these estimates for a comprehensive causal effect determination. Such an approach offers a safeguard against potential breaches of instrumental variable assumptions, ensuring the integrity of causal inferences. Exposures demonstrating statistical significance in univariable MR analyses are subjected to further evaluation using multivariable MR analysis. This advanced method allows for the independent assessment of the effects of multiple risk and protective factors, analogous to the examination of diverse treatments within a randomized controlled trial framework. Diverging from conventional MR approaches that link genetic instruments to a single risk factor, our strategy utilizes genetic instruments associated with a spectrum of measured influencing factors, adhering to the fundamental assumptions of instrumental variable analysis. This enables the application of our method to a variety of genetic variants, which may not directly relate to each risk factor individually within the model, thereby accommodating exposures that are either causally or independently associated. Such a comprehensive approach aids in unravelling the direct causal relationships between each influencing factor within the model. For the estimation of causal effects of these factors under the multivariable MR framework, the IVW method was employed, facilitating a rigorous and dependable evaluation of the intricate interactions among various risk and protective factors. To enhance the robustness of our MR analysis, we conducted a thorough suite of sensitivity analyses. We initiated this process by employing the MR-PRESSO test and the MR-Egger intercept test to investigate the presence of pleiotropy, alongside assessing heterogeneity through Cochran's Q test. In scenarios where significant heterogeneity was detected (P < 0.05), the random-effects IVW MR approach was applied to accommodate the variability among the instrumental variables. Additionally, a leave-one-out analysis was performed to evaluate the impact of individual SNPs on the MR estimates, ensuring that no single SNP disproportionately influenced the overall results. This meticulous analysis aimed to determine the stability of the findings, verifying that the conclusions were consistent irrespective of the inclusion or exclusion of specific SNPs. The MR-PRESSO outlier detection and correction technique was also utilized, recognized for its efficacy in identifying and rectifying outliers within the dataset. By addressing potential influential data points, MR-PRESSO significantly contributed to refining our analysis, thereby bolstering the reliability and validity of our findings. Collectively, these sensitivity analyses underpin the credibility of our MR outcomes, providing a solid foundation for the interpretation of our results. In our analysis, statistical significance was established at a P value threshold of less than 0.05. It is important to note that outcomes identified as significant in univariate MR analyses were considered indicative, rather than conclusive. The statistical computations were performed utilizing the R software environment (version 4.2.3), employing specialized packages such as ggplot2 (version 3.4.3), TwoSampleMR (version 0.5.7), and MR-PRESSO (version 1.0) to facilitate our analyses(Rasooly and Patel 2019). These selections of analytical tools and techniques were pivotal in executing precise and reliable statistical evaluations, thus enhancing the validity and dependability of our findings. This deliberate approach aimed at reducing analytical redundancy and maximizing the integrity of the results, thereby reinforcing the overall rigor and credibility of our study. Results Supplementary Table 1 presents detailed data for each phenotype under investigation, offering a transparent overview of critical parameters such as the Heterogeneity Test P-value, MR-Egger Intercept P-value, and additional metrics. In our dataset, all SNPs were subjected to analysis and demonstrated an F-statistic exceeding 10, signifying strong instrumental variable (IV) predictiveness for the exposure with minimal distortion by weak instruments. The majority of exposure analyses revealed moderate heterogeneity (P-value for Cochrane's Q < 0.05), necessitating the adoption of the random-effects IVW MR method. Further scrutiny through MR-PRESSO testing identified global heterogeneity within the exposure factor, indicating significant variance across genotypes and the likely presence of horizontal pleiotropy. Nevertheless, the results of the MR-outlier-corrected analysis indicated that, after accounting for individual outliers, the impact of genotype on the outcome was no longer significant. This necessitates additional analyses to thoroughly ascertain the presence of horizontal pleiotropy. Crucially, MR-Egger regression analysis across all exposures did not detect pleiotropy (all P-values > 0.05), with the MR-Egger intercept analysis further suggesting a lower probability of horizontal pleiotropy within our study's exposure factors. This highlights the robustness and impartiality of the instrumental variables employed, bolstering the validity of our causal inferences. Consequently, the causal effects deduced from our analysis are likely more reflective of the true effects, particularly in scenarios devoid of confounding genetic or environmental factors. Figure 3 delineates the results from univariate MR analyses exploring the causal associations between various hematological markers and PCa. Utilizing the IVW approach, genetically inferred MCV (OR: 0.942, 95% CI: 0.891–0.996, P = 0.035) and MCH (OR: 0.934, 95% CI: 0.882–0.988, P = 0.018) were found to be inversely associated with the risk of PCa, suggesting a protective effect. In contrast, analysis via the IVW method indicated that elevated eosinophil counts (OR: 1.081, 95% CI: 1.005–1.163, P = 0.036) and basophil counts (OR: 1.235, 95% CI: 1.006–1.516, P = 0.044) are associated with an increased risk of PCa. Nonetheless, the analysis did not substantiate a causal link between PCa and other hematological parameters assessed, including white blood cell count, neutrophil count, lymphocyte count, monocyte count, red blood cell count, hemoglobin concentration, hematocrit percentage, red blood cell distribution width, reticulocyte count, platelet count, platelet distribution width, and mean platelet volume, with all P-values exceeding 0.05. In the multivariable analysis, incorporating MCV, MCH, eosinophil count, and basophil count, a notable association was discerned, highlighting that an elevated basophil count correlates with an increased incidence of PCa. The OR for this association was determined to be 1.432, with a 95% CI spanning from 1.028 to 1.996, and a P-value of 0.034 (Fig. 4 ). Conversely, within the confines of the multivariable MR analysis, no statistically significant relationships were observed between MCV, MCH, or eosinophil count and the incidence of PCa, with all P-values exceeding 0.05. To elucidate the potential causal impact of PCa on hematological parameters, a reverse MR analysis was undertaken. This investigation was designed to evaluate the influence of PCa on various blood hematological indices (Fig. 5 ). Findings from this analysis suggest that PCa may lead to an increase in neutrophil count, with an OR of 1.012 and a 95% CI ranging from 1.001 to 1.023 (P = 0.031), as well as an elevation in red blood cell count, evidenced by an OR of 1.008 and a 95% CI from 1.000 to 1.016 (P = 0.042). Discussion In this study, univariate MR analyses indicated an inverse association between MCV or MCH and PCa, whereas eosinophil and basophil counts were positively associated with PCa risk. Nonetheless, no statistically significant causal relationships were identified between PCa and the other twelve hematological parameters examined. Subsequent multivariable MR analysis highlighted a pronounced association between elevated basophil count and an increased incidence of PCa. Within this analytical framework, no significant associations were observed for MCV, MCH, or eosinophil count with PCa risk. Further investigations into reverse causality through MR analysis suggested that PCa may lead to an increase in both neutrophil and red blood cell counts. These findings underscore the complexity of the relationships between hematological indicators and PCa, warranting additional research to clarify the mechanisms underpinning these associations. Our study's findings partially resonate with those from prior observational research, exploring the nexus between hematologic parameters and PCa risk and outcomes. The prospective analysis leveraging data from the UK Biobank by Watts et al. reported elevated risks of PCa associated with increased red blood cell and platelet counts, while lower risks were observed with higher MCV, MCH, and mean corpuscular hemoglobin concentration. Notably, they identified a link between higher white blood cell or neutrophil counts and PCa mortality(Watts et al. 2020). Our results corroborate these observations, especially our reverse Mendelian Randomization analysis indicating a neutrophil count increase in PCa, potentially elucidating the connection with PCa mortality. Porcaro AB et al. observed that white blood cell count could be an independent predictor for PCa risk in patients undergoing transurethral resection of the prostate for lower urinary tract symptoms(Porcaro et al. 2021). Although our study did not mirror this specific finding, the insight adds valuable context to the broader discussion on hematologic markers as potential PCa risk indicators. Research by Bahig H et al. on the association between neutrophil count and overall survival in localized PCa supports the notion that neutrophil count may serve as an independent predictor of patient outcomes(Bahig et al. 2015), Our findings, indicating an increase in neutrophil count associated with PCa, align with their results, suggesting a need for further investigations to elucidate the relationship and its implications for PCa prognosis. Furthermore, Hadadi A et al.'s retrospective multicenter study found a correlation between basophil count and poorer outcomes in metastatic hormone-sensitive PCa(Hadadi et al. 2022), This aligns with our experimental data, indicating a genetic association between elevated basophil count and an increased risk of developing PCa. Such congruence with existing literature underscores the potential of basophil count as a biomarker for PCa risk and outcomes, highlighting the relevance of our findings in the broader context of PCa research. Together, these studies and our own work contribute to a growing body of evidence that underscores the complex interplay between hematologic parameters and PCa, warranting further exploration to fully understand their clinical and biological significance. The existing body of research has established various associations between hematological parameters and PCa, highlighting the potential of these indicators in understanding disease progression and prognosis. Wang F et al. demonstrated a significant correlation between preoperative red cell distribution width (RDW) and lymphovascular invasion in PCa, suggesting RDW's importance in the disease's advancement(Wang et al. 2022). Similarly, Albayrak et al. found RDW to be predictive of an increased risk of PCa, further underscoring its role in disease progression(Albayrak et al. 2014). Furthermore, Yu Z et al. identified a link between elevated platelet counts and poorer prognoses in PCa, adding another layer to the complex relationship between hematological markers and cancer outcomes(Yu et al. 2023). Complementing these findings, Song W et al. reported that integrating platelet distribution width with total or free prostate-specific antigen levels could refine the diagnostic precision for PCa, enhancing the clinical value of PSA testing(Song et al. 2022). Despite these insightful correlations, our study did not replicate these associations, potentially highlighting the influence of confounding variables and the intricate dynamics governing the interaction between hematological markers and PCa. This discrepancy underscores the necessity for cautious interpretation of hematological indicators in PCa research and diagnosis, recognizing the multifaceted nature of their relationship with the disease. Our findings, diverging from the established literature, emphasize the critical need for further investigation to delineate the specific roles these markers play in PCa's pathophysiology and their utility in clinical practice. Unraveling the intricate association between hematological markers and PCa presents significant challenges, chiefly due to the paucity of definitive factors directly correlated with PCa's initiation and progression. This MR analysis brings to light the potential protective roles of MCV and MCH against PCa. These parameters, indicative of red blood cell size and hemoglobin content respectively, may reflect cellular metabolic activities that inhibit the emergence and growth of malignant cells in PCa. Additionally, our findings suggest that an increase in eosinophil and basophil counts may elevate PCa risk, possibly signaling changes in the body's inflammatory state. Chronic inflammation is closely associated with heightened cancer risk, involving complex interactions that may engage with the tumor microenvironment, inflammation, immune surveillance, and cancer progression pathways mediated by chemokines and cytokines(Nagarsheth et al. 2017, Hughes and Nibbs 2018). Therefore, the observed elevation in eosinophil and basophil counts could augment PCa risk through inflammation-related pathways(Melo et al. 2013, Yamaguchi et al. 2009). Moreover, our study indicates that PCa may lead to increased neutrophil and red blood cell counts, with the former potentially linked to inflammation-induced metabolic changes and the latter associated with PCa's tendency for bone marrow metastasis. Bone marrow stromal cells, capable of enhancing PCa cell survival by inhibiting the action of tumor necrosis factor-related apoptosis-inducing ligand, may contribute to increased blood cell production in the bone marrow(Cross et al. 2007). Therefore, sustaining the activity of PCa cells may lead to an elevation in the production of blood cells within the bone marrow. Additionally, as tumor cells advance, their oxygen consumption tends to increase(Cook et al. 2012), potentially accounting for the heightened presence of oxygen-carrying red blood cells. Our investigation elucidates causal relationships between PCa and various hematological markers, including MCV, MCH, eosinophil count, basophil count, neutrophil count, and red blood cell count, aligning with insights from prior observational studies. While these associations suggest potential underlying mechanisms, the precise biological principles and mechanisms demand further detailed exploration to be definitively established. A particularly noteworthy finding from our multivariable Mendelian randomization analysis is the distinct correlation observed between basophil count and both the incidence and progression of PCa. This correlation, hitherto underexplored in the breadth of observational literature, signals a promising new direction for scientific inquiry, inviting researchers to delve into previously uncharted territories of PCa pathophysiology. This study explores the causal relationships between various hematological markers and PCa utilizing state-of-the-art genetic tools from the latest and largest GWAS datasets for these diseases. Implementing a bidirectional and multivariate MR approach, we rigorously assessed the causal links while employing a comprehensive set of sensitivity analyses to address potential pleiotropic biases, thereby ensuring the robustness of our MR findings. The investigation was specifically focused on participants of European ancestry within the GWAS datasets to minimize the influence of population stratification bias on the results. The observed consistency of genetic susceptibility across different data sources and MR models for the 12 cancers in relation to PCa reinforces the credibility of our findings, suggesting a minimal likelihood of influence by horizontal pleiotropy. However, the interpretation of these results must be approached with caution due to several limitations. Notably, Cochran's Q test indicated the presence of heterogeneity among the IVs, leading to the adoption of the IVW random-effects method as the primary MR approach for its acknowledged robustness(12). Additionally, the MR-PRESSO test identified outliers and potential horizontal pleiotropy, which were effectively adjusted for after outlier correction, thereby reducing the impact of influential values on the overall results. The use of the MR-Egger Intercept P further assessed the likelihood of horizontal pleiotropy, indicating a low probability and thus providing further confidence in the causal inferences drawn. The study also addressed the issue of potential overfitting due to sample overlap between GWAS datasets for the same trait by carefully selecting instrumental variables from large-scale GWAS and integrating findings from multiple data sources. Despite these measures, the generalizability of the findings to populations beyond those of European ancestry remains a limitation. Variabilities in results obtained through different MR methods for the same outcome highlighted the complexity of the analysis, while the call for further biological research underscores the need for a deeper understanding of the mechanisms underlying these findings. Conclusion In conclusion, our study, leveraging MR analysis, has provided significant insights into the causal associations between various hematological indicators and PCa. We identified that MCV and MCH are associated with a decreased risk of PCa, while an elevated eosinophil count and basophil count may augment the risk of developing PCa. Additionally, our findings suggest that PCa is associated with an increase in neutrophil count and red blood cell count. These discoveries enhance our understanding of the genetic underpinnings of PCa and its potential links with a range of hematological markers. Despite these advances, it is crucial to pursue further research to confirm these findings and elucidate the complex mechanisms underlying these associations. Abbreviations PCa Prostate cancer GWAS Genome-wide association studies MR Mendelian randomization PSA prostate-specific antigen RBC red blood cell MCV mean corpuscular volume MCH mean corpuscular hemoglobin STROBE-MR Strengthening the Reporting of Observational Studies in Epidemiology for Mendelian Randomization SNPs Single Nucleotide Polymorphisms LD Linkage Disequilibrium IVs instrumental variables BMI Body Mass Index IVW Inverse Variance Weighted IV instrumental variable RDW red cell distribution width Declarations Acknowledgements We wish to acknowledge the participants and investigators of the Integrative Epidemiology Unit (IEU, https://gwas.mrcieu.ac.uk/). Author contributions YRW, and HW designed the study and drafted the paper. PSY, DWX, WFH and JWW critically revised the paper. All authors read and approved the final manuscript. Funding This work was supported by capital health development project of a multicenter randomized controlled clinical study of Qishao Tianxin prescription in the treatment of early urinary incontinence after radical prostatectomy for prostate cancer, Beijing, China,under grant number 2020-2-2033. Data availability All relevant data are within the manuscript and its additional files Conflict of interest No conflict of interest. References Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. CA: a cancer journal for clinicians, 73(1), 17–48. https://doi.org/10.3322/caac.21763. Sekhoacha M, Riet K, Motloung P, Gumenku L, Adegoke A, Mashele S (2022) Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches. Molecules (Basel, Switzerland), 27(17), 5730. https://doi.org/10.3390/molecules27175730. Schaeffer EM, Srinivas S, Adra N, et al (2023) Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology. 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Melo RC, Liu L, Xenakis JJ, Spencer LA (2013) Eosinophil-derived cytokines in health and disease: unraveling novel mechanisms of selective secretion. Allergy, 68(3), 274–284. https://doi.org/10.1111/all.12103. Yamaguchi M, Koketsu R, Suzukawa M, Kawakami A, Iikura M (2009) Human basophils and cytokines/chemokines. Allergology international : official journal of the Japanese Society of Allergology, 58(1), 1–10. https://doi.org/10.2332/allergolint.08-RAI-0056. Cross NA, Papageorgiou M, Eaton CL (2007) Bone marrow stromal cells promote growth and survival of prostate cancer cells. Biochemical Society transactions, 35(Pt 4), 698–700. https://doi.org/10.1042/BST0350698. Cook CC, Kim A, Terao S, Gotoh A, Higuchi M (2012) Consumption of oxygen: a mitochondrial-generated progression signal of advanced cancer. Cell death & disease, 3(1), e258. https://doi.org/10.1038/cddis.2011.141. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4199350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287564343,"identity":"f4f3991f-1e51-446d-bbfc-6b8cd778fd0c","order_by":0,"name":"Yirui Wei","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yirui","middleName":"","lastName":"Wei","suffix":""},{"id":287564344,"identity":"8ccd092e-e5fb-40d3-a755-79e0c8596b7d","order_by":1,"name":"Pushen Yang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pushen","middleName":"","lastName":"Yang","suffix":""},{"id":287564345,"identity":"122ed970-dd4e-45ab-9f86-9bb1cb32afe0","order_by":2,"name":"Hao Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":287564346,"identity":"3deac6c6-c25b-4d2f-a14c-78464a3bdb14","order_by":3,"name":"Dawei Xie","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Xie","suffix":""},{"id":287564347,"identity":"1946da14-0706-4e0e-b6f3-9bd135787173","order_by":4,"name":"Weifeng He","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"He","suffix":""},{"id":287564348,"identity":"3ebb6734-06c2-47c9-8cee-035aacf43012","order_by":5,"name":"Jianwen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFCCgw1AwkaOn4GxgSQtacaSDcRrAYPDiRsOEKvW4ODhxs+FbYcTN58/3PbgB4NdHmEtBw42S89sSzfediOx3bCHIbmYoBazAwcbpHm3Wctuu8HYJsHDcCCxgQgtzb95tzEzbu4/2Cb5h0gtbUBbnBU3MCS2SRNliz1QizXvvzRjiRtALTIGyYS1SM44/vg2zxlgVPYffyb5psKOsBYGiQPIPAOC6oGAn7Cpo2AUjIJRMNIBADeDRERpunE4AAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianwen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-01 08:42:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4199350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4199350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54149911,"identity":"b645df8a-d888-4545-a95a-cc8207c2ab07","added_by":"auto","created_at":"2024-04-05 10:16:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51491,"visible":true,"origin":"","legend":"\u003cp\u003eAssumptions underpinning the MR analysis for diverse hematological parameters and PCa. The MR study posits that genetic variants exhibit associations solely with the exposure of interest and are not entangled with confounding factors or alternative causal pathways.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/a942edd1ff0e08dd1faf9407.png"},{"id":54149910,"identity":"1e36479f-a3f3-4adc-99e8-75786e8012fb","added_by":"auto","created_at":"2024-04-05 10:16:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126307,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic representation of inclusion and exclusion criteria for candidate SNPs in each exposure-outcome pairing, with a subsequent application of MR and the utilization of IVW methodology, is delineated in the flowchart.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/b61a9206083269bd80c22317.png"},{"id":54149915,"identity":"48bca043-9730-4394-abe0-1e220d0dcf2c","added_by":"auto","created_at":"2024-04-05 10:16:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223860,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariable MR outcomes elucidating the impacts of diverse hematological parameters on PCa.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/1f0e7e568d6bc315a77f9025.png"},{"id":54149912,"identity":"1957b39a-97cc-49ca-99ed-272e9e54c33c","added_by":"auto","created_at":"2024-04-05 10:16:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50464,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable MR findings delineating the effects of eosinophil counts, basophil counts, mean corpuscular volume, and mean corpuscular hemoglobin on PCa.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/d4db9b706d5c4763571931f6.png"},{"id":54150760,"identity":"9159d534-0a99-4b4a-9158-957eec6595df","added_by":"auto","created_at":"2024-04-05 10:32:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247356,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariable Mendelian Randomization findings illustrating the impact of PCa on various hematological parameters.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/8938c74faaa851a581acfedf.png"},{"id":54777915,"identity":"7c78cb45-4e88-41c5-90f8-3096094de71e","added_by":"auto","created_at":"2024-04-16 15:45:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/1e539879-b38d-43ac-9da6-12aedc1f3753.pdf"},{"id":54150123,"identity":"15cfd96d-1d4a-4275-9bae-3bfda37362e6","added_by":"auto","created_at":"2024-04-05 10:24:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22299,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4199350/v1/42673059f07ec1ce4553f4fd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Associations between Prostate Cancer and Various Hematological Parameters: Insights from Multivariable and Bidirectional Mendelian Randomization Analyses","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePCa is the second most common malignancy and a leading cause of cancer-related death among men globally. In the United States, an estimated 288,300 new cases were reported in 2023, underscoring its significant impact on male health through substantial morbidity and mortality rates(Siegel et al. 2023). Clinical assessment of PCa typically entails the measurement of serum prostate-specific antigen (PSA) levels, imaging studies, and digital rectal examination to detect induration. Definitive diagnosis, however, hinges on the histopathological evaluation of samples obtained via transrectal ultrasound-guided prostate biopsy(Sekhoacha et al. 2022; Schaeffer et al. 2023).\u003c/p\u003e \u003cp\u003eOngoing research endeavors are directed towards augmenting early detection and diagnostic modalities for PCa, with an increasing emphasis on the identification of serum biomarkers(Wei et al. 2023; Cittadini et al. 2022). A prospective study leveraging data from the UK Biobank has elucidated the correlation between various hematologic parameters and the risk and mortality associated with PCa. Notably, elevated levels of red blood cell (RBC) and platelet counts have been identified as risk enhancers for PCa, whereas mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) levels are inversely related to PCa risk(Watts et al. 2020). Additionally, the width of red blood cell distribution has been recognized as an independent prognostic factor for PCa(Huang et al. 2021). Analysis of data from 16,266 patients has uncovered a linkage between an elevated neutrophil-to-lymphocyte ratio and adverse prognosis in PCa(Gu et al. 2016), Moreover, neutrophil counts have been independently correlated with the five-year survival rates in localized PCa(Bahig et al. 2015), and baseline basophil counts, as well as the basophil-to-lymphocyte ratio, have been associated with poorer clinical outcomes in metastatic hormone-sensitive PCa(Hadadi et al. 2022). These clinical observations underpin our research, aiming to explore the potential links between various hematological markers and the development and progression of PCa. Our research further seeks to clarify the genetic underpinnings of the associations between these hematologic indicators and PCa.\u003c/p\u003e \u003cp\u003eObservational epidemiological studies are frequently subject to biases including reverse causation, misclassification, and unmeasured confounding, which challenge their capacity to definitively establish causal links between PCa and various hematological markers(Grosman and Scott 2022). To address these limitations, MR approaches have been increasingly utilized(Lin et al. 2021). MR leverages genetic variants as instrumental variables for exposures, thereby enhancing the rigor of causal inference(Birney 2022). This methodology significantly reduces the influence of residual confounding, given that genetic variations are allocated randomly at conception and are independent of environmental and lifestyle variables. Furthermore, the MR framework is inherently protected against reverse causation, as the genetic determinants of exposure precede the onset and progression of disease. In this investigation, we applied both univariable and multivariable MR analyses across two independent cohorts to explore the potential causal associations between specific hematological parameters and PCa. The results of this study hold substantial implications for the development of more refined follow-up and monitoring protocols for individuals diagnosed with cancer, potentially informing targeted intervention strategies and improving patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of our investigation delineates the methodology and foundational principles of MR employed in our study. Utilizing summary-level data from GWAS, we conducted both univariate (forward and reverse MR) and multivariate MR analyses within a two-sample MR framework. As a genetic instrumental approach, MR leverages genetic variants that are randomly inherited at conception to infer causal relationships between exposures and outcomes. This technique is predicated on three critical assumptions: (1) the instrumental variables, represented by genetic variants, must have a strong association with the exposure of interest; (2) these genetic variants should not be associated with any confounders; and (3) the genetic variants should not have a direct effect on the outcome, ensuring that the observed association is mediated through the exposure.\u003c/p\u003e \u003cp\u003eOur study exclusively leveraged publicly available summary-level data from extensive GWAS and consortia, obviating the need for ethical approval. It is essential to emphasize that all primary studies included in our analysis had already obtained ethical approval from appropriate academic ethics committees, and informed consent was secured from all participants. Our research rigorously adhered to the Strengthening the Reporting of Observational Studies in Epidemiology for Mendelian Randomization (STROBE-MR) guidelines(Burgess et al. 2023, Skrivankova et al. 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGWAS data sources for hematological parameters\u003c/h2\u003e \u003cp\u003eSupplementary Table\u0026nbsp;1 meticulously catalogues essential demographic and phenotypic data pertinent to our study's cohort. This table delineates a range of variables, including the GWAS identification number, publication year, sample size, number of controls, and the demographic specifics of each phenotype examined. Our analysis predominantly utilized data from the UK Biobank consortium, ensuring that the information for each phenotype originated from a consistent population base and temporal context. Specifically, the PCa dataset (GWAS ID: ebi-a-GCST90018905, 2021) comprised 11,599 cases and 199,628 controls, all of whom were of European descent. The selection of this dataset was strategically based on its congruence in initiation year with the majority of hematological parameters assessed. Similarly, for hematological traits, data procurement was also through the UK Biobank consortium to maintain uniformity across studies. An example includes the dataset for White Blood Cell Count (GWAS ID: ebi-a-GCST90018978, 2021, PMID: 34594039), which included 350,470 participants and analysis of 19,060,103 Single Nucleotide Polymorphisms (SNPs). For comprehensive insights, detailed enumerations of all utilized datasets are available in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variables selection\u003c/h2\u003e \u003cp\u003eTo enhance the integrity of our dataset, we applied rigorous filtering criteria, excluding SNPs with high Linkage Disequilibrium (LD) (r\u0026sup2; \u0026gt; 0.001 and a clumping window\u0026thinsp;\u0026lt;\u0026thinsp;10,000 kb), thereby ensuring the inclusion of only SNPs with a P-value less than 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. Harmonization efforts involved aligning the coded and reference alleles between exposure and outcome datasets and the exclusion of palindromic SNPs with ambiguous allele frequencies. This scrupulous approach facilitated the compilation of a refined dataset, significantly reducing duplication and improving data fidelity. The influence of missing SNPs was deemed negligible, obviating the need for proxy SNPs as replacements for missing instrumental variables (IVs) in the outcome dataset. The robustness of genetic instruments was evaluated using the F-statistic, with an F\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating satisfactory instrument strength. For an exhaustive depiction of our study's methodology, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed schematic. To mitigate the influence of potential confounders or mediators such as Body Mass Index (BMI), Type II diabetes, alcohol consumption frequency, LDL cholesterol levels, current and past smoking habits, and diabetes diagnosed by a physician, we leveraged the PhenoScanner database V2. This resource was instrumental in identifying and excluding SNPs associated with confounding phenotypes, thereby enhancing the accuracy and reliability of our causal inferences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBidirectional univariable MR analyses were conducted to investigate potential causal relationships between hematological parameters and PCa. SNPs associated with the exposures were meticulously selected from GWAS to ensure the robustness of our findings. To ensure accurate allele harmonization, the SNPs were carefully aligned, ensuring that the effect alleles for both the exposure and outcome corresponded to the same allele. This rigorous approach significantly enhances the precision and reliability of our analysis, laying a robust foundation for elucidating the causal dynamics between hematological parameters and PCa.\u003c/p\u003e \u003cp\u003eWe applied three distinct MR methodologies\u0026mdash;Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median\u0026mdash;to investigate the causal links between hematological parameters and PCa. The IVW method, our primary analytic strategy, presumes that the chosen genetic variants are legitimate instrumental variables. It aggregates the variant effects to deduce the overall causal impact of the exposure on the outcome, offering unbiased estimations in the absence of horizontal pleiotropy. The MR-Egger approach facilitates the detection and adjustment for horizontal pleiotropy, thus permitting the identification and correction of directional pleiotropy influences. Despite potentially reduced statistical power, MR-Egger introduces a mechanism to mitigate potential pleiotropy-induced biases in instrumental variable analyses, particularly through examination of its regression intercept. The Weighted Median method, by deriving estimates from over half of the data weighted by valid instrumental variables, provides a resilient estimate of the overall causal effect. This method prioritizes genetic variants according to their association strength with the exposure, using a weighted median of these estimates for a comprehensive causal effect determination. Such an approach offers a safeguard against potential breaches of instrumental variable assumptions, ensuring the integrity of causal inferences.\u003c/p\u003e \u003cp\u003eExposures demonstrating statistical significance in univariable MR analyses are subjected to further evaluation using multivariable MR analysis. This advanced method allows for the independent assessment of the effects of multiple risk and protective factors, analogous to the examination of diverse treatments within a randomized controlled trial framework. Diverging from conventional MR approaches that link genetic instruments to a single risk factor, our strategy utilizes genetic instruments associated with a spectrum of measured influencing factors, adhering to the fundamental assumptions of instrumental variable analysis. This enables the application of our method to a variety of genetic variants, which may not directly relate to each risk factor individually within the model, thereby accommodating exposures that are either causally or independently associated. Such a comprehensive approach aids in unravelling the direct causal relationships between each influencing factor within the model. For the estimation of causal effects of these factors under the multivariable MR framework, the IVW method was employed, facilitating a rigorous and dependable evaluation of the intricate interactions among various risk and protective factors.\u003c/p\u003e \u003cp\u003eTo enhance the robustness of our MR analysis, we conducted a thorough suite of sensitivity analyses. We initiated this process by employing the MR-PRESSO test and the MR-Egger intercept test to investigate the presence of pleiotropy, alongside assessing heterogeneity through Cochran's Q test. In scenarios where significant heterogeneity was detected (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the random-effects IVW MR approach was applied to accommodate the variability among the instrumental variables. Additionally, a leave-one-out analysis was performed to evaluate the impact of individual SNPs on the MR estimates, ensuring that no single SNP disproportionately influenced the overall results. This meticulous analysis aimed to determine the stability of the findings, verifying that the conclusions were consistent irrespective of the inclusion or exclusion of specific SNPs. The MR-PRESSO outlier detection and correction technique was also utilized, recognized for its efficacy in identifying and rectifying outliers within the dataset. By addressing potential influential data points, MR-PRESSO significantly contributed to refining our analysis, thereby bolstering the reliability and validity of our findings. Collectively, these sensitivity analyses underpin the credibility of our MR outcomes, providing a solid foundation for the interpretation of our results.\u003c/p\u003e \u003cp\u003eIn our analysis, statistical significance was established at a P value threshold of less than 0.05. It is important to note that outcomes identified as significant in univariate MR analyses were considered indicative, rather than conclusive. The statistical computations were performed utilizing the R software environment (version 4.2.3), employing specialized packages such as ggplot2 (version 3.4.3), TwoSampleMR (version 0.5.7), and MR-PRESSO (version 1.0) to facilitate our analyses(Rasooly and Patel 2019). These selections of analytical tools and techniques were pivotal in executing precise and reliable statistical evaluations, thus enhancing the validity and dependability of our findings. This deliberate approach aimed at reducing analytical redundancy and maximizing the integrity of the results, thereby reinforcing the overall rigor and credibility of our study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSupplementary Table\u0026nbsp;1 presents detailed data for each phenotype under investigation, offering a transparent overview of critical parameters such as the Heterogeneity Test P-value, MR-Egger Intercept P-value, and additional metrics. In our dataset, all SNPs were subjected to analysis and demonstrated an F-statistic exceeding 10, signifying strong instrumental variable (IV) predictiveness for the exposure with minimal distortion by weak instruments. The majority of exposure analyses revealed moderate heterogeneity (P-value for Cochrane's Q\u0026thinsp;\u0026lt;\u0026thinsp;0.05), necessitating the adoption of the random-effects IVW MR method. Further scrutiny through MR-PRESSO testing identified global heterogeneity within the exposure factor, indicating significant variance across genotypes and the likely presence of horizontal pleiotropy. Nevertheless, the results of the MR-outlier-corrected analysis indicated that, after accounting for individual outliers, the impact of genotype on the outcome was no longer significant. This necessitates additional analyses to thoroughly ascertain the presence of horizontal pleiotropy. Crucially, MR-Egger regression analysis across all exposures did not detect pleiotropy (all P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with the MR-Egger intercept analysis further suggesting a lower probability of horizontal pleiotropy within our study's exposure factors. This highlights the robustness and impartiality of the instrumental variables employed, bolstering the validity of our causal inferences. Consequently, the causal effects deduced from our analysis are likely more reflective of the true effects, particularly in scenarios devoid of confounding genetic or environmental factors.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e delineates the results from univariate MR analyses exploring the causal associations between various hematological markers and PCa. Utilizing the IVW approach, genetically inferred MCV (OR: 0.942, 95% CI: 0.891\u0026ndash;0.996, P\u0026thinsp;=\u0026thinsp;0.035) and MCH (OR: 0.934, 95% CI: 0.882\u0026ndash;0.988, P\u0026thinsp;=\u0026thinsp;0.018) were found to be inversely associated with the risk of PCa, suggesting a protective effect. In contrast, analysis via the IVW method indicated that elevated eosinophil counts (OR: 1.081, 95% CI: 1.005\u0026ndash;1.163, P\u0026thinsp;=\u0026thinsp;0.036) and basophil counts (OR: 1.235, 95% CI: 1.006\u0026ndash;1.516, P\u0026thinsp;=\u0026thinsp;0.044) are associated with an increased risk of PCa. Nonetheless, the analysis did not substantiate a causal link between PCa and other hematological parameters assessed, including white blood cell count, neutrophil count, lymphocyte count, monocyte count, red blood cell count, hemoglobin concentration, hematocrit percentage, red blood cell distribution width, reticulocyte count, platelet count, platelet distribution width, and mean platelet volume, with all P-values exceeding 0.05.\u003c/p\u003e \u003cp\u003eIn the multivariable analysis, incorporating MCV, MCH, eosinophil count, and basophil count, a notable association was discerned, highlighting that an elevated basophil count correlates with an increased incidence of PCa. The OR for this association was determined to be 1.432, with a 95% CI spanning from 1.028 to 1.996, and a P-value of 0.034 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, within the confines of the multivariable MR analysis, no statistically significant relationships were observed between MCV, MCH, or eosinophil count and the incidence of PCa, with all P-values exceeding 0.05.\u003c/p\u003e \u003cp\u003eTo elucidate the potential causal impact of PCa on hematological parameters, a reverse MR analysis was undertaken. This investigation was designed to evaluate the influence of PCa on various blood hematological indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Findings from this analysis suggest that PCa may lead to an increase in neutrophil count, with an OR of 1.012 and a 95% CI ranging from 1.001 to 1.023 (P\u0026thinsp;=\u0026thinsp;0.031), as well as an elevation in red blood cell count, evidenced by an OR of 1.008 and a 95% CI from 1.000 to 1.016 (P\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, univariate MR analyses indicated an inverse association between MCV or MCH and PCa, whereas eosinophil and basophil counts were positively associated with PCa risk. Nonetheless, no statistically significant causal relationships were identified between PCa and the other twelve hematological parameters examined. Subsequent multivariable MR analysis highlighted a pronounced association between elevated basophil count and an increased incidence of PCa. Within this analytical framework, no significant associations were observed for MCV, MCH, or eosinophil count with PCa risk. Further investigations into reverse causality through MR analysis suggested that PCa may lead to an increase in both neutrophil and red blood cell counts. These findings underscore the complexity of the relationships between hematological indicators and PCa, warranting additional research to clarify the mechanisms underpinning these associations.\u003c/p\u003e \u003cp\u003eOur study's findings partially resonate with those from prior observational research, exploring the nexus between hematologic parameters and PCa risk and outcomes. The prospective analysis leveraging data from the UK Biobank by Watts et al. reported elevated risks of PCa associated with increased red blood cell and platelet counts, while lower risks were observed with higher MCV, MCH, and mean corpuscular hemoglobin concentration. Notably, they identified a link between higher white blood cell or neutrophil counts and PCa mortality(Watts et al. 2020). Our results corroborate these observations, especially our reverse Mendelian Randomization analysis indicating a neutrophil count increase in PCa, potentially elucidating the connection with PCa mortality. Porcaro AB et al. observed that white blood cell count could be an independent predictor for PCa risk in patients undergoing transurethral resection of the prostate for lower urinary tract symptoms(Porcaro et al. 2021). Although our study did not mirror this specific finding, the insight adds valuable context to the broader discussion on hematologic markers as potential PCa risk indicators. Research by Bahig H et al. on the association between neutrophil count and overall survival in localized PCa supports the notion that neutrophil count may serve as an independent predictor of patient outcomes(Bahig et al. 2015), Our findings, indicating an increase in neutrophil count associated with PCa, align with their results, suggesting a need for further investigations to elucidate the relationship and its implications for PCa prognosis. Furthermore, Hadadi A et al.'s retrospective multicenter study found a correlation between basophil count and poorer outcomes in metastatic hormone-sensitive PCa(Hadadi et al. 2022), This aligns with our experimental data, indicating a genetic association between elevated basophil count and an increased risk of developing PCa. Such congruence with existing literature underscores the potential of basophil count as a biomarker for PCa risk and outcomes, highlighting the relevance of our findings in the broader context of PCa research. Together, these studies and our own work contribute to a growing body of evidence that underscores the complex interplay between hematologic parameters and PCa, warranting further exploration to fully understand their clinical and biological significance.\u003c/p\u003e \u003cp\u003eThe existing body of research has established various associations between hematological parameters and PCa, highlighting the potential of these indicators in understanding disease progression and prognosis. Wang F et al. demonstrated a significant correlation between preoperative red cell distribution width (RDW) and lymphovascular invasion in PCa, suggesting RDW's importance in the disease's advancement(Wang et al. 2022). Similarly, Albayrak et al. found RDW to be predictive of an increased risk of PCa, further underscoring its role in disease progression(Albayrak et al. 2014). Furthermore, Yu Z et al. identified a link between elevated platelet counts and poorer prognoses in PCa, adding another layer to the complex relationship between hematological markers and cancer outcomes(Yu et al. 2023). Complementing these findings, Song W et al. reported that integrating platelet distribution width with total or free prostate-specific antigen levels could refine the diagnostic precision for PCa, enhancing the clinical value of PSA testing(Song et al. 2022). Despite these insightful correlations, our study did not replicate these associations, potentially highlighting the influence of confounding variables and the intricate dynamics governing the interaction between hematological markers and PCa. This discrepancy underscores the necessity for cautious interpretation of hematological indicators in PCa research and diagnosis, recognizing the multifaceted nature of their relationship with the disease. Our findings, diverging from the established literature, emphasize the critical need for further investigation to delineate the specific roles these markers play in PCa's pathophysiology and their utility in clinical practice.\u003c/p\u003e \u003cp\u003eUnraveling the intricate association between hematological markers and PCa presents significant challenges, chiefly due to the paucity of definitive factors directly correlated with PCa's initiation and progression. This MR analysis brings to light the potential protective roles of MCV and MCH against PCa. These parameters, indicative of red blood cell size and hemoglobin content respectively, may reflect cellular metabolic activities that inhibit the emergence and growth of malignant cells in PCa. Additionally, our findings suggest that an increase in eosinophil and basophil counts may elevate PCa risk, possibly signaling changes in the body's inflammatory state. Chronic inflammation is closely associated with heightened cancer risk, involving complex interactions that may engage with the tumor microenvironment, inflammation, immune surveillance, and cancer progression pathways mediated by chemokines and cytokines(Nagarsheth et al. 2017, Hughes and Nibbs 2018). Therefore, the observed elevation in eosinophil and basophil counts could augment PCa risk through inflammation-related pathways(Melo et al. 2013, Yamaguchi et al. 2009). Moreover, our study indicates that PCa may lead to increased neutrophil and red blood cell counts, with the former potentially linked to inflammation-induced metabolic changes and the latter associated with PCa's tendency for bone marrow metastasis. Bone marrow stromal cells, capable of enhancing PCa cell survival by inhibiting the action of tumor necrosis factor-related apoptosis-inducing ligand, may contribute to increased blood cell production in the bone marrow(Cross et al. 2007). Therefore, sustaining the activity of PCa cells may lead to an elevation in the production of blood cells within the bone marrow. Additionally, as tumor cells advance, their oxygen consumption tends to increase(Cook et al. 2012), potentially accounting for the heightened presence of oxygen-carrying red blood cells.\u003c/p\u003e \u003cp\u003eOur investigation elucidates causal relationships between PCa and various hematological markers, including MCV, MCH, eosinophil count, basophil count, neutrophil count, and red blood cell count, aligning with insights from prior observational studies. While these associations suggest potential underlying mechanisms, the precise biological principles and mechanisms demand further detailed exploration to be definitively established. A particularly noteworthy finding from our multivariable Mendelian randomization analysis is the distinct correlation observed between basophil count and both the incidence and progression of PCa. This correlation, hitherto underexplored in the breadth of observational literature, signals a promising new direction for scientific inquiry, inviting researchers to delve into previously uncharted territories of PCa pathophysiology.\u003c/p\u003e \u003cp\u003eThis study explores the causal relationships between various hematological markers and PCa utilizing state-of-the-art genetic tools from the latest and largest GWAS datasets for these diseases. Implementing a bidirectional and multivariate MR approach, we rigorously assessed the causal links while employing a comprehensive set of sensitivity analyses to address potential pleiotropic biases, thereby ensuring the robustness of our MR findings. The investigation was specifically focused on participants of European ancestry within the GWAS datasets to minimize the influence of population stratification bias on the results. The observed consistency of genetic susceptibility across different data sources and MR models for the 12 cancers in relation to PCa reinforces the credibility of our findings, suggesting a minimal likelihood of influence by horizontal pleiotropy.\u003c/p\u003e \u003cp\u003eHowever, the interpretation of these results must be approached with caution due to several limitations. Notably, Cochran's Q test indicated the presence of heterogeneity among the IVs, leading to the adoption of the IVW random-effects method as the primary MR approach for its acknowledged robustness(12). Additionally, the MR-PRESSO test identified outliers and potential horizontal pleiotropy, which were effectively adjusted for after outlier correction, thereby reducing the impact of influential values on the overall results. The use of the MR-Egger Intercept P further assessed the likelihood of horizontal pleiotropy, indicating a low probability and thus providing further confidence in the causal inferences drawn. The study also addressed the issue of potential overfitting due to sample overlap between GWAS datasets for the same trait by carefully selecting instrumental variables from large-scale GWAS and integrating findings from multiple data sources. Despite these measures, the generalizability of the findings to populations beyond those of European ancestry remains a limitation. Variabilities in results obtained through different MR methods for the same outcome highlighted the complexity of the analysis, while the call for further biological research underscores the need for a deeper understanding of the mechanisms underlying these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study, leveraging MR analysis, has provided significant insights into the causal associations between various hematological indicators and PCa. We identified that MCV and MCH are associated with a decreased risk of PCa, while an elevated eosinophil count and basophil count may augment the risk of developing PCa. Additionally, our findings suggest that PCa is associated with an increase in neutrophil count and red blood cell count. These discoveries enhance our understanding of the genetic underpinnings of PCa and its potential links with a range of hematological markers. Despite these advances, it is crucial to pursue further research to confirm these findings and elucidate the complex mechanisms underlying these associations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCa \u0026nbsp; Prostate cancer\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; Genome-wide association studies\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; Mendelian randomization\u003c/p\u003e\n\u003cp\u003ePSA \u0026nbsp;\u0026nbsp;prostate-specific antigen\u003c/p\u003e\n\u003cp\u003eRBC \u0026nbsp; red blood cell\u003c/p\u003e\n\u003cp\u003eMCV \u0026nbsp; mean corpuscular volume\u003c/p\u003e\n\u003cp\u003eMCH \u0026nbsp; mean corpuscular hemoglobin\u003c/p\u003e\n\u003cp\u003eSTROBE-MR \u0026nbsp;\u0026nbsp;Strengthening the Reporting of Observational Studies in Epidemiology for Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eSNPs \u0026nbsp;\u0026nbsp;Single Nucleotide Polymorphisms\u003c/p\u003e\n\u003cp\u003eLD \u0026nbsp; Linkage Disequilibrium\u003c/p\u003e\n\u003cp\u003eIVs \u0026nbsp; \u0026nbsp;instrumental variables\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp;Inverse Variance Weighted\u003c/p\u003e\n\u003cp\u003eIV \u0026nbsp; \u0026nbsp;instrumental variable\u003c/p\u003e\n\u003cp\u003eRDW \u0026nbsp; \u0026nbsp;red cell distribution width\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp; We wish to acknowledge the participants and investigators of the Integrative Epidemiology Unit (IEU, https://gwas.mrcieu.ac.uk/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp; YRW, and HW designed the study and drafted the paper. PSY, DWX, WFH and JWW critically revised the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp; \u0026nbsp;This work was supported by capital health development project of a multicenter randomized controlled clinical study of Qishao Tianxin prescription in the treatment of early urinary incontinence after radical prostatectomy for prostate cancer, Beijing, China,under grant number 2020-2-2033.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp; All relevant data are within the manuscript and its additional files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp; No conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. 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Allergy, 68(3), 274\u0026ndash;284. https://doi.org/10.1111/all.12103.\u003c/li\u003e\n \u003cli\u003eYamaguchi M, Koketsu R, Suzukawa M, Kawakami A, Iikura M (2009) Human basophils and cytokines/chemokines. Allergology international : official journal of the Japanese Society of Allergology, 58(1), 1\u0026ndash;10. https://doi.org/10.2332/allergolint.08-RAI-0056.\u003c/li\u003e\n \u003cli\u003eCross NA, Papageorgiou M, Eaton CL (2007) Bone marrow stromal cells promote growth and survival of prostate cancer cells. Biochemical Society transactions, 35(Pt 4), 698\u0026ndash;700. https://doi.org/10.1042/BST0350698.\u003c/li\u003e\n \u003cli\u003eCook CC, Kim A, Terao S, Gotoh A, Higuchi M (2012) Consumption of oxygen: a mitochondrial-generated progression signal of advanced cancer. Cell death \u0026amp; disease, 3(1), e258. https://doi.org/10.1038/cddis.2011.141.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, Mendelian randomization, hematological parameters, Single nucleotide polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-4199350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4199350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eThis study aims to investigate the causal relationship between hematologic parameters and the risk of developing PCa, employing a rigorous and methodical scientific approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe applied genome-wide association studies (GWAS) to conduct both forward and reverse Mendelian randomization (MR) analyses within a two-sample framework. We further undertook multivariable MR analyses to investigate the associations between diverse hematological parameters and PCa. To validate the robustness of our results, we implemented several sensitivity analysis techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eIn our univariable MR analysis, genetically predicted mean corpuscular volume (Odds Ratio [OR]: 0.942, 95% Confidence Interval [CI]: 0.891–0.996, P=0.035) and mean corpuscular hemoglobin (OR: 0.934, 95% CI 0.882–0.988, P=0.018) in red blood cells were associated with a decreased risk of PCa. Moreover, MR analysis revealed that genetically predicted increases in eosinophil count (OR: 1.081, 95% CI 1.005–1.163, P=0.036) and basophil count (OR: 1.235, 95% CI 1.006–1.516, P=0.044) were linked to an elevated risk of PCa (all P\u0026lt;0.05). In the multivariable analysis, we found that basophil count was associated with an increased incidence of PCa (OR:1.432, 95% CI 1.028–1.996, P=0.034). In the reverse MR analysis, we observed that genetically predicted PCa was associated with an increase in neutrophil count (OR: 1.012, 95% CI: 1.001–1.023, P=0.031) and red blood cell count (OR: 1.008, 95% CI: 1.000–1.016, P=0.042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn conclusion, our investigation elucidates the causal associations between specific hematological parameters and PCa. These insights contribute significantly to our understanding of the genetic determinants of PCa and their potential interactions with various hematological parameters.\u003c/p\u003e","manuscriptTitle":"Causal Associations between Prostate Cancer and Various Hematological Parameters: Insights from Multivariable and Bidirectional Mendelian Randomization Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 10:16:33","doi":"10.21203/rs.3.rs-4199350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0299a50-b144-4f12-ba74-06954d30fc6d","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-25T03:16:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 10:16:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4199350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4199350","identity":"rs-4199350","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}