Circulating Inflammatory Biomarkers mediates the causal effect of Aging on Female Pelvic Organ Prolapse: Mendelian Randomization Analysis

Circulating Inflammatory Biomarkers mediates the causal effect of Aging on Female Pelvic Organ Prolapse: Mendelian Randomization Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Circulating Inflammatory Biomarkers mediates the causal effect of Aging on Female Pelvic Organ Prolapse: Mendelian Randomization Analysis Xiaoyu Huang, Ya Xiao, Mao Chen, Fangyi Zhu, Liying Chen, Xiaoyu Tian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4138072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Female pelvic organ prolapse (POP) is a disease associated with aging and inflammation, though it is not determined that aging and inflammation are causative factors. The purpose of this study was to evaluate the causal effects of aging and inflammatory factors on female pelvic organ prolapse (POP). Methods Significant genetic variables were evaluated by assessing genome-wide association study (GWAS) data for POP and 5 age biomarkers (GrimAge, HorvathAge, HannumAge, PhenoAge, and leukocyte telomere length). Initially, a bidirectional MR analysis was conducted utilizing a random-effects inverse variance-weighted IVW method to elucidate the causal association. Other MR methods and sensitivity analyses were also used. Then, we also used a two-step MR analysis to analyze the mediating effect of six circulating inflammatory biomarkers in the causal relationship between age and POP. Finally, two-sample MR analysis was also used to investigate the effects of 190 inflammatory cytokines on POP risk. Results Shorter leukocyte telomere length (LTL), rather than epigenetic clocks is genetically predicted to increase the risk of POP. MR analysis showed that shorter LTL is associated with higher leukocyte count, which can lead to POP. A significant causal association was found between 44 circulating inflammatory cytokines and POP risk. After adjusting for multiple tests, CXCL14, IL17A, IL18, IL6, TNFRSF10B, and TNFSF9 remained statistically significant. Conclusions Our findings provide that leukocyte count mediates the potential genetic causal impact of shorter LTL on the development of POP. Inflammatory cytokines might to be considered as potential targets for intervention in POP. Female pelvic organ prolapse aging inflammatory biomarkers cytokines Mendelian randomization analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Female pelvic organ prolapse (POP), characterized by uterine and vaginal wall prolapse due to weakened pelvic floor support, can adversely affect a woman's health and quality of life [ 1 ]. arious factors, such as genetics, anatomy, hormones, and mechanics, contribute to POP. Studies have found a link between age and the prevalence of POP [ 2 ]. Inflammation is theorized to play a role in the pathogenesis of age-related chronic diseases [ 3 ]. Similarly, epidemiological studies have demonstrated a connection between POP and immune-mediated diseases [ 4 , 5 ]. Attributing the development of age-induced pelvic organ prolapse (POP) to chronic immune-mediated factors is challenging in observational studies due to the presence of unquantified variables and the potential for reverse causation. This requires thorough investigations from different perspectives. The utilization of genome-wide association studies (GWAS) provides insight into the complex interactions between environmental factors and genetic components in the pathogenesis of various diseases. The advancements in high-throughput technology have enabled measurements of epigenetic age (a marker of ageing), hundreds of inflammatory biomarkers, and genotyping large populations at the same time [ 6 ]. Additionally, increasing number of studies revealed notable correlations between SNPs and POP risk [ 7 , 8 ]. Mendelian randomization (MR) analysis utilizing genetic data can offer valuable insights into observational studies, often employed to elucidate causal associations between exposures and diseases [ 9 ]. However, there have been only a limited number of MR studies conducted to examine the relationship between genetically determined factors and pelvic organ prolapse [ 10 , 11 ]. In addition, these MR studies neglected to investigate the impact of epigenetics and inflammation on female POP. However, due to their association, it is necessary to investigate their effects on female pelvic prolapse comprehensively. For this research, we utilized an extensive MR analysis to investigate the genetic associations between the susceptibility to female pelvic organ prolapse and 5 biomarkers related to age, 6 biomarkers related to inflammation in circulation, and 190 cytokines related to inflammatory response in the blood (shown in Fig. 1 ). We wondered whether there was a genetic association between age and the development of POP, and a possible role for inflammation as a mediator of this link. Methods Study design and the source of data STROBE-MR guidelines were followed when designing the research [ 12 ]. In the context of bidirectional MR analysis, the initial step involved conducting forward MR analysis to assess the impact of 5 age biomarkers on the risk of POP. Subsequently, reverse MR analysis was carried out to explore the potential influence of genetic predisposition to POP on the levels of age biomarkers. There are five biomarkers related to age, which consist of four (DNA methylation) epigenetic clocks (GrimAge[ 13 ], HannumAge[ 13 ], HorvathAge[ 14 , 15 ], and PhenoAge[ 16 ]) and the length of leukocyte telomeres [ 17 ]. The source information can be found in the supplementary table 1 . In the framework of a two-step MR analysis, circulating inflammatory biomarkers were utilized as secondary exposures in the analysis. We discovered intermediary factors of circulating inflammatory markers that mediate the causal relationship between LTL and POP through the analysis. The GWAS summary data for six circulating inflammatory biomarkers, namely leukocyte count, eosinophil count, basophil count, neutrophil count, lymphocyte count, and monocyte count [ 18 ], were acquired from the GWAS Catalog (https// www.ebi.ac.uk/gwas/ ) and can be found in the supplementary table 1 . We obtained the GWAS data of 228 inflammatory cytokines (including 37 chemokines, 82 interleukins, 50 growth factors, 22 interferons, and 37 tumor necrosis factors (TNF)) for the purpose of studying the circulation of inflammatory cytokines [ 19 ]. Genetic statistics of POP were obtained from a genome-wide association meta-analysis involving 28,086 cases and 546,291 controls of European descent [ 8 ]. This study collected four sets of epigenetic age data from GWAS data, which were derived from 28 cohort studies involving 34,710 individuals of European descent [ 20 ]. Mendelian Randomization Analysis 1. Selection of Instrumental Variables We select the genetic IVs using a multistep process. First, we analyzed SNPs associated with conventional thresholds (p < 5 × 10 − 8 ). After estimating Linkage Disequilibrium (LD), the SNPs were grouped based on a threshold of R2 < 0.001 and a window size of 10,000 kb. Subsequently, we rectified or eliminated uncertain single nucleotide polymorphisms (SNPs) from the GWAS of population through the computation of beta coefficients and standard errors. To ensure the dependability of IVs, a F-statistic was computed to gauge the potency of the association between independent variables and phenotypes [ 21 ]. 2. Statistical and Sensitivity Analysis We performed a MR analysis by following procedures: (1) integrating exposure and outcome data to identify matching SNPs, and (2) investigating the genetic relationship between exposure and outcome using the IVW method. To assess heterogeneity across causal effects, sensitivity analysis was performed using Cochran's Q. A P-value below 0.05 indicates pleiotropy, which requires a random effects IVW-MR method. The MR-Egger test was used to estimate horizontal pleiotropy based on the intercept, ensuring independence between genetic variation and exposure or outcome. Furthermore, additional analyses of MR methods (weighted-median and weighted-mode methods) were conducted with different assumptions and strengths to enhance the stability and robustness of the findings. For the MR-IVW result to be effective, it must be statistically significant, free of pleiotropy and heterogeneity, and have beta values that align with other methods. We also calculated statistical power using the mRnd website (https//shiny.cnsgenomics.com/mRnd/) [ 22 ]. The direct effect of exposure on outcome in the two-step MR analysis is represented by β0 − β1 × β2 [ 23 ]. Here, β0 measures the overall causal effect of exposure on outcome, β1 represents the causal effect of exposure on mediator, β2 represents the causal effect of mediator on outcome, and β1 × β2 indicates the mediating effect from exposure to outcome causal effect. To evaluate the association between female POP and inflammatory cytokines, the initial approach involved utilizing either a Wald ratio or IVW method for inflammatory cytokines assessment. Following adjustment for multiple testing using the Bonferroni method, cytokines that exhibited significant associations were subsequently confirmed through MR‒Egger regression, weighted-median, and weighted-mode approaches. Metascape Enrichment analysis was conducted on 44 cytokines that are significantly associated with the risk of female POP using Metascape (http//metascape.org) [ 24 ]. Additionally, protein‒protein interaction enrichment analysis was performed, and the Molecular Complex Detection (MCODE) algorithm was utilized. There were 3 instances of minimum overlaps and minimum enrichment, with a P value cut-off of 0.05. Statistical analysis R (v 4.1.1) was used to conduct all the statistical analysis. MR analysis was performed by TwoSampleMR and MendelianRandomization packages. Results 1. Causal estimates between age and female pelvic organ prolapse In this study, we utilized a bidirectional MR for age biomarkers and POP, as presented in Table 1 . Table 1 Statistics of Mendelian randomization analysis for age-related biomarkers and female pelvic organ prolapse. Exposures Outcome IWV Egger Weighted mode Weighted median OR (95% CI) P- Value OR (95% CI) P- Value OR (95% CI) P- Value OR (95% CI) P- Value GrimAge POP 0.9828 (0.8986–1.0749) 0.704 0.3042 (0.1302–0.7110) 0.111 0.9586 (0.8983–1.0230) 0.203 0.9439 (0.8704–1.0236) 0.257 HorvathAge 0.9962 (0.9619–1.0318) 0.833 0.9569 (0.8538–1.0724) 0.473 1.0116 (0.9709–1.0541) 0.582 1.0114 (0.9471–1.0801) 0.745 HannumAge 0.9920 (0.9742–1.0101) 0.384 0.9792 (0.9148–1.0481) 0.552 0.9936 (0.9689–1.0188) 0.614 0.9916 (0.9336–1.0531) 0.785 PhenoAge 0.9927 (0.9713–1.0145) 0.509 0.9461 (0.8793–1.0181) 0.173 0.9964 (0.9693–1.0242) 0.797 0.9945 (0.9590–1.0313) 0.773 Leukocyte telomere length 0.8829 (0.7968–0.9782) 0.017 0.7162 (0.5051–1.0156) 0.068 0.8675 (0.6959–1.0816) 0.213 0.9507 (0.8771–1.0304) 0.218 POP GrimAge 1.1311 (0.9319–1.3728) 0.213 0.9299 (0.4797–1.8026) 0.832 1.0884 (0.8327–1.4225) 0.535 1.5258 (0.9644–2.4142) 0.085 HorvathAge 1.1021 (0.8835–1.3747) 0.389 1.8261 (0.8832–3.7757) 0.120 1.2870 (0.9729–1.7025) 0.077 1.4758 (0.9746–2.2348) 0.080 HannumAge 1.1969 (0.9455–1.5151) 0.135 2.4886 (1.2057–5.1363) 0.023 1.2076 (0.8923–1.6344) 0.222 1.8237 (1.0326–3.2208) 0.052 PhenoAge 1.1838 (0.9298–1.5072) 0.171 2.1385 (0.9385–4.8731) 0.088 1.2324 (0.8855–1.7151) 0.215 1.4309 (0.8734–2.3442) 0.172 leukocyte telomere length 1.0424 (0.9218–1.1788) 0.508 1.3243 (0.9483–1.8495) 0.1142 1.1501 (0.9122-1.4500) 0.250 1.1149 (0.9286–1.3385) 0.244 IV, instrumental variables; IWV, Inverse variance weighted. The average F-statistic of all exposures exceeded 10, indicating a low likelihood of weak instrumental variable bias (Supplementary table 2). Furthermore, there was no evidence of between-SNP heterogeneity or horizontal pleiotropy detected by the MR-Egger test (Supplementary table 2). No significant correlation was observed between any of the four phenotypic age biomarkers (GrimAge, HorvathAge, HannumAge, and PhenoAge) and the risk of POP, as per the IVW approach. The OR estimates were as follows: 0.9828 (95% CI 0.8986–1.0749) for GrimAge, 0.9962 (95% CI 0.9619–1.0318) for HorvathAge, 0.9920 (95% CI 0.9742–1.0101) for HannumAge, and 0.9927 (95% CI 0.9713–1.0145) for PhenoAge (Table 1 ). Shorter LTL, however, was associated with a significant risk of POP with an OR of 0.8829 (95% CI 0.7968–0.9782). Similar outcomes were observed with the implementation of the weighted median and weighted mode MR methods, alongside the IVW approach. 2. Causal estimates between leukocyte telomere length and female pelvic organ prolapse with circulating inflammatory biomarkers as mediator In order to investigate the mechanism behind the causal impacts of LTL on POP, we conducted a two-step MR analysis utilizing inflammatory biomarkers as mediator variables. As shown in Table 1 , we used several IVs to determine LTL and inflammatory biomarkers in the MR analysis, and no weak IV was observed. None of the exposures exhibited substantial horizontal pleiotropy for any of the exposures. However, we detected significant between-SNP heterogeneity for leukocyte count (p = 0.0271) and lymphocyte count (p = 0.0298), so the random-effect IVW approach was utilized. Our MR analysis revealed significant associations between shorter LTL and higher circulating inflammatory biomarkers, with an OR of 0.8903 (95% CI 0.8005–0.9901) for leukocyte count (Fig. 2 ). However, no significant results were detected for the other 5 inflammatory biomarkers. Analysis further revealed significant associations between higher leukocyte count and risk of POP, with an OR of 1.0018 (95% CI 1.0003–1.0033), as shown in Fig. 3 and Fig. 4 . No significant results were detected for the other 5 inflammatory biomarkers. Further investigation of circulating inflammatory biomarkers and POP causality excluded reverse causality (supplementary table 3). As a mediator of the genetic causality of aging on female pelvic organ prolapse, leukocyte count (a circulating inflammatory biomarker) was significant (p < 0.01, see Table 2 ). Table 2 Two-step MR results of leukocyte count as a mediator variable for age and POP.’ Mediator Total effect Direct effect A Direct effect B Mediation effect Mediated Proportion % (95% CI) Beta (95% CI) Beta (95% CI) Beta (95% CI) Beta (95% CI) P Leukocyte Count -0.125 (-0.227,-0.022) -0.145 (-0.266, -0.025) 0.071 (0.013,-0.128) -0.010 (-0.025, -0.001) 0.01 8.0(0.8,20.1) Total effect indicates the effect of LTL on POP; direct effect A indicates the effect of LTL on leukocyte count; direct effect B indicates the effect of leukocyte count on POP; mediation effect indicates the effect of LTL on POP through leukocyte count. Total effect, direct effect A and B were derived by IVW, mediation effect was derived by using the delta method. All statistical tests were two-sided p < 0.05 was considered significant. Association between circulating inflammatory cytokines and female pelvic organ prolapse Here, we found a genetic causality of leukocyte count (a circulating inflammatory) and female pelvic organ prolapse. To further screen for the potential intervention targets, we included 190 inflammatory cytokines (32 chemokines, 69 interleukins, 20 fibroblast growth factors, 6 transforming growth factors, 15 other growth factors, 18 interferons, and 30 TNFs) from 228 candidates for two-sample MR analysis (Supplementary Table S3) after performing quality control. Out of these, 166 cytokines possessed two or more valid genetic variants, whereas the remaining 24 cytokines had only a single valid IV. We observed significant associations between the risk of POP and 44 cytokines, including 4 chemokines, 17 interleukins, 7 growth factors, 5 interferons, and 11 TNFs (Fig. 5 ; Supplementary Table S3). Furthermore, the associations were still statistically significant for CXCL14 (p = 2.23e − 08 ), IL17A (p = 1.21e − 13 ), IL18 (p = 7.09e − 08 ), IL6 (p = 3.28e − 28 ), TNFRSF10B (p = 0.0002), and TNFSF9 (p = 3.97e − 08 ) after Bonferroni correction (0.05/190). 4. Enrichment pathway analysis To investigate the possible pathogenesis of circulating inflammatory cytokines and POP, enrichment analysis of 44 significantly associated circulating inflammatory cytokines was performed (Fig. 6 A). The functional enrichment analysis revealed that the immune and inflammatory responses were primarily linked to the inflammatory cytokines, encompassing pathways such as 'Interleukin-10 signaling', 'leukocyte proliferation regulation', 'T-cell activation regulation', and 'mononuclear cell proliferation regulation'. Interestingly, they were also enriched in the “lipid and atherosclerosis pathway”, which is closely associated with the development and progression of atherosclerosis. In the construction of the protein-protein interaction network, Cytoscape was utilized. Additionally, top two closely connected modules were developed using MCODE plug-in. As shown in Fig. 6 B, IL1A, IL18, IL10, CCL20, CCL3L1 and IL6 had a closely interaction, while IL23R, IFNA5, IL10RB, IFNA2, IFNA21, IL12RB, IL10RA, IL11 and TNFs were in the module 1. Discussion The etiology of POP is multifactorial, and age is widely recognized as a significant risk factor for POP [ 24 ]. With this comprehensive MR analysis, we investigated the causality of age on female POP risk for the first time from the perspective of GWAS. Through our analysis, we observed a significant causal relationship between shorter leukocyte telomere length and the risk of female POP. There was no identified causal association was found between four genetically predicted biomarkers for epigenetic age (GrimAge, HorvathAge, HannumAge, and PhenoAge) and POP. And we conducted a bidirectional Mendelian randomization analysis to rule out reverse causality. Yet, the exact mechanisms how shorter telomere length contributes to the development of POP remain an intriguing subject. In order to investigate potential intermediaries of the link between LTL and POP, an examination was carried out, concentrating on inflammation, which is considered a significant cause and feature of aging. The bidirectional connections between chronic inflammation and other characteristics of aging have been extensively emphasized in a recent analysis [ 25 ]. Consistent with our findings, previous research has indicated a negative correlation between leukocyte telomere length (LTL) and leukocyte counts [ 26 ]. A separate investigation suggested that inflammatory markers, including levels of C-reactive protein, leukocyte count, and neutrophil count in the bloodstream, may play a role in the pathogenesis of chronic obstructive pulmonary disease (COPD) through associations with shortened telomeres [ 27 ]. Additionally, the current research has unveiled a genetic association between leukocyte count and pelvic organ prolapse (POP), consistent with the findings of the initial exome chip analysis. This study has identified immune response activation as a key biological process linked to POP [ 28 ]. Nevertheless, in our MR study, no significant findings were seen in the other 5 inflammatory biomarkers (eosinophil, basophil, neutrophil, lymphocyte, and monocyte count). The absence of notable correlations suggests that changes in specific circulating biomarkers are not causative factors in the development of pelvic organ prolapse (POP), but instead function as elements within a multifaceted inflammatory pathway. Put differently, just a single circulating biomarker may not comprehensively capture the specific inflammatory profile of a given tissue [ 29 ]. In subsequent research, we expanded the investigation of the impact of inflammation on POP to include examination of specific inflammatory factors. Similarly, significant causal associations emerged between 44 circulating inflammatory cytokines and POP. Among them, CXCL14, IL17A, IL18, IL6, TNFRSF10B, and TNFSF9 were still statistically significant in the results after multiple corrections. Consistent with this finding, several gene expression profiling studies have discovered that the up-regulated biological process in POP patients mainly related to inflammation [ 30 , 31 ]. To date, the single-cell expression profiles of POP have revealed that the major stromal cells of the vaginal wall from POP cases gained immune regulation and cytokine secretion [ 30 , 31 ]. Additionally, the tissue of POP patients showed an accumulation of macrophages [ 30 , 31 ], and the interaction of IL18-CD48 (pro-inflammatory cytokines) was gained in fibroblasts and immune cells in POP cases [ 32 ]. To provide a deeper understanding of our findings, we conducted an analysis using 44 inflammatory cytokines to identify potential pathways associated with the development of POP. As mentioned above, we observed enriched pathways including “Interleukin-10 signaling”, “regulation of leukocyte proliferation”, "lipid and atherosclerosis pathway", and “regulation of mononuclear cell proliferation”. A recent MR study identified an inverse causal relationship between HDL cholesterol (HDL-C) levels and POP, corroborating the results of prior observational studies [ 22 , 33 ]. Our study revealed a noteworthy positive causal relationship between IL10 signaling molecules and POP, despite IL10's primary role as an anti-inflammatory cytokine that safeguards the body from excessive immune responses. However, prolonged elevation of IL10 levels has been linked to the onset of chronic infections, autoimmune disorders, and the deterioration of immune system function associated with aging [ 34 ]. Moreover, studies have established a correlation between IL10 and aberrant fibrotic mechanisms in various organs such as the lungs, liver, heart, and kidneys [ 35 ]. Moreover, fibrosis and disturbed structural organization of fibrils in the vaginal tissues of women with vaginal prolapse [ 36 ]. As a result, we speculate that IL10 signaling could affect POP development via an abnormal fibrosis process, but more research is needed to confirm this theory. There are a few advantages in the present study. MR analysis is not affected by confounders due to the allocation of genotypes during meiosis, and it is less influenced by data bias as genotype information can be efficiently collected through sequencing. Moreover, a crucial characteristic is the MR understanding of a statistically meaningful correlation as evidence that the exposure has a causal impact on that outcome, rendering MR analysis a noteworthy complement to observational inquiries in this context. Furthermore, this study extensively examined a wide range of genetic factors to investigate the correlation between aging, circulating inflammatory cells, inflammatory factors, and POP. We recognize multiple limitations in our study. First, this study focused solely on participants with European ancestry, which limits the universality of our findings to other ancestry populations. Second, the GWAS data for age-related biomarkers, inflammatory biomarkers and inflammatory cytokines in this MR investigation were gathered through blood sample analysis. Further investigation into the analysis of immune system alterations in pelvic floor supportive tissue, which serves as the primary site of pelvic organ prolapse (POP), would aid in the identification of potential biomarkers and targets for medication. While blood is commonly suggested for data sampling, exploring alternative sources is crucial. In conclusion, although our inquiry covered a fairly wide range of inflammatory characteristics, there is still a lack of comprehensive understanding regarding the roles and mechanisms of these inflammatory cytokines in relation to diseases. Conclusion To summarize, our comprehensive MR analysis indicated evidence of causality of LTL as age-related biomarker on female pelvic organ prolapse, as well as circulating leukocyte count was proven as the mediator. Inflammatory cytokines were further detected as candidate targets for inflammation-induced pelvic organ prolapse. Significant pathways and inflammatory cytokines were also identified. Nevertheless, as a result of the constraints of the research focusing exclusively on European ancestral populations, it is imperative to corroborate our discoveries through extensive GWAS summary data and investigations into the underlying mechanisms. Abbreviations MR: Mendelian randomization GWAS: Genome-wide association study IVW: Inverse variance weighted POP: Female pelvic organ prolapse UC: Ulcerative colitis CD: Crohn's disease TNF: Tumor necrosis factor IL: Interleukin IFN: Interferon CI: Confidence interval Declarations Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaoyu Huang, Ya Xiao, and Fangyi Zhu. The first draft of the manuscript was written by Xiaoyu Huang and Mao Chen. Liying Chen and Xiaoyu Tian contributed to the critical revision of the manuscript. Li Hong contributed to study concept and design, interpreting the data and critical revision of the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: This work was supported by the National Natural Science Foundation of China (82371639), Hubei Key Research and Development Program (2022BCA045) and the National Key Research and Development Program of China (2021YFC2701300; 2021YFC2701302). Availability of data and materials : GWAS data on age biomarkers, female pelvic organ prolapse and circulating inflammatory biomarkers were downloaded from https://gwas.mrcieu.ac.uk/. Data on inflammatory cytokines were downloaded from https://www.decode.com/summarydata/. The direct link and accession numbers are provided in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Acknowledgements : We thank all investigators for making their GWAS data publicly available. Ethics approval and consent to participate: This study was based on publicly available data and the ethics approval is waived. Consent for publication: Not applicable Competing interests: The authors declare that they have no competing interests. Informed Consent Statement: All methods were carried out following STROBE-MR guidelines and regulations. References Weintraub AY, Glinter H, Marcus-Braun N: Narrative review of the epidemiology, diagnosis and pathophysiology of pelvic organ prolapse . Int Braz J Urol 2020, 46 (1):5-14. Barber MD, Maher C: Epidemiology and outcome assessment of pelvic organ prolapse . Int Urogynecol J 2013, 24 (11):1783-1790. Bi J, Zhang C, Lu C, Mo C, Zeng J, Yao M, Jia B, Liu Z, Yuan P, Xu S: Age-related bone diseases: Role of inflammaging . J Autoimmun 2024, 143 :103169. Khan S, Ansari MA, Vasenwala SM, Mohsin Z: A Community Based Study on Pelvic Inflammatory Disease in Postmenopausal Females: Microbiological Spectrum and Socio-Demographic Correlates . J Clin Diagn Res 2017, 11 (3):LC05-LC10. Caglayan M, Oktayoglu P, Agacayak E, Dagli AZ, Okcu M, Nas K: A New Comorbidity in Female Patients With Ankylosing Spondylitis: Pelvic Organ Prolapse . J Clin Rheumatol 2019, 25 (1):36-40. Didriksen M, Nawaz MS, Dowsett J, Bell S, Erikstrup C, Pedersen OB, Sorensen E, Jennum PJ, Burgdorf KS, Burchell B et al : Large genome-wide association study identifies three novel risk variants for restless legs syndrome . Commun Biol 2020, 3 (1):703. Olafsdottir T, Thorleifsson G, Sulem P, Stefansson OA, Medek H, Olafsson K, Ingthorsson O, Gudmundsson V, Jonsdottir I, Halldorsson GH et al : Genome-wide association identifies seven loci for pelvic organ prolapse in Iceland and the UK Biobank . Commun Biol 2020, 3 (1):129. Pujol-Gualdo N, Lall K, Lepamets M, Estonian Biobank Research T, Rossi HR, Arffman RK, Piltonen TT, Magi R, Laisk T: Advancing our understanding of genetic risk factors and potential personalized strategies for pelvic organ prolapse . Nat Commun 2022, 13 (1):3584. Liu Z, Song C, Suo C, Fan H, Zhang T, Jin L, Chen X: Alcohol consumption and hepatocellular carcinoma: novel insights from a prospective cohort study and nonlinear Mendelian randomization analysis . BMC Med 2022, 20 (1):413. Zhang W, Ge J, Qu Z, Wu W, Lei H, Pan H, Chen H: Evaluation for causal effects of socioeconomic traits on risk of female genital prolapse (FGP): a multivariable Mendelian randomization analysis . BMC Med Genomics 2023, 16 (1):125. Liu H, Wu W, Xiang W, Yuan J: Lifestyle factors, metabolic factors and socioeconomic status for pelvic organ prolapse: a Mendelian randomization study . Eur J Med Res 2023, 28 (1):183. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N et al : Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement . JAMA 2021, 326 (16):1614-1621. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y et al : Genome-wide methylation profiles reveal quantitative views of human aging rates . Mol Cell 2013, 49 (2):359-367. Horvath S: DNA methylation age of human tissues and cell types . Genome Biol 2013, 14 (10):R115. Horvath S: Erratum to: DNA methylation age of human tissues and cell types . Genome Biol 2015, 16 (1):96. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD et al : DNA methylation GrimAge strongly predicts lifespan and healthspan . Aging (Albany NY) 2019, 11 (2):303-327. Kessler MD, Damask A, O'Keeffe S, Banerjee N, Li D, Watanabe K, Marketta A, Van Meter M, Semrau S, Horowitz J et al : Common and rare variant associations with clonal haematopoiesis phenotypes . Nature 2022, 612 (7939):301-309. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA et al : The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease . Cell 2016, 167 (5):1415-1429 e1419. Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV et al : Large-scale integration of the plasma proteome with genetics and disease . Nat Genet 2021, 53 (12):1712-1721. McCartney DL, Min JL, Richmond RC, Lu AT, Sobczyk MK, Davies G, Broer L, Guo X, Jeong A, Jung J et al : Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging . Genome Biol 2021, 22 (1):194. Sanderson E, Spiller W, Bowden J: Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization . Stat Med 2021, 40 (25):5434-5452. Brion MJ, Shakhbazov K, Visscher PM: Calculating statistical power in Mendelian randomization studies . Int J Epidemiol 2013, 42 (5):1497-1501. Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD: Mendelian randomisation for mediation analysis: current methods and challenges for implementation . Eur J Epidemiol 2021, 36 (5):465-478. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK: Metascape provides a biologist-oriented resource for the analysis of systems-level datasets . Nat Commun 2019, 10 (1):1523. Baechle JJ, Chen N, Makhijani P, Winer S, Furman D, Winer DA: Chronic inflammation and the hallmarks of aging . Mol Metab 2023, 74 :101755. Codd V, Denniff M, Swinfield C, Warner SC, Papakonstantinou M, Sheth S, Nanus DE, Budgeon CA, Musicha C, Bountziouka V et al : Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank . Nat Aging 2022, 2 (2):170-179. Wang T, Jia Z, Li S, Li Y, Yu T, Lu T, Shi Y: The association between leukocyte telomere length and chronic obstructive pulmonary disease is partially mediated by inflammation: a meta-analysis and population-based mediation study . BMC Pulm Med 2022, 22 (1):320. Kluivers KB, Lince SL, Ruiz-Zapata AM, Post WM, Cartwright R, Kerkhof MH, Widomska J, De Witte W, Pecanka J, Kiemeney LA et al : Molecular Landscape of Pelvic Organ Prolapse Provides Insights into Disease Etiology . Int J Mol Sci 2023, 24 (7). Wijeyesinghe S, Beura LK, Pierson MJ, Stolley JM, Adam OA, Ruscher R, Steinert EM, Rosato PC, Vezys V, Masopust D: Expansible residence decentralizes immune homeostasis . Nature 2021, 592 (7854):457-462. Wang WG, Chen ZSD, Sun J, Yang CM, He HB, Lu XK, Wang WY: Bioinformatic analysis of biological changes involved in pelvic organ prolapse . Medicine (Baltimore) 2023, 102 (22):e33823. Liu P, Liu D, Chen F, Luo L, Jin Y, Peng J, Yu H, Wei M, Shi X, Wang L: Effect of Nrf2 on Phenotype Changes of Macrophages in the Anterior Vaginal Wall of Women With Pelvic Organ Prolapse . Urogynecology (Phila) 2022, 28 (9):616-623. Li Y, Zhang QY, Sun BF, Ma Y, Zhang Y, Wang M, Ma C, Shi H, Sun Z, Chen J et al : Single-cell transcriptome profiling of the vaginal wall in women with severe anterior vaginal prolapse . Nat Commun 2021, 12 (1):87. Wu C, Zhou Z, Yang Y, Li H, Guo Y, Tong X: Bioinformatically deciphering immune cell infiltration and signature genes in pelvic organ prolapse . Int Urogynecol J 2023, 34 (5):1091-1101. Carlini V, Noonan DM, Abdalalem E, Goletti D, Sansone C, Calabrone L, Albini A: The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions . Front Immunol 2023, 14 :1161067. Huaux F: Interpreting Immunoregulation in Lung Fibrosis: A New Branch of the Immune Model . Front Immunol 2021, 12 :690375. Bray R, Derpapas A, Fernando R, Khullar V, Panayi DC: Does the vaginal wall become thinner as prolapse grade increases? Int Urogynecol J 2017, 28 (3):397-402. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4138072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291160160,"identity":"94fc7c11-c2fb-4cfd-a134-6e3785177a87","order_by":0,"name":"Xiaoyu Huang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Huang","suffix":""},{"id":291160161,"identity":"aa914685-96ba-4170-a98b-a864b5d6fa96","order_by":1,"name":"Ya Xiao","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Xiao","suffix":""},{"id":291160162,"identity":"0cce33fb-2f43-46c8-b906-ace29da4ddad","order_by":2,"name":"Mao Chen","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Mao","middleName":"","lastName":"Chen","suffix":""},{"id":291160163,"identity":"6babd7fb-e4d4-4961-ae78-bbe49ba59db0","order_by":3,"name":"Fangyi Zhu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Fangyi","middleName":"","lastName":"Zhu","suffix":""},{"id":291160165,"identity":"f6371a12-5bdb-4c53-8414-a693b03fc98f","order_by":4,"name":"Liying Chen","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Chen","suffix":""},{"id":291160166,"identity":"74b9820e-8d00-4904-88ba-348486efb900","order_by":5,"name":"Xiaoyu Tian","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Tian","suffix":""},{"id":291160168,"identity":"14028df0-56b0-4abc-96ca-0e7b77524b98","order_by":6,"name":"Li Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDCCAwxsIEqGjb0BzGBsIFYLDxvPAVK1MEgkEKmF7/YBtgcfd9Ty8Em+MXvMw2Aju+EA87MH+LRInktgN5x55jgPm3SOuTEPQ5rxhgNs5gb4tBicYWCT5m07BtSSu02ah+Fw4oYDPGwSxGmRPAvS8p9oLTVAZbwgLQcIa5EEapGc2QZUxpP/TXKOQbLxzMNsZni18AG1SHxsq5OTbz+WJvGmwk6273jzM7xaGBj4PwCJwzB3AjEzfvUwUEecslEwCkbBKBiZAABwSD8nER3aWwAAAABJRU5ErkJggg==","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2024-03-20 15:00:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4138072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4138072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54936740,"identity":"6e28b9af-f51f-41fa-a845-9bddc87931e7","added_by":"auto","created_at":"2024-04-18 22:12:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276102,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic summary of the study. LTL, leukocyte telomere length.\u003c/p\u003e","description":"","filename":"figure1flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/85575d4df321912e3922577c.png"},{"id":54937005,"identity":"f48bddcb-3e26-4756-bfdd-87cea628e21f","added_by":"auto","created_at":"2024-04-18 22:20:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2033487,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic association between leukocyte telomere length and inflammatory biomarkers according to Mendelian randomization analysis (IV, instrumental variable; IVW, inverse-variance-weighted method).\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/5fec65f7ddddd55b6caf9850.png"},{"id":54936741,"identity":"bbd1e456-792d-409e-bb6b-7f4ebdd444e6","added_by":"auto","created_at":"2024-04-18 22:12:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2452758,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic association between circulating inflammatory biomarkers and female pelvic organ prolapse according to Mendelian randomization analysis (IV, instrumental variable; IVW, inverse variance-weighted method).\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/113c2c03b54eab363494053c.png"},{"id":54936743,"identity":"a6c5205e-81ea-4272-86c1-fa7a9218e308","added_by":"auto","created_at":"2024-04-18 22:12:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":679278,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot showing the SNP effects on both circulating inflammatory biomarkers and female pelvic organ prolapse (The gray error bars denote the 95% confidence intervals of the effects).\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/9b8cc7789498ed516c5ee010.png"},{"id":54936739,"identity":"04f29484-f3fc-4861-845e-fcdc16e679bd","added_by":"auto","created_at":"2024-04-18 22:12:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":440863,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic association between circulating inflammatory cytokines and female pelvic organ prolapse according to Mendelian randomization analysis (we only show the point estimate in this plot. The blue dashed line denotes the threshold of p value \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/900c70bc22b9a1f3e86f8a51.png"},{"id":54936745,"identity":"399bc0f4-968d-458a-b3bb-bcf27accfe07","added_by":"auto","created_at":"2024-04-18 22:12:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":990354,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis results. A. Enrichment pathway analysis for 44 pathways significantly correlated with female POP (we only show the most important pathways in this plot); B. The protein‒protein interaction network (PPI network) constructed by Cytoscape and two closely connected modules obtained through MCODE.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/95fc57f6b51dd4d2dbfef6d8.png"},{"id":70913429,"identity":"e8aef92d-0614-4c56-a2f4-e8078ad84783","added_by":"auto","created_at":"2024-12-09 07:40:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9916823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/006745ec-8917-4aed-af5b-506c70879dbc.pdf"},{"id":54937004,"identity":"7ab8bcf6-cead-4787-90e1-2852a6dd76ce","added_by":"auto","created_at":"2024-04-18 22:20:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":92346,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4138072/v1/d38a97bc6e06134af6709920.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating Inflammatory Biomarkers mediates the causal effect of Aging on Female Pelvic Organ Prolapse: Mendelian Randomization Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemale pelvic organ prolapse (POP), characterized by uterine and vaginal wall prolapse due to weakened pelvic floor support, can adversely affect a woman's health and quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. arious factors, such as genetics, anatomy, hormones, and mechanics, contribute to POP. Studies have found a link between age and the prevalence of POP [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Inflammation is theorized to play a role in the pathogenesis of age-related chronic diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, epidemiological studies have demonstrated a connection between POP and immune-mediated diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Attributing the development of age-induced pelvic organ prolapse (POP) to chronic immune-mediated factors is challenging in observational studies due to the presence of unquantified variables and the potential for reverse causation. This requires thorough investigations from different perspectives.\u003c/p\u003e \u003cp\u003eThe utilization of genome-wide association studies (GWAS) provides insight into the complex interactions between environmental factors and genetic components in the pathogenesis of various diseases. The advancements in high-throughput technology have enabled measurements of epigenetic age (a marker of ageing), hundreds of inflammatory biomarkers, and genotyping large populations at the same time [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, increasing number of studies revealed notable correlations between SNPs and POP risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis utilizing genetic data can offer valuable insights into observational studies, often employed to elucidate causal associations between exposures and diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, there have been only a limited number of MR studies conducted to examine the relationship between genetically determined factors and pelvic organ prolapse [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, these MR studies neglected to investigate the impact of epigenetics and inflammation on female POP. However, due to their association, it is necessary to investigate their effects on female pelvic prolapse comprehensively.\u003c/p\u003e \u003cp\u003eFor this research, we utilized an extensive MR analysis to investigate the genetic associations between the susceptibility to female pelvic organ prolapse and 5 biomarkers related to age, 6 biomarkers related to inflammation in circulation, and 190 cytokines related to inflammatory response in the blood (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We wondered whether there was a genetic association between age and the development of POP, and a possible role for inflammation as a mediator of this link.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and the source of data\u003c/h2\u003e\n \u003cp\u003eSTROBE-MR guidelines were followed when designing the research [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eIn the context of bidirectional MR analysis, the initial step involved conducting forward MR analysis to assess the impact of 5 age biomarkers on the risk of POP. Subsequently, reverse MR analysis was carried out to explore the potential influence of genetic predisposition to POP on the levels of age biomarkers. There are five biomarkers related to age, which consist of four (DNA methylation) epigenetic clocks (GrimAge[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], HannumAge[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], HorvathAge[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], and PhenoAge[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]) and the length of leukocyte telomeres [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. The source information can be found in the supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIn the framework of a two-step MR analysis, circulating inflammatory biomarkers were utilized as secondary exposures in the analysis. We discovered intermediary factors of circulating inflammatory markers that mediate the causal relationship between LTL and POP through the analysis. The GWAS summary data for six circulating inflammatory biomarkers, namely leukocyte count, eosinophil count, basophil count, neutrophil count, lymphocyte count, and monocyte count [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], were acquired from the GWAS Catalog (https//\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ebi.ac.uk/gwas/\u003c/span\u003e\u003c/span\u003e) and can be found in the supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eWe obtained the GWAS data of 228 inflammatory cytokines (including 37 chemokines, 82 interleukins, 50 growth factors, 22 interferons, and 37 tumor necrosis factors (TNF)) for the purpose of studying the circulation of inflammatory cytokines [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eGenetic statistics of POP were obtained from a genome-wide association meta-analysis involving 28,086 cases and 546,291 controls of European descent [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study collected four sets of epigenetic age data from GWAS data, which were derived from 28 cohort studies involving 34,710 individuals of European descent [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMendelian Randomization Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1. Selection of Instrumental Variables\u003c/p\u003e\n \u003cp\u003eWe select the genetic IVs using a multistep process. First, we analyzed SNPs associated with conventional thresholds (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). After estimating Linkage Disequilibrium (LD), the SNPs were grouped based on a threshold of R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a window size of 10,000 kb. Subsequently, we rectified or eliminated uncertain single nucleotide polymorphisms (SNPs) from the GWAS of population through the computation of beta coefficients and standard errors. To ensure the dependability of IVs, a F-statistic was computed to gauge the potency of the association between independent variables and phenotypes [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e2. Statistical and Sensitivity Analysis\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWe performed a MR analysis by following procedures: (1) integrating exposure and outcome data to identify matching SNPs, and (2) investigating the genetic relationship between exposure and outcome using the IVW method. To assess heterogeneity across causal effects, sensitivity analysis was performed using Cochran\u0026apos;s Q. A P-value below 0.05 indicates pleiotropy, which requires a random effects IVW-MR method. The MR-Egger test was used to estimate horizontal pleiotropy based on the intercept, ensuring independence between genetic variation and exposure or outcome. Furthermore, additional analyses of MR methods (weighted-median and weighted-mode methods) were conducted with different assumptions and strengths to enhance the stability and robustness of the findings.\u003c/p\u003e\n \u003cp\u003eFor the MR-IVW result to be effective, it must be statistically significant, free of pleiotropy and heterogeneity, and have beta values that align with other methods. We also calculated statistical power using the mRnd website (https//shiny.cnsgenomics.com/mRnd/) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe direct effect of exposure on outcome in the two-step MR analysis is represented by \u0026beta;0\u0026thinsp;\u0026minus;\u0026thinsp;\u0026beta;1\u0026thinsp;\u0026times;\u0026thinsp;\u0026beta;2 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Here, \u0026beta;0 measures the overall causal effect of exposure on outcome, \u0026beta;1 represents the causal effect of exposure on mediator, \u0026beta;2 represents the causal effect of mediator on outcome, and \u0026beta;1\u0026thinsp;\u0026times;\u0026thinsp;\u0026beta;2 indicates the mediating effect from exposure to outcome causal effect.\u003c/p\u003e\n \u003cp\u003eTo evaluate the association between female POP and inflammatory cytokines, the initial approach involved utilizing either a Wald ratio or IVW method for inflammatory cytokines assessment. Following adjustment for multiple testing using the Bonferroni method, cytokines that exhibited significant associations were subsequently confirmed through MR‒Egger regression, weighted-median, and weighted-mode approaches.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eMetascape\u003c/h2\u003e\n \u003cp\u003eEnrichment analysis was conducted on 44 cytokines that are significantly associated with the risk of female POP using Metascape (http//metascape.org) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, protein‒protein interaction enrichment analysis was performed, and the Molecular Complex Detection (MCODE) algorithm was utilized. There were 3 instances of minimum overlaps and minimum enrichment, with a P value cut-off of 0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eR (v 4.1.1) was used to conduct all the statistical analysis. MR analysis was performed by TwoSampleMR and MendelianRandomization packages.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e1. Causal estimates between age and female pelvic organ prolapse\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized a bidirectional MR for age biomarkers and POP, as presented in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eStatistics of Mendelian randomization analysis for age-related biomarkers and female pelvic organ prolapse.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003eExposures\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003eOutcome\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eIWV\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eEgger\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eWeighted mode\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003eWeighted median\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP- Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP- Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP- Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eP- Value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGrimAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003ePOP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9828\u003cbr\u003e(0.8986\u0026ndash;1.0749)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.704\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.3042\u003cbr\u003e(0.1302\u0026ndash;0.7110)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.111\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9586\u003cbr\u003e(0.8983\u0026ndash;1.0230)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.203\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9439\u003cbr\u003e(0.8704\u0026ndash;1.0236)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.257\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHorvathAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9962\u003cbr\u003e(0.9619\u0026ndash;1.0318)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.833\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9569\u003cbr\u003e(0.8538\u0026ndash;1.0724)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.473\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.0116\u003cbr\u003e(0.9709\u0026ndash;1.0541)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.582\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.0114\u003cbr\u003e(0.9471\u0026ndash;1.0801)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.745\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHannumAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9920\u003cbr\u003e(0.9742\u0026ndash;1.0101)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.384\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9792\u003cbr\u003e(0.9148\u0026ndash;1.0481)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.552\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9936\u003cbr\u003e(0.9689\u0026ndash;1.0188)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.614\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9916\u003cbr\u003e(0.9336\u0026ndash;1.0531)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.785\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePhenoAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9927\u003cbr\u003e(0.9713\u0026ndash;1.0145)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.509\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9461\u003cbr\u003e(0.8793\u0026ndash;1.0181)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.173\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9964\u003cbr\u003e(0.9693\u0026ndash;1.0242)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.797\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9945\u003cbr\u003e(0.9590\u0026ndash;1.0313)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.773\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLeukocyte telomere length\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cspan type=\"BoldUnderline\" name=\"Emphasis\"\u003e0.8829\u003c/span\u003e\u003cbr\u003e\u003cspan type=\"BoldUnderline\" name=\"Emphasis\"\u003e(0.7968\u0026ndash;0.9782)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cspan type=\"BoldUnderline\" name=\"Emphasis\"\u003e0.017\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.7162\u003cbr\u003e(0.5051\u0026ndash;1.0156)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.068\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.8675\u003cbr\u003e(0.6959\u0026ndash;1.0816)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.213\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9507\u003cbr\u003e(0.8771\u0026ndash;1.0304)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.218\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003ePOP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGrimAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1311\u003cbr\u003e(0.9319\u0026ndash;1.3728)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.213\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.9299\u003cbr\u003e(0.4797\u0026ndash;1.8026)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.832\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.0884\u003cbr\u003e(0.8327\u0026ndash;1.4225)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.535\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.5258\u003cbr\u003e(0.9644\u0026ndash;2.4142)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.085\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHorvathAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1021\u003cbr\u003e(0.8835\u0026ndash;1.3747)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.389\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.8261\u003cbr\u003e(0.8832\u0026ndash;3.7757)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.120\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.2870\u003cbr\u003e(0.9729\u0026ndash;1.7025)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.077\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.4758\u003cbr\u003e(0.9746\u0026ndash;2.2348)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.080\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHannumAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1969\u003cbr\u003e(0.9455\u0026ndash;1.5151)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.135\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.4886\u003cbr\u003e(1.2057\u0026ndash;5.1363)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.2076\u003cbr\u003e(0.8923\u0026ndash;1.6344)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.222\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.8237\u003cbr\u003e(1.0326\u0026ndash;3.2208)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.052\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePhenoAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1838\u003cbr\u003e(0.9298\u0026ndash;1.5072)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.171\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.1385\u003cbr\u003e(0.9385\u0026ndash;4.8731)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.088\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.2324\u003cbr\u003e(0.8855\u0026ndash;1.7151)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.215\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.4309\u003cbr\u003e(0.8734\u0026ndash;2.3442)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.172\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eleukocyte telomere length\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.0424\u003cbr\u003e(0.9218\u0026ndash;1.1788)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.508\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.3243\u003cbr\u003e(0.9483\u0026ndash;1.8495)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.1142\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1501\u003cbr\u003e(0.9122-1.4500)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.250\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1149\u003cbr\u003e(0.9286\u0026ndash;1.3385)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.244\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eIV, instrumental variables; IWV, Inverse variance weighted.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe average F-statistic of all exposures exceeded 10, indicating a low likelihood of weak instrumental variable bias (Supplementary table 2). Furthermore, there was no evidence of between-SNP heterogeneity or horizontal pleiotropy detected by the MR-Egger test (Supplementary table 2).\u003c/p\u003e\n\u003cp\u003eNo significant correlation was observed between any of the four phenotypic age biomarkers (GrimAge, HorvathAge, HannumAge, and PhenoAge) and the risk of POP, as per the IVW approach. The OR estimates were as follows: 0.9828 (95% CI 0.8986\u0026ndash;1.0749) for GrimAge, 0.9962 (95% CI 0.9619\u0026ndash;1.0318) for HorvathAge, 0.9920 (95% CI 0.9742\u0026ndash;1.0101) for HannumAge, and 0.9927 (95% CI 0.9713\u0026ndash;1.0145) for PhenoAge (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Shorter LTL, however, was associated with a significant risk of POP with an OR of 0.8829 (95% CI 0.7968\u0026ndash;0.9782). Similar outcomes were observed with the implementation of the weighted median and weighted mode MR methods, alongside the IVW approach.\u003c/p\u003e\n\u003cp\u003e2. Causal estimates between leukocyte telomere length and female pelvic organ prolapse with circulating inflammatory biomarkers as mediator\u003c/p\u003e\n\u003cp\u003eIn order to investigate the mechanism behind the causal impacts of LTL on POP, we conducted a two-step MR analysis utilizing inflammatory biomarkers as mediator variables.\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, we used several IVs to determine LTL and inflammatory biomarkers in the MR analysis, and no weak IV was observed. None of the exposures exhibited substantial horizontal pleiotropy for any of the exposures. However, we detected significant between-SNP heterogeneity for leukocyte count (p\u0026thinsp;=\u0026thinsp;0.0271) and lymphocyte count (p\u0026thinsp;=\u0026thinsp;0.0298), so the random-effect IVW approach was utilized.\u003c/p\u003e\n\u003cp\u003eOur MR analysis revealed significant associations between shorter LTL and higher circulating inflammatory biomarkers, with an OR of 0.8903 (95% CI 0.8005\u0026ndash;0.9901) for leukocyte count (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). However, no significant results were detected for the other 5 inflammatory biomarkers.\u003c/p\u003e\n\u003cp\u003eAnalysis further revealed significant associations between higher leukocyte count and risk of POP, with an OR of 1.0018 (95% CI 1.0003\u0026ndash;1.0033), as shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. No significant results were detected for the other 5 inflammatory biomarkers.\u003c/p\u003e\n\u003cp\u003eFurther investigation of circulating inflammatory biomarkers and POP causality excluded reverse causality (supplementary table 3). As a mediator of the genetic causality of aging on female pelvic organ prolapse, leukocyte count (a circulating inflammatory biomarker) was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, see Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTwo-step MR results of leukocyte count as a mediator variable for age and POP.\u0026rsquo;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eMediator\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTotal effect\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDirect effect A\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDirect effect B\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eMediation effect\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eMediated\u003cbr\u003eProportion %\u003cbr\u003e(95% CI)\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eBeta\u003cbr\u003e(95% CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBeta\u003cbr\u003e(95% CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBeta\u003cbr\u003e(95% CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eBeta\u003cbr\u003e(95% CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eP\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLeukocyte Count\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-0.125\u003cbr\u003e(-0.227,-0.022)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-0.145\u003cbr\u003e(-0.266, -0.025)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.071\u003cbr\u003e(0.013,-0.128)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e-0.010\u003cbr\u003e(-0.025, -0.001)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e8.0(0.8,20.1)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eTotal effect indicates the effect of LTL on POP; direct effect A indicates the effect of LTL on leukocyte count; direct effect B indicates the effect of leukocyte count on POP; mediation effect indicates the effect of LTL on POP through leukocyte count. Total effect, direct effect A and B were derived by IVW, mediation effect was derived by using the delta method. All statistical tests were two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAssociation between circulating inflammatory cytokines and female pelvic organ prolapse\u003c/p\u003e\n\u003cp\u003eHere, we found a genetic causality of leukocyte count (a circulating inflammatory) and female pelvic organ prolapse. To further screen for the potential intervention targets, we included 190 inflammatory cytokines (32 chemokines, 69 interleukins, 20 fibroblast growth factors, 6 transforming growth factors, 15 other growth factors, 18 interferons, and 30 TNFs) from 228 candidates for two-sample MR analysis (Supplementary Table S3) after performing quality control.\u003c/p\u003e\n\u003cp\u003eOut of these, 166 cytokines possessed two or more valid genetic variants, whereas the remaining 24 cytokines had only a single valid IV. We observed significant associations between the risk of POP and 44 cytokines, including 4 chemokines, 17 interleukins, 7 growth factors, 5 interferons, and 11 TNFs (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e; Supplementary Table S3). Furthermore, the associations were still statistically significant for CXCL14 (p\u0026thinsp;=\u0026thinsp;2.23e\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e), IL17A (p\u0026thinsp;=\u0026thinsp;1.21e\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), IL18 (p\u0026thinsp;=\u0026thinsp;7.09e\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e), IL6 (p\u0026thinsp;=\u0026thinsp;3.28e\u003csup\u003e\u0026minus;\u0026thinsp;28\u003c/sup\u003e), TNFRSF10B (p\u0026thinsp;=\u0026thinsp;0.0002), and TNFSF9 (p\u0026thinsp;=\u0026thinsp;3.97e\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e) after Bonferroni correction (0.05/190).\u003c/p\u003e\n\u003cp\u003e4. Enrichment pathway analysis\u003c/p\u003e\n\u003cp\u003eTo investigate the possible pathogenesis of circulating inflammatory cytokines and POP, enrichment analysis of 44 significantly associated circulating inflammatory cytokines was performed (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eA). The functional enrichment analysis revealed that the immune and inflammatory responses were primarily linked to the inflammatory cytokines, encompassing pathways such as \u0026apos;Interleukin-10 signaling\u0026apos;, \u0026apos;leukocyte proliferation regulation\u0026apos;, \u0026apos;T-cell activation regulation\u0026apos;, and \u0026apos;mononuclear cell proliferation regulation\u0026apos;. Interestingly, they were also enriched in the \u0026ldquo;lipid and atherosclerosis pathway\u0026rdquo;, which is closely associated with the development and progression of atherosclerosis.\u003c/p\u003e\n\u003cp\u003eIn the construction of the protein-protein interaction network, Cytoscape was utilized. Additionally, top two closely connected modules were developed using MCODE plug-in. As shown in Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eB, IL1A, IL18, IL10, CCL20, CCL3L1 and IL6 had a closely interaction, while IL23R, IFNA5, IL10RB, IFNA2, IFNA21, IL12RB, IL10RA, IL11 and TNFs were in the module 1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe etiology of POP is multifactorial, and age is widely recognized as a significant risk factor for POP [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. With this comprehensive MR analysis, we investigated the causality of age on female POP risk for the first time from the perspective of GWAS. Through our analysis, we observed a significant causal relationship between shorter leukocyte telomere length and the risk of female POP. There was no identified causal association was found between four genetically predicted biomarkers for epigenetic age (GrimAge, HorvathAge, HannumAge, and PhenoAge) and POP. And we conducted a bidirectional Mendelian randomization analysis to rule out reverse causality. Yet, the exact mechanisms how shorter telomere length contributes to the development of POP remain an intriguing subject.\u003c/p\u003e \u003cp\u003eIn order to investigate potential intermediaries of the link between LTL and POP, an examination was carried out, concentrating on inflammation, which is considered a significant cause and feature of aging. The bidirectional connections between chronic inflammation and other characteristics of aging have been extensively emphasized in a recent analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consistent with our findings, previous research has indicated a negative correlation between leukocyte telomere length (LTL) and leukocyte counts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A separate investigation suggested that inflammatory markers, including levels of C-reactive protein, leukocyte count, and neutrophil count in the bloodstream, may play a role in the pathogenesis of chronic obstructive pulmonary disease (COPD) through associations with shortened telomeres [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, the current research has unveiled a genetic association between leukocyte count and pelvic organ prolapse (POP), consistent with the findings of the initial exome chip analysis. This study has identified immune response activation as a key biological process linked to POP [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Nevertheless, in our MR study, no significant findings were seen in the other 5 inflammatory biomarkers (eosinophil, basophil, neutrophil, lymphocyte, and monocyte count). The absence of notable correlations suggests that changes in specific circulating biomarkers are not causative factors in the development of pelvic organ prolapse (POP), but instead function as elements within a multifaceted inflammatory pathway. Put differently, just a single circulating biomarker may not comprehensively capture the specific inflammatory profile of a given tissue [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn subsequent research, we expanded the investigation of the impact of inflammation on POP to include examination of specific inflammatory factors. Similarly, significant causal associations emerged between 44 circulating inflammatory cytokines and POP. Among them, CXCL14, IL17A, IL18, IL6, TNFRSF10B, and TNFSF9 were still statistically significant in the results after multiple corrections. Consistent with this finding, several gene expression profiling studies have discovered that the up-regulated biological process in POP patients mainly related to inflammation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To date, the single-cell expression profiles of POP have revealed that the major stromal cells of the vaginal wall from POP cases gained immune regulation and cytokine secretion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, the tissue of POP patients showed an accumulation of macrophages [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and the interaction of IL18-CD48 (pro-inflammatory cytokines) was gained in fibroblasts and immune cells in POP cases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To provide a deeper understanding of our findings, we conducted an analysis using 44 inflammatory cytokines to identify potential pathways associated with the development of POP. As mentioned above, we observed enriched pathways including \u0026ldquo;Interleukin-10 signaling\u0026rdquo;, \u0026ldquo;regulation of leukocyte proliferation\u0026rdquo;, \"lipid and atherosclerosis pathway\", and \u0026ldquo;regulation of mononuclear cell proliferation\u0026rdquo;. A recent MR study identified an inverse causal relationship between HDL cholesterol (HDL-C) levels and POP, corroborating the results of prior observational studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study revealed a noteworthy positive causal relationship between IL10 signaling molecules and POP, despite IL10's primary role as an anti-inflammatory cytokine that safeguards the body from excessive immune responses. However, prolonged elevation of IL10 levels has been linked to the onset of chronic infections, autoimmune disorders, and the deterioration of immune system function associated with aging [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, studies have established a correlation between IL10 and aberrant fibrotic mechanisms in various organs such as the lungs, liver, heart, and kidneys [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, fibrosis and disturbed structural organization of fibrils in the vaginal tissues of women with vaginal prolapse [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. As a result, we speculate that IL10 signaling could affect POP development via an abnormal fibrosis process, but more research is needed to confirm this theory.\u003c/p\u003e \u003cp\u003eThere are a few advantages in the present study. MR analysis is not affected by confounders due to the allocation of genotypes during meiosis, and it is less influenced by data bias as genotype information can be efficiently collected through sequencing. Moreover, a crucial characteristic is the MR understanding of a statistically meaningful correlation as evidence that the exposure has a causal impact on that outcome, rendering MR analysis a noteworthy complement to observational inquiries in this context. Furthermore, this study extensively examined a wide range of genetic factors to investigate the correlation between aging, circulating inflammatory cells, inflammatory factors, and POP.\u003c/p\u003e \u003cp\u003eWe recognize multiple limitations in our study. First, this study focused solely on participants with European ancestry, which limits the universality of our findings to other ancestry populations. Second, the GWAS data for age-related biomarkers, inflammatory biomarkers and inflammatory cytokines in this MR investigation were gathered through blood sample analysis. Further investigation into the analysis of immune system alterations in pelvic floor supportive tissue, which serves as the primary site of pelvic organ prolapse (POP), would aid in the identification of potential biomarkers and targets for medication. While blood is commonly suggested for data sampling, exploring alternative sources is crucial. In conclusion, although our inquiry covered a fairly wide range of inflammatory characteristics, there is still a lack of comprehensive understanding regarding the roles and mechanisms of these inflammatory cytokines in relation to diseases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo summarize, our comprehensive MR analysis indicated evidence of causality of LTL as age-related biomarker on female pelvic organ prolapse, as well as circulating leukocyte count was proven as the mediator. Inflammatory cytokines were further detected as candidate targets for inflammation-induced pelvic organ prolapse. Significant pathways and inflammatory cytokines were also identified. Nevertheless, as a result of the constraints of the research focusing exclusively on European ancestral populations, it is imperative to corroborate our discoveries through extensive GWAS summary data and investigations into the underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eGWAS: Genome-wide association study\u003c/p\u003e\n\u003cp\u003eIVW: Inverse variance weighted\u003c/p\u003e\n\u003cp\u003ePOP: Female pelvic organ prolapse\u003c/p\u003e\n\u003cp\u003eUC: Ulcerative colitis\u003c/p\u003e\n\u003cp\u003eCD: Crohn\u0026apos;s disease\u003c/p\u003e\n\u003cp\u003eTNF: Tumor necrosis factor\u003c/p\u003e\n\u003cp\u003eIL: Interleukin\u003c/p\u003e\n\u003cp\u003eIFN: Interferon\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaoyu Huang, Ya Xiao, and Fangyi Zhu. The first draft of the manuscript was written by Xiaoyu Huang and Mao Chen. \u0026nbsp;Liying Chen and Xiaoyu Tian contributed to the critical revision of the manuscript. Li Hong contributed to study concept and design, interpreting the data and critical revision of the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (82371639), \u0026nbsp;Hubei Key Research and Development Program (2022BCA045) and the National Key Research and Development Program of China (2021YFC2701300; 2021YFC2701302).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eGWAS\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edata on\u0026nbsp;age biomarkers,\u0026nbsp;female pelvic organ prolapse \u0026nbsp;and circulating inflammatory biomarkers were downloaded from https://gwas.mrcieu.ac.uk/. Data on inflammatory\u0026nbsp;cytokines\u0026nbsp;were downloaded from \u003ca href=\"https://www.decode.com/summarydata/\"\u003ehttps://www.decode.com/summarydata/.\u003c/a\u003e The direct link and accession numbers are provided in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWe thank all investigators for making their GWAS data publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was based on publicly available data and the ethics approval is waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eAll methods were carried out following STROBE-MR guidelines and regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeintraub AY, Glinter H, Marcus-Braun N: \u003cstrong\u003eNarrative review of the epidemiology, diagnosis and pathophysiology of pelvic organ prolapse\u003c/strong\u003e. \u003cem\u003eInt Braz J Urol \u003c/em\u003e2020, \u003cstrong\u003e46\u003c/strong\u003e(1):5-14.\u003c/li\u003e\n\u003cli\u003eBarber MD, Maher C: \u003cstrong\u003eEpidemiology and outcome assessment of pelvic organ prolapse\u003c/strong\u003e. \u003cem\u003eInt Urogynecol J \u003c/em\u003e2013, \u003cstrong\u003e24\u003c/strong\u003e(11):1783-1790.\u003c/li\u003e\n\u003cli\u003eBi J, Zhang C, Lu C, Mo C, Zeng J, Yao M, Jia B, Liu Z, Yuan P, Xu S: \u003cstrong\u003eAge-related bone diseases: Role of inflammaging\u003c/strong\u003e. \u003cem\u003eJ Autoimmun \u003c/em\u003e2024, \u003cstrong\u003e143\u003c/strong\u003e:103169.\u003c/li\u003e\n\u003cli\u003eKhan S, Ansari MA, Vasenwala SM, Mohsin Z: \u003cstrong\u003eA Community Based Study on Pelvic Inflammatory Disease in Postmenopausal Females: Microbiological Spectrum and Socio-Demographic Correlates\u003c/strong\u003e. \u003cem\u003eJ Clin Diagn Res \u003c/em\u003e2017, \u003cstrong\u003e11\u003c/strong\u003e(3):LC05-LC10.\u003c/li\u003e\n\u003cli\u003eCaglayan M, Oktayoglu P, Agacayak E, Dagli AZ, Okcu M, Nas K: \u003cstrong\u003eA New Comorbidity in Female Patients With Ankylosing Spondylitis: Pelvic Organ Prolapse\u003c/strong\u003e. \u003cem\u003eJ Clin Rheumatol \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e(1):36-40.\u003c/li\u003e\n\u003cli\u003eDidriksen M, Nawaz MS, Dowsett J, Bell S, Erikstrup C, Pedersen OB, Sorensen E, Jennum PJ, Burgdorf KS, Burchell B\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eLarge genome-wide association study identifies three novel risk variants for restless legs syndrome\u003c/strong\u003e. \u003cem\u003eCommun Biol \u003c/em\u003e2020, \u003cstrong\u003e3\u003c/strong\u003e(1):703.\u003c/li\u003e\n\u003cli\u003eOlafsdottir T, Thorleifsson G, Sulem P, Stefansson OA, Medek H, Olafsson K, Ingthorsson O, Gudmundsson V, Jonsdottir I, Halldorsson GH\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenome-wide association identifies seven loci for pelvic organ prolapse in Iceland and the UK Biobank\u003c/strong\u003e. \u003cem\u003eCommun Biol \u003c/em\u003e2020, \u003cstrong\u003e3\u003c/strong\u003e(1):129.\u003c/li\u003e\n\u003cli\u003ePujol-Gualdo N, Lall K, Lepamets M, Estonian Biobank Research T, Rossi HR, Arffman RK, Piltonen TT, Magi R, Laisk T: \u003cstrong\u003eAdvancing our understanding of genetic risk factors and potential personalized strategies for pelvic organ prolapse\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e(1):3584.\u003c/li\u003e\n\u003cli\u003eLiu Z, Song C, Suo C, Fan H, Zhang T, Jin L, Chen X: \u003cstrong\u003eAlcohol consumption and hepatocellular carcinoma: novel insights from a prospective cohort study and nonlinear Mendelian randomization analysis\u003c/strong\u003e. \u003cem\u003eBMC Med \u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e(1):413.\u003c/li\u003e\n\u003cli\u003eZhang W, Ge J, Qu Z, Wu W, Lei H, Pan H, Chen H: \u003cstrong\u003eEvaluation for causal effects of socioeconomic traits on risk of female genital prolapse (FGP): a multivariable Mendelian randomization analysis\u003c/strong\u003e. \u003cem\u003eBMC Med Genomics \u003c/em\u003e2023, \u003cstrong\u003e16\u003c/strong\u003e(1):125.\u003c/li\u003e\n\u003cli\u003eLiu H, Wu W, Xiang W, Yuan J: \u003cstrong\u003eLifestyle factors, metabolic factors and socioeconomic status for pelvic organ prolapse: a Mendelian randomization study\u003c/strong\u003e. \u003cem\u003eEur J Med Res \u003c/em\u003e2023, \u003cstrong\u003e28\u003c/strong\u003e(1):183.\u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eStrengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement\u003c/strong\u003e. \u003cem\u003eJAMA \u003c/em\u003e2021, \u003cstrong\u003e326\u003c/strong\u003e(16):1614-1621.\u003c/li\u003e\n\u003cli\u003eHannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenome-wide methylation profiles reveal quantitative views of human aging rates\u003c/strong\u003e. \u003cem\u003eMol Cell \u003c/em\u003e2013, \u003cstrong\u003e49\u003c/strong\u003e(2):359-367.\u003c/li\u003e\n\u003cli\u003eHorvath S: \u003cstrong\u003eDNA methylation age of human tissues and cell types\u003c/strong\u003e. \u003cem\u003eGenome Biol \u003c/em\u003e2013, \u003cstrong\u003e14\u003c/strong\u003e(10):R115.\u003c/li\u003e\n\u003cli\u003eHorvath S: \u003cstrong\u003eErratum to: DNA methylation age of human tissues and cell types\u003c/strong\u003e. \u003cem\u003eGenome Biol \u003c/em\u003e2015, \u003cstrong\u003e16\u003c/strong\u003e(1):96.\u003c/li\u003e\n\u003cli\u003eLu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDNA methylation GrimAge strongly predicts lifespan and healthspan\u003c/strong\u003e. \u003cem\u003eAging (Albany NY) \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(2):303-327.\u003c/li\u003e\n\u003cli\u003eKessler MD, Damask A, O\u0026apos;Keeffe S, Banerjee N, Li D, Watanabe K, Marketta A, Van Meter M, Semrau S, Horowitz J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCommon and rare variant associations with clonal haematopoiesis phenotypes\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2022, \u003cstrong\u003e612\u003c/strong\u003e(7939):301-309.\u003c/li\u003e\n\u003cli\u003eAstle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease\u003c/strong\u003e. \u003cem\u003eCell \u003c/em\u003e2016, \u003cstrong\u003e167\u003c/strong\u003e(5):1415-1429 e1419.\u003c/li\u003e\n\u003cli\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eLarge-scale integration of the plasma proteome with genetics and disease\u003c/strong\u003e. \u003cem\u003eNat Genet \u003c/em\u003e2021, \u003cstrong\u003e53\u003c/strong\u003e(12):1712-1721.\u003c/li\u003e\n\u003cli\u003eMcCartney DL, Min JL, Richmond RC, Lu AT, Sobczyk MK, Davies G, Broer L, Guo X, Jeong A, Jung J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging\u003c/strong\u003e. \u003cem\u003eGenome Biol \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(1):194.\u003c/li\u003e\n\u003cli\u003eSanderson E, Spiller W, Bowden J: \u003cstrong\u003eTesting and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization\u003c/strong\u003e. \u003cem\u003eStat Med \u003c/em\u003e2021, \u003cstrong\u003e40\u003c/strong\u003e(25):5434-5452.\u003c/li\u003e\n\u003cli\u003eBrion MJ, Shakhbazov K, Visscher PM: \u003cstrong\u003eCalculating statistical power in Mendelian randomization studies\u003c/strong\u003e. \u003cem\u003eInt J Epidemiol \u003c/em\u003e2013, \u003cstrong\u003e42\u003c/strong\u003e(5):1497-1501.\u003c/li\u003e\n\u003cli\u003eCarter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J, Taylor AE, Davies NM, Howe LD: \u003cstrong\u003eMendelian randomisation for mediation analysis: current methods and challenges for implementation\u003c/strong\u003e. \u003cem\u003eEur J Epidemiol \u003c/em\u003e2021, \u003cstrong\u003e36\u003c/strong\u003e(5):465-478.\u003c/li\u003e\n\u003cli\u003eZhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK: \u003cstrong\u003eMetascape provides a biologist-oriented resource for the analysis of systems-level datasets\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e(1):1523.\u003c/li\u003e\n\u003cli\u003eBaechle JJ, Chen N, Makhijani P, Winer S, Furman D, Winer DA: \u003cstrong\u003eChronic inflammation and the hallmarks of aging\u003c/strong\u003e. \u003cem\u003eMol Metab \u003c/em\u003e2023, \u003cstrong\u003e74\u003c/strong\u003e:101755.\u003c/li\u003e\n\u003cli\u003eCodd V, Denniff M, Swinfield C, Warner SC, Papakonstantinou M, Sheth S, Nanus DE, Budgeon CA, Musicha C, Bountziouka V\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMeasurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank\u003c/strong\u003e. \u003cem\u003eNat Aging \u003c/em\u003e2022, \u003cstrong\u003e2\u003c/strong\u003e(2):170-179.\u003c/li\u003e\n\u003cli\u003eWang T, Jia Z, Li S, Li Y, Yu T, Lu T, Shi Y: \u003cstrong\u003eThe association between leukocyte telomere length and chronic obstructive pulmonary disease is partially mediated by inflammation: a meta-analysis and population-based mediation study\u003c/strong\u003e. \u003cem\u003eBMC Pulm Med \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):320.\u003c/li\u003e\n\u003cli\u003eKluivers KB, Lince SL, Ruiz-Zapata AM, Post WM, Cartwright R, Kerkhof MH, Widomska J, De Witte W, Pecanka J, Kiemeney LA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMolecular Landscape of Pelvic Organ Prolapse Provides Insights into Disease Etiology\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2023, \u003cstrong\u003e24\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eWijeyesinghe S, Beura LK, Pierson MJ, Stolley JM, Adam OA, Ruscher R, Steinert EM, Rosato PC, Vezys V, Masopust D: \u003cstrong\u003eExpansible residence decentralizes immune homeostasis\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2021, \u003cstrong\u003e592\u003c/strong\u003e(7854):457-462.\u003c/li\u003e\n\u003cli\u003eWang WG, Chen ZSD, Sun J, Yang CM, He HB, Lu XK, Wang WY: \u003cstrong\u003eBioinformatic analysis of biological changes involved in pelvic organ prolapse\u003c/strong\u003e. \u003cem\u003eMedicine (Baltimore) \u003c/em\u003e2023, \u003cstrong\u003e102\u003c/strong\u003e(22):e33823.\u003c/li\u003e\n\u003cli\u003eLiu P, Liu D, Chen F, Luo L, Jin Y, Peng J, Yu H, Wei M, Shi X, Wang L: \u003cstrong\u003eEffect of Nrf2 on Phenotype Changes of Macrophages in the Anterior Vaginal Wall of Women With Pelvic Organ Prolapse\u003c/strong\u003e. \u003cem\u003eUrogynecology (Phila) \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(9):616-623.\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang QY, Sun BF, Ma Y, Zhang Y, Wang M, Ma C, Shi H, Sun Z, Chen J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSingle-cell transcriptome profiling of the vaginal wall in women with severe anterior vaginal prolapse\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e(1):87.\u003c/li\u003e\n\u003cli\u003eWu C, Zhou Z, Yang Y, Li H, Guo Y, Tong X: \u003cstrong\u003eBioinformatically deciphering immune cell infiltration and signature genes in pelvic organ prolapse\u003c/strong\u003e. \u003cem\u003eInt Urogynecol J \u003c/em\u003e2023, \u003cstrong\u003e34\u003c/strong\u003e(5):1091-1101.\u003c/li\u003e\n\u003cli\u003eCarlini V, Noonan DM, Abdalalem E, Goletti D, Sansone C, Calabrone L, Albini A: \u003cstrong\u003eThe multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1161067.\u003c/li\u003e\n\u003cli\u003eHuaux F: \u003cstrong\u003eInterpreting Immunoregulation in Lung Fibrosis: A New Branch of the Immune Model\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:690375.\u003c/li\u003e\n\u003cli\u003eBray R, Derpapas A, Fernando R, Khullar V, Panayi DC: \u003cstrong\u003eDoes the vaginal wall become thinner as prolapse grade increases?\u003c/strong\u003e \u003cem\u003eInt Urogynecol J \u003c/em\u003e2017, \u003cstrong\u003e28\u003c/strong\u003e(3):397-402.\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":"Female pelvic organ prolapse, aging, inflammatory biomarkers, cytokines, Mendelian randomization analysis","lastPublishedDoi":"10.21203/rs.3.rs-4138072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4138072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eFemale pelvic organ prolapse (POP) is a disease associated with aging and inflammation, though it is not determined that aging and inflammation are causative factors. The purpose of this study was to evaluate the causal effects of aging and inflammatory factors on female pelvic organ prolapse (POP).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSignificant genetic variables were evaluated by assessing genome-wide association study (GWAS) data for POP and 5 age biomarkers (GrimAge, HorvathAge, HannumAge, PhenoAge, and leukocyte telomere length). Initially, a bidirectional MR analysis was conducted utilizing a random-effects inverse variance-weighted IVW method to elucidate the causal association. Other MR methods and sensitivity analyses were also used. Then, we also used a two-step MR analysis to analyze the mediating effect of six circulating inflammatory biomarkers in the causal relationship between age and POP. Finally, two-sample MR analysis was also used to investigate the effects of 190 inflammatory cytokines on POP risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eShorter leukocyte telomere length (LTL), rather than epigenetic clocks is genetically predicted to increase the risk of POP. MR analysis showed that shorter LTL is associated with higher leukocyte count, which can lead to POP. A significant causal association was found between 44 circulating inflammatory cytokines and POP risk. After adjusting for multiple tests, CXCL14, IL17A, IL18, IL6, TNFRSF10B, and TNFSF9 remained statistically significant.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings provide that leukocyte count mediates the potential genetic causal impact of shorter LTL on the development of POP. Inflammatory cytokines might to be considered as potential targets for intervention in POP.\u003c/p\u003e","manuscriptTitle":"Circulating Inflammatory Biomarkers mediates the causal effect of Aging on Female Pelvic Organ Prolapse: Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 22:12:37","doi":"10.21203/rs.3.rs-4138072/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":"7dbe4da1-9ddd-4eed-a5f9-751fc235aae3","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T07:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 22:12:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4138072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4138072","identity":"rs-4138072","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}