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

doi:10.21203/rs.3.rs-4138072/v1

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

https://doi.org/10.21203/rs.3.rs-4138072/v1

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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

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.

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.

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
Exposures	Outcome	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)
Mediator	Beta (95% CI)	Beta (95% CI)	Beta (95% CI)	Beta (95% CI)	P	Mediated Proportion % (95% CI)
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. 6A). 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. 6B, 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.

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.

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.

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

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.

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No competing interests reported.

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

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