Причинная роль общих аутоиммунных заболеваний в межститиальном цистите/болезнечном синдроме пузырьков: исследование менделианского случайного заболевания (MR).

1. Introduction

Autoimmune diseases (AIDs) refer to a range of ongoing disorders marked by the immune system’s failure to differentiate self-antigens, resulting in inflammation and tissue damage that may progress to organ malfunction. [ 1 – 4 ] AIDs have the potential to impact a range of organs or systems in the body, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), type 1 diabetes (T1D), Sjögren’s syndrome (SS), multiple sclerosis (MS), Crohn disease (CD), ulcerative colitis (UC), and other chronic conditions.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a severe urological pain disorder that predominantly influences the bladder, marked by various uncomfortable symptoms such as intense bladder or pelvic pain, heightened urgency and frequency of urination, and increased nighttime urination. This condition is marked by inflammation of the bladder lining. Due to its unclear etiology and potential overlap with other urinary disorders, therapeutic advancements over the past 25 years have been minimal. Currently, the diagnosis and treatment of IC/BPS remain significant challenges. [ 5 , 6 ] Immune system dysfunction is believed to be responsible for the development of AIDs and IC/BPS, it is becoming more apparent that AIDs could be connected to IC/BPS. [ 7 ] Patients diagnosed with interstitial cystitis/bladder pain syndrome exhibit a higher propensity for developing SLE in comparison to individuals within the general populace. [ 8 ] Additionally, IC/BPS shows robust connections with conditions such as rheumatoid arthritis, ankylosing spondylitis (AS), and particularly, Sjögren’s syndrome. [ 9 – 11 ] This indicates that IC/BPS could involve an autoimmune aspect in certain individuals, potentially linked to autoantibodies targeting M3 receptors in the bladder. [ 12 ] Nevertheless, the sole definitive approach to confirm that a disease is not autoimmune is by illustrating an alternative mechanism of disease development. Until now, there is no indication pointing towards autoimmune reactivity playing a primary or secondary part in the pathophysiology of IC/BPS. These factors lead us to investigate whether a genuine cause-and-effect connection exists between these 2 conditions. The existing observational studies and epidemiological evidence do not seem to be enough at the moment to establish a strong link between IC/BPS and AIDs.

In this context, Mendelian randomization (MR) presents a distinctive approach for exploring the relationship between IC/BPS and AIDs. MR is based on the random assortment of alleles during the creation of gametes, as explained by Mendel principles of heredity. [ 13 ] Using genetic differences, MR creates instrumental variables (IVs) to construct the impact of a specific exposure, enabling the evaluation of causal links between exposure and outcome. [ 14 ] MR offers a distinct advantage over traditional epidemiological methods in observational studies by minimizing confounding factors like social environment and personal lifestyle. It also ensures temporal consistency in causal inference. This approach addresses challenges inherent in observational studies, including small sample sizes, population diversity, confounding variables, genetic variability, and issues of reverse causation, [ 15 , 16 ] thus yielding more robust conclusions. Hence, this study utilizes the MR technique to examine the genetic aspect of the link between AIDs and IC/BPS.

2. Methods

2.1. Materials and methods

2.1.1. Investigation design and data origins

At the heart of MR lie 3 key premises: firstly, the IVs exhibit a robust and consistent association with the exposure under investigation; secondly, the IVs remain impervious to any influences from confounding variables; and thirdly, the IVs exert their influence on the outcome solely through the exposure. Due to our reliance on openly available condensed information from genome-wide association studies (GWAS), there was no need to seek additional ethical clearance for the execution of this research. To rigorously evaluate the potential causal connection between AIDs and IC/BPS, we designed an MR study flowchart that adheres to these assumptions (Fig. 1 ). [ 17 , 18 ]

Flowchart of the MR study between IC/BPS and 14 AIDs. AIDs = autoimmune diseases.

2.1.2. Data selection

To infer the causal relationship between AIDs and IC/BPS, [ 19 ] we downloaded summary statistics for AIDs and IC/BPS from open GWAS databases and previously published MR studies ( https://gwas.mrcieu.ac.uk/ retrieved on June 1, 2024). The AIDs include UC, CD, celiac disease (CeD), SS, RA, SLE, MS, psoriasis (PsO), primary biliary cholangitis (PBC), asthma, eczema, T1D, primary sclerosing cholangitis (PSC), and AS. The studies included in these analyzes used only individuals of European descent for both case cohorts and control groups, with little to no duplication of participants across different GWAS datasets. Detailed information on enrollment procedures and sample standards was provided in the original studies (Table 1 ). [ 20 ]

Table 1 Characteristics of the IC/BPC and autoimmune disease GWAS cohorts. Disease Study PMID Year Journal Cases Controls Sample size Datasets in the GWAS Asthma Valette K et al 34103634 2021 Commun Biol. 56,167 352,255 408,442 ebi-a-GCST90014325 RA Ha E et al 33310728 2020 Ann Rheum Dis. 14,361 43,923 58,284 ebi-a-GCST90013534 T1D Forgetta V et al 32005708 2020 Diabetes. 9266 15,574 24,840 ebi-a-GCST010681 PSC Ji et al 27992413 2017 Nat. Genet. 4796 19,955 24,751 ieu-a-1112 Eczema Ferreira MA et al 29083406 2017 Nat. Genet. 180,129 180,709 360,838 ebi-a-GCST005038 SLE Bentham J et al 26502338 2015 Nat. Genet. 5201 9066 14,267 ebi-a-GCST003156 CD Liu JZ et al 26192919 2015 Nat. Genet. 17,897 33,977 51,874 ieu-a-12 UC Liu JZ et al 26192919 2015 Nat. Genet. 13,768 33,977 47,745 ieu-a-970 MS Beecham AH et al 24076602 2013 Nat. Genet. 14,498 24,091 38,589 ieu-a-1025 PBC Liu JZ et al 22961000 2012 Nat. Genet. 2861 8514 11,375 ebi-a-GCST005581 PsO Tsoi LC et al 23143594 2012 Nat. Genet. 10,588 22,806 33,394 ebi-a-GCST005527 CeD Trynka et al 22057235 2011 Nat. Genet. 12,041 12,228 24,269 ieu-a-1058 AS Jiang L et al 34737426 2021 Nat Genet 186 456,162 456,348 ebi-a-GCST90044542 SS Jiang L et al 34737426 2021 Nat Genet 99 456,249 456,348 ebi-a-GCST90044534 AS = ankylosing spondylitis, CD = Crohn disease, CeD = celiac disease, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus, SS = Sjögren syndrome, T1D = type 1 diabetes, UC = ulcerative colitis.

Characteristics of the IC/BPC and autoimmune disease GWAS cohorts.

AS = ankylosing spondylitis, CD = Crohn disease, CeD = celiac disease, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus, SS = Sjögren syndrome, T1D = type 1 diabetes, UC = ulcerative colitis.

2.1.3. SNP selection and two-sample MR analysis

We selected the required analysis methods and IVs from the exposure data using R software, and conducted the selection of IVs and two-sample MR analysis with the “TwoSampleMR” package. IVs were carefully chosen from the exposure information following strict standards to guarantee their validity for each AIDs. Criteria for selecting IVs: SNPs showing significant association with the exposure were extracted from the GWAS combined database, setting a filtering threshold at a significance level of P  < 5E‐8. We then set clump_kb = 10,000 and clump_r2 = 0.001 to avoid linkage disequilibrium. For SS, due to the smaller number of IVs in its GWAS data, we adjusted the P -value threshold to P  < 5E‐5. Additionally, we used the harmonize function in the “TwoSampleMR” package to exclude all inconsistent alleles and ambiguous strands (e.g., A/G with A/C, A/T, or G/C). We additionally conducted weak instrument bias detection on the chosen IVs and computed the F -statistic for each IV. We made certain that the mean F -value for every group of independent IVs surpassed 10, demonstrating a strong connection between the IVs and the exposure. Within this research, we utilized a total of 5 MR analysis techniques that are grounded on distinct assumptions: (1) inverse variance weighting (IVW), (2) MR-Egger regression, (3) weighted median, (4) simple mode (SM), and (5) weighted mode. [ 17 , 18 , 21 ] IVW was the primary method of analysis utilized, with the underlying assumption being the validity of all IVs. [ 22 ] MR-Egger regression is capable of detecting and correcting for horizontal pleiotropy, with the intercept providing clear indication of the presence of pleiotropic effects. Weighted median gives greater weight to valid IVs and can provide reliable estimates even if some IVs are invalid. SM (simple median) and weighted mode (weighted median) are both models used for estimating causal effects by aggregating SNPs that share similarities and determining the causal effect estimate based on the majority of clustered SNPs. The findings were displayed using forest plots. Additionally, we undertook reverse causality analysis, treating IC/BPS as the exposure and AIDs as the outcome, to validate the strong and independent relationship between the exposure and outcomes, thus bolstering the credibility of the analysis results.

2.1.4. Quality control methods

To evaluate the consistency and dependability of the MR findings, various quality assurance techniques were employed in this research. Initially, Cochran Q test was employed to assess the diversity of the SNPs, where a P value below .05 indicates diversity in the findings. Additionally, MR-Egger regression and the MR-PRESSO global tests were applied to uncover potential horizontal and directional pleiotropy present in the SNPs. When the P -value exceeds .05, it indicates the lack of horizontal pleiotropy; if it falls below .05, the study is redone without the outlier SNPs causing pleiotropic effects. The MR-PRESSO package in R was additionally used to analyze pleiotropy and minimize bias in the IVW results by removing any outliers. When using the MR-PRESSO outlier test, it is assumed that over half of the genetic variations are legitimate instruments. Additionally, the stability of the results was evaluated through a leave-one-out analysis to detect any potentially diverse SNPs. The Steiger filtering method was utilized to confirm that the genetic instrumental variables (GIVs) impacted the outcome exclusively via the exposure. Through this approach, it was confirmed that every SNP was notably correlated with the exposure, not the outcome, thus establishing the correct causal link between the 2. [ 19 ] According to Steiger filtering, an accurate GIV should give greater weight to exposure rather than outcome. When a GIV fulfills this condition, it is labeled as “TRUE”; if not, it is labeled as “FALSE.” In the MR analysis, a P -value lower than .05 was considered to have statistical significance. [ 19 ] R software (version 4.3.3) was utilized for all statistical analyses in this study, with the MR analyses conducted through R packages TwoSampleMR (version 0.5.11), MR-PRESSO (version 1.0), and qvalue.

3. Result

3.1. Selected instrumental variables

The SNPs that made it to the final selection after the instrumental variables process were as listed below: 48 SNPs for MS, 18 SNPs for PSC,42 SNPs for SLE, 119 SNPs for CD, 18 SNPs for PBC, 37 SNPs for T1D, 85 SNPs for UC, 70SNPs for SS, 85 SNPs for RA, 71 SNPs for eczema, 15 SNPs for AS, 68 SNPs for asthma, 15 SNPs for CeD, and 66 SNPs for PsO. Comprehensive SNP details are available in Supplementary File 1, Supplemental Digital Content, http://links.lww.com/MD/O444.MR analysis summarized the causal relationships between these autoimmune diseases and IC/BPS. The average F -statistics values ranged from 15 to 285, indicating a low possibility of weak IV bias and satisfying the relevance assumption for MR analysis (Table 2 ).

Table 2 Results of MR analyses between autoimmune disease liability and IC/BPS risk. Exposure Nsnp
r
2
F MR Egger Weighted median Inverse variance weighted Simple mode Weighted mode OR CI
P
OR CI
P
OR CI
P
OR CI
P
OR CI
P
Ashma 76 0.000168 68.5165 1.734904 0.6556–4.5908 .2712 1.689901 0.0532 .9928–2.8765 1.692584 1.1474–2.4967 .3237 2.311389 0.7795–6.8541 .1356 2.026027 0.8369–4.9048 .1223 CD 120 0.00173 90.0585 0.9970347 0.6795–1.4629 .9879 0.9603302 0.7134 .7738–1.1918 1.0321402 0.8934–1.1924 .8738 0.8099553 0.4812–1.3634 .4292 0.9054768 0.6454–1.2703 .5665 CeD 15 0.0114 285.7935 1.027567 0.8422–1.2538 .793 1.034771 0.6869 .8763–1.2219 1.052851 0.9215–1.203 .0532 1.200974 0.9339–1.5444 .1754 1.050212 0.9005–1.2249 .5426 Eczema 70 0.000174 62.6772 4.874314 1.2629–18.8131 .0247 1.369135 0.3781 .6808–2.7533 1.012993 0.6266–1.6376 .5527 1.272587 0.3256–4.9741 .73 1.463634 0.4797–4.4659 .5056 MS 49 0.0018 69.8125 0.9632109 0.6593–1.4073 .8472 0.9932495 0.9635 .743–1.3278 1.0823168 0.8958–1.3077 .073 1.4580855 0.7707–2.7585 .2522 0.7962192 0.5506–1.1513 .2319 PBC 18 NA NA 1.3064769 0.6784–2.5161 .4357 1.0351869 0.7709 .8203–1.3064 1.0252525 0.8518–1.234 .7321 0.9285151 0.634–1.3599 .7079 1.0829256 0.8047–1.4574 .6058 PSC 18 0.00757 116.2914 1.008161 0.9604–1.0583 .7441 1.018871 0.4859 .9667–1.0739 1.001814 0.9638–1.0413 .2126 1.045355 0.9616–1.1364 .3029 1.022666 0.9692–1.0791 .4178 T1D 37 0.00456 116.1002 0.9650415 0.8363–1.1135 .6292 0.9720434 0.6977 .8425–1.1215 1.0508733 0.9523–1.1597 .3237 1.2760709 0.9061–1.797 .1716 1.0415318 0.9254–1.1722 .5043 UC 85 0.00194 93.2096 0.8930161 0.5791–1.377 .61 0.9835275 0.9044 .75–1.2898 0.9853226 0.821–1.1825 .8738 0.9245989 0.5401–1.5829 0.7757 0.8837778 0.6232–1.2533 .4901 AS 16 0.000103 47.061 1.028722 0.9713–1.0895 .3513 1.026682 0.3357 .9731–1.0832 1.045991 0.9994–1.0948 .0532 1.008836 0.8839–1.1515 0.8981 1.026533 0.9711–1.0851 .3706 SLE 42 0.00669 76.7697 1.0170512 0.8192–1.2627 .8791 1.0369154 0.6661 .8795–1.2225 1.03243 0.9292–1.1472 .5527 0.9175033 0.6819–1.2345 0.5729 1.0035438 0.8106–1.2424 .9743 SS 71 5E*4 18.175 1.017406 0.9932–1.0422 .1652 1.014345 0.1022 .9972–1.0318 1.011848 0.9989–1.025 .073 1.020279 0.9768–1.0657 0.3696 1.022971 0.9804–1.0674 .2991 RA 85 0.00187 109.9294 1.1025586 0.8861–1.372 .3839 1.1650134 0.1656 .9388–1.4458 1.0250934 0.8895–1.1814 .7321 0.9950567 0.6054–1.6355 0.9845 1.1006198 0.8997–1.3464 .3539 PsO 50 NA NA 1.0010539 0.9425–1.0632 .9729 1.0081701 0.7671 .9553–1.064 1.0261689 0.9853–1.0687 .2126 0.9970697 0.8962–1.1093 0.9573 1.0159119 0.9682–1.066 .5241 ORs express each exposure’s liability impact on IC/BPS risk. AS = ankylosing spondylitis, CD = Crohn disease, CeD = celiac disease, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus, SS = Sjögren syndrome, T1D = type 1 diabetes, UC = ulcerative colitis.

Results of MR analyses between autoimmune disease liability and IC/BPS risk.

ORs express each exposure’s liability impact on IC/BPS risk.

AS = ankylosing spondylitis, CD = Crohn disease, CeD = celiac disease, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus, SS = Sjögren syndrome, T1D = type 1 diabetes, UC = ulcerative colitis.

3.2. AIDs and IC/BPS

The IVW analysis outcomes revealed a substantial connection between asthma and a raised probability of IC/BPS (OR value equaled 1.693, 95% CI ranged from 1.147 to 2.497, and P -value was .008). as detailed in Figure 2 forest plot and Supplementary File 2, Supplemental Digital Content, http://links.lww.com/MD/O445 . Additionally, no diversity was found according to Cochran Q test ( P value was .05) (Fig. S1, Supplemental Digital Content, http://links.lww.com/MD/O446 ), with both MR-Egger intercept test ( P value was .554) and MR-PRESSO global test ( P value was .6) also indicating the absence of significant horizontal pleiotropy. The correlation between asthma and IC/BPS was depicted in Figure 3 ’s scatter plot, where the regression lines from 3 different analysis techniques all pointed in the same direction. The leave one out sensitivity analysis plot (Fig. S2, Supplemental Digital Content, http://links.lww.com/MD/O446 ) suggested that none of the SNPs had a significant effect on the causal relationship. Detailed analysis results are provided in Table 2 . The results of other autoimmune diseases are as follows: AS: IVW P value was .053, CD: IVW P value was .668, UC: IVW P value was .873, CeD: IVW P value was 0.449, eczema: IVW P value was .958, MS: IVW P value was .412, PBC: IVW P value was .792, PSC: IVW P value was .927, RA: IVW P value was .732, SLE: IVW P value was .552, SS: IVW P value was .073, PsO: IVW P value was .213; the results show that none of the other autoimmune diseases included in the analysis demonstrated a causal relationship with IC/BPS (IVW P value > .05, MR-Egger P value > .05, VM P value >.05) (Table 2 ). Cochran Q test showed no heterogeneity ( P value > .05), and both the MR-Egger intercept test and MR-PRESSO global test indicated no horizontal pleiotropy (Fig. S1, Supplemental Digital Content, http://links.lww.com/MD/O446 ). The scatter plots (Fig. 3 ) provided additional confirmation that there are no causal connections between other AIDs and IC/BPS.

Forest plot illustrating the results of the Mendelian randomization analysis. AS = ankylosing spondylitis, CeD = celiac disease, IC/BPS = interstitial cystitis/bladder pain syndrome, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PSo = psoriasis, RA = rheumatoid arthritis, SLE = systemic lupus erythematosus, SS = Sjögren’s syndrome, TID = type 1 diabetes, UC = ulcerative colitis.

Scatterplot of MR analysis and validation results for risk of asthma, AS, SS, RA, T1D, PSC, Eczema, SLE, CD, UC, MS, PBC, PsO, and CeD. (The slopes of the lines represent the causal effect of each method, respectively.) AS = ankylosing spondylitis, CeD = celiac disease, MS = multiple sclerosis, PBC = primary biliary cirrhosis, PSC = primary sclerosing cholangitis, PsO = psoriasis, SS = Sjögren syndrome, T1D = type 1 diabetes, UC = ulcerative colitis.

3.3. Verification of the reliability of reverse MR analysis

To validate the core belief that genetic GIVs are closely linked to exposure and to guarantee the reliability of the reverse MR analysis findings, we utilized the aforementioned selection methods for the reverse analysis. However, when using IC/BPS as the exposure and autoimmune diseases as the outcomes, the stringent selection criteria failed to extract SNPs associated with IC/BPS. We progressively relaxed the P -value threshold to P  < 5E‐6 and eventually obtained valid SNPs only in the analysis involving PSC, asthma, RA, T1D, eczema, and SLE (PSC IVW: P value was .311, asthma IVW: P value was .185, RA IVW: P value was .414, T1D IVW: P value was .693, eczema IVW: P value was 0.932,SLE IVW: P value was 0.992). For the remaining autoimmune diseases, the data did not permit further MR analysis. The IVW analysis approach demonstrated no reverse causal connection between these autoimmune diseases and IC/BPS ( P  > .05) (Fig. S3, Supplemental Digital Content, http://links.lww.com/MD/O446 ). The findings from the reverse MR analysis support the conclusion that the associations observed between IC/BPS and autoimmune diseases are unlikely due to reverse causation. The lack of significant findings reinforces the robustness of our primary MR analysis and its causal inference.

4. Discussion

Based on our information, this research is the first to employ MR analysis with extensive GWAS datasets to examine the potential causal relationship between IC/BPS and AIDs. Through the utilization of a two-sample MR design, the study aimed to investigate the genetic causal link between AIDs and IC/BPS. Our research has various strong points, with a focus on the MR design that allows for establishing causation without being affected by confounding variables or reverse causation. [ 23 ] Additionally, to ensure the causal association’s generalizability, we incorporated several independent large cohorts in the MR analysis to guarantee an adequate sample size. Furthermore, we carried out a range of additional analyses, such as sensitivity tests to examine variations and pleiotropy, along with an MR reverse causality analysis (Figs. S1, Supplemental Digital Content, http://links.lww.com/MD/O446 , S2, Supplemental Digital Content, http://links.lww.com/MD/O446 , S3, Supplemental Digital Content, http://links.lww.com/MD/O446 ), in order to evaluate the strength of the instrumental variables and validate the accuracy of our results.

The MR analysis in this research indicates that the genetically predicted risk of autoimmune diseases and the increased risk of IC/BPS were only significantly associated with asthma. Various AIDs have been found to have complex connections with IC/BPS in previous research, [ 24 – 27 ] with extensive comorbidity observed with asthma, SLE, RA, CD, SS, AS, and UC. [ 9 – 11 , 28 ] Population studies have suggested increased prevalence rates of RA, SLE, SS, AS, and inflammatory bowel disease among IC/BPS patients. [ 7 , 8 , 11 , 29 ] Other AIDs have been reported to be linked with IC/BPS in retrospective studies. [ 27 , 30 , 31 ] For example, in Taiwan’s NHIRD, a research study found a notable link between IC/BPS and autoimmune diseases like RA and SS. [ 9 ] However, these studies often suffer from small sample sizes and cannot establish clear causality. A cross-sectional analysis exploring the link between RA and IC/BPS found that 13% of classic IC/BPS patients and 4% of non-ulcerative IC/BPS patients had RA. [ 32 ] RA pathogenesis is primarily driven by the atypical proliferation of synoviocytes with fibroblast characteristics and the invasion of inflammatory cells that produce cytokines like tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6. These cytokines, notably TNF-α, have the ability to chemotactically stimulate leukocytes, endothelial cells, and osteoclasts, leading to continuous inflammation and the gradual deterioration of bone and cartilage. [ 32 , 33 ] Some studies suggest that the mechanism of bladder-related symptoms induction is through the specific cytokine release triggered by autoantibodies transported to the protein layer. [ 29 ] In case studies, pre-SS patients frequently present abnormal lab results, such as elevated immunoglobulin G, reduced complement component 4, and positive antinuclear antibodies, alongside symptoms related to IC/BPS. [ 34 , 35 ] Kujala put forward the idea that there could be a genetic predisposition or relationship between autoimmune diseases and IC/BPS in individuals with specific immune characteristics. [ 24 – 26 , 36 ] Other investigations uphold the engagement of complement constituents. [ 37 ] Even though the exact identity of the related autoantibodies has not been confirmed, some suggest an increased prevalence of autoimmune diseases among IC/BPS patients may involve a shared underlying autoimmune disorder. [ 38 ] JoopP’s study also emphasized the strong correlation between SS and IC/BPS, [ 30 ] with growing evidence suggesting that muscarinic acetylcholine receptor M3 (M3R) autoantibodies are significantly involved in the nosogenesis of exocrine dysfunction in SS. The muscarinic receptor family, which interacts with acetylcholine, controls the functions of parasympathetic nerves, managing saliva production. M3R, the primary subtype controlling salivary flow, is a target of autoantibodies in SS, potentially reducing the performance of exocrine gland. [ 39 ] Interestingly, M3R serves as the primary receptor responsible for facilitating cholinergic contractions in the detrusor muscle of the bladder. [ 40 ] Research indicates that the transfer of immunoglobulin G, which inhibits M3R antibodies, from SS patients to mice leads to enhanced contraction of detrusor muscles when stimulated by cholinergic signals. [ 24 ] These observations imply that anti-M3R antibodies could be involved in IC/BPS. However, post-statistical analysis indicates that most tested SS patients have anti-M3R autoantibodies, implying it may be a common marker. [ 26 , 30 , 39 – 43 ] Other research indicates that detrusor muscle cells can be prompted by pro-inflammatory cytokines TNF-α and IL-1β to produce IL-8, IL-6, and regulated on activation, normal T cell expressed and secreted (RANTES) (CCL5). [ 44 ] RANTES has been found to drive lower urinary tract mast cell recruitment. [ 45 ] Thus, high-affinity autoantibodies binding to M3R may lead to local mast cell accumulation through TNF-α and IL-1β release and IL-8, IL-6, and RANTES production. Many individuals diagnosed with interstitial cystitis exhibit a marked concentration of mast cells around the detrusor muscle, which is recognized as a key indicator of the disease. [ 46 ] Van de Merwe proposed that the presence of autoantibodies that obstruct muscarinic receptors could result in IC/BPS. [ 47 ] However, our analysis shows no genetic causal relationship between SS, SLE, RA, CD, UC, and AS with IC/BPS. Despite the potential involvement of muscarinic receptor autoantibodies, our analysis shows no genetic link.In the examination of IC/BPS and asthma, we identified a notable genetic link between asthma and IC/BPS risk. This finding is corroborated by IVW analysis (IVW OR: 1.693, 95% CI: 1.147–2.497, P value was .008; MR-Egger P value was .271), with Cochran Q test showing a lack of heterogeneity ( P value was .589). Additionally, the MR-Egger intercept ( P value was .554) and MR-PRESSO global tests ( P value was .596) demonstrated no significant evidence of horizontal pleiotropy. Other methods also supported the cause-and-effect connection between IC/BPS and asthma. In a clinical case study by Jae Chun Lee, a patient with IC/BPS, allergic rhinitis, and bronchial asthma experienced relief through anti-IgE therapy and specific immunotherapy. Anti-IgE therapy effectively reduced circulating IgE levels in IC/BPS patients. This study suggests the need to investigate allergic diseases and conduct pathophysiological studies in IC/BPS individuals to understand the disease processes and assess the efficacy of anti-IgE treatment and targeted immunotherapy. [ 48 ] Previous observational studies and case reports have been unable to eliminate the influence of limited sample size, population heterogeneity, potential confounding factors, genetic variation, and reverse causation interference, thus not providing definitive causal evidence.

Recent studies have revealed an increased T helper cell type 1-skewed reaction, as opposed to T helper cell type 2, and a reduction in immunosuppressive Tregs within the bladder microenvironment of both non-Hunner and Hunner interstitial cystitis/bladder pain syndrome (IC/BPS).This study suggests a new avenue for understanding IC/BPS pathogenesis, with M2/M2-like macrophages prevalent in HIC and M1-like macrophages in non-Hunner lesion interstitial cystitis, secreting various chemokines in distinct patterns. Tissue-resident macrophages release pro-inflammatory substances, while inflammatory fibroblasts produce TNF-α and IL6, which are considered significant factors in the development of IC/BPS. [ 49 ] Another study on asthma reported that in vitro hyperreactive primary bronchial epithelial cells induced by IL-17A and TNF-α resulted in C-X-C Motif Chemokine Ligand 8 release, correlating with C-X-C Motif Chemokine Ligand 8 levels and neutrophil percentage in BALF of mild asthma patients. The majority of asthma sufferers’ primary bronchial epithelial cells displayed an amplified cooperative impact when exposed to IL-17A and TNF-α, characterized as hyperresponsive. [ 50 ] Numerous studies have indicated that the IL family and TNF-α are involved in various pathological processes between IC/BPS and autoimmune diseases, including chronic inflammation and autoimmune response behaviors. Combined with previously reported findings, the frequent comorbidity, significant relief after similar treatments, and common co-occurrence of IC/BPS and autoimmune diseases, [ 31 , 51 – 54 ] we speculate that TNF-α and the IL family might be key genetic factors or pathogenic mechanisms linking IC/BPS and autoimmune diseases, warranting further exploration. The MR analysis results in this study indicated no causal link between CeD, eczema, MS, PBC, PSC, PsO, T1D, and IC/BPS. These relationships have not been documented in prior research. Therefore, this study did not further elaborate on the relationships between CeD, eczema, MS, PBC, PSC, PsO, T1D, and IC/BPS.

Several limitations are evident in this study. First, it is based on GWAS data from European cohorts, additional studies are required to assess its relevance in non-European populations. Second, the study’s findings indicated no causal link between autoimmune diseases (except asthma) and IC/BPS, which contrasts with numerous previous observational studies. The difference in results could be due to various factors influencing earlier observational research or limitations in the genetic tools employed in this investigation. Nonetheless, this study provides initial theoretical support for analyzing the genetic-level biological mechanisms linking asthma and IC/BPS among autoimmune diseases. Further, a more comprehensive set of molecular and animal research studies is necessary to examine the correlation between IC/BPS and AIDs.

5. Conclusion

To summarize, and our study points to a genetic connection between asthma and IC/BPS. Nonetheless, no such genetic causal relationships were found between IC/BPS and other AIDs. This result suggests a potential path for future investigations and highlights the crucial nature of early screening and prevention of IC/BPS in asthma patients. Additionally, it provides new insights into the genetic-level causal relationship and invigorates the theoretical study on the etiopathogenesis of IC/BPS.

Acknowledgments

The contributions of the GWAS database are greatly appreciated.

Author contributions

Conceptualization: Liming Wu, Rong Yang.

Data curation: Tianwei Wang.

Formal analysis: Liming Wu, Ming Bai.

Investigation: Jinbang Chen, Xinyan Xu, Zihan Hao.

Methodology: Liming Wu.

Resources: Yuanchun Ma.

Software: Liming Wu.

Supervision: Jiang Ning.

Validation: Qingyang Jin.

Visualization: Liming Wu.

Writing – original draft: Liming Wu, Tianwei Wang.

Writing – review & editing: Liming Wu, Tianwei Wang.

Supplementary Material

Abbreviations: AIDs autoimmune diseases AS ankylosing spondylitis CD Crohn disease CeD celiac disease GIV genetic instrumental variables GWAS genome-wide association studies IC/BPS interstitial cystitis/bladder pain syndrome IL interleukin IVs instrumental variables IVW inverse variance weighting M3R muscarinic acetylcholine receptor M3 MR Mendelian randomization MS multiple sclerosis PBC primary biliary cirrhosis PSC primary sclerosing cholangitis PsO psoriasis RA rheumatoid arthritis RANTES regulated on activation, normal T cell expressed and secreted SLE systemic lupus erythematosus SS Sjogren syndrome T1D type 1 diabetes TNF-α tumor necrosis factor-alpha UC ulcerative colitis

Abbreviations:

autoimmune diseases

ankylosing spondylitis

Crohn disease

celiac disease

genetic instrumental variables

genome-wide association studies

interstitial cystitis/bladder pain syndrome

interleukin

instrumental variables

inverse variance weighting

muscarinic acetylcholine receptor M3

Mendelian randomization

multiple sclerosis

primary biliary cirrhosis

primary sclerosing cholangitis

psoriasis

rheumatoid arthritis

regulated on activation, normal T cell expressed and secreted

systemic lupus erythematosus

Sjogren syndrome

type 1 diabetes

tumor necrosis factor-alpha

ulcerative colitis

The data in this article were obtained from the FinnGen, IEU OpenGWAS project, and GWAS Catalog databases. No Ethics approval or consent to participate was needed.

The authors have no funding and conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Wu L, Wang T, Bai M, Chen J, Ning J, Jin Q, Ma Y, Xu X, Hao Z, Yang R. Causal role of common autoimmune diseases in interstitial cystitis/bladder pain syndrome: Mendelian randomization (MR) study. Medicine 2025;104:8(e41484).

LW and TW contributed equally to this work.

The information used in this research can be found in the public database. For more information, contact the corresponding authors.

References

Associated Data

Supplementary Materials