The causal relationship between inflammatory cytokines and thrombocytopenia: A bidirectional two-sample Mendelian randomization study

The causal relationship between inflammatory cytokines and thrombocytopenia: A bidirectional two-sample Mendelian randomization study | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article The causal relationship between inflammatory cytokines and thrombocytopenia: A bidirectional two-sample Mendelian randomization study Kimsor Hong, Marady Hun, Feifeng Wu, Jueyi Mao, Yang Wang, Junquan Zhu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4893487/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 Immune thrombocytopenia (ITP) is an autoimmune hemorrhagic disease characterized by increased platelet destruction and impaired thrombopoiesis. Epidemiological and experimental evidence has linked inflammation cytokine levels to ITP etiology but is uncertain. To respond to this query, we conducted a Mendelian randomization (MR) analysis to investigate the causal effects of circulating cytokine levels on ITP development. Using summary statistics from genome-wide association studies (GWAS), we obtained data on 41 serum cytokines from 8,293 Finnish individuals and ITP data from a meta-analysis of the FinnGen consortium, UK Biobank, and BioBank Japan. The association between genetically predicted levels of inflammatory cytokines and ITP was estimated using a bidirectional Mendelian randomization (MR) study. Sensitivity analyses and the False Discovery Rate (FDR) method were also performed to verify the robustness of the results. We discovered that higher genetically predicted M-CSF levels were strongly associated with an increased risk of ITP (OR: 1.09; 95%CI: 1.03–1.16; p = 0.003) and gestational thrombocytopenia (GT) (OR: 1.17; 95%CI, 1.05–1.32; p = 0.006). Additionally, our results showed an adverse association between genetically predicted levels of the circulating HGF (OR: 0.75; 95%CI, 0.63–0.90; p = 0.002), MIF (OR: 0.90; 95%CI, 0.84–0.96; p = 0.001) and TRAIL (OR: 0.92; 95%CI, 0.87–0.97; p = 0.003) with the GT. The study result links genetic predisposition to elevated M-CSF levels with increased risks of ITP and GT, suggesting that targeting cytokines could aid in ITP prevention, though further validation is needed. Biological sciences/Immunology Biological sciences/Immunology/Cytokines Immune thrombocytopenia gestational thrombocytopenia cytokines inflammation Mendelian randomization Figures Figure 1 Figure 2 Figure 3 1. Introduction Immune thrombocytopenia (ITP) is a rare condition due to a complex autoimmune disorder characterized by immune-mediated destruction of platelets, inhibiting their production, leading to thrombocytopenia and an increased risk of bleeding 1 , 2 . The estimated incidence of ITP in children ranges from 0.46 to 12.5 cases per 100,000 person-years, whereas in adults, it ranges from 1.6 to 3.9 cases per 100,000 person-years 3 . ITP is an autoimmune disease acquired and defined by a low platelet count (< 100 × 10^9/L) without identifiable underlying reasons. Secondary immune thrombocytopenia refers to all types of immune-mediated thrombocytopenia that are not primary, including gestational thrombocytopenia (GT). Simultaneously, the bleeding rates at the time of diagnosis are comparable 4 , 5 . ITP manifests with a diverse range of symptoms, ranging from asymptomatic to severe bleeding that poses a risk to life 6 . The primary symptom is an elevated tendency to hemorrhage, which may vary from skin discoloration to severe bleeding in the mucous membranes 7 . Nevertheless, the likelihood of experiencing critical or severe bleeding is frequently low 8 . A significant number of individuals have mild to moderate symptoms and may not need treatment, while some people with ITP show no evidence of bleeding 9 , 10 . Although the particular etiology and pathophysiology of ITP remain unknown, accumulating evidence shows that dysregulated immunological responses, such as inflammation and cytokine signaling, play a critical role in its development 11 , 12 . Cytokines play a major role in the development of autoimmune disorders. IL-12, IFN-γ, and TNF-α are cytokines that serve as proinflammatory biomarkers for many autoimmune inflammations, including Rheumatoid arthritis (RA), systematic lupus erythematosus (SLE), type I diabetes, as well as ITP. 13 Studies have reported the presence of activated platelet-autoreactive T cells exhibiting an escalating cytokine imbalance, particularly notable in chronic ITP patients. 14 Although some observational studies have tried to clarify the connections between inflammatory cytokines and ITP, unpredictable factors may influence the changes in cytokines levels in actual clinical settings, causing the results of this research to be influenced by unexpected confounding variables or reverse causation, making it challenging to establish definitive causal linkages. Mendelian randomization (MR) is a robust analytical method to explore causal links between exposures and outcomes using genetic variants as instrumental variables. Because genetic variation is inherited at birth and remains constant throughout our lives, associations derived from MR are less vulnerable to causal inversions, protecting confounding or reverse causation biases than those from conventional observational studies 15 . In this study, we explore the utility of bidirectional MR in elucidating the causal relationship between inflammation cytokine and thrombocytopenia. 2. Results 2.1. Estimation of Causal Effects of Inflammatory Cytokines on Thrombocytopenia Among the 41 inflammation cytokines, we identified 452 SNPs with a higher threshold of p-value P < 5×10 − 6 , and all F-statistic values exceeded 10, indicating the minimal likelihood of weak instrument bias (Supplementary Table S2 ). To evaluate the influence of 41 inflammatory cytokines on ITP, we employed IVW as the primary method, complemented by MR-Egger and weighted median. After applying FDR adjustment for multiple testing corrections, a strong positive association was found, indicating that higher levels of M-CSF were associated with increased odds of ITP (OR: 1.09; 95%CI: 1.03–1.16; p < 0.003). The higher levels of IL-13 were associated with increased odds of ITP (OR: 1.06; 95%CI: 1.02–1.11; p = 0.005); however, the significance was excluded because of the inconsistency direction among the analysis methods. Genetically predicted levels of the other studied cytokines were not associated with ITP risk (Supplementary Table S3, Fig. 1 , 2 ). In addition, higher genetically predicted M-CSF levels were associated with an increased risk of GT (OR: 1.17; 95%CI, 1.05–1.32; p = 0.006). Our results showed an adverse association between genetically predicted levels of the circulating HGF (OR: 0.75; 95%CI, 0.63–0.90; p = 0.002), MIF (OR: 0.90; 95%CI, 0.84–0.96; p = 0.001) and TRAIL (OR: 0.92; 95%CI, 0.87–0.97; p = 0.003) with the GT risk after applying FDR adjustment for multiple testing corrections. There were suggestive associations of genetically predicted IL-1β (OR: 1.091; 95%CI, 1.01–1.18; p = 0.030), IFN-γ (OR: 1.26; 95%CI, 1.02–1.55; p = 0.035) with an increased risk of GT. Genetically predicted levels of SDF-1α (OR: 0.77; 95%CI, 0.61–0.95; p = 0.018) were suggestively associated with reduced risk of GT (Supplementary Table S3, Fig. 1 , 2 ). All the association result directions were in accordance with MR- Egger and weight median. The sensitivity analysis results (Table 1 ) ruled out potential heterogeneity and horizontal pleiotropy in causal associations by using Cochran's Q test, the intercept of MR-Egger, and the global test of MR-PRESSO (all the test p-value > 0.05), indicating that our findings were robust and reliable. Table 1 Results of Sensitivity Analyses in the Forward MR Analysis Exposure Outcome Heterogeneity test Cochran’s Q test (P value) IVW Rucker’s Q test (P value) MR-egger Pleiotropy test Egger intercept test (P value) MR-egger MR-PRESSO distortion test Outliers Global test P value M-CSF ITP 0.963 0.937 0.727 NA 0.958 HGF GT 0.635 0.739 0.403 NA 0.690 M-CSF GT 0.629 - - - - MIF GT 0.844 0.646 0.780 NA 0.874 TRAIL GT 0.972 0.965 0.588 NA 0.948 2.2. Estimation of Causal Effects of Thrombocytopenia on Inflammatory Cytokines In reverse MR analysis, we aimed to investigate the potential causal relationships between ITP and various inflammation cytokines. We extracted 10 SNPs associated with ITP and 53 SNPs associated with GT as the exposure with the cut-off threshold of p < 5×10 − 6 , all F-statistic values exceeded 10, indicating the minimal likelihood of weak instrument bias and thus strengthening the validity of our instrumental variables (Supplementary Table S4). Our initial findings provided suggestive evidence of a causal relationship between ITP and elevated levels of several cytokines. Specifically, we observed that ITP was associated with increased levels of GROα (OR: 1.08; 95%CI, 1.01–1.16; p = 0.031), MCP3 (OR: 1.14; 95%CI, 1.02–1.28; p = 0.018), IL-18 (OR: 1.07; 95%CI, 1.01–1.14; p = 0.028), PDGFbb (OR: 1.04; 95%CI, 1.01–1.08, p = 0.017) and TRAIL (OR: 1.06; 95%CI, 1.02–1.11; p = 0.004) (Supplementary Table S5) However, upon applying FDR adjustment for multiple testing corrections, none of these associations remained statistically significant, suggesting no strong significant correlations among any of the cytokines analyzed. Additionally, we use GT SNPs as the exposure variable to identify potential associations with inflammation cytokines. Despite our rigorous approach, we identified no SNPs significantly associated with cytokine outcomes in this context. 3. Discussion This study represents the comprehensive evaluation of the causal effects of 41 inflammatory factors on ITP and vice versa. Beyond extensive research that has explored the correlations between ITP and various inflammation-related cytokine types (Supplementary Table S6) using the observation from epidemiology studies, we have made significant findings by conducting two-sample MR analysis using meta-analysis from three databanks together. The results provided strong evidence of the causal relationships of genetically predicted levels of M-CSF levels with the risk of ITP and GT across European and East Asian populations. In addition, our study found that increased MIF, HGF, and TRAIL levels can reduce the risk of ITP during pregnancy. The current investigation emphasized the causative significance of M-CSF in determining a susceptibility to thrombocytopenia. M-CSF/CSF-1 is lineage-specific and promotes the survival, proliferation, and differentiation of mononuclear phagocytes and their progenitors 16 . Pregnant women were found to have elevated M-CSF serum levels and rapidly return to baseline after delivery 17 , 18 . It suggests that serum M-CSF may serve as a diagnostic and predictive biomarker for hypertensive disorders that complicate pregnancy, with elevated levels suggesting a correlation between disease severity, inflammation, and adverse pregnancy outcomes 19 . Li et al. 20 M-CSF expression is increased in decidua from women with pre-eclampsia. Yong et al. 17 demonstrated that female patients with ITP exhibit significantly elevated levels of circulating M-CSF in comparison to female controls of the same age. Increased concentrations of M-CSF in ITP suggest the degree of macrophage activation and contribute to the exacerbation of this illness. Pregnant mice have heightened levels of M-CSF in their tissues and blood, which is associated with an increase in the production of monocytes. This is supported by a fivefold rise in the amount of monocytes circulating in the blood and a more than twofold increase in the amount of precursor cells for splenic macrophages 21 . In addition, Zeigler et al. 22 provided evidence that patients diagnosed with Evan's syndrome, which is a combination of ITP and autoimmune hemolytic anemia (AIHA), exhibited significantly elevated levels of M-CSF. Furthermore, patients with a more severe form of ITP had increased levels of M-CSF, while those with a mild and chronic form of ITP had normal levels of M-CSF. Furthermore, a correlation was observed between the level of M-CSF and the clinical response to corticosteroids. Individuals who did not respond to steroids exhibited elevated levels of M-CSF compared to those who did respond. Moreover, infusions of M-CSF resulted in elevated levels of monocytes and decreased platelet count but enhanced erythrophagocytosis 23 . The drop in platelets may be attributed to the peripheral destruction of the platelets rather than to a decrease in production, as indicated by the increase in megakaryocytes. Taken together, these results suggest that elevated levels of M-CSF may significantly contribute to the ongoing destruction of platelets in cases of ITP disorder. MIF is engaged in various physiological processes, including cell proliferation and differentiation, innate immune responses, and angiogenic biological activities, and it is critical for B cell proliferation 24 , 25 . MIF is secreted by several cell types, including endothelial cells, macrophages, and active platelets 26 . Existing studies have shown that MIF activation produces considerable quantities of pro-inflammatory mediators, including TNF-α, IL-1b, IL-6, IFN- γ, matrix metalloproteinases, and nitric oxide 27 , 28 . Moreover, clinical studies have linked MIF to the etiology of autoimmunity and inflammation, including rheumatoid arthritis (RA), systematic lupus erythematosus (SLE), and multiple sclerosis (MS); it is thought to be a promising cytokine for targeted inhibition. 29 , 30 . In a study conducted by Burenbatu et al. 31 , it was discovered that the analysis of ITP patients using iTRAQ-based quantitative proteomics revealed a significant decrease in MIF compared to the standard control. However, it was observed that MIF levels were noticeably elevated during ITP remission as a result of effective treatment. In addition, Xu et al. 25 demonstrated that MIF elevation by CD72 blockade may play a role in suppressing B cell proliferation in ITP patients. HGF, also known as scatter factor (SF), was first discovered to be a factor that could stimulate the proliferation of hepatocytes in culture in plasma and platelets 32 . Furthermore, research has shown that HGF has also been linked to a variety of biological impacts, including the growth, regeneration, and remodeling of cells in the body's tissues and organs, which are particularly anti-inflammatory and anti-apoptotic properties 33 . In addition, HGF is crucial in the process of differentiating hematopoietic progenitor cells. Various studies have indicated that the administration of HGF has been found to boost platelet counts and megakaryocytes. This is likely due to its positive impact on hepatocyte TPO mRNA expression 34 , 35 . Moreover, HGF has been shown to possess anti-fibrotic characteristics. These features aid in preventing bone marrow fibrosis in ITP patients undergoing treatment with thrombopoiesis drugs 36 . TRAIL, a member of the tumor necrosis factor (TNF) superfamily, is crucial in developing autoimmune diseases, including SLE, Sjogren syndrome, and autoimmune thyroid diseases 37 – 39 . Prior research indicates that TRAIL can contribute to the maturation and apoptosis of megakaryocytes and platelet release. 40 , 41 . Yang et al. 42 observed that reduced TRAIL expression likely resulted in less activation of caspase-8 and caspase-3, reducing megakaryocyte death. However, Sedger et al. 43 observed that FasL and TRAIL double-deficient animals suffer severe lymphoproliferative illness and fatal autoimmune thrombocytopenia. In addition, low TRAIL expression in megakaryocytes may cause maturation and apoptotic damage in ITP patients. Zhou et al. 44 demonstrated that in healthy controls and individuals with ITP, the use of low-dose decitabine (DAC) has been found to have the potential to enhance TRAIL expression by reducing its promoter methylation status. This, consequently, can promote megakaryocyte maturation and the release of platelets. Our study has several advantages. The robustness of the genetic instrumental variable enabled us to conduct a pioneering and thorough Mendelian randomization (MR) investigation, examining the relationship between inflammatory variables and the risk of ITP. To provide more reliable results, we combined outcome data from three biobanks. To validate our MR analysis, we conducted various sensitivity analyses to assess the robustness of the instrumental variables, heterogeneity, and horizontal pleiotropy, which are the three main assumptions of MR. Despite the MR design being less susceptible to confounding than other observational studies, limitations exist. First, although the genome-wide significance P-value threshold of 5 × 10 − 8 was a common standard, our study used a less stringent 5 × 10 − 6 cut-off to explore more possibilities. Additionally, some instruments used in the MR analyses had few SNPs, displayed evidence of heterogeneity, and were associated with potential confounders. Secondly, the second and third assumptions could not be sufficiently addressed due to the MR analysis's limitations, which could have led to bias. Third, ethnic bias may be present, so it should be taken cautiously if the conclusions are applied to other races. Fourth, when we explored GT as the exposure to inflammation cytokines, no SNPs were found in cytokine outcomes. This lack of findings further underscores the need for more detailed studies on this field. Concurrently, While MR can shed light on the lifetime relationships between genetic variations and inflammatory variables, alterations in inflammatory cytokines can be altered by unforeseen circumstances in any given clinical situation. More research should be conducted to confirm and apply our results to clinical diagnosis procedures and therapy options. 4. Conclusion This MR study suggests that genetic predisposition to elevated circulating M-CSF levels was associated with an increased risk of ITP and GT. Our study contributes to the existing knowledge regarding the role of inflammatory biomarkers in the development of ITP. These findings have clinical implications, suggesting the potential of targeting inflammatory cytokines to prevent ITP. While we have identified numerous strong connections, further validation is necessary to evaluate the potential of these cytokines as targets for medication or lifestyle interventions in ITP prevention. 5. Methods This study was conducted following the STROBEMR (Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization) guideline 45 . We performed a bidirectional Mendelian randomization study using SNPs associated with 41 inflammatory cytokine levels as instrumental variables to assess their causal association with immune thrombocytopenia, as illustrated in Fig. 3 . 5.1. Data sources In this Mendelian Randomization investigation, we used datasets from publicly available GWAS data. For information on 41 unique inflammatory cytokines as exposures dataset, we obtained the IVs from a GWAS meta-analysis of two separate sources: The Cardiovascular Risk in Young Finns Study (YES) 46 , and FINRISK 47 in 8,293 Finnish individuals. The average ages of participants in the YFS study and the FINRISK survey are 37 years and 60 years, respectively 48 . Gene–outcome associations for ITP were obtained from three databanks: the FinnGen consortium (R10 release) 49 , UKBiobank TOPMed 50 , and BioBank Japan 51 . All the studies are ongoing cohorts, and the combined outcome summary statistics included 991,844 participants and 1,468 cases of ITP. The other set of summary-level statistics on GT was retrieved from the GWAS catalog datasets by Yang et al. 52 , using 11,138/85,294 cases/controls in East Asian (China) ancestry. The datasets used in this study are listed in Supplementary Table S1 . Due to the utilization of publicly accessible summary statistics obtained from published studies, further ethical approval from the institutional review board was unnecessary for the current investigation. 5.2. MR assumptions and Instrumental variable selection The chosen genetic variants must meet specific criteria to be considered valid instruments for the MR analysis. Firstly, they must strongly associate with the cytokine levels in circulation. Secondly, they must be independent of potential confounding variables that could affect the relationship between the exposure and the outcome. Lastly, these genetic variants should influence the outcome solely through the exposure being studied. To ensure the robustness of our conclusions regarding the mutual risk of inflammation cytokines level with ITP, we carefully selected the most suitable IVs through meticulous quality control measures. Then, we proceeded with the following steps: (1) We found specific genetic variations that showed strong connections with each exposure (cytokines level or ITP) throughout the entire genome. We used a significance threshold of P < 5 × 10 − 8 to determine the significance of these associations. Hence, given the lack of genetic variants reaching genome-wide significance for 13 cytokines, the p-value threshold was subsequently adjusted to < 5×10 − 6 . (2) We set a threshold of 0.01 for the minor allele frequency (MAF) of the specific variant we were interested in. (3) To reduce the risk of biased results, we selected single nucleotide polymorphisms (SNPs) that were highly associated with the exposure and showed no linkage disequilibrium (LD) (R2 < 0.001) within a clumping distance of 10,000 kb.; (4) we excluded palindromic SNPs to prevent any distortions in chain orientation or allele coding; (5) The degree of association between instrumental variables and exposure factors was evaluated using the F statistic. To address bias caused by weak instrumental variables, we only included single nucleotide polymorphisms (SNPs) with an F statistic greater than 10 53 . 5.3. Statistical analyses In the study, for the ITP disease outcomes datasets, we conducted a meta-analysis using summary data from the three biobanks, UKB-TOPMed, FinnGen, and biobank Japan, using the METAL software 54 . For the main analysis, we utilized three MR methods: The instrumental variable weighting (IVW) method computes the causal impact of the exposure on the result by merging ratio estimates for each single nucleotide polymorphism (SNP). This strategy essentially transforms MR estimations into a regression model that considers the impact of SNPs on both the outcome and the exposure 55 . The weighted median technique yields unbiased estimates, even in cases where unreliable independent variables contribute up to 50% of the information 56 . MR-Egger not only calculates the causal impact using the slope coefficient obtained by Egger regression but also identifies possible bias that may arise from small-study effects 57 . When only one SNP was available to create the instrumental variable, the ratio of coefficients approach was used to calculate Mendelian randomization (MR) estimates, with first-order weights utilized to establish standard errors. When multiple SNPs were available to generate the instrumental variable for a specific cytokine, the random-effects inverse-variance weighted (IVW) MR approach combined the MR estimates derived from individual SNPs. To account for multiple hypothesis testing, we calculated adjusted p-values (q-values) for the false discovery rate (FDR) in the primary IVW MR analyses using the sequential p-value technique suggested by Benjamini and Hochberg 58 . We used Cochran's Q statistics to assess heterogeneity in both the IVW and MR-Egger methodologies 59 . The presence of horizontal pleiotropy, which means that instrumental variables (IVs) may affect outcomes via routes that are unrelated to causality, was also considered 60 . To investigate the direct relationship between the chosen independent variables (IVs) and the outcomes, we used MR pleiotropy residual sum and outlier (MR-PRESSO) 61 . According to the analysis above, we determined that the IVW results were the most reliable estimates of the causal effect and were used as the main method in the study. This conclusion was based on the consistent findings across all three methods and the q-value results, which were below 10%. Additionally, there was no evidence of horizontal pleiotropy, as indicated by a P-value for the Egger intercept greater than 0.05, which was considered statistically significant. Furthermore, we searched PubMed to compare the results of the MR analyses with epidemiological evidence. We specifically looked for observational studies examining the relationship between chronic inflammatory markers and the outcomes of interest, thrombocytopenia. We used general search terms such as "cytokines," "inflammation," and "thrombocytopenia" to search. 5.4. Reverse-direction MR Analysis We explored the potential causal relationship between ITP and inflammation cytokines through reverse-direction MR analysis. Based on the number of cases among the three databanks, we obtained IVs of ITP from the FinnGen consortium as the exposure. When multiple SNPs were characterized, we utilized three MR methods: IVW, MR-Egger, and weighted median. The sensitivity analysis was conducted in the same way as the forward MR. Given the limited availability of SNP for analysis, we could not conduct assessments for heterogeneity, pleiotropy, and sensitivity. All statistical analyses and data visualization were performed in R software (version 4.3.0) with “TwoSampleMR” and “MRPRESSO” software packages. The Heatmap plot of this study was created from https://www.chiplot.online/ . Declarations Acknowledgments The authors thank the participants who contributed to the BioBank Japan Project and the UKB-TOPMed project. We want to acknowledge the participants and investigators of the FinnGen study. Sincere thanks also go to IEU GWAS database projects, which have made the GWAS data publicly available, and many members of the IEU have contributed to curating these data. Thank you to all GWAS Catalog users and authors of studies included in the catalog. This study would not have been possible without access to publicly available summary data. Declaration The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics Approval and Consent to Participate Not applicable Consent for Publication Not applicable Competing interests The authors declare that they have no competing interests. Funding National Key Clinical Specialty Scientific Research Project (No. Z2023032) Authors' contributions KH contributed to the study design. KH, MH, FW, and JM, formal analysis, methodology, and writing the original draft. KH, MH, and LZ drafted and revised the draft manuscript. JT, MH, YW, JZ, JH, XZ, BL, and XQ reviewed and edited the final manuscript. CW, LZ, and JT conceptualized and supervised the manuscript. All authors read and approved the final manuscript. Availability of Data and Materials The summary datasets of GWAS - https://gwas.mrcieu.ac.uk/datasets/ - https://www.ebi.ac.uk/gwas/ - https://www.finngen.fi/en/access_results - https://pheweb.org/UKB-TOPMed/ - https://pheweb.jp/ References Cooper, N. & Bussel, J. The pathogenesis of immune thrombocytopaenic purpura. Br J Haematol 133, 364–374 (2006). https://doi.org/10.1111/j.1365-2141.2006.06024.x Miltiadous, O., Hou, M. & Bussel, J. B. Identifying and treating refractory ITP: difficulty in diagnosis and role of combination treatment. Blood 135, 472–490 (2020). https://doi.org/10.1182/blood.2019003599 Terrell, D. R. et al. The incidence of immune thrombocytopenic purpura in children and adults: A critical review of published reports. 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Blood 115, 3258–3268 (2010). https://doi.org/https://doi.org/10.1182/blood-2009-11-255497 Zhou, H. et al. Low-dose decitabine promotes megakaryocyte maturation and platelet production in healthy controls and immune thrombocytopenia. Thromb Haemost 113, 1021–1034 (2015). https://doi.org/10.1160/th14-04-0342 Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. Bmj 375, n2233 (2021). https://doi.org/10.1136/bmj.n2233 Raitakari, O. T. et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol 37, 1220–1226 (2008). https://doi.org/10.1093/ije/dym225 Ritchie, S. C. et al. The Biomarker GlycA Is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection. Cell Syst 1, 293–301 (2015). https://doi.org/10.1016/j.cels.2015.09.007 Ahola-Olli, A. V. et al. Genome-wide Association Study Identifies 27 Loci Influencing Concentrations of Circulating Cytokines and Growth Factors. Am J Hum Genet 100, 40–50 (2017). https://doi.org/10.1016/j.ajhg.2016.11.007 Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023). https://doi.org/10.1038/s41586-022-05473-8 Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021). https://doi.org/10.1038/s41586-021-03205-y Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nature Genetics 53, 1415–1424 (2021). https://doi.org/10.1038/s41588-021-00931-x Yang, Z. et al. Genetic Basis of Altered Platelet Counts and Gestational Thrombocytopenia in Pregnancy. Blood (2023). https://doi.org/10.1182/blood.2023021925 Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res 26, 2333–2355 (2017). https://doi.org/10.1177/0962280215597579 Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010). https://doi.org/10.1093/bioinformatics/btq340 Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology 28, 30–42 (2017). https://doi.org/10.1097/ede.0000000000000559 Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40, 304–314 (2016). https://doi.org/10.1002/gepi.21965 Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32, 377–389 (2017). https://doi.org/10.1007/s10654-017-0255-x Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 57, 289–300 (1995). https://doi.org/https://doi.org/10.1111/j.2517-6161.1995.tb02031.x Sanderson, E., Spiller, W. & Bowden, J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat Med 40, 5434–5452 (2021). https://doi.org/10.1002/sim.9133 Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet 27, R195-r208 (2018). https://doi.org/10.1093/hmg/ddy163 Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50, 693–698 (2018). https://doi.org/10.1038/s41588-018-0099-7 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-4893487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":353284764,"identity":"090c9106-ad68-4ba5-bee0-cea713de9609","order_by":0,"name":"Kimsor Hong","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kimsor","middleName":"","lastName":"Hong","suffix":""},{"id":353284765,"identity":"7574f5e8-db13-4dbe-a2fa-53d479630b23","order_by":1,"name":"Marady Hun","email":"","orcid":"","institution":"Department of Pediatrics, The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marady","middleName":"","lastName":"Hun","suffix":""},{"id":353284766,"identity":"caa31463-0e6e-41e5-91fb-354ee55186d5","order_by":2,"name":"Feifeng Wu","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feifeng","middleName":"","lastName":"Wu","suffix":""},{"id":353284767,"identity":"44ece3b8-2690-4eba-aadf-1d56db84d81f","order_by":3,"name":"Jueyi Mao","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jueyi","middleName":"","lastName":"Mao","suffix":""},{"id":353284768,"identity":"9d5e57c3-c725-4787-ae44-304186b3babf","order_by":4,"name":"Yang Wang","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":353284769,"identity":"7da34630-1cf8-475b-9687-c1f6d6f7a7bc","order_by":5,"name":"Junquan Zhu","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junquan","middleName":"","lastName":"Zhu","suffix":""},{"id":353284770,"identity":"37993af8-325a-487a-8641-d80f0edc6513","order_by":6,"name":"Xin Zhou","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhou","suffix":""},{"id":353284771,"identity":"55d89803-1f1e-42e4-9b17-43a30071a403","order_by":7,"name":"Haotian Xie","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Xie","suffix":""},{"id":353284772,"identity":"3bb44d7b-00d7-4c9b-9022-8b8bdd5cb914","order_by":8,"name":"Binbin Li","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Li","suffix":""},{"id":353284773,"identity":"481b1163-19ff-45a1-903e-e0985c9aea1c","order_by":9,"name":"Xinying Qiu","email":"","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Qiu","suffix":""},{"id":353284774,"identity":"69955987-25f1-4108-91e1-285bce873cae","order_by":10,"name":"Jidong Tian","email":"","orcid":"","institution":"Department of Gastroenterology, The Second Xiangya Hospital of Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jidong","middleName":"","lastName":"Tian","suffix":""},{"id":353284775,"identity":"ce460339-77ed-48a7-a2ba-e3065b8c6858","order_by":11,"name":"Liang Zhang","email":"","orcid":"","institution":"The Affiliated Children’s Hospital of Xiangya School of Medicine, Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zhang","suffix":""},{"id":353284776,"identity":"f56ca6a4-9ec3-4339-b0a5-f6467c1cb0db","order_by":12,"name":"Chuan Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCSjND6GYSdAi2UCyFoMDxGrhn91j+LmwzSbP+PjxZxIMFdaJDexnD+C35M4ZY+mZbWnFZmdyzCQYzqQnNvDkJeDVYiCRYyDNu+1w4rYbPGwSjG2HExskeAwIaTH+zbvtf+LmGezPJBj/EafFDGjLgcQNEgxmEowNRGiRuJFWZs37LzlxxpkcY4uEY+nGbTw5+LXwz0jefJvnjF1if/vxhzc+1FjL9rOfwa8FFSQAMRsJ6kfBKBgFo2AU4AAAdk4/vVainkoAAAAASUVORK5CYII=","orcid":"","institution":"Department of Pediatrics, The Second Xiangya Hospital of Central South University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-08-11 02:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4893487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4893487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64491542,"identity":"5e3778cf-63f0-4ba9-ae8e-fdbd94e75575","added_by":"auto","created_at":"2024-09-13 20:41:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":775520,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows the causal associations between 41 inflammatory cytokines and thrombocytopenia; we mainly used the IVW method. ITP, Immune thrombocytopenia; GT, gestational thrombocytopenia.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/d49196d6ed1a37530a7df6f8.jpeg"},{"id":64491541,"identity":"86b07af2-4be5-4c0e-ae9f-b43fe794d187","added_by":"auto","created_at":"2024-09-13 20:41:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121816,"visible":true,"origin":"","legend":"\u003cp\u003eThe circular heatmap shows the odd ratio values corresponding to 41 inflammation cytokines. (A) Heatmap of odd ratios forward MR Analysis. (B) odd ratios revers MR Analysis. One asterisk denotes the association is nominally significant (p \u0026lt; 0.05), and two asterisk indicates that the association was significant when considering multiple comparison correction (FDR ≤ 10%). ITP, Immune thrombocytopenia; GT, gestational thrombocytopenia.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/5185002b7916e5096cbebd9f.jpeg"},{"id":64491697,"identity":"90e981d3-68e9-40b5-8046-3cce1b30d5da","added_by":"auto","created_at":"2024-09-13 20:49:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":865056,"visible":true,"origin":"","legend":"\u003cp\u003eStudy overview bidirectional Mendelian randomization (MR) analysis.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/b035999d346c46f595b2580d.jpg"},{"id":80367926,"identity":"e5771faf-3dd2-4abf-b291-2b31ce4c2480","added_by":"auto","created_at":"2025-04-11 06:02:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2534464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/9818216b-1c16-4165-91f1-11a7faeb67e1.pdf"},{"id":64491544,"identity":"40e5b359-1101-4ad6-8d2a-27cead267b47","added_by":"auto","created_at":"2024-09-13 20:41:10","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":322043,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/550fedd9db6430d53f686938.xlsx"},{"id":64491698,"identity":"bb0b9158-4f4a-4936-bb6f-db3c9ffb7529","added_by":"auto","created_at":"2024-09-13 20:49:10","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":611756,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4893487/v1/d2f977e8dd9eafc79bdd6b9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal relationship between inflammatory cytokines and thrombocytopenia: A bidirectional two-sample Mendelian randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eImmune thrombocytopenia (ITP) is a rare condition due to a complex autoimmune disorder characterized by immune-mediated destruction of platelets, inhibiting their production, leading to thrombocytopenia and an increased risk of bleeding \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The estimated incidence of ITP in children ranges from 0.46 to 12.5 cases per 100,000 person-years, whereas in adults, it ranges from 1.6 to 3.9 cases per 100,000 person-years \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. ITP is an autoimmune disease acquired and defined by a low platelet count (\u0026lt;\u0026thinsp;100 \u0026times; 10^9/L) without identifiable underlying reasons. Secondary immune thrombocytopenia refers to all types of immune-mediated thrombocytopenia that are not primary, including gestational thrombocytopenia (GT). Simultaneously, the bleeding rates at the time of diagnosis are comparable \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. ITP manifests with a diverse range of symptoms, ranging from asymptomatic to severe bleeding that poses a risk to life \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The primary symptom is an elevated tendency to hemorrhage, which may vary from skin discoloration to severe bleeding in the mucous membranes \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the likelihood of experiencing critical or severe bleeding is frequently low \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. A significant number of individuals have mild to moderate symptoms and may not need treatment, while some people with ITP show no evidence of bleeding \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although the particular etiology and pathophysiology of ITP remain unknown, accumulating evidence shows that dysregulated immunological responses, such as inflammation and cytokine signaling, play a critical role in its development \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCytokines play a major role in the development of autoimmune disorders. IL-12, IFN-γ, and TNF-α are cytokines that serve as proinflammatory biomarkers for many autoimmune inflammations, including Rheumatoid arthritis (RA), systematic lupus erythematosus (SLE), type I diabetes, as well as ITP.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Studies have reported the presence of activated platelet-autoreactive T cells exhibiting an escalating cytokine imbalance, particularly notable in chronic ITP patients.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Although some observational studies have tried to clarify the connections between inflammatory cytokines and ITP, unpredictable factors may influence the changes in cytokines levels in actual clinical settings, causing the results of this research to be influenced by unexpected confounding variables or reverse causation, making it challenging to establish definitive causal linkages.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a robust analytical method to explore causal links between exposures and outcomes using genetic variants as instrumental variables. Because genetic variation is inherited at birth and remains constant throughout our lives, associations derived from MR are less vulnerable to causal inversions, protecting confounding or reverse causation biases than those from conventional observational studies \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this study, we explore the utility of bidirectional MR in elucidating the causal relationship between inflammation cytokine and thrombocytopenia.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Estimation of Causal Effects of Inflammatory Cytokines on Thrombocytopenia\u003c/h2\u003e \u003cp\u003eAmong the 41 inflammation cytokines, we identified 452 SNPs with a higher threshold of p-value P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, and all F-statistic values exceeded 10, indicating the minimal likelihood of weak instrument bias (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To evaluate the influence of 41 inflammatory cytokines on ITP, we employed IVW as the primary method, complemented by MR-Egger and weighted median. After applying FDR adjustment for multiple testing corrections, a strong positive association was found, indicating that higher levels of M-CSF were associated with increased odds of ITP (OR: 1.09; 95%CI: 1.03\u0026ndash;1.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.003). The higher levels of IL-13 were associated with increased odds of ITP (OR: 1.06; 95%CI: 1.02\u0026ndash;1.11; p\u0026thinsp;=\u0026thinsp;0.005); however, the significance was excluded because of the inconsistency direction among the analysis methods. Genetically predicted levels of the other studied cytokines were not associated with ITP risk (Supplementary Table S3, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, higher genetically predicted M-CSF levels were associated with an increased risk of GT (OR: 1.17; 95%CI, 1.05\u0026ndash;1.32; p\u0026thinsp;=\u0026thinsp;0.006). Our results showed an adverse association between genetically predicted levels of the circulating HGF (OR: 0.75; 95%CI, 0.63\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.002), MIF (OR: 0.90; 95%CI, 0.84\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.001) and TRAIL (OR: 0.92; 95%CI, 0.87\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.003) with the GT risk after applying FDR adjustment for multiple testing corrections. There were suggestive associations of genetically predicted IL-1β (OR: 1.091; 95%CI, 1.01\u0026ndash;1.18; p\u0026thinsp;=\u0026thinsp;0.030), IFN-γ (OR: 1.26; 95%CI, 1.02\u0026ndash;1.55; p\u0026thinsp;=\u0026thinsp;0.035) with an increased risk of GT. Genetically predicted levels of SDF-1α (OR: 0.77; 95%CI, 0.61\u0026ndash;0.95; p\u0026thinsp;=\u0026thinsp;0.018) were suggestively associated with reduced risk of GT (Supplementary Table S3, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll the association result directions were in accordance with MR- Egger and weight median. The sensitivity analysis results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) ruled out potential heterogeneity and horizontal pleiotropy in causal associations by using Cochran's Q test, the intercept of MR-Egger, and the global test of MR-PRESSO (all the test p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that our findings were robust and reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Sensitivity Analyses in the Forward MR Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeterogeneity test Cochran\u0026rsquo;s Q test (P value) IVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRucker\u0026rsquo;s Q test (P value) MR-egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePleiotropy test Egger intercept test (P value) MR-egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMR-PRESSO distortion test Outliers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlobal test P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Estimation of Causal Effects of Thrombocytopenia on Inflammatory Cytokines\u003c/h2\u003e \u003cp\u003eIn reverse MR analysis, we aimed to investigate the potential causal relationships between ITP and various inflammation cytokines. We extracted 10 SNPs associated with ITP and 53 SNPs associated with GT as the exposure with the cut-off threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, all F-statistic values exceeded 10, indicating the minimal likelihood of weak instrument bias and thus strengthening the validity of our instrumental variables (Supplementary Table S4). Our initial findings provided suggestive evidence of a causal relationship between ITP and elevated levels of several cytokines. Specifically, we observed that ITP was associated with increased levels of GROα (OR: 1.08; 95%CI, 1.01\u0026ndash;1.16; p\u0026thinsp;=\u0026thinsp;0.031), MCP3 (OR: 1.14; 95%CI, 1.02\u0026ndash;1.28; p\u0026thinsp;=\u0026thinsp;0.018), IL-18 (OR: 1.07; 95%CI, 1.01\u0026ndash;1.14; p\u0026thinsp;=\u0026thinsp;0.028), PDGFbb (OR: 1.04; 95%CI, 1.01\u0026ndash;1.08, p\u0026thinsp;=\u0026thinsp;0.017) and TRAIL (OR: 1.06; 95%CI, 1.02\u0026ndash;1.11; p\u0026thinsp;=\u0026thinsp;0.004) (Supplementary Table S5)\u003c/p\u003e \u003cp\u003eHowever, upon applying FDR adjustment for multiple testing corrections, none of these associations remained statistically significant, suggesting no strong significant correlations among any of the cytokines analyzed. Additionally, we use GT SNPs as the exposure variable to identify potential associations with inflammation cytokines. Despite our rigorous approach, we identified no SNPs significantly associated with cytokine outcomes in this context.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study represents the comprehensive evaluation of the causal effects of 41 inflammatory factors on ITP and vice versa. Beyond extensive research that has explored the correlations between ITP and various inflammation-related cytokine types (Supplementary Table S6) using the observation from epidemiology studies, we have made significant findings by conducting two-sample MR analysis using meta-analysis from three databanks together. The results provided strong evidence of the causal relationships of genetically predicted levels of M-CSF levels with the risk of ITP and GT across European and East Asian populations. In addition, our study found that increased MIF, HGF, and TRAIL levels can reduce the risk of ITP during pregnancy.\u003c/p\u003e \u003cp\u003eThe current investigation emphasized the causative significance of M-CSF in determining a susceptibility to thrombocytopenia. M-CSF/CSF-1 is lineage-specific and promotes the survival, proliferation, and differentiation of mononuclear phagocytes and their progenitors \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Pregnant women were found to have elevated M-CSF serum levels and rapidly return to baseline after delivery \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. It suggests that serum M-CSF may serve as a diagnostic and predictive biomarker for hypertensive disorders that complicate pregnancy, with elevated levels suggesting a correlation between disease severity, inflammation, and adverse pregnancy outcomes \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Li et al. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e M-CSF expression is increased in decidua from women with pre-eclampsia. Yong et al. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e demonstrated that female patients with ITP exhibit significantly elevated levels of circulating M-CSF in comparison to female controls of the same age. Increased concentrations of M-CSF in ITP suggest the degree of macrophage activation and contribute to the exacerbation of this illness. Pregnant mice have heightened levels of M-CSF in their tissues and blood, which is associated with an increase in the production of monocytes. This is supported by a fivefold rise in the amount of monocytes circulating in the blood and a more than twofold increase in the amount of precursor cells for splenic macrophages \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, Zeigler et al. \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e provided evidence that patients diagnosed with Evan's syndrome, which is a combination of ITP and autoimmune hemolytic anemia (AIHA), exhibited significantly elevated levels of M-CSF. Furthermore, patients with a more severe form of ITP had increased levels of M-CSF, while those with a mild and chronic form of ITP had normal levels of M-CSF. Furthermore, a correlation was observed between the level of M-CSF and the clinical response to corticosteroids. Individuals who did not respond to steroids exhibited elevated levels of M-CSF compared to those who did respond. Moreover, infusions of M-CSF resulted in elevated levels of monocytes and decreased platelet count but enhanced erythrophagocytosis \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The drop in platelets may be attributed to the peripheral destruction of the platelets rather than to a decrease in production, as indicated by the increase in megakaryocytes. Taken together, these results suggest that elevated levels of M-CSF may significantly contribute to the ongoing destruction of platelets in cases of ITP disorder.\u003c/p\u003e \u003cp\u003eMIF is engaged in various physiological processes, including cell proliferation and differentiation, innate immune responses, and angiogenic biological activities, and it is critical for B cell proliferation \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. MIF is secreted by several cell types, including endothelial cells, macrophages, and active platelets \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Existing studies have shown that MIF activation produces considerable quantities of pro-inflammatory mediators, including TNF-α, IL-1b, IL-6, IFN- γ, matrix metalloproteinases, and nitric oxide \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Moreover, clinical studies have linked MIF to the etiology of autoimmunity and inflammation, including rheumatoid arthritis (RA), systematic lupus erythematosus (SLE), and multiple sclerosis (MS); it is thought to be a promising cytokine for targeted inhibition. \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In a study conducted by Burenbatu et al. \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, it was discovered that the analysis of ITP patients using iTRAQ-based quantitative proteomics revealed a significant decrease in MIF compared to the standard control. However, it was observed that MIF levels were noticeably elevated during ITP remission as a result of effective treatment. In addition, Xu et al. \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e demonstrated that MIF elevation by CD72 blockade may play a role in suppressing B cell proliferation in ITP patients. HGF, also known as scatter factor (SF), was first discovered to be a factor that could stimulate the proliferation of hepatocytes in culture in plasma and platelets \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, research has shown that HGF has also been linked to a variety of biological impacts, including the growth, regeneration, and remodeling of cells in the body's tissues and organs, which are particularly anti-inflammatory and anti-apoptotic properties \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In addition, HGF is crucial in the process of differentiating hematopoietic progenitor cells. Various studies have indicated that the administration of HGF has been found to boost platelet counts and megakaryocytes. This is likely due to its positive impact on hepatocyte TPO mRNA expression \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, HGF has been shown to possess anti-fibrotic characteristics. These features aid in preventing bone marrow fibrosis in ITP patients undergoing treatment with thrombopoiesis drugs \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. TRAIL, a member of the tumor necrosis factor (TNF) superfamily, is crucial in developing autoimmune diseases, including SLE, Sjogren syndrome, and autoimmune thyroid diseases \u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Prior research indicates that TRAIL can contribute to the maturation and apoptosis of megakaryocytes and platelet release. \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Yang et al. \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e observed that reduced TRAIL expression likely resulted in less activation of caspase-8 and caspase-3, reducing megakaryocyte death. However, Sedger et al. \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e observed that FasL and TRAIL double-deficient animals suffer severe lymphoproliferative illness and fatal autoimmune thrombocytopenia. In addition, low TRAIL expression in megakaryocytes may cause maturation and apoptotic damage in ITP patients. Zhou et al. \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e demonstrated that in healthy controls and individuals with ITP, the use of low-dose decitabine (DAC) has been found to have the potential to enhance TRAIL expression by reducing its promoter methylation status. This, consequently, can promote megakaryocyte maturation and the release of platelets.\u003c/p\u003e \u003cp\u003eOur study has several advantages. The robustness of the genetic instrumental variable enabled us to conduct a pioneering and thorough Mendelian randomization (MR) investigation, examining the relationship between inflammatory variables and the risk of ITP. To provide more reliable results, we combined outcome data from three biobanks. To validate our MR analysis, we conducted various sensitivity analyses to assess the robustness of the instrumental variables, heterogeneity, and horizontal pleiotropy, which are the three main assumptions of MR.\u003c/p\u003e \u003cp\u003eDespite the MR design being less susceptible to confounding than other observational studies, limitations exist. First, although the genome-wide significance P-value threshold of 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was a common standard, our study used a less stringent 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e cut-off to explore more possibilities. Additionally, some instruments used in the MR analyses had few SNPs, displayed evidence of heterogeneity, and were associated with potential confounders. Secondly, the second and third assumptions could not be sufficiently addressed due to the MR analysis's limitations, which could have led to bias. Third, ethnic bias may be present, so it should be taken cautiously if the conclusions are applied to other races. Fourth, when we explored GT as the exposure to inflammation cytokines, no SNPs were found in cytokine outcomes. This lack of findings further underscores the need for more detailed studies on this field. Concurrently, While MR can shed light on the lifetime relationships between genetic variations and inflammatory variables, alterations in inflammatory cytokines can be altered by unforeseen circumstances in any given clinical situation. More research should be conducted to confirm and apply our results to clinical diagnosis procedures and therapy options.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis MR study suggests that genetic predisposition to elevated circulating M-CSF levels was associated with an increased risk of ITP and GT. Our study contributes to the existing knowledge regarding the role of inflammatory biomarkers in the development of ITP. These findings have clinical implications, suggesting the potential of targeting inflammatory cytokines to prevent ITP. While we have identified numerous strong connections, further validation is necessary to evaluate the potential of these cytokines as targets for medication or lifestyle interventions in ITP prevention.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cp\u003eThis study was conducted following the STROBEMR (Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization) guideline \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We performed a bidirectional Mendelian randomization study using SNPs associated with 41 inflammatory cytokine levels as instrumental variables to assess their causal association with immune thrombocytopenia, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Data sources\u003c/h2\u003e \u003cp\u003eIn this Mendelian Randomization investigation, we used datasets from publicly available GWAS data. For information on 41 unique inflammatory cytokines as exposures dataset, we obtained the IVs from a GWAS meta-analysis of two separate sources: The Cardiovascular Risk in Young Finns Study (YES) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and FINRISK \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e in 8,293 Finnish individuals. The average ages of participants in the YFS study and the FINRISK survey are 37 years and 60 years, respectively \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGene\u0026ndash;outcome associations for ITP were obtained from three databanks: the FinnGen consortium (R10 release) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, UKBiobank TOPMed \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and BioBank Japan \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. All the studies are ongoing cohorts, and the combined outcome summary statistics included 991,844 participants and 1,468 cases of ITP. The other set of summary-level statistics on GT was retrieved from the GWAS catalog datasets by Yang et al. \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, using 11,138/85,294 cases/controls in East Asian (China) ancestry. The datasets used in this study are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Due to the utilization of publicly accessible summary statistics obtained from published studies, further ethical approval from the institutional review board was unnecessary for the current investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.2. MR assumptions and Instrumental variable selection\u003c/h2\u003e \u003cp\u003eThe chosen genetic variants must meet specific criteria to be considered valid instruments for the MR analysis. Firstly, they must strongly associate with the cytokine levels in circulation. Secondly, they must be independent of potential confounding variables that could affect the relationship between the exposure and the outcome. Lastly, these genetic variants should influence the outcome solely through the exposure being studied. To ensure the robustness of our conclusions regarding the mutual risk of inflammation cytokines level with ITP, we carefully selected the most suitable IVs through meticulous quality control measures. Then, we proceeded with the following steps: (1) We found specific genetic variations that showed strong connections with each exposure (cytokines level or ITP) throughout the entire genome. We used a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to determine the significance of these associations. Hence, given the lack of genetic variants reaching genome-wide significance for 13 cytokines, the p-value threshold was subsequently adjusted to \u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e. (2) We set a threshold of 0.01 for the minor allele frequency (MAF) of the specific variant we were interested in. (3) To reduce the risk of biased results, we selected single nucleotide polymorphisms (SNPs) that were highly associated with the exposure and showed no linkage disequilibrium (LD) (R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001) within a clumping distance of 10,000 kb.; (4) we excluded palindromic SNPs to prevent any distortions in chain orientation or allele coding; (5) The degree of association between instrumental variables and exposure factors was evaluated using the F statistic. To address bias caused by weak instrumental variables, we only included single nucleotide polymorphisms (SNPs) with an F statistic greater than 10 \u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Statistical analyses\u003c/h2\u003e \u003cp\u003eIn the study, for the ITP disease outcomes datasets, we conducted a meta-analysis using summary data from the three biobanks, UKB-TOPMed, FinnGen, and biobank Japan, using the METAL software \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. For the main analysis, we utilized three MR methods: The instrumental variable weighting (IVW) method computes the causal impact of the exposure on the result by merging ratio estimates for each single nucleotide polymorphism (SNP). This strategy essentially transforms MR estimations into a regression model that considers the impact of SNPs on both the outcome and the exposure \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The weighted median technique yields unbiased estimates, even in cases where unreliable independent variables contribute up to 50% of the information \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. MR-Egger not only calculates the causal impact using the slope coefficient obtained by Egger regression but also identifies possible bias that may arise from small-study effects \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. When only one SNP was available to create the instrumental variable, the ratio of coefficients approach was used to calculate Mendelian randomization (MR) estimates, with first-order weights utilized to establish standard errors. When multiple SNPs were available to generate the instrumental variable for a specific cytokine, the random-effects inverse-variance weighted (IVW) MR approach combined the MR estimates derived from individual SNPs. To account for multiple hypothesis testing, we calculated adjusted p-values (q-values) for the false discovery rate (FDR) in the primary IVW MR analyses using the sequential p-value technique suggested by Benjamini and Hochberg \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We used Cochran's Q statistics to assess heterogeneity in both the IVW and MR-Egger methodologies \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The presence of horizontal pleiotropy, which means that instrumental variables (IVs) may affect outcomes via routes that are unrelated to causality, was also considered \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. To investigate the direct relationship between the chosen independent variables (IVs) and the outcomes, we used MR pleiotropy residual sum and outlier (MR-PRESSO) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to the analysis above, we determined that the IVW results were the most reliable estimates of the causal effect and were used as the main method in the study. This conclusion was based on the consistent findings across all three methods and the q-value results, which were below 10%. Additionally, there was no evidence of horizontal pleiotropy, as indicated by a P-value for the Egger intercept greater than 0.05, which was considered statistically significant.\u003c/p\u003e \u003cp\u003eFurthermore, we searched PubMed to compare the results of the MR analyses with epidemiological evidence. We specifically looked for observational studies examining the relationship between chronic inflammatory markers and the outcomes of interest, thrombocytopenia. We used general search terms such as \"cytokines,\" \"inflammation,\" and \"thrombocytopenia\" to search.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Reverse-direction MR Analysis\u003c/h2\u003e \u003cp\u003eWe explored the potential causal relationship between ITP and inflammation cytokines through reverse-direction MR analysis. Based on the number of cases among the three databanks, we obtained IVs of ITP from the FinnGen consortium as the exposure. When multiple SNPs were characterized, we utilized three MR methods: IVW, MR-Egger, and weighted median. The sensitivity analysis was conducted in the same way as the forward MR. Given the limited availability of SNP for analysis, we could not conduct assessments for heterogeneity, pleiotropy, and sensitivity.\u003c/p\u003e \u003cp\u003eAll statistical analyses and data visualization were performed in R software (version 4.3.0) with \u0026ldquo;TwoSampleMR\u0026rdquo; and \u0026ldquo;MRPRESSO\u0026rdquo; software packages. The Heatmap plot of this study was created from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chiplot.online/\u003c/span\u003e\u003cspan address=\"https://www.chiplot.online/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants who contributed to the BioBank Japan Project and the UKB-TOPMed project. We want to acknowledge the participants and investigators of the FinnGen study. Sincere thanks also go to IEU GWAS database projects, which have made the GWAS data publicly available, and many members of the IEU have contributed to curating these data. Thank you to all GWAS Catalog users and authors of studies included in the catalog. This study would not have been possible without access to publicly available summary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Key Clinical Specialty Scientific Research Project (No. Z2023032)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKH contributed to the study design. KH, MH, FW, and JM, formal analysis, methodology, and writing the original draft.\u0026nbsp;KH, MH, and LZ drafted and revised the draft manuscript. JT, MH, YW, JZ, JH, XZ, BL, and XQ reviewed and edited the final manuscript. CW, LZ, and JT conceptualized and supervised the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe summary datasets of GWAS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; https://gwas.mrcieu.ac.uk/datasets/\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; https://www.ebi.ac.uk/gwas/ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; https://www.finngen.fi/en/access_results\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; https://pheweb.org/UKB-TOPMed/\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; https://pheweb.jp/ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCooper, N. \u0026amp; Bussel, J. 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Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50, 693\u0026ndash;698 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-018-0099-7\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0099-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Immune thrombocytopenia, gestational thrombocytopenia, cytokines, inflammation, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4893487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4893487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmune thrombocytopenia (ITP) is an autoimmune hemorrhagic disease characterized by increased platelet destruction and impaired thrombopoiesis. Epidemiological and experimental evidence has linked inflammation cytokine levels to ITP etiology but is uncertain. To respond to this query, we conducted a Mendelian randomization (MR) analysis to investigate the causal effects of circulating cytokine levels on ITP development. Using summary statistics from genome-wide association studies (GWAS), we obtained data on 41 serum cytokines from 8,293 Finnish individuals and ITP data from a meta-analysis of the FinnGen consortium, UK Biobank, and BioBank Japan. The association between genetically predicted levels of inflammatory cytokines and ITP was estimated using a bidirectional Mendelian randomization (MR) study. Sensitivity analyses and the False Discovery Rate (FDR) method were also performed to verify the robustness of the results. We discovered that higher genetically predicted M-CSF levels were strongly associated with an increased risk of ITP (OR: 1.09; 95%CI: 1.03\u0026ndash;1.16; p\u0026thinsp;=\u0026thinsp;0.003) and gestational thrombocytopenia (GT) (OR: 1.17; 95%CI, 1.05\u0026ndash;1.32; p\u0026thinsp;=\u0026thinsp;0.006). Additionally, our results showed an adverse association between genetically predicted levels of the circulating HGF (OR: 0.75; 95%CI, 0.63\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.002), MIF (OR: 0.90; 95%CI, 0.84\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.001) and TRAIL (OR: 0.92; 95%CI, 0.87\u0026ndash;0.97; p\u0026thinsp;=\u0026thinsp;0.003) with the GT. The study result links genetic predisposition to elevated M-CSF levels with increased risks of ITP and GT, suggesting that targeting cytokines could aid in ITP prevention, though further validation is needed.\u003c/p\u003e","manuscriptTitle":"The causal relationship between inflammatory cytokines and thrombocytopenia: A bidirectional two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-13 20:41:05","doi":"10.21203/rs.3.rs-4893487/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":"3f548226-a17c-4d48-a5ec-1298880af984","owner":[],"postedDate":"September 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37536598,"name":"Biological sciences/Immunology"},{"id":37536599,"name":"Biological sciences/Immunology/Cytokines"}],"tags":[],"updatedAt":"2025-04-11T05:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-13 20:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4893487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4893487","identity":"rs-4893487","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}