Causal relationship between immune cells and posttraumatic stress disorder: a bulk two-sample Mendelian randomization study

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However, the causal relationship between immune cells and PTSD and the possible related types of immune cells are still unclear. Aim: To determine the causal relationship between immune cells and PTSD. Two-sample Mendelian randomization (MR) analysis was then performed. Materials and Methods: The exposure and outcome data used in this study were obtained from a GWAS (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was performed to assess the causal relationships between 731 types of immune cell features and PTSD. We used 731 types of immune cells for exposure and PTSD as the outcome. Our MR analysis uses a variety of methods to ensure the robustness of the experiment. For each immune cell that may be related to PTSD, we adopted a sufficient number of methods to ensure the accuracy and effectiveness of the results. Results: Our study identified potential causal relationships between various immune cells and PTSD. We identified 10 types of immune cells that are potentially causally linked to PTSD. CD33-related cells are the most prominently related immune cells. Conclusions: Our study determined the causal relationship between a variety of immune cells and PTSD via MR, which can provide help for future clinical practice. Causal relationship immune cells posttraumatic stress disorder bulk two-sample Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlight 1. Our study explored the relationship between 731 immune cells and PTSD through Mendelian randomization. 2.Our research can help us better understand PTSD from a genetic and immunological perspective. 3.We have finally confirmed the relationship between 10 immune cells and PTSD, which is a good development for PTSD research. 1 Introduction Posttraumatic stress disorder (PTSD), previously known as battle fatigue syndrome or shell shock, is a severe mental disturbance (1). We are frustrating that it has a lifetime prevalence that is close to 10% (2). There are many factors that may induce PTSD, terrorist attacks (3), serious diseases such as COVID-19 (4), domestic violence and abuse (5), and relatives who suffer from illness (6). Special occupations and populations may also induce PTSD, which, as we well know, include refugees, police officers, doctors and nurses (healthcare workers), and veterans and firefighters (7-11). There is increasing evidence that PTSD is associated with the immune system. However, in the field of psychiatry, biological markers are rarely, if ever, used in the diagnosis of mental health disorders. Clinicians rely primarily on patient histories and behavioral symptoms to identify specific psychopathologies, which makes diagnosis highly subjective, and healthcare workers also overlook the pathophysiological indices of the disease (12). This is why we hope to determine the relationship between PTSD and immune cells. In recent research, researchers have shown that brain-derived neurotrophic factor (BDNF) levels are significantly increased in PTSD patients, and abnormal BDNF levels have been found in individuals with psychiatric disorders (13). Some researchers have shown that proinflammatory markers are associated with the connection of PTSD female task-positive networks, and they have explored the relationships between proinflammatory cytokine levels, C-reactive protein (CRP), tumor necrosis factor alpha (TNF-alpha), and interleukin-6 (IL-6) levels and resting-state functional connectivity patterns in the default mode network (DMN), central executive network (CEN), and salience network (SN) in female patients with PTSD. (14). Other researchers have attempted to identify potential biomarkers and therapeutic targets related to PTSD due to traumatic brain injury (15). These studies have attempted to explore the mechanism of PTSD from the perspective of genes, metabolites or immunity. These findings have made important contributions to a better understanding of the pathogenesis of PTSD and provide important clues for future treatment and prevention. Although researchers have made many efforts, we still do not know much about PTSD. This may be due to a lack of research samples or limitations in traditional research schemes. We should note that our experimental method of Mendelian randomization (MR) was performed because the observed associations could not be well determined due to the limitations of conventional statistical methods, such as potential confounders of either or both reverse causalities (16). It was used to explore causal relationships between exposures and outcomes (17). Genetic variants closely associated with the level of exposure are used as instrumental variables in Mendelian randomization to estimate these causal relationships. Mendelian randomization can identify potential causal factors for diseases (18) and obtain more information about whether specific factors are causes or outcomes of diseases (19). Mendelian randomization has been widely applied in studies of neurological diseases and has identified many pathogenic factors for different neurological diseases from the perspective of immune cells (20-22). In this study, two-sample Mendelian randomization analysis was conducted to detect potential causal relationships between different types of immune cells and the risk of PTSD. This study aimed to provide new possibilities for future treatment strategies. We hope to explore the mechanism of PTSD as much as possible so that current and future PTSD researchers can understand more details and explanations about PTSD to better help people with PTSD. 2 Materials and methods 2.1 Study design We conducted a two-sample MR analysis to assess the causal relationships between 731 types of immune cell characteristics and PTSD. In MR studies, effective genetic instrumental variables must obey three key assumptions for causal inference: (1) genetic variation is directly associated with the exposure, (2) genetic variation is unrelated to potential confounders between the exposure and the outcome, and (3) genetic variation does not influence the outcome through pathways other than the exposure (23). The research design is shown in Figure 1. 2.2 Data sources 2.2.1 Source of immune cell data The immune cell GWAS data were obtained from a study on the genetic characteristics of immune cells (from accession numbers GCST0001391 to GCST0002121). In this study, researchers conducted analyses of a large number of genetic variations to identify those associated with immune cell characteristics and further understand the impact of these variations on immune system function (20). The brief information is shown in Table 1 . 2.2.2 Source of posttraumatic stress disorder data Posttraumatic stress disorder data were obtained from a GWAS (https://gwas.mrcieu.ac.uk/). The GWAS ID of the dataset is “finn-b-F5_PTSD”. There were both male and female European participants. Brief information is shown in Table 1 . Table 1 : Information fundamental for the inclusion of exposure and outcome data in GWAS. Phenotype Number of SNP Cases Controls Sample size Population Immune cells 14,155,839 17008 37154 54162 European PTSD 16,380,382 1103 198110 NA European PTSD: Posttraumatic stress disorder. NA: Not available 2.3 Mendelian randomization analyses The inverse variance weighted (IVW) model was used as the chief method. The IVW improves the estimation accuracy. We used the inverse variance weighted (fixed effects), inverse variance weighted (random) and MR‒Egger methods as backup methods (24). 2.4 Selection of genetic instrumental variables The genetic variants used in the MR analyses were of genome-wide significance (p< 5×10 -8 ) and were distributed independently by pruning SNPs with a r 2 < 0.001 threshold. We removed incompatible alleles and palindromic SNPs. The remaining harmonized SNPs were the final instrumental variables exposed. 2.5 Sensitivity analyses In the present study, the Cochran Q test was used to evaluate the heterogeneity between individual genetic variation estimates. To check for violations of the MR assumption due to horizontal pleiotropy, MR‒Egger regression was performed. 2.6 Software and packages All the statistical analyses in this study were conducted using the R software package in the R language, version 4.3.3. The primary R package utilized was “TwoSampleMR”, and its version number is 0.5.8. 3 Results 3.1 Mendelian randomization results To study the causal influence of immune cell types on PTSD, a two-sample MR analysis was performed utilizing the IVW technique as a major analytical strategy and removing potential confounders and horizontal pleiotropy. Our research revealed that 10 types of immune cells have potential causal relationships with PTSD. To our surprise, CD45RA+ CD8+ T cells can be considered a protective factor against PTSD. The remaining relevant immune cells can be regarded as risk factors for PTSD. The IVW analysis results for all immune cells are as follows: CD45RA+ CD8+ T-cell %T-cell (p= 0.0146; OR 95%CI= 0.68 (0.49,0.93)), Effector Memory CD4-CD8- T-cell %CD4-CD8- T-cell (p= 0.0375; OR 95%CI= 1.27 (1.01,1.58)), CD39+ CD8+ T-cell %CD8+ T-cell (p= 0.0274; OR 95%CI= 1.23 (1.02,1.49)), CD34 on Hematopoietic Stem Cell (p= 0.0329; OR 95%CI= 1.26 (1.02,1.55)), CD28 on CD45RA- CD4 not regulatory T-cell (p= 0.0336; OR 95%CI= 1.29 (1.02,1.64)), CD33 on CD66b++ myeloid cell (p= 0.0097; OR 95%CI= 1.12 (1.03,1.22)), CD33 on CD33dim HLA DR- (p= 0.0296; OR 95%CI= 1.09 (1.01,1.18)), CD33 on basophil (p= 0.0183; OR 95%CI= 1.09 (1.01,1.17)), CD33 on Immature Myeloid-Derived Suppressor Cells (p= 0.0168; OR 95%CI= 1.10 (1.02,1.18)), SSC-A on plasmacytoid Dendritic Cell (p= 0.0455; OR 95%CI= 1.42 (1.01,2.00)). In the reverse MR results of immune cells and PTSD, PTSD as an exposure failed to operate in the reverse Mendelian randomization due to a lack of enough SNPs, indicating that PTSD has no effect on the included immune cells. The final results revealed potential causal relationships between 10 types of immune cells and PTSD. 3.2 Heterogeneity test results Due to the results of the heterogeneity test, we found that our process had no obvious heterogeneity (the Q_pval results were all greater than 0.05). This shows the robustness of our study. The details are shown in Table 2. Table 2: Heterogeneity test results Exposure Outcome Method Q Q_df Q_pval CD45RA+ CD8+ T-cell %T-cell PTSD MR Egger 0.063 1 0.80 CD45RA+ CD8+ T-cell %T-cell PTSD Inverse variance weighted 1.56 2 0.46 Effector Memory CD4-CD8- T-cell %CD4-CD8- T-cell PTSD MR Egger 3.45 2 0.18 Effector Memory CD4-CD8- T-cell %CD4-CD8- T-cell PTSD Inverse variance weighted 3.45 3 0.33 CD39+ CD8+ T-cell %CD8+ T-cell PTSD MR Egger 3.62 4 0.46 CD39+ CD8+ T-cell %CD8+ T-cell PTSD Inverse variance weighted 5.79 5 0.33 CD34 on Hematopoietic Stem Cell PTSD MR Egger 0.13 2 0.94 CD34 on Hematopoietic Stem Cell PTSD Inverse variance weighted 1.07 3 0.78 CD28 on CD45RA- CD4 not regulatory T-cell PTSD MR Egger 0.55 1 0.46 CD28 on CD45RA- CD4 not regulatory T-cell PTSD Inverse variance weighted 0.88 2 0.64 CD33 on CD66b++ myeloid cell PTSD MR Egger 1.99 2 0.37 CD33 on CD66b++ myeloid cell PTSD Inverse variance weighted 1.99 3 0.57 CD33 on basophil PTSD MR Egger 5.14 5 0.39 CD33 on basophil PTSD Inverse variance weighted 5.15 6 0.52 CD33 on Immature Myeloid-Derived Suppressor Cells PTSD MR Egger 4.94 4 0.29 CD33 on Immature Myeloid-Derived Suppressor Cells PTSD Inverse variance weighted 4.96 5 0.42 SSC-A on plasmacytoid Dendritic Cell PTSD MR Egger 5.07 3 0.17 SSC-A on plasmacytoid Dendritic Cell PTSD Inverse variance weighted 6.20 4 0.18 CD33 on CD33dim HLA DR- PTSD MR Egger 4.28 4 0.37 CD33 on CD33dim HLA DR- PTSD Inverse variance weighted 5.76 5 0.33 3.3 Pleiotropy test results Due to pleiotropy, our process has no obvious pleiotropy. The Egger intercept results are likely close to zero, and the pval results are all above 0.05. This further proves the robustness of our study. The details are shown in Table 3. Table 3: Pleiotropy test results of the study Exposure Outcome Egger_intercept se Pval CD45RA+ CD8+ T-cell %T-cell PTSD -0.067504718 0.05513117 0.4359839 Effector Memory CD4-CD8- T-cell %CD4-CD8- T-cell PTSD 0.003834236 0.18524840 0.9853660 CD39+ CD8+ T-cell %CD8+ T-cell PTSD 0.112342766 0.07628031 0.2148043 CD34 on Hematopoietic Stem Cell PTSD 0.141524832 0.14621016 0.4351829 CD28 on CD45RA- CD4 not regulatory T-cell PTSD 0.046209176 0.07980968 0.6658833 CD33 on CD66b++ myeloid cell PTSD -0.001947005 0.04334060 0.9682504 CD33 on basophil PTSD 0.006912475 0.06868845 0.9237508 CD33 on Immature Myeloid-Derived Suppressor Cells PTSD 0.010873164 0.07839998 0.8963985 SSC-A on plasmacytoid Dendritic Cell PTSD 0.142405923 0.17441351 0.4740273 CD33 on CD33dim HLA DR- PTSD -0.059946333 0.05089924 0.3041850 3.4 Leave-one-out plot detection results Due to the leave-one-out plot results, we are pleased to learn that the findings that ten types of immune cells are potentially causally linked to PTSD are robust. The immune cells identified in this study also exhibited heterogeneity and pleiotropy. The diversity of MR methods has made our results accurate and effective (Figures 3-8). 3.5 Immune cells failed to pass the sensitivity test According to the results of leave-one-out plot detection, we regret that five types of immune cells failed to pass the leave-one-out plot detection test, even though their heterogeneity and pleiotropy level test were sufficient. We chose to show our efforts in their research and hope that the team that replicates our experiment in the future may also receive some insight from it. This result may be caused by the insufficient inclusion of SNPs. The exact reason for this result is unclear (Figures 9-10). 4 Discussion Based on open, openly accessible genetic data, our study evaluated the causal relationship between 731 types of immune cells and PTSD. To our knowledge, this novel study aimed to provide more insight into the pathogenesis of PTSD in the fields of immunology and genetics. After strict screening and analysis, we identified 10 immunological characteristics that had a causal relationship with PTSD. In our study, we found a close association between CD33-related immune cell subtypes and PTSD risk factors. CD33 is a receptor belonging to the sialic acid-binding immunoglobulin-like lectin (Ig) family that is primarily expressed by myeloid cells and microglia and participates in the adhesion of human primitive immune cells and the mediation of cell‒cell interactions (25). Increasing evidence has shown that CD33-related cells are linked to Alzheimer's disease (AD). Studies have shown that the expression of CD33 in the brains of AD patients is associated with a protective allele of a SNP (26, 27), which is related to a reduction in insoluble amyloid-beta 42 (Aβ42) levels. Coincidentally, in autoimmune conditions, such as celiac disease (CeD), researchers identified a novel bioprofile characterized by elevated CD64 and reduced CD33 levels (28). However, there is no information about the links between PTSD and CD33-related immune cells. More regrettably, even though we were the first to discover this mechanism, we were unable to explore the specific mechanism of CD33-related immune cells and PTSD, but at least we provided a direction for subsequent researchers to obtain some insight. In addition to the CD33-related immune cells we mentioned, we were surprised to find that CD45RA+ CD8+ T cells are a protective mechanism against PTSD. CD45, also called the leukocyte common antigen, is a cell surface glycoprotein with tyrosine phosphatase activity that is expressed on nearly all hematopoietic cells in various isoforms related to their stage of development and activation (29). One of the isoforms is CD45RA, which identifies naive T cells (30). However, although many researchers have studied T cells or CD45, there is still not enough information on CD45RA+ CD8+ T cells or CD45RA. We look forward to future conclusions that will enable us to better understand the possible protection mechanisms of PTSD. Effector memory CD4-CD8- T cells. In a study on psoriasis, researchers have shown that electrostimulation (ES) occurs through the downregulation of the psoriasis channel Kv1.3 on T cells and the reduction of cd4/cd8 effector memory (t++em) and CD8 skin resident memory T (t+rm) cells. However, ES failed to further suppress T-cell memory in Kv1.3-deficient cells (31). This also provides us with some insight into the treatment of PTSD. Some researchers have also attempted to assess the acute effects of a single session of whole-body electromyostimulation (WB-EMS) on the physical performance and serum levels of neurotrophic factors in Parkinson's disease (PD) patients (32). Will the same mechanism occur in PTSD patients? We cannot tell that answer for now. One day in the future, after we gain enough information, we will have more treatment or prevention methods available. CD39+ CD8+ T cells. CD39 is an ectonucleotidase expressed by B cells, innate cells, regulatory T cells, and activated CD4 + and CD8 + T cells, which can result in the local production of adenosine, leading to an immunosuppressive environment (33-36). One study demonstrated the coexpression of CD39 with a marker of resident memory CD8 + T cells, indicating that CD39 plays a protective role in cancer survival in these cells (37,38), rather than being a risk factor for PTSD patients in our study. However, the exact mechanism involved in CD39 + CD8 + T cells in PTSD patients is still unknown. There are many studies on the effect of CD34 on haematopoietic stem cells. The ex vivo expansion of high-quality hematopoietic stem cells is considered a paramount issue in cell and gene therapy for hematological diseases (39). In a study of diabetes, researchers found that angiotensin (Ang)-(1-7) stimulates the vasoprotective functions of diabetic (DB) CD34 hematopoietic stem/progenitor cells partly by decreasing reactive oxygen species (ROS), increasing nitric oxide (NO) levels and decreasing TGFβ1 secretion (40). The effect of CD34 on haematopoietic stem cells is also a topic of interest in tumour research, as some haematopoietic cancers are known to be related to CD34. However, few studies on mental disorders or diseases have reported the effect of CD34 on haematopoietic stem cells. Moreover, we cannot explore further mechanisms through our results. Our study is exploratory, and we conducted a bulk two-sample MR analysis based on published large GWAS datasets; thus, this study has high statistical efficiency. Our research aimed to explore more information about PTSD from the perspective of immune cells to help us better understand PTSD and explore clues or evidence, including risk factors or possible protective mechanisms. After strict screening and analysis, our study did not show horizontal pleiotropy or heterogeneity, and our results are robust. However, the study has several limitations. First, we analysed only European data, which limits the applicability of the results to non-European populations. Second, we cannot learn the specific information of the subjects; that is, we cannot classify the patients, including from the perspective of different disease inducements and different occupational groups. Third, our MR is only linear. We hope to learn more about them in the future in different ways. 5 Conclusion Our study determined the causal relationship between 731 types of immune cells and PTSD via MR. These findings provide important insight into the pathogenesis of PTSD. These findings can provide help for future clinical practice. Declarations 6 Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. 7 Author Contributions KZ wrote the manuscript and performed the quality assessment. KZ designed the project and performed the statistical analysis. ZD and YT contributed to the revision of the manuscript and reviewed the results. Conceptualization, KZ; methodology, KZ; writing—review and editing, ZD and YT; visualization, KZ. All authors contributed to the article and approved the submitted version. 8 Funding This research received no external funding. 9 Acknowledgements We thank all the investigators who performed the GWASs. All the researchers providing GWAS data publicly are best regarded. 10 Data availability statement The datasets presented in this study can be found in online repositories (https://gwas.mrcieu.ac.uk/). The names of the repositories/repositories can be found in the article. 1 1 Ethics declarations Ethics approval and consent to participate： This study only used publicly available data. No original data were collected. Consent for publication： Not applicable. References Thakur A, Choudhary D, Kumar B, Chaudhary A. A Review on Posttraumatic Stress Disorder (PTSD): Symptoms, Therapies and Recent Case Studies. Curr Mol Pharmacol. 2022;15(3):502-516. doi: 10.2174/1874467214666210525160944 Williamson JB, Jaffee MS, Jorge RE. Posttraumatic Stress Disorder and Anxiety-Related Conditions. Continuum (Minneap Minn). 2021 Dec 1;27(6):1738-1763. doi: 10.1212/CON.0000000000001054 Pirard P, Motreff Y, Stene LE, Rabet G, Vuillermoz C, Vandentorren S, Baubet T, Messiah A. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4275442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292995959,"identity":"5efe95d0-e84e-47c5-871f-2a72d8d28792","order_by":0,"name":"Kai qi Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3RsQqCUBTG8SOCNFxqFYTrEwQnLghC1KsoQpNDY6MhNNXu4FsEzScCXYxWhwZbnBxsa4iosSZvW9D97z84HwdApfrBDMj3VYsPNj3FJEf6WhGMkjlxKDNPjnCdHKvXkgAKUfIwgzwL8Owvo+JaNjDhw6iLsD2JOdZ+rG+2bgqBcKiLmOQFCer+Co47iwH5u05iV3hgL7KGsJYkQKOY4UGYEBqypAi0BGcczUy4KUpssaM8v7X3McNBfCmbxYR3ko9MJvmad/KtUKlUqr/oCdYuRtu1zfNeAAAAAElFTkSuQmCC","orcid":"","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"qi","lastName":"Zhou","suffix":""},{"id":292995960,"identity":"1ae9501c-a777-4dbc-bc4b-27593b0a902b","order_by":1,"name":"Zhou wei Deng","email":"","orcid":"","institution":"The First Affiliated Hospital of Shao yang University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"wei","lastName":"Deng","suffix":""},{"id":292995961,"identity":"87d620d2-8ee7-47e5-a628-64dcf28666cc","order_by":2,"name":"Yu lun Tan","email":"","orcid":"","institution":"Long Hui People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"lun","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-04-16 10:58:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4275442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4275442/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55075640,"identity":"ac48cc0e-e069-4e77-b1c2-acf1dfab99f3","added_by":"auto","created_at":"2024-04-22 08:10:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123775,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic design showing the MR study process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/927155f21101c2e04f466f57.png"},{"id":55075641,"identity":"7ba92ebd-deb5-4092-ad67-ff8b458c729b","added_by":"auto","created_at":"2024-04-22 08:10:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378226,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot of the MR results\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/fe8665e6d9e95169347ae786.png"},{"id":55075393,"identity":"b252551f-a206-4a83-ab45-2ff7244083a9","added_by":"auto","created_at":"2024-04-22 08:02:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226443,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (CD33-related cells)\u003c/p\u003e\n\u003cp\u003e(Leave-one-out plot result A: CD33 on CD66b++ myeloid cells, B: CD33 on basophils, C: SSC-A on plasmacytoid dendritic cells, D: CD33 on immature myeloid-derived suppressor cells, E: CD33 on CD33dim HLA DR-)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/f658c04a2508d29d7e305a31.png"},{"id":55075639,"identity":"0681cff6-64da-4df6-83dd-11f20d6e9d0f","added_by":"auto","created_at":"2024-04-22 08:10:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51378,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (CD28 on CD45RA-CD4+ nonregulatory T cells)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/752fbb3eb4cb23f7f424c1f1.png"},{"id":55075390,"identity":"115f1414-63e3-4acb-a779-4af4c6d74a3a","added_by":"auto","created_at":"2024-04-22 08:02:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54465,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (CD34 on haematopoietic stem cells)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/b6d2c0c3d8dffcf6ef7fd4e8.png"},{"id":55075392,"identity":"147635f8-ac48-48f6-ad89-062bdac2b1b9","added_by":"auto","created_at":"2024-04-22 08:02:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59665,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (CD39+ CD8+ T cells)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/f6ad35b71ccde93ef5eef145.png"},{"id":55075642,"identity":"46509008-55df-419b-af8a-a10826817657","added_by":"auto","created_at":"2024-04-22 08:10:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":48858,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (CD45RA+ CD8+ T cells)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/03924c323ea25349f72690f7.png"},{"id":55075395,"identity":"c96cfc56-70bf-43fb-8dff-a2cc92927591","added_by":"auto","created_at":"2024-04-22 08:02:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53612,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the results of the study (Effector Memory CD4-CD8- T cells)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/44710fe0ec3d3967448f1031.png"},{"id":55075398,"identity":"cb0d9b75-6d3d-4577-b0bf-1b284ece4685","added_by":"auto","created_at":"2024-04-22 08:02:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":211966,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot for immune cells that failed to pass the sensitivity test\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/e1ec8a516b5e2615c7f40b4a.png"},{"id":55075399,"identity":"dd5f72ba-5e12-4a12-a621-22ff883b122a","added_by":"auto","created_at":"2024-04-22 08:02:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":190097,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for immune cells that failed to pass the sensitivity test\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/b8acf9e5dfda789bceab60d1.png"},{"id":93567857,"identity":"1f649f5b-dd85-49d1-ad4c-86d262da2ea4","added_by":"auto","created_at":"2025-10-15 08:40:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4275442/v1/7005f838-45c9-4083-881f-87383bd92236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between immune cells and posttraumatic stress disorder: a bulk two-sample Mendelian randomization study","fulltext":[{"header":"Highlight","content":"\u003cp\u003e1. Our study explored the relationship between 731 immune cells and PTSD through Mendelian randomization. 2.Our research can help us better understand PTSD from a genetic and immunological perspective. 3.We have finally confirmed the relationship between 10 immune cells and PTSD, which is a good development for PTSD research.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003ePosttraumatic stress disorder (PTSD), previously known as battle fatigue syndrome or shell shock, is a severe mental disturbance (1). We are frustrating that it has a lifetime prevalence that is close to 10% (2). There are many factors that may induce PTSD, terrorist attacks (3), serious diseases such as COVID-19 (4), domestic violence and abuse (5), and relatives who suffer from illness (6). Special occupations and populations may also induce PTSD, which, as we well know, include refugees, police officers, doctors and nurses (healthcare workers), and veterans and firefighters (7-11).\u003c/p\u003e\n\u003cp\u003eThere is increasing evidence that PTSD is associated with the immune system. However,\u0026nbsp;in the field of psychiatry, biological markers are rarely, if ever, used in the diagnosis of mental health disorders. Clinicians rely primarily on patient histories and behavioral symptoms to identify specific psychopathologies, which makes diagnosis highly subjective, and healthcare workers also overlook the pathophysiological indices of the disease (12). This is why we hope to determine the relationship between PTSD and immune cells. In recent research, researchers have shown that brain-derived neurotrophic factor (BDNF) levels are significantly increased in PTSD patients, and abnormal BDNF levels have been found in individuals with psychiatric disorders (13). Some researchers have shown that proinflammatory markers are associated with the connection of PTSD female task-positive networks, and they have explored the relationships between proinflammatory cytokine levels, C-reactive protein (CRP), tumor necrosis factor alpha (TNF-alpha), and interleukin-6 (IL-6) levels and resting-state functional connectivity patterns in the default mode network (DMN), central executive network (CEN), and salience network (SN) in female patients with PTSD. (14). Other researchers have attempted to identify potential biomarkers and therapeutic targets related to PTSD due to traumatic brain injury (15). These studies have attempted to explore the mechanism of PTSD from the perspective of genes, metabolites or immunity. These findings have made important contributions to a better understanding of the pathogenesis of PTSD and provide important clues for future treatment and prevention. Although researchers have made many efforts, we still do not know much about PTSD. This may be due to a lack of research samples or limitations in traditional research schemes.\u003c/p\u003e\n\u003cp\u003eWe should note that our experimental method of Mendelian randomization (MR) was performed because the observed associations could not be well determined due to the limitations of conventional statistical methods, such as potential confounders of either or both reverse causalities (16). It was used to explore causal relationships between exposures and outcomes (17). Genetic variants closely associated with the level of exposure are used as instrumental variables in Mendelian randomization to estimate these causal relationships. Mendelian randomization can identify potential causal factors for diseases (18) and obtain more information about whether specific factors are causes or outcomes of diseases (19). Mendelian randomization has been widely applied in studies of neurological diseases and has identified many pathogenic factors for different neurological diseases from the perspective of immune cells (20-22).\u003c/p\u003e\n\u003cp\u003eIn this study, two-sample Mendelian randomization analysis was conducted to detect potential causal relationships between different types of immune cells and the risk of PTSD. This study aimed to provide new possibilities for future treatment strategies. We hope to explore the mechanism of PTSD as much as possible so that current and future PTSD researchers can understand more details and explanations about PTSD to better help people with PTSD.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Study design\u003c/h2\u003e\n\u003cp\u003eWe conducted a two-sample MR analysis to assess the causal relationships between 731 types of immune cell characteristics and PTSD. In MR studies, effective genetic instrumental variables must obey three key assumptions for causal inference: (1) genetic variation is directly associated with the exposure, (2) genetic variation is unrelated to potential confounders between the exposure and the outcome, and (3) genetic variation does not influence the outcome through pathways other than the exposure (23). The research design is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 \u0026nbsp; \u0026nbsp;Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Source of immune cell data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immune cell GWAS data were obtained from a study on the genetic characteristics of immune cells (from accession numbers GCST0001391 to GCST0002121). In this study, researchers conducted analyses of a large number of genetic variations to identify those associated with immune cell characteristics and further understand the impact of these variations on immune system function (20). The brief information is shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Source of posttraumatic stress disorder data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePosttraumatic stress disorder data were obtained from a GWAS (https://gwas.mrcieu.ac.uk/). The GWAS ID of the dataset is \u0026ldquo;finn-b-F5_PTSD\u0026rdquo;. There were both male and female European participants.\u0026nbsp;Brief information is shown in\u0026nbsp;\u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e:\u0026nbsp;Information fundamental for the inclusion of exposure and outcome data in GWAS.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.539050535987748%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of SNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.539050535987748%\" valign=\"top\"\u003e\n \u003cp\u003eImmune cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e14,155,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e17008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e37154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e54162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.539050535987748%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e16,380,382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e1103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003e198110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.69218989280245%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePTSD:\u0026nbsp;Posttraumatic stress disorder. NA: Not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Mendelian randomization analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inverse variance weighted (IVW) model was used as the chief method. The IVW improves the estimation accuracy.\u0026nbsp;We used the\u0026nbsp;inverse variance weighted (fixed effects), inverse variance weighted (random) and MR‒Egger methods as backup methods (24).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u003c/strong\u003e \u003cstrong\u003eSelection of genetic instrumental variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic variants used in the MR analyses were of genome-wide significance (p\u0026lt; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) and were distributed independently by pruning SNPs with a r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001 threshold. We removed incompatible alleles and palindromic SNPs. The remaining harmonized SNPs were the final instrumental variables exposed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Sensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, the Cochran Q test was used to evaluate the heterogeneity between individual genetic variation estimates. To check for violations of the MR assumption due to horizontal pleiotropy, MR‒Egger regression was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Software and packages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses in this study were conducted using the R software package in the R language, version 4.3.3. The primary R package utilized was \u0026ldquo;TwoSampleMR\u0026rdquo;, and its version number is 0.5.8.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e \u003cstrong\u003eMendelian randomization\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study the causal influence of immune cell types on PTSD, a two-sample MR analysis was performed utilizing the IVW technique as a major analytical strategy and removing potential confounders and horizontal pleiotropy. Our research revealed that 10 types of immune cells have potential causal relationships with PTSD. To our surprise, CD45RA+ CD8+ T cells can be considered a protective factor against PTSD. The remaining relevant immune cells can be regarded as risk factors for PTSD. The IVW analysis results for all immune cells are as follows: CD45RA+ CD8+ T-cell %T-cell (p= 0.0146; OR 95%CI= 0.68 (0.49,0.93)), Effector Memory CD4-CD8- T-cell %CD4-CD8- T-cell (p= 0.0375; OR 95%CI= 1.27 (1.01,1.58)), CD39+ CD8+ T-cell %CD8+ T-cell (p= 0.0274; OR 95%CI= 1.23 (1.02,1.49)), CD34 on Hematopoietic Stem Cell (p= 0.0329; OR 95%CI= 1.26 (1.02,1.55)), CD28 on CD45RA- CD4 not regulatory T-cell (p= 0.0336; OR 95%CI= 1.29 (1.02,1.64)), CD33 on CD66b++ myeloid cell (p= 0.0097; OR 95%CI= 1.12 (1.03,1.22)), CD33 on CD33dim HLA DR- (p= 0.0296; OR 95%CI= 1.09 (1.01,1.18)), CD33 on basophil (p= 0.0183; OR 95%CI= 1.09 (1.01,1.17)), CD33 on Immature Myeloid-Derived Suppressor Cells (p= 0.0168; OR 95%CI= 1.10 (1.02,1.18)), SSC-A on plasmacytoid Dendritic Cell (p= 0.0455; OR 95%CI= 1.42 (1.01,2.00)). In the reverse MR results of immune cells and PTSD, PTSD as an exposure failed to operate in the reverse Mendelian randomization due to a lack of enough SNPs, indicating that PTSD has no effect on the included immune cells. The final results revealed potential causal relationships between 10 types of immune cells and PTSD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u003c/strong\u003e \u003cstrong\u003eHeterogeneity test\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eresults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the results of the heterogeneity test, we found that our process had no obvious heterogeneity (the Q_pval results were all greater than 0.05). This shows the robustness of our study. The details are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eHeterogeneity test results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"764\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003eQ_pval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD45RA+ CD8+ T-cell %T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD45RA+ CD8+ T-cell %T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eEffector Memory CD4-CD8- T-cell %CD4-CD8- T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eEffector Memory CD4-CD8- T-cell %CD4-CD8- T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD39+ CD8+ T-cell %CD8+ T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD39+ CD8+ T-cell %CD8+ T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD34 on Hematopoietic Stem Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD34 on Hematopoietic Stem Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD28 on CD45RA- CD4 not regulatory T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD28 on CD45RA- CD4 not regulatory T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD66b++ myeloid cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD66b++ myeloid cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on basophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on basophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on Immature Myeloid-Derived Suppressor Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on Immature Myeloid-Derived Suppressor Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eSSC-A on plasmacytoid Dendritic Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eSSC-A on plasmacytoid Dendritic Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD33dim HLA DR-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.3717277486911%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD33dim HLA DR-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.638743455497382%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.073298429319372%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602094240837696%\" valign=\"top\"\u003e\n \u003cp\u003e5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.628272251308901%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.68586387434555%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e \u003cstrong\u003ePleiotropy test\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eresults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to pleiotropy, our process has no obvious pleiotropy. The Egger intercept results are likely close to zero, and the pval results are all above 0.05. This further proves the robustness of our study. The details are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003ePleiotropy test results of the study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"716\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003eEgger_intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003ePval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD45RA+ CD8+ T-cell %T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e-0.067504718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.05513117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.4359839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eEffector Memory CD4-CD8- T-cell %CD4-CD8- T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.003834236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.18524840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.9853660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD39+ CD8+ T-cell %CD8+ T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.112342766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.07628031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.2148043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD34 on Hematopoietic Stem Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.141524832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.14621016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.4351829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD28 on CD45RA- CD4 not regulatory T-cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.046209176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.07980968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.6658833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD66b++ myeloid cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e-0.001947005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.04334060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.9682504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on basophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.006912475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.06868845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.9237508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on Immature Myeloid-Derived Suppressor Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.010873164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.07839998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.8963985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eSSC-A on plasmacytoid Dendritic Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e0.142405923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.17441351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.4740273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.3463687150838%\" valign=\"top\"\u003e\n \u003cp\u003eCD33 on CD33dim HLA DR-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.59217877094972%\" valign=\"top\"\u003e\n \u003cp\u003ePTSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.502793296089385%\" valign=\"top\"\u003e\n \u003cp\u003e-0.059946333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.128491620111731%\" valign=\"top\"\u003e\n \u003cp\u003e0.05089924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.430167597765363%\" valign=\"top\"\u003e\n \u003cp\u003e0.3041850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Leave-one-out plot detection results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the leave-one-out plot results, we are pleased to learn that the findings that ten types of immune cells are potentially causally linked to PTSD are robust. The immune cells identified in this study also exhibited heterogeneity and pleiotropy. The diversity of MR methods has made our results accurate and effective (Figures 3-8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Immune cells failed to pass the sensitivity test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the results of leave-one-out plot detection, we regret that five types of immune cells failed to pass the leave-one-out plot detection test, even though their heterogeneity and pleiotropy level test were sufficient. We chose to show our efforts in their research and hope that the team that replicates our experiment in the future may also receive some insight from it. This result may be caused by the insufficient inclusion of SNPs. The exact reason for this result is unclear (Figures 9-10).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBased on open, openly accessible genetic data, our study evaluated the causal relationship between 731 types of immune cells and PTSD. To our knowledge, this novel study aimed to provide more insight into the pathogenesis of PTSD in the fields of immunology and genetics.\u0026nbsp;After strict screening and analysis, we identified 10 immunological characteristics that had a causal relationship with PTSD.\u003c/p\u003e\n\u003cp\u003eIn our study, we found a close association between CD33-related immune cell subtypes and PTSD risk factors. CD33 is a receptor belonging to the sialic acid-binding immunoglobulin-like lectin (Ig) family that is primarily expressed by myeloid cells and microglia and participates in the adhesion of human primitive immune cells and the mediation of cell‒cell interactions (25). Increasing evidence has shown that CD33-related cells are linked to Alzheimer\u0026apos;s disease (AD). Studies have shown that the expression of CD33 in the brains of AD patients is associated with a protective allele of a SNP (26, 27), which is related to a reduction in insoluble amyloid-beta 42 (A\u0026beta;42) levels. Coincidentally, in autoimmune conditions, such as celiac disease (CeD), researchers identified a novel bioprofile characterized by elevated CD64 and reduced CD33 levels (28). However, there is no information about the links between PTSD and CD33-related immune cells. More regrettably, even though we were the first to discover this mechanism, we were unable to explore the specific mechanism of CD33-related immune cells and PTSD, but at least we provided a direction for subsequent researchers to obtain some insight.\u003c/p\u003e\n\u003cp\u003eIn addition to the CD33-related immune cells we mentioned, we were surprised to find that CD45RA+ CD8+ T cells are a protective mechanism against PTSD. CD45, also called the leukocyte common antigen, is a cell surface glycoprotein with tyrosine phosphatase activity that is expressed on nearly all hematopoietic cells in various isoforms related to their stage of development and activation (29). One of the isoforms is CD45RA, which identifies naive T cells (30). However, although many researchers have studied T cells or CD45, there is still not enough information on CD45RA+ CD8+ T cells or CD45RA. We look forward to future conclusions that will enable us to better understand the possible protection mechanisms of PTSD.\u003c/p\u003e\n\u003cp\u003eEffector memory CD4-CD8- T cells. In a study on psoriasis, researchers have shown that electrostimulation (ES) occurs through the downregulation of the psoriasis channel Kv1.3 on T cells and the reduction of cd4/cd8 effector memory (t++em) and CD8 skin resident memory T (t+rm) cells. However, ES failed to further suppress T-cell memory in Kv1.3-deficient cells (31). This also provides us with some insight into the treatment of PTSD. Some researchers have also attempted to assess the acute effects of a single session of whole-body electromyostimulation (WB-EMS) on the physical performance and serum levels of neurotrophic factors in Parkinson\u0026apos;s disease (PD) patients (32). Will the same mechanism occur in PTSD patients? We cannot tell that answer for now.\u0026nbsp;One day in the future, after we gain enough information, we will have more treatment or prevention methods available.\u003c/p\u003e\n\u003cp\u003eCD39+ CD8+ T cells. CD39 is an ectonucleotidase expressed by B cells, innate cells, regulatory T cells, and activated CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, which can result in the local production of adenosine, leading to an immunosuppressive environment (33-36). One study demonstrated the coexpression of CD39 with a marker of resident memory CD8\u0026thinsp;+\u0026thinsp;T cells, indicating that CD39 plays a protective role in cancer survival in these cells (37,38),\u0026nbsp;rather than being a risk factor for PTSD patients in our study. However, the exact mechanism involved in CD39\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+ T cells in PTSD patients is still unknown.\u003c/p\u003e\n\u003cp\u003eThere are many studies on the effect of CD34 on haematopoietic stem cells. The ex vivo expansion of high-quality hematopoietic stem cells is considered a paramount issue in cell and gene therapy for hematological diseases (39). In a study of diabetes, researchers found that angiotensin (Ang)-(1-7) stimulates the vasoprotective functions of diabetic (DB) CD34 hematopoietic stem/progenitor cells partly by decreasing reactive oxygen species (ROS), increasing nitric oxide (NO) levels and decreasing TGF\u0026beta;1 secretion (40). The effect of CD34 on haematopoietic stem cells is also a topic of interest in tumour research, as some haematopoietic cancers are known to be related to CD34. However, few studies on mental disorders or diseases have reported the effect of CD34 on haematopoietic stem cells. Moreover, we cannot explore further mechanisms through our results.\u003c/p\u003e\n\u003cp\u003eOur study is exploratory, and we conducted a bulk two-sample MR analysis based on published large GWAS datasets; thus, this study has high statistical efficiency. Our research aimed to explore more information about PTSD from the perspective of immune cells to help us better understand PTSD and explore clues or evidence, including risk factors or possible protective mechanisms. After strict screening and analysis, our study did not show horizontal pleiotropy or heterogeneity, and our results are robust. However, the study has several limitations. First, we analysed only European data, which limits the applicability of the results to non-European populations. Second, we cannot learn the specific information of the subjects; that is, we cannot classify the patients, including from the perspective of different disease inducements and different occupational groups. Third, our MR is only linear. We hope to learn more about them in the future in different ways.\u003c/p\u003e"},{"header":"5\tConclusion","content":"\u003cp\u003eOur study determined the causal relationship between 731 types of immune cells and PTSD via MR. These findings provide important insight into the pathogenesis of PTSD. These findings can provide help for future clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Conflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e7\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Author Contributions\u003c/p\u003e\n\u003cp\u003eKZ wrote the manuscript and performed the quality assessment. KZ designed the project and performed the statistical analysis. ZD and YT contributed to the revision of the manuscript and reviewed the results. Conceptualization, KZ; methodology, KZ; writing\u0026mdash;review and editing, ZD and YT; visualization, KZ. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e8\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e9\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank all the investigators\u0026nbsp;who performed the GWASs. All\u0026nbsp;the researchers providing GWAS data publicly are best regarded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10 \u0026nbsp; \u0026nbsp; Data\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories (https://gwas.mrcieu.ac.uk/). The names of the\u0026nbsp;repositories/repositories can be found in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e1 \u0026nbsp; \u0026nbsp; Ethics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate：\u003c/p\u003e\n\u003cp\u003eThis study only used publicly available data. No original data were collected.\u003c/p\u003e\n\u003cp\u003eConsent for publication：\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThakur A, Choudhary D, Kumar B, Chaudhary A. A Review on Posttraumatic Stress Disorder (PTSD): Symptoms, Therapies and Recent Case Studies. Curr Mol Pharmacol. 2022;15(3):502-516. doi: 10.2174/1874467214666210525160944\u003c/li\u003e\n\u003cli\u003eWilliamson JB, Jaffee MS, Jorge RE. Posttraumatic Stress Disorder and Anxiety-Related Conditions. Continuum (Minneap Minn). 2021 Dec 1;27(6):1738-1763. doi: 10.1212/CON.0000000000001054\u003c/li\u003e\n\u003cli\u003ePirard P, Motreff Y, Stene LE, Rabet G, Vuillermoz C, Vandentorren S, Baubet T, Messiah A. Initiation of multiple-session psychological care in civilians exposed to the November 2015 Paris terrorist attacks. 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Epub 2024 Mar 6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Causal relationship, immune cells, posttraumatic stress disorder, bulk, two-sample Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4275442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4275442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e There is increasing evidence that the immune system is associated with posttraumatic stress disorder (PTSD). However, the causal relationship between immune cells and PTSD and the possible related types of immune cells are still unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eTo determine the causal relationship between immune cells and PTSD. Two-sample Mendelian randomization (MR) analysis was then performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e The exposure and outcome data used in this study were obtained from a GWAS (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was performed to assess the causal relationships between 731 types of immune cell features and PTSD. We used 731 types of immune cells for exposure and PTSD as the outcome. Our MR analysis uses a variety of methods to ensure the robustness of the experiment. For each immune cell that may be related to PTSD, we adopted a sufficient number of methods to ensure the accuracy and effectiveness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our study identified potential causal relationships between various immune cells and PTSD. We identified 10 types of immune cells that are potentially causally linked to PTSD. CD33-related cells are the most prominently related immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study determined the causal relationship between a variety of immune cells and PTSD via MR, which can provide help for future clinical practice.\u003c/p\u003e","manuscriptTitle":"Causal relationship between immune cells and posttraumatic stress disorder: a bulk two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 08:02:15","doi":"10.21203/rs.3.rs-4275442/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":"a82fa45e-45de-4807-ac36-83bbc51675df","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T08:39:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 08:02:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4275442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4275442","identity":"rs-4275442","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}