The role relationship played by immune cells and circulating inflammatory proteins in NAFLD

The role relationship played by immune cells and circulating inflammatory proteins in NAFLD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role relationship played by immune cells and circulating inflammatory proteins in NAFLD Zhenfei Chi, Yanyun Zhang, Xuesong Bai, Baoying Zhao, Zhe Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4427607/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 Objective The mechanisms by which immune cells and inflammatory factors influence Non-Alcoholic Fatty Liver Disease (NAFLD) remain unclear. This study employs Mendelian randomization (MR) to investigate the relationship between immune cells, inflammatory factors, and NAFLD, as well as the proportion of their mutual mediation effects on NAFLD. Methods This study utilizes MR analysis, examining the causal relationship between 731 immune cell phenotypes, 91 circulating inflammatory proteins, and NAFLD. The data are sourced from publicly available data in the GWAS Catalog. The research process consists of two steps, analyzing them through the assessment of their mediating effects. To obtain reliable results, MR analysis necessitates the fulfillment of three fundamental assumptions. In the selection of instrumental variables, SNPs are screened, requiring significant associations with the exposure factors and no association with the outcomes. Statistical analyses employ methods such as IVW, WM, and MR-Egger to evaluate the causal relationship between exposure and outcomes. Sensitivity analyses are conducted, examining heterogeneity and horizontal pleiotropy. Results Ultimately, among the 731 immune cell phenotypes, 21 phenotypes are found to have a causal relationship with NAFLD, with 6 circulating inflammatory protein phenotypes playing intermediary roles. Among the 91 circulating inflammatory protein phenotypes, 7 inflammatory factor phenotypes are found to have a causal relationship with NAFLD, with 5 immune cell phenotypes playing intermediary roles. Conclusion Immune cells and circulating inflammatory proteins play a crucial role in NAFLD, and our study may provide new insights for the diagnosis and treatment of NAFLD in the future. immune cells circulating inflammatory proteins NAFLD Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduce The incidence of Non-Alcoholic Fatty Liver Disease (NAFLD) in both children and adults is gradually increasing, reaching 29–35% ( 1 , 2 ). The continuous escalation of these epidemics is projected to result in a global NAFLD prevalence of 55% by the year 2040 ( 3 ). As the main cause of liver disease at present, NAFLD encompasses a range of liver damage, progressing from simple fatty degeneration to further development of steatohepatitis, with or without accompanying fibrosis ( 4 ). If not intervened in a timely manner, liver fibrosis may progress to cirrhosis and lead to serious complications such as hepatocellular carcinoma. Therefore, we should attach great importance to this issue. However, due to the unclear pathogenesis of NAFLD, the known mechanisms of NAFLD development are closely related to factors such as insulin resistance, lipid metabolism disorder, inflammatory response, and lipid peroxidation ( 5 ). Among these factors, the inflammatory response plays a crucial role in the pathogenesis of NAFLD, which deserves special attention. In addition, research has found that innate immune activation also plays a role in the pathogenesis of insulin resistance, and the role of immune cells in NAFLD is gradually being discovered ( 5 , 6 ). In order to better understand these mechanisms, further in-depth research is needed, along with the search for more effective treatment strategies. For this liver disease, NAFLD, we need to take proactive preventive measures and treatment plans to mitigate its impact on human health. Immune response and inflammatory response are important defense mechanisms of the body against external pathogens and injuries ( 5 , 6 ). The immune response leads to the release of a series of inflammatory mediators, such as cytokines and chemokines, by immune cells to recruit and activate other immune cells, strengthening the immune response. Inflammatory cells in the inflammatory response release a series of cytokines, such as tumor necrosis factor and interleukins, to recruit and activate other immune cells, enhancing the immune response. Both are crucial to life activities, but their dysregulation can also lead to the occurrence of diseases. mendelian randomization (MR) analysis is a highly statistically efficient method based on genome-wide association studies (GWAS) that can effectively eliminate confounding factors and reveal causal relationships. Currently, there have been studies on immune cells and circulating inflammatory proteins, demonstrating their causal relationships in related diseases ( 7 , 8 ). However, the roles they play in NAFLD are still unclear. This study aims to explore the role relationship played by immune cells and circulating inflammatory proteins in NAFLD using the MR method, providing a new perspective on the pathogenesis and potential therapeutic targets of NAFLD. Methods This study employed MR analysis to investigate the causal relationships between immune cells, circulating inflammatory proteins, and NAFLD. A two-step approach was utilized to assess their potential mediating effects. Please refer to Fig. 1 a for the specific research process. Data source All GWAS data were obtained from GWAS Catalog, which is the largest GWAS meta-analysis website ( https://www.ebi.ac.uk/gwas/ ). A total of 731 different immune cell phenotypes (archive numbers ranging from GCST0001391 to GCST0002121) were included in this study ( 9 ), with the original GWAS data based on information from 3,757 Europeans. Genotyping of approximately 240,000 single nucleotide polymorphisms (SNPs) was performed using a reference panel based on the Sardinian sequence. Association testing was then conducted after adjusting for covariates such as gender, age, and year. Additionally, 91 different circulating inflammatory protein phenotypes (archive numbers ranging from GCST90274758 to GCST90274848) were included in this study ( 10 ), with the original GWAS data for these inflammatory traits derived from 11 cohorts comprising 14,824 Europeans. The NAFLD GWAS summary data (archive number GCST90091033) included 8,434 NAFLD cases and 770,180 controls of European descent from four cohorts: the electronic medical records and genomics (eMERGE), UK Biobank, FinnGen, and Estonian Biobank ( 11 ). It is worth noting that there was no overlap in the populations of the aforementioned data cohorts. For further detailed information on the summary GWAS data, please refer to the original paper. Instrumental variable selection To obtain robust results, MR analysis must satisfy three major assumptions (Fig. 1 b). Due to the limited sample size of immune cells and circulating inflammatory proteins, we adopted relatively lenient criteria for instrumental variable selection. SNPs were required to be significantly associated with the exposure (p < 1e-05) ( 7 , 12 ). Using data from the European samples in the 1000 Genomes Project as the reference panel, we calculated the linkage disequilibrium (LD) between SNPs and filtered out SNPs with an R2 5e-08). Simultaneously, we have eliminated SNPs that do not align with the outcome in the GWAS. In order to avoid weak instrumental variable bias, F-statistics and R2 were calculated based on previous studies ( 15 – 17 ). Statistical analysis In each two-sample mendelian randomization analysis, various causal inference methods, such as inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, were employed to assess the causal associations between exposure and outcome. IVW method served as the primary statistical model ( 17 ). Both fixed-effect and random-effects IVW methods are available, but we employed the more stringent fixed-effect IVW method as the primary statistical model. We placed high demands on the robustness of our results, and we could not tolerate significant heterogeneity (P < 0.05) in the results. Furthermore, different MR analysis methods were required to yield consistent effects; otherwise, the results would be deemed invalid and discarded. All results were presented as odds ratios (ORs) with their corresponding 95% confidence intervals (CIs). We also calculated the mediating effects of immune cells and circulating inflammatory proteins separately. Specific analyses can be found in Fig. 1 c, where a two-step approach was implemented for mediation effect calculation ( 18 ). The indirect effect was represented by c’=c-a*b, and the percentage of mediation was calculated as a*b/c ( 19 ). Sensitivity analysis Heterogeneity and horizontal pleiotropy are not allowed in MR analysis ( 20 ). Heterogeneity was quantified using Cochran's Q statistic, with a P value less than 0.05 considered significant. To assess the potential pleiotropic effects of instrumental variables, MR-Egger regression was employed. The level of directional pleiotropy in causal estimation can be represented by the intercept term in MR-Egger regression. In MR-PRESSO analysis, the aim was to reduce heterogeneity in the causal effect estimation by eliminating SNPs that excessively contributed to heterogeneity. To achieve this goal, 3,000 iterations were set in the MR-PRESSO analysis. Additionally, we performed leave-one-out analysis, sequentially excluding each SNP and conducting MR analysis on the remaining SNPs to detect potential outliers. All analyses needed to be negative; otherwise, the MR results would be deemed meaningless and discarded. Ethics The data used in this study were obtained from published GWAS datasets, thus ethical approval was not required. The included data sources were approved by local ethics committees and complied with local regulations, with all participants providing informed consent. The statistical analyses were conducted using the R software (version 4.2.2, available at https://R-Forge.R-project.org/projects/ ). The TwoSampleMR package was utilized for the analysis ( 21 ). Results Immune cells and NAFLD MR analysis was primarily performed using the IVW method, which required statistical significance. The results of other MR methods needed to yield consistent effects. Additionally, the sensitivity analysis results were robust, with all results being negative (all P > 0.05). Moreover, in the reverse MR analysis with NAFLD as the exposure and immune cells as the outcome, no reverse causal relationship was observed. There were 21 remaining positive results, as shown in Fig. 2 and Supplementary Material 1 (ST1). Due to space limitations, the main text of the article only presents detailed results of the mediation effects in subsequent analyses, totaling 11, as depicted in Fig. 3 . Circulating inflammatory proteins and NAFLD MR analysis was primarily performed using the IVW method, which required statistical significance. The results of other MR methods needed to yield consistent effects. Moreover, the sensitivity analysis results were robust, with all results being negative (all P > 0.05). Furthermore, in the reverse MR analysis with NAFLD as the exposure and circulating inflammatory proteins as the outcome, no reverse causal relationship was observed. There were 7 remaining positive results, as shown in Fig. 4 and ST2. Immune cells and circulating inflammatory proteins Finally, there were a total of 9 positive results indicating the effects of immune cells on NAFLD through circulating inflammatory proteins. The results of immune cells on circulating inflammatory proteins can be found in ST3 and Fig. 5 , with the mediation effects presented in Table 1 . Mediation effect refers to the influence of a mediating variable on the relationship between two variables. When the value of the mediation effect is negative, it indicates an inhibitory effect of the mediating variable on the relationship between the two variables, meaning that an increase in the mediating variable weakens the relationship between the two variables. When the value of the mediation effect is positive, it indicates a promoting effect of the mediating variable on the relationship between the two variables, meaning that an increase in the mediating variable strengthens the relationship between the two variables. There were a total of 5 positive results indicating the effects of circulating inflammatory proteins on NAFLD through immune cells. The results of circulating inflammatory proteins on immune cells can be found in ST4 and Fig. 6 , with the mediation effects presented in Table 1 . Table 1 Results of mediation effects. exposure mediator outcome effect Immune cells Inflammatory cytokines CD62L- Dendritic Cell Absolute Count Neurturin levels NAFLD -9.60% Terminally Differentiated CD8 + T cell %CD8 + T cell Neurturin levels NAFLD -20.18% Terminally Differentiated CD4-CD8- T cell %CD4-CD8- T cell Neurturin levels NAFLD -12.88% CD39 + CD8 + T cell Absolute Count Fibroblast growth factor 21 levels NAFLD -12.25% HLA DR on CD33- HLA DR+ Fibroblast growth factor 21 levels NAFLD 8.34% CD19 on IgD + CD24- B cell Interleukin-6 levels NAFLD 11.93% CD19 on IgD + CD24- B cell Leukemia inhibitory factor levels NAFLD 13.40% CD4 on Terminally Differentiated CD4 + T cell Signaling lymphocytic activation molecule levels NAFLD -3.99% CD45RA- CD4 + T cell Absolute Count Interleukin-10 receptor subunit beta levels NAFLD -6.78% Inflammatory cytokines Immune cells Interleukin-10 receptor subunit beta levels HLA DR on CD33- HLA DR+ NAFLD -6.51% Interleukin-6 levels Terminally Differentiated CD8 + T cell %CD8 + T cell NAFLD 8.75% Interleukin-6 levels Terminally Differentiated CD8 + T cell %T cell NAFLD 13.50% Signaling lymphocytic activation molecule levels Naive-mature B cell Absolute Count NAFLD 13.76% Macrophage colony-stimulating factor 1 levels HLA DR on B cell NAFLD -7.86% Discussion We have respectively explored the causal relationship between 731 types of immune cell phenotypes and 91 types of circulating inflammatory proteins with NAFLD, and evaluated their potential mediating effects. To the best of our knowledge, this is the first mendelian randomization study to investigate the causal and mediating relationship between immune cells and circulating inflammatory proteins and NAFLD. In this study, we finally discovered that among the 731 immune cell phenotypes, 21 immune cell phenotypes are causally related to NAFLD, and 6 circulating inflammatory protein phenotypes play a mediating role; among the 91 circulating inflammatory protein phenotypes, 7 circulating inflammatory protein phenotypes are causally related to NAFLD, and 5 immune cell phenotypes play a mediating role. Immune regulation is of paramount importance to the organism's vital activities ( 6 ), including the innate (general, nonspecific) immune system and the adaptive (specialized) immune system ( 22 , 23 ), involving multiple factors such as immune cells, signal transduction molecules, cytokines, etc ( 24 ). The liver is considered an important immune organ ( 5 , 25 ), and therefore, in recent years, there have been increasing studies exploring the role of immune cells in the progression of NAFLD ( 26 – 28 ), such as in the diagnosis of NAFLD, pathology is the gold standard, so currently, most studies focus on metabolic biomarkers, with few studies on immune cells. However, a recent study compared the phenotypic changes between NASH-resistant and recombination activating 1 (Rag)-deficient mice in the NASH model, and they found a key role of immune cells in the pathogenesis of NASH ( 27 ). In addition, inflammation is an important aspect of metabolic disorders ( 5 ). All adipose tissues contribute significantly to the systemic and hepatic inflammation observed in NAFLD. Studies have found that successful weight loss after bariatric surgery reduces IL-1 cytokines, especially in subcutaneous adipose tissue ( 29 ). It has also been found that a decrease in interleukin-1 inhibits the transformation of fatty degeneration to steatohepatitis and liver fibrosis ( 30 ). Currently, the specific role of dendritic cells in NAFLD is not clear ( 31 ). In this study, we found that the CD62L- Dendritic Cell Absolute Count phenotype plays a role in increasing the risk of NAFLD (OR = 1.05 (1.00-1.09)). Studies have found that the significant increase in conventional dendritic cells (cDC) in the liver is an important pathological marker. They examined blood and liver samples from individuals in the NAFLD spectrum using single-cell transcriptomics and found that the level and activity of type 1 conventional dendritic cells (cDC1) increase in the disease environment. Knocking down cDC1 in NASH mouse experiments reduces liver damage ( 32 ), which is consistent with our research results, but the specific mechanisms need further exploration. In this study, we found that the percentage of terminally differentiated CD4-CD8- T cells in total T cells increases the risk of NAFLD, and the Neurturin levels phenotype has a mediating inhibitory effect of 12.88%. CD4-CD8- double-negative (DN) T cells is a rare subset of T cells, and previous studies have found that DN T cells proliferate significantly in related autoimmune diseases and regulate the production of inflammatory cytokines and immunoglobulins in inflammatory tissues ( 32 – 36 ). Neurturin (NRTN) is one of the ligands of the neurotrophic factor (GDNF) family, belonging to the TGF-β superfamily ( 37 ). It was initially thought to be associated with neuronal growth, differentiation, and maintenance ( 38 ), but recent studies have found that it also plays a role in the digestive system and is associated with various tumorigenic processes ( 39 – 41 ). Furthermore, the expression level of NRTN in the liver is very high according to the NCBI database ( 42 ), and this study found that NRTN levels are consistently associated with the occurrence of NAFLD, and its related mechanisms need further in-depth research. In this study, we found that CD4 on Terminally Differentiated CD4 + T cell has a OR = 0.93, 95CIs=(0.88–0.97), serving as a protective factor against NAFLD. Currently, there are studies suggesting that an increase in the level of fatty acids in the local liver microenvironment reduces the number of CD4 T lymphocytes, and the loss of CD4 T lymphocytes impairs immune regulation, leading to the progression of NAFLD to cirrhosis and liver cancer ( 26 ), which is consistent with our results. Additionally, Signaling lymphocytic activation molecule levels also have an inhibitory effect, with a mediating effect of 3.99%, indicating the presence of negative feedback regulation between the two. Meanwhile, the inflammatory cytokines also play a crucial role in NAFLD ( 43 ). This study revealed that an elevation in IL-6 levels increases the risk of NAFLD (OR = 1.17 (1.02–1.35)). The mechanism behind this may involve the suppressors of the cytokine signaling-3 (SOCS-3) - sterol-regulatory-element-binding protein-1c (SREBP-1c) - fatty acyl-CoA (FA-CoA) pathway, which suppresses insulin signaling, regulates the acute phase response and chronic inflammation ( 44 – 46 ). Furthermore, research suggests that macrophages promote liver cell injury and fibrosis through the secretion of IL-6 ( 47 ). Additionally, insulin resistance is a significant etiological factor in NAFLD. In a mice experiment, it was found that obesity-induced inflammation and metabolic disorders act through the influence of IL-6 on the generation of immune cells ( 48 ). However, currently, there is no known relationship between Interleukin-10 receptor subunit beta levels and NAFLD, warranting further in-depth analysis and investigation. Leukemia inhibitory factor (LIF) is a member of the interleukin-6 (IL-6) cytokine family ( 49 ). This study discovered that Leukemia inhibitory factor levels have an inhibitory effect on the CD19 on IgD + CD24- B cell phenotype, which aligns with previous findings indicating LIF's ability to suppress immune rejection responses ( 48 ). Fibroblast growth factor 21 (FGF21) is a primarily liver-secreted cytokine that improves glucose and lipid metabolism, reduces inflammation and oxidative stress, increases insulin sensitivity, and acts on multiple organs through autocrine, paracrine, and endocrine mechanisms. Some studies propose FGF21 as a potential biomarker for NAFLD ( 50 ). Elevated serum FGF21 levels have been found to predict the development of hepatic steatosis ( 51 ), and serum FGF21 levels correlate in a dose-dependent manner with hepatic fat content in NAFLD patients ( 52 ). It is believed that a decrease in serum FGF21 levels resulting from gastric bypass surgery can lead to an improvement in NAFLD ( 53 ). In this study, it was discovered that FGF1 increases the risk of NAFLD, and the potential reason behind this could be that, under normal circumstances, FGF21 promotes fatty acid oxidation and inhibits fatty acid synthesis. However, when FGF21 levels are excessively high, it may disrupt energy metabolism, leading to fat accumulation and the occurrence of NAFLD. Elevated FGF21 levels may reflect an abnormal response in the body's energy metabolism, including insulin resistance, obesity, diabetes, and other metabolic abnormalities, all of which are associated with an increased risk of NAFLD. In the external mendelian randomization analysis conducted in this study, the genetic variations represented by these instrumental variables were found to be associated with an increase in FGF21 levels and an increased risk of developing NAFLD. However, it is important to note that further experimental validation is required for the aforementioned analyses. There is currently limited research on other inflammatory cytokines in this study, more research is needed to delve into this topic. Conclusion In conclusion, immune cells and circulating inflammatory proteins play a crucial role in NAFLD, and our study may provide new insights for the diagnosis and treatment of NAFLD in the future. Declarations Declaration of interest The authors declare no competing interests. Funding This research was conducted without any funding support. Author Contribution We, as the authors of this paper, hereby declare our individual contributions:Z.C: Responsible for the overall research design and direction, as well as the conceptualization and design of the experiments. During the experiment, responsible for data collection and analysis, and contributed to the writing of the research methods and results sections. Y.Z: Conducted extensive literature review and summarized relevant research during the initial stages of the study. Also involved in data analysis and contributed to the writing of the introduction and background sections of the paper. X.B: Responsible for data processing, statistical analysis, and some visualization of the results during the experiment. Also contributed to the writing and revision of the paper, and optimized the figures and tables. B.Z: Responsible for the overall organization and editing of the paper throughout the entire research process. During the writing process, made revisions and optimizations to the structure and language of the paper. Also contributed to the writing and revision of the results and discussion sections. Z.Z: Responsible for the overall organization and editing of the paper throughout the entire research process. During the writing process, made revisions and optimizations to the structure and language of the paper. Also contributed to the writing and revision of the results and discussion sections.As a team, we worked closely and collaboratively to complete the research and writing of this paper. We are proud of our individual contributions and would like to express our gratitude for the teamwork involved. Data availability The data used in this study is publicly available and can be accessed through https://www.ebi.ac.uk/gwas/ . References Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. 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Scrna-seq reveals new enteric nervous system roles for gdnf, nrtn, and tbx3. Cell Mol Gastroenterol Hepatol. 2021;11:1548–92. 10.1016/j.jcmgh.2020.12.014 . Man J, Zhou W, Zuo S, Zhao X, Wang Q, Ma H, et al. Tango1 interacts with nrtn to promote hepatocellular carcinoma progression by regulating the pi3k/akt/mtor signaling pathway. Biochem Pharmacol. 2023;213:115615. 10.1016/j.bcp.2023.115615 . Zhang TS, Qin HL, Wang T, Li HT, Li H, Xia SH et al. Global publication trends and research hotspots of nonalcoholic fatty liver disease: a bibliometric analysis and systematic review. Springerplus . (2015) 4: 776. 10.1186/s40064-015-1542-1 . Duan Y, Pan X, Luo J, Xiao X, Li J, Bestman PL, et al. Association of inflammatory cytokines with non-alcoholic fatty liver disease. Front Immunol. 2022;13:880298. 10.3389/fimmu.2022.880298 . Lima-Cabello E, García-Mediavilla MV, Miquilena-Colina ME, Vargas-Castrillón J, Lozano-Rodríguez T, Fernández-Bermejo M, et al. Enhanced expression of pro-inflammatory mediators and liver x-receptor-regulated lipogenic genes in non-alcoholic fatty liver disease and hepatitis c. Clin Sci (Lond). 2011;120:239–50. 10.1042/CS20100387 . Cobbina E, Akhlaghi F. Non-alcoholic fatty liver disease (nafld) - pathogenesis, classification, and effect on drug metabolizing enzymes and transporters. Drug Metab Rev. 2017;49:197–211. 10.1080/03602532.2017.1293683 . Gao B, Tsukamoto H. Inflammation in alcoholic and nonalcoholic fatty liver disease: friend or foe? Gastroenterology . (2016) 150: 1704-9. 10.1053/j.gastro.2016.01.025 . Jorgensen MM, de la Puente P. Leukemia inhibitory factor: an important cytokine in pathologies and cancer. Biomolecules . (2022) 12. 10.3390/biom12020217 . Rose-John S. Interleukin-6 family cytokines. Cold Spring Harb Perspect Biol. 2018;10. 10.1101/cshperspect.a028415 . He L, Deng L, Zhang Q, Guo J, Zhou J, Song W, et al. Diagnostic value of ck-18, fgf-21, and related biomarker panel in nonalcoholic fatty liver disease: a systematic review and meta-analysis. Biomed Res Int. 2017;2017:9729107. 10.1155/2017/9729107 . Wu G, Li H, Fang Q, Zhang J, Zhang M, Zhang L, et al. Complementary role of fibroblast growth factor 21 and cytokeratin 18 in monitoring the different stages of nonalcoholic fatty liver disease. Sci Rep. 2017;7:5095. 10.1038/s41598-017-05257-5 . Xiao F, Shi X, Huang P, Zeng X, Wang L, Zeng J, et al. Dose-response relationship between serum fibroblast growth factor 21 and liver fat content in non-alcoholic fatty liver disease. Diabetes Metab. 2021;47:101221. 10.1016/j.diabet.2020.101221 . Guo JY, Chen HH, Lee WJ, Chen SC, Lee SD, Chen CY. Fibroblast growth factor 19 and fibroblast growth factor 21 regulation in obese diabetics, and non-alcoholic fatty liver disease after gastric bypass. Nutrients. 2022;14. 10.3390/nu14030645 . Additional Declarations No competing interests reported. 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Flowchart of the present study;\u003c/p\u003e\n\u003cp\u003eb. Three major hypotheses of Mendelian randomization study;\u003c/p\u003e\n\u003cp\u003ec. Mediation analysis process\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/ce17fdd74adcb51925c652f3.jpg"},{"id":57502697,"identity":"4fd55d7a-965e-45b7-aa0f-80b9a94b6ddf","added_by":"auto","created_at":"2024-05-31 14:20:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":399404,"visible":true,"origin":"","legend":"\u003cp\u003ePositive results of Mendelian randomization study from immune cells to NAFLD.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/0265767f47668b82edba319e.jpg"},{"id":57502353,"identity":"2b8c1203-39a5-43a7-bf94-048df05d3bf3","added_by":"auto","created_at":"2024-05-31 14:12:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":565066,"visible":true,"origin":"","legend":"\u003cp\u003e11 results showing mediating effects in the positive results of Mendelian randomization\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/7b2516e5c5d25d2be1860837.jpg"},{"id":57502699,"identity":"037d0bd5-1c01-47f4-8e55-76342b649c5a","added_by":"auto","created_at":"2024-05-31 14:20:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353671,"visible":true,"origin":"","legend":"\u003cp\u003ePositive results of Mendelian randomization study from circulating inflammatory proteins to NAFLD, with a total of 7 results.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/f7f376bb88f50fe47fffdbc2.jpg"},{"id":57502357,"identity":"50d02330-539f-42f9-9f22-a22411c8e9ce","added_by":"auto","created_at":"2024-05-31 14:12:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":414612,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results from immune cells to circulating inflammatory proteins, with a total of 9 results.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/d5d89501213912bbaa1f76f4.jpg"},{"id":57502355,"identity":"013714b1-10b6-487e-bd24-0b4bf8eb56de","added_by":"auto","created_at":"2024-05-31 14:12:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":281585,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results from circulating inflammatory proteins to immune cells, with a total of 5 results.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/394c118cee2b846ef3896391.jpg"},{"id":72634781,"identity":"7fd0173f-cfe1-4050-aaa5-457713ea1d0b","added_by":"auto","created_at":"2024-12-30 15:02:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2567788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/fdfc9008-ed1e-46ae-9a55-7e98adcfeb47.pdf"},{"id":57502696,"identity":"21b4ea31-d50e-4185-be98-71490c6b67a6","added_by":"auto","created_at":"2024-05-31 14:20:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40521,"visible":true,"origin":"","legend":"","description":"","filename":"supplementmaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4427607/v1/e3dd042f4309a16c0d5a971b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role relationship played by immune cells and circulating inflammatory proteins in NAFLD","fulltext":[{"header":"Introduce","content":"\u003cp\u003eThe incidence of Non-Alcoholic Fatty Liver Disease (NAFLD) in both children and adults is gradually increasing, reaching 29\u0026ndash;35% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The continuous escalation of these epidemics is projected to result in a global NAFLD prevalence of 55% by the year 2040 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As the main cause of liver disease at present, NAFLD encompasses a range of liver damage, progressing from simple fatty degeneration to further development of steatohepatitis, with or without accompanying fibrosis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). If not intervened in a timely manner, liver fibrosis may progress to cirrhosis and lead to serious complications such as hepatocellular carcinoma. Therefore, we should attach great importance to this issue. However, due to the unclear pathogenesis of NAFLD, the known mechanisms of NAFLD development are closely related to factors such as insulin resistance, lipid metabolism disorder, inflammatory response, and lipid peroxidation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Among these factors, the inflammatory response plays a crucial role in the pathogenesis of NAFLD, which deserves special attention. In addition, research has found that innate immune activation also plays a role in the pathogenesis of insulin resistance, and the role of immune cells in NAFLD is gradually being discovered (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In order to better understand these mechanisms, further in-depth research is needed, along with the search for more effective treatment strategies. For this liver disease, NAFLD, we need to take proactive preventive measures and treatment plans to mitigate its impact on human health.\u003c/p\u003e \u003cp\u003eImmune response and inflammatory response are important defense mechanisms of the body against external pathogens and injuries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The immune response leads to the release of a series of inflammatory mediators, such as cytokines and chemokines, by immune cells to recruit and activate other immune cells, strengthening the immune response. Inflammatory cells in the inflammatory response release a series of cytokines, such as tumor necrosis factor and interleukins, to recruit and activate other immune cells, enhancing the immune response. Both are crucial to life activities, but their dysregulation can also lead to the occurrence of diseases. mendelian randomization (MR) analysis is a highly statistically efficient method based on genome-wide association studies (GWAS) that can effectively eliminate confounding factors and reveal causal relationships. Currently, there have been studies on immune cells and circulating inflammatory proteins, demonstrating their causal relationships in related diseases (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the roles they play in NAFLD are still unclear. This study aims to explore the role relationship played by immune cells and circulating inflammatory proteins in NAFLD using the MR method, providing a new perspective on the pathogenesis and potential therapeutic targets of NAFLD.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003e This study employed MR analysis to investigate the causal relationships between immune cells, circulating inflammatory proteins, and NAFLD. A two-step approach was utilized to assess their potential mediating effects. Please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea for the specific research process.\u003c/p\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eAll GWAS data were obtained from GWAS Catalog, which is the largest GWAS meta-analysis website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 731 different immune cell phenotypes (archive numbers ranging from GCST0001391 to GCST0002121) were included in this study (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), with the original GWAS data based on information from 3,757 Europeans. Genotyping of approximately 240,000 single nucleotide polymorphisms (SNPs) was performed using a reference panel based on the Sardinian sequence. Association testing was then conducted after adjusting for covariates such as gender, age, and year. Additionally, 91 different circulating inflammatory protein phenotypes (archive numbers ranging from GCST90274758 to GCST90274848) were included in this study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), with the original GWAS data for these inflammatory traits derived from 11 cohorts comprising 14,824 Europeans. The NAFLD GWAS summary data (archive number GCST90091033) included 8,434 NAFLD cases and 770,180 controls of European descent from four cohorts: the electronic medical records and genomics (eMERGE), UK Biobank, FinnGen, and Estonian Biobank (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It is worth noting that there was no overlap in the populations of the aforementioned data cohorts. For further detailed information on the summary GWAS data, please refer to the original paper.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable selection\u003c/h2\u003e \u003cp\u003eTo obtain robust results, MR analysis must satisfy three major assumptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Due to the limited sample size of immune cells and circulating inflammatory proteins, we adopted relatively lenient criteria for instrumental variable selection. SNPs were required to be significantly associated with the exposure (p\u0026thinsp;\u0026lt;\u0026thinsp;1e-05) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Using data from the European samples in the 1000 Genomes Project as the reference panel, we calculated the linkage disequilibrium (LD) between SNPs and filtered out SNPs with an R2\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (aggregate window size\u0026thinsp;=\u0026thinsp;500 kb) to ensure their independence (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). SNPs should be unrelated to the outcome (p\u0026thinsp;\u0026gt;\u0026thinsp;5e-08). Simultaneously, we have eliminated SNPs that do not align with the outcome in the GWAS. In order to avoid weak instrumental variable bias, F-statistics and R2 were calculated based on previous studies (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn each two-sample mendelian randomization analysis, various causal inference methods, such as inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, were employed to assess the causal associations between exposure and outcome. IVW method served as the primary statistical model (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Both fixed-effect and random-effects IVW methods are available, but we employed the more stringent fixed-effect IVW method as the primary statistical model. We placed high demands on the robustness of our results, and we could not tolerate significant heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the results. Furthermore, different MR analysis methods were required to yield consistent effects; otherwise, the results would be deemed invalid and discarded. All results were presented as odds ratios (ORs) with their corresponding 95% confidence intervals (CIs). We also calculated the mediating effects of immune cells and circulating inflammatory proteins separately. Specific analyses can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, where a two-step approach was implemented for mediation effect calculation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The indirect effect was represented by c\u0026rsquo;=c-a*b, and the percentage of mediation was calculated as a*b/c (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eHeterogeneity and horizontal pleiotropy are not allowed in MR analysis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Heterogeneity was quantified using Cochran's Q statistic, with a P value less than 0.05 considered significant. To assess the potential pleiotropic effects of instrumental variables, MR-Egger regression was employed. The level of directional pleiotropy in causal estimation can be represented by the intercept term in MR-Egger regression. In MR-PRESSO analysis, the aim was to reduce heterogeneity in the causal effect estimation by eliminating SNPs that excessively contributed to heterogeneity. To achieve this goal, 3,000 iterations were set in the MR-PRESSO analysis. Additionally, we performed leave-one-out analysis, sequentially excluding each SNP and conducting MR analysis on the remaining SNPs to detect potential outliers. All analyses needed to be negative; otherwise, the MR results would be deemed meaningless and discarded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eThe data used in this study were obtained from published GWAS datasets, thus ethical approval was not required. The included data sources were approved by local ethics committees and complied with local regulations, with all participants providing informed consent.\u003c/p\u003e \u003cp\u003eThe statistical analyses were conducted using the R software (version 4.2.2, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://R-Forge.R-project.org/projects/\u003c/span\u003e\u003cspan address=\"https://R-Forge.R-project.org/projects/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The TwoSampleMR package was utilized for the analysis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune cells and NAFLD\u003c/h2\u003e \u003cp\u003eMR analysis was primarily performed using the IVW method, which required statistical significance. The results of other MR methods needed to yield consistent effects. Additionally, the sensitivity analysis results were robust, with all results being negative (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Moreover, in the reverse MR analysis with NAFLD as the exposure and immune cells as the outcome, no reverse causal relationship was observed. There were 21 remaining positive results, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Material 1 (ST1). Due to space limitations, the main text of the article only presents detailed results of the mediation effects in subsequent analyses, totaling 11, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCirculating inflammatory proteins and NAFLD\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMR analysis was primarily performed using the IVW method, which required statistical significance. The results of other MR methods needed to yield consistent effects. Moreover, the sensitivity analysis results were robust, with all results being negative (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, in the reverse MR analysis with NAFLD as the exposure and circulating inflammatory proteins as the outcome, no reverse causal relationship was observed. There were 7 remaining positive results, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and ST2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmune cells and circulating inflammatory proteins\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, there were a total of 9 positive results indicating the effects of immune cells on NAFLD through circulating inflammatory proteins. The results of immune cells on circulating inflammatory proteins can be found in ST3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with the mediation effects presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mediation effect refers to the influence of a mediating variable on the relationship between two variables. When the value of the mediation effect is negative, it indicates an inhibitory effect of the mediating variable on the relationship between the two variables, meaning that an increase in the mediating variable weakens the relationship between the two variables. When the value of the mediation effect is positive, it indicates a promoting effect of the mediating variable on the relationship between the two variables, meaning that an increase in the mediating variable strengthens the relationship between the two variables. There were a total of 5 positive results indicating the effects of circulating inflammatory proteins on NAFLD through immune cells. The results of circulating inflammatory proteins on immune cells can be found in ST4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, with the mediation effects presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable 1 Results of mediation effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003emediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eeffect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory cytokines\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD62L- Dendritic Cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurturin levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerminally Differentiated CD8\u0026thinsp;+\u0026thinsp;T cell %CD8\u0026thinsp;+\u0026thinsp;T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurturin levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-20.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerminally Differentiated CD4-CD8- T cell %CD4-CD8- T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurturin levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD39\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblast growth factor 21 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA DR on CD33- HLA DR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibroblast growth factor 21 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD19 on IgD\u0026thinsp;+\u0026thinsp;CD24- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-6 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD19 on IgD\u0026thinsp;+\u0026thinsp;CD24- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeukemia inhibitory factor levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4 on Terminally Differentiated CD4\u0026thinsp;+\u0026thinsp;T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignaling lymphocytic activation molecule levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD45RA- CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterleukin-10 receptor subunit beta levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory cytokines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eImmune cells\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-10 receptor subunit beta levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLA DR on CD33- HLA DR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-6 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerminally Differentiated CD8\u0026thinsp;+\u0026thinsp;T cell %CD8\u0026thinsp;+\u0026thinsp;T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-6 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerminally Differentiated CD8\u0026thinsp;+\u0026thinsp;T cell %T cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignaling lymphocytic activation molecule levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaive-mature B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophage colony-stimulating factor 1 levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLA DR on B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.86%\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"},{"header":"Discussion","content":"\u003cp\u003eWe have respectively explored the causal relationship between 731 types of immune cell phenotypes and 91 types of circulating inflammatory proteins with NAFLD, and evaluated their potential mediating effects. To the best of our knowledge, this is the first mendelian randomization study to investigate the causal and mediating relationship between immune cells and circulating inflammatory proteins and NAFLD. In this study, we finally discovered that among the 731 immune cell phenotypes, 21 immune cell phenotypes are causally related to NAFLD, and 6 circulating inflammatory protein phenotypes play a mediating role; among the 91 circulating inflammatory protein phenotypes, 7 circulating inflammatory protein phenotypes are causally related to NAFLD, and 5 immune cell phenotypes play a mediating role.\u003c/p\u003e \u003cp\u003eImmune regulation is of paramount importance to the organism's vital activities (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), including the innate (general, nonspecific) immune system and the adaptive (specialized) immune system (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), involving multiple factors such as immune cells, signal transduction molecules, cytokines, etc (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The liver is considered an important immune organ (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and therefore, in recent years, there have been increasing studies exploring the role of immune cells in the progression of NAFLD (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), such as in the diagnosis of NAFLD, pathology is the gold standard, so currently, most studies focus on metabolic biomarkers, with few studies on immune cells. However, a recent study compared the phenotypic changes between NASH-resistant and recombination activating 1 (Rag)-deficient mice in the NASH model, and they found a key role of immune cells in the pathogenesis of NASH (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, inflammation is an important aspect of metabolic disorders (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). All adipose tissues contribute significantly to the systemic and hepatic inflammation observed in NAFLD. Studies have found that successful weight loss after bariatric surgery reduces IL-1 cytokines, especially in subcutaneous adipose tissue (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). It has also been found that a decrease in interleukin-1 inhibits the transformation of fatty degeneration to steatohepatitis and liver fibrosis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, the specific role of dendritic cells in NAFLD is not clear (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In this study, we found that the CD62L- Dendritic Cell Absolute Count phenotype plays a role in increasing the risk of NAFLD (OR\u0026thinsp;=\u0026thinsp;1.05 (1.00-1.09)). Studies have found that the significant increase in conventional dendritic cells (cDC) in the liver is an important pathological marker. They examined blood and liver samples from individuals in the NAFLD spectrum using single-cell transcriptomics and found that the level and activity of type 1 conventional dendritic cells (cDC1) increase in the disease environment. Knocking down cDC1 in NASH mouse experiments reduces liver damage (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), which is consistent with our research results, but the specific mechanisms need further exploration.\u003c/p\u003e \u003cp\u003eIn this study, we found that the percentage of terminally differentiated CD4-CD8- T cells in total T cells increases the risk of NAFLD, and the Neurturin levels phenotype has a mediating inhibitory effect of 12.88%. CD4-CD8- double-negative (DN) T cells is a rare subset of T cells, and previous studies have found that DN T cells proliferate significantly in related autoimmune diseases and regulate the production of inflammatory cytokines and immunoglobulins in inflammatory tissues (\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Neurturin (NRTN) is one of the ligands of the neurotrophic factor (GDNF) family, belonging to the TGF-β superfamily (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). It was initially thought to be associated with neuronal growth, differentiation, and maintenance (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), but recent studies have found that it also plays a role in the digestive system and is associated with various tumorigenic processes (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Furthermore, the expression level of NRTN in the liver is very high according to the NCBI database (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), and this study found that NRTN levels are consistently associated with the occurrence of NAFLD, and its related mechanisms need further in-depth research.\u003c/p\u003e \u003cp\u003eIn this study, we found that CD4 on Terminally Differentiated CD4\u0026thinsp;+\u0026thinsp;T cell has a OR\u0026thinsp;=\u0026thinsp;0.93, 95CIs=(0.88\u0026ndash;0.97), serving as a protective factor against NAFLD. Currently, there are studies suggesting that an increase in the level of fatty acids in the local liver microenvironment reduces the number of CD4 T lymphocytes, and the loss of CD4 T lymphocytes impairs immune regulation, leading to the progression of NAFLD to cirrhosis and liver cancer (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which is consistent with our results. Additionally, Signaling lymphocytic activation molecule levels also have an inhibitory effect, with a mediating effect of 3.99%, indicating the presence of negative feedback regulation between the two.\u003c/p\u003e \u003cp\u003eMeanwhile, the inflammatory cytokines also play a crucial role in NAFLD (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This study revealed that an elevation in IL-6 levels increases the risk of NAFLD (OR\u0026thinsp;=\u0026thinsp;1.17 (1.02\u0026ndash;1.35)). The mechanism behind this may involve the suppressors of the cytokine signaling-3 (SOCS-3) - sterol-regulatory-element-binding protein-1c (SREBP-1c) - fatty acyl-CoA (FA-CoA) pathway, which suppresses insulin signaling, regulates the acute phase response and chronic inflammation (\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Furthermore, research suggests that macrophages promote liver cell injury and fibrosis through the secretion of IL-6 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Additionally, insulin resistance is a significant etiological factor in NAFLD. In a mice experiment, it was found that obesity-induced inflammation and metabolic disorders act through the influence of IL-6 on the generation of immune cells (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). However, currently, there is no known relationship between Interleukin-10 receptor subunit beta levels and NAFLD, warranting further in-depth analysis and investigation. Leukemia inhibitory factor (LIF) is a member of the interleukin-6 (IL-6) cytokine family (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). This study discovered that Leukemia inhibitory factor levels have an inhibitory effect on the CD19 on IgD\u0026thinsp;+\u0026thinsp;CD24- B cell phenotype, which aligns with previous findings indicating LIF's ability to suppress immune rejection responses (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFibroblast growth factor 21 (FGF21) is a primarily liver-secreted cytokine that improves glucose and lipid metabolism, reduces inflammation and oxidative stress, increases insulin sensitivity, and acts on multiple organs through autocrine, paracrine, and endocrine mechanisms. Some studies propose FGF21 as a potential biomarker for NAFLD (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Elevated serum FGF21 levels have been found to predict the development of hepatic steatosis (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), and serum FGF21 levels correlate in a dose-dependent manner with hepatic fat content in NAFLD patients (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). It is believed that a decrease in serum FGF21 levels resulting from gastric bypass surgery can lead to an improvement in NAFLD (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In this study, it was discovered that FGF1 increases the risk of NAFLD, and the potential reason behind this could be that, under normal circumstances, FGF21 promotes fatty acid oxidation and inhibits fatty acid synthesis. However, when FGF21 levels are excessively high, it may disrupt energy metabolism, leading to fat accumulation and the occurrence of NAFLD. Elevated FGF21 levels may reflect an abnormal response in the body's energy metabolism, including insulin resistance, obesity, diabetes, and other metabolic abnormalities, all of which are associated with an increased risk of NAFLD. In the external mendelian randomization analysis conducted in this study, the genetic variations represented by these instrumental variables were found to be associated with an increase in FGF21 levels and an increased risk of developing NAFLD. However, it is important to note that further experimental validation is required for the aforementioned analyses. There is currently limited research on other inflammatory cytokines in this study, more research is needed to delve into this topic.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, immune cells and circulating inflammatory proteins play a crucial role in NAFLD, and our study may provide new insights for the diagnosis and treatment of NAFLD in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was conducted without any funding support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWe, as the authors of this paper, hereby declare our individual contributions:Z.C: Responsible for the overall research design and direction, as well as the conceptualization and design of the experiments. During the experiment, responsible for data collection and analysis, and contributed to the writing of the research methods and results sections. Y.Z: Conducted extensive literature review and summarized relevant research during the initial stages of the study. Also involved in data analysis and contributed to the writing of the introduction and background sections of the paper. X.B: Responsible for data processing, statistical analysis, and some visualization of the results during the experiment. Also contributed to the writing and revision of the paper, and optimized the figures and tables. B.Z: Responsible for the overall organization and editing of the paper throughout the entire research process. During the writing process, made revisions and optimizations to the structure and language of the paper. Also contributed to the writing and revision of the results and discussion sections. Z.Z: Responsible for the overall organization and editing of the paper throughout the entire research process. During the writing process, made revisions and optimizations to the structure and language of the paper. Also contributed to the writing and revision of the results and discussion sections.As a team, we worked closely and collaboratively to complete the research and writing of this paper. We are proud of our individual contributions and would like to express our gratitude for the teamwork involved.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data used in this study is publicly available and can be accessed through \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. 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Nutrients. 2022;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14030645\u003c/span\u003e\u003cspan address=\"10.3390/nu14030645\" 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 cells, circulating inflammatory proteins, NAFLD, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4427607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4427607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe mechanisms by which immune cells and inflammatory factors influence Non-Alcoholic Fatty Liver Disease (NAFLD) remain unclear. This study employs Mendelian randomization (MR) to investigate the relationship between immune cells, inflammatory factors, and NAFLD, as well as the proportion of their mutual mediation effects on NAFLD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilizes MR analysis, examining the causal relationship between 731 immune cell phenotypes, 91 circulating inflammatory proteins, and NAFLD. The data are sourced from publicly available data in the GWAS Catalog. The research process consists of two steps, analyzing them through the assessment of their mediating effects. To obtain reliable results, MR analysis necessitates the fulfillment of three fundamental assumptions. In the selection of instrumental variables, SNPs are screened, requiring significant associations with the exposure factors and no association with the outcomes. Statistical analyses employ methods such as IVW, WM, and MR-Egger to evaluate the causal relationship between exposure and outcomes. Sensitivity analyses are conducted, examining heterogeneity and horizontal pleiotropy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUltimately, among the 731 immune cell phenotypes, 21 phenotypes are found to have a causal relationship with NAFLD, with 6 circulating inflammatory protein phenotypes playing intermediary roles. Among the 91 circulating inflammatory protein phenotypes, 7 inflammatory factor phenotypes are found to have a causal relationship with NAFLD, with 5 immune cell phenotypes playing intermediary roles.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eImmune cells and circulating inflammatory proteins play a crucial role in NAFLD, and our study may provide new insights for the diagnosis and treatment of NAFLD in the future.\u003c/p\u003e","manuscriptTitle":"The role relationship played by immune cells and circulating inflammatory proteins in NAFLD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 14:12:53","doi":"10.21203/rs.3.rs-4427607/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":"62f4ef0e-8102-402c-9d46-53a86e0287bb","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T14:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 14:12:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4427607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4427607","identity":"rs-4427607","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}