Association between 91 inflammatory factors combined with 1400 metabolites and ankylosing spondylitis: a two-sample Mendelian randomization study

Association between 91 inflammatory factors combined with 1400 metabolites and ankylosing spondylitis: a two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between 91 inflammatory factors combined with 1400 metabolites and ankylosing spondylitis: a two-sample Mendelian randomization study Yi-fa Rong, Xue-Zhen LIANG, Kai JIANG, Hai-Feng JIA, Han-Zheng LI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4139990/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 BACKGROUND: Ankylosing spondylitis is a chronic progressive inflammatory disease of the joints. A large amount of evidence shows that ankylosing spondylitis is closely related to inflammatory factors and metabolites. However, the causal relationship between ankylosing spondylitis and inflammatory factors and metabolites is unclear. OBJECTIVE: To evaluate potential the causal relationships between 91 inflammatory cytokines combined with 1,400 metabolites and ankylosing spondylitis using the Mendelian randomization method. METHODS: A two-sample Mendelian randomization study was performed using the Genome-wide association study (GWAS) summary statistics of 91 inflammatory cytokines (n=14,824) and 1,400 serum metabolites (n=8,299) as well as GWAS data of ankylosing spondylitis from the FinnGen R10 database (3,162 cases and 2,947,070 healthy controls) were used. Inverse variance weighted, MR-Egger, weighted median, weighted model and simple model were used to examine the causal association between inflammatory cytokines combined with metabolites and ankylosing spondylitis. Sensitivity analysis was used to test whether the results of the Mendelian randomization analysis were reliable. CONCLUSION: FGF-23 and IL-7 were positively correlated with ankylosing spondylitis while CD244 and FIt3L were negatively correlated based on causal associations. FGF-23 had potential causal relationships with 62 metabolites (p<0.05), IL-7 had potential causal relationships with 68 metabolites (p<0.05), FIt3L had potential causal relationships with 37 metabolites (p<0.05), and CD244 had potential causal relationships with 61 metabolites (p<0.05). The results suggest that CD244, FGF-23, FIt3L, IL-7 may play important roles in the pathogenesis of ankylosing spondylitis, and metabolism-related inflammatory cytokines could be important in future explorations of mechanisms and drug target selections for ankylosing spondylitis. ankylosing spondylitis inflammatory cytokines metabolites Mendelian randomization causal relationships inverse variance weighted method heterogeneity horizontal pleiotropy sensitivity analysis Figures Figure 1 Figure 2 Figure 3 1 Introduction Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the joints, mainly affecting the sacroiliac joints, spine, paraspinal soft tissues, and peripheral joints, which is characterized by inflammation, stiffness, pain, and dysfunction of the spinal cord and pelvic joints [ 1 ] . According to epidemiological studies, the global prevalence of ankylosing spondylitis ranges from 0.1–1.4%, is more common in men than in women, and often occurs between the ages of 30 and 40 [ 2 ] . AS is widespread throughout the world, and its incidence varies widely between regions and populations [ 3 ] . The exact pathogenesis of ankylosing spondylitis remains unclear, and diagnosis is typically based on a combination of clinical features and imaging changes in the sacroiliac joints. Ankylosing spondylitis patients experience abnormal release of inflammatory factors such as tumor necrosis factor-alpha (TNFα), interleukin-1 β (IL-1b), and interleukin-17 (IL-17) due to a combination of environmental factors (mechanical stress stimuli, infections, and intestinal flora) and immune and genetic factors. Irregular secretion and activation of inflammatory factors can cause inflammation, pain, and tissue damage in joints and the spine [ 4 ] . Human leukocyte antigen B27 (HLA-B27) is strongly associated with immune and genetic factors and is considered the highest genetic risk factor in ankylosing spondylitis [ 5 ] . Tumor necrosis factor-α (TNF-α), IL-23, and IL-17 play important roles in the inflammatory response in ankylosing spondylitis and may be effective therapeutic targets [ 6 , 7 ] . In addition to this, there is increasing evidence that the development of ankylosing spondylitis is also closely related to metabolic abnormalities.Genre et al. showed a strong association between ankylosing spondylitis and metabolic disorders such as atherosclerosis, cardiovascular disease, diabetes mellitus, dyslipidemia, and obesity [ 8 ] . The causal effect of inflammatory factors and metabolites on ankylosing spondylitis remains uncertain due to sample size limitations and confounding factors. In epidemiologic studies, the presence of confounders greatly interferes with the causal inference of exposure and outcome. Mendelian randomization analysis is an emerging method for genetic research and is now widely used in epidemiological studies. The MR analyses relied on large samples of genotypic and phenotypic data. Instrumental variables (IVs) were used, which were single nucleotide polymorphism (SNP) loci with strong correlations with exposure factors, to determine causality between exposure and outcomes [ 9 ] . Mendelian randomization studies utilize genetic variants that follow the principle of random assignment of alleles, similar to randomized controlled experiments. This approach effectively mitigates the effects of confounders and reverse causality encountered in observational studies. This study utilized an innovative MR research method. Inflammatory factors were used as exposure factors and AS as outcome factors for MR analysis to screen for the inflammatory factors with causal associations. The inflammatory factors with causal associations were then analyzed by MR with 1400 metabolites. The objective of this study was to use a two-sample Mendelian randomization study to reveal the causal relationship between co-metabolites of inflammatory factors and AS. This study aims to provide new strategies for the prevention and treatment of AS. Materials and Methods 1.1 Study Design In this study, firstly, based on the genome-wide association study (GWAS) pooled data of 91 inflammatory factors and ankylosing spondylitis, eligible instrumental variables were screened for MR analysis to explore the causal relationship between inflammatory factors and ankylosing spondylitis. Based on the Genome-Wide Association Study (GWAS) pooled data of inflammatory factors and 1,400 metabolites that were screened positive, eligible instrumental variables were screened for MR analysis to explore the causal relationship between inflammatory factors and 1,400 metabolites that are causally related to ankylosing spondylitis. The present study strictly adhered to the three assumptions of MR analysis, which are independence assumption, exclusivity assumption and correlation assumption [ 10 ] . (figure. 1) 2 Exposure and Outcome Data Acquisition The Genome wide association study (GWAS) pooled dataset of 91 inflammatory factors was obtained from Zhao JH et al. study, which included 14824 European adults [ 11 ] . The study utilized a pooled dataset of 1,400 blood metabolites from a genome-wide association study, which is currently the most comprehensive analysis of blood metabolites available. The dataset was obtained from Chen YH and others [ 12 ] , which included 1,091 metabolites and 309 metabolite ratios from 8,299 European adults, of which 1,091 metabolites comprised 850 known metabolites and 241 unknown metabolites. The 850 known metabolites can be categorized into eight major metabolic groups (amino acids, carbohydrates, cofactors and vitamins, energy products, lipids, nucleotides, peptides, and heterologous biometabolites) by the Kyoto encyclopedia of genes and genomes (KEGG) database. Pooled GWAS data for AS were obtained from the FinnGen data ( https://www.r10.finngen.fi/ , accessed 01/12/2024), which included a case group of 3,162 AS cases and a control group of 294,770, with all participants being of European ancestry, and informed consent was obtained. 2.1 Selection of IVs The instrumental variables we chose were in line with the three core assumptions described previously, that the 91 inflammatory factors extracted as SNPs at the time of exposure should reach genome-wide significant levels (with a threshold of P < 5×10 − 5 ) [ 13 ] , and that the thresholds for linkage disequilibrium were set at r2 = 0.001 and kb = 10,000 in order to select independent instrumental variables; The F values of single nucleotide polymorphisms in the IVs were obtained by calculating the formula as R 2 = 2 × (1-MAF) × MAF × β 2 /2 × β 2 × EAF × (1-EAF) + se 2 × 2 × N × EAF (1-EAF), F = [R 2 /(1-R 2 )] × [(N-K-1)/K] [ 14 ] , with R 2 denoting the extent to which the instrumental variables explained the exposure, MAF denoting the minor allele frequency, β denoting the effect value of the alleles, K denoting the number of instrumental variables, and N denoting sample size. In addition, to exclude weak instrumental variables, we included instrumental variables with F > 10 in the MR analysis. Finally, to ensure the accuracy of the results, palindromic SNPs with intermediate allele frequencies were removed from them [ 15 ] . 2.2 Sensitivity Analysis To assess the robustness of the causal effects of inflammatory factors and ankylosing spondylitis as well as inflammatory factors and metabolites, a series of sensitivity analyses were performed, applying Cochran's Q-test to assess potential heterogeneity, with heterogeneity indicated when the p-value was ≤ 0.05. MR-Egger regression and MR-PRESSO were applied to assess potential horizontal pleiotropy; finally, Leave one out analysis was applied to assess the sensitivity of a single SNPs to the results. 2.3 MR Analysis Five MR analysis methods were used in this study, including Inverse Variance Weighted (IVW), MR-Egger, Weighted Median, Weighted Model and Simple Model. IVW was the main analysis method, and the other methods were used as supplements. When horizontal pleiotropy was absent, the results of IVW were not biased, thus making its analysis more reliable compared to other methods. When ankylosing spondylitis was used as an outcome, it was a dichotomous variable, expressed as odds ratio (OR) and 95% CI, and when metabolites were used as an outcome, it was a continuous variable, expressed as beta value and 95% confidence interval (CI). All statistical analyses were performed in R (version 4.3.1) software using the "TwoSampleMR" package and the MR-PRESSO package used for the above analyses, with a test criterion of α = 0.05. 3 Result 3.1 Inflammatory factors and AS 3.1.1 Selection of IVs In this study, the genome-wide significance threshold was set at 5 × 10 − 5 to ensure a sufficient number of SNPs for analysis. In addition, all F values exceeded 10 to mitigate bias caused by weak instrumental variables, resulting in a total of 24,207 SNPs screened. 3.1.2 MR Analysis In this study, we first analyzed the causal relationship between inflammatory factors and ankylosing spondylitis, along with the tests of pleiotropy and heterogeneity, and the results of the analysis initially suggested that there were seven inflammatory factors (CCL19, CD244, FGF-23, FIt3L, IL-18R1, IL-6, and IL-7) that might be causally associated with AS, and four of them (CD244, FGF-23, FIt3L, and IL-7) passed the test of horizontal pleiotropy and the test of heterogeneity(Table 1 ). IVW analysis (Table 2 ) showed that FGF-23 was positively associated with ankylosing spondylitis with an OR of 1.185 (95% CI = 1.013 ~ 1.387, P = 0.034). IL-7 was positively associated with ankylosing spondylitis with an OR of 1.240 (95% CI = 1.020 ~ 1.506, P = 0.030), CD244 was negatively correlated with ankylosing spondylitis with an OR of 0.885 (95% CI = 0.786–0.996, P = 0.043), and FIt3L was negatively correlated with ankylosing spondylitis with an OR of 0.895 (95% CI = 0.803 ~ 0.998, P = 0.045) (Figure. 2). The results of the leave-one-out method showed that no significant outliers were seen, and that the results of the MR study were reliable. (Figure. 3) Figure Note: Figure A. FGF-23 was positively correlated with AS, Figure B. CD244 was negatively correlated with AS, Figure C. IL-7 was negatively correlated with AS, Figure D. FIt3L was negatively correlated with AS. Figure Note: Figure A. CD244 vs. AS, Figure B. FGF-23 vs. AS, Figure C. FIt3L vs. AS, Figure D. IL-7 vs. AS Table 1 Sensitivity analysis of inflammatory factors and AS. inflammatory factor GCS_ID Heterogeneity Test Horizontal Multiplicity Test IVW method MR Egger MR Egger MR Egger Q p-value Q p-value Intercept p-value CCL19 GCST90274765 2.55E-40 9.42E-23 -0.100 0.001 CD244 GCST90274771 0.617 0.587 -0.008 0.537 FGF-23 GCST90274789 0.286 0.244 -0.004 0.812 FIt3L GCST90274791 0.4873 0.448 0.003 0.769 IL-18R1 GCST90274805 1.46E-69 4.93E-60 0.138 0.104 IL-6 GCST90274815 0.025 0.016 -0.001 0.974 IL-7 GCST90274816 0.475 0.425 -0.011 0.663 Table 2 IVW analysis of inflammatory factors and AS. exposure GCS_ID SNP number β SE OR P-value CD244 GCST90274771 33 -0.123 0.060 0.885 0.043 FGF-23 GCST90274789 28 0.170 0.080 1.185 0.034 FIt3L GCST90274791 45 -0.111 0.055 0.895 0.045 IL-7 GCST90274816 22 0.215 0.099 1.240 0.030 3.2 Positive Inflammatory Factors and Metabolites 3.2.1 Selection of IVS In this study, four inflammatory factors (CD244,FGF-23,FIt3L, IL-7) causally associated with AS were analyzed by MR as exposure and metabolites as outcome. Firstly, these 4 inflammatory factors were screened for eligible instrumental variables according to the conditions first set based on genome-wide significance, linkage disequilibrium, CD244 screened for 33 SNPs,FGF-23 screened for 28 inflammatory factors,FIt3L screened for 46 SNPs, and IL-7 screened for 22 SNPs. The F values of the four inflammatory factor single-nucleotide polymorphisms ranged from 19.53 to 442.14, which all met the requirement of F > 10, indicating that the present study has a low probability of having a weak instrumental variable bias. 3.2.2 Statistical Analysis MR analysis was performed on 1400 metabolites using 4 positive inflammatory factors as exposure factors, respectively. After 5 metabolites were excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables 1, 2), IVW analysis showed that FGF-23 was causally associated with 62 metabolites (p < 0.05), of which 55 were known metabolites and 7 were unknown metabolites, and of the known metabolites, FGF-23 was positively correlated with 18 metabolites, and negatively correlated with 37 metabolites (Supplementary Fig. 1); After 2 metabolites were excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables 3, 4), IVW analysis showed that IL-7 was causally associated with 68 metabolites (p < 0.05), of which 57 were known metabolites and 11 were unknown metabolites, and of the known metabolites, IL-7 was positively correlated with 42 metabolites and negatively correlated with 15 metabolites (Supplementary Fig. 2); One metabolite was excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables 5, 6), and IVW analysis showed that FIt3L was causally associated with 37 metabolites (p < 0.05), of which 32 were known metabolites and 5 were unknown metabolites, and of the known metabolites, FIt3L was positively correlated with 13 metabolites and negatively correlated with 19 metabolites (Supplementary Fig. 3); after excluding 7 metabolites by horizontal pleiotropy and heterogeneity tests (Supplementary Tables 7, 8), IVW analysis showed that CD244 was causally associated with 61 metabolites (p < 0.05), of which 51 were known metabolites and 10 were unknown metabolites, and of the known metabolites, CD244 was positively correlated with 18 metabolites and negatively correlated with 33 metabolites (Supplementary Fig. 4). 4 Discussion AS is closely related by inflammatory factors and metabolites and has received great attention in clinical research, but the causal relationship at the genetic level is still unclear. Therefore, based on the pooled data of GWAS, we comprehensively analyzed the causal relationship of 91 inflammatory factors on AS by using a two-sample MR method, and we also performed a two-sample MR analysis of the positive inflammatory factors with 1,400 blood metabolites, exploring the causal relationship between the inflammatory factors and metabolites associated with AS, and ultimately providing a new perspective for unraveling the genetically inherited role in the pathogenesis of AS, as well as a direction for precision diagnosis and prevention. Fang P et al investigated the causal relationship between 41 inflammatory factors and AS and performed MR analysis to conclude that three inflammatory factors (βNGF, IL-1b, and TRAIL) were causally associated with the risk of developing AS [ 16 ] . The current study used a more comprehensive set of 91 inflammatory factors with AS for MR analysis, which can more comprehensively and reliably assess the causal relationship between inflammatory factors and AS. The pathogenesis of AS remains unclear, and it is widely recognized that the key mechanism lies in the abnormal production and regulation of inflammatory factors [ 17 , 18 ] . The results of this study showed that FGF-23 and IL-7 were positively associated with the risk of ankylosing spondylitis, whereas CD244 and FIt3L were negatively associated with ankylosing spondylitis, and FGF-23 was causally associated with 62 metabolites, IL-7 with 68 metabolites, CD244 with 61 metabolites, and FIt3L with 37 metabolites. Abnormal regulation of inflammatory factors affects the regulation of metabolic processes, leading to compromised metabolite secretion and synthesis, which in turn triggers inflammatory responses. These inflammatory reactions mainly affect joints such as the spine and pelvis, leading to inflammatory injuries of the sacroiliac joints, spine, paraspinal soft tissues and peripheral joints [ 19 ] . By two-sample MR analysis, this study found that elevated levels of FGF-23 and IL-7 were associated with an increased risk of ankylosing spondylitis. Fibroblast growth factor − 23(FGF-23) is a hormone secreted by osteoblasts and osteoclasts of long bones and is involved in the regulation of serum phosphate and osteotriol (1,25(OH)2D3) levels [ 20 ] .FGF-23 has been linked to CKD-related metabolic bone disease, osteoporosis and other bone metabolic diseases [ 21 – 23 ] . Gercik O et al. showed that FGF-23 levels were significantly higher in patients with ankylosing spondylitis (170, 94.3-317.4 pg/ml) than in healthy controls (107, 63.3-192.8 pg/ml), p = 0.023 and were strongly associated with inflammation [ 24 ] . The results of this study suggest a potential causal relationship between both FGF-23 and ankylosing spondylitis. IL-7 is a multifunctional cytokine that maintains the homeostasis of the immune system, and its wide distribution includes lymphoid organs such as bone marrow, thymus, lymph nodes, and spleen, as well as non-lymphoidal sites such as the skin, lungs, intestines, and liver, and plays a crucial regulatory role in the entire immune system [ 25 , 26 ] . In addition, elevated IL-7 concentrations are strongly associated with autoimmune diseases such as rheumatoid arthritis [ 27 ] . Ciccia F et al. showed that ILC3, characterized by Lyn-RORc-Tbet + NKp44 + cells, was significantly expanded in the intestine, synovial fluid and bone marrow and produced high levels of IL-17 and IL-22, in which IL-7 played a key role, and that its levels were significantly increased in the intestines of AS patients [ 28 ] . Our study showed that elevated levels of IL-7 were associated with an increased risk of ankylosing spondylitis. Thus, IL-7 may be involved in the pathogenesis of ankylosing spondylitis, but the underlying mechanisms remain to be further elucidated. CD244 is a transmembrane protein present in NK cells, T cells, and other types of immune cells that may play a role in the development of immune-related diseases such as systemic lupus erythematosus, rheumatoid arthritis, and type 1 diabetes mellitus [ 29 ] . The relationship between CD244 and AS has not been reported in the literature, and the results of this study suggest that increased levels of CD244 are associated with a decreased risk of developing AS, which still needs to be further verified in the future. Flt3 plays an important role in rheumatoid arthritis, and Ramos M I et al. showed that Flt3 levels were significantly elevated in serum, synovial fluid, and synovial tissue of rheumatoid arthritis (RA) patients [ 30 ] . The results of the present study showed a negative correlation between Flt3 and ankylosing spondylitis, which needs to be verified by further studies in the future. AS is closely associated with metabolic abnormalities and metabolites play an important role in AS. elevated levels of FGF-23 increase the risk of developing AS.在Among the 62 metabolites with potential causal relationship with FGF-23, Li H et al. study showed that 5alpha-androstan-3beta,17beta-diol disulfate levels, Citrate levels, and Choline levels were significantly reduced in the group of patients with ankylosing spondylitis as compared to the healthy control group [ 31 ] . The results of the present study showed that FGF-23 was negatively associated with all three metabolites. The present study hypothesized that FGF-23 increases the risk of ankylosing spondylitis by down-regulating 55alpha-androstane-3beta,17beta-diol disulfate levels, Citrate levels, and Choline levels, which provides a direction for subsequent studies on the pathogenesis of AS. The results provide a direction for subsequent studies on the pathogenesis of AS. TNF-α plays an important role in the inflammatory response in AS, and Vittimberga F J et al. found that Salicylate levels may inhibit the production of TNF-α [ 32 ] . Luxia Zu et al found that salicylates blocked the lipolytic effect of TNF-α on primary adipocytes in rats [ 33 ] . In addition, salicylate-based NSAIDs are the first-line treatment for ankylosing spondylitis. The results of this study show that FGF-23, a risk factor for AS, decreases salicylate levels. The present study hypothesized that FGF-23 increases the risk of developing AS by down-regulating salicylate levels. This study also found that some metabolites are associated with other inflammatory diseases or cancers. Paine A et al. showed that Glycoursodeoxycholic acid sulfate (1) levels are sensitive and specific predictors of progression from Psoriasis (Ps) to Psoriatic Arthritis (PsA) [ 34 ] . Nystrom N et al. showed that Behenoyl sphingomyelin (d18:1/22:0) levels are involved in the inflammatory response and are increased in neonatal inflammatory bowel disease [ 35 ] . Perfluorooctane sulfonate (PFOS) levels are a toxic and carcinogenic persistent organic pollutant to the human body that induces apoptosis of immune cells, destroys the immune system, and ultimately leads to cancer and toxic damage [ 36 ] . Zhang J et al. found that malonylcarnitine levels were lower in the breast cancer case group than in the control group and is a protective factor for breast cancer [ 37 ] . 1 -Methylnicotinamide (1-MNA) was found to be a risk factor for hepatocellular carcinoma in liver cancer patients at significantly higher levels than in healthy controls [ 38 ] . In addition, Sidor K et al. demonstrated that 1-MNA reduced the activation of NLRP3 inflammatory vesicles in human macrophages through a ROS-dependent pathway and reduced the occurrence of NLRP3-associated inflammatory diseases [ 39 ] . These metabolites provide new ideas to explore the pathogenesis of ankylosing spine. Elevated levels of IL-7 increase the risk of developing ankylosing spondylitis, and there are 68 metabolites that have a potential causal relationship with IL-7. Li H et al. found that Anthranilate levels were significantly reduced in the group of ankylosing spondylitis patients compared to healthy controls [ 31 ] . Diametrically, the present study showed that IL-7 increased Anthranilate levels, which warrants further investigation of the mechanisms linking IL-7 and Anthranilate levels to AS. Although there are a number of metabolites that are not supported by the literature related to AS, they have been associated with other inflammatory diseases or cancers, and it is possible that some of the same inflammatory mechanisms may exist that are worth investigating. Taurocholenate sulfate levels may be a new candidate marker for early serodiagnosis of hepatocellular carcinoma [ 40 ] . 3-methylglutarylcarnitine (2) levels were independently associated with invasive breast cancer and estrogen receptor-positive (ER+) breast cancer, and may be a metabolic pathway for breast carcinogenesis [ 41 ] . Taurocholic acid levels may stimulate the intestinal flora to convert taurine and bile acids into genotoxins and tumor initiating factors, respectively, associated with colon cancer [ 42 ] . CD244AS protective factors with causal association with 61 metabolites. Among them, Li H et al. study found that 1-stearoyl-GPE (18:0) levels, Pregnenediol disulfate (C21H34O8S2) levels were significantly lower in AS than in healthy controls, 1-(1-enyl-stearoyl)-GPE (p-18:0) levels were significantly higher in the AS patient group than in the healthy control group [ 31 ] . The present study showed that CD244 was positively correlated with 1 -stearoyl-GPE (18:0) levels, 1 -(1 -enyl-stearoyl)- GPE (p-18:0) levels and negatively correlated with Pregnenediol disulfate (C21H34O8S2) levels. In this study, we hypothesized that CD244 reduces the risk of A by up-regulating 1-stearoyl-GPE (18:0) levels and down-regulating 1-(1-enyl-stearoyl)-GPE (p-18:0) levels.1. Furthermore, studies such as Imrich R and Kirnap M showed that no significant difference in Cortisol levels was seen in AS patients compared to healthy controls [ 43 , 44 ] . However, the results of the present study suggest that CD244 decreases cortisol levels. This warrants an in-depth study of how CD-244 affects the pathogenesis of AS by modulating cortisol. The strength of the current study lies in the fact that it has a strong theoretical basis and important clinical research value from the perspective of molecular mechanisms to explore the causal relationship between 91 inflammatory factors and the risk of AS development, and then between positive inflammatory factors and 1400 metabolites as exposure factors; In this study, strict quality control conditions and analytical methods were used, which can overcome the effects of potential confounding and reverse causality, avoid the waste of human, material and financial resources, etc., and the results of the study are reliable and stable; In contrast to previous Mendelian randomization studies of single exposure factors, the present study involves a large number of 91 inflammatory factors and 1400 metabolites, which is a very large workload and analytical challenge. There are some limitations to this study, firstly the data are only from a European population and further validation is needed to see if similar genetic variants exist in other populations; Among the metabolites obtained by analysis that are causally related to inflammatory factors associated with ankylosing spondylitis there are a number of unknown metabolites with uncertainties in their functional structure; The veracity of the MR analysis depends to a greater extent on the interpretation of the instrumental variables of exposure, and follow-up studies still need further expanded sample sizes to provide a more precise assessment of the MR analysis. 5 Conclussion In this study, a two-sample Mendelian randomization method was used to comprehensively assess the causal relationship between 91 inflammatory factors combined with 1400 metabolites and AS. The results of this study showed that four inflammatory factors, FGF-23, IL-7, CD244, and FIt3L, were causally associated with ankylosing spondylitis. In addition, FGF-23 was potentially causally associated with 62 metabolites, IL-7 with 68 metabolites, CD244 with 61 metabolites, and FIt3L with 37 metabolites. From a public health perspective, the results of this study are conducive to an in-depth understanding of the genetic relationship between inflammatory factors and metabolites and AS, and to the use of inflammatory factors and blood metabolites as potential biomarkers, which can provide a potential direction for exploring the pathogenesis of ankylosing spondylitis, disease prevention, and targeted drug therapy. Abbreviations Ankylosing spondylitis (AS) confidence interval (CI) genome-wide association studies (GWAS) inverse variable weighting (IVW) instrumental variables (IVs) Mendelian randomization (MR) single-nucleotide polymorphism (SNP) odds ratio (OR) fibroblast growth factor-23 (FGF-23) Interleukin 7 (IL-7), Fms-like tyrosine kinase 3 ligand (Flt3L) tumor necrosis factor-α (TNF-α) 1 -Methylnicotinamide (1-MNA) Declarations 8.1 Data Availability Statement Publicly available datasets were analysed in this study. These datasets can be found at the following URLs: FinnGen (https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_M13_ANKYLOSPON.gz) and GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). 8.2 Ethics Approval and Informed Consent All data used in this work are publicly available from studies with relevant participant consent and ethical approval. 8.3 Consent for Publication All participating authors give their consent for this work to be published. 8.4 Acknowledgements We thank the IEU OpenGWAS data‑base and FINNGEN database for sharing the data. 8.5 Funding None 8.6 Author Contributions Author Contributions All authors made significant contributions to the reported work and agreed to accept responsibility for all aspects of the work.R.Y.F. and L.G. designed the experiments.R.Y.F. and L.X.Z. performed the data preparation, R.Y.F., J.K., J.H.F., and L.H.Z. analyzed data.R.Y.F., L.B.W., and L.X.Z. prepared the first draft of the manuscript. R.Y.F., L.B.W. and L.X.Z. prepared the manuscript.L.G. provided critical feedback during the research process or during the submission of the manuscript. All authors finally approved the submitted version and agreed to publish it in the journal. 8.7 Declaration of interest The authors declare no conflicts of interest in the research. References Taurog JD, Chhabra A, Colbert RA. Ankylosing Spondylitis and Axial Spondyloarthritis[J]. N Engl J Med. 2016;374(26):2563–74. Dean LE, Jones GT, MacDonald AG, et al. Global prevalence of ankylosing spondylitis[J]. Rheumatology (Oxford). 2014;53(4):650–7. Exarchou S, Lindstrom U, Askling J, et al. The prevalence of clinically diagnosed ankylosing spondylitis and its clinical manifestations: a nationwide register study[J]. Arthritis Res Ther. 2015;17(1):118. Tam LS, Gu J, Yu D. Pathogenesis of ankylosing spondylitis[J]. Nat Rev Rheumatol. 2010;6(7):399–405. Hwang MC, Ridley L, Reveille JD. 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Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.pdf Supplementaryfigure2.pdf Supplementaryfigure3.pdf Supplementaryfigure4.pdf Supplementarytable1.csv Supplementarytable2.csv Supplementarytable3.csv Supplementarytable4.csv Supplementarytable5.csv Supplementarytable6.csv Supplementarytable7.csv Supplementarytable8.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4139990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282232268,"identity":"799ef1e2-55d2-4df9-8ea7-8a61095d436d","order_by":0,"name":"Yi-fa Rong","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yi-fa","middleName":"","lastName":"Rong","suffix":""},{"id":282232269,"identity":"edbc93a9-0d3b-49a8-b292-ca7f1d1df1a8","order_by":1,"name":"Xue-Zhen LIANG","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xue-Zhen","middleName":"","lastName":"LIANG","suffix":""},{"id":282232270,"identity":"54965de8-5f94-4af6-b40b-1a43b8fdb283","order_by":2,"name":"Kai JIANG","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"JIANG","suffix":""},{"id":282232271,"identity":"abc47c83-95ad-47d3-a0c5-f380050b149a","order_by":3,"name":"Hai-Feng JIA","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hai-Feng","middleName":"","lastName":"JIA","suffix":""},{"id":282232272,"identity":"063222b2-cc42-474c-9450-075d6b8d4423","order_by":4,"name":"Han-Zheng LI","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Han-Zheng","middleName":"","lastName":"LI","suffix":""},{"id":282232273,"identity":"8f8f1604-4b1e-452e-9526-f359e56cdabc","order_by":5,"name":"Bo-Wen LU","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bo-Wen","middleName":"","lastName":"LU","suffix":""},{"id":282232274,"identity":"5729d4b0-91f4-488b-a073-c327d9d4e704","order_by":6,"name":"Gang LI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYFCCBCAuYJBjY28/QIoWAwZjPp4zCaRpSZwn4WBAnAb59vTHn3kMatPbJICaf1RsI6zF4MwbA2Meg+O5bdKNBxh7ztwmQotEDkMyj8Gx3DaZAwnMjG1EaJGfkf7gMFBLOptEggFxWhhuJBg28xjUJBCvBegXY8Y5BgcM24CBfJAov4BC7MObijp5+fb2gw9+VBDjMCBg4mE4DGYcIE49EDD+YKgjWvEoGAWjYBSMQAAAeHw76VsavKQAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2024-03-21 01:32:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4139990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4139990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53579284,"identity":"a52dcb0f-be23-4b74-a32e-ec986e619767","added_by":"auto","created_at":"2024-03-27 17:22:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82284,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design Flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/23e8c2b8aa156873e72cb028.png"},{"id":53579280,"identity":"f5b620d3-4c8a-42e8-8e46-13b6d7bfd56a","added_by":"auto","created_at":"2024-03-27 17:22:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":646577,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of the causal relationship between inflammatory factors and AS.\u003c/p\u003e\n\u003cp\u003eFigure Note: Figure A. FGF-23 was positively correlated with AS, Figure B. CD244 was negatively correlated with AS, Figure C. IL-7 was negatively correlated with AS, Figure D. FIt3L was negatively correlated with AS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/673bd2825320277ced34be6b.png"},{"id":53580811,"identity":"c51a050f-c6af-4779-8387-5a6dffabcf78","added_by":"auto","created_at":"2024-03-27 17:30:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":601640,"visible":true,"origin":"","legend":"\u003cp\u003eThe leave-one-out test for inflammatory factors and AS\u003c/p\u003e\n\u003cp\u003eFigure Note: Figure A. CD244 vs. AS, Figure B. FGF-23 vs. AS, Figure C. FIt3L vs. AS, Figure D. IL-7 vs. AS\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/ef0a4d6b0d976358b3f13e46.png"},{"id":54087162,"identity":"d2efb8e1-718c-4198-be04-5b0d30dea387","added_by":"auto","created_at":"2024-04-04 11:44:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1048717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/140af31d-cd67-481f-998e-a5927b5f20e6.pdf"},{"id":53579281,"identity":"5a9662e6-7d27-4c00-85c8-aec97de0a108","added_by":"auto","created_at":"2024-03-27 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17:22:15","extension":"csv","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":7268,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable7.csv","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/6ed2a137c015baafff3a7931.csv"},{"id":53579289,"identity":"cfc17545-6bd3-401f-ae6e-5f1a6cc71434","added_by":"auto","created_at":"2024-03-27 17:22:14","extension":"csv","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":16761,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable8.csv","url":"https://assets-eu.researchsquare.com/files/rs-4139990/v1/ae066617a884a4a59db91120.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between 91 inflammatory factors combined with 1400 metabolites and ankylosing spondylitis: a two-sample Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAnkylosing spondylitis (AS) is a chronic progressive inflammatory disease of the joints, mainly affecting the sacroiliac joints, spine, paraspinal soft tissues, and peripheral joints, which is characterized by inflammation, stiffness, pain, and dysfunction of the spinal cord and pelvic joints\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to epidemiological studies, the global prevalence of ankylosing spondylitis ranges from 0.1\u0026ndash;1.4%, is more common in men than in women, and often occurs between the ages of 30 and 40\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. AS is widespread throughout the world, and its incidence varies widely between regions and populations\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The exact pathogenesis of ankylosing spondylitis remains unclear, and diagnosis is typically based on a combination of clinical features and imaging changes in the sacroiliac joints. Ankylosing spondylitis patients experience abnormal release of inflammatory factors such as tumor necrosis factor-alpha (TNFα), interleukin-1 β (IL-1b), and interleukin-17 (IL-17) due to a combination of environmental factors (mechanical stress stimuli, infections, and intestinal flora) and immune and genetic factors. Irregular secretion and activation of inflammatory factors can cause inflammation, pain, and tissue damage in joints and the spine\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Human leukocyte antigen B27 (HLA-B27) is strongly associated with immune and genetic factors and is considered the highest genetic risk factor in ankylosing spondylitis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Tumor necrosis factor-α (TNF-α), IL-23, and IL-17 play important roles in the inflammatory response in ankylosing spondylitis and may be effective therapeutic targets\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In addition to this, there is increasing evidence that the development of ankylosing spondylitis is also closely related to metabolic abnormalities.Genre et al. showed a strong association between ankylosing spondylitis and metabolic disorders such as atherosclerosis, cardiovascular disease, diabetes mellitus, dyslipidemia, and obesity\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The causal effect of inflammatory factors and metabolites on ankylosing spondylitis remains uncertain due to sample size limitations and confounding factors.\u003c/p\u003e \u003cp\u003eIn epidemiologic studies, the presence of confounders greatly interferes with the causal inference of exposure and outcome. Mendelian randomization analysis is an emerging method for genetic research and is now widely used in epidemiological studies. The MR analyses relied on large samples of genotypic and phenotypic data. Instrumental variables (IVs) were used, which were single nucleotide polymorphism (SNP) loci with strong correlations with exposure factors, to determine causality between exposure and outcomes\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Mendelian randomization studies utilize genetic variants that follow the principle of random assignment of alleles, similar to randomized controlled experiments. This approach effectively mitigates the effects of confounders and reverse causality encountered in observational studies. This study utilized an innovative MR research method. Inflammatory factors were used as exposure factors and AS as outcome factors for MR analysis to screen for the inflammatory factors with causal associations. The inflammatory factors with causal associations were then analyzed by MR with 1400 metabolites. The objective of this study was to use a two-sample Mendelian randomization study to reveal the causal relationship between co-metabolites of inflammatory factors and AS. This study aims to provide new strategies for the prevention and treatment of AS.\u003c/p\u003e \u003cp\u003eMaterials and Methods\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study Design\u003c/h2\u003e \u003cp\u003eIn this study, firstly, based on the genome-wide association study (GWAS) pooled data of 91 inflammatory factors and ankylosing spondylitis, eligible instrumental variables were screened for MR analysis to explore the causal relationship between inflammatory factors and ankylosing spondylitis. Based on the Genome-Wide Association Study (GWAS) pooled data of inflammatory factors and 1,400 metabolites that were screened positive, eligible instrumental variables were screened for MR analysis to explore the causal relationship between inflammatory factors and 1,400 metabolites that are causally related to ankylosing spondylitis. The present study strictly adhered to the three assumptions of MR analysis, which are independence assumption, exclusivity assumption and correlation assumption\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. (figure. 1)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2 Exposure and Outcome Data Acquisition","content":"\u003cp\u003eThe Genome wide association study (GWAS) pooled dataset of 91 inflammatory factors was obtained from Zhao JH et al. study, which included 14824 European adults\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The study utilized a pooled dataset of 1,400 blood metabolites from a genome-wide association study, which is currently the most comprehensive analysis of blood metabolites available. The dataset was obtained from Chen YH and others\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, which included 1,091 metabolites and 309 metabolite ratios from 8,299 European adults, of which 1,091 metabolites comprised 850 known metabolites and 241 unknown metabolites. The 850 known metabolites can be categorized into eight major metabolic groups (amino acids, carbohydrates, cofactors and vitamins, energy products, lipids, nucleotides, peptides, and heterologous biometabolites) by the Kyoto encyclopedia of genes and genomes (KEGG) database. Pooled GWAS data for AS were obtained from the FinnGen data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r10.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://www.r10.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed 01/12/2024), which included a case group of 3,162 AS cases and a control group of 294,770, with all participants being of European ancestry, and informed consent was obtained.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Selection of IVs\u003c/h2\u003e \u003cp\u003eThe instrumental variables we chose were in line with the three core assumptions described previously, that the 91 inflammatory factors extracted as SNPs at the time of exposure should reach genome-wide significant levels (with a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and that the thresholds for linkage disequilibrium were set at r2\u0026thinsp;=\u0026thinsp;0.001 and kb\u0026thinsp;=\u0026thinsp;10,000 in order to select independent instrumental variables; The F values of single nucleotide polymorphisms in the IVs were obtained by calculating the formula as R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2 \u0026times; (1-MAF) \u0026times; MAF\u0026thinsp;\u0026times;\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e/2\u0026thinsp;\u0026times;\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026times;\u0026thinsp;EAF \u0026times; (1-EAF)\u0026thinsp;+\u0026thinsp;se\u003csup\u003e2\u003c/sup\u003e \u0026times; 2 \u0026times; N \u0026times; EAF (1-EAF), F = [R\u003csup\u003e2\u003c/sup\u003e/(1-R\u003csup\u003e2\u003c/sup\u003e)] \u0026times; [(N-K-1)/K]\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, with R\u003csup\u003e2\u003c/sup\u003e denoting the extent to which the instrumental variables explained the exposure, MAF denoting the minor allele frequency, β denoting the effect value of the alleles, K denoting the number of instrumental variables, and N denoting sample size. In addition, to exclude weak instrumental variables, we included instrumental variables with F\u0026thinsp;\u0026gt;\u0026thinsp;10 in the MR analysis. Finally, to ensure the accuracy of the results, palindromic SNPs with intermediate allele frequencies were removed from them\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the causal effects of inflammatory factors and ankylosing spondylitis as well as inflammatory factors and metabolites, a series of sensitivity analyses were performed, applying Cochran's Q-test to assess potential heterogeneity, with heterogeneity indicated when the p-value was \u0026le;\u0026thinsp;0.05. MR-Egger regression and MR-PRESSO were applied to assess potential horizontal pleiotropy; finally, Leave one out analysis was applied to assess the sensitivity of a single SNPs to the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MR Analysis\u003c/h2\u003e \u003cp\u003eFive MR analysis methods were used in this study, including Inverse Variance Weighted (IVW), MR-Egger, Weighted Median, Weighted Model and Simple Model. IVW was the main analysis method, and the other methods were used as supplements. When horizontal pleiotropy was absent, the results of IVW were not biased, thus making its analysis more reliable compared to other methods. When ankylosing spondylitis was used as an outcome, it was a dichotomous variable, expressed as odds ratio (OR) and 95% CI, and when metabolites were used as an outcome, it was a continuous variable, expressed as beta value and 95% confidence interval (CI). All statistical analyses were performed in R (version 4.3.1) software using the \"TwoSampleMR\" package and the MR-PRESSO package used for the above analyses, with a test criterion of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Inflammatory factors and AS\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Selection of IVs\u003c/h2\u003e \u003cp\u003eIn this study, the genome-wide significance threshold was set at 5 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5 to ensure a sufficient number of SNPs for analysis. In addition, all F values exceeded 10 to mitigate bias caused by weak instrumental variables, resulting in a total of 24,207 SNPs screened.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 MR Analysis\u003c/h2\u003e \u003cp\u003eIn this study, we first analyzed the causal relationship between inflammatory factors and ankylosing spondylitis, along with the tests of pleiotropy and heterogeneity, and the results of the analysis initially suggested that there were seven inflammatory factors (CCL19, CD244, FGF-23, FIt3L, IL-18R1, IL-6, and IL-7) that might be causally associated with AS, and four of them (CD244, FGF-23, FIt3L, and IL-7) passed the test of horizontal pleiotropy and the test of heterogeneity(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). IVW analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that FGF-23 was positively associated with ankylosing spondylitis with an OR of 1.185 (95% CI\u0026thinsp;=\u0026thinsp;1.013\u0026thinsp;~\u0026thinsp;1.387, P\u0026thinsp;=\u0026thinsp;0.034). IL-7 was positively associated with ankylosing spondylitis with an OR of 1.240 (95% CI\u0026thinsp;=\u0026thinsp;1.020\u0026thinsp;~\u0026thinsp;1.506, P\u0026thinsp;=\u0026thinsp;0.030), CD244 was negatively correlated with ankylosing spondylitis with an OR of 0.885 (95% CI\u0026thinsp;=\u0026thinsp;0.786\u0026ndash;0.996, P\u0026thinsp;=\u0026thinsp;0.043), and FIt3L was negatively correlated with ankylosing spondylitis with an OR of 0.895 (95% CI\u0026thinsp;=\u0026thinsp;0.803\u0026thinsp;~\u0026thinsp;0.998, P\u0026thinsp;=\u0026thinsp;0.045) (Figure. 2). The results of the leave-one-out method showed that no significant outliers were seen, and that the results of the MR study were reliable. (Figure. 3)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure Note: Figure A. FGF-23 was positively correlated with AS, Figure B. CD244 was negatively correlated with AS, Figure C. IL-7 was negatively correlated with AS, Figure D. FIt3L was negatively correlated with AS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure Note: Figure A. CD244 vs. AS, Figure B. FGF-23 vs. AS, Figure C. FIt3L vs. AS, Figure D. IL-7 vs. AS\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis of inflammatory factors and AS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003einflammatory factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGCS_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHeterogeneity Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHorizontal Multiplicity Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55E-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.42E-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIt3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-18R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46E-69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93E-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIVW analysis of inflammatory factors and AS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCS_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIt3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90274816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.030\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 \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Positive Inflammatory Factors and Metabolites\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Selection of IVS\u003c/h2\u003e \u003cp\u003eIn this study, four inflammatory factors (CD244,FGF-23,FIt3L, IL-7) causally associated with AS were analyzed by MR as exposure and metabolites as outcome. Firstly, these 4 inflammatory factors were screened for eligible instrumental variables according to the conditions first set based on genome-wide significance, linkage disequilibrium, CD244 screened for 33 SNPs,FGF-23 screened for 28 inflammatory factors,FIt3L screened for 46 SNPs, and IL-7 screened for 22 SNPs. The F values of the four inflammatory factor single-nucleotide polymorphisms ranged from 19.53 to 442.14, which all met the requirement of F\u0026thinsp;\u0026gt;\u0026thinsp;10, indicating that the present study has a low probability of having a weak instrumental variable bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Statistical Analysis\u003c/h2\u003e \u003cp\u003eMR analysis was performed on 1400 metabolites using 4 positive inflammatory factors as exposure factors, respectively. After 5 metabolites were excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables\u0026nbsp;1, 2), IVW analysis showed that FGF-23 was causally associated with 62 metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 55 were known metabolites and 7 were unknown metabolites, and of the known metabolites, FGF-23 was positively correlated with 18 metabolites, and negatively correlated with 37 metabolites (Supplementary Fig.\u0026nbsp;1); After 2 metabolites were excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables\u0026nbsp;3, 4), IVW analysis showed that IL-7 was causally associated with 68 metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 57 were known metabolites and 11 were unknown metabolites, and of the known metabolites, IL-7 was positively correlated with 42 metabolites and negatively correlated with 15 metabolites (Supplementary Fig.\u0026nbsp;2); One metabolite was excluded by horizontal pleiotropy and heterogeneity tests (Supplementary Tables\u0026nbsp;5, 6), and IVW analysis showed that FIt3L was causally associated with 37 metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 32 were known metabolites and 5 were unknown metabolites, and of the known metabolites, FIt3L was positively correlated with 13 metabolites and negatively correlated with 19 metabolites (Supplementary Fig.\u0026nbsp;3); after excluding 7 metabolites by horizontal pleiotropy and heterogeneity tests (Supplementary Tables\u0026nbsp;7, 8), IVW analysis showed that CD244 was causally associated with 61 metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 51 were known metabolites and 10 were unknown metabolites, and of the known metabolites, CD244 was positively correlated with 18 metabolites and negatively correlated with 33 metabolites (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAS is closely related by inflammatory factors and metabolites and has received great attention in clinical research, but the causal relationship at the genetic level is still unclear. Therefore, based on the pooled data of GWAS, we comprehensively analyzed the causal relationship of 91 inflammatory factors on AS by using a two-sample MR method, and we also performed a two-sample MR analysis of the positive inflammatory factors with 1,400 blood metabolites, exploring the causal relationship between the inflammatory factors and metabolites associated with AS, and ultimately providing a new perspective for unraveling the genetically inherited role in the pathogenesis of AS, as well as a direction for precision diagnosis and prevention. Fang P et al investigated the causal relationship between 41 inflammatory factors and AS and performed MR analysis to conclude that three inflammatory factors (βNGF, IL-1b, and TRAIL) were causally associated with the risk of developing AS\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The current study used a more comprehensive set of 91 inflammatory factors with AS for MR analysis, which can more comprehensively and reliably assess the causal relationship between inflammatory factors and AS.\u003c/p\u003e \u003cp\u003eThe pathogenesis of AS remains unclear, and it is widely recognized that the key mechanism lies in the abnormal production and regulation of inflammatory factors\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The results of this study showed that FGF-23 and IL-7 were positively associated with the risk of ankylosing spondylitis, whereas CD244 and FIt3L were negatively associated with ankylosing spondylitis, and FGF-23 was causally associated with 62 metabolites, IL-7 with 68 metabolites, CD244 with 61 metabolites, and FIt3L with 37 metabolites. Abnormal regulation of inflammatory factors affects the regulation of metabolic processes, leading to compromised metabolite secretion and synthesis, which in turn triggers inflammatory responses. These inflammatory reactions mainly affect joints such as the spine and pelvis, leading to inflammatory injuries of the sacroiliac joints, spine, paraspinal soft tissues and peripheral joints\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBy two-sample MR analysis, this study found that elevated levels of FGF-23 and IL-7 were associated with an increased risk of ankylosing spondylitis. Fibroblast growth factor \u0026minus;\u0026thinsp;23(FGF-23) is a hormone secreted by osteoblasts and osteoclasts of long bones and is involved in the regulation of serum phosphate and osteotriol (1,25(OH)2D3) levels\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.FGF-23 has been linked to CKD-related metabolic bone disease, osteoporosis and other bone metabolic diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Gercik O et al. showed that FGF-23 levels were significantly higher in patients with ankylosing spondylitis (170, 94.3-317.4 pg/ml) than in healthy controls (107, 63.3-192.8 pg/ml), p\u0026thinsp;=\u0026thinsp;0.023 and were strongly associated with inflammation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The results of this study suggest a potential causal relationship between both FGF-23 and ankylosing spondylitis. IL-7 is a multifunctional cytokine that maintains the homeostasis of the immune system, and its wide distribution includes lymphoid organs such as bone marrow, thymus, lymph nodes, and spleen, as well as non-lymphoidal sites such as the skin, lungs, intestines, and liver, and plays a crucial regulatory role in the entire immune system\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In addition, elevated IL-7 concentrations are strongly associated with autoimmune diseases such as rheumatoid arthritis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Ciccia F et al. showed that ILC3, characterized by Lyn-RORc-Tbet\u0026thinsp;+\u0026thinsp;NKp44\u0026thinsp;+\u0026thinsp;cells, was significantly expanded in the intestine, synovial fluid and bone marrow and produced high levels of IL-17 and IL-22, in which IL-7 played a key role, and that its levels were significantly increased in the intestines of AS patients\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Our study showed that elevated levels of IL-7 were associated with an increased risk of ankylosing spondylitis. Thus, IL-7 may be involved in the pathogenesis of ankylosing spondylitis, but the underlying mechanisms remain to be further elucidated. CD244 is a transmembrane protein present in NK cells, T cells, and other types of immune cells that may play a role in the development of immune-related diseases such as systemic lupus erythematosus, rheumatoid arthritis, and type 1 diabetes mellitus\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The relationship between CD244 and AS has not been reported in the literature, and the results of this study suggest that increased levels of CD244 are associated with a decreased risk of developing AS, which still needs to be further verified in the future. Flt3 plays an important role in rheumatoid arthritis, and Ramos M I et al. showed that Flt3 levels were significantly elevated in serum, synovial fluid, and synovial tissue of rheumatoid arthritis (RA) patients\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The results of the present study showed a negative correlation between Flt3 and ankylosing spondylitis, which needs to be verified by further studies in the future.\u003c/p\u003e \u003cp\u003eAS is closely associated with metabolic abnormalities and metabolites play an important role in AS. elevated levels of FGF-23 increase the risk of developing AS.在Among the 62 metabolites with potential causal relationship with FGF-23, Li H et al. study showed that 5alpha-androstan-3beta,17beta-diol disulfate levels, Citrate levels, and Choline levels were significantly reduced in the group of patients with ankylosing spondylitis as compared to the healthy control group\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The results of the present study showed that FGF-23 was negatively associated with all three metabolites. The present study hypothesized that FGF-23 increases the risk of ankylosing spondylitis by down-regulating 55alpha-androstane-3beta,17beta-diol disulfate levels, Citrate levels, and Choline levels, which provides a direction for subsequent studies on the pathogenesis of AS. The results provide a direction for subsequent studies on the pathogenesis of AS. TNF-α plays an important role in the inflammatory response in AS, and Vittimberga F J et al. found that Salicylate levels may inhibit the production of TNF-α\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Luxia Zu et al found that salicylates blocked the lipolytic effect of TNF-α on primary adipocytes in rats\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In addition, salicylate-based NSAIDs are the first-line treatment for ankylosing spondylitis. The results of this study show that FGF-23, a risk factor for AS, decreases salicylate levels. The present study hypothesized that FGF-23 increases the risk of developing AS by down-regulating salicylate levels. This study also found that some metabolites are associated with other inflammatory diseases or cancers. Paine A et al. showed that Glycoursodeoxycholic acid sulfate (1) levels are sensitive and specific predictors of progression from Psoriasis (Ps) to Psoriatic Arthritis (PsA)\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Nystrom N et al. showed that Behenoyl sphingomyelin (d18:1/22:0) levels are involved in the inflammatory response and are increased in neonatal inflammatory bowel disease\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Perfluorooctane sulfonate (PFOS) levels are a toxic and carcinogenic persistent organic pollutant to the human body that induces apoptosis of immune cells, destroys the immune system, and ultimately leads to cancer and toxic damage\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Zhang J et al. found that malonylcarnitine levels were lower in the breast cancer case group than in the control group and is a protective factor for breast cancer\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. 1 -Methylnicotinamide (1-MNA) was found to be a risk factor for hepatocellular carcinoma in liver cancer patients at significantly higher levels than in healthy controls\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In addition, Sidor K et al. demonstrated that 1-MNA reduced the activation of NLRP3 inflammatory vesicles in human macrophages through a ROS-dependent pathway and reduced the occurrence of NLRP3-associated inflammatory diseases\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. These metabolites provide new ideas to explore the pathogenesis of ankylosing spine.\u003c/p\u003e \u003cp\u003eElevated levels of IL-7 increase the risk of developing ankylosing spondylitis, and there are 68 metabolites that have a potential causal relationship with IL-7. Li H et al. found that Anthranilate levels were significantly reduced in the group of ankylosing spondylitis patients compared to healthy controls\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Diametrically, the present study showed that IL-7 increased Anthranilate levels, which warrants further investigation of the mechanisms linking IL-7 and Anthranilate levels to AS. Although there are a number of metabolites that are not supported by the literature related to AS, they have been associated with other inflammatory diseases or cancers, and it is possible that some of the same inflammatory mechanisms may exist that are worth investigating. Taurocholenate sulfate levels may be a new candidate marker for early serodiagnosis of hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. 3-methylglutarylcarnitine (2) levels were independently associated with invasive breast cancer and estrogen receptor-positive (ER+) breast cancer, and may be a metabolic pathway for breast carcinogenesis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Taurocholic acid levels may stimulate the intestinal flora to convert taurine and bile acids into genotoxins and tumor initiating factors, respectively, associated with colon cancer\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCD244AS protective factors with causal association with 61 metabolites. Among them, Li H et al. study found that 1-stearoyl-GPE (18:0) levels, Pregnenediol disulfate (C21H34O8S2) levels were significantly lower in AS than in healthy controls, 1-(1-enyl-stearoyl)-GPE (p-18:0) levels were significantly higher in the AS patient group than in the healthy control group\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The present study showed that CD244 was positively correlated with 1 -stearoyl-GPE (18:0) levels, 1 -(1 -enyl-stearoyl)- GPE (p-18:0) levels and negatively correlated with Pregnenediol disulfate (C21H34O8S2) levels. In this study, we hypothesized that CD244 reduces the risk of A by up-regulating 1-stearoyl-GPE (18:0) levels and down-regulating 1-(1-enyl-stearoyl)-GPE (p-18:0) levels.1. Furthermore, studies such as Imrich R and Kirnap M showed that no significant difference in Cortisol levels was seen in AS patients compared to healthy controls\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. However, the results of the present study suggest that CD244 decreases cortisol levels. This warrants an in-depth study of how CD-244 affects the pathogenesis of AS by modulating cortisol.\u003c/p\u003e \u003cp\u003eThe strength of the current study lies in the fact that it has a strong theoretical basis and important clinical research value from the perspective of molecular mechanisms to explore the causal relationship between 91 inflammatory factors and the risk of AS development, and then between positive inflammatory factors and 1400 metabolites as exposure factors; In this study, strict quality control conditions and analytical methods were used, which can overcome the effects of potential confounding and reverse causality, avoid the waste of human, material and financial resources, etc., and the results of the study are reliable and stable; In contrast to previous Mendelian randomization studies of single exposure factors, the present study involves a large number of 91 inflammatory factors and 1400 metabolites, which is a very large workload and analytical challenge.\u003c/p\u003e \u003cp\u003eThere are some limitations to this study, firstly the data are only from a European population and further validation is needed to see if similar genetic variants exist in other populations; Among the metabolites obtained by analysis that are causally related to inflammatory factors associated with ankylosing spondylitis there are a number of unknown metabolites with uncertainties in their functional structure; The veracity of the MR analysis depends to a greater extent on the interpretation of the instrumental variables of exposure, and follow-up studies still need further expanded sample sizes to provide a more precise assessment of the MR analysis.\u003c/p\u003e"},{"header":"5 Conclussion","content":"\u003cp\u003eIn this study, a two-sample Mendelian randomization method was used to comprehensively assess the causal relationship between 91 inflammatory factors combined with 1400 metabolites and AS. The results of this study showed that four inflammatory factors, FGF-23, IL-7, CD244, and FIt3L, were causally associated with ankylosing spondylitis. In addition, FGF-23 was potentially causally associated with 62 metabolites, IL-7 with 68 metabolites, CD244 with 61 metabolites, and FIt3L with 37 metabolites. From a public health perspective, the results of this study are conducive to an in-depth understanding of the genetic relationship between inflammatory factors and metabolites and AS, and to the use of inflammatory factors and blood metabolites as potential biomarkers, which can provide a potential direction for exploring the pathogenesis of ankylosing spondylitis, disease prevention, and targeted drug therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAnkylosing spondylitis (AS)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;confidence interval (CI)\u003c/p\u003e\n\u003cp\u003egenome-wide association studies (GWAS)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;inverse variable weighting (IVW)\u003c/p\u003e\n\u003cp\u003einstrumental variables (IVs)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mendelian randomization (MR)\u003c/p\u003e\n\u003cp\u003esingle-nucleotide polymorphism (SNP)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;odds ratio (OR)\u003c/p\u003e\n\u003cp\u003efibroblast growth factor-23 (FGF-23)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interleukin 7 (IL-7),\u003c/p\u003e\n\u003cp\u003eFms-like tyrosine kinase 3 ligand (Flt3L) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;)\u003c/p\u003e\n\u003cp\u003e1 -Methylnicotinamide (1-MNA)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e8.1 Data Availability Statement\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets were analysed in this study. These datasets can be found at the following URLs: FinnGen (https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_M13_ANKYLOSPON.gz) and GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).\u003c/p\u003e\n\u003ch2\u003e8.2 Ethics Approval and Informed Consent\u003c/h2\u003e\n\u003cp\u003eAll data used in this work are publicly available from studies with relevant participant consent and ethical approval.\u003c/p\u003e\n\u003ch2\u003e8.3 Consent for Publication\u003c/h2\u003e\n\u003cp\u003eAll participating authors give their consent for this work to be published.\u003c/p\u003e\n\u003ch2\u003e8.4 Acknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank the IEU OpenGWAS data‑base and FINNGEN database for sharing the data.\u003c/p\u003e\n\u003ch2\u003e8.5 Funding\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003e8.6 Author Contributions\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions All authors made significant contributions to the reported work and agreed to accept responsibility for all aspects of the work.R.Y.F. and L.G. designed the experiments.R.Y.F. and L.X.Z. performed the data preparation, R.Y.F., J.K., J.H.F., and L.H.Z. analyzed data.R.Y.F., L.B.W., and L.X.Z. prepared the first draft of the manuscript. R.Y.F., L.B.W. and L.X.Z. prepared the manuscript.L.G. provided critical feedback during the research process or during the submission of the manuscript. All authors finally approved the submitted version and agreed to publish it in the journal.\u003c/p\u003e\n\u003ch2\u003e8.7 Declaration of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest in the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaurog JD, Chhabra A, Colbert RA. Ankylosing Spondylitis and Axial Spondyloarthritis[J]. N Engl J Med. 2016;374(26):2563\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDean LE, Jones GT, MacDonald AG, et al. Global prevalence of ankylosing spondylitis[J]. Rheumatology (Oxford). 2014;53(4):650\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExarchou S, Lindstrom U, Askling J, et al. 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Gut Microbes. 2016;7(3):201\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImrich R, Rovensky J, Zlnay M, et al. Hypothalamic-pituitary-adrenal axis function in ankylosing spondylitis[J]. Ann Rheum Dis. 2004;63(6):671\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirnap M, Atmaca H, Tanriverdi F, et al. Hypothalamic-pituitary-adrenal axis in patients with ankylosing spondylitis[J]. Horm (Athens). 2008;7(3):255\u0026ndash;8.\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":"ankylosing spondylitis, inflammatory cytokines, metabolites, Mendelian randomization, causal relationships, inverse variance weighted method, heterogeneity, horizontal pleiotropy, sensitivity analysis","lastPublishedDoi":"10.21203/rs.3.rs-4139990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4139990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND: \u003c/strong\u003eAnkylosing spondylitis is a chronic progressive inflammatory disease of the joints. A large amount of evidence shows that ankylosing spondylitis is closely related to inflammatory factors and metabolites. However, the causal relationship between ankylosing spondylitis and inflammatory factors and metabolites is unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOBJECTIVE: \u003c/strong\u003eTo evaluate potential the causal relationships between 91 inflammatory cytokines combined with 1,400 metabolites and ankylosing spondylitis using the Mendelian randomization method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eA two-sample Mendelian randomization study was performed using the Genome-wide association study (GWAS) summary statistics of 91 inflammatory cytokines (n=14,824) and 1,400 serum metabolites (n=8,299) as well as GWAS data of ankylosing spondylitis from the FinnGen R10 database (3,162 cases and 2,947,070 healthy controls) were used. Inverse variance weighted, MR-Egger, weighted median, weighted model and simple model were used to examine the causal association between inflammatory cytokines combined with metabolites and ankylosing spondylitis. Sensitivity analysis was used to test whether the results of the Mendelian randomization analysis were reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION: \u003c/strong\u003eFGF-23 and IL-7 were positively correlated with ankylosing spondylitis while CD244 and FIt3L were negatively correlated based on causal associations. FGF-23 had potential causal relationships with 62 metabolites (p\u0026lt;0.05), IL-7 had potential causal relationships with 68 metabolites (p\u0026lt;0.05), FIt3L had potential causal relationships with 37 metabolites (p\u0026lt;0.05), and CD244 had potential causal relationships with 61 metabolites (p\u0026lt;0.05). The results suggest that CD244, FGF-23, FIt3L, IL-7 may play important roles in the pathogenesis of ankylosing spondylitis, and metabolism-related inflammatory cytokines could be important in future explorations of mechanisms and drug target selections for ankylosing spondylitis.\u003c/p\u003e","manuscriptTitle":"Association between 91 inflammatory factors combined with 1400 metabolites and ankylosing spondylitis: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:22:09","doi":"10.21203/rs.3.rs-4139990/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":"3b2f3c99-ee7b-4d72-a84a-72b821246471","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-04T11:36:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 17:22:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4139990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4139990","identity":"rs-4139990","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}