Causal relationship between gut microbiota, circulating inflammatory proteins and IgA nephropathy: two-sample and mediated Mendelian randomisation analysis

Causal relationship between gut microbiota, circulating inflammatory proteins and IgA nephropathy: two-sample and mediated Mendelian randomisation analysis | 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 Causal relationship between gut microbiota, circulating inflammatory proteins and IgA nephropathy: two-sample and mediated Mendelian randomisation analysis Pengtao Dong, Xiaoyu Li, Xue Feng, Siyu Huang, Bing Cui, Qing Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4472698/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： IgA nephropathy (IgAN) is an immune-inflammatory glomerulonephritis mediated by both genetic and environmental factors. Recent research indicates a close association between gut microbiota dysbiosis and IgAN development. Additionally, circulating inflammatory proteins also play a significant role in the progression of IgAN.However, the causal relationship among gut microbiota, circulating inflammatory proteins, and IgAN remains unclear. Methods： This study utilized publicly available genome-wide association study (GWAS) data for Mendelian randomization (MR) analysis to investigate the causal relationship among gut microbiota circulating inflammatory proteins and IgAN, as well as to examine the mediating role of circulating inflammatory proteins in the association between gut microbiota and IgAN. The primary analytical method employed in this study was inverse variance-weighted (IVW) analysis with specific attention given to Bayesian-weighted MR results and supported by MR-Egger regression, weighted median, median model and simple model approaches. Several sensitivity analyses were performed to evaluate the robustness of MR analysis findings. Results： (1)MR analysis of gut microbiota and IgAN indicates negative associations between g_Roseburia, g_Faecalibacterium, s_Odoribacter_splanchnicus, and s_Roseburia_unclassified with IgAN risk, while positive associations exist between s_Paraprevotella_unclassified and s_Lachnospiraceae_bacterium_7_1_58FAA with IgAN risk.(2) Circulating inflammatory proteins to IgAN in MR analysis showed that IL-10RA was negatively correlated with the risk of IgAN, while TSGP-CD5, FGF23, LIF, and TGF-α levels were positively correlated with the risk of IgAN.(3)Mediation analysis suggests that TGF-αserves as a mediator between s_Odoribacter_splanchnicus and the causality of IgAN. (4) The results of the reverse MR analysis suggest no significant causal effect of IgAN on gut flora and circulating inflammatory proteins.Sensitivity analyses consistently support the reliability of the study results. Conclusion： Our research findings, obtained through genetic methods, substantiate the causal link between gut microbiota, circulating inflammatory proteins, and IgAN. The identification of biomarkers offers novel insights into the potential mechanisms underlying IgAN, which can be advantageous for early diagnosis and the development of more effective treatment strategies. IgA nephropathy Gut microbiota Circulating Inflammatory proteins Mendelian randomization Analysis of mediation Causality Figures Figure 1 Figure 2 Figure 3 1 Introduction IgA nephropathy (IgAN) is an immune-inflammatory mediated glomerulonephritis, characterized by the deposition of immunoglobulin A or IgA-dominant immune complexes in the mesangial area of the glomeruli[ 1 ]. Microscopic or gross hematuria, with or without varying degrees of proteinuria, represents the most common clinical manifestation. As the most prevalent primary glomerular disease globally, approximately 25% of IgAN patients progress to end-stage renal disease (ESRD) within 20 years[2 , 3]. Furthermore, IgAN predominantly affects young and middle-aged individuals, significantly impacting the productive workforce and imposing substantial psychological and economic burdens on family members[ 4 ]. The specific pathogenesis of IgAN is complex and not fully elucidated at present, while its definitive diagnostic methods remain limited to renal biopsy techniques. Hence, researchers urgently need to identify potential risk factors for the disease, facilitating early diagnosis and the development of new treatments for IgAN. The human gut hosts millions of microorganisms, and maintaining the relative stability of the gut microenvironment is vital for human health. In recent years, an increasing number of studies have shown a close association between dysbiosis of the gut microbiota and IgAN[ 5 – 7 ]. Alterations in gut microbiota abundance can disrupt systemic or local mucosal immune responses, resulting in increased release of immunoglobulins into the bloodstream, subsequently depositing in the kidneys and leading to the development of IgAN[ 8 ]. LIANG et al, sequenced fecal samples from both IgAN patients and healthy controls, revealing a higher abundance of Bacteroides in IgAN patients[ 9 ]. 16S rRNA sequencing of fecal samples from untreated IgAN patients revealed a significant increase in the abundance of Escherichia-Shigella, which decreased significantly in clinically relieved patients after treatment with immunosuppressants and a 6-month follow-up[ 10 ].Furthermore, a study demonstrated that IgAN patients exhibited lower levels of Bifidobacterium, with its abundance showing a negative correlation with levels of hematuria and proteinuria[ 11 ].The gut microbiota plays a pivotal role in the pathogenesis of IgAN. IgAN is a disease closely linked to immune inflammation. Macrophages, pivotal immune cells in the body, can differentiate into M1 (pro-inflammatory) and M2 (anti-inflammatory) phenotypes. Deposition of immune complexes in the kidneys can activate macrophages to polarize towards the M1 phenotype, resulting in the secretion of large amounts of pro-inflammatory cytokines (such as TNF-α, IL-1, IL-6, IL-12), ultimately leading to kidney function loss[ 12 ]. As the disease progresses and M2 macrophages polarize, anti-inflammatory cytokines can promote kidney tissue repair and facilitate renal fibrosis[ 13 ]. Inflammatory cytokines play a pivotal role throughout the development of IgAN. Abnormal gut microbiota can cause barrier dysfunction, inflammation and local immune responses in IgAN[ 14 ]. However, the exact causal relationship and mediation proportions among gut microbiota, circulating inflammatory proteins and IgAN are still unclear. Mendelian randomization (MR) is a method used to assess causal associations between exposure and outcome by substituting for randomized controlled trials. Because genetic variation is randomly allocated at conception. MR analysis using genetic variants as instrumental variables can mitigate the influence of common confounders and avoid reverse causation bias[ 15 ]. Mediation analysis is employed to assess pathways through which exposure affects outcomes[ 16 ]. In this study, we performed two-sample and mediation MR analyses using publicly available summary-level data from genome-wide association studies (GWAS) to assess the causal relationship between gut microbiota, circulating inflammatory proteins and IgAN to determine the mediating effect of circulating inflammatory proteins. 2 Materials and Methods 2.1 Study design The workflow of this MR study is illustrated in Figure 1. Firstly, we acquired publicly available summary-level data from GWAS concerning gut microbiota, circulating inflammatory proteins and IgAN. Two-sample MR methods were utilized to evaluate the causal relationships between gut microbiota and IgAN, as well as between circulating inflammatory proteins and IgAN. Subsequently, circulating inflammatory proteins with a mediating role were identified in the positive findings of the causal relationship between gut microbiota and IgAN. 2.2 Data sources GWAS summary statistics for IgAN are sourced from the FinnGen consortium (R10), involving 653 IgAN cases and 411,528 controls. The data can be directly accessed from https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_N14_IGA_NEPHROPATHY.gz. GWAS summary data for gut microbiota are derived from a whole-genome association study conducted by the Netherlands Microbiome Project team, which involved 7,738 participants and assessed 412 features (including 207 gut microbial taxa and 205 functional pathways)[17]. The GWAS data for circulating inflammatory proteins were obtained from the study by Zhao et al., which recruited 14,824 participants and identified 91 circulating inflammatory proteins using Olink Target Inflammation Immunoassay Panels to analyze whole-genome genetic data and plasma proteomic data[18]. The GWAS summary statistics for circulating inflammatory proteins can be found in the EBI GWAS Catalog (accession numbers GCST90274758-GCST90274848)[18]. 2.3 Instrumental variable selection Qualified instrumental variables (IVs) must meet three core assumptions: (1) Selected IVs are directly linked to the exposure factor; (2) IVs are unrelated to any confounding factors influencing the "exposure-outcome" relationship; (3) Selected IVs influence the outcome solely through the exposure factor[19]. Strict criteria are necessary for screening IVs to ensure the credibility of MR study findings. When screening single nucleotide polymorphisms (SNPs) associated with gut microbiota and circulating inflammatory proteins, we initially employed a stringent threshold (P<5×10-8) for selection, resulting in a limited number of SNPs being chosen. To increase the number of SNPs for the study, we adjusted the threshold to P<1×10-5, based on the majority of previous studies[20-22], and subsequently established parameters kb = 10000, r2= 0.001 to mitigate interference from linkage disequilibrium[23]. Additionally, we performed reverse MR analysis, with the criteria for selecting IgAN-related SNPs set at P<5×10-6, kb = 10000, r2= 0.001.Weak instrumental variables exhibit a feeble association with the exposure factor, thereby compromising result accuracy. The strength of each SNP was calculated using the F statistic, and IVs with F<10 were excluded as weak instruments[24]. Palindromic SNPs were eliminated by reconciling exposure-outcome datasets[24]. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method were employed to remove outlier SNPs[25]. Following stringent screening based on the aforementioned criteria, the remaining SNPs were utilized for subsequent MR analysis. 2.4 Statistical analysis 2.4.1 Two-sample Mendelian randomization Five analytical methods—Inverse Variance Weighted (IVW), MR-Egger regression, Weighted Median Model (WME), Simple Model (SM), and Weighted Model (WM)—were employed to assess causality[26]. When all SNPs are valid and no horizontal pleiotropy exists, the IVW method combines estimated values of individual IVs using inverse variance weights to yield a consistent and unbiased estimate of the causal effect[27]. MR-Egger regression and WME are valuable tools in MR for addressing situations involving horizontal pleiotropy or violations of the IVs assumption. They enable researchers to estimate causal effects while accounting for pleiotropy effects and offer additional insights into the relationship between exposure and outcome variables[25]. Nevertheless, the non-parametric nature of WME might result in reduced estimation accuracy, while MR Egger, relying on regression modeling, may diminish statistical power[28]. SM groups SNP categories with similar values and evaluates causal associations based on the group with the most SNPs[29]. WM requires identifying multiple variables as valid instruments to detect the same causal effect.IVW analysis has the highest statistical power, and this study utilized IVW analysis as the primary method for MR analysis[30]. Additionally, Bayesian Weighted Mendelian Randomization (BWMR) was employed to validate positive results. BWMR served as our primary reference, and negative outcomes from BWMR were disregarded. BWMR accounts for uncertainty stemming from polygenicity, resulting in weak instrument effects, and tackles violations of the IV core assumption attributed to horizontal pleiotropy through Bayesian weighted outlier detection[31]. Sensitivity analysis was performed to evaluate the robustness of the results. Cochran's Q test was employed to assess heterogeneity among IVs, where a significance level of P < 0.05 indicated substantial heterogeneity among SNPs, warranting the use of a random-effects model; otherwise, a fixed-effects model was employed[32]. MR-Egger regression was utilized to evaluate horizontal pleiotropy and its statistical significance. Absence of a significant intercept term in MR-Egger (P > 0.05) indicates the absence of horizontal pleiotropy[33]. Leave-one-out analysis was conducted by sequentially excluding individual IVs to investigate whether any SNP exerts a dominant influence on the causal association. Significant influence on the MR results upon removal of a specific SNP suggests that the outcome is impacted by a single IV. Subsequent analysis will omit results identified as outliers and displaying horizontal pleiotropy by MR-PRESSO. Additionally, this study is exploratory in nature, and to achieve more positive and mediating results, we did not apply multiple testing corrections. All analyses were performed using R (version 4.3.2) along with the R-TwoSampleMR package (version 0.5.10) and the R-MR-PRESSO package. 2.4.2 Reverse Mendelian randomization analysis To investigate the causal relationship between IgAN and gut microbiota as well as circulating inflammatory proteins (P IVW < 0.05), we also performed reverse MR analysis.In this scenario, SNPs associated with IgAN are treated as exposures, while gut microbiota and categories of circulating inflammatory proteins are regarded as outcomes. The steps involved in reverse MR analysis mirror those of standard MR analysis. 2.4.4. Mediation analysis Mediation analysis aims to evaluate the pathways through which exposure influences the outcome, assisting in the exploration of potential mechanisms through which exposure affects the outcome. Mediation analysis comprises four specific steps. In Step 1, we have already derived the total effect (beta_all) of gut microbiota on IgAN through two-sample MR analysis. In Step 2, reverse MR analysis was performed on the outcomes of Step 1 to assess the causal association between IgAN and gut microbiota; mediation analysis can only proceed when the mediator is independent of the exposure[16]. Step 3 involved conducting two-sample MR analysis on circulating inflammatory proteins and IgAN to determine the effect size (beta2) of circulating inflammatory proteins on IgAN. Step 4 involved subjecting the gut microbiota obtained in Step 1 and circulating inflammatory proteins obtained in Step 3 to two-sample MR analysis to derive the effect size (beta1). The mediation effect is calculated as beta1 × beta2, where beta_direct is the difference between the total effect and the mediation effect, and the mediation ratio is determined as (mediation effect / total effect) × 100%. The delta method was used to estimate the 95% confidence interval (CI) for the mediation effect and mediation ratio[16]. 3 Results 3.1 Causal effects of gut microbiota on IgA nephropathy According to Figure 2 (P IVW 0.05). Ultimately, two genera and four species of bacteria were confirmed to be causally associated with IgAN. Specific information about SNPs is provided in Supplementary Table S3.At the genus level, IVW analysis revealed a significant negative correlation between g_Roseburia (OR=0.639, 95% CI: 0.426 - 0.958, P=0.030) and g_Faecalibacterium (OR=0.558, 95% CI: 0.328 - 0.947, P=0.030) and IgAN, thereby reducing the risk of IgAN. At the species level, s_Odoribacter_splanchnicus (OR=0.677, 95% CI: 0.485 - 0.944, P=0.021) and s_Roseburia_unclassified (OR=0.743, 95% CI: 0.562 - 0.983, P=0.037) were negatively associated with the risk of IgAN. Conversely, s_Paraprevotella_unclassified (OR=1.494, 95% CI: 1.021 - 2.186, P=0.039) and s_Lachnospiraceae_bacterium_7_1_58FAA (OR=1.614, 95% CI: 1.052 - 2.477, P=0.029) were positively correlated with the risk of IgAN.Similarly, BWMR results aligned with IVW analysis findings and demonstrated statistical significance, thereby reinforcing the robustness of our results. For a more comprehensive overview of results, please refer to Supplementary Table S4. These findings confirm the causal association between particular gut microbiota and the risk of IgAN. Sensitivity analyses were performed to ensure the robustness of the findings (Supplementary Table S5). Cochran's Q tests indicated that both IVW and MR-Egger had Q-pvalues greater than 0.05, suggesting no significant heterogeneity (Table 1); additionally, MR-Egger demonstrated no significant intercept terms (P > 0.05), indicating the absence of significant horizontal pleiotropy (Table 1). Cochran's Q tests indicated that both IVW and MR-Egger had Q-pvalues greater than 0.05, suggesting no significant heterogeneity (Table 1); additionally, MR-Egger demonstrated no significant intercept terms (P > 0.05), indicating the absence of significant horizontal pleiotropy (Table 1). The leave-one-out analysis did not reveal any single SNP dominating the overall outcomes (Supplementary Figure S2). Table 1: Sensitivity analysis of the causal effects of gut microbiota on IgA nephropathy. exposure outcome MR method SNPs Cochran's Q pleiotropy Q Q-df Q-pval Egger_intercept Se P-value g_Roseburia IgA nephropathy IVW 14 17.089 12 0.146 g_Roseburia IgA nephropathy MR Egger 14 17.472 13 0.179 -0.047 0.091 0.613 g_Faecalibacterium IgA nephropathy IVW 8 4.580 7 0.711 g_Faecalibacterium IgA nephropathy MR Egger 8 4.579 6 0.599 0.005 0.123 0.972 s_Odoribacter_splanchnicus IgA nephropathy IVW 14 14.494 13 0.340 s_Odoribacter_splanchnicus IgA nephropathy MR Egger 14 14.195 12 0.288 -0.033 0.066 0.624 s_Paraprevotella_unclassified IgA nephropathy IVW 9 5.534 8 0.595 s_Paraprevotella_unclassified IgA nephropathy MR Egger 9 6.223 7 0.622 0.158 0.191 0.434 s_Lachnospiraceae_bacterium_7_1_58FAA IgA nephropathy IVW 11 11.034 10 0.273 s_Lachnospiraceae_bacterium_7_1_58FAA IgA nephropathy MR Egger 11 11.214 9 0.341 0.047 0.124 0.711 s_Roseburia_unclassified IgA nephropathy IVW 14 18.256 13 0.148 s_Roseburia_unclassified IgA nephropathy MR Egger 14 18.116 12 0.112 -0.031 0.101 0.766 3.2 The causal effect of circulating inflammatory proteins on IgA nephropathy. Based on the IVW method, the results indicate that there are seven causal relationships between circulating inflammatory proteins and IgAN (P IVW 0.05), finally establishing five circulating inflammatory proteins with causal relationships with IgAN (Figure 3). Specific information on SNPs can be found in Supplementary Table S6. IVW analysis results show that Interleukin-10 receptor subunit alpha levels (IL-10RA) (OR=0.591, 95% CI: 0.404–0.864, P=0.007) are negatively associated with the risk of IgAN, while T-cell surface glycoprotein CD5 (TSGP-CD5) levels (random-effects model) (OR=1.456, 95% CI: 1.018–2.081, P=0.039), Fibroblast growth factor 23 (FGF23) levels (OR=1.473, 95% CI: 1.039–2.090, P=0.030), Leukemia inhibitory factor (LIF) levels (OR=1.878, 95% CI: 1.344–2.625, P=0.0002), and Transforming growth factor-alpha (TGF-α) levels (OR=1.638, 95% CI: 1.159–2.314, P=0.005) are positively associated with the risk of IgAN. Detailed results are available in Supplementary Table S7. It is worth noting that although we did not perform statistical correction for the results, for LIF, its P-value is less than the most stringent statistical correction threshold (P=0.0002 < 0.05/91=0.00055), and except for the SM method, all other methods have shown a strong causal effect of LIF on IgAN. Sensitivity analysis (Supplementary Table S8) shows that the Cochran's Q test of TSGP-CD5 levels under MR-Egger is less than 0.05, indicating some heterogeneity among SNPs, so we analyzed this result using the random-effects model, while the Q-pvals of other results are all greater than 0.05, indicating no obvious heterogeneity (Table 2). In addition, the MR-Egger regression intercept P is greater than 0.05, indicating no significant horizontal pleiotropy (Table 2). Similarly, leave-one-out analysis also did not show outliers. (Supplementary Figure S4) Table 2: Sensitivity analysis of the causal effects of circulating inflammatory proteins on IgA nephropathy. exposure outcome MR method SNPs Cochran's Q pleiotropy Q Q-df Q-pval Egger_intercept Se P-value T-cell surface glycoprotein CD5 levels IgA nephropathy IVW 29 40.397 28 0.061 0.005 0.046 0.917 T-cell surface glycoprotein CD5 levels IgA nephropathy MR Egger 29 40.380 27 0.047 Fibroblast growth factor 23 levels IgA nephropathy IVW 28 33.885 27 0.169 -0.030 0.033 0.372 Fibroblast growth factor 23 levels IgA nephropathy MR Egger 28 4.579 26 0.167 Interleukin-10 receptor subunit alpha levels IgA nephropathy IVW 19 22.241 18 0.221 -0.003 0.038 0.936 Interleukin-10 receptor subunit alpha levels IgA nephropathy MR Egger 19 22.232 17 0.176 Leukemia inhibitory factor levels IgA nephropathy IVW 26 24.126 25 0.486 -0.026 0.036 0.469 Leukemia inhibitory factor levels IgA nephropathy MR Egger 26 23.585 24 0.512 Transforming growth factor-alpha levels IgA nephropathy IVW 27 21.217 26 0.731 0.003 0.039 0.944 Transforming growth factor-alpha levels IgA nephropathy MR Egger 27 21.212 25 0.681 3.3 Reverse MR Analysis Reverse MR analysis was conducted using IgAN SNPs as exposure to assess the causal effects of the mentioned gut microbiota and circulating inflammatory proteins. No potential causal relationships between IgAN and the mentioned microbiota and circulating inflammatory proteins were found in the reverse MR analysis (P IVW > 0.05). Refer to Supplementary Tables S9 and S10 for details. 3.4 Mediation analysis We have shown that both gut flora and circulating inflammatory proteins causally affect the risk of IgAN development, with circulating inflammatory proteins mediating the pathway from gut flora to IgAN.Employing the previously identified gut microbiota and circulating inflammatory proteins, we conducted a mediation MR analysis and identified TGF-α as a mediator from gut microbiota to IgAN.TGF-α (beta=-0.043, se=0.087) accounts for 10.7% of the total effect of s_Odoribacter_splanchnicus on IgAN, as illustrated in Table 3.No mediating effects of other inflammatory proteins were observed.(Refer to Supplementary Table S11 for details). Table 3: Mediators of the Pathogenic Effect of Gut Microbiota on IgA Nephropathy by Circulating Inflammatory Proteins exposure beta1 Intermediate beta2 outcome beta_all beta se Intermediate ratio (%) s_Odoribacter_splanchnicus -0.085 Transforming growth factor-alpha levels 0.493 IgA nephropathy -0.390 -0.043 0.087 10.734 se, standard error; beta, mediation effect 4 Discussion This study employed a two-sample MR analysis to explore the causal relationships between gut microbiota, circulating inflammatory proteins, and IgAN, yielding promising results.Additionally, in the mediation analysis, we found that TGF-α, a circulating inflammatory protein, mediates the causal effect between s_Odoribacter_splanchnicus and IgAN. IgAN is an immune-inflammatory mediated disease characterized by mesangial aggregate deposition and defective galactose-deficient IgA1 (Gd-IgA1)[ 34 ]. The pathogenesis of IgAN remains unclear, and the gut-kidney axis is considered central to the pathogenic mechanisms of IgAN.Substantial evidence currently supports the association between gut microbiota and IgAN.Studies have reported significant dysbiosis of gut microbiota in IgAN patients[8 , 10]. Imbalance in gut microbiota can disrupt the intestinal mucosal barrier, allowing the absorption of metabolic toxins, activating intestinal lymphoid tissue, and inducing elevated Gd-IgA1 levels, ultimately leading to IgAN development[ 35 ]. Additionally, immune cells on the intestinal mucosa contribute to maintaining intestinal microbial homeostasis and enhancing epithelial barrier function, regulating normal immune responses in the body[ 36 ]. Furthermore, gut microbiota can contribute to IgAN development by producing various metabolites[ 37 ]. Short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, are major metabolites produced by gut microbiota[ 38 ]. Studies have reported that SCFAs, especially butyrate, can enhance tight junction complexes, maintain structural integrity of the intestinal mucosa, reduce absorption of harmful substances, maintain intestinal environmental stability, and potentially protect kidney function[ 39 ].MR analysis revealed g_Roseburia, g_Faecalibacterium, and s_Odoribacter_splanchnicus as protective factors, and s_Lachnospiraceae_bacterium_7_1_58FAA as a risk factor for IgAN. These findings offer a new perspective on the role of gut microbiota in IgAN. Fecal microbiota transplantation may offer a promising alternative therapy for chronic kidney disease. Studies have shown that fecal microbiota transplantation in chronic kidney disease patients reduces renal damage and increases the abundance of Firmicutes, Bacteroidetes, and g_Roseburia, suggesting its potential benefits for kidney function recovery[ 40 ]. Wang et al. demonstrated that supplementation with g_Faecalibacterium significantly improved intestinal dysbiosis and barrier dysfunction in sleep-deprived mice[ 41 ]. s_Odoribacter_splanchnicus synthesizes butyrate through carbohydrate fermentation and plays a vital role in immune inflammation regulation[ 42 ]. A novel anti-inflammatory sulfonolipid compound has been identified as a bacterial metabolite secreted by Odoribacter[ 43 ]. As a well-known SCFA-producing group of three bacteria[ 44 ], We speculate that these three microorganisms reduce the risk of developing IgAN by possibly influencing the production of SCFA.s_Lachnospiraceae_bacterium_7_1_58FAA belongs to the Lachnospiraceae family. In their study, Maria De Angelis et al. observed a higher proportion of Lachnospiraceae in the fecal microbiota of advanced IgAN patients compared to healthy individuals[ 45 ]. Based on our findings, we speculate that the elevation of s_Lachnospiraceae_bacterium_7_1_58FAA levels may accelerate the progression of IgAN. Chronic and persistent low-grade inflammation in the kidneys plays a crucial role in IgAN development. In IgAN pathogenesis, inflammatory factors play pivotal roles in mediating and regulating immune responses via paracrine, autocrine, and endocrine mechanisms by binding to respective receptors[ 46 ]. Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase widely expressed in human and mouse B cells, playing a pathogenic role in IgAN[ 47 ]. In IgA-stimulated human mesangial cells, inflammatory cytokine production (e.g., IL-6, IL-8, MCP-1) is SYK-dependent, and SYK inhibition markedly attenuates proteinuria, glomerular macrophage infiltration, and inflammation in renal tubular epithelial cells induced by glomerular-tubular interactions in a nephritis animal model[ 48 ]. Interactions among inflammatory factors are intricate. In IgAN, these interactions can trigger a cascade of inflammatory reactions, culminating in glomerular damage, proteinuria, hematuria, and eventually renal interstitial fibrosis. Our findings indicate that LIF, FGF23, TGF-α, and TSGP-CD5 elevate IgAN risk, whereas IL-10RA lowers it.LIF belongs to the IL-6 family and plays a role in mucosal immunity[ 49 ]. Few studies have elucidated its mechanism as a risk factor for IgAN.Koshi Yamada and colleagues compared IgA1-secreting cell lines isolated from peripheral blood cells of both IgAN patients and healthy individuals. They observed that in IgA1-producing cells from IgAN patients, LIF induced a higher level of STAT1 phosphorylation compared to healthy individuals. Furthermore, knockdown of STAT1 using siRNA significantly attenuated the LIF-induced increase in Gd-IgA1 in these cells[50 , 51].FGF23, primarily produced by osteocytes, regulates vitamin D and phosphate metabolism in the kidneys. Over the past decade, it has emerged as a crucial biomarker for cardiovascular diseases[ 52 ]. Studies have shown an association between FGF23 and proteinuria as well as renal function decline in IgAN patients[ 53 ], implying its potential role as a risk factor for IgAN development.TSGP-CD5, a glycoprotein found on T cell surfaces, is generally regarded as having negative immune regulatory properties, inhibiting T cell activation and aiding in autoimmune balance maintenance[ 54 ]. Administering anti-CD5 monoclonal antibodies can ameliorate proteinuria and mesangial matrix damage in rats with glomerulonephritis[ 55 ]. This appears contradictory to our results, however, considering it is based on a rat model rather than clinical experimentation and the intricate interactions involving immune inflammation, further clinical investigations are warranted to elucidate the role of TSGP-CD5 in IgAN.TGF-α, a member of the epidermal growth factor family, has been primarily studied in the context of tumors. Its role in kidney diseases remains contentious with no consensus reached. The findings of this study suggest the potential role of TGF-α as a risk factor for IgAN.MR studies have shown that IL-10RA acts as a protective factor in IgAN.IL-10RA, a subunit of the IL-10 receptor, plays a crucial role in the IL-10 signaling pathway.IL-10 is an anti-inflammatory cytokine, and research indicates that upregulation of IL-10 can suppress the activation of Th1 and Th17 cells in the intestine, ameliorating renal damage in crescentic glomerulonephritis[ 56 ]. Additionally, IL-10 can suppress the secretion of inflammatory cytokines like IL-6 and TNF-α, alleviate glomerular inflammation, suppress cell proliferation, and preserve renal function[ 57 ]. This aligns with our discovery that IL-10RA, an isoform of IL-10, is implicated in mitigating the genetic susceptibility to IgAN. The association between gut microbiota and IgAN is currently a subject of interest. Intestinal immune inflammation activation represents a risk factor for IgAN.Research has indicated that dysregulated gut microbiota can bind to Toll-like receptors (TLRs) on mucosal dendritic cell surfaces, triggering the production of inflammation and pro-inflammatory cytokines[ 58 ]. Additionally, Tang and colleagues discovered that in pseudo-infertile mice with IgAN, aberrant gut microbiota results in increased levels of inflammatory factors (including TLR4, IL-6, TNF-α, and NF-κB) in intestinal and renal tissues, exacerbating renal inflammation[ 13 ].The interaction between gut microbiota and inflammatory proteins is crucial in the pathogenesis of IgAN.Our findings suggest that s_Odoribacter_splanchnicus may mitigate the risk of IgAN by downregulating TGF-α expression.This offers a specific direction for future research focusing on the gut microbiota - inflammation response - IgAN axis. This study employed MR for the first time to investigate the causal relationships among gut microbiota, circulating inflammatory proteins, and IgAN.Rigorous sensitivity analyses were conducted on the results, and reverse MR studies were performed to mitigate the potential interference of reverse causality. Ultimately, the study validated the causal relationships between gut microbiota, circulating inflammatory proteins, and IgAN, identifying inflammatory proteins as intermediate factors. This not only offers theoretical support for the treatment and prevention of IgAN and broadens the applicability of the gut-kidney axis but also contributes to a deeper understanding of its underlying mechanisms.Co-regulation of gut microbiota and inflammatory factors holds the potential for substantial breakthroughs in preventing and treating IgAN. Nonetheless, this study faces several limitations. (Ⅰ) Since all participants were of European ancestry, and given the notable ethnic variations in IgAN incidence, further investigation is warranted to ascertain the generalizability of these findings to other ethnic groups, a common challenge in current MR studies. (Ⅱ) Given the exploratory nature of this study, statistical adjustments were not applied to the results to yield more informative insights into gut microbiota and inflammatory proteins. (Ⅲ) The GWAS data we acquired lacks demographic details, including IgAN progression, patients' age, and gender, precluding subgroup analyses. Moving forward, researchers should encompass diverse ethnic populations, augment sample sizes, and employ advanced research methodologies and statistical approaches to delineate the precise relationships between gut microbiota, circulating inflammatory proteins, and IgAN. 5 Conclusion In summary, our MR study uncovered causal links between six gut microbiota species and five circulating inflammatory proteins with IgAN. Additionally, mediation analysis demonstrated that the circulating inflammatory protein TGF-α mediated the pathway from gut microbiota to IgAN. The gut microbiota and circulating inflammatory proteins identified in this study can potentially serve as biomarkers for diagnosing and treating IgAN, and aid in elucidating its underlying mechanisms. Declarations The authors declare that they have no conflicts of interest. Acknowledgements. We would like to thank the authors of the public databases and GWAS analyses mentioned in the article. Author contributions. Pengtao Dong and Xiaoyu Li conducted the data analysis and authored the article. Xue Feng was responsible for data collection and preprocessing. Siyu Huang and Ziran Zhao summarized the current status of the study. Qing Zhang revised the manuscript and addressed errors in expertise.Zheng Wang and Bing Cui provided guidance throughout the entire project and granted final approval to the manuscript. Funding. This study received support from the Cultivation of Top Talents in Traditional Chinese Medicine in Henan Province (Project Approval No. 2022ZYBJ08), the Scientific Research Special Project of Traditional Chinese Medicine in Henan Province (Project Approval No. 2019JDZX2116), and the Science and Technology Tackling Project of Henan Province (Project Approval No. 232102310458). Data availability. The GWAS data utilized in this study are all publicly available. References Pattrapornpisut P, Avila-Casado C and Reich HN. (2021) IgA Nephropathy: Core Curriculum 2021. AMERICAN JOURNAL OF KIDNEY DISEASES.78, 429–441. Schena FP and Nistor I. (2018) Epidemiology of IgA Nephropathy: A Global Perspective. SEMINARS IN NEPHROLOGY.38, 435–442. Jarrick S, Lundberg S, Welander A, Carrero JJ, Höijer J, Bottai M and Ludvigsson JF. (2019) Mortality in IgA Nephropathy: A Nationwide Population-Based Cohort Study. JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY.30, 866–876. 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ARCHIVES OF MICROBIOLOGY.206, 137. De Angelis M, Montemurno E, Piccolo M, Vannini L, Lauriero G, Maranzano V, Gozzi G, Serrazanetti D, Dalfino G, Gobbetti M and Gesualdo L. (2014) Microbiota and metabolome associated with immunoglobulin A nephropathy (IgAN). PLoS One.9, e99006. Kawasaki Y. (2022) Treatment strategy with multidrug therapy and tonsillectomy pulse therapy for childhood-onset severe IgA nephropathy. Clinical and Experimental Nephrology.26, 501–511. Yiu WH, Chan KW, Chan L, Leung J, Lai KN and Tang S. (2021) Spleen Tyrosine Kinase Inhibition Ameliorates Tubular Inflammation in IgA Nephropathy. Frontiers in Physiology.12, 650888. Kim MJ, McDaid JP, McAdoo SP, Barratt J, Molyneux K, Masuda ES, Pusey CD and Tam FW. (2012) Spleen tyrosine kinase is important in the production of proinflammatory cytokines and cell proliferation in human mesangial cells following stimulation with IgA1 isolated from IgA nephropathy patients. JOURNAL OF IMMUNOLOGY.189, 3751–3758. Kiryluk K, Li Y, Scolari F, Sanna-Cherchi S, Choi M, Verbitsky M, Fasel D, Lata S, Prakash S, Shapiro S, Fischman C, Snyder HJ, Appel G, Izzi C, Viola BF, Dallera N, Del VL, Barlassina C, Salvi E, Bertinetto FE, Amoroso A, Savoldi S, Rocchietti M, Amore A, Peruzzi L, Coppo R, Salvadori M, Ravani P, Magistroni R, Ghiggeri GM, Caridi G, Bodria M, Lugani F, Allegri L, Delsante M, Maiorana M, Magnano A, Frasca G, Boer E, Boscutti G, Ponticelli C, Mignani R, Marcantoni C, Di Landro D, Santoro D, Pani A, Polci R, Feriozzi S, Chicca S, Galliani M, Gigante M, Gesualdo L, Zamboli P, Battaglia GG, Garozzo M, Maixnerová D, Tesar V, Eitner F, Rauen T, Floege J, Kovacs T, Nagy J, Mucha K, Pączek L, Zaniew M, Mizerska-Wasiak M, Roszkowska-Blaim M, Pawlaczyk K, Gale D, Barratt J, Thibaudin L, Berthoux F, Canaud G, Boland A, Metzger M, Panzer U, Suzuki H, Goto S, Narita I, Caliskan Y, Xie J, Hou P, Chen N, Zhang H, Wyatt RJ, Novak J, Julian BA, Feehally J, Stengel B, Cusi D, Lifton RP and Gharavi AG. (2014) Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. NATURE GENETICS.46, 1187–1196. Yamada K, Huang ZQ, Raska M, Reily C, Anderson JC, Suzuki H, Kiryluk K, Gharavi AG, Julian BA, Willey CD and Novak J. (2020) Leukemia Inhibitory Factor Signaling Enhances Production of Galactose-Deficient IgA1 in IgA Nephropathy. Kidney Diseases.6, 168–180. Yamada K, Huang ZQ, Reily C, Green TJ, Suzuki H, Novak J and Suzuki Y. (2024) LIF/JAK2/STAT1 Signaling Enhances Production of Galactose-Deficient IgA1 by IgA1-Producing Cell Lines Derived From Tonsils of Patients With IgA Nephropathy. Kidney International Reports.9, 423–435. Yang Z, Wang J, Ma J, Ren D, Li Z, Fang K and Shi Z. (2024) Fibroblast growth factor 23 during septic shock and myocardial injury in ICU patients. Heliyon.10, e27939. Lundberg S, Qureshi AR, Olivecrona S, Gunnarsson I, Jacobson SH and Larsson TE. (2012) FGF23, albuminuria, and disease progression in patients with chronic IgA nephropathy. Clinical Journal of the American Society of Nephrology.7, 727–734. Velasco-de AM, Casadó-Llombart S, Català C, Leyton-Pereira A, Lozano F and Aranda F. (2020) Soluble CD5 and CD6: Lymphocytic Class I Scavenger Receptors as Immunotherapeutic Agents. Cells.9. Ikezumi Y, Kawachi H, Toyabe S, Uchiyama M and Shimizu F. (2000) An anti-CD5 monoclonal antibody ameliorates proteinuria and glomerular lesions in rat mesangioproliferative glomerulonephritis. KIDNEY INTERNATIONAL.58, 100–114. Soukou-Wargalla S, Kilian C, Velasquez LN, Machicote A, Letz P, Tran HB, Domanig S, Bertram F, Stumme F, Bedke T, Giannou A, Kempski J, Sabihi M, Song N, Paust HJ, Borchers A, Garcia PL, Pelczar P, Liu B, Ergen C, Steglich B, Muscate F, Huber TB, Panzer U, Gagliani N, Krebs CF and Huber S. (2023) Tr1 Cells Emerge and Suppress Effector Th17 Cells in Glomerulonephritis. JOURNAL OF IMMUNOLOGY.211, 1669–1679. Kamyshova ES, Shvetsov MY, Kutyrina IM, Burdennyi AM, Zheng A, Nosikov VV and Bobkova IN. (2016) [Clinical value of TNF, IL-6, and IL-10 gene polymorphic markers in chronic glomerulonephritis]. TERAPEVTICHESKII ARKHIV.88, 45–50. He H, Shen M, Tang Y, Sun W and Xu X. (2024) LPS/TLR4 Pathway Regulates IgA1 Secretion to Induce IgA Nephropathy. ALTERNATIVE THERAPIES IN HEALTH AND MEDICINE.30, 419–425. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.xls Supplementaryfile2.doc 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. 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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-4472698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310959942,"identity":"4c76fbc2-c8d1-4b2a-9ded-2a62f5f6af0f","order_by":0,"name":"Pengtao Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDCCA2DSgoeBgfnAgQ8/iNaSIAHUwpZ4cGYPCVqABI/xYQ42InTwHe89/Lrwh4SMwY2cD4cZeBjk+cUO4NcieeZcmvUMoMMkZ+RuOFxgwWA4c3YCfi1Aw82MeYBa+CWAWmbwMCQY3CZWC5tEzoPDPGzEaTF+DLElh4E4LZJnzpgxz0gD+qXnmQEwkCUI+4XveI/x5wIbG3uD48mPP3z4YSPPL01ACxCwSYMpAbBKCYLKQYD5M5jiP0CU6lEwCkbBKBiBAAA5lkPi+yIsVQAAAABJRU5ErkJggg==","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Pengtao","middleName":"","lastName":"Dong","suffix":""},{"id":310959943,"identity":"2848681d-c30d-413c-abf8-8a95c4694b5b","order_by":1,"name":"Xiaoyu Li","email":"","orcid":"","institution":"School of Public Health, Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Li","suffix":""},{"id":310959944,"identity":"bc872b76-0574-47c6-8466-c2cb2bab27b7","order_by":2,"name":"Xue Feng","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Feng","suffix":""},{"id":310959945,"identity":"d6678682-fc08-4ea1-92f3-9f84bf7709cf","order_by":3,"name":"Siyu Huang","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Huang","suffix":""},{"id":310959946,"identity":"364f99c4-27d5-40aa-94df-7661acf05ff1","order_by":4,"name":"Bing Cui","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Cui","suffix":""},{"id":310959947,"identity":"c31a90a9-eeea-4f1b-988c-efc202c4af5a","order_by":5,"name":"Qing Zhang","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhang","suffix":""},{"id":310959948,"identity":"89ad363c-6716-4d61-accd-9922a9e6e147","order_by":6,"name":"Ziran Zhao","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ziran","middleName":"","lastName":"Zhao","suffix":""},{"id":310959952,"identity":"d8ef4791-d489-458d-8d8a-71ac24f23b7f","order_by":7,"name":"Zheng Wang","email":"","orcid":"","institution":"Nephrology Diagnosis and Treatment Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-24 13:16:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4472698/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4472698/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57884296,"identity":"6c0c7531-7b85-49ea-a5ca-0c681fb39fa3","added_by":"auto","created_at":"2024-06-07 04:10:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":713618,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study. MR: Mendelian randomization\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/bab8ce8e2a8fb20a1e0977b3.png"},{"id":57884151,"identity":"d2ea561e-3e2c-4c42-ba4c-b45a8fc1da5d","added_by":"auto","created_at":"2024-06-07 04:02:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2309636,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows the causal effects of gut microbiota on IgA nephropathy.BWMR: Bayesian Weighted Mendelian Randomization; nsnp: number of SNPs; OR: odds ratio; CI: confidence interval; beta: allele effect value; se: standard error of beta.\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/47046376cb83f0fbfb078793.png"},{"id":57884153,"identity":"48ea3d2e-6be9-401c-bc2f-c29c1ef64d08","added_by":"auto","created_at":"2024-06-07 04:02:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2387017,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows the causal effects of circulating inflammatory proteins on IgA nephropathy. BWMR: Bayesian Weighted Mendelian Randomization; nsnp: number of SNPs; OR: odds ratio; CI: confidence interval; beta: allele effect value; se: standard error of beta; Results with Pval \u0026lt; 0.05 are marked.\u003c/p\u003e","description":"","filename":"fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/27cf63f4e27ef1d3ef13799c.png"},{"id":57884310,"identity":"56b7e7ad-8381-4747-9426-d305958744dd","added_by":"auto","created_at":"2024-06-07 04:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4880074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/3533169a-1298-4479-874d-fedff3d17baf.pdf"},{"id":57883954,"identity":"1227bebf-710e-4563-90ca-d144e9d31c06","added_by":"auto","created_at":"2024-06-07 03:54:39","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":438784,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/a2f4e8c7dcd598533c36076e.xls"},{"id":57883957,"identity":"3d2913db-b1be-4603-8775-380e409dcb4e","added_by":"auto","created_at":"2024-06-07 03:54:39","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3238400,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.doc","url":"https://assets-eu.researchsquare.com/files/rs-4472698/v1/61d036bf8ab0ef5af21a91bb.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between gut microbiota, circulating inflammatory proteins and IgA nephropathy: two-sample and mediated Mendelian randomisation analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIgA nephropathy (IgAN) is an immune-inflammatory mediated glomerulonephritis, characterized by the deposition of immunoglobulin A or IgA-dominant immune complexes in the mesangial area of the glomeruli[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Microscopic or gross hematuria, with or without varying degrees of proteinuria, represents the most common clinical manifestation. As the most prevalent primary glomerular disease globally, approximately 25% of IgAN patients progress to end-stage renal disease (ESRD) within 20 years[2\u003csup\u003e,\u003c/sup\u003e 3]. Furthermore, IgAN predominantly affects young and middle-aged individuals, significantly impacting the productive workforce and imposing substantial psychological and economic burdens on family members[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The specific pathogenesis of IgAN is complex and not fully elucidated at present, while its definitive diagnostic methods remain limited to renal biopsy techniques. Hence, researchers urgently need to identify potential risk factors for the disease, facilitating early diagnosis and the development of new treatments for IgAN.\u003c/p\u003e \u003cp\u003eThe human gut hosts millions of microorganisms, and maintaining the relative stability of the gut microenvironment is vital for human health. In recent years, an increasing number of studies have shown a close association between dysbiosis of the gut microbiota and IgAN[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Alterations in gut microbiota abundance can disrupt systemic or local mucosal immune responses, resulting in increased release of immunoglobulins into the bloodstream, subsequently depositing in the kidneys and leading to the development of IgAN[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. LIANG et al, sequenced fecal samples from both IgAN patients and healthy controls, revealing a higher abundance of Bacteroides in IgAN patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 16S rRNA sequencing of fecal samples from untreated IgAN patients revealed a significant increase in the abundance of Escherichia-Shigella, which decreased significantly in clinically relieved patients after treatment with immunosuppressants and a 6-month follow-up[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Furthermore, a study demonstrated that IgAN patients exhibited lower levels of Bifidobacterium, with its abundance showing a negative correlation with levels of hematuria and proteinuria[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].The gut microbiota plays a pivotal role in the pathogenesis of IgAN.\u003c/p\u003e \u003cp\u003eIgAN is a disease closely linked to immune inflammation. Macrophages, pivotal immune cells in the body, can differentiate into M1 (pro-inflammatory) and M2 (anti-inflammatory) phenotypes. Deposition of immune complexes in the kidneys can activate macrophages to polarize towards the M1 phenotype, resulting in the secretion of large amounts of pro-inflammatory cytokines (such as TNF-α, IL-1, IL-6, IL-12), ultimately leading to kidney function loss[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As the disease progresses and M2 macrophages polarize, anti-inflammatory cytokines can promote kidney tissue repair and facilitate renal fibrosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Inflammatory cytokines play a pivotal role throughout the development of IgAN.\u003c/p\u003e \u003cp\u003eAbnormal gut microbiota can cause barrier dysfunction, inflammation and local immune responses in IgAN[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the exact causal relationship and mediation proportions among gut microbiota, circulating inflammatory proteins and IgAN are still unclear. Mendelian randomization (MR) is a method used to assess causal associations between exposure and outcome by substituting for randomized controlled trials. Because genetic variation is randomly allocated at conception. MR analysis using genetic variants as instrumental variables can mitigate the influence of common confounders and avoid reverse causation bias[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Mediation analysis is employed to assess pathways through which exposure affects outcomes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, we performed two-sample and mediation MR analyses using publicly available summary-level data from genome-wide association studies (GWAS) to assess the causal relationship between gut microbiota, circulating inflammatory proteins and IgAN to determine the mediating effect of circulating inflammatory proteins.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow of this MR study is illustrated in Figure 1. Firstly, we acquired publicly available summary-level data from GWAS concerning gut microbiota, circulating inflammatory proteins and IgAN. Two-sample MR methods were utilized to evaluate the causal relationships between gut microbiota and IgAN, as well as between circulating inflammatory proteins and IgAN. Subsequently, circulating inflammatory proteins with a mediating role were identified in the positive findings of the causal relationship between gut microbiota and IgAN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS summary statistics for IgAN are sourced from the FinnGen consortium (R10), involving 653 IgAN cases and 411,528 controls. The data can be directly accessed from https://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_N14_IGA_NEPHROPATHY.gz. GWAS summary data for gut microbiota are derived from a whole-genome association study conducted by the Netherlands Microbiome Project team, which involved 7,738 participants and assessed 412 features (including 207 gut microbial taxa and 205 functional pathways)[17]. The GWAS data for circulating inflammatory proteins were obtained from the study by Zhao et al., which recruited 14,824 participants and identified 91 circulating inflammatory proteins using Olink Target Inflammation Immunoassay Panels to analyze whole-genome genetic data and plasma proteomic data[18]. The GWAS summary statistics for circulating inflammatory proteins can be found in the EBI GWAS Catalog (accession numbers GCST90274758-GCST90274848)[18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Instrumental variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualified instrumental variables (IVs) must meet three core assumptions: (1) Selected IVs are directly linked to the exposure factor; (2) IVs are unrelated to any confounding factors influencing the \u0026quot;exposure-outcome\u0026quot; relationship; (3) Selected IVs influence the outcome solely through the exposure factor[19]. Strict criteria are necessary for screening IVs to ensure the credibility of MR study findings. When screening single nucleotide polymorphisms (SNPs) associated with gut microbiota and circulating inflammatory proteins, we initially employed a stringent threshold (P\u0026lt;5\u0026times;10-8) for selection, resulting in a limited number of SNPs being chosen. To increase the number of SNPs for the study, we adjusted the threshold to P\u0026lt;1\u0026times;10-5, based on the majority of previous studies[20-22], and subsequently established parameters kb = 10000, r2= 0.001 to mitigate interference from linkage disequilibrium[23]. Additionally, we performed reverse MR analysis, with the criteria for selecting IgAN-related SNPs set at P\u0026lt;5\u0026times;10-6, kb = 10000, r2= 0.001.Weak instrumental variables exhibit a feeble association with the exposure factor, thereby compromising result accuracy. The strength of each SNP was calculated using the F statistic, and IVs with F\u0026lt;10 were excluded as weak instruments[24]. Palindromic SNPs were eliminated by reconciling exposure-outcome datasets[24]. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method were employed to remove outlier SNPs[25]. Following stringent screening based on the aforementioned criteria, the remaining SNPs were utilized for subsequent MR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.1 Two-sample Mendelian randomization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive analytical methods\u0026mdash;Inverse Variance Weighted (IVW), MR-Egger regression, Weighted Median Model (WME), Simple Model (SM), and Weighted Model (WM)\u0026mdash;were employed to assess causality[26]. When all SNPs are valid and no horizontal pleiotropy exists, the IVW method combines estimated values of individual IVs using inverse variance weights to yield a consistent and unbiased estimate of the causal effect[27]. MR-Egger regression and WME are valuable tools in MR for addressing situations involving horizontal pleiotropy or violations of the IVs assumption. They enable researchers to estimate causal effects while accounting for pleiotropy effects and offer additional insights into the relationship between exposure and outcome variables[25]. Nevertheless, the non-parametric nature of WME might result in reduced estimation accuracy, while MR Egger, relying on regression modeling, may diminish statistical power[28]. SM groups SNP categories with similar values and evaluates causal associations based on the group with the most SNPs[29]. WM requires identifying multiple variables as valid instruments to detect the same causal effect.IVW analysis has the highest statistical power, and this study utilized IVW analysis as the primary method for MR analysis[30]. Additionally, Bayesian Weighted Mendelian Randomization (BWMR) was employed to validate positive results. BWMR served as our primary reference, and negative outcomes from BWMR were disregarded. BWMR accounts for uncertainty stemming from polygenicity, resulting in weak instrument effects, and tackles violations of the IV core assumption attributed to horizontal pleiotropy through Bayesian weighted outlier detection[31].\u003c/p\u003e\n\u003cp\u003eSensitivity analysis was performed to evaluate the robustness of the results. Cochran\u0026apos;s Q test was employed to assess heterogeneity among IVs, where a significance level of P \u0026lt; 0.05 indicated substantial heterogeneity among SNPs, warranting the use of a random-effects model; otherwise, a fixed-effects model was employed[32]. MR-Egger regression was utilized to evaluate horizontal pleiotropy and its statistical significance. Absence of a significant intercept term in MR-Egger (P \u0026gt; 0.05) indicates the absence of horizontal pleiotropy[33]. Leave-one-out analysis was conducted by sequentially excluding individual IVs to investigate whether any SNP exerts a dominant influence on the causal association. Significant influence on the MR results upon removal of a specific SNP suggests that the outcome is impacted by a single IV. Subsequent analysis will omit results identified as outliers and displaying horizontal pleiotropy by MR-PRESSO.\u003c/p\u003e\n\u003cp\u003eAdditionally, this study is exploratory in nature, and to achieve more positive and mediating results, we did not apply multiple testing corrections.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R (version 4.3.2) along with the R-TwoSampleMR package (version 0.5.10) and the R-MR-PRESSO package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.2 Reverse Mendelian randomization analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the causal relationship between IgAN and gut microbiota as well as circulating inflammatory proteins (P \u003csub\u003eIVW\u003c/sub\u003e \u0026lt; 0.05), we also performed reverse MR analysis.In this scenario, SNPs associated with IgAN are treated as exposures, while gut microbiota and categories of circulating inflammatory proteins are regarded as outcomes. The steps involved in reverse MR analysis mirror those of standard MR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.4. Mediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMediation analysis aims to evaluate the pathways through which exposure influences the outcome, assisting in the exploration of potential mechanisms through which exposure affects the outcome. Mediation analysis comprises four specific steps. In Step 1, we have already derived the total effect (beta_all) of gut microbiota on IgAN through two-sample MR analysis. In Step 2, reverse MR analysis was performed on the outcomes of Step 1 to assess the causal association between IgAN and gut microbiota; mediation analysis can only proceed when the mediator is independent of the exposure[16]. Step 3 involved conducting two-sample MR analysis on circulating inflammatory proteins and IgAN to determine the effect size (beta2) of circulating inflammatory proteins on IgAN. Step 4 involved subjecting the gut microbiota obtained in Step 1 and circulating inflammatory proteins obtained in Step 3 to two-sample MR analysis to derive the effect size (beta1). The mediation effect is calculated as beta1 \u0026times; beta2, where beta_direct is the difference between the total effect and the mediation effect, and the mediation ratio is determined as (mediation effect / total effect) \u0026times; 100%. The delta method was used to estimate the 95% confidence interval (CI) for the mediation effect and mediation ratio[16].\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Causal effects of gut microbiota on IgA nephropathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Figure 2 (P\u003csub\u003e\u0026nbsp;IVW\u003c/sub\u003e \u0026lt; 0.05), three genera and four species of bacteria were identified as having a causal relationship with IgAN, but g_Erysipelotrichaceae_noname was excluded when combined with BWMR results (P \u003csub\u003eBWMR\u003c/sub\u003e \u0026gt; 0.05). Ultimately, two genera and four species of bacteria were confirmed to be causally associated with IgAN. Specific information about SNPs is provided in Supplementary Table S3.At the genus level, IVW analysis revealed a significant negative correlation between g_Roseburia (OR=0.639, 95% CI: 0.426 - 0.958, P=0.030) and g_Faecalibacterium (OR=0.558, 95% CI: 0.328 - 0.947, P=0.030) and IgAN, thereby reducing the risk of IgAN. At the species level, s_Odoribacter_splanchnicus (OR=0.677, 95% CI: 0.485 - 0.944, P=0.021) and s_Roseburia_unclassified (OR=0.743, 95% CI: 0.562 - 0.983, P=0.037) were negatively associated with the risk of IgAN. Conversely, s_Paraprevotella_unclassified (OR=1.494, 95% CI: 1.021 - 2.186, P=0.039) and s_Lachnospiraceae_bacterium_7_1_58FAA (OR=1.614, 95% CI: 1.052 - 2.477, P=0.029) were positively correlated with the risk of IgAN.Similarly, BWMR results aligned with IVW analysis findings and demonstrated statistical significance, thereby reinforcing the robustness of our results. For a more comprehensive overview of results, please refer to Supplementary Table S4. These findings confirm the causal association between particular gut microbiota and the risk of IgAN. Sensitivity analyses were performed to ensure the robustness of the findings (Supplementary Table S5). Cochran\u0026apos;s Q tests indicated that both IVW and MR-Egger had Q-pvalues greater than 0.05, suggesting no significant heterogeneity (Table 1); additionally, MR-Egger demonstrated no significant intercept terms (P \u0026gt; 0.05), indicating the absence of significant horizontal pleiotropy (Table 1). Cochran\u0026apos;s Q tests indicated that both IVW and MR-Egger had Q-pvalues greater than 0.05, suggesting no significant heterogeneity (Table 1); additionally, MR-Egger demonstrated no significant intercept terms (P \u0026gt; 0.05), indicating the absence of significant horizontal pleiotropy (Table 1). The leave-one-out analysis did not reveal any single SNP dominating the overall outcomes (Supplementary Figure S2).\u003c/p\u003e\n\u003cp\u003eTable 1: Sensitivity analysis of the causal effects of gut microbiota on IgA nephropathy.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.872340425531913%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eexposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.943262411347519%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eoutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2695035460992905%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.404255319148938%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCochran\u0026apos;s Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.29078014184397%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003epleiotropy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.228464419475657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.232209737827715%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-df\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60299625468165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-pval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.973782771535582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEgger_intercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.606741573033707%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.355805243445692%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003eg_Roseburia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003eg_Roseburia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e17.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003eg_Faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\" valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003eg_Faecalibacterium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e4.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Odoribacter_splanchnicus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e14.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Odoribacter_splanchnicus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e14.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Paraprevotella_unclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Paraprevotella_unclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e6.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Lachnospiraceae_bacterium_7_1_58FAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Lachnospiraceae_bacterium_7_1_58FAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e11.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Roseburia_unclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.95373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003es_Roseburia_unclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98932384341637%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.252669039145907%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.185053380782918%\"\u003e\n \u003cp\u003e18.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.362989323843417%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.9644128113879%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.93950177935943%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.295373665480427%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 The causal effect of circulating inflammatory proteins on IgA nephropathy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the IVW method, the results indicate that there are seven causal relationships between circulating inflammatory proteins and IgAN (P\u003csub\u003e\u0026nbsp;IVW\u003c/sub\u003e \u0026lt; 0.05) (Figure 3). After considering the BWMR results, two inflammatory proteins, Interleukin-12 subunit beta levels and Programmed cell death 1 ligand 1 levels, were excluded (P\u003csub\u003e\u0026nbsp;BWMR\u003c/sub\u003e \u0026gt; 0.05), finally establishing five circulating inflammatory proteins with causal relationships with IgAN (Figure 3). Specific information on SNPs can be found in Supplementary Table S6. IVW analysis results show that Interleukin-10 receptor subunit alpha levels (IL-10RA) (OR=0.591, 95% CI: 0.404\u0026ndash;0.864, P=0.007) are negatively associated with the risk of IgAN, while T-cell surface glycoprotein CD5 (TSGP-CD5) levels (random-effects model) (OR=1.456, 95% CI: 1.018\u0026ndash;2.081, P=0.039), Fibroblast growth factor 23 (FGF23) levels (OR=1.473, 95% CI: 1.039\u0026ndash;2.090, P=0.030), Leukemia inhibitory factor (LIF) levels (OR=1.878, 95% CI: 1.344\u0026ndash;2.625, P=0.0002), and Transforming growth factor-alpha (TGF-\u0026alpha;) levels (OR=1.638, 95% CI: 1.159\u0026ndash;2.314, P=0.005) are positively associated with the risk of IgAN. Detailed results are available in Supplementary Table S7. It is worth noting that although we did not perform statistical correction for the results, for LIF, its P-value is less than the most stringent statistical correction threshold (P=0.0002 \u0026lt; 0.05/91=0.00055), and except for the SM method, all other methods have shown a strong causal effect of LIF on IgAN. Sensitivity analysis (Supplementary Table S8) shows that the Cochran\u0026apos;s Q test of TSGP-CD5 levels under MR-Egger is less than 0.05, indicating some heterogeneity among SNPs, so we analyzed this result using the random-effects model, while the Q-pvals of other results are all greater than 0.05, indicating no obvious heterogeneity (Table 2). In addition, the MR-Egger regression intercept P is greater than 0.05, indicating no significant horizontal pleiotropy (Table 2). Similarly, leave-one-out analysis also did not show outliers. (Supplementary Figure S4)\u003c/p\u003e\n\u003cp\u003eTable 2: Sensitivity analysis of the causal effects of circulating inflammatory proteins on IgA nephropathy.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eexposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eoutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMR method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55772646536412%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCochran\u0026apos;s Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044404973357015%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003epleiotropy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.53731343283582%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.805970149253731%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-df\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.044776119402986%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ-pval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEgger_intercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.044776119402986%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.671641791044776%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\"\u003e\n \u003cp\u003eT-cell surface glycoprotein CD5 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\" valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eT-cell surface glycoprotein CD5 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\"\u003e\n \u003cp\u003e40.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\"\u003e\n \u003cp\u003eFibroblast growth factor 23 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\" valign=\"bottom\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eFibroblast growth factor 23 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\"\u003e\n \u003cp\u003e4.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\"\u003e\n \u003cp\u003eInterleukin-10 receptor subunit alpha levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.241\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\" valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eInterleukin-10 receptor subunit alpha levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\"\u003e\n \u003cp\u003e22.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeukemia inhibitory factor levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\" valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eLeukemia inhibitory factor levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\"\u003e\n \u003cp\u003e23.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eTransforming growth factor-alpha levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.217\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\" valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.912966252220247%\" valign=\"bottom\"\u003e\n \u003cp\u003eTransforming growth factor-alpha levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.966252220248668%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.236234458259325%\" valign=\"bottom\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.282415630550622%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.348134991119005%\"\u003e\n \u003cp\u003e21.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.571936056838366%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.946714031971581%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.63765541740675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.460035523978686%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Reverse MR Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReverse MR analysis was conducted using IgAN SNPs as exposure to assess the causal effects of the mentioned gut microbiota and circulating inflammatory proteins. No potential causal relationships between IgAN and the mentioned microbiota and circulating inflammatory proteins were found in the reverse MR analysis (P\u003csub\u003eIVW\u003c/sub\u003e \u0026gt; 0.05). Refer to Supplementary Tables S9 and S10 for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Mediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have shown that both gut flora and circulating inflammatory proteins causally affect the risk of IgAN development, with circulating inflammatory proteins mediating the pathway from gut flora to IgAN.Employing the previously identified gut microbiota and circulating inflammatory proteins, we conducted a mediation MR analysis and identified TGF-\u0026alpha;\u0026nbsp;as a mediator from gut microbiota to IgAN.TGF-\u0026alpha;\u0026nbsp;(beta=-0.043, se=0.087) accounts for 10.7% of the total effect of s_Odoribacter_splanchnicus on IgAN, as illustrated in Table 3.No mediating effects of other inflammatory proteins were observed.(Refer to Supplementary Table S11 for details).\u003c/p\u003e\n\u003cp\u003eTable 3: Mediators of the Pathogenic Effect of Gut Microbiota on IgA Nephropathy by Circulating Inflammatory Proteins\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eexposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntermediate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eoutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta_all\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003ebeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003ese\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntermediate ratio (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003es_Odoribacter_splanchnicus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eTransforming growth factor-alpha levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003eIgA nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003ese, standard error; beta, mediation effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThis study employed a two-sample MR analysis to explore the causal relationships between gut microbiota, circulating inflammatory proteins, and IgAN, yielding promising results.Additionally, in the mediation analysis, we found that TGF-α, a circulating inflammatory protein, mediates the causal effect between s_Odoribacter_splanchnicus and IgAN.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIgAN is an immune-inflammatory mediated disease characterized by mesangial aggregate deposition and defective galactose-deficient IgA1 (Gd-IgA1)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The pathogenesis of IgAN remains unclear, and the gut-kidney axis is considered central to the pathogenic mechanisms of IgAN.Substantial evidence currently supports the association between gut microbiota and IgAN.Studies have reported significant dysbiosis of gut microbiota in IgAN patients[8\u003csup\u003e,\u003c/sup\u003e 10]. Imbalance in gut microbiota can disrupt the intestinal mucosal barrier, allowing the absorption of metabolic toxins, activating intestinal lymphoid tissue, and inducing elevated Gd-IgA1 levels, ultimately leading to IgAN development[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, immune cells on the intestinal mucosa contribute to maintaining intestinal microbial homeostasis and enhancing epithelial barrier function, regulating normal immune responses in the body[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, gut microbiota can contribute to IgAN development by producing various metabolites[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, are major metabolites produced by gut microbiota[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Studies have reported that SCFAs, especially butyrate, can enhance tight junction complexes, maintain structural integrity of the intestinal mucosa, reduce absorption of harmful substances, maintain intestinal environmental stability, and potentially protect kidney function[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].MR analysis revealed g_Roseburia, g_Faecalibacterium, and s_Odoribacter_splanchnicus as protective factors, and s_Lachnospiraceae_bacterium_7_1_58FAA as a risk factor for IgAN. These findings offer a new perspective on the role of gut microbiota in IgAN. Fecal microbiota transplantation may offer a promising alternative therapy for chronic kidney disease. Studies have shown that fecal microbiota transplantation in chronic kidney disease patients reduces renal damage and increases the abundance of Firmicutes, Bacteroidetes, and g_Roseburia, suggesting its potential benefits for kidney function recovery[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Wang et al. demonstrated that supplementation with g_Faecalibacterium significantly improved intestinal dysbiosis and barrier dysfunction in sleep-deprived mice[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. s_Odoribacter_splanchnicus synthesizes butyrate through carbohydrate fermentation and plays a vital role in immune inflammation regulation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A novel anti-inflammatory sulfonolipid compound has been identified as a bacterial metabolite secreted by Odoribacter[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As a well-known SCFA-producing group of three bacteria[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], We speculate that these three microorganisms reduce the risk of developing IgAN by possibly influencing the production of SCFA.s_Lachnospiraceae_bacterium_7_1_58FAA belongs to the Lachnospiraceae family. In their study, Maria De Angelis et al. observed a higher proportion of Lachnospiraceae in the fecal microbiota of advanced IgAN patients compared to healthy individuals[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Based on our findings, we speculate that the elevation of s_Lachnospiraceae_bacterium_7_1_58FAA levels may accelerate the progression of IgAN.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChronic and persistent low-grade inflammation in the kidneys plays a crucial role in IgAN development. In IgAN pathogenesis, inflammatory factors play pivotal roles in mediating and regulating immune responses via paracrine, autocrine, and endocrine mechanisms by binding to respective receptors[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase widely expressed in human and mouse B cells, playing a pathogenic role in IgAN[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In IgA-stimulated human mesangial cells, inflammatory cytokine production (e.g., IL-6, IL-8, MCP-1) is SYK-dependent, and SYK inhibition markedly attenuates proteinuria, glomerular macrophage infiltration, and inflammation in renal tubular epithelial cells induced by glomerular-tubular interactions in a nephritis animal model[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Interactions among inflammatory factors are intricate. In IgAN, these interactions can trigger a cascade of inflammatory reactions, culminating in glomerular damage, proteinuria, hematuria, and eventually renal interstitial fibrosis. Our findings indicate that LIF, FGF23, TGF-α, and TSGP-CD5 elevate IgAN risk, whereas IL-10RA lowers it.LIF belongs to the IL-6 family and plays a role in mucosal immunity[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Few studies have elucidated its mechanism as a risk factor for IgAN.Koshi Yamada and colleagues compared IgA1-secreting cell lines isolated from peripheral blood cells of both IgAN patients and healthy individuals. They observed that in IgA1-producing cells from IgAN patients, LIF induced a higher level of STAT1 phosphorylation compared to healthy individuals. Furthermore, knockdown of STAT1 using siRNA significantly attenuated the LIF-induced increase in Gd-IgA1 in these cells[50\u003csup\u003e,\u003c/sup\u003e 51].FGF23, primarily produced by osteocytes, regulates vitamin D and phosphate metabolism in the kidneys. Over the past decade, it has emerged as a crucial biomarker for cardiovascular diseases[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Studies have shown an association between FGF23 and proteinuria as well as renal function decline in IgAN patients[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], implying its potential role as a risk factor for IgAN development.TSGP-CD5, a glycoprotein found on T cell surfaces, is generally regarded as having negative immune regulatory properties, inhibiting T cell activation and aiding in autoimmune balance maintenance[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Administering anti-CD5 monoclonal antibodies can ameliorate proteinuria and mesangial matrix damage in rats with glomerulonephritis[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This appears contradictory to our results, however, considering it is based on a rat model rather than clinical experimentation and the intricate interactions involving immune inflammation, further clinical investigations are warranted to elucidate the role of TSGP-CD5 in IgAN.TGF-α, a member of the epidermal growth factor family, has been primarily studied in the context of tumors. Its role in kidney diseases remains contentious with no consensus reached. The findings of this study suggest the potential role of TGF-α as a risk factor for IgAN.MR studies have shown that IL-10RA acts as a protective factor in IgAN.IL-10RA, a subunit of the IL-10 receptor, plays a crucial role in the IL-10 signaling pathway.IL-10 is an anti-inflammatory cytokine, and research indicates that upregulation of IL-10 can suppress the activation of Th1 and Th17 cells in the intestine, ameliorating renal damage in crescentic glomerulonephritis[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Additionally, IL-10 can suppress the secretion of inflammatory cytokines like IL-6 and TNF-α, alleviate glomerular inflammation, suppress cell proliferation, and preserve renal function[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This aligns with our discovery that IL-10RA, an isoform of IL-10, is implicated in mitigating the genetic susceptibility to IgAN.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe association between gut microbiota and IgAN is currently a subject of interest. Intestinal immune inflammation activation represents a risk factor for IgAN.Research has indicated that dysregulated gut microbiota can bind to Toll-like receptors (TLRs) on mucosal dendritic cell surfaces, triggering the production of inflammation and pro-inflammatory cytokines[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Additionally, Tang and colleagues discovered that in pseudo-infertile mice with IgAN, aberrant gut microbiota results in increased levels of inflammatory factors (including TLR4, IL-6, TNF-α, and NF-κB) in intestinal and renal tissues, exacerbating renal inflammation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].The interaction between gut microbiota and inflammatory proteins is crucial in the pathogenesis of IgAN.Our findings suggest that s_Odoribacter_splanchnicus may mitigate the risk of IgAN by downregulating TGF-α expression.This offers a specific direction for future research focusing on the gut microbiota - inflammation response - IgAN axis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThis study employed MR for the first time to investigate the causal relationships among gut microbiota, circulating inflammatory proteins, and IgAN.Rigorous sensitivity analyses were conducted on the results, and reverse MR studies were performed to mitigate the potential interference of reverse causality. Ultimately, the study validated the causal relationships between gut microbiota, circulating inflammatory proteins, and IgAN, identifying inflammatory proteins as intermediate factors. This not only offers theoretical support for the treatment and prevention of IgAN and broadens the applicability of the gut-kidney axis but also contributes to a deeper understanding of its underlying mechanisms.Co-regulation of gut microbiota and inflammatory factors holds the potential for substantial breakthroughs in preventing and treating IgAN.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNonetheless, this study faces several limitations. (Ⅰ) Since all participants were of European ancestry, and given the notable ethnic variations in IgAN incidence, further investigation is warranted to ascertain the generalizability of these findings to other ethnic groups, a common challenge in current MR studies. (Ⅱ) Given the exploratory nature of this study, statistical adjustments were not applied to the results to yield more informative insights into gut microbiota and inflammatory proteins. (Ⅲ) The GWAS data we acquired lacks demographic details, including IgAN progression, patients' age, and gender, precluding subgroup analyses. Moving forward, researchers should encompass diverse ethnic populations, augment sample sizes, and employ advanced research methodologies and statistical approaches to delineate the precise relationships between gut microbiota, circulating inflammatory proteins, and IgAN.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, our MR study uncovered causal links between six gut microbiota species and five circulating inflammatory proteins with IgAN. Additionally, mediation analysis demonstrated that the circulating inflammatory protein TGF-α mediated the pathway from gut microbiota to IgAN. The gut microbiota and circulating inflammatory proteins identified in this study can potentially serve as biomarkers for diagnosing and treating IgAN, and aid in elucidating its underlying mechanisms.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003eWe would like to thank the authors of the public databases and GWAS analyses mentioned in the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u003c/strong\u003ePengtao Dong and Xiaoyu Li conducted the data analysis and authored the article. Xue Feng was responsible for data collection and preprocessing. Siyu Huang and Ziran Zhao summarized the current status of the study. Qing Zhang revised the manuscript and addressed errors in expertise.Zheng Wang and Bing Cui provided guidance throughout the entire project and granted final approval to the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e This study received support from the Cultivation of Top Talents in Traditional Chinese Medicine in Henan Province (Project Approval No. 2022ZYBJ08), the Scientific Research Special Project of Traditional Chinese Medicine in Henan Province (Project Approval No. 2019JDZX2116), and the Science and Technology Tackling Project of Henan Province (Project Approval No. 232102310458).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u003c/strong\u003eThe GWAS data utilized in this study are all publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePattrapornpisut P, Avila-Casado C and Reich HN. 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(2024) LPS/TLR4 Pathway Regulates IgA1 Secretion to Induce IgA Nephropathy. ALTERNATIVE THERAPIES IN HEALTH AND MEDICINE.30, 419\u0026ndash;425.\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":"IgA nephropathy, Gut microbiota, Circulating Inflammatory proteins, Mendelian randomization, Analysis of mediation, Causality","lastPublishedDoi":"10.21203/rs.3.rs-4472698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4472698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground：\u003c/strong\u003eIgA nephropathy (IgAN) is an immune-inflammatory glomerulonephritis mediated by both genetic and environmental factors. Recent research indicates a close association between gut microbiota dysbiosis and IgAN development. Additionally, circulating inflammatory proteins also play a significant role in the progression of IgAN.However, the causal relationship among gut microbiota, circulating inflammatory proteins, and IgAN remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eThis study utilized publicly available genome-wide association study (GWAS) data for Mendelian randomization (MR) analysis to investigate the causal relationship among gut microbiota circulating inflammatory proteins and IgAN, as well as to examine the mediating role of circulating inflammatory proteins in the association between gut microbiota and IgAN. The primary analytical method employed in this study was inverse variance-weighted (IVW) analysis with specific attention given to Bayesian-weighted MR results and supported by MR-Egger regression, weighted median, median model and simple model approaches. Several sensitivity analyses were performed to evaluate the robustness of MR analysis findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003e(1)MR analysis of gut microbiota and IgAN indicates negative associations between g_Roseburia, g_Faecalibacterium, s_Odoribacter_splanchnicus, and s_Roseburia_unclassified with IgAN risk, while positive associations exist between s_Paraprevotella_unclassified and s_Lachnospiraceae_bacterium_7_1_58FAA with IgAN risk.(2) Circulating inflammatory proteins to IgAN in MR analysis showed that IL-10RA was negatively correlated with the risk of IgAN, while TSGP-CD5, FGF23, LIF, and TGF-α levels were positively correlated with the risk of IgAN.(3)Mediation analysis suggests that TGF-αserves as a mediator between s_Odoribacter_splanchnicus and the causality of IgAN. (4) The results of the reverse MR analysis suggest no significant causal effect of IgAN on gut flora and circulating inflammatory proteins.Sensitivity analyses consistently support the reliability of the study results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003eOur research findings, obtained through genetic methods, substantiate the causal link between gut microbiota, circulating inflammatory proteins, and IgAN. The identification of biomarkers offers novel insights into the potential mechanisms underlying IgAN, which can be advantageous for early diagnosis and the development of more effective treatment strategies.\u003c/p\u003e","manuscriptTitle":"Causal relationship between gut microbiota, circulating inflammatory proteins and IgA nephropathy: two-sample and mediated Mendelian randomisation analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 03:54:34","doi":"10.21203/rs.3.rs-4472698/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":"3f8b38b8-af52-4f45-988d-92a432ee56ab","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[{"value":"featured","date":"2024-06-07 16:35:08"}],"updatedAt":"2025-11-12T10:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 03:54:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4472698","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4472698","identity":"rs-4472698","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}