{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:40:45Z","timestamp":1774543245663,"version":"3.50.1"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Primary immune thrombocytopenia (ITP) is an immune-mediated hematologic disorder characterized by a reduction in platelet count, increasing the risk of bleeding. Recent studies have indicated a close association between alterations in gut microbiota and the development of ITP. However, the mechanisms by which gut microbiota influence the occurrence and progression of ITP through plasma metabolites remain poorly understood. Evidence suggests extensive interactions between gut microbiota and plasma metabolites, implying a potential role for gut microbiota in influencing ITP through alterations in plasma metabolites, which requires further investigation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, summarized GWAS data (including 211 gut microbiota taxa, 1,400 plasma metabolites or ratios, and an ITP patient cohort) were retrieved from the MiBioGen and GWAS Catalog databases. Using a two-sample Mendelian randomization (MR) approach, we screened gut microbiota and plasma metabolites potentially causally related to ITP. We further identified plasma metabolites serving as mediators through which gut microbiota affect ITP and calculated the strength of the mediation effect. To ensure result stability, we primarily used the inverse variance weighted (IVW) method as the main judgment index. We also utilized MR Egger and inverse variance weighted methods to detect heterogeneity in the results, and employed MR-Egger and MR-PRESSO methods to assess the presence of pleiotropy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Though two-sample MR analysis, 8 gut microbiota taxa were found to have causal relationships with ITP. After excluding six plasma metabolites with pleiotropy, 39 plasma metabolites were found to be causally related to ITP (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). Eleven plasma metabolites were identified as having causal relationships between gut microbiota and plasma metabolites. Finally, using the delta method, it was calculated that Sphingomyelin levels (8.0%, 95%CI: 0.9% to 11.5%, <jats:italic>P<\/jats:italic> = 0.047) and Glucose-to-mannose ratio (6.5%, 95%CI: 0.7% to 9.5%, <jats:italic>P<\/jats:italic> = 0.039) are intermediates for Intestinimonas influencing ITP, while Bilirubin (Z,Z) to etiocholanolone glucuronide ratio (5.6%, 95%CI: 4.7% to 6.9%, <jats:italic>P<\/jats:italic> = 0.043) is an intermediate for Senegalimassilia influencing ITP.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Gut microbiota can influence the development of ITP through changes in plasma metabolites. Sphingomyelin levels, Glucose-to-mannose ratio, and Bilirubin (Z,Z) to etiocholanolone glucuronide ratio are newly discovered intermediates through which gut microbiota influence ITP, providing potential indicators and targets for clinical diagnosis and treatment. This study highlights the intricate relationship between gut microbiota and plasma metabolites in the context of ITP, suggesting new avenues for clinical diagnosis and treatment.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2024.1447729","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T11:39:02Z","timestamp":1730115542000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Genetically predicted plasma metabolites mediate the causal relationship between gut microbiota and primary immune thrombocytopenia (ITP)"],"prefix":"10.3389","volume":"15","author":[{"given":"Yang","family":"Hong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1093\/ije\/dyv080","article-title":"Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression","volume":"44","author":"Bowden","year":"2015","journal-title":"Int. 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