{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:51:09Z","timestamp":1790718669094,"version":"4.1.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"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. Pharmacol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Sepsis remains a major cause of mortality and morbidity worldwide. Recent studies suggest that gut microbiota-derived metabolites, such as \u03b1-hydroxybutyrate (\u03b1-HB), may play a critical role in the progression of sepsis. However, the molecular mechanisms underlying \u03b1-HB\u2019s involvement in sepsis remain unclear. This study aims to explore the targets of \u03b1-HB and their association with sepsis progression using multi-database data mining, machine learning, and unsupervised clustering analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>\u03b1-HB-related targets were identified through comprehensive data mining from three databases: SEA, SuperPred, and SwissTargetPrediction. Sepsis-related targets were obtained from the GEO dataset GSE26440, and the intersection of these datasets was analyzed to reveal common targets. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. Additionally, a nomogram was constructed to predict sepsis progression. Clustering, GSVA, and ssGSEA analyses were performed to explore sepsis subtypes. Molecular docking simulations was conducted to investigate interactions between \u03b1-HB and key targets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 42 common targets were identified between \u03b1-HB and sepsis, with significant enrichment in pathways related to immune response, hypoxia, and cancer. Machine learning-based feature selection identified four robust biomarkers (APEX1, CTSD, SLC40A1, PIK3CB) associated with sepsis. The constructed nomogram demonstrated high predictive accuracy for sepsis risk. Unsupervised clustering revealed two distinct \u03b1-HB-related sepsis subtypes with differential immune cell infiltration patterns and pathway activities, particularly involving immune and inflammatory pathways. Subtype 1 was predominantly associated with non-survivors, while Subtype 2 was more frequent among survivors, showing a significant difference in survival status. Molecular docking analysis further indicated potential interactions between \u03b1-HB and key targets (APEX1, CTSD, SLC40A1, PIK3CB), providing insights into the molecular mechanisms of \u03b1-HB in sepsis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study identifies key \u03b1-HB-related targets and biomarkers for sepsis, offering new insights into its pathophysiology. The findings highlight the potential of \u03b1-HB in modulating immune responses and suggest that \u03b1-HB-related targets could serve as promising therapeutic targets for sepsis management.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1615269","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T05:42:14Z","timestamp":1758087734000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["The role of \u03b1-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learning, and unsupervised clustering"],"prefix":"10.3389","volume":"16","author":[{"given":"Qing","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dayong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.cnc.2021.08.003","article-title":"Sepsis","volume":"33","author":"Ackerman","year":"2021","journal-title":"Crit. care Nurs. 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