{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T05:22:19Z","timestamp":1764220939675,"version":"3.46.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"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. Mol. Biosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rosacea is a chronic inflammatory skin disease characterized by vascular and neurological dysregulation, presenting with diverse clinical subtypes whose pathological mechanisms remain incompletely elucidated. Recent studies suggest that metabolic dysregulation may play a key role in disease onset and progression; however, systematic metabolomic studies targeting different subtypes remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study employed untargeted metabolomic analysis to systematically compare plasma metabolic characteristic differences between patients with erythematotelangiectatic rosacea (ETR), papulopustular rosacea (PPR), and healthy controls (HC), aiming to identify potential disease biomarkers and provide new insights for understanding the pathogenesis of rosacea.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to compare metabolic profiles of plasma samples from ETR, PPR, and HC groups. Key differential metabolites identified were subjected to correlation analysis with disease severity and skin physiological parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ETR patients primarily involved amino acid metabolism, carbon metabolism, and cholesterol metabolism pathways, with key metabolites including upregulated SSA and 2,3-DHPA, and downregulated TCDCA and Met. PPR patients primarily involved tryptophan and linoleic acid metabolism pathways, with key metabolites including upregulated 12-HSA, DGLA, and 5-ALA, and downregulated 5-HTP and 3-HPPA. Metabolic differences between different rosacea subtypes were associated with steroid hormone biosynthesis. DGLA showed positive correlation with disease severity, while 5-HTP showed negative correlation with disease severity. Met was closely related to skin barrier function. Both 12-HSA and DGLA showed positive correlation with sebum secretion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings elucidate the metabolic characteristics of rosacea and their associations with disease severity and skin physiological parameters, providing new theoretical foundation and potential targets for subtype diagnosis and precision treatment of the disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmolb.2025.1652995","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T05:13:39Z","timestamp":1764220419000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of blood untargeted metabolomic characteristics of different subtypes of rosacea"],"prefix":"10.3389","volume":"12","author":[{"given":"Kejia","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sichun","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxian","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxi","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5934","DOI":"10.3390\/molecules29245934","article-title":"Advancements in mass spectrometry-based targeted metabolomics and lipidomics: implications for clinical research","volume":"29","author":"Anh","year":"2024","journal-title":"Molecules"},{"key":"B2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s40360-021-00491-0","article-title":"Transcriptome investigation of anti\u2010inflammation and immuno\u2010regulation mechanism of taurochenodeoxycholic acid","volume":"22","author":"Bao","year":"2021","journal-title":"BMC Pharmacol. 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