{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T18:44:50Z","timestamp":1788374690455,"version":"build-2803163510"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"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>Alopecia areata (AA) is a common autoimmune disorder marked by non-scarring hair loss, which imposes significant psychosocial stress on patients. To investigate key metabolites and ions involved in AA\u2019s pathogenesis, we utilized gas chromatography-mass spectrometry (GC-MS) for non-targeted metabolomics and inductively coupled plasma mass spectrometry (ICP-MS) for ionomics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 36 six-week-old Kunming mice were divided into control (n = 12), an AA model (n = 12), and tofacitinib-treated groups (n = 12). A mouse model of AA was established by sodium sulfide (Na<jats:sub>2<\/jats:sub>S) induction in both the model and treatment groups, while the treatment group (n = 12) received tofacitinib treatment at a dose of 1\u00a0mg\/kg. GC-MS was used to determine the metabolic profiling in serum samples, and ICP-MS was applied to assess ionomic changes in the serum samples. Potential metabolites and ions were identified using orthogonal partial least squares-discriminant analysis (OPLS-DA). Subsequently, MetaboAnalyst 5.0 and the Kyoto Encyclopedia of Genes and Genomes database (KEGG) were used to map the metabolic pathways. Spearman correlation analysis was conducted to identify relationships and potential regulatory interactions between differential metabolites and individual ions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Metabolomics analysis revealed that D-lactic acid, glycolic acid, linoleic acid, petroselinic acid, and stearic acid are key differential metabolites between the control, AA model, and tofacitinib groups. Pathway analysis highlighted that the biosynthesis of unsaturated fatty acids and linoleic acid metabolism are pivotal pathways implicated in the onset and progression of AA. Furthermore, ionomics analysis identified magnesium, aluminum, titanium, and nickel as differential ions among the three groups. The integrated metabolomics and ionomics analysis indicated that linoleic acid, a key differential metabolite according to the KEGG database, shows a positive correlation with phosphorus, vanadium, magnesium, and zinc. Among these, Mg<jats:sup>2+<\/jats:sup> (Mg<jats:sup>2+<\/jats:sup>) play a crucial role in modulating CD8<jats:sup>+<\/jats:sup> T cell infiltration, thereby influencing the disease progression in AA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Tofacitinib inhibits CD8<jats:sup>+<\/jats:sup> T cell infiltration in hair follicles affected by sodium sulfide-induced AA by modulating the linoleic acid metabolism-Mg<jats:sup>2+<\/jats:sup> pathway. Our findings offer new insights and potential avenues for the clinical diagnosis and treatment of AA, suggesting that targeting metabolic and ionic pathways could enhance therapeutic outcomes.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1507348","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:06:34Z","timestamp":1747213594000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Associations between ionomic profile and metabolic abnormalities in a murine model of sodium sulfide induced alopecia areata"],"prefix":"10.3389","volume":"16","author":[{"given":"Luning","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujuan","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"13809","DOI":"10.1038\/s41598-021-92853-1","article-title":"Adiponectin as a novel biomarker of disease severity in alopecia areata","volume":"11","author":"Anna","year":"2021","journal-title":"Sci. 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