{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T06:01:43Z","timestamp":1785391303946,"version":"3.55.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Meige syndrome (MS), a complex form of segmental craniocervical dystonia, currently lacks objective diagnostic biomarkers.This study aims to identify biological biomarkers that can stratify patients with MS and to reveal the complex pathophysiological heterogeneity of this disorder, with particular attention to the peripheral immune-metabolic profiles that may reflect underlying neuroimmune dysfunction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected clinical and laboratory data from 1,782 patients with MS during their first hospitalization at the MS Center of The Third People\u2019s Hospital of Henan Province between 2023 and 2025.\u00a0A total of 66 clinical and laboratory indicators were included. An interpretable clustering analysis framework was applied to perform a series of pipeline analyses, including imputation, dimensionality reduction, outlier removal, and clustering. Next, feature importance was assessed. A multi-class classification model was then constructed and evaluated using XGBoost. Finally, the Shapley Additive exPlanations(SHAP) algorithm was employed to interpret the contribution of each feature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The preprocessing pipeline effectively imputed missing values (ranging from 0.06% to 14.14%) and removed outliers. Unsupervised clustering analysis revealed three heterogeneous subtypes of MS. A total of 23 important features were selected to construct a multi-class XGBoost classification model. The model demonstrated excellent performance, with a macro-average AUC of 0.9790 and an Obuchowski index of 0.9784. The SHAP analysis further elucidated the unique hematological and biochemical metabolic characteristics of each subtype.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study establishes a novel, data-driven subtyping system for MS based on objective blood and metabolic profiles. The three identified subtypes display distinct immune-metabolic signatures: a hypercoagulable-inflammatory profile, a metabolic-excess profile with impaired vascular protection, and a frailty-associated antioxidant-deficient profile. These peripheral biomarker patterns suggest testable hypotheses regarding neuroimmune mechanisms\u2014including fibrinogen-driven microglial activation, oxidized lipid-mediated blood-brain barrier disruption, and uric acid-dependent antioxidant depletion\u2014that may contribute to the pathophysiology of different MS subtypes. This framework may inform future prospective investigations into precision medicine approaches for MS, though substantial validation is required before clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1904682","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T05:40:51Z","timestamp":1785390051000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Use of explainable cluster analysis to identify distinct subtypes of Meige syndrome patients and associated biomarker profiles"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaoli","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, The Third People\u2019s Hospital of Henan Province","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, The Third People\u2019s Hospital of Henan Province","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runing","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, The Third People\u2019s Hospital of Henan Province","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1017\/9781316459324.003","article-title":"Definition 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