{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:16:21Z","timestamp":1787026581236,"version":"build-2736575974"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"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>Tuberculosis-diabetes mellitus (TB-DM) comorbidity presents significant clinical challenges due to poor treatment outcomes. This study investigated peripheral blood lymphocyte profiles and cytokine dynamics in TB-DM patients compared to healthy controls (HCs) and DM patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Subjects from the healthy controls (HCs), DM, and TB-DM were recruited, and peripheral blood samples were collected. The absolute counts of lymphocyte subsets were detected by flow cytometry, and the cytokines were quantitatively analyzed using the Olink ultra-sensitive targeted protein detection technology for micro-samples. Methods such as differential expression analysis, principal component analysis (PCA), correlation analysis, KEGG pathway enrichment analysis, and GO functional annotation were used to screen out the biomarkers related to TB-DM. Based on this, a TB-DM internal model performance was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate its diagnostic efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study demonstrated significantly reduced NK cells (P\n                      <jats:sub>TB-DM vs. HC<\/jats:sub>\n                      &amp;lt; 0.0001 and P\n                      <jats:sub>TB-DM vs. DM<\/jats:sub>\n                      = 0.0292), total T cells (P\n                      <jats:sub>TB-DM vs. HC<\/jats:sub>\n                      = 0.0018 and P\n                      <jats:sub>TB-DM vs. DM<\/jats:sub>\n                      &amp;lt; 0.0001), and CD8+ T cells (P\n                      <jats:sub>TB-DM vs. HC<\/jats:sub>\n                      = 0.0009 and P\n                      <jats:sub>TB-DM vs. DM<\/jats:sub>\n                      = 0.0072) in TB-DM versus HCs and DM groups. TB-DM patients showed decreased CD4+ T (P\n                      <jats:sub>TB-DM vs. DM<\/jats:sub>\n                      &amp;lt; 0.0001) and B cells (P\n                      <jats:sub>TB-DM vs. DM<\/jats:sub>\n                      = 0.0004) compared to DM controls. Cytokine profiling revealed 5 upregulated and 17 downregulated factors in TB-DM. Three biomarkers (IL-6, IFN-\u03b3, CXCL10) demonstrated superior diagnostic performance (AUC = 0.9841, sensitivity=88.89%, specificity=92.86%) when combined.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings identify distinct immunological alterations in TB-DM and propose a novel cytokine-based diagnostic panel for this high-risk population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1713654","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T06:29:13Z","timestamp":1768458553000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Identification of biomarkers and construction of discriminating model for tuberculosis patients with diabetes mellitus based on proteomics: a cross-sectional 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Department of Tuberculosis, Chinese PLA General Hospital","place":["Beijing, China"]},{"name":"Graduate School, Hebei North University","place":["Zhangjiakou, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Senior Department of Tuberculosis, Chinese PLA General Hospital","place":["Beijing, China"]},{"name":"Graduate School, Hebei North University","place":["Zhangjiakou, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Graduate School, Hebei North University","place":["Zhangjiakou, Hebei, China"]},{"name":"Department of Geriatrics, the Eighth Medical Center of PLA General Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenping","family":"Gong","sequence":"additional","affiliation":[{"name":"Senior Department of Tuberculosis, Chinese 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