{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T23:10:22Z","timestamp":1788649822038,"version":"build-2803163510"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Advanced age is a primary risk factor for adverse COVID-19 outcomes, potentially attributed to immunosenescence and dysregulated inflammatory responses. In the post-pandemic era, with containment measures lifted, the elderly remain particularly susceptible, highlighting the need for intensified focus on immune health management.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 281 elderly patients were enrolled in this study and categorized based on their clinical status at the time of admission into three groups: non-severe (n = 212), severe survivors (n = 49), and severe non-survivors (n = 20). Binary logistic regression analysis was employed to identify independent risk factors associated with disease severity and in-hospital outcomes. The diagnostic performance of risk factors was assessed using the receiver operating characteristic (ROC) curves. Kaplan-Meier survival analysis and log-rank test were utilized to compare the 30-day survival rates. Furthermore, the transcriptomic data of CD4<jats:sup>+<\/jats:sup> T cells were extracted from Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) was applied to reveal biological processes and pathways involved.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the comparison between severe and non-severe COVID-19 cases, significant elevations were observed in the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and Serum Amyloid A (SAA) levels, concurrent with a notable reduction in CD8<jats:sup>+<\/jats:sup> T cells, CD4<jats:sup>+<\/jats:sup> T cells, natural killer (NK) cells, and monocytes (all <jats:italic>p<\/jats:italic> &amp;lt; 0.05). CD4<jats:sup>+<\/jats:sup> T cells (OR: 0.997 [0.995-1.000], <jats:italic>p<\/jats:italic>&amp;lt;0.05) and NLR (OR: 1.03 [1.001-1.060], <jats:italic>p<\/jats:italic>&amp;lt;0.05) were independent risk factors affecting disease severity. The diagnostic accuracy for COVID-19 severity, as measured by the area under the curve (AUC) for CD4<jats:sup>+<\/jats:sup> T cells and NLR, was 0.715 (95% CI: 0.645-0.784) and 0.741 (95% CI: 0.675-0.807), respectively. Moreover, patients with elevated NLR or IL-6 levels at admission exhibited significantly shorter survival times. Gene Set Enrichment Analysis (GSEA) revealed several biological pathways that are implicated in the regulation of immune responses and metabolic processes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Lymphocytopenia and the cytokine storm onset are significant predictors of an unfavorable prognosis in elderly patients. The decrease in CD4<jats:sup>+<\/jats:sup> T cells among elderly patients is detrimental to disease recovery, and the biological pathways regulated by these cells could potentially heighten vulnerability to SARS-CoV-2 infection, thereby exacerbating the development of associated complications.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2024.1450196","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T10:33:08Z","timestamp":1732876388000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Immunological characteristics in elderly COVID-19 patients: a post-COVID era analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Yunhui","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.1111\/all.14866","article-title":"Profound dysregulation of t cell homeostasis and function in patients with severe covid-19","volume":"76","author":"Adamo","year":"2021","journal-title":"Allergy."},{"key":"B2","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1186\/s12967-024-05556-2","article-title":"Immune signature in vaccinated versus non-vaccinated aged people with covid-19 pneumonia","volume":"22","author":"Alessandra","year":"2024","journal-title":"J. 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