{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:54:51Z","timestamp":1777924491319,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>As life expectancy increases and the global population ages, the incidence of sarcopenia is also increasing, highlighting the need for better diagnosis and treatment methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      To\n                      \n                      \n                      study the genetic expression of sarcopenia using bioinformatics methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A Weighted Gene Coexpression Network Analysis (WGCNA) was conducted to construct coexpression networks, along with protein-protein interaction networks. Diagnostic biomarker potential was evaluated using receiver operating characteristic curves. An analysis of Single-Sample Gene Set Enrichment Analysis (ssGSEA) was performed in order to determine the amount of immune cell infiltration. We analyzed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment using the KEGG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>WGCNA identified modules linked to bone metabolism, ssGSEA showed unique gene enrichment patterns, and 268 genes were found to be differentially expressed in sarcopenia. Fourteen co-expression modules related to bone metabolism were identified, with one showing a strong positive correlation. KEGG pathway analysis indicated downregulation of the renin-angiotensin system and Alzheimer's disease pathways. The differentially expressed genes were primarily involved in adipocyte differentiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study analyzes genetic changes and immune cell patterns in sarcopenia, providing insights into its causes and potential diagnostic markers for future research on treatments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329241291323","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:40:47Z","timestamp":1733974847000},"page":"1140-1153","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Unraveling the genetic mysteries of sarcopenia: A bioinformatics approach"],"prefix":"10.1177","volume":"33","author":[{"given":"Hui","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Geriatrics, 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