Russo Cristina, Valle Maria Stella, Cambria Maria Teresa, Malaguarnera Lucia
Section of Pathology, Department of Biomedical and Biotechnological Sciences, School of Medicine, University of Catania, 95123 Catania, Italy.
Section of Physiology, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy.
Int J Mol Sci. 2025 Aug 17;26(16):7932. doi: 10.3390/ijms26167932.
Sarcopenia and type 2 diabetes mellitus (T2DM) are chronic conditions that gradually affect the elderly, often coexisting and interacting in complex ways. Sarcopenia, which is characterized by the progressive loss of muscle mass and function, is frequently observed in individuals with T2DM. Although the clinical association is well known, the molecular mechanisms remain unclear. Gene expression datasets were retrieved from the Gene Expression Omnibus database. DEGs were identified using the limma package in R (R 4.4.0). Shared DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction networks were constructed using the STRING database and were visualized with Cytoscape. Hub genes were identified via six topological algorithms in the CytoHubba plugin. Pearson's correlation analysis was conducted between hub genes and selected metabolic regulators. GO and KEGG enrichment analyses indicated that mitochondrial function, oxidative phosphorylation, and immune-inflammatory responses were significantly enriched. A PPI network revealed a mitochondrial hub of five key genes involved in energy metabolism, whose downregulation suggests mitochondrial dysfunction as a shared mechanism in sarcopenia and T2DM. Our results provide new insight into the molecular overlap between T2DM and sarcopenia, highlighting potential biomarkers and therapeutic targets for addressing both metabolic disruption and muscle decline.
肌肉减少症和2型糖尿病（T2DM）是逐渐影响老年人的慢性疾病，它们常常同时存在并以复杂的方式相互作用。以肌肉质量和功能逐渐丧失为特征的肌肉减少症在T2DM患者中经常出现。尽管这种临床关联已为人所知，但其分子机制仍不清楚。从基因表达综合数据库中检索基因表达数据集。使用R语言中的limma软件包（R 4.4.0）识别差异表达基因（DEG）。对共享的DEG进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。使用STRING数据库构建蛋白质-蛋白质相互作用网络，并用Cytoscape进行可视化。通过CytoHubba插件中的六种拓扑算法识别枢纽基因。对枢纽基因与选定的代谢调节因子进行Pearson相关性分析。GO和KEGG富集分析表明，线粒体功能、氧化磷酸化和免疫炎症反应显著富集。蛋白质-蛋白质相互作用网络揭示了一个由五个参与能量代谢的关键基因组成的线粒体枢纽，其下调表明线粒体功能障碍是肌肉减少症和T2DM的共同机制。我们的研究结果为T2DM和肌肉减少症之间的分子重叠提供了新的见解，突出了应对代谢紊乱和肌肉衰退的潜在生物标志物和治疗靶点。
