NFKBIA as a Potential Aging-Related Comorbidity Gene Linking Renal Fibrosis and Age-Associated Muscle Dysfunction: A Hypothesis-Generating Multi-Omics Analysis with Tissue-Specific Regulatory Patterns

Aging accelerates renal fibrosis (RF) and age-associated muscle dysfunction (muscle aging), but their shared mechanisms remain unclear. We integrated multi-omics bioinformatics to identify aging-related comorbidity genes linking RF and muscle aging. Bidirectional two-sample Mendelian randomization (MR) used estimated glomerular filtration rate (eGFR) and appendicular lean mass (ALM) as proxies for kidney function and muscle mass, followed by differential expression analysis, four machine learning algorithms (LASSO, SVM-RFE, Random Forest, XGBoost), single-cell transcriptomics, pseudotime ordering, cell–cell communication, virtual knockout, and virtual drug screening with molecular docking. MR suggested a potential bidirectional genetic association (forward beta = −0.529, 95% CI −0.816 to −0.242, p < 0.001; reverse beta = −0.031, 95% CI −0.050 to −0.012, p = 0.001). Intersecting renal- and muscle aging-derived differentially expressed genes (DEGs) with aging-related genes yielded 11 candidates; NFKBIA was consistently selected across all algorithms and both datasets. The NFKBIA-based model showed preliminary discrimination in the available datasets (renal: training AUC = 0.816, external expression-based AUC = 0.875; muscle aging: apparent training AUC = 0.892, repeated cross-validated AUC = 0.903, external expression-based AUC = 0.758). NFKBIA was predominantly expressed in immune cells in RF and fibroblasts in the muscle aging dataset, suggesting tissue-specific regulatory patterns. Virtual knockout suggested MHC class II and Pde4d/Cdon-related network effects, respectively. Virtual screening identified Tamoxifen and Raloxifene as candidate compounds that may target NFKBIA. Molecular docking showed favorable binding affinities (−6.7 and −8.6 kcal/mol, respectively). This study suggests NFKBIA as a potential aging-related comorbidity gene linking RF and age-associated muscle dysfunction, providing a preliminary computational basis for future experimental validation and drug repurposing. Importantly, the muscle datasets capture age-associated transcriptional changes rather than clinically adjudicated sarcopenia.