Epigenetic-Metabolic interplay in chronic kidney disease mortality: insights from grimage acceleration and transcriptomic profiling

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BACKGROUND: Chronic kidney disease (CKD) is associated with a substantially elevated risk of mortality. Although GrimAge acceleration (GAA) and metabolic syndrome (MetS) are both implicated in this risk, their combined impact and the underlying biological mechanisms remain poorly understood. METHODS: This study integrated data from National Health and Nutrition Examination Survey (NHANES; n = 2,529), Gene Expression Omnibus (GEO), and public aging-related genes. Participants with CKD were divided into four groups: low GAA without MetS (reference), high GAA without MetS (GAA), low GAA with MetS (MetS), and high GAA with MetS (GAA-MetS). Weighted Cox proportional hazards models were used to evaluate associations with all-cause mortality. Independent transcriptomic analyses of CKD (GSE66494), MetS (GSE98895) and aging-related genes datasets included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (LASSO and SVM-RFE) to identify hub genes. Immune cell infiltration was estimated using CIBERSORT. RESULTS: Patients with CKD in the GAA (HR = 2.331, 95% CI: 1.785-3.043, p < 0.001), MetS (HR = 1.314, 95% CI: 1.057-1.635, p = 0.014), and GAA-MetS (HR = 2.112, 95% CI: 1.525-2.927, p < 0.001) groups exhibited significantly higher mortality risks compared to the reference group. High GAA (HR = 2.083, 95% CI: 1.533-2.831, p < 0.001) independently predicted mortality in patients aged ≥ 72 years. Sex subgroup analyses revealed elevated risks for males in both the GAA (HR = 2.440, 95% CI: 1.471-4.048, p < 0.001) and GAA-MetS (HR = 2.320, 95% CI: 1.395-3.859, p = 0.001) groups, and for females in the GAA-MetS group (HR = 1.763, 95% CI: 1.054-2.950, p = 0.031). Transcriptomic analysis identified five hub genes (ZMPSTE24, RELB, STAT6, DKC1, E2F3) implicated in CKD pathogenesis, cellular senescence, and metabolic pathways, whose expression profiles were correlated with distinct alterations in immune cell infiltration within CKD tissues. CONCLUSION: GAA is an independent predictor of all-cause mortality in CKD. The GAA-MetS identifies a particularly high-risk phenotype. Complementary transcriptomic analyses offer a testable hypothesis for the interplay between epigenetic and metabolic dysregulation of CKD pathogenesis.

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