ArticleClinical epigenetics2026
Epigenetic-Metabolic interplay in chronic kidney disease mortality: insights from grimage acceleration and transcriptomic profiling.
Article in Clinical epigenetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundChronic 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.
methodsThis 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.
resultsPatients 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.
conclusionGAA 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.