Epigenetic age plus eGFR for mortality prediction: cross-cohort validation
In an abstract, a mortality model using creatinine-based kidney function was developed in the UK Biobank and validated in NHANES 1999–2002; adding GrimAge improved discrimination, with a C-statistic change of 0.16. [1]
Automatic summary · not editorially reviewed
The key findings
A mortality model including estimated glomerular filtration rate was developed in the UK Biobank and externally validated in NHANES 1999–2002. [1]
Adding GrimAge improved discrimination for mortality prediction, with a change in the C-statistic of 0.16. [1]
The abstract states GrimAge was particularly effective in identifying high-risk individuals who might not be detected by standard approaches. [1]
The source at a glance
- Study type / format
- Cross-cohort validation of a mortality risk prediction model (abstract)
- Population / context
- Participants in the UK Biobank and in NHANES 1999–2002, as described in the abstract.
- Summary based on
- Study abstract
- Original publication
- August 1, 2026
Kidney function and epigenetic age mortality model
The abstract asks whether combining DNA methylation-based biological age with estimated glomerular filtration rate (eGFR, an estimate of kidney filtration) improves mortality risk prediction beyond conventional models. A model including eGFR was developed in the UK Biobank and externally validated in the National Health and Nutrition Examination Survey (1999–2002). Four epigenetic age measures were evaluated: Horvath, Hannum, PhenoAge and GrimAge.
Supporting source [1]
GrimAge improved discrimination in the abstract
Among the four measures, GrimAge showed the largest improvement when combined with creatinine-based kidney function estimates. Adding GrimAge improved discrimination for mortality prediction, with a change in the C-statistic of 0.16. The abstract states that GrimAge was particularly effective in identifying high-risk individuals who might not be detected by standard approaches.
Supporting source [1]
Abstract-only scope of the mortality model
The abstract reports improved mortality prediction from integrating epigenetic age with renal function indicators and suggests more precise risk stratification in kidney health assessments. It does not report sample sizes, follow-up duration, effect estimates with confidence intervals, or adjustment details. The full text was not reviewed. These are predictive associations, not evidence that epigenetic age or kidney function causes mortality.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. Sample sizes, follow-up, confidence intervals and adjustment details are not reported in the abstract. Findings are predictive associations, not causal effects.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Wang X, Song L, Lv Q, Li L, Wang Q, Liu C, Chen M, Liu F, Yu Y, Shen Y, Cui J, Zhao Y, Deng Q. · europepmc.org · August 1, 2026