ArticleGeroScience2026
Associations of accelerated epigenetic aging with cancer and mortality risk in the USA.
Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
DNA methylation (DNAm) algorithms have been developed to assess biological aging and its association with cancer. Despite their potential, direct comparisons to identify the most accurate algorithm for predicting cancer risk and mortality remain limited. The study population (n = 2532) consisted of adults aged 50 years and older from the National Health and Nutrition Examination Survey, with 17-year follow-up mortality data. Twelve DNAm algorithms were evaluated using Illumina EPIC BeadChip array. Logistic regression models were used to assess both overall and site-specific cancer risk, while Cox proportional hazards models and Fine-Gray sub-distribution hazard model were employed to assess cancer mortality. Three hundred forty-three cancer cases were observed at baseline, and 271 cancer-caused deaths were recorded during the follow-up. GrimAgeMortAcc, GrimAge2MortAcc, and VidalBraloAgeAcc were positively associated with overall cancer risk, with multivariable-adjusted odds ratios per standard deviation increase of 1.44 (95% CI: 1.06-1.95), 1.32 (95% CI: 1.01-1.72), and 1.20 (95% CI: 1.01-1.44), respectively. PhenoAgeAcc, GrimAgeMortAcc, and GrimAge2MortAcc were associated with increased cancer risk in women (particularly non-Hispanic White women), while no significant associations were observed in men, including for prostate cancer specifically. Several DNAm algorithms showed strong inverse associations with skin cancer risk. In addition, higher HorvathAgeAcc was linked to an increased risk of cancer mortality, with multivariable adjusted hazard ratio 1.19 (95% CI: 1.04-1.37). This study reveals a close association between several DNAm algorithms (particularly GrimAge) and cancer risk and mortality. These algorithms offer promising tools for advancing precision medicine, with potential applications in cancer prevention and risk stratification.
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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.