DNA Methylation Signatures of Aging Could Help Assess Mortality Risk

Aging is associated with the progressive degeneration and loss of function across multiple physiological systems. Chronological age is the most common indicator of aging; however, there is significant variation in the aging process among individuals of the same age due to differences in their organ systems. This variability complicates the understanding of biological aging and the prediction of related diseases and mortality.

Recent advancements in DNA methylation (DNAm) algorithms have provided new ways to estimate biological age, with the potential to predict mortality and age-related diseases. However, despite the development of multiple DNAm algorithms, it remains unclear which tool most accurately represents biological aging and its impact on health outcomes. Now, a new study sought to assess 12 DNAm signatures of aging to explore their correlation with mortality and determine which was the most predictive.

The study by researchers at Shanghai Jiao Tong University School of Medicine (SJTUSM, Shanghai, China) involved a large cohort from the U.S. National Health and Nutrition Examination Survey 1999-2000. Participants aged 50 or older were followed for a median of 17.17 years, with mortality data linked to the National Death Index. The study incorporated twelve DNAm estimators, including HorvathAge, HannumAge, GrimAge, and others. It aimed to evaluate their associations with mortality and compare their predictive abilities.

The research found that most DNAm estimators were significantly correlated with chronological age and mortality. However, when all 12 algorithms were included in the same model, only GrimAge2 remained significantly associated with all-cause mortality. GrimAge2 was found to have superior predictive abilities compared to chronological age, with a hazard ratio (HR) of 2.69 per standard deviation increase.

Based on their findings published in Tsinghua University Press, the researchers concluded that DNAm signatures of aging are independently associated with all-cause mortality, with GrimAge2 being the most robust predictor. Additionally, the team acknowledged that GrimAge2 outperformed other DNAm estimators and chronological age in predicting mortality, suggesting its potential for assessing mortality risk and evaluating healthy aging interventions.

The study also identified that while these findings are promising, further validation in diverse populations is necessary. Moving forward, the researchers plan to explore the underlying mechanisms behind GrimAge2’s effect on mortality and conduct additional studies to confirm these results in broader cohorts.

Related Links:
SJTUSM