Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

Biological Age Linked to Gene Activity A study published on July 20 in npj Aging analyzed 3,227 participants from the Health and Retirement Study, a long-term American study of people over 50 years old. The researchers measured both methylation, or chemical marks on DNA, and RNA levels, which indicate active genes in blood cells. Epigenetic clocks are formulas that convert DNA marks into an estimate of biological age, and they were trained to solve different tasks: Horvath and Hannum predict calendar age, PhenoAge and GrimAge predict health indicators and mortality risk, and DunedinPACE measures the pace of age-related changes in multiple systems. The authors found that 49 genes were statistically associated with high Horvath values, while 3,204 genes were associated with DunedinPACE. No single gene was common to all five clocks, and the same result was obtained when comparing pathways in Reactome, a catalog of biological processes. On a broader level, the estimates converge, all affecting metabolism, development, immune function, and cellular regulation. The researchers created transcriptomic aging signatures, TAGS, based on the profiles of genes associated with each clock. They were calculated based on RNA levels of genes associated with the corresponding clocks and were validated on 2,584 participants. The connection between TAGS and the corresponding clocks was moderate, reflecting current gene activity in the blood, while the original clocks reflected chemical marks on DNA. In a group of 645 people, TAGS were more strongly associated with several outcomes than the corresponding DNA methylation clocks. 🔗 Read original →