Do epigenetic clocks measure causes or effects?

The founder of the PhenoAge epigenetic clock methodology authored this 2020 article: “The Ge[r]oscience paradigm suggests that targeting the aging process could delay or prevent the risk of multiple major age-related diseases. We need clinically valid measures of the underlying biological process and/or classification criteria for what it means to be biologically, rather than chronologically, … Continue reading Do epigenetic clocks measure causes or effects?

An epigenetic clock review by committee

This 2019 worldwide review of epigenetic clocks was a semi-anonymous mishmash of opinions, facts, hypotheses, unwarranted extrapolations, and beliefs. The diversity of viewpoints among the 21 coauthors wasn’t evident. 1. Citations of the coauthors’ works seemed excessive, and they apologized for omissions. However: Challenge 5 was titled “Single-cell analysis of aging changes and disease” and … Continue reading An epigenetic clock review by committee

A strawman argument against epigenetic clocks

This 2019 review of epigenetic clocks by Washington cancer researchers ignored the elephant in the room, and repeatedly returned to an argument for randomness as a cause for aging and disease: “A time-dependent stochastic event process, like epigenetic drift, could lead to cancer formation through the accumulation of random epigenetic alterations that, through chance, eventually … Continue reading A strawman argument against epigenetic clocks

Statistical inferences vs. biological realities

A 2019 UCLA study introduced a derivative of the epigenetic clock named GrimAge: “DNAm GrimAge, a linear combination of chronological age, sex, and DNAm-based surrogate biomarkers for seven plasma proteins and smoking pack-years, outperforms all other DNAm-based biomarkers, on a variety of health-related metrics. An age-adjusted version of DNAm GrimAge, which can be regarded as … Continue reading Statistical inferences vs. biological realities