ClockBase Reveals Biological Age Patterns and Why Aging Clocks Disagree

The samples sit in public archives — more than two thousand DNA-methylation datasets, roughly two hundred thousand human and mouse molecular profiles. The platform reads them all. Eleven clocks compute eleven biological ages: Horvath, Hannum, PhenoAge, DunedinPACE. And the clocks do not agree — adjusted for chronological age, DunedinPACE correlates negatively with most of the rest. The question: what is a single biological-age number worth? The defensible answer: trends across many clocks matter; no lone number is definitive.

Concrete Findings from ClockBase

One notable finding from ClockBase analysis of dataset GSE60446 involved the DNA-methyltransferase inhibitor zebularine. In cultured cells, zebularine significantly reduced epigenetic age across nearly all clocks and slowed the DunedinPACE/POAm pace of aging. However, it is important to note that zebularine directly alters the methylation machinery, so methylation clocks must be interpreted with caution in this context.

Another finding from dataset GSE63704 showed that pulmonary fibrosis patients had significantly higher epigenetic age (as measured by Horvath, PedBE, Zhang, and Hannum clocks) and a faster DunedinPACE pace of aging compared to healthy controls. This aligns with previous research showing that factors like smoking and stress accelerate epigenetic aging, while interventions like parabiosis and iPSC reprogramming decrease it.

Why Aging Clocks Disagree

One key insight from ClockBase is that different aging clocks often disagree with each other. After adjusting for chronological age, DunedinPACE correlated negatively with most other clocks, and the correlation between DunedinPOAm and DunedinPACE was only -0.05. This highlights that different clocks capture different aspects of aging, and no single biological age number should be considered definitive. Instead, trends and patterns across multiple clocks are more meaningful.

ClockBase provides a powerful tool for researchers and the public to explore aging patterns, with features like searching diseases and treatments, running group comparisons and correlations on biological age, and uploading personal methylation data. However, it is crucial to interpret findings cautiously, especially when interventions directly affect methylation machinery. Educational, not medical advice.