An Ageome to Represent States of Aging Across Different Functional Areas of Cellular Biochemistry
Cellular metabolism is highly complex, but that complexity can be divided into functional modules that only interact with one another indirectly. Those indirect interactions do exist, however, and so loss of function in one module will tend to affect others. In this way aging is a process of countless distinct changes, but the effects of those changes are felt everywhere. Or so we might hypothesis, analogously to our experience that declining function in one organ (the kidney, say) will have negative effects on the function of all of the other organs in the body. That said, should we should expect aging to occur uniformly across distinct functional areas of cell biochemistry? Researchers here present data in support of the idea that the progression of aging is not uniform at all when considered at this level.
The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans.
Ageome is based on the premise that an organism's overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks.
Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.
Means mouse data is useless to solve human aging.
"A key finding of our study is the differential predictive power of various pathways in aging. After adjusting for gene set size, we found that lipid metabolism pathways in humans and ion transport and neurological signaling pathways in mice were most predictive of aging."