More Work on Proteomic Clocks to Measure Biological Age

Researchers are these days producing a fair number of novel metrics capable of measuring age and mortality. Machine learning or similar approaches are used to mine epigenetic, proteomic, and transcriptomic data sets, in order to establish algorithmic combinations of epigenetic marks or expression of specific genes that change in characteristic ways with age. The work here is an example of the type, focused on the proteome, the set of proteins produced by cells, and how it shifts over the course of a lifetime. Unlike first generation epigenetic clocks, this approach appears to be able to pick up the difference to the pace of aging caused by regular exercise and consequent physical fitness, suggesting that it is probably a better class of biomarker, given what is known of the effects of exercise on long-term health. We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1...
Source: Fight Aging! - Category: Research Authors: Tags: Daily News Source Type: blogs