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More Work on Proteomic Clocks to Measure Biological Age


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Posted 16 October 2020 - 10:11 AM


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) significantly extend life span when manipulated in mice or fish.

By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.

Link: https://doi.org/10.1111/acel.13256


View the full article at FightAging




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