The last decade of research has shown that machine learning can be used to mine biological data and produce algorithms that report chronological age and biological age sensitivity. Biological data changes over time, and many of those changes are characteristic of age. The perfect measure of biological age would accurately predict mortality risk and reflect the burden of damage and dysfunction due to aging. While this may be difficult to achieve, accurate biological aging clocks will greatly speed progress towards aging therapies.
Aging processes interact with each other, and the progression of aging is a stochastic process, resulting in varying outcomes even in identical bodies. Today’s open-access paper demonstrates that aging clocks for specific organs can be derived from data on circulating proteins, showing that a fraction of people exhibit accelerated aging in one organ. This approach may help improve research, medicine, and lifestyle choices.
The paper “Organ aging signatures in the plasma proteome track health and disease” shows that using data on circulating proteins, machine learning approaches can derive aging clock algorithms specific to organs. The study demonstrates that accelerated organ aging is linked to higher mortality risk and specific organ-related diseases. The findings suggest that accelerated brain and vascular aging predict Alzheimer’s disease progression independently from other biomarkers. This research introduces a method to study organ aging using plasma proteomics data.
. If the provided articles seems to be less than 200 characters or it is an intro of the author, then try to generate an articles using this title.Building Aging Clocks for Specific Organs from Circulating Protein Levels – Fight Aging!
