Longevity researchers have used artificial intelligence toa diagnostic tool that can identify people who are aging quicker. Dubbed the ‘inflammatory aging clock’, it is a machine-learning algorithm that predicts a person’s immunological decline through immune-related biomarkers and inflammation levels.
It is an established fact that there is a difference between a person’s chronological age and their Biological Age. While chronological age is simply the number of years which have passed since a person was born, Biological Age is the actual age of a person’s cells and the biological damage that they have accumulated over the years, and it is usually not the same as one’s chronological age, hence why in old age, some people deteriorate far worse than others, while others remain relatively healthy.
Using the inflammatory aging clock’s AI algorithm, a new, more precise and more quantifiable metric has been added to the components that make up Biological Age: inflammatory age, or iAge.
This inflammatory age indicates the severity of systemic chronic immune-system inflammation that a person has.
Although the immune system deploys ‘acute inflammation’ as a short-term localized protective response to fix tissue damage and microbial invasion, long term system-wide inflammation is damaging and has been linked to most age-related diseases. So being able to accurately predict the severity of ‘bad’ chronic inflammation as opposed to ‘good’ acute inflammation, was critical for researchers behind the inflammatory aging clock.
“Contrary to the acute response, which is typically triggered by infection, chronic and systemic inflammation is thought to be triggered by physical, chemical, or metabolic stimuli such as those released by damaged cells and environmental insults, generally termed ‘damage-associated molecular patterns’,” systems biologist David Furman, the lead author of the published paper on iAge explained.
Furman and scientists at Stanford University used AI to sift through and analyze blood samples collected from 2009 to 2016 from 1001 healthy individuals between the ages of 8 and 96. This allowed for the identification of protein markers in the blood that indicate a person’s iAge.
One of the protein markers, a cytokine called CXCL9 which carries immune cells to infected sites, emerged as the most likely to influence a person’s iAge.
The study revealed that there is a spike in CXCL9 levels at age 60, which can cause functional problems in the endothelial cells that make up blood vessel walls. Further studies showed that when CXCL9 genes were knocked-off, these functional problems were fixed.
According to Furman this means that iAge can not only be used to inform physicians about a patient’s ‘inflammatory burden’, but it can also allow for preventative interventions, as it can identify individuals who are at risk of age-related diseases despite said individuals showing no clinical evidence of such diseases.
“Using iAge it’s possible to predict seven years in advance who is going to become frail,” Furman said. “That leaves us lots of room for intervention.”
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