The connection between smoking and biological aging on the other hand is somewhat less clear. An international team of scientists led by Insilico Medicine have demonstrated that smoking status can be predicted using blood biochemistry, cell count results, and artificial intelligence which may change how smoking is evaluated on a biochemical level.
Setting out to determine biological age differences between smokers and those who don’t, the team evaluated impacts of smoking via blood biochemistry using artificial intelligence. By employing advances in AI age prediction models developed in supervised deep learning techniques several biochemical markers including measures based on glycated hemoglobin, urea, fasting, glucose, and ferritin were analyzed, as published in Scientific Reports.
Results were carried out based on blood profiles of 149,000 adults in which male and female smokers were predicted to be twice as old as their chronological ages, all smokers demonstrated higher aging ratios.
The team concluded that results propose deep learning analysis of routine blood tests could complement or even may be able to replace unreliable methods of self reporting of smoking status and evaluate influence that other lifestyle and environmental factors have on aging.
Smoking is a most serious issue that can destroy health, cause premature death, and cause many serious diseases. Applying AI has helped researchers to prove for the first time that smoking significantly increases your biological age.
