Deep-learned biochemistry clocks effectively determine biological age of smokers and predict smoking status. By employing age-prediction models developed by supervised deep learning techniques, the study analyzed a number of biochemical markers, including measures based on glycated hemoglobin, urea, fasting glucose and ferritin. The findings of the study are published in Scientific Reports.
Smoking has long been proven to negatively affect people's overall health in multiple ways. The study by Insilico scientists set out to determine biological age differences between smokers and non-smokers, and to evaluate the impact of smoking using blood biochemistry and recent advances in artificial intelligence.
According to study's results, smokers demonstrated a higher aging ratio, and both male and female smokers were predicted to be twice as old as their chronological age as compared to nonsmokers. The results were carried out based on the blood profiles of 149,000 adults.