By employing age-prediction models developed using supervised deep learning techniques, a research team at Insilico Medicine, Inc. — one of the leaders in artificial intelligence for drug discovery, biomarker development, digital medicine, and aging research — found that tobacco smokers exhibited higher aging rates than non-smokers. The study was published in the journal Scientific Reports.

Smoking has long been proven to negatively affect people’s overall health in multiple ways.

Insilico Medicine senior researcher Polina Mamoshina and colleagues 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.

By employing age-prediction models, the researchers examined data from 149,000 fully anonymized individual records linked to smoking status (49,000 smokers). The number of females, males, smokers and non-smokers within each age group was comparable. The median age was 55 years.

The team analyzed a number of biochemical markers, including measures based on glycated hemoglobin, urea, fasting glucose and ferritin.

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 non-smokers.

The findings also suggested that deep learning analysis of routine blood tests could replace the current unreliable method of self-reporting of smoking status and evaluate the influence that other lifestyle and environmental factors have on aging.

“I am pleased to be part of the research study, which provides fascinating scientific evidence that smoking is likely to accelerate aging,” Dr. Mamoshina said.

“Smoking is a real problem that destroys people’s health, causes premature deaths, and is often the cause of many serious diseases.”

“We applied artificial intelligence to prove that smoking significantly increases your biological age.”

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Polina Mamoshina et al. 2019. Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Scientific Reports 9, article number: 142; doi: 10.1038/s41598-018-35704-w