Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers

TY - JOUR

T1 - Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers

AU - Mamoshina, Polina

AU - Kochetov, Kirill

AU - Cortese, Franco

AU - Kovalchuk, Anna

AU - Aliper, Alexander

AU - Putin, Evgeny

AU - Scheibye-Knudsen, Morten

AU - Cantor, Charles R.

AU - Skjodt, Neil M.

AU - Kovalchuk, Olga

AU - Zhavoronkov, Alex

PY - 2019

Y1 - 2019

N2 - There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging.

AB - There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging.

U2 - 10.1038/s41598-018-35704-w

DO - 10.1038/s41598-018-35704-w

M3 - Journal article

C2 - 30644411

AN - SCOPUS:85060044993

SN - 2045-2322

VL - 9

JO - Scientific Reports

JF - Scientific Reports

M1 - 142

ER -