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Sci Rep. 2019 Jan 15;9(1):142. doi: 10.1038/s41598-018-35704-w.

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

Author information

1
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., 9601 Medical Center Dr, Suite 127, JHU, Rockville, MD, 20850, USA.
2
Canada Cancer and Aging Research Laboratories, Ltd, Lethbridge, Alberta, T1K7X8, Canada.
3
Computer Science Department, University of Oxford, Oxford, United Kingdom.
4
Computer Technologies Lab, ITMO University, St. Petersburg, 197101, Russia.
5
Biogerontology Research Foundation, Research Department, Oxford, United Kingdom.
6
Canadian Longevity Alliance, Ontario, Canada.
7
University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada.
8
Leaders in Medicine Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, T2N 4N1, Canada.
9
Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
10
Boston University, Department of Biomedical Engineering, Boston, Massachusetts, 02215, USA.
11
Canada Cancer and Aging Research Laboratories, Ltd, Lethbridge, Alberta, T1K7X8, Canada. olga.kovalchuk@uleth.ca.
12
University of Lethbridge, Lethbridge, Alberta, T1K3M4, Canada. olga.kovalchuk@uleth.ca.
13
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., 9601 Medical Center Dr, Suite 127, JHU, Rockville, MD, 20850, USA. alex@insilicomedicine.com.
14
Canada Cancer and Aging Research Laboratories, Ltd, Lethbridge, Alberta, T1K7X8, Canada. alex@insilicomedicine.com.
15
Biogerontology Research Foundation, Research Department, Oxford, United Kingdom. alex@insilicomedicine.com.
16
Buck Institute for Research on Aging, 8001 Redwood Boulevard, Novato, CA, 94945, USA. alex@insilicomedicine.com.

Abstract

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.

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