Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., 9601 Medical Center Dr, Suite 127, JHU, Rockville, MD, 20850, USA.
Canada Cancer and Aging Research Laboratories, Ltd, Lethbridge, Alberta, T1K7X8, Canada.
Sci Rep. 2019 Jan 15;9(1):142. doi: 10.1038/s41598-018-35704-w.
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.
吸烟与癌症、心血管疾病和全因死亡率之间存在关联。然而，目前尚无经济实惠且信息量丰富的测试方法可用于评估吸烟对生物衰老速度的影响。在这项研究中，我们首次证明可以使用血液生化和细胞计数结果以及人工智能（AI）的最新进展来预测吸烟状况。通过使用基于监督式深度学习技术开发的年龄预测模型，我们发现，无论胆固醇比率和空腹血糖水平如何，吸烟者的衰老速度都高于不吸烟者。我们进一步使用这些模型来量化由于吸烟而导致的生物衰老加速。与不吸烟者相比，女性吸烟者的预测年龄是其实际年龄的两倍，而男性吸烟者的预测年龄是其实际年龄的 1.5 倍。我们的研究结果表明，对常规血液测试进行深度学习分析可以补充甚至取代目前易出错的自我报告吸烟状况的方法，并且可以扩展到评估其他生活方式和环境因素对衰老的影响。
