Scientific Product Assessment Center, Japan Tobacco Inc, 6-2 Umegaoka, Aoba-ku, Yokohama, 227-8512, Kanagawa, Japan.
BMC Public Health. 2023 Mar 29;23(1):589. doi: 10.1186/s12889-023-15511-3.
Exposure to harmful and potentially harmful constituents in cigarette smoke is a risk factor for cardiovascular and respiratory diseases. Tobacco products that could reduce exposure to these constituents have been developed. However, the long-term effects of their use on health remain unclear. The Population Assessment of Tobacco and Health (PATH) study is a population-based study examining the health effects of smoking and cigarette smoking habits in the U.S.
Participants include users of tobacco products, including electronic cigarettes and smokeless tobacco. In this study, we attempted to evaluate the population-wide effects of these products, using machine learning techniques and data from the PATH study.
Biomarkers of exposure (BoE) and potential harm (BoPH) in cigarette smokers and former smokers in wave 1 of PATH were used to create binary classification machine-learning models that classified participants as either current (BoE: N = 102, BoPH: N = 428) or former smokers (BoE: N = 102, BoPH: N = 428). Data on the BoE and BoPH of users of electronic cigarettes (BoE: N = 210, BoPH: N = 258) and smokeless tobacco (BoE: N = 206, BoPH: N = 242) were input into the models to investigate whether these product users were classified as current or former smokers. The disease status of individuals classified as either current or former smokers was investigated.
The classification models for BoE and BoPH both had high model accuracy. More than 60% of participants who used either one of electronic cigarettes or smokeless tobacco were classified as former smokers in the classification model for BoE. Fewer than 15% of current smokers and dual users were classified as former smokers. A similar trend was found in the classification model for BoPH. Compared with those classified as former smokers, a higher percentage of those classified as current smokers had cardiovascular disease (9.9-10.9% vs. 6.3-6.4%) and respiratory diseases (19.4-22.2% vs. 14.2-16.7%).
Users of electronic cigarettes or smokeless tobacco are likely to be similar to former smokers in their biomarkers of exposure and potential harm. This suggests that using these products helps to reduce exposure to the harmful constituents of cigarettes, and they are potentially less harmful than conventional cigarettes.
暴露于香烟烟雾中的有害和潜在有害成分是心血管和呼吸道疾病的一个风险因素。已经开发出了能够降低这些成分暴露的烟草产品。然而,它们对健康的长期影响仍不清楚。人口评估烟草和健康(PATH)研究是一项基于人群的研究,旨在检查吸烟和美国吸烟习惯对健康的影响。
参与者包括烟草制品的使用者,包括电子烟和无烟烟草。在这项研究中,我们试图使用机器学习技术和 PATH 研究的数据来评估这些产品的人群效应。
使用 PATH 研究第 1 波中吸烟者和前吸烟者的暴露生物标志物(BoE)和潜在危害(BoPH)来创建二进制分类机器学习模型,将参与者分类为当前吸烟者(BoE:N=102,BoPH:N=428)或前吸烟者(BoE:N=102,BoPH:N=428)。将电子烟(BoE:N=210,BoPH:N=258)和无烟烟草(BoE:N=206,BoPH:N=242)使用者的 BoE 和 BoPH 数据输入到模型中,以调查这些产品使用者是否被分类为当前或前吸烟者。还调查了被分类为当前或前吸烟者的个体的疾病状况。
BoE 和 BoPH 的分类模型都具有很高的模型准确性。在 BoE 的分类模型中,超过 60%的使用电子烟或无烟烟草的参与者被分类为前吸烟者。在 BoPH 的分类模型中,不到 15%的当前吸烟者和双重使用者被分类为前吸烟者。这两种分类模型都呈现出类似的趋势。与被分类为前吸烟者相比,被分类为当前吸烟者的人患心血管疾病(9.9-10.9%对 6.3-6.4%)和呼吸道疾病(19.4-22.2%对 14.2-16.7%)的比例更高。
电子烟或无烟烟草使用者的暴露和潜在危害生物标志物可能与前吸烟者相似。这表明使用这些产品有助于减少对香烟有害成分的暴露,并且它们可能比传统香烟的危害小。