Lee Peter N, Coombs Katharine J, Fry John S
P.N. Lee Statistics and Computing Ltd., 17 Cedar Road, Sutton, SM2 5DA, UK.
RoeLee Statistics Ltd., 17 Cedar Road,, Sutton, SM2 5DA, UK.
Harm Reduct J. 2025 Mar 30;22(1):45. doi: 10.1186/s12954-025-01188-x.
Reliable epidemiological data are limited on the lung cancer risk of groups using e-cigarettes (ECIGs) and groups using heated tobacco products (HTPs).
We describe a methodology to estimate the lung cancer risk of these groups according to their levels of biomarkers of exposure (BOEs) and of potential harm (BOPHs).
Using 28 search terms for BOEs and 82 for BOPHs we sought publications reporting biomarker-specific data from North America and Europe comparing individuals who smoke cigarettes and individuals who use other established products (ETPs; cigars, pipes, smokeless tobacco (ST) and/or snuff/snus). Publications were selected using defined inclusion/exclusion criteria. Additionally using lung cancer relative risk (RR) estimates for users of specific ETPs derived from recent meta-analyses of epidemiological studies in these regions, we derived a regression model predicting the lung cancer RR by level of each specific biomarker. Separately for groups using ECIGs and using HTPs the lung cancer risk was then estimated by combining RR estimates for selected biomarkers. Our primary estimates only considered biomarkers statistically significantly (p < 0.01) related to lung cancer risk which showed no significant (p < 0.01) misfit to the RR of 1.0 for non-users-those with no use of ETPs, ECIGs or HTPs.
Based on 38 available publications, we extracted biomarker-specific data for ETPs for 56 BOEs within 21 of the 28 search terms considered and for 54 BOPHs within 29 of the 82. The regression slope fitted to the lung cancer risk was significant (p < 0.01) for 22 BOEs and six BOPHs. However, the predicted RR for non-users significantly (p < 0.01) differed from 1.0 for 16 of these biomarkers. We estimated the lung cancer RR for using ECIGs, derived from 30 estimates for 10 biomarkers, as 1.88 (95% CI 1.60-2.22), the excess risk (ER = RR - 1) being 6.8% of that for smokers of cigarettes. The RR estimate varied little in most sensitivity analyses conducted, but increased markedly after removing the restriction to significant model fit. We estimated the lung cancer RR for using HTPs, combining estimates for four BOEs, as 1.44 (0.41-5.08), the ER being 3.4% of that for smokers of cigarettes.
Despite some methodological limitations, our approach estimates risk when reliable epidemiological data are unavailable. Using the biomarkers considered here, the model indicates that the lung cancer risk for individuals using ECIGs is much lower than for smokers of cigarettes, and suggests that the risk for those using HTPs is also low. Research using additional data could add precision to these findings.
关于使用电子烟（ECIG）人群和使用加热烟草制品（HTP）人群的肺癌风险，可靠的流行病学数据有限。
我们描述一种方法，根据暴露生物标志物（BOE）和潜在危害生物标志物（BOPH）的水平来估计这些人群的肺癌风险。
我们使用28个关于BOE的检索词和82个关于BOPH的检索词，查找报告来自北美和欧洲的生物标志物特异性数据的出版物，这些数据比较了吸烟人群和使用其他已确定产品（ETP；雪茄、烟斗、无烟烟草（ST）和/或鼻烟/口含烟）的人群。使用定义的纳入/排除标准选择出版物。此外，利用从这些地区近期流行病学研究的荟萃分析中得出的特定ETP使用者的肺癌相对风险（RR）估计值，我们推导了一个回归模型，通过每种特定生物标志物的水平预测肺癌RR。然后分别针对使用ECIG的人群和使用HTP的人群，通过合并选定生物标志物的RR估计值来估计肺癌风险。我们的主要估计仅考虑与肺癌风险在统计学上显著相关（p < 0.01）的生物标志物，这些生物标志物与未使用者（即未使用ETP、ECIG或HTP的人群）的RR为1.0无显著（p < 0.01）不匹配。
基于38篇可用出版物，我们提取了所考虑的28个检索词中的21个内56种BOE以及82个检索词中的29个内54种BOPH的ETP生物标志物特异性数据。拟合肺癌风险的回归斜率对于22种BOE和6种BOPH是显著的（p < 0.01）。然而，对于其中16种生物标志物，非使用者的预测RR与1.0有显著（p < 0.01）差异。我们根据10种生物标志物的30个估计值，估计使用ECIG的肺癌RR为1.88（95% CI 1.60 - 2.22），超额风险（ER = RR - 1）为吸烟者的6.8%。在进行的大多数敏感性分析中，RR估计变化不大，但在去除对显著模型拟合的限制后显著增加。我们结合4种BOE的估计值，估计使用HTP的肺癌RR为1.44（0.41 - 5.
