American Association for Cancer Research
epi-23-0538_supplementary_table_s40_suppst40.pdf (89.07 kB)

Supplementary Table S40 from Influence of Half-life and Smoking/Nonsmoking Ratio on Biomarker Consistency between Waves 1 and 2 of the Population Assessment of Tobacco and Health Study

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journal contribution
posted on 2024-01-09, 08:21 authored by David L. Ashley, Wanzhe Zhu, Deepak Bhandari, Lanqing Wang, Jun Feng, Yuesong Wang, Lei Meng, Baoyun Xia, Jeffery M. Jarrett, Cindy M. Chang, Heather L. Kimmel, Benjamin C. Blount

Supplementary Table S40 shows regression coefficients of time since last smoked a cigarette referenced to “within the last hour” from weighted multiple linear regression analysis of log-transformed urinary biomarkers for each evaluated in this study and the intra-class correlation coefficients from this alternative model


National Institute on Drug Abuse (NIDA)

United States Department of Health and Human Services

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Centers for Disease Control and Prevention (CDC)

U.S. Food and Drug Administration (FDA)



Biomarkers of exposure are tools for understanding the impact of tobacco use on health outcomes if confounders like demographics, use behavior, biological half-life, and other sources of exposure are accounted for in the analysis. We performed multiple regression analysis of longitudinal measures of urinary biomarkers of alkaloids, tobacco-specific nitrosamines, polycyclic aromatic hydrocarbons, volatile organic compounds (VOC), and metals to examine the sample-to-sample consistency in Waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) Study including demographic characteristics and use behavior variables of persons who smoked exclusively. Regression coefficients, within- and between-person variance, and intra-class correlation coefficients (ICC) were compared with biomarker smoking/nonsmoking population mean ratios and biological half-lives. Most biomarkers were similarly associated with sex, age, race/ethnicity, and product use behavior. The biomarkers with larger smoking/nonsmoking population mean ratios had greater regression coefficients related to recency of exposure. For VOC and alkaloid metabolites, longer biological half-life was associated with lower within-person variance. For each chemical class studied, there were biomarkers that demonstrated good ICCs. For most of the biomarkers of exposure reported in the PATH Study, for people who smoke cigarettes exclusively, associations are similar between urinary biomarkers of exposure and demographic and use behavior covariates. Biomarkers of exposure within-subject consistency is likely associated with nontobacco sources of exposure and biological half-life. Biomarkers measured in the PATH Study provide consistent sample-to-sample measures from which to investigate the association of adverse health outcomes with the characteristics of cigarettes and their use.