Variability in Urinary Nicotine Exposure Biomarker Levels Between Waves 1 (2013-2014) and 2 (2014-2015) in the Population Assessment of Tobacco and Health Study

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

IMPACT

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.

Validating Wave 1 (2014) Urinary Cotinine and TNE-2 Cut-points for Differentiating Wave 4 (2017) Cigarette Use from Non-use in the United States Using Data from the PATH Study

BACKGROUND

Sex and racial/ethnic identity-specific cut-points for validating tobacco use using Wave 1 (W1) of the Population Assessment of Tobacco and Health (PATH) Study were published in 2020. The current study establishes predictive validity of the W1 (2014) urinary cotinine and total nicotine equivalents-2 (TNE-2) cut-points on estimating Wave 4 (W4; 2017) tobacco use.

METHODS

For exclusive and polytobacco cigarette use, weighted prevalence estimates based on W4 self-report alone and with exceeding the W1 cut-point were calculated to identify the percentage missed without biochemical verification. Sensitivity and specificity of W1 cut-points on W4 self-reported tobacco use status were examined. ROC curves were used to determine the optimal W4 cut-points to distinguish past 30-day users from non-users, and evaluate whether the cut-points significantly differed from W1.

RESULTS

Agreement between W4 self-reported use and exceeding the W1 cut-points was high overall and when stratified by demographic subgroups (0.7%-4.4% of use was missed if relying on self-report alone). The predictive validity of using the W1 cut-points to classify exclusive cigarette and polytobacco cigarette use at W4 was high (>90% sensitivity and specificity, except among polytobacco Hispanic smokers). Cut-points derived using W4 data did not significantly differ from the W1-derived cut-points [e.g., W1 exclusive = 40.5 ng/mL cotinine (95% confidence interval, CI: 26.1-62.8), W4 exclusive = 29.9 ng/mL cotinine (95% CI: 13.5-66.4)], among most demographic subgroups.

CONCLUSIONS

The W1 cut-points remain valid for biochemical verification of self-reported tobacco use in W4.

IMPACT

Findings from can be used in clinical and epidemiologic studies to reduce misclassification of cigarette smoking status.