Evidence map›Paper›PMID 35348750›Full record

ArticleNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2023

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.

David L Ashley, Wanzhe Zhu, Lanqing Wang, Connie Sosnoff, Jun Feng, Arseima Y Del Valle-Pinero, Yu-Ching Cheng, Cindy M Chang, Dana van Bemmel, Nicolette Borek and 3 more

Abstract read
In one paragraph

Article in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Consistency of Biomarkers of Exposure in the Population Assessment of Tobacco and Health Study from Samples Taken 3 to 5 Years Apart.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026
    Article
  2. 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.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024
    Article
  3. 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.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

David L AshleyDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.ORCID 0000-0002-1060-5893
Wanzhe ZhuDivision of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.
Lanqing WangDivision of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.ORCID 0000-0001-6620-7096
Connie SosnoffDivision of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.ORCID 0000-0001-9162-633X
Jun FengDivision of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.
Arseima Y Del Valle-PineroOffice of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Yu-Ching ChengOffice of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Cindy M ChangOffice of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Dana van BemmelOffice of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Nicolette BorekOffice of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Heather L KimmelDivision of Epidemiology, Services and Prevention Research, National Institute for Drug Abuse, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0001-8278-0095
Marushka L SilveiraDivision of Epidemiology, Services and Prevention Research, National Institute for Drug Abuse, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-4880-2550
Benjamin C BlountDivision of Laboratory Sciences, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.

Funding

FDA HHSNIDA NIH HHS HHSN271201100027CNIDA NIH HHS HHSN271201600001CNIH HHS HHSN271201100027C
6 · The paper itself

Abstract

introductionTo date, no studies have evaluated the consistency of biomarker levels in people who smoke over a long-time period in real-world conditions with a large number of subjects and included use behavior and measures of nicotine metabolism. We evaluated the variability of biomarkers of nicotine exposure over approximately a 1-year period in people who exclusively smoke cigarettes, including intensity and recency of use and brand switching to assess impact on understanding associations with product characteristics. AIMS AND

methodsMultivariate regression analysis of longitudinal repeated measures of urinary biomarkers of nicotine exposure from 916 adults in the Population Assessment of Tobacco and Health (PATH) Study with demographic characteristics and use behavior variables. Intraclass correlation coefficients (ICCs) were calculated to examine individual variation of nicotine biomarkers and the uncertainty of repeat measures at two time points (Waves 1 and 2).

resultsAge, race, and urinary creatinine were significant covariates of urinary cotinine. When including use behavior, recency, and intensity of use were highly significant and variance decreased to a higher extent between than within subjects. The ICC for urinary cotinine decreased from 0.7530 with no use behavior variables in the model to 0.5763 when included. Similar results were found for total nicotine equivalents.

conclusionsUrinary nicotine biomarkers in the PATH Study showed good consistency between Waves 1 and 2. Use behavior measures such as time since last smoked a cigarette and number of cigarettes smoked in the past 30 days are important to include when assessing factors that may influence biomarker concentrations. IMPLICATIONS: The results of this study show that the consistency of the nicotine biomarkers cotinine and total nicotine equivalents in spot urine samples from Waves 1 to 2 of the PATH Study is high enough that these data are useful to evaluate the association of cigarette characteristics with biomarkers of exposure under real-world use conditions.

Indexed as

NicotineTobacco ProductsAdultBiomarkersCotinineHumansBiomarkersCotinineNicotine

Identifiers

PMID35348750
PMCPMC10032194

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.