Evidence map›Paper›PMID 40747480›Full record

ArticleAnnals of cancer epidemiology2019

Chemical Biomarkers of Exposure and Early Damage from Potentially Carcinogenic Airborne Pollutants.

Kai Luo, Irina Stepanov, Stephen S Hecht

Abstract read
In one paragraph

Article in Annals of cancer epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
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  8. 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

3 authors.

Kai LuoMasonic Cancer Center, University of Minnesota, 2231 6th St SE, Minneapolis, MN 55455, USA.
Irina StepanovMasonic Cancer Center, University of Minnesota, 2231 6th St SE, Minneapolis, MN 55455, USA.
Stephen S HechtMasonic Cancer Center, University of Minnesota, 2231 6th St SE, Minneapolis, MN 55455, USA.

Funding

Untargeted Analysis ResourceU2CES026533 · NIEHS · UNIVERSITY OF MINNESOTA · PI PETERSON, LISA A · 2015 to 2025
$14.0M
METABOLISM OF CARCINOGENIC TOBACCO SPECIFIC NITROSAMINESR01CA081301 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI STEPHEN S HECHT · 1999 to 2026
$7.1M
Metabolism of Carcinogenic Tobacco-Specific NitrosaminesR37CA081301 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI HECHT, STEPHEN S · 2004 to 2013
$5.5M
NCI NIH HHS R01 CA081301NCI NIH HHS R37 CA081301NIEHS NIH HHS U2C ES026533
6 · The paper itself

Abstract

Background: Epidemiology and laboratory studies support the conclusion that air pollution is carcinogenic to humans. Chemically specific biomarkers are useful for determining human exposure to, and metabolism of, potentially toxic and carcinogenic components of air pollution, and distinguishing exposures due to air pollution from those resulting from tobacco smoke. Methods: Representative chemically specific biomarkers, quantified by mass spectrometry and related techniques, are discussed. The biomarkers are related to exposure to polycyclic aromatic hydrocarbons, volatile toxicants and carcinogens, oxidants, DNA damaging compounds, and metals found in polluted air. Results: Quantifiable chemically specific biomarkers potentially useful in studies of air pollution carcinogenesis include urinary 1-hydroxypyrene, metabolites of benzo[ Conclusions: The following validated chemically specific biomarkers are currently the optimal ones for use in studies of air pollution and cancer: urinary 1-hydroxypyrene, phenanthrene metabolites,

Indexed as

Air pollutioncarcinogenesis biomarkersDNA adductsmetalsoxidative damagepolycyclic aromatic hydrocarbonsvolatile toxicants and carcinogens

Identifiers

PMID40747480
PMCPMC12312103

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Registered trials

None linked

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.