Relationship between Biomarkers of Cigarette Smoke Exposure and Biomarkers of Inflammation, Oxidative Stress, and Platelet Activation in Adult Cigarette Smokers
Cigarette smoking is a risk factor for several diseases, including cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer, but the role of specific smoke constituents in these diseases has not been clearly established. The relationships between biomarkers of potential harm (B...
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Veröffentlicht in: | Cancer epidemiology, biomarkers & prevention biomarkers & prevention, 2011-08, Vol.20 (8), p.1760-1769 |
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Zusammenfassung: | Cigarette smoking is a risk factor for several diseases, including cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer, but the role of specific smoke constituents in these diseases has not been clearly established.
The relationships between biomarkers of potential harm (BOPH), associated with inflammation [white blood cell (WBC), high sensitivity C-reactive protein (hs-CRP), fibrinogen, and von Willebrand factor (vWF)], oxidative stress [8-epi-prostaglandin F(2α) (8-epiPGF(2α))] and platelet activation [11-dehydro-thromboxin B(2) (11-dehTxB(2))], and machine-measured tar yields (grouped into four categories), biomarkers of exposure (BOE) to cigarette smoke: nicotine and its five metabolites (nicotine equivalents), 4-methylnitrosamino-1-(3-pyridyl)-1-butanol (total NNAL), carboxyhemoglobin, 1-hydroxypyrene, 3-hydroxypropylmercapturic acid, and monohydroxybutenyl-mercapturic acid, were investigated in 3,585 adult smokers and 1,077 nonsmokers.
Overall, adult smokers had higher levels of BOPHs than nonsmokers. Body mass index (BMI), smoking duration, tar category, and some of the BOEs were significant factors in the multiple regression models. Based on the F value, BMI was the highest ranking factor in the models for WBC, hs-CRP, fibrinogen, and 8-epiPGF(2α), respectively, and gender and smoking duration for 11-dehTxB(2) and vWF, respectively.
Although several demographic factors and some BOEs were statistically significant in the model, the R(2) values indicate that only up to 22% of the variability can be explained by these factors, reflecting the complexity and multifactorial nature of the disease mechanisms.
The relationships between the BOEs and BOPHs observed in this study may help with the identification of appropriate biomarkers and improve the design of clinical studies in smokers. |
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ISSN: | 1055-9965 1538-7755 |
DOI: | 10.1158/1055-9965.epi-10-0987 |