A study to estimate and correlate cigarette smoke exposure in smokers in Germany as determined by filter analysis and biomarkers of exposure

A clinical study, conducted in Germany, compared two methods of estimating exposure to cigarette smoke. Estimates of mouth level exposure (MLE) to nicotine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), pyrene and acrolein were obtained by chemical analysis of spent cigarette filters for nicotine content. Estimates of smoke constituent uptake were achieved by analysis of corresponding urinary biomarkers: for nicotine; total nicotine equivalents (nicotine, cotinine, trans-3′-hydroxycotinine plus their glucuronide conjugates), for NNK; (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) plus glucuronide, for pyrene; 1-hydroxy pyrene (1-OHP) plus glucuronide and for acrolein; 3-hydroxylpropyl-mercapturic acid (3-HPMA) plus the nicotine metabolite cotinine in plasma and saliva. Two hundred healthy volunteer subjects were recruited; 50 smokers of each of 1–2 mg, 4–6 mg and 9–10 mg ISO tar yield cigarettes and 50 non-smokers (NS). Smokers underwent two periods of home smoking, each followed by residence in a clinic. Smoking was permitted ad libitum, and spent cigarette filters, cigarette consumption data, 24 h urine, as well as plasma and saliva samples were collected. Significant correlations ( p < 0.001) were found between MLE and the relevant biomarker for each smoke constituent. The Pearson correlation coefficients ( r) were 0.83 (nicotine), 0.76 (NNK), 0.82 (acrolein) and 0.63 (pyrene). Mean MLE estimates for nicotine, NNK and pyrene showed a dose response in line with ISO tar yield smoked, with 10 mg > 4 mg > 1 mg, and for acrolein 10 mg > 4 mg > *1 mg (where * indicates not significant at 95% confidence level). The mean exposure estimates from biomarkers for nicotine, NNK and acrolein also showed a dose response in line with ISO tar yield with 10 mg > 4 mg > 1 mg > NS, and for pyrene 10 mg > *4 mg > 1 mg > NS. This study shows that estimates of exposure obtained by filter analysis and biomarkers of exposure correlate significantly over a wide range of smoke exposures and that filter analysis may provide a simple and effective alternative to biomarkers for estimating smokers’ exposure.