© 2006 British Occupational Hygiene Society Published by Oxford University Press
Original Article |
Identification of markers for PCB exposure in plasma from Swedish construction workers removing old elastic sealants
1 Department of NBC Defence, Swedish Defence Research Agency, FOI, SE-901 82 Umeå, Sweden; 2 Department of Occupational and Environmental Medicine, Örebro University Hospital, SE-701 85 Örebro, Sweden; 3 Department of Chemistry, Environmental Chemistry, SE-901 87 Umeå, Sweden
* Author to whom correspondence should be addressed. Tel: +46 90 10 6740; fax: +46 90 10 6806; e-mail: hakan.wingfors{at}foi.se
The objective of the present study was to identify PCB-indicators of occupational exposure related to the removal of old elastic sealants. Blood samples were collected from workers involved in removing elastic sealants. Samples were also taken from age- and sex-matched controls. A majority of the exposed workers were re-sampled after 10 months. All samples were analysed for 19 PCBs. The levels in the exposed workers were twice as high as those in the controls, 575 and 267 ng g–1 lipid, respectively, but were essentially unchanged at the second sampling. The PCB patterns also differed. Levels of many less chlorinated PCBs were much higher in the exposed workers, compared to the controls, and principal component analysis (PCA) revealed that easily metabolized PCBs decreased in the exposed workers during the study. This finding indicates that elimination exceeded uptake during the study period, and that the safety information given to the workers had been effective. PCA was also used to identify exposure markers. The relatively persistent PCBs 56/60 and 66, the easily metabolized PCBs 44, 70 and 110 (with vicinal hydrogens in meta/para-positions) and the very persistent PCBs 153 and 180 were found to be good markers for occupational, recent occupational and background (dietary) exposure, respectively. A PCA model based on these markers was equally effective in differentiating between exposed individuals and controls, and between recent and less recent exposure, as a model based on all PCBs.
Keywords: multivariate, analysis • PCB • profiles • principal component analysis