D2.2-VITO: Generation and Application of a Model to Determine Intra- and Inter-individual Variation in Biomarkers

Description

Biomarkers which reflect levels of pollutants in the human body offer a unique opportunity to investigate impact of environmental pollution on humans. Biomarker levels in the general population are influenced by genetic, physiological and external factors. Knowledge on which factors are important may help to prioritise risk management measures. This information is also crucial for comparison biomarker values among different populations.

This project aimed to analyze systematically human biomarker data from large environmental health programs which have been running over the last years in Belgium and in Denmark to attain more insight in inter-individual variability of biomarkers. The databases contained data from the general population and from exposed workers. Different age groups were included and information on covariates such as gender, body composition, occupational exposure, smoking behavior, nutrition and other life style factors was available.
The project focused on urinary 1-hydroxypyrene. This urinary metabolite of pyrene is a widely used marker for exposure to polyaromatic hydrocarbons (PAHs). PAH are non-persistent compounds to which the general population is exposed through different sources (traffic, cooking, heating, food,…). Although pyrene is not carcinogenic, some 4 to 7-ring PAH are. Urinary 1-hydroxypyrene is a biomarker with a relatively short half-life (18-20 hours).

As a second marker of exposure to PAHs, DNA adducts were studied. This is a biomarker of effective dose. As a third marker, serum levels of marker PCBs were investigated. These compounds are persistent and accumulate in body fat. PCBs have endocrine properties and are suspect to interfere with early development. Exposure of the general population occurs mainly through food.

One of the major goals of the project was to develop a strategy to describe biomarker data to get clear information on levels and ranges in the general population. It dealt with biomarker values below the limits of detection (LOD), outliers, missing values, detection and quantification of significant covariates by multivariate regression models. The multiple linear regression model allowed to identify the major determinants of variability and to quantify their importance. It was found that, for 1-hydroxypyrene, in adolescents, 2.5% of the variability was explained by biological factors and 2.3% by life style factors. In adults, respective data were 8.5% and 15.7%. Further, in adolescents, 36% of the variability in serum levels of marker PCBs is explained by biological factors, 14% by local factors and 1% by food consumption, and in adults, 31% by biological factors, 3% by local factors and 4% by food consumption. It was shown that the approach which has been developed can be extrapolated to other biomarkers in different matrices (PCBs in cord blood) or to other chlorinated substances (e.g. HCB, p,p’-DDE in serum).