Exposure mixtures calculation
The most common model of cumulative risk assessment is to focus on substances that belong to the same common assessment groups (CAG). Substances in such a group belong to the same chemical family and may or may not have a similar mode of action. In assessing the risk, possible interactions between substances are often ignored and, moreover, little information is available about synergistic effects at low doses. More information is needed about the combined effects of such substances, but it is impractical to investigate all possible mixtures, and therefore instruments are needed to select the most relevant substances for further investigation. This selection should not only be based on the hazard (highest relative potencies) but also on the exposure of the population to these substances. The potential risk of being exposed to chemicals in a mixture depends on the food consumption patterns of individuals in a population. A regular diet can contain hundreds of substances, so the number of combinations of substances to which an individual in a population is exposed can be numerous. The exposures mixtures module is used to identify the most relevant components to which a population is exposed.
Risk based, standardised or unweighted exposures
Before performing the mixture exposure assessment, the data are preprocessed. Three optional choices are available, see settings, exposure approach type.
Risk based exposures: exposures are multiplied by the relative potency factor (\(\operatorname{RPF}\)) of each substance and thus exposures for different substances are on the same and comparable scale.
Standardised exposures: all exposures are standardised to equal variance (unit variance).
Unweighted exposures: exposures are taken as such, this is equivalent to \(\operatorname{RPF}\) s equal to 1 for each substance.
Exposure mixtures are identified using a quantitative approach: sparse non-negative matrix underapproximation (SNMU) (Gillis and Plemmons (2013)) and answers the question what combination of substances predominantly determine the underlying patterns in the exposure matrix (substance x person (day)).
After identifying the components, a cluster analysis is applied to group individuals with similar profiles of exposure to the obtained component (Crépet et al. (2022)).
Figure 67 Example of co-exposure distribution (from >1 substance per individual-day, red) super-imposed on the total exposure distribution (blue).