Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures

Abstract

Background: Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge.

Objectives: We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products.

Methods: We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products.

Results: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways.

Discussion: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.

Figure 1.

Data processing pipeline for frequent itemset mining of chemicals in consumer purchasing. Consumer purchasing data was obtained from Nielsen, mapped with a database linking chemicals to products and integrated with chemical functional use information, and purchases were aggregated by month to focus on chemical coexposure. For analysis, chemicals were limited to a broad set from the nonconfidential Toxic Substances Control Act (TSCA) inventory and a smaller pathway-based case study (endocrine-disrupting chemicals).

Figure 2.

Prevalence and ranking of individual chemicals. Heat map illustrating the ranked support for the 20 most prevalent chemicals. For a demographic, green color denotes a downward shift of rank relative to the global (lower priority/potential exposure), and red denotes an upward shift (higher priority/potential exposure). Cell numbers quantify the unit change in rank (for households in that demographic) relative to the global rank (all households). Note that ranks are not comparable across demographics as a quantitative measure but are intended to suggest shifts in potential exposure for different demographics with respect to all households. Column annotations indicate demographic categories, and row annotations indicate harmonized functional use of chemicals.

Figure 3.

Ranking of co-occurring chemicals. Heat map illustrating the ranked support for the 20 most prevalent chemical combinations. For a demographic, green color denotes a downward shift of rank relative to the global (lower priority), and red denotes an upward shift (higher priority). Cell numbers quantify the unit change in rank relative to the global rank. Note that ranks are not comparable across demographics as a quantitative measure but are intended to suggest shifts in potential exposure for different demographics with respect to all households. Column annotations indicate demographic categories and row annotations indicate harmonized functional use of chemicals. (A) and (B) annotate groups with similar co-occurrence patterns across demographics as discussed in the main text. Rows and columns were clustered using complete linkage hierarchical clustering based on correlation of rank departures.

Figure 4.

Total number of frequent chemical combinations across demographic groups for five product groups. For each combination of product group and demographic, the purchasing data were reduced to only those chemicals in products contained in the product group and only those households matching the specific demographic category. Parameters for frequent itemset mining: $\text{minimum support}=0.1\%$, $\text{minimum set length}=2$, and $\text{maximum set length}=10$.

Figure 5.

Prevalence and ranking of individual endocrine active chemicals (EACs). Heat map illustrating the ranked support for the 20 most prevalent EACs (0.1% prevalence threshold). For a demographic, green color denotes a downward shift of rank relative to the global (lower priority), and red denotes an upward shift (higher priority). Cell numbers quantify the unit change in rank relative to the global rank. Note that ranks are not comparable across demographics as a quantitative measure but are intended to suggest shifts in potential exposure for different demographics with respect to all households. Column annotations indicate demographic categories and row annotations indicate harmonized functional use of chemicals and their predicted target receptor.

Figure 6.

Ranking of co-occurring endocrine active chemicals (EACs). Heat map illustrating the ranked support for the 20 most prevalent EAC combinations. For a demographic, green color denotes a downward shift of rank relative to the global (lower priority), and red denotes an upward shift (higher priority). Cell numbers quantify the unit change in rank relative to the global rank. Note that ranks are not comparable across demographics as a quantitative measure but are intended to suggest shifts in potential exposure for different demographics with respect to all households. Column annotations indicate demographic categories and row annotations indicate harmonized functional use of chemicals and their predicted target receptors.