Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

Abstract

Objective: To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

Recent findings: Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

Conclusions: There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

Keywords: Exposome; Metabolomics; Perfluorinated compounds; Persistent organic pollutants; Polyfluoroalkyl substances.

Figure 1.. PRISMA Flow diagram depicting process of article selection.

^a WOS: Web of Science Core Collection as licensed at Yale Institution. Abbreviation: PFAS, per- and polyfluoroalkyl substances.

Figure 2.. A summary of the commonly detected metabolite compound classes (in at least three studies) and categories associated with four individual PFAS compounds and PFAS mixtures.

Individual PFAS compounds and the mixtures were differentiated in color, and the total number of significant associations summing across PFAS types reported in the eleven studies were organized according to the metabolite compound class and categories in human metabolome database (HMDB). Abbreviations: PFAS, per- and polyfluoroalkyl substances; PFHxS, perfluorohexane sulfonic acid; PFOS, perfluorooctane sulfonate; PFOA, perfluorooctanoic acid; PFNA, perfluorononanoic acid.

Figure 3.. A summary of the commonly detected metabolic pathways (in at least three studies) associated with four individual PFAS compounds and PFAS mixtures.

Individual PFAS compounds and the mixtures were differentiated in color and the total number of significant associations summing across PFAS types reported in the eleven studies were organized according to the metabolism category in Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Abbreviations: PFAS, per- and polyfluoroalkyl substances; PFHxS, perfluorohexane sulfonic acid; PFOS, perfluorooctane sulfonate; PFOA, perfluorooctanoic acid; PFNA, perfluorononanoic acid.