PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies.

We conducted a systematic review following PRISMA guidelines [ 96 ] and selected studies that investigated human PFAS exposures (either studied as single compounds or as a mixture) in relation to metabolic profiling using high-throughput metabolomics technologies. Inclusion criteria were detailed in advance and registered in PROSPERO (ID: 327,196; access registration via https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=327196 ). We searched research articles found on EMBASE and PubMed databases from their inception through July 19, 2022. Two reviewers independently performed a study selection of eligible epidemiological research articles. Our search was based on matching words contained in the title, abstract, or as keywords including “PFAS” or “perfluoroalkyl” or “polyfluoroalkyl,” and “metabolomics” or “metabolome” or “metabolic profiles,” among others. A full list of terms and search strategy is provided in the Supplementary Material ( Supplementary Material , Methods ) as well as in PROSPERO. Eligible studies included original epidemiological research articles reported as full-text articles in English that predominantly focused on at least one PFAS exposure and with metabolomics data (either as a primary or secondary outcome, or either as targeted or untargeted). Lipidomics studies were also included under the targeted study category. All research studies made use of high-throughput approaches for metabolite profiling. Research articles that focused solely on metabolic biomarkers with no metabolomic analytic approach used were excluded from this review. Studies with metabolomics data were eligible from any human biofluid sample as long as the study was epidemiological; animal studies, as well as experimental studies with human samples conducted in vitro or in vivo were ineligible ( Supplementary Material , Methods ). Data extraction of eligible studies included first-author, year of publication, study design, sample size, participant characteristics and location, years of follow-up (if applicable), number and nomenclature of PFAS examined, window of exposure (and year), number of metabolites measured (outcome), metabolomics analytical method (including reported level of confidence in metabolites [ 97 ]), data pre-processing, statistical methods (including false discovery rate correction), chemical databases used for identification or classification of the metabolites and pathways, summary of findings (analyzed metabolites and pathways), biological significance, adjusted confounders, effect modifiers, limitations, and additional findings. When possible, we classified each PFAS-associated metabolite using the criteria denoted by the human metabolome database (HMDB) ( https://hmdb.ca/ ) and pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( https://www.genome.jp/kegg/pathway.html ). When a particular metabolite identifier was not found in the HMDB database, the Chemical Entities of Biological Interest (ChEBI) ( https://www.ebi.ac.uk/chebi/ ) and LipidMaps ( https://www.lipidmaps.org/ ) were used instead. We evaluated the quality of each study based on their limitations and potential biases. We created a quality score following 5-point criteria based on epidemiological [ 98 ] and metabolomics guidelines [ 99 , 100 ] where points were assigned for each of the following: sample size (≥ 100 vs < 100 participants), adjustment for confounders (adjusted vs. none), false discovery rate (FDR-adjustment vs. no correction), study design (prospective vs. not prospective), and reporting of confidence level 1 metabolites identified according to the Metabolomics Standards Initiative (MSI). A higher score indicated a higher quality of evidence. We summarized the evidence for reported PFAS associations with individual metabolites or dysregulated metabolic pathways to identify consistent findings and research gaps. In this review, we refer to consistent findings as those metabolites, metabolite classes, or pathways reported most frequently across studies (Figs. 2–6, Table S5 ), but we also report consistent directionalities based on the total number of metabolite associations independent of study number ( Figs. S1 – S3 , Table S3 – S4 ); we either presented the number of studies reporting specific PFAS-metabolite associations or the total number of reported PFAS-metabolite associations in all studies. We classified each reported metabolite by HMDB superclass, class, and subclass categories. We also specified the directionality of the association reported, as well as the PFAS exposure (either a combination of several PFAS mixtures or the individual PFAS studied) and metabolomics approach followed (either targeted or untargeted). Pathway analyses across studies were also summarized by KEGG modules and submodules. In our summary, we included results from correlation analyses, analyses adjusting for covariates, and analyses implementing FDR-correction. If a study presented more than one type of analysis, we extracted the results that were considered more rigorous (FDR-corrected and/or covariate-adjusted). In order to complement the summary of pathways reported across studies, we extracted each reported metabolite by study and conducted an independent pathway analysis using a systematic method with Reactome ( https://reactome.org ) [ 101 ]. We extracted each identified metabolite name reported in the articles and converted them to KEGG IDs. In the case of lipids, when unsure, we searched on LipidMaps, and if a m/z ratio was provided, we matched it with what was reported by the study. Studies were included in Reactome analyses if they reported any PFAS-metabolite association with an available metabolite KEGG ID recognizable by Reactome ( n = 26). We then used the KEGG IDs for each compound and uploaded them in Reactome to evaluate the pathways inferred from positive associations, negative associations, and all associations for each study separately. Lastly, we calculated combined p -values for top pathways across studies [ 240 , 241 ]. Quantitative summaries for descriptive statistics and visualizations for main findings were implemented in R (version 4.1.2).

Out of 358 records retrieved from EMBASE ( n = 207) and PubMed ( n = 151), a total of 28 unique records for original studies evaluating PFAS exposures and human metabolomics that were observational in design and in English language were eligible for this systematic review ( Fig. 1 ). Effects of 31 different PFAS exposures (including isomers) have been evaluated in relation to the human metabolome across studies, with the most common exposure being legacy long-chain PFAS, such as PFOA, PFOS, and PFHxS ( Table 1 ). We accounted for PFAS that are included in the final analytic dataset across studies ( Table S2 ) and described their reported distributions in Table 1 . While the majority of studies examined single and mixed exposures to PFAS, there are several studies that examined other pollutants as well [ 31 , 102 – 110 ]. Only one study [ 109 ] examined PFAS-metabolite associations adjusting for other pollutants in partial correlation analyses and another two studies (Li et al. [ 31 ] and Matta et al. [ 104 ]) implemented multi-pollutant models (PCBs in combination with PFAS). Most PFAS were measured in samples collected during the 2000s and only four studies had samples measured prior to 1999 [ 31 , 111 – 113 ]. The majority of studies measured PFAS exposures during adulthood ( n = 15), followed by prenatally or perinatally ( n = 9), and/or in childhood or adolescence ( n = 7) ( Tables 2 and 3 , Table S2 ). Sample sizes across eligible studies also ranged widely from 40 to 1,105 participants recruited across three different continents as follows: America ( n = 11 in the USA), Asia ( n = 9), and Europe ( n = 2 in Sweden, n = 2 in Finland, n = 1 in France, n = 1 in Spain, and n = 2 across Europe). A total of 19 studies reported a non-targeted metabolomics approach, 7 studies used a targeted metabolomics approach, and 2 studies included both approaches. The most common analytical method used in samples was LC–MS or LC-HRMS ( n = 11), followed by UHPLC-, UPLC-, HPLC-, or LC-qTOF-MS ( n = 7), HPLC-, UPLC-, or LC–MS/MS ( n = 7), and LC/Orbitrap-MS or UPLC-Q-Orbitrap HRMS ( n = 2). Other methods such as 1 H-NMR ( n = 1) or GC–MS ( n = 1) were less commonly used for metabolomics analyses. Metabolomics data were collected from several media across studies: in serum ( n = 16), plasma ( n = 10), urine ( n = 2), or semen samples ( n = 1). Most studies measured metabolomics in a single sample collected from participants, with only two studies integrating repeated metabolomics measures in plasma [ 114 ] or urine [ 102 ]. The majority of studies were cross-sectional ( n = 25), including four [ 30 , 102 , 114 , 115 ] with additional prospective analyses of PFAS measured prior to metabolomics. Several studies focused as well on examining associations with a specific disease or biomarker for disease ( n = 18). Cardiometabolic outcomes were the most studied diseases in relation to PFAS exposure, including overall diabetes [ 105 ], type 2 diabetes [ 111 , 113 ] type 1 diabetes [ 30 ], obesity [ 105 ], hypertension [ 105 ], dyslipidemia [ 105 ], hyperuricemia [ 105 ], non-alcoholic fatty liver disease or steatohepatitis [ 108 , 116 ], in addition to cardiometabolic markers (insulin resistance, lipids, glucose level, adiposity, BMI, or liver injury) [ 29 , 106 , 109 , 115 , 117 , 118 ]. A few other studies also examined reproductive outcomes (semen quality [ 107 ] or endometriosis [ 104 ]), birth weight or small-for-gestational age [ 119 ], breast cancer [ 31 ], celiac disease [ 114 ], and COVID-19 [ 120 ]. Overall, most articles had a medium to high-level quality score with Kingsley et al. [ 121 ], Maitre et al. [ 102 ], Salihovic et al. [ 117 ], Hu et al. [ 112 ], and Chang et al. [ 119 ] studies scoring the highest based on the aforementioned criteria ( Table S2 ). A total of 546 unique metabolite features, either classifiable or identifiable under HMDB, ChEBI, or LipidMaps (with reported names), were found to be dysregulated by PFAS exposures across studies. PFAS were associated more frequently with lipid metabolites, followed by organic acids and derivatives ( Fig. 2 , Figures S1 - S3 ). More specifically, metabolites that appeared significant most frequently across all studies pertained to the class of chemicals fatty acyls (present in n = 26 studies), carboxylic acids and derivatives ( n = 26 studies), glycerophospholipids ( n = 23 studies), steroids and steroid derivatives ( n = 18 studies), or sphingolipids ( n = 16 studies) ( Fig. 2 ). Overall, a positive association between PFAS and metabolites was more frequently reported across studies, particularly for fatty acyls, imidazopyrimidines, and benzene and substituted derivatives, whereas a negative association was reported for sphingolipids in more studies. A sensitivity descriptive analysis was conducted restricting to studies that included a metabolite confidence level 1 in their results ( Figure S4 ), which yielded consistent findings for most metabolite classes; PFAS had an overall positive association with imidazopyrimidines, benzene and substituted derivatives, organooxygen and organonitrogen compounds, while a negative association was found for sphingolipids across studies. Other metabolite classes had approximately a similar positive-to-negative association number ratio ± 1 study, with the exception of fatty acyls which were observed to revert in directionality with respect to PFAS but the number of studies in our sensitivity analysis was reduced significantly (from n = 26 studies that initially reported any PFAS-metabolite associations to only n = 11). Among all untargeted metabolomics data, the most common metabolites reported were glycerophospholipids (40.3%), fatty acyls (23.2%), sphingolipids (9.3%), carboxylic acids and derivatives (8.1%), glycerolipids (6.1%), and steroids (4.7%) ( Figure S1 ). A similar distribution of metabolites was also observed in studies reporting a targeted approach, where PFAS were found to be related the most to the following metabolite classes: glycerophospholipids (33.6%), glycerolipids (15.6%), sphingolipids (13.0%), carboxylic acids and derivatives (10.0%), steroids (9.1%), and fatty acyls (6.2%) ( Figure S2 ). Overall, the most frequent PFAS-metabolite associations were observed with respect to glycerophospholipids, followed by fatty acyls ( Figure S3 ). Long-chain legacy PFAS were the predominant PFAS compound forms that were most examined in relation to metabolomics and also the most frequently associated with different metabolic profiles ( Table S3 ). These include primarily PFOA, PFOS, and PFHxS, followed by PFDA, PFNA, and PFUnDA. We observed a fewer number of associations for other long-chain PFAS such as PFTrDA, PFHpS, and EtFOSAA, short-chain PFAS (PFHpA, PFPeA), or novel PFAS (Cl-PFESAs). In terms of the total number of metabolites, an overall consistency in the directionality of associations was particularly observed between PFAS homologues and increased number of fatty acids [ 17 , 29 , 105 , 110 , 113 , 117 , 120 , 122 , 123 ], glycerophospholipids (glycerophosphocholines and glycerophosphoethanolamines) [ 105 – 108 , 111 , 115 , 117 , 124 , 125 ], glycerolipids (triradylcglycerols, diradylglycerols, monoradylglycerols) [ 106 , 108 , 111 , 113 , 114 , 124 ], bile acids [ 31 , 105 , 108 , 109 , 114 , 119 , 124 ], and to a lesser extent, for fatty acid esters (acylcarnitines) [ 17 , 105 , 107 , 112 , 118 , 120 ], amino acids [ 17 , 29 , 102 , 105 , 106 , 108 , 111 , 112 , 115 , 116 , 119 , 120 ], phosphosphingolipids (sphingomyelins) [ 105 , 111 , 115 , 124 , 125 ], purines [ 17 , 105 , 112 , 117 , 119 ], sulfated steroids [ 105 , 119 ], glycerophosphoinositols [ 105 ], carnitine [ 105 , 112 ], and benzoic acids [ 105 , 106 , 120 ]. On the other hand, cholestane steroids [ 105 , 108 , 112 ], quinone, and hydroquinone lipids [ 105 ] appeared to be consistently downregulated in relation to PFAS. Ceramides were frequently reported as being potentially altered by PFAS, though with unclear or inconsistent directionality [ 105 , 108 , 124 ]. At the metabolite-level, we found an overlap in the number of significant metabolites across studies ( Table S4 ). A particularly high number of metabolites that were reported in at least three studies in relation to PFAS exposures were an increase in docosahexaenoic acid (Schillemans et al. [ 113 ], Li et al. [ 122 ], Salihovic et al. [ 117 ]), phosphatidylcholine (PC) 40:6 (Salihovic et al. [ 117 ], Sinisalu et al. [ 124 ], You et al. [ 105 ]), creatine (Jin et al. [ 116 ], Hu et al. [ 112 ], You et al. [ 105 ]), and uric acid (Salihovic et al. [ 117 ], You et al. [ 105 ], Chang et al. [ 119 ]). Overall, an additional 15 metabolites were consistently upregulated (PC 35:1, PC 36:5, PC 38:5, PC 38:6, PC 40:5, ether-linked phosphatidylcholines PC O-38:5, PC O-40:4, triacylglycerols TG 54:2, TG 54:5, TG 54:1, glycochenodeoxycholic acid, pyroglutamic acid, phenylalanine, succinate or succinic acid, carnitine), two were downregulated (glycine, betaine), and two were either upregulated and downregulated (deoxycholic acid) or for which directionality was not reported (methionine) across several studies. In pathway analyses, a total of 101 different pathways were reported to be significantly altered in relation to PFAS exposures. In Table S5 , we classified these PFAS-related pathways based on KEGG identifiers. There were 10 untargeted studies (Jin et al. [ 116 ], Lu et al. [ 17 ], Kingsley et al. [ 121 ], Li et al. [ 31 ], Alderete et al. [ 29 ], Li et al. [ 122 ], Salihovic et al. [ 117 ], Hu et al. [ 112 ], Chang et al. [ 119 ], Chen et al. [ 118 ]) and 3 targeted studies (Sen et al. [ 108 ], Ji et al. [ 120 ], Stratakis et al. [ 115 ]) that examined metabolic pathways related to PFAS using Mummichog, pathway enrichment analysis in MetaboAnalyst, or the KEGG database. Out of these 13 studies, the majority (8 studies) conducted analyses adjusting for confounders and incorporating FDR-correction (Jin et al. [ 116 ], Kingsley et al. [ 121 ], Alderete et al. [ 29 ], Li et al. [ 122 ], Salihovic et al. [ 117 ], Hu et al. [ 112 ], Chang et al. [ 119 ], Ji et al. [ 120 ]). The most frequently reported alteration of metabolism in most studies was among amino acids followed by lipid, carbohydrate, and metabolism of cofactors and vitamins, accounting for 27%, 25%, 15%, and 9% of the total significant PFAS-induced pathway associations across studies, respectively ( Table S5 , Fig. 3 ). The predominant pathways of amino acid metabolism included alanine and aspartate ( n = 7), aspartate and asparagine ( n = 6), arginine and proline ( n = 6), urea cycle ( n = 5), lysine ( n = 5), and glutamate ( n = 5). The predominant pathways of lipid metabolism involved glycerophospholipid ( n = 8), linoleate/linoleic acid ( n = 6), glycosphingolipid ( n = 5), glycosphingolipid biosynthesis ( n = 5), bile acid-related pathways ( n = 3), or fatty acid-related pathways ( n = 15). The most prevalent pathways of carbohydrate metabolism involved butanoate ( n = 4), TCA cycle ( n = 3), sialic acid ( n = 3), glycolysis/gluconeogenesis ( n = 3), glyoxylate and dicarboxylate ( n = 3). The most frequent pathways of vitamin and cofactor metabolism involved vitamin A ( n = 3), vitamin B3 ( n = 4), and vitamin D3 ( n = 3). In addition, PFAS exposure also contributed to the alteration of several additional pathways: nucleotide metabolism (5%, i.e., purine and pyrimidine metabolism), glycan biosynthesis and metabolism (5%, i.e., N-glycan degradation/biosynthesis, keratan/chondroitin/heparin sulfate degradation), energy metabolism (4%, i.e., nitrogen metabolism), metabolism of other amino acids (3%, i.e., beta-alanine metabolism), or xenobiotic metabolism (3%). Results for other pathways were reported less consistently across studies. Similar pathway results were found in our analyses using Reactome where metabolites participating in membrane transport, lipid-related, or amino acid-related mechanisms were predominant across studies ( Fig. 4A – C and Tables S6 - S8 ). A complete list of Reactome pathways including those that appeared less frequently across studies were also shown in Tables S9 - S11 . Top pathways associated with metabolites involved in the potential deleterious effects of PFAS belonged to transmembrane transport, transport of bile salts and organic acids, metal ions and amine compounds, plasma lipoprotein remodeling pathways (including HDL), phospholipid and phospholipase-related pathways (PLC beta mediated events or phospho-PLA2), phagocytosis, amino acid transport across the plasma membrane, Golgi-to-ER transport, glucose-dependent insulinotropic polypeptide, or acyl chain remodeling of lipids, among others ( Fig. 4C , Table S6 ). Additional pathways were observed when we conducted analyses separately with metabolites from positive and negative PFAS associations ( Fig. 4A , B , Tables S7 - S8 ). For instance, while pathways implicated in triglyceride metabolism were present in studies that reported positive PFAS-metabolite associations ( Table S7 ), immune system or ceramide signaling pathways, as well as amino acid transport across the plasma membrane, were predominant across studies that reported metabolites with inverse PFAS associations ( Table S8 ). In addition to the top pathways (present in 25% of the studies and with an FDR < 0.05), summaries for Reactome pathways specific to lipid and amino acid metabolism are shown in Figs. 5 and 6 . It is noteworthy that at least 3 studies showed a pathway enrichment for PPAR-αregulation of lipid metabolism, lipid particle organization, sphingolipid, and phospholipid metabolism ( Fig. 5 ), as well as catabolism of tryptophan, threonine, and choline, creatine metabolism, and carnitine synthesis ( Fig. 6 ). Nearly half of the studies included in this review ( n = 13) made reference to populations with a specific disease or populations at increased risk for disease for whom metabolomics data were analyzed. Three studies evaluated metabolomics in patient populations: Jin et al. [ 116 ] in children with nonalcoholic fatty liver disease (NAFLD), Ji et al. in COVID-19 patients [ 120 ], and Sen et al. [ 108 ] in NAFLD patients undergoing a laparoscopic bariatric surgery. Another three metabolomics studies were conducted in population subgroups at high risk for disease: Chen et al. [ 118 ] focused on children who had a history of being overweight or obese but did not have diabetes or other disease, Mitro et al. [ 111 ] focused on adult participants at higher risk for diabetes (with BMIs of above 24 and with high levels of fasting plasma glucose), and Alderete et al. [ 29 ] focused on children with high risk of T2D but without clinical diagnosis. Lastly, nearly seven studies reported PFAS-associated metabolite features separately in disease patients (or at increased risk for disease) and control populations: Stratakis et al. [ 115 ] compared children with high vs. low liver injury risk, Schillemans et al. [ 113 ] compared T2D and control pairs, Li et al. [ 31 ] and Hu et al. [ 112 ] examined breast cancer cases and controls but no metabolomics comparison was conducted, You et al. [ 105 ] compared cases with hyperuricemia and controls, Matta et al. [ 104 ] compared women with and without endometriosis, and Sinisalu et al. [ 114 ] evaluated metabolomics on celiac disease patients and healthy controls. When comparing PFAS-metabolite associations in a cardiometabolic disease population with a healthy control group, Stratakis et al. [ 115 ] found primarily increases in several branched-amino acids, and lipid alterations (glycerophospholipids and sphingomyelins), Schillemans et al. [ 113 ] study observed primarily increases of glycerophospholipids and diacylglycerols linked to T2D odds ratios, and You et al. [ 105 ] compared differential metabolites in hyperuricemia patients versus controls (but disregarding PFAS exposure status) showing alteration in several lipids and aminoacids. Sinisalu et al. [ 114 ] conducted metabolomics analyses with respect to celiac patients and controls and showed alterations in lipid and bile acid metabolism triggered by PFAS exposure in infants who developed celiac disease later in life in comparison to healthy controls. Lastly, Matta et al. [ 104 ] observed a differential metabolome (lipids and functional metabolite ratios) between cases of endometriosis and controls but disregarding PFAS exposure levels between groups. Given the different ages from populations included in reviewed studies, we evaluated potential differential patterns across life stages focusing on early life exposures or sensitive windows of exposure. We observed reports of alterations to amino acids during the prenatal period [ 102 , 119 ], as well as alterations in the metabolism of several vitamins (B3, D, and retinol) [ 112 , 119 , 122 ] and dysregulation of imidazopyrimidines in pregnant women [ 112 , 119 ]. These findings (dysregulation of imidazopyrimidines, amino acids, and vitamins) were also consistent with findings in adults. Additionally, we observed that aromatic amino acids [ 29 , 116 ] and arginine or related pathways [ 29 , 116 , 121 ], were particularly specific to associations found in children across studies in this review.

PFAS are ubiquitous chemicals that can alter health via disruption of key metabolites and pathways in the human body. In this review, we summarized and identified alterations in several metabolites (amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines) and related metabolic pathways that could underlie PFAS-associated diseases in humans, including lipid, amino acid, carbohydrate, nucleotide, glycan, or energy metabolism, and metabolism of cofactors and vitamins. Future studies should consider prospective designs optimizing methods for exposure-metabolomics analyses with longitudinal measures, additional confounder adjustment, or assessment of emerging PFAS and mixture effects to address existing limitations in this field.

This systematic review highlights the potential for PFAS exposures to alter several metabolic pathways in humans as reported by recent investigations of the human metabolome. The studies summarized in this review were recently published, with the oldest publication being in 2017, highlighting the relevance of this emerging field. We found consistent evidence across several observational studies suggesting that PFAS exposures are associated with dysregulations in lipid and amino acid metabolites and related pathways particularly relevant to metabolic disease in humans. Many of these pathways may be involved in energy and cell membrane disruption. We additionally observed both potentially similar and divergent effects of PFAS by age or developmental stages. Research gaps from the reviewed studies include the lack of prospective studies or longitudinal measures of PFAS and metabolomics over the life-course, as well as limited adjustment for relevant confounders. The majority of studies to date have evaluated long-chain legacy PFAS. Therefore, future research to study emerging and shorter chain PFAS, either as individual compounds or as exposure mixtures, is warranted. Growing evidence from previous epidemiological and experimental studies indicate that PFAS can alter health via increased oxidative stress, inflammation, peroxisome proliferator-activated receptor (PPAR) signaling, or sex steroid hormone mechanisms [ 23 – 26 , 126 ]. PPARs are nuclear receptors that regulate fatty acids, lipids, and glucose metabolism [ 127 ]. A variety of PFAS have been shown to activate PPARα, primarily expressed in the liver, in human cells in vitro [ 128 – 130 ]. Several mechanisms for PFAS toxicity in mammals also show that PFAS can incur damage via non-PPARα-dependent pathways, such as other nuclear receptors (i.e., PPARγ, CAR, ERα) [ 131 – 135 ] inducing gene expression changes, altering mitochondrial function [ 136 – 138 ] modifying membrane fluidity [ 139 ], or inhibiting gap junction intercellular communication [ 131 , 140 , 141 ]. Similarly, findings from Reactome pathway analyses in this review also highlighted that PFAS may modify transmembrane, lipid, and amino acid metabolism. Of note, alterations in PPAR-mediated mechanisms were also among the most recurrent (present in at least 3 studies with FDR < 0.05) PFAS-induced pathways related to lipid metabolism in this review. At the molecular level, the potential for PFAS as endocrine disruptors is reflected in their capacity to bind to other proteins, their structure similar to that of fatty acids, and their putative involvement in the displacement of endogenous ligands, which could explain their high retention in human serum and the potential to alter lipid metabolism and the hormonal system. For instance, PFAS can interfere with binding to albumin [ 142 , 143 ], sex hormone-binding globulin [ 144 ], corticosteroid-binding globulin [ 71 ], and liver fatty acid-binding protein (L-FABP) [ 145 ]. PFAS also can compete with thyroxine in binding to thyroid hormone transport protein or receptors [ 146 , 147 ], which could lead to a potential decrease in the normal levels of circulating hormones and derivatives (i.e., thyroid hormone, SHBG) resulting in hormone dysregulation. In our review, we found that PFAS altered levels of steroid-related and bile acid-related pathways and metabolites, such as pregnane steroids [ 102 , 122 ], overall levels of sulfated steroids [ 105 , 119 ], and bile acids were increased [ 105 , 106 , 108 , 109 , 114 , 119 , 122 , 124 ], and levels of cholestane steroids were decreased [ 105 , 108 , 112 ]. Similarly, previous metabolomic studies on PFAS toxicity in animals indicated that PFAS exposures modulated metabolic pathways related to sterols and bile acids in mice [ 148 ]. This is consistent with findings from pathway analyses in Reactome indicating bile acid transport dysregulation and a moderate alteration of steroid metabolism. Overall, the findings from our systematic review parallel prior findings in animal studies, which together support a causal role for PFAS on disrupting endocrine pathways. Our review detected a disproportionately higher number of lipids and membrane function-related pathways across studies and across pathway analyses. Glycerophospholipids, the main type of lipid in the cell membrane, may be an important component in driving cellular accumulation of PFAS. More specifically, in our review an overall increase in glycerophosphocholines, and in particular of phosphatidylcholines, may indicate mitochondrial membrane disruption with a subsequent hydrolysis of phospholipids [ 149 ]. Furthermore, glycosphingolipid and sphingolipid metabolism were recurrent in pathway analyses across the studies [ 29 , 31 , 118 , 119 , 121 ]; related metabolites (altered ceramides and increased phosphosphingolipids, such as sphingomyelin) were also reported frequently across the studies [ 29 , 104 , 105 , 108 , 111 , 115 , 124 , 125 ] and corroborated in Reactome showing consistent pathways related to ceramide signaling, phospholipids, sphingolipids, and phospholipase activity which may exacerbate inflammatory response [ 150 ]. Furthermore, given that sphingolipids constitute a major part of the fluidity, structure, and permeability of membranes and are involved in cell-to-cell signaling [ 151 – 154 ], a PFAS-induced membrane alteration mechanism may be a common pathway by which PFAS may exert cellular damage. We also found a relative consistent positive association with PFAS among glycerolipids, particularly with triacylglycerides (TGs). While the association between PFAS and increased levels of unhealthy lipids, including triglycerides or LDL-cholesterol, has not been consistent across animal [ 155 , 156 ] and epidemiological studies [ 157 – 161 ], the majority of overall PFAS-TG associations across human metabolomics studies included in this systematic review were positive in directionality [ 105 , 108 , 111 , 114 , 124 ]. Furthermore, pathway analyses in Reactome confirmed that mechanisms related to TG metabolism were involved in the positive association between higher PFAS exposure levels and increased TGs. Similarly, lipoprotein pathways, including dysregulation of healthy cholesterol pathways such as high-density lipoprotein (HDL), were observed to be predominant across studies reporting PFAS-TG associations [ 105 , 108 , 111 , 114 , 124 ]. In humans, PFAS associations with serum triglycerides rendered inconsistent effects across different PFAS in previous non-metabolomics epidemiological studies [ 158 , 160 , 162 , 163 ], though more positive associations were observed for PFOA [ 157 , 158 , 164 – 166 ], PFOS [ 157 , 165 , 167 , 168 ], PFHxS [ 166 , 169 ], or PFNA [ 165 , 166 ] exposures across various populations of healthy and unhealthy adults, children, and adolescents. Moreover, lipidomics studies in mice and rats have suggested more consistent associations with hepatic triglycerides for several long-chain PFAS (PFDoDA, PFOS, PFHxS, APFO, PFNA) [ 131 , 170 – 173 ]. The mechanism underlying a potential PFAS-induced alteration in triglycerides points at initiation by PPARs. An in vitro study with PFAS-exposed human liver cells where PFOS, PFOA, and PFNA activated PPARα signaling, suggested that the subsequent observed increase in cellular triglyceride levels could be due to an induction of lipid droplet-associated proteins or glyceroneogenesis [ 174 ]. PPARγ could be also implicated in the PFAS-induced lipid alteration as a regulator of lipids and triglyceride fat storage in adipose tissue [ 175 ], though mechanisms for PFAS-induced damage via changes in triglyceride levels are poorly understood. PFAS may contribute to lipid dysregulation by alterations in energy metabolism. Across studies, we observed that PFAS were positively associated with acylcarnitines [ 17 , 105 , 107 , 112 , 118 , 120 ], which are intermediate metabolites involved in the transport of fatty acids and long-chain acyl-CoA from the cytosol into the mitochondria. Hence, the potential for acylcarnitines to be used as a marker of mitochondrial functioning and fatty acid oxidation [ 176 ], given that elevated levels could reflect either mitochondrial dysfunction or an adaptive change to disturbed lipid metabolism. Our findings across the reviewed studies are consistent with the studies in mice, the latter suggesting an overall increase in acylcarnitines (particularly hepatic) in relation to PFAS [ 148 , 177 ]. Similarly, we observed an implication of PFAS exposures on fatty acid metabolism, carbohydrate, and amino acid metabolism. Binding of PFAS to fatty acid binding proteins may be implicated in the reduction of bioavailable binding sites for endogenous fatty acids resulting in higher concentrations of fatty acids. Fatty acids may interact with PPARs [ 178 ] and liver X receptors and could be involved in the regulation of gene expression and inflammation. Furthermore, several branched-chain amino acids (leucine, isoleucine, and valine), relevant in fatty acid oxidation and prevalent in the metabolome of those suffering from obesity-related conditions [ 179 ], were overall increased across reviewed studies [ 111 , 115 ]. Similarly, aromatic amino acids, namely tyrosine and phenylalanine, which have been shown to be related to insulin resistance or diabetes [ 180 , 181 ], were also increased across studies [ 29 , 115 , 116 ]. Pathway analyses in Reactome for metabolites sharing a negative association with PFAS, implicated pathways related to acyl-chain remodeling and amino acid transport across the plasma membrane, which could explain abundant levels of amino acids in obese subjects via dysregulation of key metabolites helping in the processing of fatty acids and amino acids. It is hypothesized that impaired branched-chain amino acid metabolism could lead to accumulation of toxic branched-chain keto acids and acyl-CoA precursors facilitating conversions into acylcarnitines [ 182 , 183 ], which are increased in obese and T2D individuals [ 180 , 184 ], consistent with our findings across studies in this review. Increased levels of acylcarnitines may reflect incomplete long-chain fatty acid beta-oxidation and limited intermediates or tricarboxylic acid (TCA) cycle utilization [ 184 ]. Furthermore, glycan-related pathways were also recurrent across studies in this review. Glycans are polysaccharides and alterations in glycosylation (a post-translational modification) have been suggested to be involved in diabetes etiology in prior epidemiological studies [ 185 – 187 ]. Perturbations of several metabolites associated with the TCA cycle, along with the presence of enriched carbohydrate metabolism across studies in our review, such as electron transport chain, glycolysis and gluconeogenesis, or beta-oxidation, is also consistent with the notion of the toxicity exerted by PFAS. Thus, changes in energy metabolism may have widespread implications on development, growth, aging, protection against infectious and toxic exposures, and multiple disease processes. In humans, a myriad of studies have linked environmental endocrine-disrupting chemicals to a variety of diseases and metabolic dysregulations. In vitro studies with animal and human cells, have shown that PFAS can exert immunotoxicity [ 188 ], hepatic toxicity [ 172 ], developmental [ 189 ], and endocrine toxicity [ 147 ]. Research indicates that PFAS affect cardiometabolic markers of disease, can increase cancer risk, alter immune response, and impair reproductive health, thyroid, liver, and kidney function, among others [ 4 , 10 , 12 , 14 , 18 , 190 – 192 ]. For instance, various PFAS, such as PFOA, PFBS, or PFNA have been linked to diabetes across different populations and epidemiological study designs [ 193 – 195 ]. Interestingly, Reactome pathways related to disease, the immune system, and glucose-dependent insulinotropic polypeptide (GIP), which is a hormone regulating insulin secretion, were shared across reviewed studies. Several of the studies included in our review indicated that PFAS increased the risk of multiple cardiometabolic conditions. Given that prior epidemiological studies not including metabolomics reported inconsistent findings on PFAS and cardiometabolic outcomes [ 1 , 196 – 200 ], either due to their cross-sectional design or heterogeneous populations, epidemiological studies including metabolomics are warranted to elucidate potential mechanisms at the metabolite-level in the plausible link between PFAS exposure and cardiometabolic outcomes. In the liver, PFAS increased liver enzymes characterizing injury risk in Stratakis et al.’s study [ 115 ]. In Jin et al.’s study [ 116 ], PFOS and PFHxS were associated with increased odds for liver fibrosis, lobular inflammation, or nonalcoholic steatohepatitis (NASH) [ 116 ]. PFAS exposure mixtures together with other environmental chemicals increased NAFLD risk in Sen et al.’s study [ 108 ]. A potential mechanism for liver injury could be via increased oxidative stress, as reflected in increased liver function biomarkers (i.e., ALT, AST) [ 201 , 202 ]. Furthermore, PFAS was associated as well with increased glucose in several studies in our review [ 29 , 118 ]. Mitro et al. 2021 [ 111 ] identified particularly several sphingomyelins, phosphatidylethanolamines, and DGs and TGs related to legacy PFAS (PFOA and PFOS) in a population at high risk of developing T2D. Similarly, increased T2D risk was also reported for PFNA-associated diacylglycerols in Schillemans et al. 2021 [ 113 ]. Lipids are mainly stored in mature white adipocytes in the form of TGs. Increased circulating free fatty acids and accumulation of TGs and derivatives, such as diacylglycerols, are deemed contributing factors to insulin resistance [ 203 ]. Along with TGs, diacylglycerols were overall increased across the studies in this review [ 105 , 108 , 113 ]. Furthermore, prenatal PFAS exposure contributed to increased postnatal risk of type 1 diabetes in neonates in one other study (McGlinchey et al. [ 30 ]), and PFHxS contributed to metabolic syndrome and lower levels of healthy cholesterol (HDL) in Bessonneau et al.’s study in occupationally-exposed adult women [ 109 ]. Elevated levels of acylcarnitines, which were increased across studies in our review (“Potential Dysregulated Pathways Linked with PFAS Exposures in Humans”), have been linked to risk of cardiovascular disease in prior cohort studies [ 204 , 205 ]. Other metabolic disorders such as hyperuricemia were found to increase in risk upon PFAS exposures [ 105 ]. This is consistent with findings from several large cohorts from US and Chinese populations where PFOA, PFNA, PFOS, and PFHxS were reported to increase risk of hyperuricemia [ 2 , 206 , 207 ]. There was evidence across the included studies from this review that PFAS exposures also altered reproductive outcomes and immune-related diseases. PFNA concentration was significantly associated with higher odds of small-for-gestational age (SGA) birth in a population of African-American women in Chang et al.’s study [ 119 ], and PFHxS was linked to lower sperm concentration in Chinese males in Huang et al.’s study [ 107 ]. Chemical mixtures including PFAS exposures also exacerbated conditions like endometriosis in Matta et al.’s study [ 104 ], suggesting that an endometriosis metabolic pattern could be characterized by dysregulation of bile acid homeostasis. PFAS was also found to increase risk of COVID-19 in one metabolomics study via impaired kynurenine metabolism (involved in immune responses), and eicosanoids (involved in inflammatory responses) [ 120 ], consistent with previous research linking PFAS and impaired immune system. Overall, we observed consistent findings for reported PFAS-metabolite associations between studies conducted in population subgroups with a specific disease compared to population-based studies, particularly in regard to alterations in lipid metabolites (glycerolipids [ 104 , 105 , 108 , 111 , 113 , 114 , 117 , 124 ], sphingolipids [ 29 , 30 , 104 – 106 , 108 , 111 , 115 , 124 , 125 ], and fatty acyls [ 17 , 29 , 104 – 107 , 109 , 110 , 112 , 113 , 117 – 120 , 122 – 124 ]), bile acids [ 105 , 106 , 108 , 109 , 114 , 119 , 122 , 124 ], and amino acids [ 17 , 29 , 30 , 102 , 105 , 106 , 108 , 111 – 113 , 115 – 120 , 123 ]. Additionally, the field of metabolomics is at its early stages and could have the potential to become a key tool for disease biomarker detection and help elucidate pre-diagnostic stages of disorders that could be amenable to intervention. The lack of multi-omics studies integrating metabolomics with genomics, epigenomics, toxicogenomics, transcriptomics, proteomics, the microbiome, and other fields, highlights the need to advance this area of research as multi-omics may be a promising avenue of exposomics research where combined applications can provide mechanistic explanations for differential levels of environmental contaminants in the body and corresponding phenotypic states across individuals. Different populations may undergo distinct effects from PFAS at different stages throughout their life course. Of particular concern are the effects of PFAS in the womb due to the critical windows of exposure increasing the risk of disease onset in the offspring. Based on experimental research in mice, it is hypothesized that PFAS exposures lead to a reduction in transport of amino acid analogues from mothers to the fetus [ 208 ]. Though across studies we observed a perturbation of amino acids in mothers, we observed inconsistent results throughout pregnancy; for instance, amino acids, such as glycine, were increased in mothers at 8–14 weeks in the Chang et al. study [ 119 ]; in the Maitre et al. study, glycine decreased at trimester 3 [ 102 ]. Consequences of amino acid perturbation in the developing fetus could indicate improper nutrition, affect fetal growth, and contribute to small gestational size [ 119 , 209 , 210 ]. An association between PFAS and disrupted metabolism related to cofactors and vitamins B and D was also observed across studies and are key contributors to proper fetus development. Metabolism for vitamin B3 (nicotinate and nicotinamide) [ 29 , 112 , 119 , 121 ], retinol [ 17 , 121 , 122 ], and vitamin D [ 109 , 112 , 118 , 119 , 121 ] were reported consistently across studies, three of them being conducted in pregnant women [ 112 , 119 , 122 ]. In pregnant women, vitamin A (retinol) and its analogs can regulate gene transcription impacting embryonic development [ 211 ] and the immune system [ 212 – 214 ]. Moreover, vitamin B is a well-known antioxidant for fetal growth (i.e., folate), and Vitamin D can be implicated also in oxidative stress and inflammatory response mechanisms, as well as in the metabolism of glucose and fetal growth skeletal development or placental function [ 215 – 217 ]. An overall increase of imidazopyrimidines [ 17 , 105 , 112 , 117 , 119 ], including increased uric acid [ 117 , 119 ], was observed across studies in this review, with two studies being conducted in pregnant women [ 112 , 119 ]. In pregnant women, it is hypothesized that PFAS may lead to decreases in uric acid secretion, resulting in elevated serum uric acid concentrations, which in turn, may trigger placental inflammation and oxidative stress, inhibit amino acid transport to placentas, alter the development of endothelial and trophoblast cell development in the fetus, or lead to higher risk for pre-eclampsia [ 119 , 218 – 221 ]. Seven studies in total have focused on PFAS and metabolomics and were conducted in a population of children or adolescents [ 29 , 106 , 114 , 116 , 118 , 121 , 125 ]. Interestingly, out of these studies, three [ 116 , 121 , 29 ] performed pathway analyses with PFAS-associated metabolites and numerous amino acid pathways were reported in all three studies: tyrosine metabolism, aspartate and asparagine metabolism, glycine, serine, alanine and threonine metabolism, urea cycle/amino group metabolism, arginine and proline metabolism, alanine and aspartate metabolism, and glutamate metabolism. We did not see consistent associations between PFAS-induced alterations on branched-amino acids (valine, isoleucine, and leucine) and related pathways across reviewed studies in children, contrary to previous findings from adult studies [ 105 , 111 ]. However, an alteration in aromatic amino acids was evident in children. Tyrosine and phenylalanine are aromatic metabolites that were primarily increased in children populations across reviewed studies [ 29 , 116 ]. Aromatic amino acids, as well as associated pathways, have been previously linked to increased risk of developing insulin resistance or obesity in children [ 222 – 226 ], or increased liver injury in adolescents [ 227 ]. We also observed that arginine or arginine-related pathways, previously linked to diabetes [ 228 ], were recurrent across the reviewed studies in children [ 29 , 116 , 121 ]. These findings are consistent with the hypothesis that environmental contaminants, namely PFAS, could alter the susceptible metabolism of children in a more remarkable way than at other developmental stages across the lifespan suggesting a potential window of susceptibility on certain amino acid types, particularly aromatic. A possible avenue of future research could also focus on infant PFAS exposures and other environmental contaminants via breast milk. This review also intended to discern any potential patterns of effects across PFAS subtypes. Sulfonic acids are considered more potent than carboxylic acids [ 229 ], and it is hypothesized that longer carbon-chain PFAS may exert more deleterious effects [ 230 ], however, this premise has been contested [ 231 ] as novel PFAS emerge. We observed similar effects across the major studied PFAS (PFOS, PFOA, PFHxS), though a more clear-cut positive association seems to be apparent between the long-chain sulfonate PFOS and amino acids, fatty acid esters, or glycerophosphocholines. Overall, the findings observed across studies indicated that PFAS with longer half-lives were associated with relatively more metabolites. Similarly, we found that more legacy or long-chain PFAS were associated with more metabolites compared to short-chain or novel PFAS [ 113 , 122 ], yet this could also be due to the frequency of the chemical being studied across studies and/or their detectability, where novel shorter-chain PFAS have lower exposure levels across samples, and their exposure assessment could be more prone to measurement error due to shorter half-lives. Animal studies have shown that PPARα receptor activation was increased the longer the carbon chain of the PFAS chemical [ 128 , 232 ]. Furthermore, inhibition of gap junction intercellular communication is also considered more prominent for longer chain PFAS than in shorter chain PFAS [ 140 ]. Although animal studies can provide some validity as to the reason why we may be encountering different potency across PFAS groups, we would need more evidence for emerging or short-chain PFAS, which may be similarly toxic but are understudied, to further assess their deleterious effects on the human metabolome and involvement in disease. Several common limitations were found across studies rendering a lower quality score. These included confounding factors not accounted for, lack of false discovery rate (FDR) adjustment, a cross-sectional design introducing potential reverse causation bias, a small sample size, or not reporting a high confidence level for metabolites. Out of all 28 studies included in the review, no study reached a maximum quality score of 5, meaning that no study accounted altogether for a longitudinal study design, a large sample size, multiple testing correction, MSI metabolite confidence level 1, and covariate adjustment in the study design. Future PFAS-metabolomics studies may consider addressing these limitations in their design. Longitudinal measures of PFAS and metabolomics with longer follow-up from birth through adulthood are needed to better capture metabolite variability and to elucidate persistent effects over the life-course. Metabolomics approaches handling repeated exposures and longitudinal -omics data are also needed to corroborate potential windows of enhanced vulnerability. Variability encountered in the laboratory methods for metabolomics profiling as well as the intra-individual variability in the samples could have also reduced the power to detect associations [ 233 ], so it is likely that the observed associations may be an underestimate of the true associations. We also expect that technical methods are more reliable than intra-individual correlation, which could have low reliability over time, as metabolites may be dependent on sex, age, fasting status, and diet of the individual. On the other hand, there could be potential false positives (type I error) due to lack of multiple testing correction or lack of covariate-adjustment in metabolomics analyses. Overall, in metabolomics analyses, 16 studies included in the review reported FDR-correction ( Tables 2 and 3 , Table S2 ). Similarly, only 8 studies reporting pathway analyses both applied FDR-correction and adjusted for any covariates ( Table S5 ). Moreover, nearly 40% of the studies in this review ( n = 11) report to have included metabolites with a confidence level 1. Our sensitivity analyses including only these studies that reported the highest confidence level indicated a similar pattern of PFAS-associated metabolites for most metabolite groups examined ( Figure S4 ). Given that more than half of the studies either did not include MSI confidence level 1, or did not mention at all confidence levels, caution when interpreting both findings should be given. Compliance with the Metabolomics Standards Initiative when reporting metabolomics methods is needed in future studies [ 99 , 100 ]. Confounding bias could be present in reviewed studies that examined an association without controlling for sociodemographic and lifestyle variables, such as diet or exposure to other correlated chemicals. Diet was included as a confounder in the metabolomic analyses for only 2 studies [ 102 , 117 ]. Interestingly, high level of docosahexaenoic acid (DHA) was observed across several studies [ 113 , 117 , 122 ]. Evidence from animal studies indicated that undergoing fish oil supplements prevented the PFOA-induced increase in hepatic triglyceride content by depressing the formation of triglycerides by DHA [ 234 ]. This suggests that potential deleterious effects exerted by PFAS exposures may be attenuated if fish consumption, a potential negative confounder of PFAS and omega-3 fatty acids, is not adjusted for in the analyses. Given that fish is a DHA-rich food source but also has high PFAS content [ 235 – 237 ], findings cannot be attributed to PFAS exposures as the only causative factor. The same would apply to a high correlation between packaged foods with high caloric density content and PFAS contamination [ 238 ], which could instead skew results over-representing lipid metabolism. Similarly, the presence of several benzenoids and xenobiotic metabolism across studies may indicate that PFAS may act in conjunction with other exogenous chemicals to alter metabolite levels in humans. Therefore, it is not possible to parse out whether findings are due to the effects from PFAS exposures solely, or instead, stem in part from potential confounders, a combination of both (mixture), or effect modification. Of note, there is a research gap regarding co-exposures since most of the reviewed articles on PFAS and metabolomics do not adjust for other pollutants in their analyses and correlated exposures are not systematically taken into account. The vast majority of studies presented in this review were conducted in White and Asian populations with only a few studies including Hispanic and African-American populations in the US [ 29 , 119 ]. Increased representation of minority ethnic groups with potentially different socioeconomic backgrounds, lifestyle, and dietary behaviors is needed in future studies to reassure generalizability of findings to minority populations that are disproportionally affected by metabolic and other chronic diseases. Moreover, most of the evidence compiled in this review is drawn from blood samples and additional studies comparing PFAS effects via the metabolome across tissues and other biofluid matrices can be informative. Additionally, genetic susceptibility to PFAS exposure in relation to metabolomics was taken into account in only one study [ 30 ]. Emerging evidence suggest gene-PFAS interactions in disease risk in humans [ 239 ]. To that end, an emerging field of “multi-omics” data incorporating both metabolomics and genomics has just recently started to be applied in the investigation of environmental exposures and disease. Together with novel PFAS exposures, effect modification by genetic variations or incorporating “multi-omics” approaches could be a focus of future research. Improvements in the sensitivity of analytic tools (i.e., HRM) and harmonization in chemical annotation and standardization of pre-processing and data preparation (i.e., imputation, transformations, CV %) can help address these limitations in this field of research and enable a better reproducibility of metabolomic studies. This review included metabolite classifications from the HMDB and ChEBI databases and KEGG pathway classifications, which are not fully comprehensive, challenging the summary and classification of findings across specific metabolite groups. About a third of unique metabolite features reported in our review, were either unmatched to a KEGG ID (not found) or their KEGG ID was not recognized by Reactome. Conversions were not possible particularly for several lipid metabolites and vitamins and were underrepresented when performing pathway analyses compared to other molecules, in spite of appearing frequently in results across studies. Thus, we can expect that lipid metabolism, yet present across studies in a significant manner, is underestimated in Reactome pathway analyses, although this also seems to be the case for individual studies conducting other pathway enrichment analyses. We also recognize that differentiation of targeted and untargeted studies is arguable in the field of metabolomics, so we attempted to refer to this classification as reported by the studies. Additionally, laboratory methods likely influencing quality assessment are not reported systematically throughout each study, and therefore, we were not able to account for other factors into the quality score. One important strength is that despite the high heterogeneity noted across epidemiology and laboratory methods, study design, and populations (i.e., country of study, sex, race, age, occupational, unhealthy), we found consistent findings for several metabolites and pathways across studies, and thus, results are more likely to reflect true associations. Furthermore, our study provides a comprehensive systematic review of all human studies published on PFAS and metabolomics, including both targeted and untargeted metabolomics, as well as lipidomics studies. Lastly, by using Reactome we provided a pathway analysis, which is considered a more systematic method to summarize and visualize findings.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a group of widespread human-made fluorinated compounds that can contribute to deleterious health effects and chronic diseases in humans [ 1 , 2 ]. Estimates from recent data derived from the National Health And Nutrition Examination Survey (NHANES) show that PFAS can be detected in nearly every sample [ 3 , 4 ], with perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorohexanesulfonic acid (PFHxS) being present in at least 99% of the U.S. population [ 5 ]. While certain long-chain, or so-called legacy PFAS, such as PFOS and PFOA are being phased out in many countries [ 6 ], a new generation of short-chain PFAS chemicals are being introduced with limited knowledge on their short- and long-term health effects in humans. Early indications in observational studies point at the persistent nature of these chemicals through overall long half-life, bioaccumulation, and slow degradation rate in the environment and in the human body [ 7 – 9 ]. In prior research, PFAS exposures have been linked to a wide array of diseases including, but not limited to, increased risk of cancer [ 10 , 11 ], asthma [ 12 ], altered immune [ 13 ], thyroid [ 14 , 15 ], or liver function [ 16 ], kidney damage [ 17 ], altered pregnancy outcomes [ 18 ], and cardiometabolic disease [ 19 – 21 ]. These effects are observed in occupational and non-occupational settings and in both adult and child cohorts. Particular attention has been placed on the metabolic effects of PFAS in metabolic syndrome [ 1 ], a complex combination of metabolic abnormalities including insulin resistance, dyslipidemia, glucose intolerance, hypertension, and obesity [ 22 ]. Mechanisms underlying PFAS toxicity in humans are not yet fully understood, but exposure to PFAS has been hypothesized to play a role in pathways modulating insulin resistance and lipid metabolism, such as oxidative stress, inflammation, peroxisome proliferator-activated receptor (PPAR) signaling, metabolic hormones (i.e., adipokines, insulin) or sex steroid hormones [ 21 , 23 – 27 ]. Newly available emerging high-throughput technologies have made possible the investigation of the human metabolome as a novel way to understand mechanisms of health and disease [ 28 ]. Metabolomics is the study of metabolites (or small molecules) within the body, tissues, and cells, at a large-scale. Metabolomic approaches have the ability to characterize the human exposome and are considered a promising tool to potentially unravel the etiology of certain diseases [ 29 – 31 ]. Only one scoping review has been conducted on PFAS and metabolomics previously, including untargeted metabolomics studies only [ 32 ]. To our knowledge, we compile for the first time a systematic review of all epidemiological studies that have examined PFAS exposures and metabolomics using both targeted and untargeted approaches. In this review, we summarized the human metabolite pathways associated with PFAS exposure, highlighting the major and common pathways identified across studies to gain more insight into biological responses of PFAS exposures in humans. We also discuss current research gaps and possible avenues of future research. PFAS are a group of ubiquitously manufactured chemicals that have been broadly used worldwide in consumer and industrial products for their properties as surfactants and their coating resistance to heat, oil, stains, or water. PFAS are aliphatic substances made of strong carbon-fluoride bonds that give these compounds their slow degradation rate and enduring properties in the environment and within the human body [ 33 ]. PFAS half-lives in humans reported in published literature to date are summarized in Table S1 . PFAS compounds have a characteristic perfluoroalkyl moiety, where for one or more carbon atoms the hydrogens have been replaced by fluorine atoms, as well as a functional group [ 34 ] conferring them both hydrophobic and lipophobic properties [ 35 , 36 ]. Depending on the number of carbons, a PFAS compound is referred to as either long-chain or short-chain ( Table 1 ). Long-chain PFAS usually refer to any perfluoroalkyl carboxylic acid containing 8 carbons or greater, or any perfluoroalkyl sulfonic acid containing 6 carbons or greater. Perfluoroalkyl carboxylic acids and sulfonic acids containing less than 8 and 6 per-fluorinated carbons, respectively, are commonly referred to as short-chain PFAS [ 34 , 37 ]. The longer their fluorinated carbon chain the more PFAS are thought to bioaccumulate, as suggested in previous animal studies [ 38 – 40 ], and thus are more subjected to monitoring [ 41 ]. PFOA and PFOS are two of the most common long-chain PFAS studied. While regulation tends to focus more on long-chain PFAS [ 42 ], short-chain PFAS also present a wide array of concerns. Short-chain PFAS can be considered toxic [ 43 ] and as persistent as long-chain PFAS [ 44 ]. They have a potential long-range transport in both biotic and abiotic environments compared to their long-chain counterparts [ 45 , 46 ]. This high mobility [ 47 , 48 ] means that they may reach bodies of water from which they are harder to remove than long-chain PFAS due to their lower adsorption potential [ 46 , 49 ]. A limited number of PFAS have been considered in biomonitoring and health studies to date. However, it is estimated that more than 4700 PFAS exist [ 50 – 52 ] and at least 3000 are readily available in products currently on the market [ 53 , 54 ]. PFAS have been in use in industry and consumer products since their introduction in the 1940s [ 55 ]. Common PFAS sources range from site-specific and occupational exposures (military bases, airport sites, fire-fighting foams) to everyday consumer products, such as cookware, food packaging, impermeable gear, furniture, carpeting, coatings and paint, or cosmetics, among others [ 56 – 58 ]. The most prevalent PFAS exposure routes in humans are via dietary sources or contaminated water (oral route), followed by inhalation of dust or air particles, and dermal absorption [ 59 , 60 ]. To date, PFAS have been detected in rivers, rain water, soil, and ambient air of major cities around the world, as well as in remote areas [ 61 ]. There are no current adequate safety limits to PFOS or other PFAS in drinking water in most countries, and there are no proven safe levels of PFAS at lower dosages. For instance, even low doses of PFAS have been linked to adverse health effects in humans, such as decreased antibody response [ 62 – 64 ]. Additional routes of exposure also include early life exposure routes via the placenta and breastfeeding [ 65 – 67 ]. Prenatal and early-life PFAS exposures are particularly important, yet understudied, as they may contribute significantly to programming of later chronic health outcomes in adulthood [ 68 ]. Given the persistent nature of PFAS, with an average half-life ranging from a few days or months to several decades ( Table S1 ), bioaccumulation in humans has raised concerns. PFAS are structurally similar to fatty acids, but unlike other persistent organic pollutants, such as organochlorine and brominated compounds, PFAS compounds do not tend to accumulate in lipid tissue. Instead, due to their polar hydrophobic fluorine content, PFAS have a higher affinity for proteins [ 69 – 71 ]. After ingestion, PFAS tend to concentrate in high protein density tissues, such as compounds in blood, binding to serum albumin [ 72 ]. Protein-rich tissues, such as the liver and blood, are major repositories of perfluorinated acids [ 73 , 74 ]. Interestingly, an intervention study showed a decrease in serum and plasma PFAS levels of Australian firefighters after blood donation [ 75 ]. Not surprisingly, PFAS are also commonly detected in human breast milk [ 76 ], which has high antimicrobial and digestive enzymatic activity [ 77 , 78 ]. In addition to primarily accumulating after adsorption in media with high-protein content, such as liver and serum, PFAS can also accumulate in other organs, including the lungs, bone, brain, and kidney [ 79 , 80 ]. While the primary route of PFAS clearance is through the kidney in animal studies [ 72 , 81 ], renal urine excretion in humans has been modeled to be low for these compounds [ 82 ] highlighting the persistent nature and difficult elimination of these compounds, particularly long-chain PFAS. Despite PFAS secretion via urine, an albumin-based reabsorption mechanism in the kidneys contributes to low elimination rates [ 83 ]. Sex steroid hormones have also been observed to take part in facilitating renal clearance in animals [ 84 ]. Additionally, fecal excretion has been suggested as a potential route of PFAS elimination [ 85 , 86 ]. In women, lactation, parity, and menstruation are other alternative excretion routes that may provide an advantage, compared to males, in terms of more rapid PFAS secretion from the body [ 87 – 90 ]. This is consistent with the higher levels of some PFAS found in males compared to women [ 3 ], yet this sex difference in clearance may narrow as a function of age and menopausal status [ 91 , 92 ]. The metabolome refers to the collection of all the molecules (organic or inorganic) playing a role in physiological processes in the body or present in cells and tissues. The human metabolome can include metabolites involved in endogenous cellular processes, as well as metabolites external to the body such as dietary factors, xenobiotics, and environmental contaminants. Analytical methods using high-throughput technologies have emerged as novel tools to examine comprehensively both exogenous chemicals as well as endogenous metabolites in the body at a granular level. Targeted methods have been developed for a priori preselected metabolites while untargeted methods holistically and systematically analyze a wide array of metabolites in an organism by maximizing the number of metabolite features detected. Numerous laboratory methods, such as nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC–MS), enable the detection of metabolites. Recent advances, particularly in high-resolution metabolomics (HRM), such as ultra-high-resolution mass spectrometry (UHRMS), have allowed for an even more comprehensive characterization of exogenous and endogenous metabolites present at lower concentrations in the body [ 28 , 93 ]. The use of metabolomics as a tool to better understand the mechanisms associated with disease is still at its early stages. So far, metabolomics has facilitated the assessment of the effects from PFAS exposure in humans in recent studies, indicating putative intermediary markers for the initiation and progression of various diseases, such as cardiometabolic disease [ 29 ], diabetes [ 30 ], and cancer [ 31 ]. For instance, candidate metabolites like glucose or creatine have been hypothesized as biomarkers for diagnosis or prognosis of diseases [ 94 ] as metabolomics data encompass the downstream phenotypic product after gene transcription and translation [ 95 ], as well as suggest possible interaction effects or cross-talk between the exposome and endogenous biological systems.