Skip to main content

Individual and mixture associations of perfluoroalkyl substances on liver function biomarkers in the Canadian Health Measures Survey

Abstract

Background

Perfluoroalkyl substances can disrupt hepatic metabolism and may be associated with liver function biomarkers. We examined individual and mixture associations of PFAS on liver function biomarkers in a representative sample of Canadian adults. We explored the potential for effect modification by sex and body mass index, as well as by physical activity level which may attenuate the deleterious effect of PFAS on metabolic disorders.

Methods

We analyzed data from participants aged 20–74 from the Canadian Health Measures Survey. We used linear regression to examine associations between plasma concentrations of PFOA, PFOS, PFHxS, PFNA, PFDA, and PFUDA on serum concentrations of aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT) and total bilirubin. We used quantile g-computation to estimate associations with a PFAS mixture for each simultaneous, one-quartile change in PFAS concentrations.

Results

Each doubling of PFOA, PFOS, PFHxS, or PFNA concentrations was associated with higher AST, GGT, and ALP concentrations. Each doubling of PFOA concentrations was associated with 16.5% (95%CI: 10.4, 23.0) higher GGT concentrations among adults not meeting Canada’s physical activity guidelines vs. 6.6% (95%CI: -1.6, 15.5) among those meeting these guidelines. Sex and BMI also modified some associations, though to a lesser extent. We did not observe associations between ALT and PFOA (1.2% change; 95%CI: -2.5, 4.9), PFOS (2.2% change; 95%CI: -0.8, 5.3), or PFHxS (1.5% change; 95%CI: -0.4, 3.4). We also did not observe consistent associations for PFDA and PFUDA or with total bilirubin. In quantile g-computation models, each simultaneous one-quartile increase in the PFAS mixture was positively associated with AST (7.5% higher; 95%CI: 4.0, 10.4), GGT (9.7% higher; 95%CI: 1.7, 17.0), and ALP (2.8% higher; 95%CI: 0.5, 5.4).

Conclusion

Higher plasma concentrations of PFOA, PFOS, PFHxS, and PFNA – both individually and as a mixture – were associated with higher serum concentrations of liver function biomarkers. These results contribute to emerging evidence suggesting that higher levels of physical activity appear to be protective against the hepatotoxic effects of PFOA. This work contributes to a growing body of evidence supporting the hepatotoxic effects of PFAS.

Peer Review reports

Introduction

Perfluoroalkyl substances (PFAS) are a class of highly fluorinated chemicals with lipophobic and hydrophobic properties that are useful for manufacturing a wide range of consumer products, including non-stick cookware, food packaging, personal care and beauty products, fire retardant foams, and carpet treatment applications [1, 2]. Ingestion of contaminated drinking water and food are the primary sources of exposure among the general population [3]. These substances persist in the environment and have been detected in human populations worldwide [1, 4,5,6,7,8]. They bioaccumulate with half-lives ranging from days to years [9] and exposure is nearly ubiquitous among humans [10].

Experimental and mechanistic evidence indicate that PFAS can disrupt hepatic metabolism and promote lipid accumulation in the liver [11, 12]. These effects are at least partially mediated by activation of peroxisome proliferator-activated receptor alpha (PPARα) [13, 14], a regulator of hepatic lipid metabolism. Several other PPARα-independent mechanisms have also been proposed [11, 13], which may be more relevant to PFAS-induced hepatic toxicity in humans. Serum measures of liver function biomarkers are useful clinical indicators of overall liver injury and are commonly used to diagnose non-alcoholic fatty liver disease [15, 16]. These include aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT) and total bilirubin. In addition, these liver function biomarkers are predictive of several long-term health outcomes, including incident vascular events [17] and diabetes [18], risk of developing hypertension [19] and metabolic syndrome [20], as well as all-cause mortality [21]. Higher circulating serum levels of these liver function biomarkers may represent markers of effect for PFAS hepatotoxicity.

Epidemiological studies have demonstrated deleterious associations between PFAS and liver function biomarkers in both highly exposed [22,23,24] and general populations [25,26,27,28,29]. These studies have mostly focused on perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). Only one study has considered the cumulative effect of exposure to a mixture of PFAS [12], showing that exposure to a mixture of PFAS was associated with increased risk of liver injury in childhood, including higher concentrations of ALT, AST, and GGT [30]. Further studies on PFAS mixtures and liver enzymes are needed [12]. In addition, associations may be modified by obesity [22, 25, 27] and there is some evidence for sex-specific associations in adolescents [26], although evidence is limited in adults [23, 29]. Physical activity has been shown to attenuate the deleterious effect of PFAS on obesity [31], incident diabetes [32], as well as hypercholesterolemia and hypertriglyceridemia [33]. To our knowledge, the potential for effect modification by physical activity level has not been examined for the association between PFAS and several other liver function biomarkers, including transaminases. Further studies are needed to address these limitations.

Our objectives were to: 1) estimate associations between PFAS and liver function biomarkers in a representative sample of Canadian adults, 2) investigate the potential for effect modification by sex, body mass index (BMI), and physical activity level, and 3) estimate the combined (mixture) association with exposure to PFAS to account for potential confounding by co-exposure to multiple PFAS.

Methods

Study design and population

We used data from cycles 1 (2007–2009), 2 (2009–2011), and 5 (2016–2017) of the Canadian Health Measures Survey (CHMS), a nationally representative cross-sectional survey that collects information regarding Canadians’ health through household interviews, questionnaires, and direct measurements. Sampling methodology is described in detail elsewhere [34, 35]. Briefly, a stratified multistage sampling strategy was used to select participants aged 3 to 79 years (6 to 79 years in cycle 1) from 15, 18, and 16 sites for cycles 1, 2, and 5, respectively. The CHMS is representative of 96% of the Canadian population in the ten provinces and excludes those living on-reserves and other Indigenous settlements, institutionalized populations, and full-time members of the Canadian forces. We restricted our analysis to individuals 20–74 years of age who were not pregnant. Participants completed a household interview and were subsequently invited to visit a mobile examination centre where physical health measurements and blood samples were collected. Six PFAS and five liver function biomarkers were analyzed across all three cycles included in this analysis, with some inconsistences – perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUDA) were not measured in cycle 1, and ALT and total bilirubin were not measured in cycle 5. The different sample sizes in this paper reflect this inconsistency in data availability. The CHMS was approved by the Health Canada and Public Health Agency of Canada Research Ethics Board [36]. Written informed consent was obtained from all participants included in this analysis [37].

Measurement of plasma PFAS

A detailed description of plasma PFAS measurement is available for each cycle of the CHMS [1, 38, 39]. Plasma samples were collected using sterile EDTA vacutainer tubes, and were processed, aliquoted, and frozen at the mobile examination clinic. Analysis of PFAS was performed by the Laboratoire de Toxicologie, Institut National de Santé Publique du Québec (Quebec City, Quebec, Canada), which is accredited by the Standards Council of Canada. Analysis was done using ultra performance liquid chromatography coupled to a Waters Xevo TQ-S tandem mass spectrometer (ACQUITY UPLC System; Waters Corporation, Milford, Massachusetts). In the current paper, we included PFAS with > 40% detection, including PFOA, PFOS, perfluorohexane sulfonate (PFHxS), PFNA, PFDA, and PFUDA. Limits of detection (LOD) across the three cycles are provided in Additional File 1 (see Supplemental Table 1).

Table 1 Description of plasma concentrations of perfluoroalkyl substances stratified by sex

Measurement of liver function biomarkers

Blood samples were centrifuged in the mobile examination clinic laboratory and either refrigerated or kept on dry ice within two hours of collection. Analyses were performed using a Vitros 5600 FS analyzer (Ortho Clinical Diagnostics) [40]. AST, ALT, GGT, and ALP were analyzed using enzymatic multiple point rate/reflectance spectrophotometry and total bilirubin was analyzed using colorimetry/reflectance spectrophotometry. Method coefficients of variation were < 5%.

Covariates

We identified covariates a priori based on previous assessments of risk factors for elevated liver function biomarkers [15], previously identified predictors or correlates of PFAS concentrations [11], as well as previous reports of associations between select PFAS and liver function biomarkers [22,23,24,25,26,27]. Covariates included: age (grouped by ages 20–29, 30–39, 40–49, 50–59, 60–74 years), race (white and other), biological sex (male and female), BMI (under/normal weight, overweight, and obese with cut-offs at 25 and 30 kg/m2, respectively [41]), reported alcohol consumption (regular, occasional, former, and never), smoking (current, former, and never), education (less than secondary, secondary, and at least some post-secondary), accelerometer-measured average moderate-to-vigorous physical activity (i.e., movement intensity at or greater than three metabolic equivalents of task) in minutes/day [42, 43], and household income (low, lower middle, upper middle, and high). The questionnaire item for household income was changed in cycle 5 and replaced by an ordinal unit-less scale that classifies respondents into 10 categories, from 1 (lowest income) to 10 (highest income) [37]. For cycles 1 and 2, we categorized less than C$40,000 as low, C$40,000 to C$59,999 as lower middle, C$60,000 to C$99,999 as upper middle, and C$100,000 or more as high. In cycle 5, we categorized groups 1 to 3 as low, groups 4 and 5 as lower middle, groups 6 and 7 as upper middle, and groups 8–10 as high. We used BMI as a measure of adiposity to allow for direct comparison with previous studies [25, 27, 29], but recognize that BMI may not be the best measure of adiposity and that other measures, such as waist circumference, are more strongly associated with cardiometabolic disease [44].

Statistical analysis

All analyses were conducted using SAS EG 7.1 (SAS Institute), Standalone SUDAAN 11 (Research Triangle Institute), and R version 4.0.3 (R Core Development Team). The combined cycle 1 and 2 weights file was used for cycles 1 and 2 [45]. Since there is no combined weights file for cycles 1, 2 and 5 and cycles 2 and 5, the individual cycle weight files were used and weights and bootstrap weights were divided by number of cycles combined as recommended by Statistics Canada [45]. A 5% significance level (α = 0.05) was used in all analyses. Participant characteristics, plasma PFAS concentrations, and serum concentrations of liver function biomarkers are presented for men and women, separately, using frequencies or geometric means (SE), as appropriate. PFOA, PFOS, PFHxS, and PFNA were detected in > 98% of samples across cycles, while PFDA was detected in 86% of samples. Values below the limit of detection were assigned a value of one-half of the cycle-specific LOD. The detection frequency for PFUDA was 45%. PFUDA was therefore categorized into three groups: ≤ LOD, LOD–75th percentile, and ≥ 75th percentile and treated as a categorical variable.

In linear regression models, liver function biomarkers were natural log-transformed and PFAS were log2 transformed (with the exception of PFUDA) to normalize the distributions and simplify the interpretation of results. Parameter estimates were back-transformed and can be interpreted as the percent change in the outcome for each two-fold increase in the exposure. Residual analysis was implemented to verify statistical assumptions. We explored the potential for non-linearity using 3-knot restricted cubic splines [46]; non-linear associations were not identified and results from the more parsimonious linear models are presented. We tested for effect modification by sex, BMI, and physical activity level by including multiplicative (product) terms in separate models; if interaction terms were not statistically significant (i.e., if effect modification was not observed) the product term was removed from the model. Physical activity level was dichotomized as meeting vs. not meeting the Canadian physical activity guidelines, which recommend that adults obtain ≥ 150 min of moderate-to-vigorous physical activity per week [47]. In addition to the main analysis, we conducted a sensitivity analysis where we excluded individuals who reported being diagnosed with liver disease (n = 150). We did not consider this variable as a confounder (either through adjustment or restriction) in the main analysis, since it may be along the causal pathway.

Associations between PFOA, PFOS, and PFHxS and AST, ALP, and GGT were examined using combined data from CHMS cycles 1, 2 and 5, resulting in a sample of size n = 4657. The associations between PFOA, PFOS, and PFHxS and ALT and total bilirubin were examined using combined data from CHMS cycles 1 and 2, resulting in a sample of size n = 3659. Finally, the associations between PFNA, PFDA, and PFUDA and AST, GGT, and ALP were examined using combined data from CHMS cycles 2 and 5, resulting in a sample of size n = 1957. Sample sizes for specific PFAS-liver function biomarker analyses are provided in Additional File 1 (see Supplemental Table 2). Satterthwaite adjusted F test was used in the linear regression models.

Table 2 Geometric mean (SE) concentrations of liver function biomarkers and reference ranges by sex

We used quantile g-computation, a generalized linear model based implementation of g-computation [48], to estimate the combined (joint) association with exposure to a mixture of PFOA, PFOS, PFHxS, PFNA, and PFDA. This method estimates the parameters of a marginal structural model that characterizes the change in the expected potential outcome for a simultaneous, one quartile increase in all of the exposures in the specified mixture. We did not include PFUDA in the mixture because of the low detection level.

Results

Sociodemographic characteristics of participants in cycles 1, 2, and 5 of the CHMS included in this analysis are provided in Additional File 1 (see Supplemental Table 3). Plasma concentrations of PFAS, stratified by sex, are presented in Table 1. Concentrations of liver function biomarkers, stratified by sex, are provided in Table 2 along with sex-specific laboratory/statistical reference ranges [40] which reflect the expected range of values from 95% of the broader population [49]. It is important to note that these are not health based reference ranges. For instance, a healthy ALT concentration from individuals without clinical risk factors for liver disease ranges from 29 to 33 U/L for males and 19 to 25 U/L for females [49]. Correlations between PFAS ranged from 0.20 to 0.71 (Additional File 1, see Supplemental Table 4).

In linear regression models, PFOA, PFOS, PFHxS, and PFNA were consistently positively associated with AST, GGT, and ALP (Fig. 1 and Additional File 1, see Supplemental Table 5)). Each two-fold increase in plasma PFOA concentrations was associated with 7.9% (95%CI: 5.4, 10.4) higher AST and 3.9% (95%CI: 1.7, 6.1) higher ALP. Some associations with each of PFOA, PFOS, and PFHxS were modified by sex, BMI, or physical activity levels for different outcomes.

Fig. 1
figure 1

Percent change (95%CI) in AST, GGT, and ALP for PFAS individually and as a mixture. Coefficients for individual PFAS represent the percent change for each two-fold increase in PFAS concentrations. Coefficients for qgComp mixture represents a simultaneous one-quartile increase in PFAS concentrations derived using quantile g-computation. Active and inactive refers to effects among individuals who either meet, or do not meet, Canada’s physical activity guidelines, respectively

Fig. 2
figure 2

Percent change (95%CI) in AST, GGT, and ALP between categorized levels of PFUDA. The reference group consists of those participants with concentrations below the limit of detection (LOD)

The association between PFOA and GGT was considerably stronger, and statistically significant, among individuals obtaining less than 150 vs. 150 min or more of moderate-to-vigorous physical activity per week (16.5% vs. 6.6%). Each two-fold increase in plasma PFOS concentrations was associated with 7.6% (95%CI: 3.0, 12.4) higher GGT and 4.1% (95%CI: 2.4, 5.9) higher ALP. The association between PFOS and AST was stronger among males vs. females (7.6% vs. 3.3%). Each two-fold increase in plasma PFHxS concentrations was associated with 3.1% (95%CI: 1.9, 4.4) higher AST and 3.9% (95%CI: 1.2, 6.6) higher GGT. The association between PFHxS and ALP was only apparent among normal/under weight individuals, which was significantly different from those with obesity (5.9% vs. 0.2%), but not significantly different from overweight individuals. Each two-fold increase in plasma PFNA concentrations was associated with 6.4% (95%CI: 3.8, 9.0) higher AST, 13.8% (95%CI: 4.2, 24.3) higher GGT, and 3.2% (95%CI: 0.3, 6.3) higher ALP. Associations for PFNA did not differ by sex, BMI, or physical activity levels. No associations were observed for PFDA. Participants with categorized PFUDA concentrations between the LOD and 75th percentile had significantly higher level of AST concentrations compared to those participants with concentrations less than the LOD (Fig. 2). No difference was observed for participants with PFUDA concentrations > 75th percentile. No associations were observed with ALT or total bilirubin (Fig. 3).

Fig. 3
figure 3

Percent change (95%CI) in ALT and total bilirubin for PFOA, PFOS, and PFHxS. Coefficients for individual PFAS represent the percent change for each two-fold increase in PFAS concentrations

In quantile g-computation models, each simultaneous one-quartile increase in the mixture of PFOA, PFOS, PFHxS, PFNA, and PFDA was associated with 7.5% (95%CI: 4.0, 10.4) higher AST, 9.7% (95%CI: 1.7, 17.0) higher GGT, and 2.8% (95%CI: 0.5, 5.4) higher ALP (Fig. 1 and Additional File 1, see Supplemental Table 6)). Generally, PFOA and PFNA provided the strongest positive weights (up to 0.53 for PFOA and 0.50 for PFNA) while PFDA consistently provided the strongest negative weight (Additional File 1, see Supplemental Table 6).

Results were similar after excluding 150 individuals with reported liver disease in a sensitivity analysis (Additional File 1, see Supplemental Table 7), except for the association between PFOS and total bilirubin which was statistically significant, although with a similar effect size compared to the main analysis (7.3% change; 95%CI: 0.4, 14.7).

Discussion

In this paper, we examined associations between six PFAS and five liver function biomarkers in a representative sample of Canadian adults from three cycles of the CHMS. We showed that plasma concentrations of PFOA, PFOS, PFHxS, and PFNA were consistently positively associated with serum concentrations of AST, GGT, and ALP. These results are generally consistent with previous epidemiological studies demonstrating deleterious associations between PFAS and liver function biomarkers in both highly exposed [22,23,24] and general populations [25,26,27,28,29]. However, this literature has focused on individual effects whereas people are routinely exposed to a mixture of PFAS [12]. We extend this literature by demonstrating that the combination, or mixture, of these PFAS is also associated with liver function biomarkers. In quantile g-computation, the weights represent the independent, adjusted beta coefficients (scaled to between -1 and + 1) for the individual quantized exposures. The weights will suffer from the same variance inflation issues as other methods which estimate independent effects (e.g., multiple regression). Weights can be thoughts of as representing the proportion of the negative or positive partial effect due to a specific exposure. Generally, PFOA and PFNA contributed the strongest positive weights in the quantile g-computation models while PFDA consistently contributed the strongest negative weight. This is in line with associations observed in the individual PFAS models where associations for PFOA and PFNA were generally the strongest among the six PFAS, and associations for PFDA were generally the weakest.

The liver function biomarkers examined in this paper are useful clinical indicators of overall liver injury and are commonly used to diagnose liver diseases, including non-alcoholic fatty liver disease [15, 16]. We observed consistent associations with GGT and ALP, which are both early markers of cholestatic liver disorders including biliary obstruction and cholestasis [50]. For total bilirubin, another indicator of such conditions, we observed similar effect sizes but only the association with PFOS was statistically significant after excluding participants with existing reported liver disease. This is consistent with evidence from a cohort study examining liver biopsies which demonstrated that PFAS can impact metabolic pathways involved in bile acid metabolism [51]. We also observed consistent positive associations with AST, a non-specific marker of hepatocyte damage that is released into the circulation following hepatocyte death [52]. PFAS are associated with biomarkers of hepatocyte apoptosis [53], which supports this finding. However, we did not observe associations for ALT, a specific marker of hepatocyte damage, which is not consistent with previous analyses in the US or China [22,23,24,25, 27, 28]. In these studies, the concentrations of PFOA and PFOS were higher than in the CHMS, which may explain this discrepancy.

A previous analysis of CHMS data by Cakmak et al. (2022) similarly demonstrated associations between several PFAS and GGT, but only PFOA was associated with AST. Contrary to our results, they identified some positive associations with ALT and total bilirubin and also identified negative associations between PFOS and ALP as well as between PFHxS and GGT (among those aged > 33 years old in an age-stratified analysis). They did not identify interactions with sex or BMI. These discrepancies could be because we restricted the analytical sample to adult participants (vs. participants aged 3–79 as in Cakmak et al. (2022)), we additionally adjusted for accelerometer-measured physical activity levels, we had a lower sample size in our paper as a result of restricting our analysis for the subset of individuals who participated in the accelerometer portion of the CHMS, and we did not include recruitment site as a random effect in our models. Despite these differences, both of these analyses provide much needed Canadian evidence and our results are generally consistent with the broader literature supporting the role of PFAS as a hepatotoxin [11, 12].

The strongest association that we observed was among physically inactive adults – each two-fold increase in PFOA concentrations was associated with 16.5% higher GGT concentrations. This association was twice as strong – or 9.9% higher, in absolute terms – compared to those adults meeting Canada’s physical activity guidelines. This suggests that higher levels of physical activity may be protective against the hepatotoxic effects of PFOA. This is consistent with results from a randomized clinical trial of a two-year lifestyle intervention on weight change, which included maintaining > 150 min of moderate-to-vigorous physical activity. These authors showed that the deleterious effects of PFAS, and PFOA in particular, on cholesterol and triglycerides was apparent only in the physically inactive control group, and not in the physically active intervention group [33]. Similar findings have been observed from this intervention study for obesity [31], and incident diabetes [32]. One explanation for our finding is that higher levels of exposure to PFOA may modify individuals’ sensitivity to other risk factors for liver diseases. This is consistent with the “two-hit” hypothesis, whereby exposure to an environmental chemical may compromise the liver’s protective responses against other lifestyle risk factors [13]. This has primarily been studied in the context of chemical-nutrition interaction effects (e.g., hypercaloric or high-fats diets) [12], but physical inactivity could play a similar role. Ultimately, further research is needed to corroborate this finding and to investigate the role of physical activity as a potential modifier of other associations between PFAS and health. Accelerometers are considered the gold-standard field-based measure of physical activity [54]. The use of objectively measured physical activity data from accelerometers is a strength of the current study, and should be considered for use in future analyses.

We found that the association between PFOA and AST was twice as strong among men vs. women. The hepatotoxic effects of PFAS may be sex-specific because of sexually dimorphic responses to PFAS exposure [11], lipid metabolism, and expression of several nuclear receptors involved in energy homeostasis [55]. In addition, this could be because menstruation [55, 56], pregnancy (via transplacental transfer [56]), and breastfeeding [57] are all prominent excretion pathways for PFAS for women which would reduce their overall PFAS body burden [58, 59]. However, previous epidemiological evidence for sex-specific associations between PFAS and liver function biomarkers is equivocal [12]. Using data from adolescents in the US National Health and Nutrition Examination Survey (NHANES), Attanasio (2019) reported deleterious, though inconsistent, associations between PFAS and liver function biomarkers among female adolescents, and largely mixed associations among male adolescents. However, sex-specific associations were not observed in a previous analysis of participants aged 3–79 in the CHMS [29], as well as an analysis of adult participants exposed to high levels of PFOA in the C8 Health Study [23].

We found that the association between PFHxS and ALP was stronger among individuals with normal/underweight vs. those with obesity. Obesity can induce lipid accumulation in the liver and is a risk factor for non-alcoholic fatty liver disease [60]. Therefore, we would have expected to see stronger associations among individuals with obesity. However, obesity has been be associated with higher ALP concentrations [61, 62], so it is also possible that obesity could obscure the relationship with PFHxS. Two previous analyses of NHANES data identified associations between PFOA [25] as well as PFHxS and PFNA [27] and liver function biomarkers among individuals with obesity. However, Lin et al. (2010) did not examine ALP, Jain and Ducatman (2019) did not observe associations with ALP, and neither study formally tested for interaction across BMI categories. A previous analysis of CHMS data did not identify effect modification by BMI for these liver function biomarkers [29]. None of these studies adjusted for objectively measured physical activity levels, which could explain the discrepancy with our findings.

This study has several limitations. The cross-sectional design of the CHMS precludes us from establishing temporality, a critical component of causality. However, given the long half-lives of these PFAS [63], plasma concentrations may reflect long term exposure. In addition, we cannot rule out the possibility of reverse causality if PFAS tend to bioaccumulate more (either through altered distribution or elimination) in individuals with existing markers of liver damage. We attempted to minimize this concern in a sensitivity analysis by excluding individuals with reported liver disease, but this does not eliminate the possibility that higher PFAS concentrations could be the result of more advanced, yet still sub-clinical, liver disease. However, when interpreting our findings within the context of longitudinal and experimental evidence demonstrating the hepatotoxic effects of PFAS, as summarized in a recent systematic review and meta-analysis [12], it is unlikely that our findings are the result of reverse causality. Not all of the PFAS and liver function biomarkers were measured across all cycles of the CHMS. As a result, in the mixture model we opted to include PFNA and PFDA and to examine associations with a smaller number of outcomes. This meant we did not examine associations with a smaller mixture on ALT and total bilirubin, for which the individual results were null in the main analysis. Finally, total bilirubin represents the sum of both direct (conjugated) and indirect (unconjugated) bilirubin, which were not measured individually in the CHMS. This is a limitation, since direct bilirubin is likely a more relevant marker of effect for PFAS-related hepatotoxicity. This may help to explain the relatively wide confidence intervals that we observed for the null associations with total bilirubin in this analysis. Future studies should consider using direct, rather than total, bilirubin.

Conclusion

In this nationally representative sample of Canadian adults, we showed that higher plasma concentrations of PFOA, PFOS, PFHxS, and PFNA – both individually and as a mixture – were associated with higher serum concentrations of the liver function biomarkers AST, GGT, and ALP. We show, for the first time, that higher levels of physical activity appear to be protective against the hepatotoxic effects of PFOA. Future research is warranted to investigate the stability of this finding in other populations. This work contributes to a growing body of evidence supporting the hepatotoxic effects of PFAS.

Availability of data and materials

The data from the Canadian Health Measures Survey (CHMS) are publicly available here: https://www.statcan.gc.ca/eng/statistical-programs/document/5071_D5_T9_V1.

Abbreviations

ALP:

Alkaline phosphatase

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

BMI:

Body mass index

CHMS:

Canadian Health Measures Survey

GGT:

Gamma-glutamyltransferase

LOD:

Limit of detection

PFDA:

Perfluorodecanoic acid

PFHxS:

Perfluorohexane sulfonate

PFNA:

Perfluorononanoic acid

PFOA:

Perfluoroctanoic acid

PFOS:

Perfluorooctane sulfonate

PFUDA:

perfluoroundecanoic acid

References

  1. Health Canada. Fifth Report on Human Biomonitoring of Environmental Chemicals in Canada [Internet]. 2019. Available from: https://www.canada.ca/en/health-canada/services/environmental-workplace-health/reports-publications/environmental-contaminants/fifth-report-human-biomonitoring.html. Cited 17 Mar 2021.

    Google Scholar 

  2. Buck RC, Franklin J, Berger U, Conder JM, Cousins IT, de Voogt P, et al. Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins. Integr Environ Assess Manag [Internet]. Wiley-Blackwell. 2011;7:513–41 Available from: (http://www.ncbi.nlm.nih.gov/pubmed/21793199). Cited 11 Jan 2019.

    CAS  Google Scholar 

  3. Trudel D, Horowitz L, Wormuth M, Scheringer M, Cousins IT, Hungerbühler K. Estimating Consumer Exposure to PFOS and PFOA. Risk Anal [Internet]. 2008;28:251–69. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/18419647). Cited 11 Jan 2019.

    Article  Google Scholar 

  4. Kannan K, Corsolini S, Falandysz J, Fillmann G, Kumar KS, Loganathan BG, et al. Perfluorooctanesulfonate and related fluorochemicals in human blood from several countries. Environ Sci Technol [Internet]. American Chemical Society. 2004;38:4489–95. Available from: https://doi.org/10.1021/es0493446. Cited 10 May 2019.

    CAS  Article  Google Scholar 

  5. Kato K, Wong L-Y, Jia LT, Kuklenyik Z, Calafat AM. Trends in Exposure to Polyfluoroalkyl Chemicals in the U.S. Population: 1999–2008. Environ Sci Technol [Internet]. 2011;45:8037–45. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/21469664). Cited 10 May 2019.

    CAS  Article  Google Scholar 

  6. Glynn A, Berger U, Bignert A, Ullah S, Aune M, Lignell S, et al. Perfluorinated Alkyl Acids in Blood Serum from Primiparous Women in Sweden: Serial Sampling during Pregnancy and Nursing, And Temporal Trends 1996–2010. Environ Sci Technol [Internet]. 2012;46:9071–9. Available from: https://doi.org/10.1021/es301168c American Chemical Society. Cited 10 May 2019

    CAS  Article  Google Scholar 

  7. Haines DA, Saravanabhavan G, Werry K, Khoury C. An overview of human biomonitoring of environmental chemicals in the Canadian Health Measures Survey: 2007–2019. Int J Hyg Environ Health [Internet]. 2017;220:13–28. Available from: (http://www.sciencedirect.com/science/article/pii/S1438463916300888).

    CAS  Article  Google Scholar 

  8. Kärrman A, Mueller JF, van Bavel B, Harden F. Toms L-ML, Lindström G, Levels of 12 perfluorinated chemicals in pooled australian serum, collected 2002–2003, in relation to age, gender, and region. Environ Sci Technol [Internet]. 2006;40:3742–8. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/16830536). Cited 10 May 2019.

    Article  CAS  Google Scholar 

  9. Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JL, et al. Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexanesulfonate, and perfluorooctanoate in retired fluorochemical production workers. National Institute of Environmental Health Science. Environ Health Perspect [Internet]. 2007;115:1298–305. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/17805419). Cited 2 Jan 2019.

    CAS  Article  Google Scholar 

  10. Haines DA, Khoury C, Saravanabhavan G, Werry K, Walker M, Malowany M. Human biomonitoring reference values derived for persistent organic pollutants in blood plasma from the Canadian Health Measures Survey 2007–2011 Urban & Fischer. Int J Hyg Environ Health [Internet]. 2017;220:744–56. Available from: (https://www.sciencedirect.com/science/article/pii/S143846391630476X). Cited 4 Jan 2019.

    CAS  Article  Google Scholar 

  11. Fenton SE, Ducatman A, Boobis A, DeWitt JC, Lau C, Nang C, et al. Per- and Polyfluoroalkyl Substance Toxicity and Human Health Review: Current State of Knowledge and Strategies for Informing Future Research [Internet]. Wiley Blackwell. Environ Toxicol Chem. 2021;40:606–30. Available from: (https://setac.onlinelibrary.wiley.com/doi/full/10.1002/etc.4890). Cited 17 Jun 2021.

    CAS  Article  Google Scholar 

  12. Costello E, Rock S, Stratakis N, Eckel SP, Walker DI, Valvi D, et al. Exposure to per- and Polyfluoroalkyl Substances and Markers of Liver Injury: A Systematic Review and Meta-Analysis. Environ Health Perspect [Internet]. 2022;130:46001. Available from: (https://ehp.niehs.nih.gov/doi/10.1289/EHP10092). Cited 3 May 2022.

    CAS  Article  Google Scholar 

  13. Wahlang B, Jin J, Beier JI, Hardesty JE, Daly EF, Schnegelberger RD, et al. Mechanisms of Environmental Contributions to Fatty Liver Disease. NIH Public Access. Curr Environ Heal reports [Internet]. 2019;6:80 Available from: /pmc/articles/PMC6698418/. Cited 17 Dec 2021.

    CAS  Article  Google Scholar 

  14. Wolf CJ, Takacs ML, Schmid JE, Lau C, Abbott BD. Activation of mouse and human peroxisome proliferator-activated receptor alpha by perfluoroalkyl acids of different functional groups and chain lengths. Toxicol Sci [Internet]. 2008;106:162–71. Available from: (https://pubmed.ncbi.nlm.nih.gov/18713766/). Cited 29 June 2021.

    CAS  Article  Google Scholar 

  15. Pratt DS, Kaplan MM. Evaluation of Abnormal Liver-Enzyme Results in Asymptomatic Patients. Massachusetts Medical Society. N Engl J Med [Internet]. 2000;342:1266–71. Available from: (https://www.nejm.org/doi/10.1056/NEJM200004273421707). Cited 17 June 2021.

    CAS  Article  Google Scholar 

  16. Targher G, Byrne CD. Circulating Markers of Liver Function and Cardiovascular Disease Risk. Arterioscler Thromb Vasc Biol [Internet]. Lippincott Williams and Wilkins. 2015;35:2290–6. Available from: (http://atvb.ahajournals.org/site/misc/). Cited 16 June 2021.

    CAS  Google Scholar 

  17. Fraser A, Harris R, Sattar N, Ebrahim S, Smith GD, Lawlor DA. Gamma-glutamyltransferase is associated with incident vascular events independently of alcohol intake: Analysis of the British Women’s Heart and Health study and meta-analysis Lippincott Williams & Wilkins. Arterioscler Thromb Vasc Biol [Internet]. 2007;27:2729–35. Available from: (http://ahajournals.org). Cited 16 June 2021.

    CAS  Article  Google Scholar 

  18. Fraser A, Harris R, Sattar N, Ebrahim S, Smith GD, Lawlor DA. Alanine aminotransferase, γ-glutamyltransferase, and incident diabetes. Diabetes Care [Internet]. Diabetes Care. 2009;32:741–50. Available from: (https://pubmed.ncbi.nlm.nih.gov/19131466/). Cited 16 June 2021.

    CAS  Article  Google Scholar 

  19. Liu C, Gu Y, Wang H, Fang N. Gamma-Glutamyltransferase Level and Risk of Hypertension: A Systematic Review and Meta-Analysis. Public Library of Science. PLoS One [Internet]. 2012;7:e48878 Available from: /pmc/articles/PMC3492247/. Cited 16 June 2021.

    CAS  Article  Google Scholar 

  20. Liu C, Zhou W, Fang N. Gamma-glutamyltransferase levels and risk of metabolic syndrome: A meta-analysis of prospective cohort studies. Int J Clin Pract [Internet]. 2012;66:692–8. Available from: (https://pubmed.ncbi.nlm.nih.gov/22698421/). Cited 16 June 2021.

    CAS  Article  Google Scholar 

  21. Kunutsor SK, Apekey TA, Seddoh D, Walley J. Liver enzymes and risk of all-cause mortality in general populations: A systematic review and meta-analysis. Oxford University Press. Int J Epidemiol. 2014;43:187–201. Available from: (https://academic.oup.com/ije/article/43/1/187/731274). Cited 16 June 2021.

    Article  Google Scholar 

  22. Gallo V, Leonardi G, Genser B, Lopez-Espinosa MJ, Frisbee SJ, Karlsson L, et al. Serum perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) concentrations and liver function biomarkers in a population with elevated PFOA exposure. Public Health Services, US Dept of Health and Human Services. Environ Health Perspect. 2012;120:655–60.

    CAS  Article  Google Scholar 

  23. Darrow LA, Groth AC, Winquist A, Shin HM, Bartell SM, Steenland K. Modeled perfluorooctanoic acid (PFOA) exposure and liver function in a Mid-Ohio Valley community. Public Health Services, US Dept of Health and Human Services. Environ Health Perspect. 2016;124:1227–33.

    CAS  Article  Google Scholar 

  24. Nian M, Li QQ, Bloom M, Qian Z (Min), Syberg KM, Vaughn MG, et al. Liver function biomarkers disorder is associated with exposure to perfluoroalkyl acids in adults: Isomers of C8 Health Project in China. Academic Press Inc. Environ Res [Internet]. 2019;172:81–8. Available from: (https://pubmed.ncbi.nlm.nih.gov/30776734/). Cited 18 June 2021.

    CAS  Article  Google Scholar 

  25. Lin CY, Lin LY, Chiang CK, Wang WJ, Su YN, Hung KY, et al. Investigation of the associations between low-dose serum perfluorinated chemicals and liver enzymes in US adults. Nature Publishing Group. Am J Gastroenterol [Internet]. 2010;105:1354–63. Available from: https://doi.org/10.1038/ajg.2009.707.

    CAS  Article  Google Scholar 

  26. Attanasio R. Sex differences in the association between perfluoroalkyl acids and liver function in US adolescents: Analyses of NHANES 2013-2016. Elsevier Ltd. Environ Pollut. 2019;254:113061.

    CAS  Article  Google Scholar 

  27. Jain RB, Ducatman A. Selective Associations of Recent Low Concentrations of Perfluoroalkyl Substances with Liver Function Biomarkers: NHANES 2011 to 2014 Data on US Adults Aged ≥20 Years. J Occup Environ Med. 2019;61:293–302.

    CAS  Article  Google Scholar 

  28. Gleason JA, Post GB, Fagliano JA. Associations of perfluorinated chemical serum concentrations and biomarkers of liver function and uric acid in the US population (NHANES), 2007–2010. Environ Res Academic Press Inc. Environ Res. 2015;136:8–14.

    CAS  Article  Google Scholar 

  29. Cakmak S, Lukina A, Karthikeyan S, Atlas E, Dales R. The association between blood PFAS concentrations and clinical biochemical measures of organ function in participants of the Canadian Health Measures Survey (CHMS). Sci Total Environ. 2022;827:153900.

    CAS  Article  Google Scholar 

  30. Stratakis N, Conti DV, Jin R, Margetaki K, Valvi D, Siskos AP, et al. Prenatal Exposure to Perfluoroalkyl Substances Associated With Increased Susceptibility to Liver Injury in Children John Wiley & Sons, Ltd. Hepatology [Internet]. 2020;72:1758–70. Available from: (https://onlinelibrary.wiley.com/doi/full/10.1002/hep.31483). Cited 30 June 2022.

    CAS  Article  Google Scholar 

  31. Cardenas A, Hauser R, Gold DR, Kleinman KP, Hivert MF, Fleisch AF, et al. Association of Perfluoroalkyl and Polyfluoroalkyl Substances With Adiposity Netw Open. JAMA Netw Open [Internet]. 2018;1:e181493. Available from: (https://pubmed.ncbi.nlm.nih.gov/30646133/). Cited 16 Nov 2021.

    Article  Google Scholar 

  32. Cardenas A, Hivert MF, Gold DR, Hauser R, Kleinman KP, Lin PID, et al. Associations of perfluoroalkyl and polyfluoroalkyl substances with incident diabetes and microvascular disease. American Diabetes Association Inc. Diabetes Care [Internet]. 2019;42:1824–32 Available from: /pmc/articles/PMC6702604/. Cited 17 Nov 2021.

    CAS  Article  Google Scholar 

  33. Lin PID, Cardenas A, Hauser R, Gold DR, Kleinman KP, Hivert MF, et al. Per- and polyfluoroalkyl substances and blood lipid levels in pre-diabetic adults-longitudinal analysis of the diabetes prevention program outcomes study. Environ Int [Internet]. 2019;129:343–53. Available from: (https://pubmed.ncbi.nlm.nih.gov/31150976/). Cited 17 Nov 2021.

    CAS  Article  Google Scholar 

  34. Tremblay M, Wolfson M, Gorber SC. Canadian Health Measures Survey: rationale, background and overview. Health Rep. 2007;18(Suppl):7–20.

    Google Scholar 

  35. Giroux S. Canadian Health Measures Survey: sampling strategy overview. Heal reports [Internet]. 2007;18:31–6. Available from: (https://pubmed.ncbi.nlm.nih.gov/18210868/). Cited 29 June 2021.

    Google Scholar 

  36. Day B, Langlois R, Tremblay M, Knoppers B-M. Canadian Health Measures Survey: Ethical, legal and social issues. Heal Rep [Internet]. 2007;18(suppl):37–51. Available from: (https://www150.statcan.gc.ca/n1/pub/82-003-s/2007000/article/10364-eng.pdf). Cited 5 Jan 2022.

    Google Scholar 

  37. Statistics Canada. Canadian Health Measures Survey (CHMS) Data User Guide: Cycle 5. Available upon request: STATCAN.infostats-infostats.STATCAN@canada.ca. 2020.

  38. Health Canada. Second Report on Human Biomonitoring of Environmental Chemicals in Canada. [Internet]. Ottawa, ON; 2013. Available from: https://www.canada.ca/content/dam/hc-sc/migration/hc-sc/ewh-semt/alt_formats/pdf/pubs/contaminants/chms-ecms-cycle2/chms-ecms-cycle2-eng.pdf Cited 3 May 2019

  39. Health Canada. Report on Human Biomonitoring of Environmental Chemicals in Canada. [Internet]. 2010. Available from: https://www.canada.ca/en/health-canada/services/environmental-workplace-health/reports-publications/environmental-contaminants/8-results-chemical-group-report-human-biomonitoring-environmental-chemicals-canada-health-canada-2010.html#n8_5. Cited 15 Jan 2019.

    Google Scholar 

  40. Adeli K, Higgins V, Nieuwesteeg M, Raizman JE, Chen Y, Wong SL, et al. Biochemical Marker Reference Values across Pediatric, Adult, and Geriatric Ages: Establishment of Robust Pediatric and Adult Reference Intervals on the Basis of the Canadian Health Measures Survey. Oxford Academic. Clin Chem [Internet]. 2015;61:1049–62. Available from: (https://academic.oup.com/clinchem/article/61/8/1049/5611534). Cited 17 Nov 2021.

    CAS  Article  Google Scholar 

  41. Statistics Canada. Table 13–10–0373–01 Overweight and obesity based on measured body mass index, by age group and sex. 2021.

    Google Scholar 

  42. Colley RC, Gorber SC, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Heal Reports [Internet]. 2010;21:63–9. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/20426228).

    Google Scholar 

  43. Colley RC, Tremblay MS. Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer. J Sports Sci [Internet]. 2011;29:783–9. Available from: (http://www.tandfonline.com/doi/abs/10.1080/02640414.2011.557744).

    Article  Google Scholar 

  44. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr [Internet]. 2004;79:379–84. Available from: (https://pubmed.ncbi.nlm.nih.gov/14985210/). Cited 6 Jul 2022.

    CAS  Article  Google Scholar 

  45. Statistics Canada. Instructions for Combining Multiple Cycles of Canadian Health Measures Survey (CHMS) Data. Available upon request: STATCAN.infostats-infostats.STATCAN@canada.ca. 2017.

  46. Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. John Wiley & Sons, Ltd. Stat Med [Internet]. 2010;29:1037–57. Available from: (http://doi.wiley.com/10.1002/sim.3841). Cited 7 Aug 2019.

    Google Scholar 

  47. Ross R, Chaput J-P, Giangregorio LM, Janssen I, Saunders TJ, Kho ME, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep1. https://doi.org/101139/apnm-2020-0467 [Internet]. NRC Research Press 1840 Woodward Drive, Suite 1, Ottawa, ON K2C 0P7. Appl Physiol Nutr Metab. 2020;45:S57-102. Available from: (https://cdnsciencepub.com/doi/abs/10.1139/apnm-2020-0467). Cited 22 Sep 2021.

    Article  Google Scholar 

  48. Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Env Heal Perspect. 2019;128:1–10.

    Google Scholar 

  49. Kwo PY, Cohen SM, Lim JK. ACG Clinical Guideline: Evaluation of Abnormal Liver Chemistries. Am J Gastroenterol [Internet]. 2017;112:18–35. Available from: (https://journals.lww.com/ajg/Fulltext/2017/01000/ACG_Clinical_Guideline__Evaluation_of_Abnormal.13.aspx). Cited 30 June 2022.

    CAS  Article  Google Scholar 

  50. Hall P, Cash J. What is the Real Function of the Liver ‘Function’ Tests? Ulster Medical Society. Ulster Med J [Internet]. 2012;81:30 /pmc/articles/PMC3609680/.

    Google Scholar 

  51. Sen P, Qadri S, Luukkonen PK, Ragnarsdottir O, McGlinchey A, Jäntti S, et al. Exposure to environmental contaminants is associated with altered hepatic lipid metabolism in non-alcoholic fatty liver disease. J Hepatol [Internet]. 2022;76(2):283–93. Elsevier B.V.; 2021;0. Available from: http://www.journal-of-hepatology.eu/article/S0168827821021048/fulltext. Cited 17 Dec 2021

  52. Oh RC, Hustead TR, Ali SM, Pantsari MW. Mildly Elevated Liver Transaminase Levels: Causes and Evaluation. Am Fam Physician. 2017;96:709–15.

    Google Scholar 

  53. Bassler J, Ducatman A, Elliott M, Wen S, Wahlang B, Barnett J, et al. Environmental Perfluoroalkyl Acid Exposures Are Associated with Liver Disease characterized by Apoptosis and Altered Serum Adipocytokines NIH Public Access. Environ Pollut [Internet]. 2019;247:1055 Available from: /pmc/articles/PMC6404528/. Cited 17 Dec 2021.

    CAS  Article  Google Scholar 

  54. Borghese MM, Tremblay MS, LeBlanc AG, Leduc G, Boyer C, Chaput JP. Comparison of ActiGraph GT3X+ and Actical accelerometer data in 9–11-year-old Canadian children. J Sports Sci [Internet]. 2017;35:517–24. Available from: (http://www.ncbi.nlm.nih.gov/pubmed/27103499).

    CAS  Google Scholar 

  55. Le Magueresse-Battistoni B. Endocrine disrupting chemicals and metabolic disorders in the liver: What if we also looked at the female side? [Internet]. Chemosphere. Elsevier Ltd; 2021;268:129212. Available from: https://pubmed.ncbi.nlm.nih.gov/33359838/. cited 2021 Jan 6

  56. Mamsen LS, Björvang RD, Mucs D, Vinnars M-T, Papadogiannakis N, Lindh CH, et al. Concentrations of perfluoroalkyl substances (PFASs) in human embryonic and fetal organs from first, second, and third trimester pregnancies Pergamon. Environ Int [Internet]. 2019;124:482–92. Available from: (https://www.sciencedirect.com/science/article/pii/S0160412018326102?via%3Dihub). Cited 24 Jun 2019.

    CAS  Article  Google Scholar 

  57. Lakind JS, Verner MA, Rogers RD, Goeden H, Naiman DQ, Marchitti SA, et al. Current Breast Milk PFAS Levels in the United States and Canada: After All This Time, Why Don’t We Know More? Environ Health Perspect [Internet]. Environmental Health Perspectives. 2022;130(2). Available from: https://ehp.niehs.nih.gov/doi/full/10.1289/EHP10359. Cited 30 Jun 2022

  58. Ding N, Harlow SD, Batterman S, Mukherjee B, Park SK. Longitudinal trends in perfluoroalkyl and polyfluoroalkyl substances among multiethnic midlife women from 1999 to 2011: The Study of Women’s Health Across the Nation. Elsevier Ltd. Environ Int. 2020;135:105381.

    CAS  Article  Google Scholar 

  59. Wong F, MacLeod M, Mueller JF, Cousins IT. Enhanced elimination of perfluorooctane sulfonic acid by menstruating women: Evidence from population-based pharmacokinetic modeling. Environ Sci Technol [Internet]. American Chemical Society. 2014;48:8807–14. Available from: (https://pubs.acs.org/sharingguidelines). Cited 29 Mar 2021.

    CAS  Google Scholar 

  60. Fabbrini E, Sullivan S, Klein S. Obesity and Nonalcoholic Fatty Liver Disease: Biochemical, Metabolic and Clinical Implications NIH Public Access. Hepatology [Internet]. 2010;51:679 Available from: /pmc/articles/PMC3575093/. Cited 5 Jan 2022.

    CAS  Article  Google Scholar 

  61. Ali N, Sumon AH, Fariha KA, Asaduzzaman M, Kathak RR, Molla NH, et al. Assessment of the relationship of serum liver enzymes activity with general and abdominal obesity in an urban Bangladeshi population Nature Publishing Group. Sci Rep [Internet]. 2021;11:6640 Available from: /pmc/articles/PMC7988042/. Cited 30 Jun 2022.

    CAS  Article  Google Scholar 

  62. Khan AR, Awan FR, Najam SS, Islam M, Siddique T, Zain M. Elevated serum level of human alkaline phosphatase in obesity - PubMed. J Pak Med Assoc [Internet]. 2015;65:1182–5. Available from: (https://pubmed.ncbi.nlm.nih.gov/26564289/). Cited 30 Jun 2022.

    Google Scholar 

  63. Agency for Toxic Substances and Disease Registry (ATSDR). Toxicological profile for perfluoroalkyls [Internet]. Atlanta, Georgia; 2021. Available from: https://stacks.cdc.gov/view/cdc/59198

Download references

Acknowledgements

The authors acknowledge Health Canada for supporting this study. The authors also acknowledge colleagues at Statistics Canada for conducting the Canadian Health Measures Survey. The authors thank Kavita Singh and Subramanian Karthikeyan for their helpful comments. The Canadian Health Measures Survey is conducted by Statistics Canada in partnership with Health Canada and the Public Health Agency of Canada. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 

Funding

The Canadian Health Measures Survey is conducted by Statistics Canada in partnership with Health Canada and the Public Health Agency of Canada. Funding for the biomonitoring component of the Canadian Health Measures Survey is provided by the Government of Canada’s Chemicals Management Plan. This research using the CHMS data did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 

Author information

Authors and Affiliations

Authors

Contributions

MMB: conceived the research question and designed the analytical plan, wrote the original draft manuscript, edited the manuscript, and visualized results. CLL contributed to the analytical plan, conducted the statistical analysis, and reviewed the manuscript. JO contributed to the analytical plan, conducted the statistical analysis, and reviewed the manuscript. MF contributed to conceiving the research question and designing the analytical plan, and reviewed the manuscript. The author(s) read and approved the final manuscript. 

Corresponding author

Correspondence to Michael M. Borghese.

Ethics declarations

Ethics approval and consent to participate

All components of the Canadian Health Measures Survey were approved by the Health Canada and Public Health Agency of Canada Research Ethics Board.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

 Supplemental Table 1. Limits of detection (µg/L) for plasma concentrations of six perfluoroalkyl substances. Supplemental Table 2. Sample sizes for analyses of PFAS and liver function biomarkers across cycles 1 (2007-2009), 2 (2009-2011), and 5 (2016-2017) of the Canadian Health Measures survey. Supplemental Table 3. Descriptive statistics for perfluoroalkyl substances and sociodemographic characteristics stratified by sex. Supplemental Table 4. Pearson correlations between Log2-transformed plasma concentrations of perfluoroalkyl substances. Supplemental Table 5. Percent difference (95%CI) in liver function biomarkers for individual PFAS. Supplemental Table 6. Percent difference (95%CI) in liver function biomarkers for a mixture of PFAS. Supplement Table 7. Sensitivity analysis of percent difference (95%CI) in liver function biomarkers for individual PFAS. Supplement Table 8. Description of liver diseases reported by 150 individuals in cycles 1, 2, and 5 of the Canadian Health Measures Survey.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Borghese, M.M., Liang, C.L., Owen, J. et al. Individual and mixture associations of perfluoroalkyl substances on liver function biomarkers in the Canadian Health Measures Survey. Environ Health 21, 85 (2022). https://doi.org/10.1186/s12940-022-00892-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12940-022-00892-6

Keywords

  • Hepatotoxicity
  • Epidemiology
  • Perfluorinated compounds
  • Biomonitoring
  • Liver enzymes
  • Environmental chemicals