Association between levels of serum perfluorooctane sulfate and ...

International Journal of Cardiology 168 (2013) 3309–3316

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Association between levels of serum perfluorooctane sulfate and carotid artery intima–media thickness in adolescents and young adults Chien-Yu Lin a, b, Lian-Yu Lin c, Ting-Wen Wen d, Guang-Wen Lien d, Kuo-Liong Chien e, Sandy H.J. Hsu f, Chien-Chang Liao g, Fung-Chang Sung h, Pau-Chung Chen d,⁎, Ta-Chen Su c,⁎⁎ a

Department of Internal Medicine, En Chu Kong Hospital, Taipei County, Taiwan School of Medicine, Fu Jen Catholic University, Taipei County, Taiwan Department of Internal Medicine and Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan d Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan e Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan f Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan g Department of Anaesthesiology, Taipei Medical University Hospital, Taipei, Taiwan h Institute of Environmental Health, China Medical University College of Public Health, Taichung 404, Taiwan b c

a r t i c l e

i n f o

Article history: Received 5 May 2012 Received in revised form 28 March 2013 Accepted 6 April 2013 Available online 7 May 2013 Keywords: Perfluorinated chemicals Perfluorooctane sulfate Perfluorononanoic acid Carotid intima–media thickness

a b s t r a c t Background: Perfluorinated chemicals (PFCs) have been widely used for years in a variety of products worldwide. Although epidemiological findings have shown that PFC levels are positively associated with cholesterol and uric acid levels, it is unknown whether PFCs are associated with atherosclerosis. Methods: We recruited 664 subjects (12–30 years) from a population-based sample of adolescents and young adults based on a mass urine screening to determine the relationship between serum levels of PFCs and carotid intima–media thickness (CIMT). Results: The median concentrations and ranges of perfluorooctanoic acid (PFOA), perfluorooctane sulfate (PFOS), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFUA) were 3.49 (0.75–52.2) ng/mL, 8.65 (0.11–85.90) ng/mL, 0.38 (0.38–25.4) ng/mL, and 6.59 (1.50–105.7) ng/mL, respectively. After controlling for age, gender, smoking status, systolic blood pressure, body mass index, low-density lipoprotein cholesterol, triglyceride, high-sensitivity C-reactive protein, and homeostasis model assessment of insulin resistance, multiple linear regression analysis revealed that CIMT increased significantly across quartiles of PFOS (0.434 mm, 0.446 mm, 0.458 mm, 0.451 mm; P for trend b0.001). Subpopulation analysis showed the association between PFOS and CIMT was more evident and significant in females, non-smokers, subjects of age 12–19 years, BMI b 24, and those with APOE genotype of E2 carrier and E3/E3. Conclusions: Higher serum concentrations of PFOS were associated with an increase of carotid IMT in this cohort of adolescents and young adults. Further studies are warranted to clarify the causal relationship between PFOS and atherosclerosis. © 2013 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Perfluorinated compounds (PFCs) are a class of chemicals that consist of a C–F backbone and a terminal charged moiety. PFCs are widely used in industrial applications as surfactants and emulsifiers and in consumer products such as food packaging, non-stick pan coatings, firefighting foams, paper and textile coatings, and personal care products. ⁎ Correspondence to: P.-C. Chen, Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, #17 Syujhou Road, Taipei 10055, Taiwan. Tel.: +886 2 3322 8088; fax: +886 2 358 2402. ⁎⁎ Correspondence to: T.-C. Su, Department of Internal Medicine and Cardiovascular Center, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei 10002, Taiwan. Tel.: +886 2 23123456x66719; fax: +886 2 23712361. E-mail addresses: [email protected] (P.-C. Chen), [email protected] (T.-C. Su). 0167-5273/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.04.042

Their wide application in industry and common consumer products over the past several decades has resulted in persistent and widespread pollution [1]. The two most widely known PFCs are perfluorooctanoic acid (PFOA) and perfluorooctane sulfate (PFOS), which belong to the 8-carbon backbone subgroup. The major manufacturer of PFOS, 3 M, has phased this chemical out of production since 2002, it has been added to Annex B of the Stockholm Convention on persistent organochlorine pollutants in 2009, and the production and use of PFOS have been regulated in Europe since 2008 [2]. However, the public health relevance of exposure to PFCs is still being examined because of its biological persistence and a lack of information on its possible long-term health implications [3]. The possible routes of human exposure to PFCs are currently being investigated. Potential routes include contaminated drinking water,

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C.-Y. Lin et al. / International Journal of Cardiology 168 (2013) 3309–3316

dust, food, food packaging, and cookware. Dietary exposure has been suggested as the main exposure route of PFCs in general populations, while consumption of fish and shellfish is a major source of serum PFC concentrations from food [4]. PFCs mainly distribute extracellularly. PFCs have a binding affinity for apolipoprotein B-lipoproteins, as well as albumin and liver fatty acid-binding protein. PFCs are not metabolized and distributed through enterohepatic circulation to the serum and the kidney. However, PFCs are mainly distributed to the liver with concentrations being several times higher than serum concentrations [3]. Toxicological studies have shown that PFOS and PFOA cause increased incidence of tumorigenicity [5,6], increases in liver weight [7], immune suppression [8], developmental delays [9], alteration of thyroid hormone levels [10], increases in oxidative stress [11,12], and hypocholesterolemia [13,14] in exposed animals. In human cell systems, exposure to PFOS and PFOA has been found to cause increased oxidative stress [15–17]. The trigger for hepatotoxicity and immunosuppression, and developmental effects as well as carcinogenesis of PFCs may be partially or completely attributable to the activation of the PPARα. Correspondingly, a change in expression of the genes that control lipid metabolism, energy homeostasis, cell differentiation, and peroxisome proliferation might be involved [18]. In epidemiological studies, evidence of the occurrence of particular cancer diseases, most often urinary bladder and prostate cancers, has been observed [19,20]. There is a recent upturn in the number of publications on reproductive and developmental effects. The results from these studies are, however, inconsistent, and the observed adverse effects are weak [18]. Unlike in animal studies, there are inconsistent reports of the effect of PFCs on hypercholesterolemia in human beings. A few cross-sectional and longitudinal occupational studies have proposed a weak positive correlation of PFOA with serum lipid and liver enzyme levels but without clinical relevance [21,22]. In a nonworking population, examination of PFOA exposure through contaminated drinking water suggests that exposure to PFOA, and possibly to PFOS, is associated with increased total cholesterol and low-density lipoprotein cholesterol (LDL-C) [23,24], and increased uric acid (UA) levels [25]. In the general population, using data from the National Health and Nutrition Examination Survey (NHANES), a positive association has been demonstrated between concentrations of PFOS, PFOA, and PFNA and total and non-high-density cholesterol [26]. PFOA also has been linked to thyroid disease [27] or elevation of serum Free T4 [28], and elevated levels of liver enzymes [29], while PFOS has been associated with insulin resistance in a US population [30]. Given the findings for cholesterol, insulin resistance, oxidative stress, and UA, it is reasonable to ask whether PFC is associated with atherosclerosis and cardiovascular disease. Published data to date are inadequate to establish whether PFCs increase the risk of cardiovascular disease. Existing studies have been restricted to mortality studies of US worker populations, with limited sample size and without clear results [31,32]. Common carotid artery intima–media thickness (CIMT) assessed by ultrasonography is a well-known marker of subclinical atherosclerosis [33,34], and as an independent predictor of stroke, myocardial infarction, and coronary events [35–37]. Endocrine-disrupting agents have been associated with disruption of the endocrine [38], reproductive [39], and neurobehavioral disorders [40] in humans, and some reports have studied the association between some of these pollutants (bisphenol A, phthalates and persistent organic pollutants) and CIMT [41,42]. We designed a cross-sectional study in adolescents and young Taiwanese adults based on a nationwide mass urine screening. Since young subjects typically have less clinical evident cardiovascular disease, we used CIMT as a surrogate marker of atherosclerosis in this study. To our knowledge, an association between PFCs and CIMT has never been investigated. The goal of this study was to assess the association between serum PFC levels and CIMT.

2. Methods 2.1. Participants and study design From 2006 to 2008 we established a cohort (the Young Taiwanese Cohort Study) based on these students with or without elevated blood pressure in childhood, selected from the 1992–2000 mass urine screening program in Taiwan [43,44]. Detailed information is available in recent reports [28,41,44,45]. In this current study, we selected 790 subjects who lived in the Taipei area whose serum samples were available for further analysis. The interview and cardiovascular health check-up were conducted in the National Taiwan University Hospital during 2006–2008. The study was approved by the ethics committee of the National Taiwan University Hospital. Informed consent was obtained for each participant when they joined the cohort follow-up study. Because of the limited availability of serum samples, we did not measure serum PFCs in all 790 subjects. Individuals without serum tests for PFCs were excluded (N = 146). A detailed flow chart of the selection process is shown in Fig. 1. A total of 644 participants were included in the final analysis. 2.2. Anthropometric and biochemical data Socio-demographic information such as age, gender, history of medication, and household income was recorded during the interview. The extent of alcohol intake was determined by questionnaire and categorized into two groups — “current alcohol consumption” and “no alcohol consumption.” Smoking status was categorized as “active smoker,” “passive smoker,” or “has never smoked.” Household income was categorized as either “above 50,000 new Taiwan dollars (NTD) per month” or “below 50,000 NTD.” Body mass index (BMI) was calculated as body weight (in kg) divided by the square of body height (in meters). Two seated blood pressure and heart rate measurements were made at least 1 min apart after 5 min of rest by using a mercury manometer and the appropriate cuff size. Serum levels of cholesterol, triglyceride (TG), low and high density lipoprotein cholesterol (LDL-C and HDL-C), and glucose were measured with an autoanalyzer (Technician RA 2000 Autoanalyzer, Bayer Diagnostic, Mishawaka, IN). Serum insulin levels were measured with the commercial kit IMMULITE 2000 (Siemens Healthcare Diagnostics, Tarrytown, NY). Serum high sensitivity C-reactive protein (hs-CRP) levels were measured using a chemiluminescent enzyme-labeled immunometric assay (Immulite C-Reactive Protein, Diagnostic Products Co., Los Angeles, CA). The homeostasis model assessment of insulin resistance (HOMA-IR) index (the product of basal glucose and insulin levels divided by 22.5) is regarded as a simple, inexpensive, and reliable surrogate measure of insulin resistance [46]. Diabetes mellitus was defined as a fasting serum glucose ≥ 126 mg/dL or selfreported current use of oral hypoglycemic agents or insulin. Hypertension status was determined by self-reported current use of anti-hypertensive medication or averaged BP ≥ 140/90 mm Hg. Childhood elevated BP was defined as either systolic blood pressure (SBP), or diastolic blood pressure (DBP), or both, greater than or equal to the modified sexand age-specific criteria for BP values [47]. 2.3. Measurement of CIMT The CIMT at extracranial carotid arteries (ECCAs) was measured by an experienced technician, with a high-resolution B-mode ultrasonograph (GE Vivid ultrasound system, Horten, Norway) equipped with a 3.5–10 MHz real-time B-mode scanner. In addition, a software package for vascular ultrasound was applied for offline automatic calculation after examination. The maximum and mean IMTs at the common carotid artery (CCA) proximal to the carotid bifurcation, bulb, and internal carotid artery were obtained bilaterally. CCA1 and CCA2 are points located at 0–1 cm and 1–2 cm, respectively, on the CCA distal from the carotid bifurcation. CIMT of the posterior wall of the distal CCA was measured as the distance from the leading edge of the first echogenic line (interface between lumen and vascular intima) to the leading edge of the second line (interface between vascular media and adventitia). CIMT was defined as the distance from the front edge of the first echogenic line (lumen–intima interface) to the front edge of the second echogenic line (media–adventitia interface) in the far wall of the vessel [48,49]. All scans were recorded on a digitalized memory system in DICOM format for subsequent off-line analysis. A moving-image clip of the carotid bulb and CCA with duration of 5 s was acquired and stored. The digitized M-mode was later analyzed off-line using a computer program, in which each image was recalled with magnification and the CIMT between 2 successive R waves was measured by automated analyzing software provided by the manufacturer. Automated measurement, the mean value of 150 measurements on a 10-mm segment of the CCA, is efficient, reliable and less time-consuming than manual measurement. Because the measurements of CIMT in the CCA are more strongly associated with cardiovascular risk factors than CIMT in the bulb and the internal carotid artery [50], CIMT in this analysis was determined by averaging four measurements on bilateral CCA. To determine the reliability of repeated measurements, the technician conducted a second reading for the same participant of random selected 30 subjects after 2 weeks later. The reliability of CIMT measurement at bilateral CCA (mean of right and left CCA) had excellent intraobserver coefficient of correlation reliability (ICCR) around 98.8% and 98.5%, respectively. 2.4. Measurement of PFC concentrations Plasma samples were stored at − 80 °C before analysis. Twelve kinds of PFCs were analyzed in our study. However, eight kinds of PFCs, over 70% of the PFCs


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Fig. 1. Flowchart of recruitment of participants in the Young Taiwanese Cardiovascular Cohort Study. 790 subjects with previous mean age of 12.5 year-old adolescents were recruited to NTUH after a mean of 9 years later for preclinical cardiovascular examination and carotid duplex during 2006–2008.

were below the limits of quantification. Therefore, we analyzed levels of PFOA, PFOS, PFNA, and perfluorodecanoic acid (PFUA) in this study. We analyzed plasma samples using a Waters ACQUITY UPLC system (Waters Corporation, Milford, MA) coupled

with a Waters Quattro Premier XE triple quadrupole mass spectrometer (Waters Corporation, Milford, MA). Details of the analytical method have been described elsewhere [51].

Table 1 Basic demographics of the sample subjects including sex- and age-adjusted geometric means and their 95% confidence intervals of 4 PFC concentrations. No.

PFOA (ng/mL)

Sex Males Females

250 394

2.62 (1.20–5.60) 2.60 (1.09–3.95)

Age (years) 12–19 20–30

231 413

Household income b50,000 NT dollar per month ≥50,000 NT dollar per month

PFNA (ng/mL)

PFUA (ng/mL)

8.97 (3.24–12.72)⁎ 7.21 (4.41–11.75)⁎

1.19 (0.56–3.92) 1.00 (0.24–1.01)

5.97 (5.65–26.74) 5.78 (1.96–6.81)

2.78 (0.68–11.10) 2.52 (0.21–1.67)

7.25 (2.44–23.69) 8.21 (6.27–34.71)

0.97 (0.16–4.10) 1.14 (0.44–5.31)

5.84 (5.19–86.92) 5.86 (1.43–11.12)

259 384

2.52(0.92–5.23) 2.66(1.18–4.20)

7.56 (2.70–10.43) 8.05 (5.86–17.03)

0.99 (0.19–1.57) 1.13 (0.54–2.43)

5.92 (2.23–12.26) 5.79 (3.35–11.81)

Smoking status Never smoked Passive smoker Active smoker

545 20 73

2.63 (1.51–4.45) 2.08 (0.02–63.94) 2.70 (0.10–4.74)

7.71 (5.53–13.29) 10.22 (0.47–29.31) 8.50 (0.66–20.41)

1.02 (0.56–1.92) 1.60 (0.01–8.49) 1.36 (0.03–5.01)

5.73 (4.30–12.32) 7.12 (0.01–47.94) 6.41 (0.34–21.18)

Current alcohol consumption Yes No

62 581

2.89 (0.07–4.40) 2.57 (1.47–4.25)

8.46 (0.59–33.28) 7.79 (5.27–12.37)

1.32 (0.06–19.49) 1.05 (0.46–1.59)

6.33 (0.88–89.93) 5.79 (3.60–10.24)

Body mass index (kg/m2) b24 ≥24

487 157

2.77 (1.16–3.78)⁎ 2.16 (1.02–7.60)⁎

7.55 (3.96–10.65)⁎ 8.84 (5.44–24.61)⁎

1.09 (0.36–1.47) 1.04 (0.43–4.31)

5.80 (3.17–10.28) 6.01 (2.70–20.03)

Currently hypertensive Yes No

47 597

2.12 (0.16–12.23) 2.65 (1.41–4.06)

6.40 (5.21–602.45) 7.98 (4.50–10.19)

0.99(0.55–44.52) 1.08 (0.39–1.34)

5.50 (0.66–47.23) 5.88 (3.69–10.49)

Diabetes mellitus Yes No

15 628

2.23 (4.13–517.50) 2.61 (1.20–3.39)

6.66 (0.40–689.52) 7.88 (4.99–11.45)

0.70 (0.01–9.90) 1.08 (0.46–1.56)

5.67 (0.16–68.85) 5.85 (3.71–10.36)

APOE genotypea E2 carrier E3/E3 E4 carrier Total

107 415 113 644

2.75 2.57 2.67 2.61

7.27 (2.60–18.97) 8.00 (4.78–14.25) 7.69 (3.15–18.54) 7.85 (5.13–11.78)

1.07 1.10 0.99 1.08

6.25 5.63 6.15 5.85

(0.84–10.74) (1.26–4.52) (0.53–6.77) (1.37–3.80)

PFOS (ng/mL)

(0.11–2.08) (0.53–2.36) (0.19–3.68) (0.47–1.56)

(0.82–11.66) (3.57–12.54) (2.18–24.17) (3.74–10.24)

Abbreviations: APOE, Apolipoprotein E; PFC, perfluorinated chemicals; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonate; PFNA, perfluorononanoic acid; PFUA, perfluoroundecanoic acid. ⁎ P b 0.0125. a E2 carriers include E2/2 and E2/3; E4 carriers include E3/4 and E4/4.

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The limits of quantification for PFOA, PFOS, PFNA, and PFUA were 1.5, 0.22, 0.75, and 1.5 ng/mL, respectively. PFOS was not observed in any blank samples, but trace amounts of PFOA (up to 1.5 ng/mL), PFNA (up to 0.75 ng/mL), and PFUA (up to 3 ng/mL) were detected. Consequently, the background levels found in the blank samples of each batch were subtracted from the actual measurements to give the reported concentrations of PFOA, PFNA, and PFUA. For concentrations below the detection limits (39.5% for PFOA, 1.6% for PFOS, 55.7% for PFNA, and 25.8% for PFUA), a value of half the detection limit was used. All laboratory analyses were conducted by investigators blinded to the characteristics of study subjects. 2.5. Genotyping of APOE alleles Apolipoprotein E (APOE) alleles play a key role in lipid transport and metabolism and are important genetic markers for dyslipidemia, coronary heart disease (CHD), and ischemic stroke [52,53]. Many studies have also shown a clear association of APOE genotype with CIMT. The E2 allele was associated with lower, while the E4 allele was associated with higher, CIMT [54,55]. DNA was extracted from peripheral leukocytes of the 644 subjects using standard methods. Genotyping of the APOE epsilon alleles (2, 3, and 4) was performed using sequence-specific primer-PCR methodologies [56]. As in most previous studies [52–55], we defined carriers of the E4 allele as those who had the E3/4 and E4/4 genotypes, and carriers of the E2 allele as those who had the E2/3 and E2/2 genotypes. 2.6. Statistical analysis SPSS for Windows (version 16.0, SPSS Inc., Chicago, IL) was used for all statistical analyses. PFC concentrations were expressed as geometric means and their 95% confidence intervals. The relation of PFC variables to categorical variables was tested using the Mann–Whitney U test or the Kruskal–Wallis test (if three or more groups). We also used a linear regression model to investigate the relationship between cardiovascular risk factors and PFC levels. Analyses were conducted using linear regression with SBP, BMI, LDL, and HOMA-IR as the outcomes. Natural log transformation was performed for hs-CRP, HOMA-IR, and TG levels with significant deviation from the normal distribution before further analysis. Moreover, we used an extended model approach for covariates to adjust for potential confounders in multiple linear regression models to study the association between PFCs and CIMT. Model 1 adjusted for age and gender. Model 2 adjusted for age, gender, smoking status, SBP, BMI, LDL, CRP, TG, and HOMA-IR. To avoid “model-dependent association,” an association was considered significant only when it was statistically significant in both models. Each PFC was modeled separately in separate analyses, and the four PFCs were modeled together in a composite analysis. Logistic regression analyses were conducted to examine the odds ratios (ORs) of thicker CIMT associated with a 50% increase in PFOS concentrations in different

APOE genotypes, and the interaction between PFOS and PFNA. We used the Bonferroni correction to correct the multiple comparisons and testing. Because we tested four kinds of PFCs in our study, P b 0.0125 was considered significant in the study.

3. Results The demographic characteristics of the sample population are outlined in Table 1. The study sample consisted of 250 males and 394 females. The sex- and age-adjusted geometric mean and 95% confidence interval of concentrations of PFOA, PFOS, PFNA, and PFUA in different subpopulations are shown in Table 1. Males had a higher median concentration of PFOS than females (P b 0.001). In addition, PFOA concentrations were lower in those with higher BMI (P = 0.011), while PFOS concentrations were higher in the higher BMI cohort (P = 0.005). The PFC concentrations were not different between other subpopulations. Not all four PFCs were correlated with one another; PFNA and PFUA were most strongly correlated, with a Spearman correlation coefficient of 0.51 (P b 0.001). Adjusted cardiovascular risk factors with increasing levels of PFCs are shown in Table 2. The only positive finding is that the mean level of log-HOMA-IR decreased significantly with increasing levels of PFNA (P = 0.009). A summary of the association between serum concentrations of PFOS and PFNA and CIMT after adjusting for other potential covariates is listed in Table 3. When one of the four PFCs was entered into the full regression models (controlling age, gender, smoking status, SBP, BMI, LDL-C, TG, hs-CRP, and HOMA), mean CIMT increased significantly with increasing levels of PFOS (P for trend b 0.001 in the full model), while it decreased insignificantly with increasing levels of PFNA. When the four PFCs were entered into the full regression models at the same time, mean CIMT increased significantly with increasing levels of PFOS (P for trend b 0.001). A summary of the association between serum concentrations of PFOA and PFUA and CIMT after adjusting for other potential covariates is listed in Supplement Table 1. The complete multiple linear regression models which

Table 2 Mean and standard error of adjusteda cardiovascular risk factors across categories of PFCs in linear regression models (n = 644). SBP mm Hg

BMI kg/m2

LDL-C mg/dL

log-TG mg/dL

UA mg/dL

log-HOMA-IR

PFOA (ng/mL) ≤3.49 (≤50th) ≤6.54 (50th–75th) ≤9.62 (75th–90th) >9.62 (>90th) P for trend

109.4 108.6 106.3 107.3 0.177

(1.13) (1.27) (1.53) (1.83)

22.3 (0.37) 21.7 (0.41) 21.2 (0.50) 21.6 (0.60) 0.130

107.4 105.5 100.3 100.4 0.117

(3.07) (3.42) (3.95) (4.66)

4.37 (0.04) 4.39 (0.05) 4.32 (0.06) 4.20 (0.07) 0.015

6.08 (0.10) 6.08 (0.11) 6.11 (0.14) 6.13 (0.17) 0.983

−0.29 −0.21 −0.22 −0.33 0.694

(0.08) (0.09) (0.11) (0.13)

PFOS (ng/mL) ≤5.41 (≤25th) ≤8.65 (25th–50th) ≤13.52 (50th–75th) >13.52 (>75th) P for trend

109.4 108.8 108.3 107.5 0.613

(1.29) (1.34) (1.35) (1.30)

21.4 (0.42) 21.9 (0.44) 21.8 (0.44) 22.3 (0.42) 0.374

102.8 101.1 105.3 109.3 0.209

(3.51) (3.50) (3.50) (3.48)

4.43 (0.05) 4.32 (0.05) 4.37 (0.05) 4.33 (0.05) 0.074

6.09 (0.13) 6.13 (0.13) 6.04 (0.13) 6.12 (0.13) 0.891

−0.25 −0.27 −0.21 −0.28 0.885

(0.09) (0.09) (0.10) (0.09)

PFNA (ng/mL) ≤1.58 (≤60th) ≤6.78 (60th–90th) >6.78 (>90th) P for trend

108.8 (1.27) 109.4 (1.41) 106.6 (1.85) 0.321

22.4 (0.41) 22.4 (0.45) 21.1 (0.59) 0.043

101.7 (2.58) 101.8 (3.00) 104.3 (4.15) 0.811

4.34 (0.04) 4.34 (0.04) 4.40 (0.06) 0.593

6.13 (0.11) 6.04 (0.13) 6.10 (0.17) 0.689

−0.05 (0.09) −0.26 (0.10) −0.27 (0.13) 0.009

PFUA (ng/mL) ≤1.50 (≤25th) ≤6.59 (25th–50th) ≤13.66 (50th–75th) >13.66 (>75th) P for trend

108.9 107.1 108.1 109.3 0.447

21.9 (0.42) 21.5 (0.44) 21.8 (0.43) 22.1 (0.42) 0.536

102.1 101.3 100.4 104.2 0.718

4.32 (0.05) 4.33 (0.05) 4.35 (0.05) 4.39 (0.05) 0.459

5.96 (0.12) 6.11 (0.12) 6.10 (0.12) 6.18 (0.12) 0.385

−0.20 −0.27 −0.27 −0.27 0.853

(1.30) (1.36) (1.31) (1.28)

(3.09) (3.24) (3.12) (3.04)

(0.09) (0.10) (009) (0.09)

Data are means (standard error). Abbreviations: BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low density lipoprotein cholesterol; PFC, perfluorinated chemicals; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonate; PFNA, perfluorononanoic acid; PFUA, perfluoroundecanoic acid; SBP, systolic blood pressure; TG, triglyceride; UA, uric acid. a Adjusted for age, gender, smoking status, alcohol drinking, body mass index.


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Table 3 Mean and standard error of carotid intima–media thickness across different categories of serum PFOS and PFNA levels in linear regression models. Carotid intima–media thickness, mm (S.E.) Separate analysis Model 1 (n = 644) PFOS (ng/mL) ≤5.41 (≤25th) ≤8.65 (25th–50th) ≤13.52 (50th–75th) >13.52 (>75th) PFNA (ng/mL) ≤1.58 (≤60th) ≤6.78 (60th–90th) >6.78 (>90th)

Composite analysis P for trend

Model 2 (n = 637)

0.441 0.454 0.465 0.459

(0.004) (0.004) (0.004) (0.004)

0.434 0.446 0.458 0.451

(0.006) (0.006) (0.006) (0.006)

0.009 0.460 (0.003) 0.450 (0.004) 0.440 (0.007)

P for trend

Model 1 (n = 644)

b0.001

0.001

0.429 0.445 0.457 0.455

(0.005) (0.005) (0.005) (0.005)

Model 2 (n = 637)

0.462 (0.003) 0.444 (0.004) 0.433 (0.007)

P for trend b0.001

0.428 0.441 0.454 0.450

(0.006) (0.006) (0.006) (0.006)

b0.001

0.122 0.452 (0.005) 0.444 (0.005) 0.441 (0.007)

P for trend b0.001

0.014 0.453 (0.005) 0.441 (0.006) 0.436 (0.008)

Data are means (standard error). Model 1: adjusted for age, gender, Model 2: adjusted for age, gender, smoking status, systolic blood pressure, body mass index, low density lipoprotein cholesterol, triglyceride, high sensitivity C-reactive protein, homeostasis model assessment of insulin resistance. Abbreviations: PFOS, perfluorooctane sulfonate; PFNA, perfluorononanoic acid.

investigated the association between serum concentrations of PFOS and PFNA and CIMT were available in Supplement Table 2. Linear regression coefficients (SE) of CIMT with quartile distribution of PFOS in the different subpopulations of the sample subjects are shown in Table 4. The positive association between CIMT and PFOS was significant in females, non-smokers, subjects of age 12–19 years, BMI b 24, and those with APOE genotype of E2 carrier or E3/E3. A negative association between IMT and PFNA was significant in females, in subjects > 20 years, and those with APOE genotype of E2 and E4 carriers (Supplement Table 2). In addition, the interaction between a higher level of PFOS and a lower level of PFNA associated with higher ORs of thicker CIMT is shown in Fig. 2. Using subjects with PFOS ≤ 50th and PFNA > 60th as a reference, subjects with PFOS > 50th and PFNA ≤ 60th, and PFOS > 50th and PFNA > 60th had a higher prevalence of thicker CIMT (greater than 50th percentile) (OR 3.011 [95% CI 1.682–5.390], P b 0.001 and OR 2.026 [95% CI 1.141–3.599], P = 0.016, respectively). Odds ratios (ORs) (95% confidence intervals [CIs]) of thicker CIMT with a 50% increase in serum PFOS concentration by variants of APOE genotype are summarized in Supplement Table 3. Increased serum PFOS concentrations were associated with a higher prevalence of thicker CIMT in APOE2 carriers

(OR 2.93 [95% CI 1.16–7.42], P = 0.034) or APOE3/E3 individuals (OR 1.84 [95% CI 1.21–2.81], P = 0.004). 4. Discussion This cross-sectional study in adolescents and young adults demonstrates a significant association between PFOS and CIMT. The importance of such studies is three fold. First, studying potential health consequences of an environmental exposure of PFCs in adolescents and young adults may provide greater insight because these groups are likely to have fewer factors confounding underlying associations (e.g., prevalent chronic or acute disease or medication use) than do older adults. Second, the robust associations of PFOS with CIMT in younger adults, coupled with the long half-life of PFOS, suggest the possibility of a causal relationship. Third, if such associations are etiologic, exposure prevention would be important to reduce long-term health consequences. We report a median concentration of PFUA of 6.73 ng/mL in men and 6.56 ng/mL in women in this study. Our finding is compatible with another study that analyzed PFCs in umbilical cord blood in Taiwan [51]. However, this concentration is 10 times higher than

Table 4 Mean and standard error of carotid intima–media thickness (mm) across different quartiles of PFOS in subpopulations of the sample subjects in linear regression models. No.

≤25th

25th–50th

50th–75th

>75th

Sex Males Females

245 392

0.452 (0.009) 0.418 (0.009)

0.452 (0.009) 0.433 (0.009)

0.466 (0.008) 0.447 (0.009)

0.457 (0.007) 0.439 (0.010)

0.401 0.001

Age, years 12–19 20–30

228 409

0.398 (0.012) 0.447 (0.007)

0.420 (0.012) 0.451 (0.007)

0.432 (0.011) 0.466 (0.007)

0.434 (0.013) 0.453 (0.006)

b0.001 0.084

BMI (kg/m2) b24 ≥24

482 155

0.427 (0.006) 0.461 (0.013)

0.441 (0.006) 0.461 (0.013)

0.450 (0.006) 0.486 (0.012)

0.448 (0.007) 0.463 (0.011)

0.001 0.155

Smoking status Never smoked Has smoked

544 93

0.437 (0.004) 0.436 (0.014)

0.450 (0.004) 0.443 (0.013)

0.464 (0.004) 0.459 (0.012)

0.457 (0.004) 0.438 (0.013)

b0.001 0.495

HOMA-IR ≤0.93 >0.93

318 319

0.434 (0.008) 0.436 (0.009)

0.445 (0.008) 0.448 (0.008)

0.455 (0.008) 0.463 (0.008)

0.452 (0.008) 0.451 (0.008)

0.039 0.023

APOE genotypea E2 carrier E3/E3 E4 carrier

107 409 112

0.415 (0.018) 0.433 (0.007) 0.450 (0.016)

0.427 (0.018) 0.443 (0.007) 0.466 (0.016)

0.458 (0.019) 0.458 (0.007) 0.465 (0.016)

0.419 (0.017) 0.452 (0.007) 0.480 (0.017)

0.007 0.004 0.378

P for trend

Data are means (standard error). Adjusted for age, gender, smoking status, systolic blood pressure, body mass index, low density lipoprotein cholesterol, triglyceride, high sensitivity C-reactive protein, homeostasis model assessment of insulin resistance. Abbreviations: PFOS, perfluorooctane sulfonate. a E2 carriers include E2/2 and E2/3, E4 carriers include E3/4 and E4/4.

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Fig. 2. Odds ratio (95% confidence interval) for thicker CIMT (greater than 50th percentile) in different PFOS and PFNA subgroups in the fully adjusted model (age, gender, smoking status, systolic blood pressure, body mass index, low density lipoprotein cholesterol, triglyceride, high sensitivity C-reactive protein, Homeostasis model assessment of insulin resistance). Subjects with PFOS ≤ 50th and PFNA > 60th were used as the reference group.

previously reported in the biomonitoring literature for PFUA, while the PFOA, PFOS, and PFNA levels we observed are comparable to other reports [57]. It is possible that a high PFUA concentration is unique to Taiwan. According to a study that investigated the influence of the semiconductor and electronics industries on PFC contamination in receiving rivers in Taiwan, PFUA was detected in wastewater although it was not the major PFC [58]. The source of exposure for our study population is not clear and needs further investigation. We found that PFOS levels were associated with CIMT, and that this association remained after adjustment for multiple potential confounders. However, there are no previous studies for comparison. In previous epidemiologic studies, PFOS levels have been shown to be associated with alterations in lipid metabolism [23,24], elevations in uric acid levels [25], and increased insulin resistance [30]. All these factors have been implicated in atherosclerosis pathophysiology. In this study, however, the association between PFOS and CIMT appeared to be independent of traditional risk factors such as age, gender, BMI, HOMA-IR, blood pressure, and elevated serum cholesterol levels. If the association is etiologic, the mechanisms by which PFOS may influence the progression of CIMT are unknown. Possible physiological explanations include immune modulation [8], increased endothelial cell permeability [17,59], and increased oxidative stress [15–17]. Qian et al. [17] exposed human microvascular endothelial cells to PFOS. They found that PFOS induced ROS production in the cells which resulted in a reorganization of actin filaments and an increased endothelial permeability. We are conducting a study of the population to further investigate a possible relation between PFOS and oxidative stress. The dose–response effects of PFOS on CIMT may not be a linear relationship in humans in this study. PFOS exerts the maximal effects at the 50th–75th category and no further relevant changes occur at an even higher level. The nonmonotonic or biphasic dose–response curves observed in this follow an inverted U shape. This is a common finding for other endocrine-active chemicals [60], for which high doses inhibit the low-dose response system while initiating a wide array of other adverse effects via different response mechanisms [61]. In subgroup analysis, the association between serum PFOS and CIMT was more evident in females, non-smokers, younger subjects, those with lower BMI, and those with non-E4 carrier status of APOE genotype, despite the lower concentration of PFOS in young, female, and low-BMI populations. These findings deserve more attention,

particularly in searching for novel risk factors of atherosclerosis in relatively healthy populations. APOE has allele- and gender-dependent effects on reverse cholesterol transport, platelet aggregation, and oxidative processes that are likely to affect overall atherogenic potential [62]. However, the relationship between PFOS, APOE, and CIMT has not been elucidated at a mechanistic level. One possible explanation is that the effect of PFOS on CIMT is much weaker than the effect of gender, age, obesity, tobacco smoke, and APOE genotype. When considering the atherosclerotic effect of PFOS in the above populations, the trend is too small to be statistically significant. Alternatively, it is also possible that the association between PFOS and gender, age, obesity, and tobacco smoke is due to an opposite synergistic effect. Several cross-sectional studies observed a positive association between concentrations of PFOS, PFOA, and total and non-high-density cholesterol in both the NHANES population [26] and in a non-working population by examination of PFOA exposure through contaminated drinking water [23,24]. These associations were non-linear, confined to LDL-C, and the strength of the associations appeared to decline and plateau at concentrations approximating 50 ng/mL. In contrast, studies of occupationally exposed workers have found associations of serum PFOS or PFOA with serum cholesterol that are either absent or are much weaker than those observed in non-occupational populations [21,22]. The positive associations are unexpected, based on toxicological/mechanistic studies [13,14], suggesting that the associations may have a biological, rather than a causal basis. One preliminary study supposes PFOS and PFOA distribute into serum lipoprotein fractions, such that increases in serum lipoproteins would result in corresponding increases in serum concentrations of PFOS and PFOA [63]. In a recent longitudinal study of a group of 204 workers engaged in the demolition of former fluorochemical manufacturing facilities, the change in non-HDL cholesterol was not associated with the changes in PFOA or PFOS. An increase in HDL was associated with an increase in PFOA, although the magnitude was small [64]. For PFOA, the most relevant data are those from a phase I clinical trial in 41 cancer patients given ammonium PFOA doses of up to 1200 mg/week for a median of 6.5 weeks, and in which non-HDL-C was reduced as a treatment effect [65]. In the current study, our results did not observe an association of PFOA, PFOS and cholesterol after multivariate analysis. Further study will be necessary to identify potential factors that may contribute to the associations of non-HDL-C with serum PFOS and PFOA.


We also observed an association of HOMA-IR with decreasing PFNA. The direction of the observed association was similar to our previous study using 1999–2000 and 2003–2004 NHANES data to study the association between PFCs and glucose homeostasis [30]. In that study we showed that in adolescents, increased serum PFNA concentrations were associated with decreased insulin resistance. Moreover, CIMT tended to decrease with increasing PFNA levels, although this was not statistically significant after multivariate adjustment. Unlike PFOA and PFOS, which do not activate mouse or human PPAR-gamma [66], PFNA activates both PPAR-alpha and PPAR-gamma in animal studies [67]. In humans, higher serum PFNA concentrations were associated with elevated serum adiponectin concentrations in our previous study [41]. If the association between PFNA, adiponectin, and insulin resistance has etiologic implications, PFNA may increase serum adiponectin levels and decrease serum insulin levels by acting as an agonist to PPAR-gamma. Increasing PFNA levels in the study population might be inversely associated atherosclerosis through its role in decreasing insulin resistance. In further analysis, while considering the effects on CIMT by testing the interaction of combining PFOS and PFNA, we found that a concurrent presence of higher PFOS and lower PFNA levels (PFOS > 50th and PFNA ≤ 60th) could contribute to a higher risk of thicker CIMT in Fig. 2. This finding further supported the above results of PFOS or PFNA on CIMT respectively and interactively. That meant an environmental–environmental interaction should be taken into consideration while studying the environmental factors in the pathogenesis of atherosclerosis. There are several limitations to our study. First, the cross-sectional design does not permit causal inference. Second, we did not include other environmental factors (for example, urban particulate matter air pollution [68,69]) that may be important confounders or explanatory variables for the outcomes of our study. Third, our study population is made up of adolescents and young adults with abnormal urinalysis in childhood and living in the Taipei area, and therefore we cannot infer that the same association might be similar in the general population. Fourth, we did not take into account all medications that may have impact on CIMT, which will be a confounding variable. However, more than 95% of participants self-reported no significant clinical diseases and no medication history. Finally, a common physiology could influence both serum PFC levels and CIMT, rather than exposure affecting the outcome. 5. Conclusion In conclusion, in a Taiwanese population of adolescents and young adults, we found that serum concentration of PFOS was associated with CIMT. This association appeared to be independent of traditional risk factors such as age, gender, BMI, HOMA-IR, blood pressure, and elevated serum cholesterol levels. Although the potential biological significance of the relationship between PFOS and CIMT is small and subclinical in the Taiwanese population, our data indicated the potential atherosclerosis potential (indexed by CIMT) among individuals with low-level exposure to PFOS, particularly females, adolescents, non-smokers, low-BMI subjects, and non-E4 carriers of the APOE genotype. If a causal relation between PFOS and CIMT exists, there could be potentially serious consequences in the form of increased risk of cardiovascular disease which data published to date are inadequate to establish. Our results provide clues about where to focus future epidemiologic and toxicology research. Acknowledgments We thank the many persons who have contributed to the data we have examined, including all of the anonymous participants in the study. This study was supported by grants from the National Health Research Institute of Taiwan (NHRI EX97-9721PC, EX98-9721PC, EX99-9721PC, and EX100-9721PC; EX95-9531PI, EX96-9531PI and

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