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Diabetes Care Publish Ahead of Print, published online December 29, 2008 Perjluoroalkyl chemicals, glucose homeostasis and metabolic syndrome Association Among Serum Perfluoroalkyl Chemicals, Glucose Homeostasis and Metabolic Syndrome in Adolescents and Adults Running title: PeruofGplkyl chemicals, glucose homeostasis and metabolic syndrome Chien-Yu Lin1-2'3, MD, MPH; pau-Chung Chen2, MD, PhD; Yu-Chuan Lin2' 4, MD; Lian-Yu Lin, MD, PhD5 1, Department of Internal iMedicine o f Nephrology, En Chu Kong Hospital, Taipei County, Taiwan 2, Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan 3, School o f Medicine, Fu Jen Catholic University, Taipei County, Taiwan 4, Department of Pediatrics, Taipei City H ospita*>Zhongxiao Branch, Taipei Taiwan 5, Department of Internal Medicine o f Cardiology, National Taiwan University Hospital, Taipei, Taiwan Corresponding author: Lian-Yu Lin E-mail: hspenosf5tvahoo.com.tw Submitted 3 October 2008 and accepted 17 December 2008. This is an uncopyedited electronic version of an article accepted for publication in Diabetes ^are- The American Diabetes Association, publisher of Diabetes Care, is not responsible for any errors or o rN 'ssions in this version of the manuscript or any version derived from it by third parties. The definitive nubi isherauthenticated version will be available in a future issue of Diabetes Care in print and onlin:e at http://care.diabetesjournals.org. Copyright American Diabetes Association, Inc., 2008 63 P-2 Diabetes Care Publish Ahead of Print, published online December 29,2008 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome Association Among Serum Perfluoroalkyl Chemicals, Glucose Homeostasis and Metabolic Syndrome in Adolescents and Adults Running title: PerfluorGC'lkyl chemicals, glucose homeostasis and metabolic syndrome 's. Chien-Yu Lin1,2,3, MD, MPH; pau-Chung Chen2, MD, PhD; Yu-Chuan Lin2'4, MD; Lian-Yu '' Lin, MD, PhD5 1, Department of Internal Medicine o f ^ephrology, En Chu Kong Hospital, Taipei County, , ' Taiwan 2, Institute of Occupational Nfedicme and' Industrial Hygiene, National Taiwan University College o f Public Health, Taipei, Taiwan 3, School o f Medicine, Fu Jen Cathoffr University, Taipei County, Taiwan 4, Department of Pediatrics, Taipei City Hospital, Zhongxiao Branch, Taipei Taiwan 5, Department of Internal Medicine o f Cardiology^ ^National Taiwan University Hospital, Taipei, Taiwan Corresponding author: Lian-Yu Lin E-mail: hspenos@vahoo.com.tw Submitted 3 October 2008 and accepted 17 December 2008. This is an uncopyedited electronic version of an article accepted for publication in Diabetes areAmerican Diabetes Association, publisher of Diabetes Care, is not responsible for any errors or or,'q`ss'ons in this version of the manuscript or any version derived from it by third parties. The definitive pubi`s^er" authenticated version will be available in a future issue of Diabetes Care in print and onlin:e at http://care.diatetesjournals.org. Copyright American Diabetes Association, Inc., 2008 P-3 Perjluoroalkyl chemicals, glucose homeostasis and metabolic syndrome Objective: Perfluoroalkyl chemicals (PFCs) have been used worldwide in a variety o f consumer products. The effect o f PFCs on glucose homeostasis is not known. Research design and methods-. We examined 474 adolescents and 969 adults with reliable serum measures o f metabolic syndrome (MS) profile from the National Health and Nutrition Examination Survey 1999-2000 and 2003-2004. Results: In adolescents, increased serum perfluorononanoic acid (PFNA) concentrations were associated with hyperglycemia (OR = 3.16, 95% Cl = 1.39-7.16, P < 0.05). Increased serum PFNA concentrations also have favorable associations with serum HDL-C (OR = 0.67,95% Cl = 0.45-0.99, P < 0.05). Overall, increased serum PFNA concentrations were inversely correlated with the prevalence of the MS (OR = 0.37, 95% Cl = 0.21-0.64, P < 0.005). In adults, increased serum perfluorooctanoic acid (PFOA) concentrations were significantly associated with increased P cell function (pcoeff= 0.070.03, P < 0.05). Increased serum perfluorooctane sulfate (PFOS) concentrations were associated with increased blood insulin (pcoeff = 0.140.05, P < 0.01), HOMA-IR (Pcoeff = 0.140.05, P < 0.01) and P cell function (Pcoeff = 0.150.05, P < 0.01). Serum PFOS concentrations were also unfavorably correlated with serum HDL-C (OR = 1-61, 95% Cl = 1.15-2.26, P < 0.05). Conclusions-. Serum PFCs were associated with glucose homeostasis and indicators of MS. Further clinical and animal studies are warranted to clarify putative causal relationships. 2 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome The perfluoroalkyl chemicals (PFCs) are a family of perfluorinated chemicals that have shown that several categories of genes are commonly altered by some PFCs including peroxisome proliferation, fatty acid consist of a carbon backbone typicalmlye4ta-b1o4liisnm, lipid transport, cholesterol length and a charged functional moiety [1J. synthesis, proteosome activation and PFCs have been used extensively since the proteolysis, cell communication, and 1950s in commercial applications, including inflammation [1], The agonistic properties of as a component in surfactants, lubricants, PFCs on PPAR-a (peroxisome proliferator- paper and textile coatings, polishes, food activated receptors-a) are well supported and packaging, and fire-retardant foams [2], Some are thought to be a major mechanism leading o f these PFCs, including the most widely to PFC-mediated liver damage [8, 9]. Since known examples of perfluorooctanoic acid activation of PPAR-a can decrease serum (PFOA) and perfluorooctane sulfate (PFOS), triglycerides, normalize low-density persisted in humans and the environment and lipoprotein cholesterol and increase HDL-C, have been detected worldwide in wildlife [2]. we hypothesized that PFCs might have The routes of human exposure to PFCs are favorable effects on lipid homeostasis and currently being investigated. Possible may also be associated with reduced insulin exposure pathways that are being examined resistance, improved serum lipid profile and include drinking water, dust in homes, and lower prevalence of the metabolic syndrome food or migration from food packaging and (MS). The goal of the present study is to test cookware. Animal studies have shown that this hypothesis by examining data from the they are well absorbed orally, but poorly National Health and Nutrition Examination eliminated; they are not metabolized, and Survey (NHANES) collected from 1999-2000 undergo extensive uptake from enterohepatic and 2003-2004. circulation and are distributed mainly to the serum, kidney and liver [1]. Although some RESEARCH DESIGN AND METHODS PFCs have been voluntarily removed from the Study design and population: Data market by manufacturers, PFOA and PFOS were from NHANES 1999-2000 and 2003 and their derivatives are still produced 2004. The NHANES is a population-based commercially and its potential risk to humans survey designed to collect information on the continues to be evaluated. health and nutrition o f the U.S. household In animal studies, exposure to PFOS and population, and to obtain a representative PFOA is associated with adverse health sample o f the non-institutionalized civilian effects, including carcinogenicity [3, 4], US population. The survey data are released hepatotoxicity [4, 5], and developmental and every 2 years. Detailed survey operations reproductive toxicity [4]. For human beings, manuals, consent documents, and brochures PFC exposure has been shown to be o f the NHANES 1999-2000 and 2003-2004 associated with certain types o f cancers [6], are available on the NHANES website [10]. Maternal exposure to PFOS and PFOA also We limited our analyses to the 3685 has been linked to low birth weight [7]. participants at least 12 years of age who had a The causal biochemical mechanisms blood test for PFCs. Among these subjects, leading to the adverse health outcomes after only 1788 subjects had a morning exposure to PFCs are largely unknown. examination and had fasting plasma glucose, However, recent studies using advanced insulin and triglyceride (TG) data available. technologies in genomics and bioinformatics O f these 1788 participants, we included the P-5 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome 1443 subjects without missing data for further analyses. Anthropometric and biochemical data: According to the statements on the NHANES website, data were collected at all study sites by trained personnel according to standardized procedures. Sociodemographic information such as age, gender, and race/ethnicity, education level and household income was collected during the household interview. Alcohol intake was determined by the questionnaire "in any one year, have you had at least 12 drinks of any type of alcohol beverage?" and was dichotomized. For adolescents, since there were too many missing data, alcohol intake was not entered for analysis. Smoking status was categorized as active smoker, former/passive smoker and non-smoker by smoking questionnaire and serum cotinine levels as previously described [11]. Laboratory measurements were performed in a mobile examination center. Weight and height were measured using standard methods and digitally recorded. Three and sometimes 4 blood pressure (BP) determinations were collected by a physician using a mercury sphygmomanometer. BP was measured in the right arm unless otherwise specified. Averaged systolic BP (SBP) and diastolic BP (DBP) were obtained. Blood specimens were processed locally, and then stored and shipped to central laboratories for analysis. Levels o f serum total cholesterol (TC) and TG were measured enzymatically. Levels of HDL-C were measured after precipitation of other lipoproteins on a Hitachi model 704 analyzer (Roche Diagnostics, Indianapolis, Ind). Serum CRP levels were measured by latex-enhanced nephelometry. Plasma insulin was determined by immunoenzymometric assay. Insulin resistance status (HOMA-IR) and p-cell function were estimated by the updated homeostasis model assessment (HOMA2) [ 12]. Definition of metabolic syndrome: For subjects above 18 years old, presence of the metabolic syndrome was calculated by sex as defined by the National Cholesterol Education Program Third Adult Treatment Panel (NCEP/ATP 111) [13] guideline of presenting with at least 3 o f the following qualifications: waist measurement greater than 88cm for women and greater than 102 cm for men; serum TG s5 1.69mmol/L; serum HDL-C <1.03 mmol/L in men and <1.29 mmol/L in women; SBP =5130 mmHg or DBP =585 mmHg or a self report of taking anti-hypertensive medications; and fasting glucose =56.10 mmol/L or a self report of taking anti-hyperglycemic medications. To define the MS among the young participants aged between 12 to 17 years, we used a previously proposed modification of the definition proposed in the NCEP/ATP III. The participants had to meet 3 of the following 5 criteria: serum concentration of T G ^1.24 mmol/L, HDL-C ^ 1 .0 4 mmol/L, WC =5sex specific 90th percentile [14], glucose concentration^55.55 mmol/L or a self report of taking anti-hyperglycemic medications [15], and SBP or DBP =5age, height and sex- specific 90th percentile or a self-report of taking anti-hypertensive medications [16], Assessment of serum PFCs: As part o f NHANES, serum samples o f PFOA, PFOS, perfluorohexane sulfonic acid (PFHS) and perfluorononanoic acid (PFNA) were collected for analysis. The analytical method has been described in detail [17]. Briefly, without protein precipitation, only dilution with 0.1M formic acid, one aliquot of 100 pL serum was injected into a commercial column switching system allowing for concentration o f the analytes on a Cl 8 solid-phase extraction column. This column was placed automatically in front o f a C8 analytical high performance liquid chromatography column for chromatographic separation of the analytes. Detection and quantification were 4 87 P-6 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome done using negative-ion TurbolonSpray ionization, tandem mass spectrometry. Isotope-labeled internal standards were used for quantification. Statistics: Data were expressed as the mean standard error (S.E). Participants were divided into adolescents (12 to 20 years o f age) and adult (greater than 20 years of age) groups for analysis. The strength of the associations between concentrations o f various serum PFCs and blood glucose, insulin and HOMA-IR levels was tested using multiple linear regression models. Logistic regression analyses were conducted to examine the odds ratios o f MS (yes or no for having at least 3 components o f MS) and its components (yes/no for that component) associated with one unit increase in log-PFCs. Log transformation was performed for variables with significant deviation from normal distribution before further analyses. For linear regression, we used an extended model approach for covariates adjustment: Model 1 = age, gender, race; Model 2 = Model 1 + health behaviors (smoking status, alcohol intake and household income); Model 3 = Model 2 + measurement data (waist measurement, CRP, and insulin/giucose/HOMA) + current medications (anti-hypertensive, anti hyperglycemic and anti-hyperlipidemic agents). For logistic regression, the models for adjustment were Model 4 = age, gender, race, health behaviors (smoking status, alcohol intake and household income), measurement data (CRP, HOMA/insulin) and current medications (anti-hypertensive, anti hyperglycemic and anti-hyperlipidemic agents). Model 5 = Model 4 + other components of the MS. A P<0.05 was considered statistically significant. To avoid "model dependent association", the association was considered significant only when it remained statistically significant in all models. Sampling weights that account for unequal probabilities o f selection, oversampling, and nonresponse were applied for all analyses using the Complex Sample Survey module of SPSS Version 13.0 for Windows XP (SPSS Inc. Chicago, Illinois, U.S.A.). RESULTS The basic demography o f the participants is summarized in Table 1. The study sample consisted o f 474 adolescents (age between 12 to 20 years) and 969 adults (age above 20 years). The serum PFHS levels were significantly higher in adolescents than in adults (log-PFHS ng/mL, 0.950,10 vs. 0.600.04 respectively, P < 0.001) while the serum PFNA concentrations were lower in adolescents than in adults (log-PFNA ng/mL, -0.350.07 vs. -0.210.07, P = 0.001). The serum PFOA and PFOS concentrations were not different between these two groups. The associations between the serum PFCs levels and glucose homeostasis markers are shown in Table 2. In adolescents, increased serum PFNA concentrations were associated with decreased blood insulin (Pcoeff = -0.100.05, P < 0.05) and p cell function (pcoeff -0.120.06, P < 0.05) with borderline significant (P = 0.05-0.09 in all models and < 0.05 in final model). Other PFCs were not associated with the serum markers for glucose homeostasis. In adults, increased serum PFOA concentrations were significantly associated with increased p cell function (pcoeff = 0.070.03, P < 0.05). Increased serum PFOS concentrations were also associated with increased blood insulin (pcoeff = 0.140.05, P < 0.01), HOMA-IR (Pcoeff = 0.140.05, P < 0.01) and p cell function (Pcoeff= 0.150.05, P < 0.01). The associations between the serum PFCs and the MS/MS components are summarized in Table 3. In adolescents, increased serum PFNA concentrations were associated with lower prevalence o f the MS (OR = 0.37, 95% Cl = 0.21-0.64, P < 0.005) 88 p. 7 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome and HDL-C below the MS criteria (OR = 0.67, 95% Cl = 0.45-0.99, P < 0.05). Increased serum PFNA concentrations were also correlated with higher prevalence of blood glucose above the MS definition (OR = 3.16, 95% Cl = 1.39-7.16, P < 0.05). We also found that the serum PFOS (OR = 0.37, 95% Cl = 0.16-0.82, P < 0.05) concentrations were inversely correlated with a lower prevalence o f waist circumference below the MS definition. In adult subjects, among all the PFCs and MS components, only serum PFOS concentrations were associated with higher prevalence of HDL-C below MS definition (O R= 1.61, 95% Cl = 1.15-2.26, P < 0.05). DISCUSSION To our knowledge, this report is the first to systemically analyze the link among serum PFC concentration, glucose homeostasis, and the MS/MS components in a nationally representative sample. In this study, we showed that PFCs were differentially associated with glucose homeostasis in adolescents and adults. We should take into careful consideration about extrapolating and interpreting data between laboratory animal studies and the corresponding biological effects (at high ppm range) compared to general human populations (at low ppb range). We found that the concentrations reported for PFCs in the occupational studies have been two and three orders o f magnitude higher than what has been measured in the general population. While there were several studies about the maternal exposure and child development, worker's exposure and health outcome, the relationship of serum PFCs levels to medical diseases and laboratory abnormality in a nationally representative survey has never been explored. Our results might suggest that low dose PFCs exposure might have effects on glucose metabolism in general population. Our analysis showed that the serum PFOA and PFOS concentrations were not different between adolescents and adults. Unlike other lipophilic persistent pollutants that display increasing serum concentrations as people age, the lack of this general trend in PFOS and PFOA could be explained by intra uterine transfer, exposure early in life, ongoing exposures being much higher than earlier historical exposures or a combination of these factors [18]. By contrast, the mean concentrations of PFHS were higher for adolescents than for adults, as previously reported [2, 19]. The higher concentrations of PHFS in children and adolescents could be related to their increased contact with carpeted floors containing PFHS, which is used for specific postmarket carpel-treatment applications [2, 19]. We showed that in adolescents, increased serum PFNA concentrations were associated with decreased blood insulin, impaired p cell function (borderline significance) and clinical hyperglycemia. On the other hand, we found that increased serum PFNA had a favorable correlation with semm HDL. Overall, increased serum PFNA concentrations were inversely associated with the prevalence o f MS in adolescents. In adults, serum PFOS concentrations were independently associated with increases in both blood insulin and insulin resistance status (HOMA-IR). Interestingly, both serum PFOA and PFOS were also positively correlated withp cell function. The balance between increased insulin resistance and p cell function has a neutral effect on blood glucose. Increased PFOS also showed an unfavorable association with serum HDL-C in adults. Overall, the PFCs in the present study had neutral effects on the prevalence of the MS in adults. There has been a great deal of progress in the last few years in understanding the toxicology and distribution of PFCs in the environment, in wildlife, and in humans. However, there is a paucity o f information pertaining to many specific PFCs [1], Thus 6 89 p.8 tH * PerJJuoroalkyl chemicals, glucose homeostasis and metabolic syndrome far, there are no published reports o f either in vitro or in vivo data pertaining to effects of PFCs on glucose homeostasis. The underlying mechanisms o f this linkage are unknown and might partially be related to peroxisome activation. The liver toxicity o f PFOS and PFOA has been linked to their PPAR-a agonist property [8, 9]. PFNA also has been shown to be a strong peroxisomal p oxidation inducer in animals [20, 21], Fibrates, amphipathic carboxylic acids that activate PPAR-a, can decrease TG, normalize the lowdensity lipoprotein cholesterol profile, and increase HDL-C [22]. However, our results are not entirely consistent with previous animal findings, suggesting an alternative or even multiple pathways in association among PFCs, glucose and lipid metabolism. For example, Luebker et al. have demonstrated that both PFOS and PFOA can interfere with the binding affinity o f L-FABP (liver-fatty acid binding protein) in rodents [23]. Interestingly, they also found that among the tested PFCs, PFOS exhibited the highest level o f inhibition of L-FABP binding, which might partially explain the unfavorable association between increased serum PFOS and HDL-C. CONCLUSION In conclusion, we present the first report of a relationship between serum PFCs, glucose homeostasis and metabolic syndrome. Since PFCs have been widely used worldwide in a variety of consumer products, further longitudinal clinical and in vitro studies are urgently warranted to elucidate the putative casual relationships between PFCs and metabolism. Study limitation: Our study has several limitations. First, the cross-sectional design does not permit any causal inference. Second, due to a number of missing data, drinking status was not included in our analyses o f adolescents. 7 P-9 Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome REFERENCES 1. Lau C, Anitole K, Hodes C, Lai D, Pfahles-Hutchens A, Seed J: Perfluoroalkyl acids: A review of monitoring and toxicological findings. Toxicol Sci 99:366-394,2007. 2. Calafat AM, Wong LY, Kuklenyik Z, Reidy JA, Needham LL: Polyfluoroalkyl chemicals in the US population: data from the National Health and Nutrition Examination Survey (NHANES) 2003-2004 and comparisons with NHANES 1999-2000. Health Perspect 115:1596-1602,2007. 3. 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Toxicology 176:175-185,2002. 9 p. 11 Perftuoroalkyl chemicals, glucose homeostasis and metabolic syndrome Table 1 Basic demography and serum concentrations of PFCs of the sample subjects Age, years Gender, % Male Female Race, % Mexican American Non-Hispanic White Non-Hispanic Black Smoking, % Active smoker Former/passive smoker Non-smoker Alcohol drinking status, % = 12 drinks last year < 12 drinks last year Annual household income, % <25000 25000-55000 >55000 MS, % MS waist, % MS glucose, % MS HDL-C, % MS triglyceride, % MS blood pressure, % DM medication Hypertension medication Hyperlipidemia medication Log-CRP, mg/dL Log-Insulin, pmol/L Log-PFHS, ng/mL Log-PFNA, ng/mL Log-PFOA, ng/mL Log-PFOS, ng/mL Unweighted No. adolescent/adult 474/969 266/476 208/493 182/273 123/510 169/186 66/197 246/144 162/628 /659 /310 186/352 163/320 125/297 38/382 124/786 35/212 96/313 86/364 49/470 0/79 0/245 0/118 474/969 474/969 474/969 474/969 474/969 474/969 Adolescents (12 yrs, <20 yrs) 15.50.2 56.63.1 43.4=3.1 11.72.5 71.73.5 16.62.6 19.12.1 49.63.6 31.43.8 N/A N/A 27.33.1 33.83.6 38.93.9 8.62.1 26.42.7 7.32.1 23.83.1 21.82.8 8.31.6 0 0 0 -2.940.07 4.050.04 0.950.I0 -0.350.07 1.50.05 3.11 0.05 Adults 20 yrs 46.20.8 50.21.7 49.8 1.7 8.71.7 80.82.5 10.5.7 25.32.5 14.51.4 60.22.5 73.32.3 26.72.3 25.22.4 33.42.7 41.42.2 36.22.0 81.21.8 15.71.5 32.62.3 34.62.2 42.02.2 4.80.7 19.5 1.5 9.7I.O -l.540.05 3.990.04 0.600.04 -0.21 0.07 1.480.04 3.190.04 Abbreviations. MS, metabolic syndrome; HDL-C, high density lipoprotein cholesterol; CRP, C-reactive protein; PFHS, perfluorohexane sulfonic acid; PFNA, perfhiorononanoic acid; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfate; N/A, not assessment PerflUoroalkyl chemicals, glucose homeostasis and metabolic syndrome Table 2 Linear regression coefficients with one unit increase in log-PFCs in adolescents and adults Adolescent Glucose Model 1 Model 2 Model 3 Log-insulin Model 1 Model 2 Model 3 Log-HOMA-IR Model 1 Model 2 Model 3 Log-P cell function Model 1 Model 2 Model 3 Adult Glucose Model 1 Model 2 Model 3 Log-insulin Model 1 Model 2 Model 3 Log-HOMA-IR Model 1 Model 2 Model 3 Log-p cell function Model 1 Model 2 Model 3 Log-PFHS pcoeff -0.020.03 -0.020.03 -0.01 0.03 0.020.04 0.030.04 0.060.03 0.020.04 0.020.05 0.050.03 0.030.04 0.030.04 0.050.03 -0.070.09 -0.050.09 -0.020.06 -0.040.05 -0.040.05 0.010.03 -0.050.05 -0.040.05 0.000.04 -0.020.04 -0.020.04 0.010.03 Log-PFNA Pcoeff 0.040.04 0.050.05 0.070.04 -0.090.05 -0.100.05 -0 .100.05* -0.090.05 -0.090.05 -0.080.04 -0 .120.07 -0.120.06 -0.120.06* -0.050.04 -0.020.05 0.000.04 -0.060.04 -0.050.04 -0.040.03 -0.060.04 -0.060.05 -0.040.04 -0.050.03 -0.050.04 -0.040.03 Log-PFOA Pcoeff -0.040.05 -0.040.05 -0.030.05 0.050.08 0.070.09 0.080.07 0.040.08 0.060.09 0.080.05 0.060.10 0.080.10 0.080.08 -0.110.10 -0.1I0.11 -0.090.08 0.080.04 0.080.04 0.070.03* 0.060.05 0.070.05 0.060.04 0.090.04* 0.090.04* 0.070.03* Log-PFOS pcoeff -0.030.06 -0.040.06 -0.030.06 0.060.07 0.070.07 0.150.08 0.050.07 0.070.07 0.150.07 0.060.08 0.080.08 0.130.09 -0.030.08 -0.230.09 -0.030.07 0.130.05* 0.130.05* 0.140.05f 0.120.05* 0.120.05* 0.140.05f 0.140.06* 0.140.06* 0.150.05f *, p<0.05; f, p<0.01; {, p<0.005; Model 1 adjusted forage, gender, race; Model 2 adjusted for Model 1 + health behaviors (smoking status, alcohol intake and household income); Model 3 adjusted for Model 2 -r measurement data (waist circumference, CRP, and insulin/glucose/HOMA) + medications Perfluoroalkyl chemicals, glucose homeostasis and metabolic syndrome Table 3 Odds ratios o f metabolic syndrome and its components associated with one unit increase in iog-PFCs in adolescents and adults A dolescent MS Model 4 MS Waist Model 4 Model 5 MS Gluocse Model 4 Model 5 MSHDL-C Model 4 Model 5 MS Triglyceride Model 4 Model 5 A d u lt MS Model 4 MS Waist Model 4 Model 5 MS Gluocse Model 4 Model 5 MS HDL-C Model 4 Model 5 MS Triglyceride Model 4 Model 5 Log-PFHS OR (95% Cl) Log-PFNA OR (95% Cl) Log-PFOA OR (95% Cl) Log-PFOS OR (95% Cl) 0.56(0.22-1.45) 0.72 (0.48-1.09) 0.64 (0.45-0.91)* 1.10(0.46-2.62) 0.98 (0.44-2.17) 0.93 (0.58-1.47) 0.93 (0.60-1.43) 1.07(0.76-1.52) 1.08(0.83-1.40) 0.37 (0.21 -0.64) % 0.99(0.59-1.63) 1.09(0.61-1.95) 3.15(1.39-7.12)* 3.16(1.39-7.16)* 0.59 (0.42-0.83)f 0.67 (0.45-0.99)* 0.68(0.40-1.15) 0.71 (0.37-1.34) 0.79 (0.30-2.12) 0.61 (0.32-1.13) 0.58 (0.34-1.00)* 0.46(0.25-0.85)* 0.55(0.24-1.25) 1.20 (0.60-2.39) 1.50(0.67-3.36) 1.64 (0.72-3.73) 1.15 (0.54-2.47) 0.49(0.18-1.30) 0.4! (0.21-0.83)* 0.37(0.16-0.82)* 0.58 (0.31-1.10) 0.58(0.28-1.14) 0.89 (0.51-1.55) 1.38(0.61-3.14) 0.95 (0.50-1.80) 0.78(0.41-1.49) 0.93(0.73-1.19) 0.73 (0.53-0.99)* 0.80 (0.58-1.10) 0.79(0.53-1.16) 0.76(0.54-1.07) 0.90(0.69-1.18) 1.00(0.73-1.37) 0.80 (0.64-0.99)* 0.78(0.60-1.02) 0.92(0.69-1.24) 1.25 (0.88-1.74) 1.34(0.93-1.92) 0.81 (0.62-1.07) 0.86(0.66-1.12) 0.80(0.65-0.99)* 0.81 (0.65-1.00) 0.98 (0.82-1.16) 0.99(0.81-1.19) 1.07(0.73-1.57) 0.95(0.63-1.45) 0.97(0.65-1.46) 0.89(0.63-1.26) 0.87(0.61-1.26) 1.14(0.84-1.55) 1.22 (0.86-1.71) 0.91 (0.69-1.20) 0.86(0.65-1.13) 1.25 (0.86-1.82) 0.89 (0.59-1.34) 0.91 (0.59-1.41) 0.83 (0.64-1.08) 0.81 (0.62-1.05) 1.47(1.07-2.00)* 1.61 (1.15-2.26)* 0.97(0.73-1.27) 0.86(0.65-1.16) *> p<0.05; f, p<0.01; %, p<0.005; Model 4 adjusted for age, gender, race, health behaviors (smoking status, alcohol intake and household income), measurement data (CRP, HOMA/insulin) and medications; Mode] 5 adjusted for Model 4 + other components o f the metabolic syndrome 12