{"paper_id":"778f2d24-48f7-4eb6-8177-fcee339b7fd9","body_text":"This may be the author’s version of a work that was submitted/accepted\nfor publication in the following source:\nKim, Y oung Ran, White, Nicole, Braunig, Jennifer, Vijayasarathy, Soumini,\nMueller, Jochen F ., Knox, Christine L., Harden, Fiona A., Pacella, Rosana,\n& Toms, Leisa Maree L.\n(2020)\nPer- and poly-fluoroalkyl substances (PFASs) in follicular fluid from women\nexperiencing infertility in Australia.\nEnvironmental Research, 190, Article number: 109963.\nThis file was downloaded from: https://eprints.qut.edu.au/205314/\n© 2020 Elsevier Inc\nThis work is covered by copyright. Unless the document is being made available under a\nCreative Commons Licence, you must assume that re-use is limited to personal use and\nthat permission from the copyright owner must be obtained for all other uses. 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If there is any doubt, please refer to the published source.\nhttps://doi.org/10.1016/j.envres.2020.109963\n\n1 \n \nPer- and poly -fluoroalkyl substances  (PFASs) in follicular fluid  from women \nexperiencing infertility in Australia \n \nAuthors: Young Ran Kim 1, Nicole White 1, Jennifer Bräunig 3, Soumini Vijayasarathy 3, \nJochen F. Mueller3, Christine L. Knox 2, Fiona A. Harden 4, Rosana Pacella 5, Leisa- Maree L. \nToms 1 \n \n1. School of Public Health and Social Work, Faculty of Health, Institute of Health and \nBiomedical Innovation, Queensland University of Technology, QLD, Australia \n2. School of Biomedical Science, Faculty of Health, Institute of Health and Biomedical \nInnovation, Queensland University of Technology, QLD, Australia \n3. Queensland Alliance for Environmental Health Sciences (QAEHS), The University of \nQueensland, QLD, Australia \n4. Hunter Industrial Medicine, Newcastle, NSW, Australia \n5.  Institute for Lifecourse Development, Faculty of Education, Health and Human \nSciences, University of Greenwich, UK  \n \nCorresponding author: Young Ran Kim email: youngran.kim@hdr.qut.edu.au Postal \naddress: School of Public Health and Social Work, Faculty of Health, Queensland \nUniversity of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD 4059, \nAustralia \n \nKeywords \nPer- and poly -fluoroalkyl substances  (PFASs), Perfluorooctane sulfonate (PFOS), \nPerfluorooctanoic acid (PFOA), Perfluorohexane sulfonate (PFHxS), Perfluorononanoic acid \n(PFNA), Australia, infertility \n\n2 \n \nAbstract 1 \nPer- and poly-fluoroalkyl substances (PFASs) have been widely used and detected in human 2 \nmatrices. Evidence that PFAS exposure may be associated with adverse human reproductive 3 \nhealth effects exists, however, data is limited. The use of a human matrix such as follicular 4 \nfluid to determine chemical exposure, along with reproductive data will be used to investigate 5 \nif there is a relationship between PFAS exposure and human fertility.  6 \n 7 \nObjective 8 \nThis study aims to: (1) assess if associations exist between PFAS concentrations  and/or age 9 \nand fertilisation rate (as determined in follicular fluid of women in Australia who received 10 \nassisted reproductive treatment (ART) ); and  (2) assess if associations exist between PFAS 11 \nconcentrations and infertility aetiology.  12 \n 13 \nMethods 14 \nFollicular fluids were originally collected from participants who underwent fully stimulated 15 \nART treatment cycles at an in vitro fertilisation (IVF) clinic in the period 2006-2009 and 2010-16 \n11 in Queensland, Australia.  The samples were available for analys is of 32 PFASs including 17 \nperfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonate 18 \n(PFHxS), and perfluorononanoic acid (PFNA) using high performance liquid chromatography 19 \ntandem mass spectrometry (HPLC -MS/MS). 9 7 samples were matched with limited 20 \ndemographic data (age and fertilisation rate) and five infertility factors (three known female 21 \nfactors): 1) endometriosis, 2) polycystic ovarian syndrome (PCOS),  and 3) genital tract 22 \ninfections - tubal/pelvic inflammation disease; as well as 4) male factor, and 5 ) idiopathic or 23 \nunknown from either males or females. SPSS was used for linear regression analysis.  24 \n 25 \nResults 26 \nPFASs were detected in all follicular fluid samples with the mean concentrations of PFOS and 27 \nPFOA, 4.9, and 2.4 ng/ml, respectively. A lower fertilisation rate was observed at higher age  28 \nwhen age was added as a covariate, but there was no relationship between PFAS concentrations 29 \nand fertilisation rate.  There were few statistically significant associations between PFAS 30 \nconcentrations in follicular fluid and infertility factors. Log-transformed PFHxS concentrations 31 \nwere lower in females with endometriosis (factor 1) than in women who had reported ‘ male 32 \n\n3 \n \nfactors’ as a reason of infertility, while PFHpA was higher in women who had infertile due to 1 \nfemale factors (factor 1-3) compared to those who had infertile due to male factor. 2 \n 3 \nConclusion: PFASs were detected  in follicular fluid of Australian women  who had been 4 \ntreated at an IVF clinic. PFAS exposure found in follicular fluids is linked to increased risk of 5 \nsome infertility factors, and increased age was associated with decreased fertilisation rate in 6 \nour data. But there was no relationship between PFAS and ferlitisation rate. Further large-scale 7 \ninvestigations of PFAS and health effects including infertility are warranted.8 \n\n4 \n \n1. Introduction 9 \nPer- and poly-fluoroalkyl substances (PFASs), are chemicals that have been used widely as 10 \nsurfactants, lubricants, floor waxes, fire -fighting foams, denture cleane rs, shampoos, 11 \npharmaceutical products, and in food packaging since the 1950s (Kantiani et al., 2010). The 12 \nmost common exposure route for PFAS is via ingestion, followed by dermal contact and 13 \ninhalation (Quaak et al., 2016; D’Hollander et al., 2014; Jian et al., 2017).  14 \n 15 \nStudies have shown potential associations between PFAS exposure and adverse health effects 16 \nfor metabolism, thyroid function, neurodevelopment, cancers, cardiovascular diseases, 17 \nreproductive functions, and immunity  (as reviewed by Kirk et al., 2018) . Kirk et al. (2018)  18 \nconfirmed that while there are increasing numbers of studies investigating the health effects of 19 \nexposure to PFASs, the results are limited or inconsistent.  When looking specifically in terms 20 \nof reproductive health outcomes, conflicting results have been observed  (Fei et al., 2009; Fei 21 \net al., 2012; Whitworth et al., 2012; Jorgensen et al., 2014; Velez et al., 2015; Vestergaard et 22 \nal., 2012; Buck Louis et al., 2013; Bach et al., 2015; Barrett et al., 2015). For example, lower 23 \nlevels of reproductive hormones, such as estradiol and progesterone, were related to higher 24 \nconcentrations of perfluorooctane sulfonate (PFOS),  and perfluorooctane sulfonamide 25 \n(PFOSA) in nulliparous women (women who have never given birth) (Barrett et al., 2015). 26 \nHowever, the results were not consistent for other PFASs, including perfluorooctanoic acid 27 \n(PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), 28 \nperfluoroundecanoic acid (PFUnDA)  and perfluorohexane sulphonate (PFHxS) in parous 29 \nwomen (women who have given birth) (Barrett et al., 2015). Fei et al. (2009; 2012) found lower 30 \nfecundability (ability to achieve pregnancy), when comparing higher PFOS (26.1-43.2 ng/mL) 31 \nto lower PFOS exposure (<26.1 ng/mL) , when stratified by parity. There was an association 32 \nbetween PFOS exposure and increased odds of infertility in three higher quartiles of PFOS 33 \nconcentrations when compared with the lowest quartile (Fei et al., 2009). No association was 34 \nfound between PFOS exposure and subfecundability or infertility (Velez et al., 2015; Bach et 35 \nal., 2015).   36 \n 37 \nPFASs are known as possible endocrine disrupting chemicals (EDCs) with adverse health 38 \neffects on the endocrine system ( Stahl et al., 2011; Caserta et al., 201 3; DeWitt 2015). For 39 \nexample, the pituitary gland produces fertility hormones, including follicle stimulating 40 \nhormones and luteinizing hormone, which are vital for ovulation and successful conception. 41 \n\n5 \n \n(Stach et al., 2011). Interference by PFASs or other chemicals on the en docrine system may 42 \ncause reproductive health issues, such as infertility or hormone imbalance in women (Caserta 43 \net al., 2013; DeWitt 2015; Kim et al., 2019).  44 \n 45 \nPFASs are persistent and bioaccumulate with concentrations detected in human samples 46 \nworldwide (Cho et al., 2015 (South Korea); Stubleski et al., 2016 (Sweden); Whitworth et al., 47 \n2012 (Norway); Olsen et al., 2017 (USA); Gao et al., 2019 (China)). In Australia, PFAS were 48 \ndetected in human serum samples dating back to 2002 with levels simila r to or higher than in 49 \nEuropean and Asian countries (Toms et al., 2014). There is recent interest in Australia and 50 \nworldwide as to whether or not  PFAS exposure may be linked to adverse health effects 51 \nspecifically in communities with PFAS exposure through drinking water and in occupationally 52 \nexposed groups such as firefighters (Rotander et al., 2015). 53 \n  54 \nInfertility, defined as the inability to conceive after one year of unprotected intercourse, is a 55 \nglobal public health issue affecting about 15% of the population (Datta et al., 2016). Female 56 \nfertility rate, defined as the average number of children born to a woman during her 57 \nreproductive years, is likely to decrease with increasing age, and/ or an underlying medical 58 \ncondition that might affect ovulation or hormone imbalance, or cause blocked fallopian tubes 59 \n(Barbieri 2018; Jaward et al., 2018). The most common medical conditions experienced by 60 \ninfertile women are endometriosis, polycystic ovarian syndrome, or pelvic inflammatory 61 \ndisease while poor semen quality is considered the main male cause (Hruska et al., 2000; Piotr 62 \net al., 2016; Skakkebaek et al., 2016; Sifakis et al., 2017; Barbieri 2018). 63 \n  64 \nHuman exposure to PFAS can be measur ed by analysis of food/drinking water, and through 65 \nanalysis of human matrices, such as blood serum, urine or breast milk.  In this study, PFASs 66 \nwere examined in follicular fluid.  This is a liquid in the ovarian follicle, which can be collected 67 \nwhen a woman unde rgoes egg harvest during assisted reproductive technology (ART) 68 \ntreatment. Studies have used follicular fluid to measure PFASs, likely due to ease of collection, 69 \nwhich is relatively non-invasive if carried out opportunistically (Governini et al., 2011; McCoy 70 \net al., 2017; Petro et al., 2014; Heffernan et al., 2018). Despite the determination of PFASs in 71 \nfollicular fluid, current data is limited to conclude whether associations exist between PFAS 72 \nconcentrations and adverse fertility effects.  73 \n 74 \n\n6 \n \nTherefore, this study aims to  assess if associations exist between : (1) PFAS concentrations 75 \nand/or age and fertilisation rate (as determined in follicular fluid of women in Australia who 76 \nreceived ART); and (2) PFAS concentrations and infertility aetiology.  77 \n 78 \n 79 \n2. Materials and Methods 80 \n2.1. Sample collection 81 \nFollicular fluid  samples were collected from female participants who underwent fully 82 \nstimulated ART treatment cycles at an IVF (in vitro fertilisation) clinic in Queensland in the 83 \nperiod 2006-2009 and 2010-2011 (as part of the “Asymptomatic upper genital tract infections 84 \nin infertile couples and assisted reproductive technology outcomes (ART)” and  “Prevalent 85 \nmicroorganisms detected in the female upper genital tract: the effect of these  microorganisms 86 \non oocytes and on assisted reproductive technology outcomes” projects  (Pelzer et al., 2013)). 87 \nThe samples were obtained when the participants were undergoing egg  harvest for IVF as 88 \ndescribed previously (Pelzer et al., 2013). The data  available included date of birth, infertility 89 \naetiology, fertilisation rate, and past clinical history of infertility. Study participants had been 90 \nclassified into groups depending on the aetiology of infertility for the couples including: three 91 \nfemale factors with 1= endometriosis, 2= polycystic ovarian syndrome (PCOS), 3= genital tract 92 \ninfections (tubal/pelvic inflammation disease) ; 4= male factor  (this is infertility due to only 93 \nmale p artners issues, but detailed health information was not given) ; and 5= idiopathic or 94 \nunknown. Factor 5, idiopathic, means causes of infertility were not identified from either the 95 \nfemale or the male. We considered infertility aetiology 1, 2, and 3 as female case groups, and 96 \ninfertility aetiology factor 4 as a control group. Whilst factor 5 was included in the analysis it 97 \nwas not included as either a case group or a control group due to its unknown causes. ART 98 \ntreatment cycle(s) outcomes were also recorded for each couple. It should be noted that the 99 \ndate of sample collection was not supplied, only that the samples were collected between 2006 100 \nand 2010. In order to calculate an age at date of collection, we have taken a mid-point of 2008 101 \nand used participant date of birth to calculate an approximate age.  102 \n 103 \n2.2 Ethics statement 104 \nWe sought and received  a waiver of consent to use follicular samples for analysis of PFAS 105 \nfrom the Queensland University of Technology (QUT) ethics committee (approval number: 106 \n\n7 \n \n1800000016) and The University of Queensland Human Research Ethic Committee (approval 107 \nnumber: 2018000550).  108 \n 109 \n2.3 Chemical Analysis for PFASs 110 \nAnalysis of the follicular fluid samples w as undertaken at the Queensland Alliance for 111 \nEnvironmental Health Sciences (QAEHS), The University of Queensland. Samples were 112 \nanalyzed for 32 PFASs; perfluorobutanoic acid (PFBA), perfluoropetanoic acid (PFPeA), 113 \nperfluorohexanoic acid (PFHxA), perfluorohepatanoic acid (PFHpA), perfluorooctanoic acid 114 \n(PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), 115 \nperfluoroundecanoic acid (PFUnDA), perfluorododecanoic acid (PFDoDA), 116 \nperfluorotridecanoic acid (PFTrDA), perfluorotetra decanoic acid (PFTeDA), 117 \nperfluorohexadecanoic acid (PFHxDA), perfluorooctadecanoic acid (PFODA), 118 \nperfluorobutane sulphonate (PFBS), perfluoropentane  sulphonate (PFPeS), perfluorohexane  119 \nsulphonate (PFHxS), perfluoroheptane sulphonate (PFHpS), perfluorooctane sulfonate (PFOS), 120 \nperfluorononane sulfonate (PFNS), perfluordecane  sulphonate (PFDS), perfluordodecane  121 \nsulphonate (PFDoDS) , sodium 1H,1H,2H,2H -perfluorohexane sulfonate (4:2) (8:2 FTS), 122 \nsodium 1H,1H,2H,2H -perfluorohexane sulfonate (4:2) (4:2 FTS), Sodium 1H,1H,2H,2H -123 \nperfluorooctane sulfonate (6:2) (6:2 FTS), sodium 1H,1H,2H,2H -perfluorodecane sulfonate 124 \n(8:2) (8:2 FTS), perfluoroethylcyclohexane sulfonate (PFECHS), perfluoro-1-octane 125 \nsulfonamide (FOSA), n-ethylperfluoro-1-octane sulfonamidoacetic acid (NEtFOSAA), n-126 \nmethylperfluoro-1-octane sulfonamidoacetic acid (NMeFOSAA), n-methylperfluoro-1-octane 127 \nsulfonamide (NMeFOSA), n-ethylperfluoro-1-octane sulfonamide (NEtFOSA), 2 -(N-128 \nmethylperfluoro-1-octane sulfonamido)-ethanol (NMe FOSE), 2 -(N-ethylperfluoro-1-octane 129 \nsulfonamido)-ethanol (NEt FOSE) (Supplementary information Table S1). A 200 μl aliquot of 130 \nfollicular fluid was transferred to a 2  ml Eppendorf tube, followed by addition of the internal 131 \nstandards. Proteins were precipitated with acetonitrile , centrifuged, filtered (2 μm GHP 132 \nmembrane; Pall, East Hills, NY, USA), and concentrated to 200µl under a gentle stream of 133 \nnitrogen. Samples were reconstitut ed to 500µl with  5 mM ammonium acetate in water  and 134 \nspiked with recovery standards prior to analysis via high performance liquid chromatography 135 \ntandem mass spectrometry (HPLC -MS/MS) using a Nexera HPLC (Shimadzu Corp., Kyoto, 136 \nJapan) coupled to a Triple Quad 6500+ mass spectrometer (Sciex, Melbourne, Australia) with 137 \nelectrospray ionizati on (ESI) in terface operating in negative mode. Chromatographic 138 \nseparation of the analyt es was achieved with a Gemini C18 column (50  x 2.0 mm, 4 μm; 139 \n\n8 \n \nPhenomenex, Torrance, CA), maintained at 45°C, with a flow rate of 0.3 mL/min and injection 140 \nvolume of 5 μL. Mobile phases consisted of methanol water (1:99, v/v) (A), and methanol: 141 \nwater (95:5, v/v) (B), with 5mM ammonium acetate in both phases. An isolator column 142 \n(Phenomenex) was included inline directly after the mobile phase mixing chamber to delay 143 \nelution of solvent-derived background PFASs contamination. Data acquisition and processing 144 \nwas carried out using analyst® TF 1.6 and MultiQuantTM software (Sciex). If the PFASs were 145 \ndetected in less than 60% of samples, they were excluded from statistical analysis (eg: PFBA, 146 \nand PFDoDA, 38.4% and 4% respectively) . Linear PFAS congeners were determined for the 147 \ncurrent study. 148 \n 149 \nQuality control 150 \nLaboratory blanks (MilliQ water) were extracte d and analyzed in parallel with each batch of 151 \nsamples. Batches included inter-batch replicates which  generally showed CV < 15%. T he 152 \nmethod limit of quantification (LOQ) was calculated by multiplying the  SD obtained from 153 \ninjecting the lowest calibration standard seven times by 10. Percentage average recovery 154 \nranged from 70 to 110%. 155 \n 156 \n2.4 Data analysis 157 \nDescriptive statistics were calculated to summarize overall cohort characteristics and PFAS 158 \nconcentrations in follicular fluid samples. Evidence of associations between PFAS 159 \nconcentrations and fertility variables were evaluated using linear modelling. A linear regression 160 \nmodel was fitted to examine the inf luence of PFAS concentrations and age on expected 161 \nfertilisation rates, which was defined as number of oocytes fertilized by ART. This model 162 \nspecified fertilisation rate as the dependent variable, with participant age and individual PFAS 163 \nconcentrations incl uded as continuous independent variables. To assess whether PFAS 164 \nconcentrations varied by aetiology infertility factors, a one-way analysis of variance (ANOVA) 165 \nwas fitted, with log-transformed concentrations as the dependent variable, and aetiology factor 166 \nas a categorical independent variable. Our decision to apply a log transformation was informed 167 \nby findings of preliminary analysis to satisfy residual assumptions. Linear modelling outcomes 168 \nwere summarized by parameter estimates and corresponding 95% confidence intervals. 169 \nHypothesis testing of estimates was conducted to determine if associations were statistically 170 \nsignificant, assuming a significance level of 0.05. All statistical analyses were undertaken i n 171 \nSPSS version 25. 172 \n\n9 \n \n 173 \n 174 \n3. Results 175 \nThe mean age of female participants w as 35 years, and the mean fertilisation rate was 63% 176 \n(Table 1). Most common aetiology factors of females participating in ART treatment in this 177 \nstudy was ‘idiopathic (24.7%)’, and male partner’s factors of infertility (22.7%)’ (Table 1). In 178 \ntotal, 97 follicular samples matched with demographic information were analysed for 32 179 \nPFASs. 8 PFASs were detected in most samples (PFOS, PFOA, PFHxS, PFNA, PFDA, PFHpS, 180 \nPFUnDA, and PFHpA) and were included in further analysis. The concentrations from highest 181 \nto lowest were:  PFOS (Mean  = 4.8; Range = 0.7 to 22.4 ng/ml), PFOA (2.4; 0.3 to 14.5 ng/ml), 182 \nPFHxS (1.7; 0.2 to 21.3 ng/ml),  PFNA (0.5; 0.08 to 2.0 ng/ml), PFDA (0.2; 0.05 to 0.9 ng/ml), 183 \nPFHpS (0.1; 0.05 to 1.1 ng/ml), PFUnDA (0.1;  <LOD to 0.4 ng/ml), and PFHpA (0.01; <LOD 184 \nto 0.6 ng/ml) (Table 2). The remaining 24 PFASs were detected in a small number of samples 185 \nor at <LOD), thus they were not discussed further, details are available in the SI.   186 \n 187 \n 188 \nTable 1. Characteristics and fertility outcomes of participating women for ART (N=97)  189 \nVariable Mean (SD); Range \nAge (years) 35 (4); 23 to 42 \nFertilisation rate (%) 63 (22); 20 to 100 \nAetiology of infertility  n (%) of 97 participants  \nEndometriosis (factor 1) 18 (18.6) \nPolycystic ovarian syndrome (factor 2) 18 (18.6) \nGenital tract infections (factor 3) 15 (15.5) \nMale factors of infertility (factor 4) 22 (22.7) \nIdiopathic (factor 5) 24 (24.7) \nSD: standard deviation 190 \n 191 \n 192 \nTable 2. Descriptive data of 8 PFASs in follicular fluids  (97 demographic information 193 \nmatched samples) 194 \n\n10 \n \nLOD: Limit of detection 195 \n 196 \n3.1.  PFAS concentrations, and fertilisation rate and age  197 \nFertilisation rate is defined as the  number of oocytes fertilized by ART  divided by the total 198 \nnumber of oocytes collected. Information was available for 92 samples (5 missing data points).  199 \nIt was found that age was negatively associated with expected fertilisation rate (Estimate = -200 \n1.49, 95%CI: -2.64 to -0.35; p = 0.013). Inconsistent results were observed in the relationship 201 \nbetween fertilisation rate, age and PFAS concentrations. The concentration of selected PFASs 202 \nin follicu lar fluid was positively ( eg; PFHpA, PFOA, PFUnDA) or negatively ( eg; PFDA, 203 \nPFHpS) associated with fertilisation rate, however high levels of uncertainty in parameter 204 \nestimates meant that none of these associations were statistically significant (Table 3).  205 \n 206 \nTable 3. Associations between PFAS concentrations and fertilisation rate and age  CI 207 \n(Confidence interval) 208 \nVariables Estimate  95% CI  Test statistic p-value \nAge -1.49  -2.64 to -0.35 -2.609 0.013 \nPFOS 2.279 -0.556 to 5.114 1.599 0.114 \nPFHxS  0.692 -0.855 to 2.239 0.890 0.376 \nPFHpS  -48.371 -111.980 to 15.238 -1.512 0.134 \nPFOA  0.706 -2.219 to 3.631 0.480 0.633 \nPFNA 15.647 -18.849 to 50.143 0.902 0.370 \nng/ml \nDetection \nfrequency \n(%) \nMinimum \n \nMaximum \n \nMean      SD \n \nPFOS  98 0.7 22.4 4.8 3.1 \nPFHxS  98 0.2 21.3 1.7 2.7 \nPFHpS  96 0.05 1.1 0.1 0.1 \nPFOA  98 0.3 14.5 2.4 1.7 \nPFNA  97 0.08 2.0 0.5 0.3 \nPFDA  98 0.05 0.9 0.2 < LOD \nPFHpA  90 < LOD 0.6 0.01 0.008 \nPFUnDA  97 < LOD 0.4 0.1 0.007 \n\n11 \n \nPFDA  -60.830 -129.250 to 7.590 -1.768 0.081 \nPFHpA  17.390 -65.276 to 100.055 0.418 0.677 \nPFUnDA  73.581 -3.413 to 150.574 1.900 0.061 \n 209 \n 210 \n3.2. PFAS concentrations and infertility by aetiology factors 211 \nAnalysis of PFAS concentrations by aetiology factor provided mixed results, with statistically 212 \nsignificant differences observed for PFHpA (test statistic = 2.4; p -value = 0.04) and PFHxS 213 \n(test statistic = 4.7; p -value = 0.002) (Table 4). For PFHxS, average log transformed 214 \nconcentrations were seen to be lowest for endometriosis (Mean = -0.3; SE = 0.1) and highest 215 \nfor polycystic ovarian syndrome (Mean = 0.3; SE = 0.1). For PFHpA, average concentrations 216 \nwere observed to be lowest for male-related infertility (Mean =-1.5; SE = 0.1) and highest 217 \ngenital tract infections and idiopathic factors (Mean = -1.2; SE = 0.1) (Table 4).  218 \n 219 \nTable 4. Association between PFAS concentrations and infertility by 5 aetiology factors. 220 \nSummary statistics are presented for log -transformed concentrations. SE: Standard 221 \nerror. 222 \nPFAS \n(log \ntransformed \nng/ml) \nMean (SE)  \nAetiology \nfactor 1 \n \nEndometriosis  \nAetiology \nfactor 2 \nPolycystic \novarian \nsyndrome  \nAetiology \nfactor 3 \nGenital tract \ninfections  \nAetiology \nfactor 4 \nMale factors \nof infertility  \nAetiology \nfactor 5 \nIdiopathic  \nTest \nstatistic  \n(p-\nvalue) \nPFOS  0.6 (0.04) 0.7 (0.1) 0.5 (0.04) 0.6 (0.03) 0.5 (0.06) 2.8 \n(0.26) \nPFHxS  -0.3 (0.1) 0.3 (0.1) 0.02 (0.04) 0.01 (0.03) -0.02 (0.1) 4.7 \n(0.002) \nPFHpS  -0.8 (0.04) -0.7 (0.06) -0.9 (0.04) -0.8 (0.03) -0.9 (0.05) 3.12 \n(0.15) \nPFOA  0.3 (0.1) 0.4 (0.1) 0.2 (0.1) 0.3 (0.03) 0.3 (0.04) 1.7 \n(0.15) \nPFNA  -0.3 (0.1) -0.3 (0.1) -0.3 (0.1) -0.3 (0.02) -0.3 (0.02) 0.21 \n(0.93) \nPFDA  -0.7 (0.1) -0.8 (0.1) -0.6 (0.1) -0.7 (0.03) -0.7 (0.04) 0.11 \n(0.97) \n\n12 \n \nPFHpA  -1.4 (0.1) -1.3 (0.1) -1.2 (0.1) -1.5 (0.1) -1.2 (0.1) 2.4 \n(0.04) \nPFUnDA  -1.04 (0.7) -0.9 (0.1) -0.9 (0.1) -0.9 (0.03) -0.9 (0.04) 0.64 \n(0.63) \n 223 \n 224 \n4. Discussion  225 \nIn this study, PFASs were detected in follicular fluid in women experiencing infertility . We 226 \nobserved association between PFAS, such as PFHpA, or PFHxS, and the 5 aetiology factors of 227 \ninfertility. From the limited studies investigating follicular fluid and fertility effects, McCoy et 228 \nal. (2017 ) found  no significant associations between ovarian response measures and PFAS 229 \nconcentrations, and also found decreased blastocyst conversion rate in follicular fluid exposed 230 \nto perflurononanoic acid (PFNA), and perfluorodecanoic acid (PFDA). Heffernan et al. (2018) 231 \nconducted a study using serum and follicular fluid of women with and without polycystic 232 \novarian syndrome (PCOS) undergoing fertility treatment and found higher serum PFOS 233 \nconcentrations in PCOS cases than controls , and in women with irregular menstrual cycles 234 \ncompared to women with regular menstrual cycles. Governini et al. (2011) reported that PFASs 235 \nwere present in human follicular fluid and suggested PFAS concentrations had a potentially 236 \ndetrimental effect on oocyte fe rtilisation capacity but the sample size was limited (n=16). 237 \nHigher levels of PFNA have also been associated with increased risk of infertility in women 238 \n(Jorgensen et al., 2014).  While in another study, the presence of higher PFASs in human 239 \nfollicular fluid (a linear combination of PFOA, PFOS, PFNS and PFHxS) had a higher chance 240 \nof an oocyte developing into a high-quality embryo (Petro et al., 2014). 241 \n 242 \nAssociations were found between PFHxS, and PFHpA, and 5 aetiology factors of infertility. 243 \nEpidemiological studies have reported an association between PFASs, and infertility caused by 244 \nendometriosis (Vagi et al., 2014; Campbell et al., 2016; Wang et al., 2017). In Chinese females 245 \nseeking ART treatment due to endometriosis, plasma levels of perfluorobutane sulfonic acid 246 \n(PFBS), which were excluded in this current study due to low detection rate, was related to an 247 \nincreased risk of infertility (Wang et al., 2017). In the United States of America, women with 248 \nendometriosis had higher levels of PFNA, PFOA and PFOS (Campbell et al., 2016). There was 249 \nalso evidence of a relationship between PCOS and PFASs exposure. Vagi et al. (2014) reported 250 \nhigher serum levels of PFOA and PFOS in females with PCOS compared to females with no 251 \nPCOS. Although incidence of genital tract infections in both females and males is related to 252 \n\n13 \n \nrisk of infertility, the effects of PFAS exposure on genital tract infections have not been well 253 \nunderstood (Pellati et al., 2008). Further epidemiology studies are needed to identify if these 254 \ndisease-causing infertilities are associated with PFAS exposure.  255 \n 256 \nIn the current study, we used individual samples of follicular fluid, and were therefore able to 257 \nmeasure the range of PFAS concentrations (minimum to maximum) from individual persons. 258 \nThis is advantageous as the body of exposure data on PFAS in Australia uses pooled blood 259 \nserum data which has the limitation of not being able to identify extreme concentrations, but 260 \nrather provides a mean of the concentrations of the individuals in that pool (Toms et al., 2019).  261 \nIndividual’s serum samples have been analysed for PFASs in pregnant women from Western 262 \nAustralia (Callan et al., 2016), community residents ( Bräunig et al., 2017), and workers 263 \nexposed to PFASs (Rotander et al., 2015) in Australia . In the current study, we were able to 264 \ncalculate the inter quartile range (IQR) for PFASs in Australia which showed little variation in 265 \nconcentrations among the 97 women . This little variation was also observed in 98 pregnant 266 \nwomen in the Western Australian study by Callen et al. (2016) (PFOS 0.45 to 8.1 µg/L, PFHxS 267 \n0.06 to 3.3 µg/L, PFOA 0.21  to 3.1 µg/L).  PFOS, PFOA, and PFHxS were detected at the 268 \nhighest concentrations in the follicular fluid as has been seen in human serum in Australia 269 \n(Toms et al., 2009). This finding is consistent with follicular fluid studies from Belgium (Petro 270 \net al., 2014), the United States of America (McCoy et al., 2017), and the United Kingdom 271 \n(Heffernan et al., 2018). 272 \n 273 \nIn terms of age effects on PFASs, there were no trends in any  of the PFAS concentrations by 274 \nage. As is expected from samples from females of child -bearing age, the age range is 275 \nreasonably narrow making the assessment of trends difficult.  Age trends have been identified 276 \nin previous studies of Australian serum coveri ng the full lifecycle where different PFAS 277 \nconcentrations varied with age  (Toms et al., 2019).  We found decreased fertilisation rate in 278 \nfollicular fluids is related to increased age, confirming accepted evidence that fertilisation rate 279 \ndecreases with age (Barbieri 2018). 280 \n 281 \n 282 \nLimitations 283 \nLimited demographic information available on participants and a small sample size limited the 284 \ninterpretation of this dataset. Another limitation arose from simplifying causes of infertility to 285 \n\n14 \n \nfive infertility aetiology factors, as human infertility is influenced by various factors, not only 286 \nphysiologically, or pathogenically, but also environmentally. Thus, considering specific 287 \ndiseases relating to male factors or other causes would be beneficial in future studies.   288 \n 289 \n 290 \n4. Conclusion 291 \n 292 \nIn conclusion, we identified PFAS in follicular fluid of Australian women who had been treated 293 \nat an IVF clinic. PFOS, PFOA, and PFHxS were detected in the highest concentrations in the 294 \nfollicular fluids . Increased age was associated with decreased fertilisation rate in our data. 295 \nThere were significant differences in PFAS concentrations between female infertility factors 296 \nand the control group that showed links between PFAS exposures and increased risk of  297 \ninfertility factors. Further studies are needed to  investigate the  relationship between PFAS 298 \nlevels and health effects including human infertility.  299 \n 300 \nAcknowledgement 301 \nYR is funded by a Postgraduate research scholarship from Queensland University of 302 \nTechnology.  303 \n 304 \nConflict of Interests 305 \nThe authors declare that there is no conflict of interests regarding the publication of this paper.  306 \n 307 \n 308 \n 309 \n 310 \n 311 \n 312 \n 313 \n 314 \n 315 \n 316 \n 317 \n 318 \n\n15 \n \n 319 \n 320 \nReferences 321 \n 322 \nAylward, L., Green, E., Porta, M., Toms, L., Den Hond, E., Schulz, C., … Mueller, J. (2014). 323 \nPopulation variation in biomonitoring data for persistent organic pollutants (POPs): An 324 \nexamination of multiple population -based datasets for application to Austra lian pooled 325 \nbiomonitoring data. Environment International, 68, 127–138. 326 \nBräunig, J., Baduel, C., Heffernan, A., Rotander, A., Donaldson, E., & Mueller, J. (2017). 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