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Title Is there a Relationship Between Polycyclic Aromatic Hydrocarbon Exposure and Ovarian Reserve Markers in Humans?

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Author Nelson, Gillian

Publication Date 2016

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IRVINE

Is there a Relationship Between Polycyclic Aromatic Hydrocarbon Exposure and Ovarian Reserve Markers in Humans?

THESIS

submitted in partial satisfaction of the requirements

for the degree of

MASTERS OF SCIENCE

in Environmental Health Sciences

Gillian Loraine Nelson, MD

Thesis Committee:

Professor Ulrike Luderer, MD, PhD, MPH, Irvine, Chair

Professor Dean Baker, MD, MPH

Associate Professor Jun Wu, PhD

2016

Page

LIST OF FIGURES iii

LIST OF TABLES iv-v

ACKNOWLEDGMENTS vi

ABSTRACT OF THE THESIS vii

1.0 INTRODUCTION 1

2.0 FEMALE REPRODUCTIVE PHYSIOLOGY 31

3.0 CONFOUNDERS OF OVARIAN RESERVE 39

4.0 STUDY OBJECTIVES 41

5.0 METHODS 41

6.0 STATISTICAL ANALYSIS METHODOLOGY 47

7.0 RESULTS 49

8.0 DISCUSSION 76

9.0 CONCLUSION 82

REFERENCES 84

LIST OF FIGURES

Page

Figure 1 Benzo[ a ]pyrene Structure 3

Figure 2 Metabolism of Benzo[ a]pyrene and Phenanthrene 14

Figure 3 Folliculogenesis 35

Figure 4 AMH Decreases With Age 38

Figure 5 AMH Parallels Antral Follicle Count 39 figure 6 OH-PAH Principal Components Factor Analysis 55

iii

LIST OF TABLES

Page

Table 1: PAH Urinary Metabolite names and abbreviations 17

Table 2: PAH Parent Compound and OH-PAH metabolites evaluated in this thesis 24

Table 3: Participant Characteristics (Demographics) 50

Table 4: Participants' and NHANES PAH Geometric Means 51

Table 5: Correlations among mean OH-PAH metabolites in cycle 1 52

Table 6: Correlations among mean OH-PAH metabolites in cycle 2 52

Table 7: Correlations among mean OH-PAH metabolites in cycle 3 53

Table 8: Principal Component Factor Analysis of OH-PAHs 54

Table 9: Correlations of natural log transformed Endocrine Endpoints 56

Table 10: Correlations between age and BMI with OH-PAH metabolites 56

Table 11: Correlations of Potential Confounders with Ovarian Reserve Markers 57

Table 12: Bivariate Associations between OH-PAH Metabolites and Race/Ethnicity 59

Table 13: Bivariate Associations between OH-PAH Metabolites and Smoking Status 60

Table 14: Bivariate Associations between OH-PAH Metabolites and EtOH 61

Table 15: Bivariate Associations of OH-PAH Metabolites and Caffeinated Coffee 62

Table 16: Bivariate Associations between OH-PAH metabolites and Caffeinated Tea 62

Table 17: Bivariate Associations between OH-PAH Metabolites and Soda/Energy Drink 63

Page

Table 18: Bivariate Associations of OH-PAH Metabolites and Education 64

Table 19: Bivariate Associations of OH-PAH Metabolites and Family Income past year 65

Table 20: Bivariate associations of Race/Ethnicity and Ovarian Reserve Markers 67

Table 21: Bivariate Associations of Cigarette use and Ovarian Reserve Markers 67

Table 22: Bivariate Associations between ETOH and Ovarian Reserve Markers 68

Table 23: Bivariate Associations of Caffeinated Coffee and Ovarian Reserve Markers 68

Table 24: Bivariate Associations between Caffeinated Tea and Ovarian Reserve Markers 69

Table 25: Bivariate Associations of Soda/Energy Drink and Ovarian Reserve Markers 69

Table 26: Bivariate Associations between Education level and Ovarian Reserve Markers 70

Table 27: Bivariate Associations of Family Income past year and Ovarian Reserve Markers 70

Table 28: AMH Multiple Linear Regression Model 71

Table 29: Inhibin B Multiple Linear Regression Model 71

Table 30: FSH Multiple Linear Regression Model 72

Table 31: AMH Effect Modification by GSTT1 74

Table 32: AMH Effect Modification by GSTM1 74

Table 33: Inhibin B Effect Modification by GSTT1 75

Table 34: Inhibin B Effect Modification by GSTM1 75

Table 35: FSH Effect Modification by GSTT1 75

Table 36: FSH Effect Modification by GSTM1 76

ACKNOWLEDGMENTS

I will be forever grateful and proud to have learned from my committee chair, Dr. Ulrike Luderer during my journey through this thesis project. I sincerely thank her for her valuable time, unmatched knowledge, and endless patience with me during this endeavor.

I am equally proud and sincerely thankful to have been given the opportunity to have learned from Drs. Dean Baker and Jun Wu. My thesis project would not have been possible without the input and patience of every one of the committee members.

ABSTRACT OF THESIS

Is there a Relationship Between Polycyclic Aromatic Hydrocarbon Exposure and Ovarian Reserve Markers in Humans?

Gillian Nelson, MD

Master of Science in Environmental Health Sciences

University of California, Irvine, 2016

Ulrike Luderer, MD, PhD, MPH, Chair

About 12% of American women suffer from infertility, and premature ovarian failure

(POF) is a major cause of infertility. Most cases of POF are of unknown etiology. Environmental toxicants such as polycyclic aromatic hydrocarbons (PAHs) have been shown in animal models and human tissue cultures to destroy ovarian primordial follicles. The aim of this study is to assess whether PAH exposure is associated with diminished ovarian reserve in humans.

Hypotheses: Women with higher levels of PAH exposure will have decreased ovarian reserve compared to women with lower levels of PAH exposure. Women who have enzyme polymorphisms that alter Phase II metabolism of PAHs will have increased susceptibility to the ovarian toxicity of PAHs compared to women who do not have these polymorphisms.

Methods: This cross-sectional data analysis of a longitudinal study involved 51 women of reproductive age who resided in Orange County, California. Urinary monohydroxylated PAH metabolites (OH-PAHs) were used as biomarkers for exposure to PAHs. Anti-Mullerian hormone

vii

(AMH), follicle-stimulating hormone (FSH) and inhibin B served as markers of ovarian reserve.

Null polymorphisms in the Phase II enzymes GSTT1 and GSTM1 were determined. Multiple linear regression models were generated and Principal Component Factor analysis with varimax rotation was used to assess co-linearity of the OH-PAHs. The OH-PAHs with the strongest associations with each of the ovarian reserve markers were forced into the regression models.

Potential confounders identified in bivariate analyses were entered into the models and retained if they changed the beta coefficients for the OH-PAHs by 10% or more.

Results: Concentrations of urinary OH-PAHs in the study population were similar to those reported for women in the National Health and Nutrition Examination Survey ( NHANES'). The

OH-PAHs with the strongest associations with the ovarian reserve markers were 2-hydroxy naphthalene (NAP2) and 9-hydroxy fluorene (FLUO9) for the AMH model, 3- hydroxy fluorene

(FLUO3) for the inhibin B model, and 3-hydroxy phenanthrene (PHEN3) for the FSH model.

Factor analysis identified two factors that explained 37% and 63 % of the variation in the OH-

PAHs, respectively. Factor 1 primarily loaded on 1-hydroxy phenanthrene (PHEN1), 2-hydroxy phenanthrene (PHEN2), PHEN3, and FLUO9. Factor 2 primarily loaded on 2-hydroxy fluorene

(FLUO2), FLUO3, 1-hydroxy naphthalene (NAP1), and NAP2. In the final models, the beta coefficients for the OH-PAHs were in the expected direction (negative for AMH and inhibin B models and positive for the FSH model), but none of them was statistically significant at the

P=0.05 level.

We examined effect modification by GSTM1 and GSTT1 null polymorphisms in stratified analyses. Our models revealed non-significant associations between OH-PAHs and ovarian

viii reserve markers in the expected directions for the strata with ≥ 1 allele and no or opposite than expected associations for the strata with null alleles. However, the numbers in each stratum were very small.

Conclusions: Our findings are suggestive that environmental exposure to PAHs may be associated with decreased ovarian reserve. Larger studies are needed and could be conducted with techniques used in this pilot study.

1.0 INTRODUCTION

1.1 Polycyclic Aromatic Hydrocarbons (PAHs)

The International Union of Pure and Applied Chemistry (IUPAC) defines PAHs as compounds consisting of only carbon and hydrogen atoms with multiple aromatic rings (two to six )(Levy, B et. al., 2011). PAHs' rings are fused, such that two adjacent benzene rings share two carbons (CDC, 2013). PAHs are a group of over a hundred organic chemicals found in nature in mixtures. Most PAHs are created during combustion and pyrolysis processes of anthropogenic and natural origin. Many PAHs are emitted from incomplete combustion of organic compounds (Domingo, JL et al., 2015). Naphthalene is the only PAH that is commercially produced in the US (CDC, 2013). Most PAHs persist in the environment and are insoluble in water. All the PAHs are solids although some have low vapor pressures and are volatile, such as naphthalene (ATSDR, 1995). The manufactured or pure PAHs, range from colorless to white or pale yellow-green solids (USEPA, 2006). The large PAHs (composed of 5 or more rings) such as

7,12-dimethylbenzanthracene (DMBA), 3-methylcholanthrene (3-MC) and benzo[ a]pyrene

(BaP) have been the most studied in general and with regard to the ovary.

Naphthalene (two rings) is a PAH used as an intermediate (NAP2) in the manufacturing of pesticides, as a precursor for various dyestuffs, pigments, in rubber processing chemicals and other miscellaneous chemicals such as toilet bowel deodorants, tanning agents, and pharmaceuticals. Fluorene (three rings) is used as an intermediate in several chemical processes such as forming pesticides, to form polyradicals for resins and in manufacturing dyestuffs; anthracene (three rings) is used in the manufacturing of dyes, lubricating materials and thermosetting plastics (Case Studies, 2009; ATSDR, 2013; ATSDR, 1995; USEPA, 2006; CDC,

2013; Ladou, M et al., 2014; Collin , G et al., 2003). PAH compounds with up to three or four aromatic rings (phenanthrene, naphthalene, and pyrene) predominate in mixtures (Ladou, M et al., 2014).

PAHs with five or six rings are classified as carcinogenic, mutagenic, and immunosuppressive (Ladou, M et al., 2014). The International Agency for Research on Cancer

(IARC) and the US National Toxicology Program (NTP) have classified specific PAH-containing chemical mixtures (e.g., soot, coke oven emissions, coal tars and coal tar pitches) as human carcinogens. The NTP determined in 2000 that naphthalene is reasonably anticipated to be a human carcinogen (NTP, 2005). IARC classifies naphthalene as a possible human carcinogen

(CDC, 2013, Preuss, R et al., 2003). Because of these health effect concerns, the US

Occupational Safety and Health Administration (OSHA) has developed criteria on the allowable levels of these chemicals in the workplace (CDC, 2013).

Each ring structure in a PAH has a unique ultraviolet spectrum, dictating the unique absorbance of each isomer, which is used in the identification of that PAH. BaP is internationally used as a marker compound for PAH measurements in air and is classified as the primary ( IARC

Group 1) carcinogen in tobacco smoke (Angerer, J et al., 1997; Levy, BS et al., 2011). Figure 1 is a structural representation of BaP:

Figure 1. Benzo[ a]pyrene (BaP)

The Agency for Toxic Substances and Disease Registry (ATSDR) identified 17 PAHs as being of greatest health concerns (ATSDR, 1995). These PAHs were selected using the following four criteria: (1) toxicity (2) potential for human exposure (3) frequency of occurrence at the United

States Environmental Protection Agency's (USEPA) National Priorities List (NPL) of hazardous waste sites, and (4) the extent of information available. The 17 PAHs are listed below.

• acenaphthene • acenaphthylene • anthracene • benz[a]anthracene • benzo[a]pyrene • benzo[e]pyrene • benzo[b]fluoranthene • benzo[g,h,i]perylene • benzo[j]fluoranthene • benzo[k]fluoranthene • chrysene • dibenz[a,h]anthracene • fluoranthene • fluorene • indeno[

1,2,3-c,d]pyrene • phenanthrene • pyrene

The USEPA priority air pollutant list of 16 PAHs include s those that have less than two dozen ring carbons or 4 to 6 rings and overlaps with the ATSDR's list except that naphthalene is included and benzo[e]pyrene and benzo[j]fluoranthene are excluded (NCBI).

1.2 PAH Sources and Exposure Pathways

1.2.1 Air

There are multiple sources of airborne PAH mixtures. Important non-natural PAH sources in air include tobacco smoke, motor vehicle exhaust (major source in urban and suburban areas), residential and industrial heating sources, coal, crude oil and natural gas processing, and waste incineration (CDC, 2013; ATSDR, 1995). As previously mentioned, it is generally agreed upon in the literature that naphthalene, phenanthrene, and pyrene are dominant constituents in PAH mixtures. Diesel exhaust has PAH adsorbed to particulates (primarily fluoranthrene, phenanthrene, and anthracene) and PAH vapor emissions dominated by phenanthrene and anthracene (Westerholm, R et al., 1994; Rogge, WF et al., 1994; Kelly, G et al., 1993). Two or three ring PAHs are usually emitted in gaseous form and condense quickly, adsorbing onto particles in soot or particulate matter air pollution (WHO, 1987). Fluorene is mostly detected in the vapor phase (but present in air particulates) of various PAH emission sources, including coal tar pitch, petroleum refineries, diesel exhaust fumes, and tobacco smoke, where it is the second (BaP is the first as mentioned above) most abundant PAH (Fang, GC et al., 2006; Ding, YS et al., 2005). Larger, five or more ring PAHs are emitted in the particulate phase (Byeong-Kyu, L et al., 2010). The smaller or fine particulates (e.g., particulate matter equal to or less than 2.5 millimeters (PM 2.5)) have higher concentrations of PAHs than larger or coarse particulates

(Boström, CE et al., 2002; Rehwagen, M et al., 2005). Naphthalene is a constituent of diesel and jet fuel (Clark, CR et al., 1982; McDougal, JN et al., 2000). Cigarette smoke contributes significantly to the naphthalene exposure of humans (Schmeltz , I et al., 1976; Hoffmann, D et al. 2001). Together these exposures lead to a background burden of PAH exposure for the

4 general population. Natural airborne sources of PAHs include volcanoes, forest fires, crude oil, and shale oil (Case Series, 2009; ATSDR, 1995). PAH concentrations in the ambient air have shown seasonal variation (IPCS, 1998; Rehwagen, M et. al., 2005).

Indoors, PAHs exist in the vapor phase, with the remainder partitioning to suspended or re- suspended particles as well as dust deposited on room surfaces (settled dust). Inhaled indoor air (both gas and particle phases) and ingestion of settled house dust are two prominent pathways of human exposure to PAHs (Harrad, S et al., 2010). Indoor concentrations of PAHs are influenced by outdoor concentrations (Lioy, PL et al., 1988), and indoor sources include tobacco smoke, unvented radiant and convective kerosene space heaters, and gas cooking and heating appliances (ATSDR, 1995). The levels of PAHs vary with different types of tobacco varieties (Ding, YS et al., 2008). Households with smokers and gas stoves have the highest concentration of PAHs (ATSDR, 1995). Typical ambient and residential air concentrations of

PAHs are in the nanogram per meter cubed (ng/m 3) range in the US (Jung, KH et al., 2014;

Naumova, YY et al., 2002).

1.2.2 Water

PAHs have been found in some drinking water supplies in the US. Background levels of PAHs in drinking water range from 4 to 24 nanograms per liter (ng/L) (ATSDR, 1995). Concentrations of BaP in treated drinking water have been shown to range from 0.0002 to 0.024 micrograms per liter (μg/L) (WHO, 1987; IARC, 1983), which are generally lower than those in untreated water and about 100-fold lower than the USEPA's drinking water standard (0.2 parts per billion

(ppb), since chlorination eliminates aqueous PAHs (Kennish, MJ 1996; Case Studies, 2009).

These treatments decrease personal exposure to PAHs surface water deposition of airborne

PAHs. Atmospheric fallout includes wet and dry depositions of particulate and vapor phases of

PAHs during precipitation, as some are semi-volatile compounds such as pyrene (Guo, W et al.,

2007; Ma, WL et al., 2010; Zhang, Y et al., 2004). Surface water PAH contamination also comes from municipal waste water discharge, urban storm water runoff, runoff from coal storage areas , effluents from wood treatment plants and other industries, oil spills such as during oil shipment at sea, and petroleum processing (ATSDR, 1995) as PAHs can leach from soil into water (Case Studies, 2009).

1.2.3 Soil

Srogi reported in 2007 that the US was second only to Austria in soil concentrations of PAHs at 59 microgram per gram (μg/g) compared to 79 μg/g (Srogi, K 2007). PAHs are found in soil near areas where wood, coal, gasoline, and other products have been burned, such as former manufactured-gas-factory sites and wood-preserving facilities (ATSDR, 1995). Documented levels near oil refineries in the past have been as high as 200,000 micrograms per kilogram (μg

/kg) of dried soil (Case Studies, 2009). Soil samples obtained near cities and areas with heavy traffic have generally averaged less than 2,000 μg/kg (IARC, 1973; Case Studies, 2009). There is a national waste site clean-up effort administered by the EPA based on a mandate from the

Comprehensive Environmental Response, Compensation, and Liability Act. The US EPA's

Superfund Program locates, investigates, and cleans up the worst hazardous waste sites in the

US. Fifty-four PAHs have been identified at a number of NPL hazardous waste sites (NCBI).

There are two NPL sites in Orange County California near which participants in the present study may have lived. The first site is the El Toro Marine Corps Air Station in Santa Ana, which is a former location of the Fleet Marine Forces in the Pacific Ocean. The area around the site

6 changed from an agricultural area to an urban area (NETR, 1990). The second area is the McColl site in Fullerton which is an inactive waste disposal facility covering 8 acres. The residents are mostly exposed to waste from oil refinery acid sludge , which is predominantly sulfur and hydrocarbons - including PAHs. They also have inhalation exposure to organic gas mixtures , which would include PAHs (NETR, 1983).

1.2.4 Food

Up to 70% of PAH exposure for nonsmokers in a non-occupational setting can be associated with diet (Phillips, DH 1999). PAHs are found in caffeinated tea, caffeinated roasted coffee, cereals, grains, soy, flour, bread, vegetables, refined vegetable oil, fruits, meat, and processed or pickled foods (Levy, B et al., 2011). Food grown in contaminated soil or air may also contain

PAHs. A few crops such as wheat, rye, and lentils, may synthesize PAHs or absorb them through water, air, or soil (Menzie, CA et al., 1992; Shabad, LM et al., 1972; Grimmer, G, 1968; IARC,

1973). Smoked meat and fish contain more PAHs than do uncooked products with up to 2.0

μg/kg of BaP detected in smoked fish (Case Studies, 2009). Cooking the meat (muscle meat such as beef, pork, poultry, and fish) or other food at high temperature increases the amount of

PAHs, which are formed from the fat and the juices of the meats in the food (Jägerstad, M et al., 2005). For example, the PAH level for charred meat can be as high as 10–20 µg/kg (Phillips,

DH 1999) which has been shown to be 3 to 5 times more PAHs compared to grilling (Mc Donald,

JD et al., 2003). Rose et al., reported that barbecuing with charcoal plus wood chips resulted in the formation of BaP in most foods; barbecuing beef burgers over charcoal without using wood chips gave the highest levels in the study as they were closer to the heat source (Rose, M et al.,

2015). Epidemiology studies support these findings , such as the Li et al., study that reported

7 urinary concentrations of PAHs after consumption of barbecued chicken were in similar ranges or as high as occupational exposure to PAHs (Li, Z et al., 2012). Secondly, self-reported grilled/broiled meat consumption was reported to be associated with increased concentrations of most PAH metabolites in a cross-sectional analysis of 5,060 participants over 6 years of age using data from the 1999-2002 National Health and Nutrition Examination Survey (NHANES)

(Suwan-ampai, P et al., 2009). Third, Vyskocil et al. reported that food consumption represented the main source of PAH exposure for children in big cities that were not heavily polluted by PAH from industrial sources (Vyskocil, A et al., 2000). Finally, Kim, SR et al., found in their study of nonsmoking women that for ﬂuorene and phenanthrene, ingested doses were the dominant pathway of exposure (Kim, SR et al., 2008). The level of PAHs in the typical US diet is less than 2 µg/kg of food (ATSDR, 1995). However, the recent reports on PAHs and diet by Ma, Y et al., 2015, for example indicate that US dietary exposure to PAHs ranges from 0.1 to

55 μg/day, depending on the amount of kgs of food intake (Ma et al., 2015). Zelinkova, Z et al.,

2015 reviewed the occurrence of the 16 USEPA air pollutant priority list of PAHs in food in the

US and in Europe. Their findings were consistent with other authors' prior reports on federal guidelines that address the consumption of foods containing PAHs. Transplacental and lactational routes are important exposure routes to PAHs for fetuses and infants. PAH metabolites are excreted in breast milk and they readily cross the placenta (Case Studies, 2009).

The World Cancer Research Fund/American Institute for Cancer Research issued a report in

2007 recommending guidelines for the consumption of red and processed (including smoked) meats; however, no recommendations were provided for PAH levels in smoked meat products

(WCRF/AICR, 2010). To date, no maximum limits for PAH contents of pre-cooked foodstuffs have been implemented into the US legislation (Zelinkova, Z et al., 2015).

1.2.5 Topical Medications

Coal tar shampoos, lotions, and creams found in prescription and nonprescription formulas for treatment of psoriasis, eczema, and dandruff contain PAHs (ATSDR, 1995; Case Studies,

2009). Disdier et. al., found fluorene, phenanthrene, pyrene, and naphthalene, among other

PAHs in an analysis of coal tar creams (Disdier, B et al., 2000).

1.2.6 Occupational Exposure

The highest risks of PAH exposure to workers are in industries or trades involving coal and coal products (Case Studies, 2009). Specific jobs in these industries include but are not limited to chimney sweeps , fishermen using coal tar on nets, machinists, printers, steel foundry workers (Case Studies, 2009). Workers such as engine mechanics and drivers are exposed to

PAHs by inhaling auto and diesel engine exhaust. Other workers are exposed by using products that contain PAHs in the following industries: mining, oil refining, chemical production, and the electrical industry's graphite electrode workers (ATSDR, 1995; Case Studies, 2009). Specifically, truck drivers, underground miners, and railroad workers have documented exposures to methylated naphthalenes and phenanthrenes from diesel exhaust exposure and utility workers to fluorene (Ladou, J et al., 2014).

Creosote is used as a wood preservative by carpenters, farmers, railroad workers, and telegraph pole workers and in tunnel construction. It's major constituent is naphthalene and its alkyl homologues. It is impregnated into lumber under high pressure. It is a constituent of fuel

9 oil, a lubricant for die molds, as well as pitch for roofing (Ladou, J et al., 2014; Siegel, E et al.,

1986). According to the US Department of Labor's April 2013 report, the number of women employed in the US construction industry grew by 81.3% from 1985 to 2007 (US Bureau Labor

Statistics, 2010).

1.3 PAH Biodegradation

The degradation of PAHs in the environment occur s through photo and chemical oxidation as well as biotic metabolism in estuarine and marine systems. In seawater, PAHs are degraded directly by photolysis reactions or photo-induced reactions involving a molecule of ozone oxygen, hydroxyl radicals, and other oxidizing agents in the upper part of the water column

(Kennish, MJ 1996). Bacteria under aerobic conditions, compared to fungi and aquatic fauna, more quickly degrade the majority of PAHs, especially in highly contaminated sites such as oil spills (Kennish, MJ 1996).

1.4 PAH Toxicokinetics

1.4.1 Absorption

PAH's are highly lipid soluble and are well-absorbed from the lung, gut, and skin of mammals (WHO, 1987). In a 12 week exposure of rats to either inhaled (radio-labeled on carbon number14 (14C)-BaP adsorbed on carbon black particles or inhaled pure BaP , the authors reported a 100-fold higher level of 14C in the lungs for the adsorbed BaP (Wolff, RK et al., 1989). Biphasic respiratory clearance of PAHs have been shown in female rats where the bronchial airways partially clear some of the particles during mucociliary transport mechanisms, leaving the rest to penetrate into the bronchial epithelial cells where metabolism takes place

(WHO, 1987). In addition to inhalation, BaP, phenanthrene and pyrene have been shown to

10 rapidly penetrate the skin of mice and rats. In mice after dermal exposure, 80% of BaP was recovered from feces after 7 days, while a total of 42% was recovered from feces and urine in rats (Sanders, CL et al., 1984; Yang, JJ et al., 1986).

All routes of absorption have been illustrated in human studies. In humans, they are largely absorbed from carrier particles through the respiratory route (Ladou, J et al., 2014) as well as through the gut. Human skin absorption has been demonstrated after using topical BaP (Kao, J et al., 1985). Evidence of absorption measured with urinary levels of 1-hydroxypyrene (PYR1), the urinary metabolite of pyrene, has been found in patients treated with topical coal tar applications (Larsen, JC 1995). Absorption after inhalation and ingestion of PAHs has also been demonstrated in workers exposed to creosote oil, in coal tar distillery workers, and in road- surfacing. Evidence of absorption through inhalation and ingestion in coke-oven workers, other workers exposed to bitumen fumes while working with binders to waterproof roofs and workers in crude oil refineries has been reported (Larsen, JC 1995). Respiratory absorption was also illustrated in coke-oven workers and city residents where significant correlations were obtained between the metabolite PYR1 and levels of pyrene and BaP in the ambient air (Larsen,

JC 1995).

1.4.2 Distribution

PAHs are rapidly and widely distributed in organisms independent of the route of exposure.

Similar patterns of distribution were found with intrathecal, intravenous, and subcutaneous exposures to PAHs in rats and mice. Detectable BaP levels are observed in most internal organs from minutes to hours after administration with the highest levels in the liver (IARC, 1983; Foth,

H et al., 1988). Fatty tissues are significant storage depots for PAHs without accumulation.

Hepatobiliary excretion results in an abundance of PAH metabolites distributed in the gastrointestinal tract (Schlede, E et al., 1970; Wiersma, DA 1983). BaP readily crosses the placental barrier of rats and mice (Neubert, D et al., 1988; Withey, JI et al., 1993), consistent with the fetal and developmental toxicity of the substance (WHO, 1987).

1.4.3 Metabolism

Metabolizing capacity of PAHs varies among animal tissues. The highest metabolizing capacity in descending order takes place in liver, lung, intestinal mucosa, skin and kidneys. PAH metabolism may also take place in nasal tissues, mammary glands, spleen, brain, hair follicles, erythrocytes, platelets, leukocytes, gonads, placenta and uterus. Animal and human fetal tissues metabolize PAHs at a lower rate compared to their respective adult tissues (Anderson,

LM et al., 1989). The ovary has been shown to metabolize PAHs (Rekhadevi, PV et al., 2014).

Phase I metabolism in the ovary involves the cytochrome P450 (CYP) enzymes (Bhattacharya, P et al., 2012; Klaassen, C et al., 2008) and microsomal epoxide hydrolases (EHs), similar to other tissues.

The major pathways involved in the metabolic transformation of PAHs are the CYP pathway mentioned above and the aldo-keto reductase pathway (AKR pathway) (Zhang, L et al., 2012).

In most cases, oxidation of PAHs by CYP enzymes is an initial step in the activation process to produce the polar biochemically reactive electrophilic species capable of interacting with cellular macromolecules, particularly nucleic acids and proteins (Xue, W et al., 2005). See BaP and phenanthrene in Figure 2 for example. Specifically, PAHs are activated by the microsomal mixed function oxidases, mainly CYP1A1 and CYP1B1, which oxidize the non-polar PAHs into polar hydroxy and reactive epoxide-derivative intermediates. The reactive epoxides are

12 enzymatically metabolized by microsomal EHs to dihydrodiols or spontaneously convert to phenols (Ladou, J et al., 2014; Shimata, T et al., 2004; Hall, M et al., 1989 Glatt, HR et al., 1987).

The aryl hydrocarbon receptor (AHR) is in the cytosol and is responsible for the inducible expression of several xenobiotic-metabolizing enzymes including CYP1A1 and CYP1B1 by PAHs

(Shimada, T et al., 2004). AHR is a receptor for low molecular weight sterically planar ligands that are approximately three benzene rings in size (such as the PAHs examined in the current study), the most widely recognized being, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Denison,

MS et al., 2003). AHR is expressed in many tissues, including the ovaries (Furue, M et al., 2014;

Schnekenburger, M et al., 2007).

The AKRs are a superfamily of monomeric nicotinamide adenine dinucleoside phosphate- oxidase (NADPH)-dependent oxidoreductases in the cytosol (Disdier, B et al., 2000; Zhang, L et al., 2012). The PAH phenanthrene is metabolized through this later pathway along with BaP

(Xue, W et al., 2005). Xue, et al., reported on dihydrodiol metabolism. Their metabolites are oxidized through the AKR pathway as an alternative. The dihydrodiol dehydogenases are converted to ketols, which then tautomerize to catechols. The later, oxidizes first to an o-semi- quinone anion radical and hydrogen peroxide and then to o-quinone and superoxide anion radical (Xue, W et al., 2005).

Figure 2. Cancer Epidemiology Biomarkers Prev., October 1, 2006 vol. 15 no. 10 1805-1811

Metabolism of BaP to trans, anti -BaP-tetraol via BPDE (metabolic activation pathway) and to phenols, such as 3- HO-BaP (metabolic detoxification pathway). (top) Metabolism of phenanthrene (Phe) to PheT and phenanthrols, such as 3-HOPhe. (bottom).

PAHs are not considered toxic as parent compounds (Chaloupka, K et al., 1994). They require activation to become toxic. The structures and reactions in Figure 2 illustrate that phenanthrene metabolism is similar to BaP's. The lower half of Figure 2 illustrates phenanthrene with the generic symbols for CYP1A1 and 1B1. Phenanthrene is oxidized by

CYP1A1 and 1B1 to an epoxide (represented by Phen 1,2 -epoxide –the cyclic ether with the 3- ring atoms, commonly referred to as an aromatic epoxide or arene oxide or oxirane)(Anzenbacher, P et al., 2012). EHs are hydrolytic enzymes that use water as a cofactor but instead of breaking the substrate apart like other hydrolytic enzymes, they add water to the substrate (Phen 1,2- epoxide) producing a dihydrodiol (represented by Phen1,2 diol in Figure 2).

These diols are much less reactive, thus the actions of EHs are generally accepted as detoxification. The diols usually spontaneously rearrange to phenols from the arene oxides

(Anzenbacher, P et al., 2012; ATSDR, 2005). However, the diols can be re-oxidized by CYP1A1 and 1B1 to a more reactive dihydrol expoxide (anti-PheDE). The dihydrodiol epoxides react with DNA forming adducts during Phase I metabolism. Detoxification of PAH dihydrodiol epoxides occur in Phase II metabolism predominantly through glutathione (GSH) conjugation

(Jernstrom, B et al., 1996; Romert, L et al., 1989). GSH is a ubiquitous antioxidant compound, present in cells, and composed of glycine, cysteine and glutamic acid. GSH xenobiotic conjugation is catalyzed by the glutathione S-transferase (GST) family of enzymes, which include the isoforms alpha, pi, mu, omega and theta, which comprise about 10% of the total cellular protein (Klaassen, C et al., 2008). Pi, Alpha, and Mu class human GSTs enzymes have been reported to be active in catalyzing GSH conjugation of PAH dihydrodiol epoxides

(Jernstrom, B et al., 1996; Seidel, A et al. 1998).

Fluorene is a tricyclic PAH examined in this thesis. Fluorene along with other tricyclic PAH parent compounds has been shown to induce the CYP system. A study showed that tricyclic

PAHs induced hepatic CYP1A2 gene expression in B6C3F1 mice by an AHR-independent pathway (Chaloupka, K et al., 1994). The CYP1A2 protein is the major CYP1A inducible isoenzyme in humans and preferentially catalyzes the oxidative metabolism of diverse substrates, including methoxyresorufin (O-dealkylation) and 17 beta-estradiol (E2) (2- hydroxylation)(Chaloupka, K et al., 1994; Okey, AB 1990). The study by Chaloupka, et al., showed that all six tricyclic PAHs tested significantly induced hepatic microsomal methoxyresorufin 0-deethylase (MROD) activity in B6C3F1 mice. CYP1A2 was induced by the

15 reconstituted 3-ring PAH mixture as well, which contained acenaphthene, acenaphthylene, anthracene, phenanthrene, fluorene, and dibenzofuran (38%)(Chaloupka, K et al., 1994). These findings implicate PAHs as ovotoxicants through induction of CYP1A enzymes independent of the AHR pathway discussed above in reference to larger PAHs (> 4 rings) such as 3-MC (that bind the AHR to induce CYP1A1 and CYP1A2 gene expression and ovotoxicity in rodents)(0key,

AB 1990). Estradiol (E2) oxidation by enzyme induction by PAHs in this manner can also result in lower levels of E2 in women leading to irregular ovulation and/or anovulatory cycles (see discussion on the menstrual cycle).

Half-lives for OH-PAHs have been reported in multiple human studies. PAHs in general have short half-lives (hours to a couple of days)(ATSDR, 1995; Li, Z et al. 2008). Motorykin et al.,

2015 reported a half-life for the parent compound pyrene as 3.3 hours(h) with overall half-lives of the OH-PAHs being higher with a range of 1.7 h for FLUO9 to 7 h for FLUO3. Li reported the half-lives of NAP2 and FLUO3 to be 2.5 h and 6.1 h, respectively (Li, Z et al., 2012). Buchet reported the half-life of PYR1 as 18 h (95% CI : 16.1 h, 19.8 h)(Buchet, JP et al., 1992), whereas

Buckley and Lioy estimated it to be 4.4 h (3.1 h to 5.9 h)(Buckley, J et al., 1992) . Smith's group estimated the half-lives of NAP1 and NAP2 as 13.6h (95% CI: 9.5h, 24.0 h) and 15.9 h (95% CI:

12.0 h, 23.8 h) respectively (Smith, KW et al., 2012). Li, Z et al., in 2012 reported median half- lives with confidence intervals for all of the OH-PAHs that will be examined in this thesis. See

Table 1 .

Table 1: PAH Urinary Metabolite names, abbreviations, median half-lives in h, and 95% CI's

PAH Metabolite ng/g Abbreviation Median half-life (h) 95% CI creatinine

1-Hydroxnaphthalene NAP1 4.3 3.3, 6.2

2-Hydroxynaphthalene NAP2 2.5 2.0, 3.4

2-Hydroxyfluorene FLUO2 2.9 2.3, 4.0

3-Hydroxyfluorene FLUO3 6.1 4.9, 8.1

9-Hydroxyfluorene FLUO9 3.1 2.6, 3.8 *

1-Hydroxyphenanthrene PHEN1 5.1 4.3, 6.1 *

2-Hydroxyphenanthrene PHEN2 3.9 3.4, 4.6

3-Hydroxyphenanthrene PHEN3 4.1 3.3, 5.6 *

1-Hydroxypyrene PYR1 3.9 3.0, 5.7

Table modified from Motorykin, O et al., 2015 ⁎ Statistically different (p-value < 0.05). Half-lives and confidence intervals (CI) from Li, Z et al., 2012.

1.4.4 Elimination

PAHs are rapidly metabolized and almost 100 percent eliminated in the urine and feces.

Urinary metabolites are eliminated within a few days (Ramesh, A et al., 2004). Metabolites of

PAHs with two and three rings are excreted preferentially in the urine, whereas higher- molecular-weight metabolites are excreted in the feces ( Ramesh, A et al., 2004).

BaP's major route of elimination is through the feces independent of the route of administration in rats (Foth et al., 1988; Van de Wiel et al., 1993). The PAHs examined in this thesis are small PAHs , and animal studies confirm that their major route of elimination is in the

17 urine. Animal studies of administered naphthalene for example, showed it is eliminated as metabolites in the urine: 77–93% in urine, 6–7% in feces following oral administration (Bakke, J et al. 1985), and 70–87% in urine, 6–14% in expired air, 2–4% in feces following dermal administration (Turkall, RM et al. 1994). Bakke et al. (1985) recovered 7–20% of an administered dose as NAP1 and NAP2 in rats’ urine. Kilanowicz et al., (1999) also reported that over 88% of intraperitoneally administered naphthalene was eliminated in urine during the ﬁrst

72 h in rats. Maximum levels in plasma were observed at the second hour post-administration, following a biphasic decline with half-lives of 0.8 h in phase I and 99 h in phase II. These data are largely conﬁrmed in human studies involving urinary NAP1 excretion (1 to 2 h in phase I and

14 to 46 h in phase II) (Heikkila , P et al., 1995). Additionally, Bieniek (1994) determined a mean urinary excretion rate of 57 mg/h, with a half-life of approximately 4 h in occupationally exposed workers employed in distillation of naphthalene oil.

Excretion kinetics involving carbon labeled PAHs (Grova, N et al., 2002) showed that phenanthrene and pyrene behaved identically, with low milk transfer and high excretion in the urine 103 h after a single oral ingestion in lactating goats. The feces excretion kinetics supported Foth and Van de Wiel's finding for BaP, showing that 88.2% of the ingested BaP radioactivity was recovered in the feces. The radioactivity detected in feces due to phenanthrene and pyrene were 21.7% and 25.5%, respectively, further supporting their major urinary excretory route.

Animal and human studies demonstrate that fluorene is also predominantly, if not totally, eliminated in the urine . Dewhurst, F et al., (1962) reported that fluorene resembled

18 naphthalene in its metabolism as it is almost completely absorbed in the gut and its metabolites excreted mainly, if not entirely, in the urine of rats and rabbits. Further, as far back as 1948,

Neish reported finding metabolites obtained from the urine of rabbits after ingesting fluorene were FLUO2 and the glucuronide of this phenol. He also concluded from his study that little if any unchanged fluorene was excreted in the feces, which was in agreement with prior studies dating 5 years back (Neish, WJ, 1948). The data on excretion of PAHs has remained consistent over the years.

Motorykin et al., (2015), reported post-inhalation excretion rates of 4 parent PAHs and 10

OH-PAHs during the smoking of salmon, and Santiago-Delago, L et al.(2015) reported on excretion of the same compounds after the consumption of smoked salmon. The maximum concentration excretions were higher for ingestion exposure compare to inhalation for most of the metabolites (Motorykin, O et al., 2015).

1.4.5 PAH-induced Ovarian Toxicity

PAH-induced ovotoxicity has been assessed in epidemiology studies of women smokers over the past 5 decades. It has long been documented that women smokers experience a 1 to 4 year earlier age onset of menopause (Jick H et al., 1977). Multiple epidemiology studies reveal a strong association between cigarette smoking and impaired fertility (Mark-Kappeler CJ et al.,

2011). The risk factors for premature ovarian failure( POF) established to date include rare autosomal mutations, disorders of the X-chromosome, treatment with anti-neoplastic drugs, pelvic ionizing radiation, and cigarette smoking. Animal studies have been relied upon to confirm PAH-induced primordial follicle depletion which is the true marker of ovarian reserve,

19 because it is not possible to retrieve primordial follicles from humans without destroying their ovaries. Mattison, DR et al., found that the diol epoxide metabolites of BaP were the ovotoxic metabolites in B6 and F1 mice, decreasing ovarian primordial and primary follicle counts

(Mattison, et al., 1989). Higher animal species such as rhesus monkey have demonstrated gonadotropin-dependent metabolism of DMBA in the ovary as well (Bengtsson, M et al., 1989).

DMBA-induced primordial and primary follicle destruction were later illustrated in a dose and time-dependent manner in grafted human ovarian tissues (Matikainen, T et al. 2001). The evidence that the mechanism of PAH-induced ovotoxicity involves CYP metabolism was further confirmed when GSH was shown to prevent DMBA-induced apoptosis and the rise in reactive oxygen species (ROS) that occurs prior to apoptosis in cultured pre-ovulatory rat follicles (Tsai-

Turton, M et al., 2007). Finally, Archibong illustrated that inhaled BaP dose-dependently- induced ovarian endocrine disruption in F-344 adult rats prior to mating. The results revealed decreased ovulation rates and number of pups per litter in BaP exposed rats at low dose compared to controls. The E2 and luteinizing hormone (LH) plasma levels at proestrus were also significantly reduced. Finally, the activity of the AHR activity in ovarian and liver tissues as well as the concentrations of BaP 7,8-diol and 3,6-dione metabolites increased in an exposure concentration-dependent manner (Archibong, E et al., 2012).

Epidemiology studies have examined PAHs with regard to female fertility and in vitro fertilization assessments and have supported prior animal and other epidemiology studies implicating PAH-induced female suboptimal fertility. Neal found BaP in the ovarian follicular fluid of women who did not conceive to be significantly higher than that of women who did conceive during IVF (Neal, M et al., 2008). Cigarette smoke has been shown in animal studies to

20 destroy primordial follicles. Tuttle documented mainstream smoke exposure in mice and reported cigarette smoke induced a significant reduction in the number of primordial follicles

(Tuttle, AM et al., 2009). Finally, PAH-induced ovotoxicty has been established for decades, yet research to advance this knowledge for clinical application is still lacking. There is an urgent need to support the use of this knowledge to help mitigate fertility problems in women.

Bhattachary states " it is well-known that ovaries express xenobiotic bioactivating and detoxifying enzymes, yet the knowledge gaps remain for established ovotoxicants" , which acknowledges the need for studies like the current study (Bhattachary, P et al., 2012).

1.4.6 Effects of Genetic Polymorphism on PAH Metabolism

Genetic polymorphisms in metabolic enzymes, such as the CYP family, are known to influence individual metabolism of xenobiotic compounds (Strickland, P et al., 1999). Genetic polymorphisms in potential PAH metabolizing phase I and II enzymes have been shown to contribute to inter-individual differences in urine concentration of PAH metabolites in occupationally and non-occupationally exposed populations.

Multiple human studies have identified many genetic polymorphisms in CYP 1A1 and 1B1, some of which have been linked with susceptibilities to lung and breast cancer, however race- related differences in occurrence of genetic polymorphisms in these CYP species make it difficult to interpret these studies as there are also reports of no relationship of polymorphisms of CYP1A1 and 1B1 to cancer susceptibility (Nakachi, K et al., 1993; Watanabe, J et al., 2000;

Kelsey, KT et al., 1994; Nebert, DW et al., 1999). Abnet et al., investigated multiple polymorphisms, including CYP1A1 and CYP1B1 polymorphisms, on the urinary PYR1 glucuronide

(PYR1G) concentrations in 199 healthy subjects and found CYP1B1 to have a strong association

21 with PYR1G concentrations in that population and a modest but significant associations with one variant in each of the AHR, CYP1A1 and CYP1A2 genes (Abnet, CC et al., 2007). Another study evaluated the correlation between external PAH exposure and biomarkers of exposure and investigated to what extent genetic polymorphisms in metabolic enzymes could explain inter-individual variation in PYR1 levels. They found that the part of the variance that could be explained by differences in biotransformation genotypes seemed to be of the same order of magnitude as the variance explained by differences in exposure. They concluded that PYR1, in addition to being a sensitive indicator of recent human exposure to PAHs, may also to some extent reflect the inter-individual variation in susceptibility to PAHs (Alexandrie, AK et al.,

2000). Yang et al., reported polymorphisms in metabolic enzymes CYP2E1, GSTM1, and N- acetyltransferase (NAT2) influenced urinary naphthol levels and also suggested that the GSTT1 null genetic polymorphism has the potential to affect the biological monitoring of PAHs with

PYR1, and might act as a genetic factor in PAH-related toxicity in subjects who did not have occupational PAH exposure (Yang, M et al., 1999).

Inter-individual variability in the excretion of urinary OH-PAHs has been shown with different GSTM1 and GSTT1 genotypes. Hong's group found in their study with bus drivers, that the excretion of PYR1G in GSTM1-deficient smokers was higher than that in GSTM1-positive smokers. In addition, PYR1G excretion was higher in GSTT1-positive smokers than in GSTT1- deficient smokers (Hong, YC et al., 1999). Another group found significantly increased PYR1 levels (p <0.001) in PAH occupationally exposed coke oven workers but no occupational induction of the effect biomarkers (DNA strand breaks for example) after adjustment for cigarette smoking. Their multi-variate analysis did not show a significant influence of GSTM1

22 and GSTT1 polymorphisms on any of their biomarkers (van Delft, JH et al., 2001). The later finding was consistent with Kawamoto's group's report of no effect of polymorphisms in

CYP1A1 or GSTM1 on urinary PYR1 (Kawamoto, T et al., 1998). More recently, Petchpoung's group found that urinary PYR1 concentrations were associated with exposure to air pollution, cigarette smoking and polymorphisms of CYP1A1 and GSTM1 but not with polymorphisms in

GSTT1 (Petchpoung, K et al., 2011). Finally, the literature is mixed with regards to different ethnic/racial groups, regarding the importance of polymorphisms in PAH metabolism. For example, as opposed to the findings of Hong's group cited earlier, Lee's group found that smoking had a more profound effect on urinary OH-PAH metabolites than did genetic polymorphism in Koreans. They investigated Korean aircraft maintenance workers' genetic polymorphisms in CYP1A1 , CYP2E1, and GSTM1 and GSTT1 and their effects on PYR1 and NAP2 levels as well as estimated their levels of exposures to PAHs (Lee, CY et al., 2001). Nan's group examined the same polymorphisms in Korean coke oven workers and Korean university students, and found that In the occupationally exposed population, CYP2E1 and GSTM1 appeared to play an important role in the metabolism of pyrene and naphthalene. However, in individuals that were not occupationally exposed to PAHs, GSTM1 and smoking seemed to influence the urinary concentration of NAP2 (Nan, HM et al., 2001). Lastly, there are also differences in gene allele frequencies within races such as those found among Asians. Garte's group found a significant difference between Japanese and other Asians for GSTM1 and GSTT1, with the Japanese showing lower frequencies of both deletions as well as major and significant differences in frequencies between Caucasians for these deletions (Garte, S et al., 2001). All of these findings indicate that more work needs to be done in order to understand the role of

polymorphisms in individuals' PAH metabolism and ultimate susceptibility to PAH-induced

ovotoxicity.

1.5 PAH Biomonitoring

Table 2 illustrates PAH parent compounds, their structures, and urinary hydroxylated metabolite names.

Table 2: PAH Parent Compounds and OH-PAH metabolites evaluated in this thesis

PAH Parent PAH Parent Structure Metabolite (s) Naphthalene 1-Hydroxynapthalene

2-Hydroxynapthalene

Fluorene 2-Hydroxyfluorene

3-Hydroxyfluorene

9-Hydroxyfluorene Phenanthrene 1-Hydroxyphenanthrene

2-Hydroxyphenanthrene

3-Hydroxyphenanthrene

4-Hydroxyphenanthrene*

Pyrene 1-Hydroxypyrene

PAHs in the National Bio-monitoring Program. * Not measured in this thesis.

Specific PAHs evaluated in this thesis are hydroxylated metabolites of small PAHs. The best studied PAHs (DMBA, 3-MC, and BaP) in terms of ovarian toxicity have been established in multiple investigations to destroy primordial follicles in laboratory animals. There is sufficient evidence for using small urinary OH-PAHs as biomarkers of exposure to larger PAHs because larger PAH metabolites such as BaP metabolites have been found in urine samples along with the urinary OH-PAH metabolites involved in this thesis in several studies. Buratti, M et al., detected 3-hydroxybenzo[a]pyrene in spot urine samples containing 15 inhaled priority PAHs that included FLUO2, NAP2, PHEN3, and PYR1 collected at three different times during a work week from asphalt workers (Buratti, M et al., 2007). Moreover, small urinary metabolites OH-

PAHs used in this thesis have been found to represent inhalation exposure to BaP in multiple studies. Merlo's group investigated the relationships between exposure to ambient air PAHs and PYR1 and indoor air pollution using BaP air measurements as an index compound of PAH levels in the air in traffic police officers and indoor subjects respectively. Stratified analysis showed higher levels of PYR1 in the urine of those who smoked 15 or less cigarettes per day and who carried the CYP1A1 polymorphism relative to those who didn't (Merlo, F et al., 1998).

Finally, since large PAHs are mostly eliminated in the feces, they are impractical in terms of specimen collection in human studies.

A number of techniques have been developed for the biological monitoring of human exposures to PAHs. Among them are PAHs and their metabolites in blood (serum) and urine, measurement of mutagenicity in urine and feces, chromosome aberrations and sister chromatid exchanges in peripheral blood lymphocytes, and DNA and protein adduct measurement in the serum and in other tissues (Larsen, JC et al., 1995). The exchange of sister

25 chromatids, chromosomal aberrations, and mutagenicity of urine and feces have poor specificity for PAHs (Hatijan, BA et al., 1995; Popp, W et al., 1995; Perera, FP et al., 1994;

Santella, RM et al., 1994). High performance liquid chromatography (HPLC) was shown in the past to be more specific in the detection of PAH-DNA adducts than enzyme-linked immunosorbent assay (ELISA), however, it was concluded that most of the analytical methods used to determine PAH-DNA adducts were too nonspecific to produce accurate and comparable results (Angerer, J et al., 1997). Ferreira, et al., examined employees at a graphite electrode production company and in a foundry for internal doses of PAHs by measuring protein adducts from hemoglobin in an attempt to avoid measurement errors introduced by DNA repair mechanism found in prior studies using DNA-adducts. His group found increasing adduct concentrations and increased diagnostic sensitivity, but found a weak correlation coefficient

(0.26) (although a statistically significant relationship) between the external exposure (sum of

13 PAHs) and the internal dose (level of hemoglobin adducts) of PAHs (Ferreira Júnior, MF et al.,

1994). Tas, S et al., assessed the risk of exposure to PAHs in steel foundries and a graphite electrode producing plant using albumin and HPLC. They found that the relationship between the BaP-diol epoxide adducts to albumin (BPDE-alb) and the intensity of exposure from airborne personal sampling to PAHs and PYR1 excretion in post-shift urine to be significantly higher in exposed workers compared to controls. However, there was a wide inter-individual variation between subjects' adduct levels with the same level of occupational exposure measured with airborne personal sampling. Therefore they concluded that as long as BPDE- protein adduct levels had not been demonstrated to be better related to the risk of adverse genotoxic effects caused by PAHs in target organs than the PYR1 level in urine, the latter

26 remained the most practical biological parameter for assessing the risk of exposure to PAHs

(Tas, S et al., 1994). The sensitivity and specificity issues of the serum and urine biomarkers persisted throughout the literature over the years and helped shape the current preferred methods of biomonitoring of PAH exposure in humans.

Metabolites of parent PAHs smaller than pyrene are usually excreted in the urine in the form of OH-PAHs, facilitating their effectiveness as biomarkers of PAH exposure (Dor, F et al.

1999; Li, Z et al. 2008). PYR1 has been found to be a good biomarker of exposure in both environmental and occupational studies (ATSDR, 1995; Li, Z et al. 2008). Urinary PAH metabolite biomonitoring of PYR1 has proven to be sensitive and reliable in many studies

(Gerde P et al., 2001; Campo, L et al., 2006; McClean, MD et al., 2004;IPCS, 1998).

Biomonitoring of OH-PAHs in the US is part of an ongoing effort to reduce and monitor particulate matter in air pollution. Asphalt is a source of occupational and environmental particulate matter through bitumen fumes. PYR1 has been useful in the study of high occupational exposure to bitumen fumes produced when it is stored and handled at temperatures up to 200-230 Centigrade (C) (Mc Clean, MD et al., 2004). Historically, the relative ease of measurement of PYR1 and its' prevalence in human urine have been instrumental in epidemiological studies aimed at illustrating the need for biomarkers of fine particle exposure.

Some of those studies showed apparent associations between ambient particulate matter concentrations and health effects among adults. They also illustrated the insufficiency of the current regulation for particulate matter levels in the US and other countries that address the soot and sulphur dioxide from the 1950's, at preventing respiratory adverse health effects and

27 mortality (Pope, CA 1989; Schwartz, J et al., 1992; Dockery, DW et al., 1993; Pope, CA et al.,

1995). Since the early 2000's, the Centers for Disease Control and Prevention's NHANES has used urinary OH-PAH concentrations to establish reference range concentrations for the US population, and to set benchmarks for future epidemiologic and biomonitoring studies (Li, Z et al., 2008). The survey focuses on the PAHs of concern to IARC, the NTP, and on the ASTDR's and

USEPA's NPL.

Pyrene is 90% metabolized into PYR1 and it has a high concentration in all mixtures of PAHs

(Cattaneo, A et al., 2010). There are positive correlations between external exposure to pyrene and its metabolite PYR1 (Cattaneo, A et al., 2010). The NHANES reports on urinary OH-PAH's from samples of thousands of people including children, adolescents and adult in the US population in two year intervals, which support these statements. The NHANES reports are a reliable assessment of human exposure and excretory route. The report from 2001 and 2002, of

22 OH-PAH urinary metabolites showed detectable levels ranging from nearly 100% for the metabolites of naphthalene, fluorene, phenanthrene, and pyrene, to less than 5% for metabolites from parent compounds with higher molecular weight such as chrysene, benzo[ c]phenanthrene, and benz[ a]anthracene (Li, Z, et al., 2008), which supports the prior sized-based statements regarding major routes of excretions for PAHs. Further, the geometric mean for PYR1—the most commonly used biomarker for PAH exposure—was 49.6 ng/L urine, or 46.4 ng/g creatinine in that study. These authors found that children (ages 6–11) generally have higher levels than adolescents (ages 12–19) or adults (ages 20 and older). Their model- adjusted, least-square geometric means for PYR1 were 87, 53 and 43 ng/L for children, adolescents (ages 12–19) and adults (ages 20 years and older), respectively. Log-transformed

28 concentrations for major detectable OH-PAHs were significantly correlated with each other.

The correlation coefficients between PYR1 and other metabolites ranged from 0.17 to 0.63 (Li, Z et al., 2008), which illustrates that PYR1 is correlated with some PAHs more than others.

Motorykin, O et al., illustrated the usefulness of the NHANES reference values in their study of the metabolism and excretion rates of different OH- PAHs after oral and inhalation of PAHs, when they used the NHANES adult geometric means from the 2008 tables for urinary creatinine adjusted OH-PAHs to compare to their values from study participants. Their baseline values were in the range of the 25th to 75th percentile of NHANES concentrations (Motorykin, O et al.,

2015).

Examples of other studies using PAH urinary metabolites as the main biomonitoring measurement tool include a controlled human exposure study for the significance of ingested

PAHs in addition to occupational exposures to PAHs. The investigators used urinary metabolites to demonstrate that diet in addition to occupational exposure need to be considered in exposure assessments of PAHs (Buckely, TJ et al., 1992). Urinary excretion of multiple PAH metabolite biomarkers were used to assess the exposure of soil remediation workers on a former creosote wood impregnation site polluted with creosote oil. The PAH biomarker data

(concentrations and diurnal excretion profiles) showed significant work-related exposure in both non-smoking and smoking participants (Elovaara, E et al., 2006).

There are inter and intra-individual PAH concentration variability throughout the day.

Studies using urinary PAH metabolites as biomarkers of exposure have used 24-hour composite urine samples as well as first morning void measurements in occupational and environmental

29 studies to represent daily PAH exposure. The internal validity of these studies have been adversely affected by 24 hour urine collection attempts due to compliance issues involving forgetfulness, inconvenience, and misplaced samples or poor sample container capacities, contamination during storage, or loss of urine (Garde, AH et al., 2004). Han, IK et al. (2008), investigated whether the convenience of using the first morning voids for the PYR1 concentrations can reflect the daily average PYR1 concentrations. They also investigated whether creatinine adjustment could reduce the inter and/or intra-individual differences between the first morning urine and 24-h composite urine measurements, since historically researchers have used it to adjust for the large inter and intra-individual urine output volume rates. This method is still controversial because diurnal variation in creatinine excretion also varies with intake of meat, age, gender, and other factors (Boeniger, MF et al., 1993; Barr, DB et al., 2005). Han reported adult coke oven workers versus non-coke oven workers' significant

(40% to 62%) intra-individual differences between the first morning voids and 24-h PYR1 concentrations. The ﬁrst morning PYR1 was substantially correlated with 24-h PYR1 and appeared to discriminate occupationally exposed workers from those not so exposed.

Creatinine adjustments of PYR1 concentrations reduced the intra-individual difference between

ﬁrst morning and 24-h composite urine measurements by approximately 10% but did not signiﬁcantly improve overall correlations between the two measurements, although winter 24- h urine and spot urine data correlated higher than fall data (Han, IK et al., 2008). Urinary OH-

PAH biomonitoring is the measurement tool of exposure in this thesis. Inter and intra-individual differences in PAH urinary metabolite concentrations will not pose a methods concern because the endpoint of analysis is the average of measurements taken over a six month period in this

30 convenience sample where the interest is in assessment of the long-term effects of PAHs on ovarian reserve markers.

2.0 Female Reproductive Physiology

2.1 Hypothalamic-Pituitary Ovarian Axis

Environmental and internal stimuli trigger gonadotropin releasing hormone (GnRH), a decapeptide, synthesized and released by specific neuronal endings in the anterior and mediobasal hypothalamus in a constant pulsatile pattern (Homburg, R, 2014). GnRH reaches the anterior pituitary gland through portal vessels that connect the hypothalamus to the anterior pituitary gland (Devoto, L et al., 2000). In response, the anterior pituitary gland releases FSH and LH (gonadotropins), which act on the ovarian follicle cells (Homburg, R, 2014).

FSH and LH stimulate the ovary to produce the sex steroids (E2 and progesterone) (Dekker, M

1999; Homburg, R, 2014). Inhibins are hormones secreted by the granulosa cells of small ovarian follicles that selectively inhibit the anterior pituitary FSH secretion (Welt, CK et al.,

2001; Homburg, R, 2014). Inhibin A and inhibin B dimers differ in their pattern of secretion.

Inhibin A concentrations are low during most of the follicular phase of the menstrual cycle, but start to rise during its latest stages and peak in the mid-luteal phase. In contrast, inhibin B concentrations start rising early in the follicular phase, paralleling but lagging behind the FSH rise (Homburg, R, 2014). Inhibin B reflects the size of the follicle cohort (Homburg, R, 2014).

During the menstrual cycle, the effect of inhibins, E2 and progesterone (on the hypothalamus and anterior pituitary) are mainly inhibitory (Homburg, R, 2014). However, when E2 surpasses a threshold level, its feedback switches from negative to positive on the hypothalamus and anterior pituitary gland (Homburg, R, 2014). FSH stimulates the growth of antral follicles,

31 inhibin synthesis, E2 production, as well as granulosa cell proliferation and differentiation in the ovary (Homburg, R, 2014; Boundless, 2013).

2.2 Menstrual Cycle

The menstrual cycle lasts from the onset of menses (the first day) until the next onset of menses (Boundless, 2013). During the follicular phase (days 5-15) basal secretion of LH and FSH occur, and E2 secretion predominates (Homburg, R, 2014). E2 secretion, by the large ovarian follicles, increases gradually throughout the first 11 days of the cycle. As days 13 to 15 approach, there is a dramatic rise in estrogen followed by an LH surge (upon which meiosis is resumed in the oocyte), and to a lesser extent an FSH surge, which trigger the dominant follicle to ovulate (Adashi, EY et al., 1994; Boundless, 2013; Homburg, R, 2014). After ovulation, the dominant follicle reorganizes to produce the corpus luteum (Adashi, EY et al., 1994). The possibility of fertilization of the ovum and implantation takes place about day 21 , after which the luteal phase ends with return of basal secretion of FSH and LH. The corpus luteum grows and secretes progesterones after ovulation to prepare the uterus for implantation of a fertilized ovum and to maintain pregnancy (Boundless 2013). If fertilization does not occur, the corpus luteum spontaneously regresses (Adashi, EY et al., 1994) which results in menstruation, and the negative feedback effects of E2, progesterone and inhibin A on FSH secretion are suddenly lost so that FSH is secreted in relatively large quantities during menstruation itself. The granulosa cells in the ovary have FSH receptors and the theca cells in the ovary have LH receptors

(Homburg, R, 2014).

At the beginning or menses phase of each cycle, a group of the most mature follicles (2–5 mm in diameter) is recruited for further growth, granulosa cell differentiation and

32 multiplication (Homburg, R, 2014). The follicles more sensitive to FSH rather than those less mature are selected for further development at the time of the FSH inter-cycle rise (Homburg,

R, 2014). The follicle most sensitive to FSH will ovulate and use FSH to increase aromatase activity and produce E2s and inhibin B (Homburg, R, 2014). Inhibin B is secreted from the small and large antral follicles. As FSH concentrations fall in response to rising E2 and inhibin B levels it becomes less available and only the most sensitive follicle with the lowest threshold for a response to FSH, survives and continues to thrive and produce the most E2 and LH receptors.

The induction of LH receptors on the largest developing follicle(s) (>10mm diameter) allows response to LH, which leads to E2 production and follicular growth in the mid-late follicular phase. It also enables LH to have a role in the development of the dominant follicle in the late follicular phase and prepare it for the oncoming LH surge (Homburg, R, 2014).

2.3 Folliculogenesis

At the follicular level, the primordial follicles (small preantral follicle) are quiescent and arrested in the diplotene stage of the first meiotic prophase (in second trimester of pregnancy and onward)( Zheng, W et al., 2012; Juengal, JL et al., 2005). They have a single incomplete flat cell layer of granulosa cells. Throughout life, primordial follicles continuously enter the growing pool, and a small fraction of these recruited follicles progress successfully through all stages of maturation, (primordial, primary, secondary (large preantral), antral, preovulatory/large antral) to the ovulatory stage (Parrot, JA et al., 1999; Otsuka, F et al., 2011). As the follicles mature their granulosa cells differentiate and become cuboidal and proliferate. Once there is a single layer of granulosa cells they are called primary follicles. When there is more than one layer, they are called secondary follicles. At this point a basement membrane is formed to separate

33 the theca cells from the granulosa cells (Zoller, LC 1991 ). The first meiotic division of the oocyte is completed as a result of the preovulatory LH surge ( Boundless, 2013). AMH, a dimeric glycoprotein and member of the transforming growth factor-beta family (Homburg, R, 2014), is secreted by the granulosa cells of the primary, secondary, and small antral follicles (van Houten,

EL et al., 2010). See Figure 3.

Folliculogenesis is under paracrine regulation. AMH is gonadotropin-independent (Iorio, R et al., 2014) and regulates folliculogenesis at the two extremes (Homburg, R, 2014; Durlinger, AL et al., 2002). First, it restricts the progress of developing primordial follicles (Durlinger, AL et al.,

1999) and second it inhibits the sensitivity of antral follicles to FSH (in selection of the dominant follicle). It also inhibits aromatase activity that catalyzes E2 production during an ovulatory cycle (Durlinger, AL et al., 2002; Homburg, R, 2014). As previously mentioned, the first meiosis is completed just before ovulation. The oocyte only completes the second meiosis if it is fertilized (Zoller, LC 1991 ). Because of the established roles of AMH and the fact that it is gonadotropin-independent, it serves as a reliable indirect marker of the number of small antral follicles in a cohort available for ovarian stimulation- a very sensitive marker of ovarian reserve irrespective of age (Homburg, R, 2014). See Figure 3 for a pictorial representation of the ovarian follicle stages that secrete the markers of ovarian reserve evaluated in this thesis (AMH, inhibin

B, and FSH). It illustrates the selective activity of AMH, FSH, and inhibin B on folliculogenesis and the author's proposal that Inhibin B may have a paracrine positive influence on folliculogenesis (Hillier, SG 1991, Findlay, JK et al., 2000, Visser et al. 2006, Broekmans, FJ et al.

2008).

Figure 3 . Folliculogenesis , Endocrine Related Cancer, Torino, F et al., 2012

2.4 The Ovary and Aging

Reproductive aging in women is characterized by the depletion of a relatively fixed number of ovarian follicles over a woman's lifetime (Johnson, J et al., 2004). At birth women have more than a million primordial follicles and by puberty the number drops to about 400,000 secondary to atresia, a noncyclical, non-gonadotropin dependent, unremitting lifelong process in women believed to be irreversible (Seifer, DB et al., 2014). Primordial follicle quiescence, activation, and atresia are controlled by phosphoinositrol-3-kinase signaling (Zheng, W et al., 2012). The optimal fertility period of females is between ages 18 and 30 years, after which a slow decline ensues into the late 30ths followed by a faster decline as menopause approaches (Figure 3;

Hansen, KR et al., 2008). The average age of menopause is 50 - 51 years (range 45 to 59) (Gold,

EB et al., 2001). At menopause, the follicle count becomes essentially zero. Assuming fixed time differences between the reproductive milestones perimenopause (the time period when ovulation and menses becomes irregular) and menopause, fertility will not be lost completely during that time period which lasts about for 4 years on average (Nikolaou, D et al., 2003).

2.5 Ovarian Reserve

Ovarian reserve refers to the number and quality of remaining primordial follicles, which is the measure of a women's reproductive potential or fecundity. Clinically, diminished ovarian reserve (DOR) has been defined as reduced response to ovarian stimulation in women of reproductive age with regular menses and without any other anatomical or other non- hormonal reason preventing the ability to reproduce (Scott, RT et al., 1995). It indicates that there is a low number and/or impaired development of primordial follicles. Women with markedly DOR have low chances of conception with their own gametes, even with assisted reproductive technologies (Sharara, FI et al., 1998). Diminishing ovarian reserve is normal perimenopausally, and happens in women during their mid to late thirties and at times earlier

(Scott, RT et al., 1995) . In 2002, 7.4 % of married women in the US experienced some type of infertility (Stephen, EH et al., 2006). Specifically 10.9% of US women aged 15 to 44 years, which is within the age range of the thesis study participants, experience impaired fecundity based on data from 2006 to 2010 (CDC, 2015). Premature ovarian failure (POF) affects 1% of the general population (Coulam, CB et al., 1986) and refers to women who reach menopause before the age of 40 by means other than medical intervention (Coulam, CB et al., 1982). The women (with

36 intact, functioning ovaries and no other known reason for delayed conception) who don't meet the criteria for POF are classified as having DOR (Scott, RT et al., 1995).

2.6 Markers of Ovarian Reserve

As indicated earlier, true ovarian reserve can only be measured with primordial follicle count, which is impossible in humans without destroying the ovary. Therefore, indirect measures of ovarian reserve are relied upon and involve measurements of the hormones involved in folliculogenesis discussed above for assessment of ovarian reserve both in research and in clinical practice in humans. Serum levels of FSH, AMH, inhibin B, and E2 are all used in assessment of ovarian reserve. Measurements of antral follicle count (AFC) with ultrasound is also used in clinical assessments of ovarian reserve. The ovarian reserve markers that are examined in this thesis (AMH, inhibin B, and FSH) will be the focus of the remainder of this discussion. Low levels of inhibin B indicates a reduction in the small and large antral follicle count and have been used as a test of functional reserve of ovaries to facilitate prediction of low response to in vitro fertilization standard protocols (Sharara, FI et al., 1998). Clinically, an increase in a women's basal FSH levels measured on day 3 of the menstrual cycle occurs due to follicle depletion and is used to classify women as having DOR (Scott, RT et al., 1995; Karande,

V et al., 1999) and poor oocyte quality (Karande, V et al.,1999). The latter two ovarian reserve markers fluctuate with the menstrual cycle as discussed above. AMH secretion is gonadotropin- independent which means it stays consistent throughout the menstrual cycle, making it the superior indirect marker of ovarian reserve over FSH and inhibin B. Both median and mean

AMH serum levels decrease with age and the wide year to year variation in values reflects

37 antral follicular supply independent of age. (Seifer, DB, et al., 2010; Seifer, DB et al., 2011). See

Figures 4 and 5.

Figure 4. AMH decreases with age

AMH Parallels Antral Follicle Count

Figure 5. AMH parallels antral follicle count, Modified from Seifer, DB, et al., Fertility and Sterility, 2011

3.0 Confounders of Ovarian Reserve

A few studies have explored factors that may affect ovarian reserve. One study examined body mass index (BMI) and ovarian reserve markers, including markers evaluated in this thesis, to assess the association between BMI and ovarian reserve in late reproductive age (40-52) women. Su et al., reported that obese (BMI > 30) women had significantly lower levels of AMH

(p =0.02) and inhibin B (p=0.08) than normal (BMI < 25) weight, age-matched women respectively. FSH, AFC, and E2 were not found to be associated with BMI and the AMH differences appeared to result from physiological processes other than decreased ovarian reserve (Su, HI et al., 2008).

Pal, et al.,(2010) reported in a cross sectional study of infertile women that FSH, Mullerian

Inhibitor Substance (same as AMH), and inhibin B are associated with chronic stress measured by morning cortisol levels and psychosocial stressor indicators. Age, family history of early menopause and chronic stress were all predictive of DOR (Pal, L et al., 2010). Finally, Kinney, A et al., investigated whether alcohol, caffeine, and smoking were associated with FSH, inhibin B,

E2, and AFC as indicators of ovarian age. They found that current smoking increased FSH (95%

CI 0.04-0.39), but had no influence on the other measures and neither alcohol or caffeine was related to any ovarian age indicator (Kinney, A et al., 2007). The etiology of DOR is likely multifaceted and will take research efforts such as those involved in this thesis to positively impact the future of women in the US and likely the world, who suffer from its'consequences.

To the best of the authors' knowledge, the research involved in this thesis is the first to investigate polycyclic aromatic carbon-induced ovotoxicity using serum markers of ovarian reserve in humans.

4.0 Study Objective

The study objective was to evaluate the association between PAH exposure determined by urinary biomonitoring of 9 OH-PAH metabolites and three serum markers of ovarian reserve,

AMH, inhibin B, and FSH in reproductive aged women. We hypothesized that women with higher PAH exposure levels would have decreased ovarian reserve (decreased AMH and inhibin

B, and increased FSH) compared to women with lower levels of PAH exposure. A second hypothesis was that a woman's susceptibility to PAH effects on ovarian reserve would be affected by whether she had polymorphisms in enzymes that detoxify PAHs (homozygous null,

GSTM1 and GSTT1). We hypothesized that women who were homozygous null GSTM1 and or

GSTT1 would have lower ovarian reserve markers compared to women who were not.

5.0 Methods

5.1 Study Design

This is a cross sectional study analysis of data collected from a prospective pilot study investigating whether the average exposure to PAHs over time, measured as the average concentration of urinary OH-PAHs collected over a 6 month period, had a dose-dependent adverse effect on ovarian reserve markers in reproductive aged women and whether the effect was modified by polymorphisms of PAH metabolizing enzymes. The general aim was to understand how toxicants cause ovarian dysfunction in order to develop future preventative measures . This study aimed to measure OH-PAH metabolite excretion profiles in the urine and baseline serum ovarian reserve markers and genetic polymorphisms in women who did not intend to become pregnant during the study period. Fifty-one women who were not on hormonal therapy were selected for the urinary PAH biomonitoring group from a convenience

41 sample of women who did not live in segments selected for the National Children's Study (NCS).

This pilot study was designed to test whether the methods used would be feasible to use in a study with a larger sample size, as an adjunct to the planned NCS. Initially, eligible women were identified and recruited by door to door household contact with fliers, follow-up telephone and email contact by study staff. Subsequently, when the NCS shifted to other recruitment strategies, eligible women were recruited at public events such as health fairs at universities and colleges, work places and those sponsored by community groups. Visits to the study center, questionnaires, diaries, email, telephone, mail, and web-based communications, facilitated by the study staff were used for data collection and education.

5.2 Description of Study Population (Participant Sampling Frame)

5.2.1 Inclusion and Exclusion Criteria

Eligible women were between the ages of 18 and 44, residing in Orange County California, who were not pregnant currently, not planning to conceive, and not using hormonal contraception. Exclusion criteria included ovarian failure (surgical, infertility treatments), history of chemotherapy, pelvic radiation, pelvic inflammatory disease, polycystic ovarian syndrome, endometriosis, women without menses for 5 or more months, inability to speak

English or Spanish, inability to understand the consent form, and residence in one of the

Orange County, CA NCS segments.

5.3 Study Encounters and Specimen Processing

5.3.1 Baseline Study Visit

After completing informed consent, the recruited women scheduled baseline evaluations at one of two Orange County locations of the UC Irvine Institute for Clinical and Translational

Science (ICTS) on dates projected to be 5 to 9 days prior to their next expected menstrual cycle.

ICTS nursing staff measured height, weight, and blood pressures and collected blood samples.

Study staff administered baseline questionnaires, which were adapted from the standardized preconception questionnaire developed for the NCS (NCS, 2007) and from the NCS First

Trimester Maternal In-Person Questionnaire (NCS, 2007). They obtained the demographic information (age, birthplace, ethnicity, household income, education), medical history (cancer, chronic illnesses, medication use), reproductive history (menstrual history, pregnancy and contraceptive history, fertility and reproductive problems), tobacco, alcohol use (EtOH), exercise, and illicit drug use history, environmental tobacco smoke exposure, exercise history, occupational history (job title, industry, exposure to specific chemicals and hazards), use of cleaning agents and pesticides in the home and yard, as well as pets. Participants were provided with urine collection kits and instructions for urine collection and storage.

Daily Diary: Participants were given and asked to record daily information about occurrence of menstrual bleeding, heaviness of menstrual flow (assessed with the help of a menstrual pictogram, as described by Wyatt (Wyatt, KM et al. 2001), use of contraception, number of cigarettes smoked, illness, use of medications, use of alcohol, and whether they had sexual intercourse (Tingen, C et al., 2004).

5.3.2 Measurement of Urinary Hydroxylated PAH Metabolites

Participants were instructed to collect one urine sample per menstrual cycle (on cycle day

10) into a plastic beaker, pour 10 ml from the beaker into each of 3 tubes, and to store them in the box provided with the kit in their home freezers for pickup by study staff every month.

Urine samples were used for the measurement of hydroxylated PAH metabolites as biomarkers of PAH exposure (Li, Z et al., 2008), and creatinine. Urine samples for measurements of 9 urinary OH-PAHs were collected over a 6 menstrual cycle period (2010-2012). Urinary OH-PAH metabolites were measured for each subject for two (2 participants) or three (49 participants) cycles during the testing period by the California Department of Public Health Environmental

Health Laboratory using procedures developed by the Center for Disease Control and

Prevention NHANES study (CDC, 2013; Li, Z et al., 2008). If a participant only had collected two or three cycles of urinary hormone data, (collected for a different aim of this data set) then OH-

PAHs were measured in all cycles, as the original plan for the other aim was to get hormone data on 6 cycles over the 6 month study period. If the participant collected urinary hormone data for more than three cycles, then the first three cycles with the fewest missing days of urinary hormone data were selected for OH-PAH measurements. Urine samples were enzymatically deconjugated and subjected to solid phase extraction, followed by gas chromatography/isotope dilution high resolution mass spectrometry for nine PAH metabolites,

NAP1 and NAP2, PYR1, PHEN1, PHEN2 and PHEN3, and FLUO2, FLUO3, and FLUO9, (Romanoff,

LC et al., 2006; Li, Z et al., 2008). Urinary OH-PAH concentrations were expressed per gram urinary creatinine or unadjusted OH-PAH concentrations were used in separate analysis. A

44 small percentage of analytes were below the limits of detection (LOD) of the assay. These were set to the LOD divided by the square root of two (Ogden, TL et al., 2010).

Participants also performed human chorionic gonadotropin Clearblue® Easy pregnancy tests

(Swiss Precision Diagnostics) by urinary dipstick if their menses onset had not occurred within

10 days after the expected date.

5.3.3 Serum Ovarian Reserve Markers

One 10 ml tube of blood was collected and allowed to clot at room temperature, then centrifuged at room temperature for 20 min at 1500xg. The serum was then removed for measurements of AMH, FSH, and inhibin B and stored at –70°C until assays were performed.

AMH was measured in ng/ml, inhibin B in ng/L, and FSH in units per liter (U/L). De-identified frozen serum samples were shipped by express mail on dry ice to Dr. Jenny Visser’s laboratory

(Erasmus Medical Center, Rotterdam, The Netherlands) for hormone analyses (AMH, FSH, and inhibin B immunoassays). AMH was measured using two monoclonal mouse anti-human antibodies directed against two different antigenetic determinants of AMH (Kevenaar, ME et al., 2006). FSH was measured by chemiluminescent immunoassay (Diagnostic Products

Corporation, Los Angeles), in the same manner as AMH. Serum inhibin B was measured using enzyme-linked immunoassay (ELISA) (Serotec Limited, Kidlington, UK), which used two antibodies against the alpha and beta-subunits of the inhibin B molecule (de Vet, A et al., 2002).

For all assays, each subject sample was run in duplicate and the average of the two samples were used for statistical analyses and data presentation.

5.3.4 GSTM1 and GSTT1 null polymorphism Genotyping

Genotyping for the GSTM1 and GSTT1 null polymorphisms was performed using DNA extracted from clotted whole blood samples, according to the QIAGEN Supplementary Protocol:

"Purification of archive-quality DNA from clotted whole blood using Clotspin Baskets (Qiagen product no. 158932) and the Gentra Puregene Blood Kit" (Qiagen Cat#158389) by the UC Irvine

ICTS laboratory. Tagman Copy Number Assays were used (Assay # Hs00010004_cn for GSTT1 and Hs02575461_cn for GSTM1). In these assays, RNase P (RNAP) was used as an internal control representing a single-copy reference gene (1/1 genotype). Reaction volumes were 20 μl and they contained: 4 μl of water, 1 μL of 20x TagMan@ primer/probe mix for target gene

(FAM labeled), 1μl of 20x TagMan@ primer/probe mix for RNAP(VIC labeled), 10 ul of 2x

TagMan @ Genotyping Mast Mix (Applied Biosystems), and 4μl of genomic DNA at a concentration of 5 ng/μl. Reactions were run on a 7900HT Thermocycler under these conditions: 95 degree Celsius (C) hot-start for 10 min, followed by 40 cycles of 95 degree centigray for 15 seconds (s), at 60 degrees C for 1 min. Each sample was run in quadruplicate, with the deltaCt averaged over the 4 replicates. Data was analyzed using Copy Caller Software, supplied by Applied Biosystems, which determined gene copy number.

56 to 77 % of the Caucasian population have the GSTM1 null genotype (Farin, FM et al.,

2001; Wang H et al., 2002; Wenslaff, AS et al., 2005; McCarroll, SA et al., 2006) or deficiency, compared to 44 to 23% who have a copy of the gene. 21.3 % of the Southern California Hispanic population have the GSTT1 null genotype compared to 78.7 % who have a copy of the gene

(Gilliland, FD et al., 2002). 40.9% of the Southern California Hispanic population have the

GSTM1 null genotype, compared to 59.1 % who have a copy of the gene (Gilliland, FD et al.,

2002).

6.0 Statistical Analysis Methodology

6.1 Exposure Variables

Creatinine adjusted OH-PAH urinary metabolites were natural-log (In) transformed for right skewed distributions and the transformed variables were found to be normally distributed. The natural-log transformations were used for the remainder of the analysis. Correlations among the In-OH-PAHs were calculated using Pearson correlation coefficients. We used the arithmetic mean concentrations of all 3 cycles for each In-OH- PAH metabolite because we were interested in long-term exposure effects on ovarian reserve markers. One-way analysis of variance

(ANOVA) was used to test for bivariate associations between alcohol consumption (EtOH), income, education and race/ethnicity and OH-PAH urinary metabolites. Independent sample t- test (equality of variance was not assumed) were used to test for bivariate associations between cigarette use, caffeinated coffee, caffeinated tea, soda/energy drink and the averages of each OH-PAH urinary metabolite collected over the study period.

6.2 Outcome Variables

The ovarian reserve markers were found to be right skewed and natural-log transformed for normal distribution. The natural-log transformed data was used for all subsequent analyses.

Pearson correlations were performed on the ovarian reserve markers as well as between the ovarian reserve markers and the age and BMI of the participants for bivariate analysis. ANOVA was used to test for bivariate associations between EtOH, income, education and race/ethnicity and ovarian reserve markers. Independent sample T test were used to test for bivariate

47 associations between cigarette use, caffeinated coffee, caffeinated tea, and soda/energy drink and ovarian reserve markers AMH, inhibin B, and FSH.

6.3 Adjusted Measures of Association

Multiple linear regression models were used to investigate associations between the OH-

PAH urinary markers and serum concentrations of the ovarian reserve markers with adjustments for important covariates. Each outcome marker was regressed against the OH-

PAHs, and OH-PAHs were retained in the models at a significance level of 0.20. Age, BMI, education, caffeinated tea, soda/energy drink and EtOH were selected as covariates, using a cut-off of p < 0.20 for their associations as potential confounders with the exposure and outcome variables in the bivariate analysis. Caffeinated coffee and cigarette use were not included because they are considered sources of PAH exposure. Family income was not chosen to represent socio-economic status because there were too many (N=7) missing values. All pre- selected covariates were entered into the Statistical Program for Social Sciences (SPSS) program while building the models for the 3 outcome variables (The Enter Method). The covariates were retained in the models if they changed the standardized beta coefficient of the PAH(s) more than 10%.

Principal Component Factor analysis with variance maximizing (varimax) rotation was used to reduce the dimensions in the PAH predictors to better understand the multicollinearity of the PAHs.

6.3.1 Effect Modification of Polymorphisms

Bivariate associations between the genotypes GSTM1 and GSTT1 were investigated with independent t-test with all 9 of the OH-PAH metabolites and none were found to be significant

(p > 0.05). Bivariate associations between GSTM1 and GSTT1 and the ovarian reserve marker endpoints (AMH, inhibin B, and FSH) were investigated using independent t-test. To evaluate the effect modification of the homozygous null GSTM1 and homozygous null GSTT1, split file, compare group analysis were performed. The participants were stratified on the null genotype for both polymorphisms and compared to all others in separate multivariable analyses. The adjusted multivariable models for the ovarian reserve markers were run comparing the homozygous null genotypes to the heterozygous/wild-type genotype group. All statistical analyses were done with SPSS version 23.

7.0 RESULTS

The mean age of the participants was 29.86 (SD 6.46) and the mean BMI was 24.9 (SD 5.29), which is considered normal weight. Fifty three percent of the participants had a bachelors degree or higher and just over half consumed a moderate amount of alcohol (55%) on a regular basis. Table 3 summarizes selected participant characteristics.

Table 3: Participant Characteristics (Demographics)

Characteristic Mean SD Age (years)at baseline 29.90 6.50 BMI (Kg/m2)* 24.90 5.30 Number Percent Race/Ethnicity Hispanic 11 21.60 Non-Hispanic White 22 43.10 Non-Hispanic Asian 14 27.50 Non-Hispanic Black 4 7.80 Education - ** Some High School/High School)/Some 23 45.10 College/Vocational Program Bachelor's Degree 16 31.40 Graduate/Doctoral Degree 11 21.60 Ever Smoked or Current Smoker Yes 5 9.80 < 1per month - no 46 90.20 Alcohol Use > 1 a week 12 24 >1 per month to 1/week 16 31 Never to <1/month 21 41 Family Income in the past year**** < $ 50,000 12 23.50 > $ 50,000 - > $100,000 14 27.40 > $100.000 18 35.20 Caffeinated Beverages (Yes Routinely) Coffee 28 55 Tea 21 41 Soda/Energy Drinks 12 24 * 3 missing BMIs * *1 missing Education level *** 2 missing Alcohol *** *7 missing Family Income/Median household income for Orange County 2009-2013 = 75K

7.1 PAH Metabolite Measurements

The participants' geometric mean (GM) concentrations of PAHs for the unadjusted (ng/L) and

the creatinine adjusted (ng/g creatinine) data were compared to the geometric means and 95%

CIs for women from the NHANES data for the US general population during the study period

(Table 4). Creatinine unadjusted NAP2, FLUO3, FLUO9, and PHEN3 did not fall within the

NHANES 95% CI for the geometric means, but did fall within the 95% CI for the 75th percentile.

The geometric mean of unadjusted PHEN2 fell below the NHANES geometric mean 95% CI. All

of the geometric means of the creatinine OH-PAHs in the current study fell below the NHANES

95% CI for the geometric means, which indicates on average, that our population OH-PAH

exposure data were lower than NHANES measurements of OH-PAHs for the general US

population during the study period.

Table 4: Participants' OH-PAH GM compared to NHANES female GM (95% CI) 2011-2012 with ranges

Parent/Metabolites Geometric Mean of ng/L Geometric Mean ng/g of creatinine Participants NHANES Participants NHANES Naphthalene 1-Hydroxynaphthalene 1632 1490 (1200-1840) 1388 2040 (1680-2470)

2-Hydroxynaphthalene 5797 4030 (3500-4650) 4965 5530 (5010-6110)

Fluorene/ 2-Hydroxyfluorene 213 207 (182-236) 184 284 (255-316)

3-Hydroxyfluorene 105 78.8 (68.1-91.1) 91 108 (94.7-123)

9-Hydroxyfluorene 278 217 (193-244) 240 297 (269-329)

Phenanthrene/ 1-Hydroxyphenanthrene 125 119 (109-131) 107 163 (148-180) 2-Hydroxyphenanthrene 35 53.0 (48.7-57.5) 31 72.5 (66.9-78.6)

3-Hydroxyphenanthrene 64 53.4 (48.3-59.0) 55 73.2 (67.0-80.0) Pyrene/ 1-Hydroxypyrene 110 102 (92.6-113) 95 141 (130-154)

Geometric mean urine concentrations for the US population from the NHANES Fourth National Exposure Report, Updated Tables, February 2015 pages 461--497. Tables 5-7 show correlations among the mean values of the participants' nine OH-PAH

metabolites for cycles 1, 2, and 3. All of the OH-PAHs were significantly correlated with each

other except for NAP2, which was not significantly correlated with any other OH-PAH in any of

the three cycles. NAP1 exhibited a statistically significant positive correlation only with FLUO9

51 in cycle 1, but exhibited significant positive correlations with most of the other PAH metabolites

(except NAP2) in cycles 2 and 3.

Table 5: Pearson correlations of mean concentrations of OH-PAH metabolites (Cycle 1)

PAH FLUO2 FLUO3 FLUO9 PHEN1 PHEN2 PHEN3 PYR1 NAP1 NAP2 FLUO2 1 FLUO3 0.877** 1 FLUO9 0.406** 0.347* 1 PHEN1 0.691** 0.607** 0.613** 1 PHEN2 0.599** 0.541** 0.595** 0.768** 1 PHEN3 0.587** 0.657** 0.435** 0.716** 0.806** 1 PYR1 0.352* 0.447** 0.246 0.506 0.522 0.590 1 NAP1 0.148 0.206 0.329* 0.243 0.054 -0.026 -0.199 1 NAP2 0.059 0.100 -0.218 -0.078 0.051 0.049 0.194 0.057 1 ** Correlation is statistically significant at the 0.01 level (2-tailed)* Correlation is statistically significant at the 0.05 level (2-tailed).

Table 6: Pearson correlations of mean concentrations of OH-PAH metabolites (Cycle 2)

PAH FLUO2 FLUO3 FLUO9 PHEN1 PHEN2 PHEN3 PYR1 NAP1 NAP2 FLUO2 1 FLUO3 0.880** 1 FLUO9 0.478** 0.470** 1 PHEN1 0.627** 0.507** 0.733** 1 PHEN2 0.545** 0.493** 0.605** 0.728** 1 PHEN3 0.661** 0.625** 0.740** 0.796** 0.711** 1 PYR1 0.373** 0.401** 0.445** 0.606** 0.566** 0.678** 1 NAP1 0.440** 0.498** 0.357* 0.430** 0.212 0.480** 0.209 1 NAP2 0.098 0.059 -0.061 -0.024 -0.008 -0.083 0.049 -0.025 1 ** Correlation is statistically significant at the 0.01 level (2-tailed)* Correlation is statistically significant at the 0.05 level (2-tailed).

Table 7: Pearson correlations of mean concentrations of OH-PAH metabolites (Cycle 3)

PAH FLUO2 FLUO3 FLUO9 PHEN1 PHEN2 PHEN3 PYR1 NAP1 NAP2 FLUO2 1 FLUO3 0.901** 1 FLUO9 0.566** 0.572** 1 PHEN1 0.631** 0.624** 0.609** 1 PHEN2 0.623** 0.587** 0.673** 0.756** 1 PHEN3 0.651** 0.762** 0.710** 0.679** 0.778** 1 PYR1 0.417** 0.534** 0.439** 0.696** 0.629** 0.709** 1 NAP1 0.433** 0.506** 0.530** 0.536** 0.497** 0.488** 0.544** 1 NAP2 -0.001 0.009 -0.183 -0.011 -0.086 -0.041 0.130 0.267 1 ** Correlation is statistically significant at the 0.01 level (2-tailed)* Correlation is statistically significant at the 0.05 level (2-tailed).

7.8 Principal Component Analysis

The overall principal component factor analysis for OH-PAHs (Table 8, Figure 6) rotations' converged in 3 iterations. Two main factors were identified. Factor 1, which explained 37% of the variance in the PAH metabolites, primarily loaded on PHEN1, PHEN2, PHEN3, and FLUO9 was minimally represented. Factor 2, which explained 63 % of the variance, primarily loaded on

FLUO2, FLUO3, and NAP1. NAP2 was the only metabolite to have a negative loading (in Factor

1), however, it had a positive loading of 0.487 in Factor 2.

Table 8: OH-PAH Principal Components Factor Analysis with Varimax Rotation

Statistical Factor Factor Loadings (Varimax Kaiser normalized) Analysis Extraction: Principal components Natural log Average Factor 1 Factor 2 (ng/g creatinine) NAP1 0.171 0.624 NAP2 -0.591 0.487 FLUO2 0.413 0.771 FLUO3 0.347 0.852 FLUO9 0.735 0.221 PHEN1 0.793 0.398 PHEN2 0.794 0.246 PHEN3 0.762 0.422 PYR1 0.527 0.257 Total 3.327 2.435 Proportion Total 36.972 64.025 Transformation Matrix Component 1 0.800 0.601 2 -0.601 0.800

Figure 6: OH- PAH Principal Components Factor Analysis with Varimax Rotation.

7.2 Ovarian Reserve Marker Measurements

The correlation between AMH and inhibin B was positive, high, and statistically significant (r

= 0.650, p = 0.01). There was non-significant low, negative correlation between AMH and FSH (r

= -0.032). The correlation between FSH and inhibin B was positive, low, and statistically significant (r = 0.371, p = 0.01). Table 9.

Table 9: Pearson Correlations of natural log transformed Endocrine Endpoints

AMH ng/ml Inhibin B ng/L FSH U/L AMH 1 Inhibin B 0.650** 1 FSH -0.032 0.371** 1 ** Correlation is statistically significant at the 0.01 level (2-tailed).

7.3 Bivariate Correlations of Potential Confounding Variables with OH-PAHs

The correlations between age and BMI and the creatinine adjusted average PAH metabolite concentrations for all cycles showed little to no linear relationship, with about half of the associations being positive and half negative. None were statistically significant (Table 10).

Table 10: Correlations between Age and BMI with OH-PAH metabolite means for all cycles

Age BMI PAH Metabolite 0.106 - 0.140 NAP1 - 0.253 - 0.092 NAP2 - 0.069 0.173 FLUO2 - 0.060 0.169 FLUO3 0.076 0.022 FLUO9 0.169 0.025 PHEN1 0.180 0.101 PHEN2 - 0.086 0.096 PHEN3 - 0.082 - 0.036 PYR1 Pearson Correlation Coefficients * Statistically significant if p<0.20 (2-tailed).

7.5 Bivariate Correlations of Ovarian Reserve Markers with potential Confounding Variables

Table 11: Correlations of Potential Confounders with Ovarian Reserve Markers

Age BMI AMH (ng/ml) - 0.280* - 0.293* Inhibin B (ng/L) - 0.465** - 0.287* FSH (U/L) 0.025 0.225 Pearson Correlations *Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed).

There were negative associations between age and BMI with AMH and inhibin B that were significant. There were 11 participants 35 or older. FSH was not significantly correlated with

BMI or age (Table 11).

7.6 Bivariate Analysis of PAHs and Ovarian Reserve Markers with Participant Characteristics

Cigarette use, EtOH, and education were associated with more than two hydroxylated PAH metabolites in the analyses of associations between potential confounders and OH- PAH average concentrations across cycles per woman (Tables 13, 14 and 18). Only 5 participants used cigarettes. Despite this low number of smokers, cigarette use was associated with 5 of the

9 (> 50%) OH-PAH metabolites, with higher concentrations of the metabolites in smokers. There were less than two OH-PAH associations that are different by strata, observed between race/ethnicity, caffeinated coffee, caffeinated tea, soda/energy drink, and family income in the past year, using OH- PAH average concentrations across cycles per woman (Tables 12, 15, 16,

17, and 19).

Bivariate associations were conducted with independent t-test between the genotypes

GSTM1 and GSTT1 with all 9 of the OH-PAH metabolites and none were found to be significant

(p > 0.05) (data not shown). Bivariate associations between GSTM1 and GSTT1 and the ovarian reserve marker endpoints (AMH, inhibin B, and FSH) were investigated using independent t-test

57 as well and there was a statistically significant association between GSTM1 and AMH (p = 0.04).

There were no other statistically significant associations between the genotypes and the ovarian reserve markers observed (data not shown).

Table 12: Bivariate associations between OH-PAH metabolites and Race/Ethnicity

PAH Metabolite Race/Ethnicity N Mean p NAP1 Hispanic 11 7.10 > 0.20 Black 4 7.60 Asian 14 7.29 White 22 7.19 NAP2 Hispanic 11 8.56 > 0.20 Black 4 8.40 Asian 14 8.49 White 22 8.51 FLUO2 Hispanic 11 5.26 > 0.20 Black 4 5.35 Asian 14 5.05 White 22 5.26 FLUO3 Hispanic 11 4.52 > 0.20 Black 4 4.80 Asian 14 4.35 White 22 4.56 FLUO9 Hispanic 11 5.42 > 0.20 Black 4 4.98 Asian 14 5.55 White 22 5.56 PHEN1 Hispanic 11 4.84 > 0.20 Black 4 4.47 Asian 14 4.53 White 22 4.72 PHEN2 Hispanic 11 3.58 < 0.20* Black 4 3.05 Asian 14 3.37 White 22 3.44 PHEN3 Hispanic 11 3.89 > 0.20 Black 4 4.06 Asian 14 3.99 White 22 4.08 PYR1 Hispanic 11 4.53 > 0.20 Black 4 4.57 Asian 14 4.44 White 22 4.63 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 13: Bivariate Associations between OH-PAH Metabolites and Smoking Status

PAH Metabolite Cigarette Use N Mean p NAP1 Yes 5 7.46 > 0.20 No 45 7.22 NAP2 Yes 5 8.56 < 0.20** No 45 8.52 FLUO2 Yes 5 5.53 > 0.20 No 45 5.18 FLUO3 Yes 5 4.96 < 0.20** No 45 4.48 FLUO9 Yes 5 5.45 > 0.20 No 45 5.48 PHEN1 Yes 5 4.59 < 0.20** No 45 4.69 PHEN2 Yes 5 3.32 < 0.20** No 45 3.42 PHEN3 Yes 5 4.04 < 0.20** No 45 4.00 PYR1 Yes 5 4.51 > 0.20 No 45 4.55 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 14: Bivariate associations between OH-PAH Metabolites and EtOH

PAH Metabolite EtOH N Mean p NAP1 > 1 /week 12 7.50 < 0.20* > 1 /mo - 1/week 16 6.89 Never o < 1/mo 21 7.30 NAP2 > 1 /week 12 8.27 > 0.20 > 1 /mo - 1/week 16 8.53 Never o < 1/mo 21 8.65 FLUO2 > 1 /week 12 5.34 > 0.20 > 1 /mo - 1/week 16 5.12 Never o < 1/mo 21 5.22 FLUO3 > 1 /week 12 4.68 > 0.20 > 1 /mo - 1/week 16 4.40 Never o < 1/mo 21 4.52 FLUO9 > 1 /week 12 5.58 > 0.20 > 1 /mo - 1/week 16 5.36 Never o < 1/mo 21 5.48 PHEN1 > 1 /week 12 4.89 < 0.20* > 1 /mo - 1/week 16 4.61 Never o < 1/mo 21 4.61 PHEN2 > 1 /week 12 3.56 > 0.20 > 1 /mo - 1/week 16 3.28 Never o < 1/mo 21 3.42 PHEN3 > 1 /week 12 4.27 < 0.20* > 1 /mo - 1/week 16 3.91 Never o < 1/mo 21 3.93 PYR1 > 1 /week 12 4.80 < 0.20* > 1 /mo - 1/week 16 4.58 Never o < 1/mo 21 4.36 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 15: Bivariate Associations of OH-PAH Metabolites and Caffeinated Coffee

PAH Metabolite Caffeinated Coffee N Mean p NAP1 Yes 28 7.32 > 0.20 No 23 7.12 NAP2 Yes 28 8.47 < 0.20** No 23 8.56 FLUO2 Yes 28 5.33 > 0.20 No 23 5.06 FLUO3 Yes 28 4.65 > 0.20 No 23 4.34 FLUO9 Yes 28 5.57 > 0.20 No 23 5.36 PHEN1 Yes 28 4.82 > 0.20 No 23 4.49 PHEN2 Yes 28 3.53 > 0.20 No 23 3.28 PHEN3 Yes 28 4.13 < 0.20** No 23 3.86 PYR1 Yes 28 4.67 > 0.20 No 23 4.40 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 16: Bivariate Associations between OH-PAH metabolites and Caffeinated Tea

PAH Metabolite Caffeinated Tea N Mean p NAP1 Yes 21 7.30 > 0.20 No 29 7.16 NAP2 Yes 21 8.30 < 0.20** No 29 8.68 FLUO2 Yes 21 5.25 > 0.20 No 29 5.13 FLUO3 Yes 21 4.52 > 0.20 No 29 4.47 FLUO9 Yes 21 5.57 > 0.20 No 29 5.38 PHEN1 Yes 21 4.79 > 0.20 No 29 4.57 PHEN2 Yes 21 3.58 > 0.20 No 29 3.30 PHEN3 Yes 21 4.10 < 0.20** No 29 3.94 PYR1 Yes 21 4.62 > 0.20 No 29 4.50 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 17: Bivariate Associations between OH-PAH Metabolites and Soda/Energy Drink

PAH Metabolite Soda/Energy Drink N Mean p NAP1 Yes 12 7.33 > 0.20 No 39 7.20 NAP2 Yes 12 8.48 > 0.20 No 39 8.51 FLUO2 Yes 12 5.28 > 0.20 No 39 5.19 FLUO3 Yes 12 4.55 > 0.20 No 39 4.50 FLUO9 Yes 12 5.56 > 0.20 No 39 5.45 PHEN1 Yes 12 4.62 > 0.20 No 39 4.68 PHEN2 Yes 12 3.44 > 0.20 No 39 3.41 PHEN3 Yes 12 4.01 > 0.20 No 39 4.01 PYR1 Yes 12 4.44 < 0.20** No 39 4.59 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Table 18: Bivariate Associations between OH-PAH Metabolites and Education

PAH Metabolite Education N Mean p NAP1 < Bachelor's 23 6.99 < 0.20* Bachelor's 16 7.35 Graduate Degree 11 7.61 NAP2 < Bachelor's 23 8.67 > 0.20 Bachelor's 16 8.41 Graduate Degree 11 8.29 FLUO2 < Bachelor's 23 5.20 > 0.20 Bachelor's 16 5.26 Graduate Degree 11 5.12 FLUO3 < Bachelor's 23 4.47 > 0.20 Bachelor's 16 4.62 Graduate Degree 11 4.39 FLUO9 < Bachelor's 23 5.27 < 0.20* Bachelor's 16 5.49 Graduate Degree 11 5.80 PHEN1 < Bachelor's 23 4.59 > 0.20 Bachelor's 16 4.73 Graduate Degree 11 4.74 PHEN2 < Bachelor's 23 3.30 < 0.20* Bachelor's 16 3.38 Graduate Degree 11 3.63 PHEN3 < Bachelor's 23 3.91 < 0.20* Bachelor's 16 4.02 Graduate Degree 11 4.15 PYR1 < Bachelor's 23 4.53 > 0.20 Bachelor's 16 4.53 Graduate Degree 11 4.63 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log OH-PAH concentrations in ng/g creatinine.

P value exactly equal to 0.20 with average PHEN3.

Table 19: Bivariate Associations between OH-PAH Metabolites and Family Income past year

PAH Metabolite Income Past Year N Mean p NAP1 < = 50,000 12 7.32 > 0.20 > 50,000 14 7.44 > 100,000 18 7.09 NAP2 < = 50,000 12 8.61 < 0.20* > 50,000 14 8.70 > 100,000 18 8.29 FLUO2 < = 50,000 12 5.40 < 0.20* > 50,000 14 5.30 > 100,000 18 5.02 FLUO3 < = 50,000 12 4.71 > 0.20 > 50,000 14 4.62 > 100,000 18 4.39 FLUO9 < = 50,000 12 5.50 > 0.20 > 50,000 14 5.49 > 100,000 18 5.51 PHEN1 < = 50,000 12 4.72 > 0.20 > 50,000 14 4.70 > 100,000 18 4.67 PHEN2 < = 50,000 12 3.41 > 0.20 > 50,000 14 3.33 > 100,000 18 3.48 PHEN3 < = 50,000 12 4.03 > 0.20 > 50,000 14 4.00 > 100,000 18 4.03 PYR1 < = 50,000 12 4.77 > 0.20 > 50,000 14 4.48 > 100,000 18 4.58 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20, Income in US Dollars. Means are natural-log OH-PAH concentrations in ng/g creatinine.

Race/Ethnicity means of ovarian reserve values were all significantly different from one

another (Table 20). Black AMH means were statistically significantly different based on the

ANOVA and it appears they were lower than the White mean AMH levels. Black mean Inhibin B

levels were statistically significantly different based on the ANOVA, and it appears they were

lower than Asian and White mean levels. Hispanics had significantly lower FSH levels than

Blacks and Blacks had significantly different FSH mean levels based on the ANOVA and it

appears they have higher FSH levels than Asian and Whites. These differences must be

65 interpreted with caution because they are based on very small numbers, as there were only four black participants.

The EtOH with ovarian reserve markers were only significant for AMH (Table 22). The mean

AMH was significantly different based on the t-tests, and it appears higher in the group who drank the most alcohol compared to the group who drank the least amount. There was a statistically different level of FSH in coffee drinkers based on the t-tests, and it appears that there is a lower level of FSH in coffee drinkers (Table 23) compared to non-coffee drinkers.

Women who did not drink tea had higher mean levels of all the ovarian reserve markers compared to those who did (Table 24). The mean AMH levels were significantly different based on the t-tests, and it appears higher in those who drank soda/energy drink compared to those who did not (Table 25). There were statistically significant differences in inhibin B levels based on the t-tests, and it appears that there was a higher mean Inhibin B levels among those with a graduate degree or higher. The lowest statistically significant Inhibin B level occurred in the group with at least a Bachelor’s degree (Table 26). There were no statistically significant differences in the means of income observed for any ovarian reserve marker (Table 27).

Table 20: Bivariate Associations of Race/Ethnicity and Ovarian Reserve Markers

Ovarian Reserve Race/Ethnicity N Mean p Marker AMH ng/ml Hispanic 11 0.629 < 0.20* Black 3 -0.237 Asian 14 0.726 White 22 0.928 Inhibin B ng/L Hispanic 11 2.719 < 0.20* Black 4 2.883 Asian 14 3.098 White 22 3.110 FSH U/L Hispanic 11 1.033 < 0.20* Black 4 2.119 Asian 14 1.389 White 22 1.170 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log concentrations.

Table 21: Bivariate Associations of Cigarette use and Ovarian Reserve Markers

Ovarian Reserve Cigarette Use N Mean p Marker AMH ng/ml Yes 5 0.536 < 0.20** No 45 0.752 Inhibin B ng/L Yes 5 2.059 < 0.20** No 45 2.985 FSH U/L Yes 5 1.080 > 0.20 No 45 1.275 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log concentrations.

Table 22: Bivariate Associations between ETOH and Ovarian Reserve Markers

Ovarian Reserve ETOH N Mean p Marker AMH ng/ml > 1 /week 12 0.982 < 0.20* > 1 /mo - 1/week 16 0.952 Never o < 1/mo 21 0.353 Inhibin B ng/L > 1 /week 12 3.225 > 0.20 > 1 /mo - 1/week 16 2.957 Never o < 1/mo 21 2.578 FSH U/L > 1 /week 12 1.388 < 0.20* > 1 /mo - 1/week 16 1.039 Never to < 1/mo 21 1.313 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log concentrations.

Table 23: Bivariate Associations between Caffeinated Coffee and Ovarian Reserve Markers

Ovarian Reserve Caffeinated Coffee N Mean p Marker AMH ng/ml Yes 28 0.688 > 0.20 No 23 0.797 Inhibin B ng/L Yes 28 2.828 > 0.20 No 23 3.050 FSH U/L Yes 28 1.167 < 0.20** No 23 1.407 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log concentrations.

Table 24: Bivariate Associations between Caffeinated Tea and Ovarian Reserve Markers

Ovarian Reserve Caffeinated Tea N Mean p Marker AMH ng/ml Yes 21 0.575 > 0.20 No 29 0.807 Inhibin B ng/L Yes 21 2.843 > 0.20 No 29 2.970 FSH U/L Yes 21 1.277 > 0.20 No 29 1.301 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log concentrations.

Table 25: Bivariate Associations between Soda/Energy Drink and Ovarian Reserve Markers

Ovarian Reserve Soda/Energy Drink N Mean p Marker AMH ng/ml Yes 12 0.904 < 0.20** No 38 0.683 Inhibin B ng/L Yes 12 2.887 > 0.20 No 39 2.939 FSH U/L Yes 12 0.998 > 0.20 No 39 1.361 ** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log concentrations.

Table 26: Bivariate Associations between Education level and Ovarian Reserve Markers

Ovarian Reserve Education Level N Mean p Marker AMH ng/ml < Bachelor's 23 0.854 > 0.20 Bachelor's 15 0.529 Graduate Degree 11 0.756 Inhibin B ng/L < Bachelor's 23 3.165 < 0.20* Bachelor's 16 2.440 Graduate Degree 11 3.174 FSH U/L < Bachelor's 23 1.211 > 0.20 Bachelor's 16 1.221 Graduate Degree 11 1.520 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log concentrations.

Table 27: Bivariate Associations of Family Income past year and Ovarian Reserve Markers

Ovarian Reserve Income Past Year N Mean p Marker AMH ng/ml < = 50,000 12 0.886 > 0.20 > 50,000 14 0.896 > 100,000 17 0.730 Inhibin B ng/L < = 50,000 12 3.11 > 0.20 > 50,000 14 3.25 > 100,000 18 2.82 FSH U/L < = 50,000 12 1.15 > 0.20 > 50,000 14 1.14 > 100,000 18 1.27 *Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log concentrations.

7.7 Multiple Linear Regressions

When all 9 OH-PAHs were entered as predictors in a linear regression model with AMH as the outcome and age, BMI, and EtOH as covariates, NAP2 and FLUO9 were the only PAH metabolites that were associated with AMH at p<0.2 (Table 28). FLUO9 loaded strongly on

Factor 1, and NAP2 loaded strongly on Factor 2. FLUO3 was the only PAH metabolite that was associated with inhibin B at p<0.2 when all 9 OH-PAHs were entered as predictors in a linear regression model with inhibin B as the outcome (Table 29). FLUO3 loaded strongly on Factor 2.

PHEN3 was the only PAH metabolite that was associated with FSH at p<0.2 when all 9 OH-PAHs were entered as predictors in a linear regression model with FSH as the outcome (Table 30).

PHEN3 loaded strongly on Factor 1.

The next step in the modeling was to enter the potential confounders identified for each outcome variable in the bivariate analyses into the regression models. These potential confounders were retained in the final models only if they altered the adjusted beta coefficients for the PAHs by 10% or more. Thus age, BMI, and EtOH were retained in the final

AMH model (Table 28), age and EtOH were retained in the final inhibin B model (Table 29), and

BMI and education were retained in the final FSH model (Table 30). The final AMH, inhibin B, and FSH models explain 15.1 % , 27 % and 1.9% of the variations in the outcome variables respectively (Tables 28-30).

Table 28: AMH Multiple Linear Regression Model

AMH = B 80% CI p 6.698 + 1.938, 11.458 0.007* NAP2 - 0.222 - 0.675, 0.115 0.159 FLUO9 - 0.220 - 0.643, 0.096 0.142 Age - 0.233 - 0.068, 0.009 0.126 BMI - 0.183 - 0.072, 0.018 0.233 EtOH - 0.243 - 0.541, 0.062 0.116 Adjusted R squared 0.151 Model p = 0.042 B = Standardized Beta * Significant if p < 0.05. + = intercept

Table 29: Inhibin B Multiple Linear Regression Model

Inhibin B = B 80% CI p 6.963 + 5.192, 8.734 0.000* FLUO3 - 0.117 - 0.549, 0.088 0.352 Age - 0.493 - 0.102, -0.052 0.000* EtOH - 0.277 - 0.556, -0.147 0.030* Adjusted R squared 0.270 Model p = 0.001 B = Standardized Beta * Significant if p < 0.05. + = intercept

Table 30: FSH Multiple Linear Regression Model

FSH = B 80% CI p 0.265 + -1.063, 1.594 0.796 PHEN3 0.028 - 0.282, 0.373 0.857 BMI 0.233 0.005, 0.047 0.119 Education 0.156 - 0.033, 0.266 0.316 Adjusted R squared 0.019 Model p = 0.286 B = Standardized Beta * Significant if p < 0.05. + = intercept

7.8 Multiple Linear Regressions with Polymorphism Effect Modification Analysis

Several stratified analyses were performed to test for effect modification by Phase II biotransformation enzyme polymorphisms in GSTM1 and GSTT1. To do this, the regression models described in the prior section were run with the population stratified by GSTM1 genotype (null compared to greater than or equal to one allele) or by GSTT1 genotype (null compared to greater than or equal to one allele) (Tables 31- 36). We hypothesized that the associations between ovarian reserve markers and OH-PAHs would be stronger in those with the null alleles (that is more negative beta coefficients for AMH and inhibin B and more positive beta coefficients for FSH). However, we instead observed weaker associations (less negative or positive) of NAP2 and FLUO9 with AMH for GSTT1 null alleles compared to one or more GSTT1 alleles (negative associations). FLUO9's beta coefficient changed from -0.220 in the linear regression AMH model for all participants to -0.248 in the AMH model run on participants with

GSTM1 null alleles and -0.199 in the model with participants with 1 or more GSTM1 alleles

(p>0.2)(Tables 28 and 32). Moreover, we observed reversed associatons for inhibin B for

GSTM1 null alleles (negative association with FLUO3) compared to those with one or more

GSTM1 alleles (positive association with FLUO3) and reversed associations for FSH for GSTT1 and GSTM1 null alleles (negative associations with PHEN3) compared to one or more GSTT1 or

GSTM1 alleles (positive associations with PHEN3). FLUO3's beta coefficient changed from -0.117 in the linear regression inhibin B model for all participants to -0.342 in the inhibin B model run for participants with GSTM1 null and to 0.066 for participants with 1 or more GSTM1 allele

(P<0.05)(Tables 29 and 34). The associations persisted when covariates such as BMI, EtOH, and age were added or removed from the models (not shown). There was a significant association between GSTM1 and AMH (p=0.04) in the bivariate analysis of the polymorphisms and the ovarian reserve markers (data not shown). However, the GSTM1 genotype did not show a significant effect modification in the AMH model (Table 32). Age remained significant in the expected direction in the models run on participants in all of the models for inhibin B

(p<0.05)(Tables 33 and 34), except in the model run on participants with GSTM1 null genotype

(p>0.2)(Table 34). BMI was significant and in the expected direction for the model run on participants with GSTM1 null genotype in the regression model for FSH (p<0.05)(Table 36). Age did not reman a significant predictor of AMH in either model for effect modification by GST polymorphisms constructed in this study (Tables 31 and 32).

Table 31: AMH Effect Modification by GSTT1

GSTT1 null (N = 17) B 80%CI for B p-value NAP2 0.107 - 0.373, 0.546 > 0.2

FLUO9 0.076 - 0.289, 0.394 > 0.2

Age - 0.009 - 0.040, 0.039 > 0.2

BMI 0.011 - 0.144, 0.148 > 0.2 EtOH - 0.445 - 0.621, 0.098 > 0.2 GSTT1 > or =1 copy NAP2 - 0.272 - 0.707, -0.048 > 0.05

FLUO9 - 0.290 - 0.767, -0.093 > 0.05

Age - 0.347 - 0.085, -0.016 > 0.05 BMI - 0.066 - 0.046, 0.026 > 0.2 EtOH - 0.239 - 0.551, 0.004 > 0.2 * Bolded indicates Statistically Significant p < 0.05.

Table 32: AMH Effect Modification by GSTM1

GSTM1 null (N = 18) B 80%CI for B p-value NAP2 - 0.121 - 0.566, 0.230 > 0.2 FLUO9 - 0.248 - 0.765, 0.088 > 0.2 Age - 0.219 - 0.077, 0.020 > 0.2 BMI - 0.226 - 0.083, 0.017 > 0.2 EtOH - 0.157 - 0.799, 0.312 > 0.2 GSTM1 > or =1 copy NAP2 - 0.270 - 0.895, 0.262 > 0.2 FLUO9 - 0.199 - 0.741, 0.267 > 0.2 Age - 0.274 - 0.095, 0.023 > 0.2 BMI - 0.030 - 0.072, 0.063 > 0.2 EtOH - 0.256 - 0.699, 0.224 > 0.2 * Bolded indicates Statistically Significant p < 0.05.

Table 33: Inhibin B Effect Modification by GSTT1

GSTT1 null (N = 17) B 80%CI for B p-value FLUO3 0.188 -0.257, 0.898 > 0.2

Age - 0.588 - 0.126, -0.035 < 0.05* EtOH - 0.265 - 0.665, 0.153 > 0.2 GSTT1 > or = 1 copy FLUO3 - 0.138 - 0.733, 0.137 > 0.2 Age - 0.505 - 0.118, -0.051 < 0.05* EtOH - 0.272 - 0.693, -0.102 > 0.05 *Bolded indicates Statistically Significant p < 0.05.

Table 34 : Inhibin B Effect Modification by GSTM1

GSTM1 null (N= 18) B 80%CI for B p-value FLUO3 - 0.342 - 1.220, -0.160 > 0.05 Age - 0.167 -0.073, 0.023 > 0.2

EtOH - 0.358 -1.352, -0.083 > 0.05 GSTM1 > or = 1 copy FLUO3 0.066 - 0.305, 0.560 > 0.2 Age - 0.608 - 0.133, -0.063 < 0.05* EtOH - 0.327 - 0.658, -0.115 > 0.05 * Bolded indicates Statistically Significant p < 0.05.

Table 35: FSH Effect Modification by GSTT1

GSTT1 null ( N = 17) B 80%CI for B p-value PHEN3 - 0.307 -1.167, 0.230 > 0.2

BMI 0.038 - 0.111, 0.133 > 0.2 Education 0.502 - 0.041, 0.656 > 0.05 GSTT1 > or = 1 copy PHEN3 0.142 - 0.223, 0.703 > 0.2 BMI 0.163 - 0.011, 0.046 > 0.2 Education 0.055 - 0.163, 0.248 > 0.2 *Bolded indicates Statistically Significant p < 0.05.

Table 36: FSH Effect Modification by GSTM1

GSTM1 null ( N = 18) B 80%CI for B p-value PHEN3 - 0.578 - 3.001, -0.825 < 0.05* BMI 0.774 0.057, 0.161 <0.05*

Education 0.321 - 0.006, 0.672 > 0.05 GSTM1 > or = 1 copy PHEN3 0.254 - 0.018, 0.615 > 0.2 BMI - 0.049 - 0.030, 0.020 > 0.2 Education 0.135 - 0.074, 0.339 > 0.2 *Bolded indicates Statistically Significant p < 0.05.

8.0 Discussion

We hypothesized that women with higher exposures to PAHs, measured as mean urinary

OH-PAH metabolite concentration over 2-3 menstrual cycles, would have decreased ovarian reserve compared to women with lower urinary concentrations of OH-PAHs. Consistent with our hypothesis, we observed negative associations of PAH metabolites (FLUO9 and NAP2) with

AMH, negative association of FLUO3 with inhibin B, and positive association of PHEN3 with FSH.

FLUO3, FLUO9, and PHEN3's parent compounds are found in diesel exhaust and tobacco smoke

(Fang, GC et al., 2006; Ding, YS et al., 2005; Westerholm, R et al., 1994; Rogge, WF et al., 1994;

Kelly, G et al., 1993) and pose inhalation exposure risks. NAP2 as well as fluorene metabolites pose dermal exposure risks through their use in the manufacturing of dyes and lubricating materials (Case Studies, 2009) and food ingestion (FLUO3, FLUO9, and PHEN3) (Kim, SR et al.,

2008). The associations did not remain statistically significant after adjustment for confounding, which may be related to the relatively small number of participants.

Bivariate analyses identified several expected bivariate associations. These included the observed inverse correlation of AMH and inhibin B with age. There were 11 participants 35 or

76 older in this study. The inverse correlations of AMH and inhibin B with BMI was also expected.

FSH had positive correlations with age and BMI, but the correlations were low and not statistically significant. We expected these results since AMH and inhibin B are made by the granulosa cells, and AMH doesn’t fluctuate with the menstrual cycle phase. AMH is thus poorly correlated with both FSH and inhibin B. FSH is not made by the granulosa cells but gets feedback inhibition of E2 from growing follicles. Early reproductive age women predominate in this study, with only 6 participants over 37 years old. Six participants had BMIs over 30 in this study. Su et al., found obesity (BMI > 30) in late reproductive age (40-52) women lowered serum AMH (77%, p 0.02) and inhibin B (24%, p = 0.08) compared to normal weight (BMI < 25) women, but had no effect on serum FSH and E2 levels. The group concluded that the lower

AMH levels resulted from physiologic processes other than decreased ovarian reserve (Su, HI et al., 2008). The finding of lower AMH levels among blacks compared to all other participants, and the higher FSH levels in blacks likely reflected random variation.

During the construction of the final multiple regression models, we regressed all of the PAHs against each ovarian reserve marker separately and observed the expected directions of associations between the PAHs and the ovarian reserve models with most of the PAHs. At least one of the PAHs was observed to have an opposite association with a marker of ovarian reserve

(FLUO3 and FSH), but this association did not approach statistical significance.

The multiple linear regressions were limited by the small sample size, the collinearity of the

PAHs (Tables 5-7), the fact that ovarian reserve markers inhibin B and FSH fluctuate with the menstrual cycle , and baseline serum collections of ovarian reserve markers were outside of the

77 study design intent to collect on 5 to 9 days prior to the next expected menstruation. These factors explain why two of the three final models were not significant (Tables 29 and 30, inhibin

B , p = 0.352 and FSH, p = 0.857). All of the aforementioned may also explain the strength of the

OH-PAHs' associations in the multiple linear regressions with the ovarian reserve markers when all 9 OH-PAHs were included in the regressions. Unexplained confounding may have also played a role in the outcomes of the regression models, as we had 7 missing values in income (Table 1), which precluded it from our regression models as a surrogate for SES, over half (28 or 55%) of the participants drank coffee (Table 1) which is a source of PAHs and was therefore excluded from the regression analysis as well as cigarette use, which was under-represented in this small sample size (5 participants smoked cigarettes, Table 1).

The collinearity of the PAHs was addressed in the principal component factor analysis with varimax rotation (Table 8 and Figure 6), which revealed that 34% of the cumulative variance was explained by all of the PAH metabolites in the study except for NAP2, and was predominated by all of the phenanthrene metabolites evaluated in the study, including PHEN3

(0.762) which was retained in the model with FSH (p= 0.857) and FLUO9 (p=0.735), which was retained in the model with AMH (p = 0.142). About 64.% of the cumulative variance was explained predominantly by FLUO3 (which had a principal component factor analysis coefficient of 0.852) which was retained in the model with inhibin B (p= 0.352) and FLUO2

(0.771), and the parent compound naphthalene, where NAP2 (0.487) was retained in the model with AMH (p = 0.159). The Principal Component analysis identified weights of individual OH-

PAHs in the overall mixture of PAHs which does not address whether they were associated with the outcome variables. For example, even though all of the OH-metabolites of

78 phenanthrene loaded stronger on Factor 1 compared to FLUO9 and NAP2 loaded less strongly on Factor 2 compared to NAP1, FLUO2, and FLUO3, the multivariable analysis selected OH-PAHs

(different) that were most strongly associated with the 3 outcome variables in the models

(Tables 28 - 30). The clinical importance of associating specific PAHs with the various ovarian reserve monitors would help construct future exposure risk models for various exposures to

PAHs as they exist in mixtures. However, there was not enough evidence in this small pilot study to reveal which specific PAHs are associated with the different markers of ovarian reserve. Despite the use of principal component analysis, the sample sizes were too small (the resulting factor analysis supported the associations found in the adjusted regression analysis).

Overall, the PAHs were not significant (p > 0.05) in the models compared to the confounders

(Tables 29 - 30).

Our cohort had similar mean PAH levels as the NHANES data (Table 4), and therefore, although we had a small convenience sample, we had good representation of excretion profiles for the part of the general US population of interest in this study. We found significant associations between all of the parent PAHs and cigarette use as expected (Table 13) even though we had a small number (5) of participants who reported cigarette use (9.8%) (Van Rooij,

JG et al., 1994). More than half (55%) of the participants reported drinking coffee, however we did not obtain information on whether they drank boiled and/or brewed coffee (brewed contains more PAHs) (Tfouni, SAV et al., 2013). This may explain why we only found significant associations between two PAH parent compounds and coffee drinkers (Table 15). We observed associations between EtOH and 4 OH-PAHs (NAP1, PHEN1, PHEN3, and PYR1), (p < 0.20) when all 9 OH-PAHs were evaluated, however, no consistent patterns were observed (Table 14).

There is a dose-dependent use of CYP2E1 to metabolize alcohol with acute alcohol intake (Seitz,

HK et al., 2006), however, the use of this enzyme to explain the individual participants' ovarian reserve marker levels or PAH levels as they relate to EtOH were not examined in this thesis.

Moreover, Van Rooij et al., did not find a significant association between alcohol consumption and baseline urinary excretion of PYR1 in non-occupationally exposed participants compared to the major significant findings associated with their dietary PAH intake, age, and tobacco consumption (Van Rooij, JG et al., 1994). This study did not find significant associations between PAHs and age or BMI (Table 10) which was expected due to the narrow age-range (18-

44 years) and BMI range (SD 5.29) of the study participants (Table 1).

Effect modification analysis was use to assess whether inter-individual variations in PAH ovotoxicity could be explained by polymorphisms in the enzymes GSTM1 and GSTT1 that detoxify PAHs. The results did not prove or disprove our hypothesis. The negative association of

FLUO9 with GSTM1 null in the AMH model was the strongest but not significant (Table 32).

Evaluations of the GSTM1 null and GSTT1 null did not reveal any associations between the PAHs in the inhibin B and FSH models (Tables 33 -36), partly because of the small sample size, which leads to the wide confidence intervals. GST polymorphisms have been associated with some

PAHs in some studies and not in others, and therefore our findings were not entirely surprising

(Liu, M et al., 2013; Gao, Q et al., 2011; Mc Carty, KM et al., 2009).

Our stratified regression models revealed associations between OH-PAHs and ovarian reserve markers in the unexpected (though insignificant) directions for the strata with null alleles for inhibin B and AMH in the GSTT1 null regression and for FSH in both the GSTT1 and

GSTM1 null regressions. The nonsignificant associations persisted when covariates such as BMI,

ETOH, and age were added or removed from the models (data not shown). Both non-deleted

GST genotypes in this study changed PAH coefficients in directions expected for the deleted genotypes and there were stronger associations with PAHs in the non-deleted genotypes compared to the deleted group, however, none were significant. Interestingly, despite the significant association between GSTM1 and AMH (p = 0.04) in our bivariate analysis of the polymorphisms and the ovarian reserve markers (data not shown), we did not observe a GSTM1 null genotype-effect modification in the AMH model. This may indicate that GST is involved in the metabolism of AMH. To the best of the author's knowledge, there are no reports of GST's associated with ovarian reserve markers as we observed in our bivariate analysis between

GSTM1 null and AMH (data not shown).

Limitations of this study included a small sample size and our inability to collect serum samples for the ovarian reserve markers in the study time-frame. If the samples were collected in the study time-frame (5 to 9 days prior to the next expected menstrual cycle), then we may have been able to capture more meaningful differences in the FSH levels and possibly have more significant associations with OH-PAHs. Significant differences between covariate groups with respect to the outcome variables could have been assessed by using pairwise t-testing. The results could have been compared with prior research on these topics (Kinney, A et al., 2007), however, that was not the focus of this thesis.

This pilot study demonstrated that the recruitment strategies, sample collection procedures, measurements used, and biomarker used are efficacious and would serve as robust analysis of

81 the hypotheses addressed in this thesis in a larger sample size. It also validated the techniques used for genotyping polymorphisms in PAH metabolizing enzyme genes. To the best of authors' knowledge, this is the first study evaluating the effects of PAHs on ovarian reserve in human subjects along with whether their effects are modified by polymorphisms in PAH-detoxifying metabolic enzymes.

9.0 Conclusion

In conclusion, we analyzed the long-term effects of exposure to PAHs on ovarian reserve markers, AMH, inhibin B, and FSH and whether there were effect modifications by GSTM1 and

GSTT1 genetic polymorphisms in a cohort of reproductive age women. We found a trend suggesting that women exposed to higher concentrations of PAHs over time would have lower ovarian reserve as measured by serum markers compared to women exposed to lower concentrations. The results of this pilot study, although limited by statistical power, adds to the body of knowledge in the field of reproductive toxicology with regards to PAH-induced ovotoxicity. We did not prove or disprove PAH-induced ovotoxicity in this particular pilot study.

Nor did we illustrate that GSTM1 null and GSTT1 null polymorphisms add to the effects of the

PAH-induced decreased ovarian reserve.

To the best of the authors' knowledge, this is the first study to investigate the association between PAHs and diminished ovarian reserve in humans and possible mechanisms of inter- individual variations in those associations with effect modification analysis involving enzyme polymorphisms (GST's) in the detoxification of PAHs. This study is a step in the right direction towards a preventative approach to diminished ovarian reserve and contributes to the body of

82 knowledge aimed at addressing air pollution policy and smoking cessation efforts. This study could be an inspiration to nutrition investigators to design studies involving PAHs that would help create guideline recommendations for consumption and processing of red meat (Kaiser, J

1997; Browner, CM 1997; Lijinsky, W et al., 1964; Lioy, PJ 1988). This pilot study also illustrated the feasibility of the study design and methods. Finally, data from this study (small sample size) could be used in a power analysis for a larger study examining the associations between OH-

PAHs and ovarian reserve markers.

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