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

Permalinkhttps://escholarship.org/uc/item/54z6194j

AuthorNelson, Gillian

Publication Date2016-01-01

LicenseCC BY-NC-ND 4.0 Peer reviewed|Thesis/dissertation

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UNIVERSITY OF CALIFORNIA,

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

by

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

Copyright 2016 Gillian Loraine Nelson, MD

ii

TABLE OF CONTENTS

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

iii

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

iv

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

v

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

vi

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.

vii

ABSTRACT OF THESIS

Is there a Relationship Between Polycyclic Aromatic Hydrocarbon Exposure and

Ovarian Reserve Markers in Humans?

by

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

viii

(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

ix

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

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,

2

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:

3

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 includes 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).

4

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

5

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/m3) 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

6

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

7

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

8

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

9

(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

10

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 occurs 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

11

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.

12

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

13

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).

14

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).

15

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

16

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.

17

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

PAH Metabolite ng/g

creatinine

Abbreviation Median half-life (h) 95% CI

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

18

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

19

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,

20

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

21

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

22

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

23

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

24

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.

25

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

26

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

27

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

28

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

29

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

30

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

31

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,

32

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

33

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

34

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).

35

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;

36

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

37

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

38

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

39

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).

40

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.

41

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

42

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.

43

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).

44

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

45

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.

46

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

47

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

48

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.

49

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.

50

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

College/Vocational Program

23 45.10

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.

51

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

52

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).

53

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.

54

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

Statistical Factor

Analysis

Factor Loadings (Varimax Kaiser normalized)

Extraction: Principal components

Natural log Average

(ng/g creatinine)

Factor 1 Factor 2

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

55

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

56

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).

57

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

58

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).

59

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

PAH Metabolite Race/Ethnicity N Mean p

NAP1 Hispanic

Black

Asian

White

11

4

14

22

7.10

7.60

7.29

7.19

> 0.20

NAP2 Hispanic

Black

Asian

White

11

4

14

22

8.56

8.40

8.49

8.51

> 0.20

FLUO2 Hispanic

Black

Asian

White

11

4

14

22

5.26

5.35

5.05

5.26

> 0.20

FLUO3 Hispanic

Black

Asian

White

11

4

14

22

4.52

4.80

4.35

4.56

> 0.20

FLUO9 Hispanic

Black

Asian

White

11

4

14

22

5.42

4.98

5.55

5.56

> 0.20

PHEN1 Hispanic

Black

Asian

White

11

4

14

22

4.84

4.47

4.53

4.72

> 0.20

PHEN2 Hispanic

Black

Asian

White

11

4

14

22

3.58

3.05

3.37

3.44

< 0.20*

PHEN3 Hispanic

Black

Asian

White

11

4

14

22

3.89

4.06

3.99

4.08

> 0.20

PYR1 Hispanic

Black

Asian

White

11

4

14

22

4.53

4.57

4.44

4.63

> 0.20

*Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log OH-PAH

concentrations in ng/g creatinine.

60

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

PAH Metabolite Cigarette Use N Mean p

NAP1 Yes

No

5

45

7.46

7.22

> 0.20

NAP2 Yes

No

5

45

8.56

8.52

< 0.20**

FLUO2 Yes

No

5

45

5.53

5.18

> 0.20

FLUO3 Yes

No

5

45

4.96

4.48

< 0.20**

FLUO9 Yes

No

5

45

5.45

5.48

> 0.20

PHEN1 Yes

No

5

45

4.59

4.69

< 0.20**

PHEN2 Yes

No

5

45

3.32

3.42

< 0.20**

PHEN3 Yes

No

5

45

4.04

4.00

< 0.20**

PYR1 Yes

No

5

45

4.51

4.55

> 0.20

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH

concentrations in ng/g creatinine.

61

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

PAH Metabolite EtOH N Mean p

NAP1 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

7.50

6.89

7.30

< 0.20*

NAP2 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

8.27

8.53

8.65

> 0.20

FLUO2 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

5.34

5.12

5.22

> 0.20

FLUO3 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

4.68

4.40

4.52

> 0.20

FLUO9 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

5.58

5.36

5.48

> 0.20

PHEN1 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

4.89

4.61

4.61

< 0.20*

PHEN2 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

3.56

3.28

3.42

> 0.20

PHEN3 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

4.27

3.91

3.93

< 0.20*

PYR1 > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

4.80

4.58

4.36

< 0.20*

*Bolded Indicates Statistically Significant One-Way ANOVA, p < 0.20. Means are natural-log OH-PAH

concentrations in ng/g creatinine.

62

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

PAH Metabolite Caffeinated Coffee N Mean p

NAP1 Yes

No

28

23

7.32

7.12

> 0.20

NAP2 Yes

No

28

23

8.47

8.56

< 0.20**

FLUO2 Yes

No

28

23

5.33

5.06

> 0.20

FLUO3 Yes

No

28

23

4.65

4.34

> 0.20

FLUO9 Yes

No

28

23

5.57

5.36

> 0.20

PHEN1 Yes

No

28

23

4.82

4.49

> 0.20

PHEN2 Yes

No

28

23

3.53

3.28

> 0.20

PHEN3 Yes

No

28

23

4.13

3.86

< 0.20**

PYR1 Yes

No

28

23

4.67

4.40

> 0.20

** 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

No

21

29

7.30

7.16

> 0.20

NAP2 Yes

No

21

29

8.30

8.68

< 0.20**

FLUO2 Yes

No

21

29

5.25

5.13

> 0.20

FLUO3 Yes

No

21

29

4.52

4.47

> 0.20

FLUO9 Yes

No

21

29

5.57

5.38

> 0.20

PHEN1 Yes

No

21

29

4.79

4.57

> 0.20

PHEN2 Yes

No

21

29

3.58

3.30

> 0.20

PHEN3 Yes

No

21

29

4.10

3.94

< 0.20**

PYR1 Yes

No

21

29

4.62

4.50

> 0.20

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH

concentrations in ng/g creatinine.

63

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

PAH Metabolite Soda/Energy Drink N Mean p

NAP1 Yes

No

12

39

7.33

7.20

> 0.20

NAP2 Yes

No

12

39

8.48

8.51

> 0.20

FLUO2 Yes

No

12

39

5.28

5.19

> 0.20

FLUO3 Yes

No

12

39

4.55

4.50

> 0.20

FLUO9 Yes

No

12

39

5.56

5.45

> 0.20

PHEN1 Yes

No

12

39

4.62

4.68

> 0.20

PHEN2 Yes

No

12

39

3.44

3.41

> 0.20

PHEN3 Yes

No

12

39

4.01

4.01

> 0.20

PYR1 Yes

No

12

39

4.44

4.59

< 0.20**

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log OH-PAH

concentrations in ng/g creatinine.

64

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

PAH Metabolite Education N Mean p

NAP1 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

6.99

7.35

7.61

< 0.20*

NAP2 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

8.67

8.41

8.29

> 0.20

FLUO2 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

5.20

5.26

5.12

> 0.20

FLUO3 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

4.47

4.62

4.39

> 0.20

FLUO9 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

5.27

5.49

5.80

< 0.20*

PHEN1 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

4.59

4.73

4.74

> 0.20

PHEN2 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

3.30

3.38

3.63

< 0.20*

PHEN3 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

3.91

4.02

4.15

< 0.20*

PYR1 < Bachelor's

Bachelor's

Graduate Degree

23

16

11

4.53

4.53

4.63

> 0.20

*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.

65

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

PAH Metabolite Income Past Year N Mean p

NAP1 < = 50,000

> 50,000

> 100,000

12

14

18

7.32

7.44

7.09

> 0.20

NAP2 < = 50,000

> 50,000

> 100,000

12

14

18

8.61

8.70

8.29

< 0.20*

FLUO2 < = 50,000

> 50,000

> 100,000

12

14

18

5.40

5.30

5.02

< 0.20*

FLUO3 < = 50,000

> 50,000

> 100,000

12

14

18

4.71

4.62

4.39

> 0.20

FLUO9 < = 50,000

> 50,000

> 100,000

12

14

18

5.50

5.49

5.51

> 0.20

PHEN1 < = 50,000

> 50,000

> 100,000

12

14

18

4.72

4.70

4.67

> 0.20

PHEN2 < = 50,000

> 50,000

> 100,000

12

14

18

3.41

3.33

3.48

> 0.20

PHEN3 < = 50,000

> 50,000

> 100,000

12

14

18

4.03

4.00

4.03

> 0.20

PYR1 < = 50,000

> 50,000

> 100,000

12

14

18

4.77

4.48

4.58

> 0.20

*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

66

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).

67

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

Ovarian Reserve

Marker

Race/Ethnicity N Mean p

AMH ng/ml Hispanic

Black

Asian

White

11

3

14

22

0.629

-0.237

0.726

0.928

< 0.20*

Inhibin B ng/L Hispanic

Black

Asian

White

11

4

14

22

2.719

2.883

3.098

3.110

< 0.20*

FSH U/L Hispanic

Black

Asian

White

11

4

14

22

1.033

2.119

1.389

1.170

< 0.20*

*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

Marker

Cigarette Use N Mean p

AMH ng/ml Yes

No

5

45

0.536

0.752

< 0.20**

Inhibin B ng/L Yes

No

5

45

2.059

2.985

< 0.20**

FSH U/L Yes

No

5

45

1.080

1.275

> 0.20

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log

concentrations.

68

Table 22: Bivariate Associations between ETOH and Ovarian Reserve Markers

Ovarian Reserve

Marker

ETOH N Mean p

AMH ng/ml > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

0.982

0.952

0.353

< 0.20*

Inhibin B ng/L > 1 /week

> 1 /mo - 1/week

Never o < 1/mo

12

16

21

3.225

2.957

2.578

> 0.20

FSH U/L > 1 /week

> 1 /mo - 1/week

Never to < 1/mo

12

16

21

1.388

1.039

1.313

< 0.20*

*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

Marker

Caffeinated Coffee N Mean p

AMH ng/ml Yes

No

28

23

0.688

0.797

> 0.20

Inhibin B ng/L Yes

No

28

23

2.828

3.050

> 0.20

FSH U/L Yes

No

28

23

1.167

1.407

< 0.20**

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log

concentrations.

69

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

Ovarian Reserve

Marker

Caffeinated Tea N Mean p

AMH ng/ml Yes

No

21

29

0.575

0.807

> 0.20

Inhibin B ng/L Yes

No

21

29

2.843

2.970

> 0.20

FSH U/L Yes

No

21

29

1.277

1.301

> 0.20

** 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

Marker

Soda/Energy Drink N Mean p

AMH ng/ml Yes

No

12

38

0.904

0.683

< 0.20**

Inhibin B ng/L Yes

No

12

39

2.887

2.939

> 0.20

FSH U/L Yes

No

12

39

0.998

1.361

> 0.20

** Bolded Indicates Statistically Significant Independent Sample T tests, P <0.20. Means are natural-log

concentrations.

70

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

Ovarian Reserve

Marker

Education Level N Mean p

AMH ng/ml < Bachelor's

Bachelor's

Graduate Degree

23

15

11

0.854

0.529

0.756

> 0.20

Inhibin B ng/L < Bachelor's

Bachelor's

Graduate Degree

23

16

11

3.165

2.440

3.174

< 0.20*

FSH U/L < Bachelor's

Bachelor's

Graduate Degree

23

16

11

1.211

1.221

1.520

> 0.20

*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

Marker

Income Past Year N Mean p

AMH ng/ml < = 50,000

> 50,000

> 100,000

12

14

17

0.886

0.896

0.730

> 0.20

Inhibin B ng/L < = 50,000

> 50,000

> 100,000

12

14

18

3.11

3.25

2.82

> 0.20

FSH U/L < = 50,000

> 50,000

> 100,000

12

14

18

1.15

1.14

1.27

> 0.20

*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.

71

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

72

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

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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).

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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.

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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.

76

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

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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

78

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

79

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).

80

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

81

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

82

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

83

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.

84

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