Transgenerational Effects of Maternal Age on Fertility of Offspring THESIS

Transgenerational effects of maternal age on fertility of offspring

THESIS

Presented in partial fulfillment of the requirements for the Degree Master of Science in in the Graduate School of The Ohio State University

Tamara S. Reynolds, BA

Graduate Program in Genetic Counseling

The Ohio State University

2017

Master’s Examination Committee:

Amanda Ewart Toland, PhD, Advisor

Dawn C. Allain, MS, LGC

Courtney D. Lynch, PhD, MPH

Judith Westman, MD

Tamara S. Reynolds

2017

Abstract

Infertility, defined as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse (Zegers-Hochschild et al, 2009), affects 9-18% of the population (Aghajanova, 2016). Numerous factors have been linked to infertility

(Hanson et al, 2016) with increasing maternal age believed to have the strongest association (American College of Obstetricians and Gynecologists Committee, 2014).

Ten to thirty percent of infertility remains unexplained (Quass & Hansen, 2016). More women are having children later in life due to educational and career pursuits despite confirmed increase in infertility (Martin et al, 2015) (Sauer, 2015). Women are born with all the egg cells they will ever have and as women age, their egg cells age along with them. An increase in egg cell nondisjunction, changes to the oocyte nuclear genome, changes to its mitochondrial genome, telomere length alterations, and epigenetic modifications in oocytes all are thought to influence age related infertility.

While it is clear that the age of a woman (G1) is associated with her own fertility, what is not known is whether the age of the woman (G1) at the birth of her daughter (G2) is correlated with the fertility of the daughter (G2). During fetal development of G2 which happens in the milieu of the G1 environment, all of the egg cells that will become G3 are formed. Epigenetic modifications to the G3 egg cell which may compromise its integrity are a potential mechanism for disruption to the future fertility of G2. Associations

ii between age of G1 and outcomes in both G2 and G3 have been found.

To test the impact of G1 age on fertility of G2, we conducted an online survey of over

3000 women. Survey questions assessed participant fertility, ages of participants and their parents, family reproductive history, and long-term health outcome of offspring.

Analyses showed that women (G2) born to teenage mothers (G1) were 40% more likely to experience infertility than women (G2) whose mothers (G1) were 20-29 (95% CI 1.08-

1.81; p-value= 0.01). This effect persisted after controlling for participant (G2) age. No significant difference in fertility was found in participants (G2) with older mothers (G1); however, only a small number of participants (G2) had mothers (G1) over age 40, which may not have sufficiently powered the analyses for that group. The results of our study did not support the hypothesis that advanced age of mother (G1) at the birth of her daughter (G2) is associated with infertility in the daughter (G2). Additional studies should be done to confirm the association of G1 age of less than 20 with increased infertility as this may have reproductive implications.

iii

Dedication

For my parents, Harriet and Michael, my husband, Jeremy, and my babies, Vianne and

Kai.

Acknowledgements

It is with the utmost gratitude that I acknowledge the support, commitment, and expertise of my thesis advisor, Dr. Amanda Ewart Toland on this project. Without her initial support and ongoing contributions, this project would never have materialized. I feel very fortunate that she has had such a large role in shaping my professional development.

Dawn Allain has also been instrumental in not only her contribution to the writing of this document, but to my graduate school training for which I am very grateful. I would also like to thank Dr. Courtney Lynch for lending her endless energy, resources, and expertise to this work. Thank you also to Dr. Judith Westman for generously volunteering so much of her time to this project and the genetic counseling graduate program. I would also like to formally acknowledge the tremendous sacrifice and assistance from my parents, husband, and children in order for this research to be completed.

Vita

May 1999…………………………B.A., Psychology, University of Michigan, Ann Arbor

2015- present…………...M.S. Candidate, Genetic Counseling, The Ohio State University

Fields of Study

Major field: Genetic Counseling

Table of Contents

Abstract …………………………………………………………………………………...ii

Dedication………………………………………………………………………………...iii

Acknowledgements………………………………………………………………………..v

Vita …………………………………………………………………………………….....vi

Fields of Study……………………………………………………………………………vi

Table of Contents ………………………………………………………………………..vii

List of Tables ……………………………………………………………………………..x

List of Figures ……………………………………………………………………………xi

Chapter 1: Introduction…………………………………………………………………....1

Infertility…………………………………………………………………………….1

Trends in childbearing and implications for fertility…..……………………………1

Introduction of Transgenerational Terms…………………………………………...5

Associations between disease and advanced grandparental age…………………....3

Age related Infertility…………………………………………………………….....6

Gametogenesis to Fertilization: a window of vulnerability for the egg cell………..9

Male gametogenesis……………………………………………………………….13

Associations between disease in G2 and advanced maternal and paternal age in

G1………………………………………………………………………………….14

Associations between disease in G3 and advanced grandparental age in G1……..16

vii Transgenerational effects of age on fertility……………………………………….17

Epigenetics and the Developing Female Reproductive System…………………...18

Epigenetic Modifications as a Mechanism of Inheritance and

Transgenerational Transmission of Epigenetic Alterations across Generations….22

Study hypothesis…………………………………………………………………..23

Chapter 2: Methods……………………………………………………………………...25

Participant Recruitment…………………………………………………………....25

Survey Description………………………………………………………………...26

Data Analysis and Statistics……………………………………………………….27

Chapter 3: Results………………………………………………………………………..31

Characteristic of Study Participants……………………………………………….31

Participants (G2) born to teenage mothers (G1) were more likely to experience

infertility…………………………………………………………………………33

Chapter 4: Discussion……………………………………………………………………36

Strengths………………………………………………………………………...... 37

Limitations…………………………………………………………………………37

Study Design………………………………………………………………...37

Demographics……………………………………………………………….38

Infertility Outcome Variable………………………………...... 38

Ongoing Studies…………………………………………………………………...39

Conclusion…………………………………………………………………………40

References………………………………………………………………………………..41

Appendix A. Initial ResearchMatch Email ………………………………...……………52

viii Appendix B. Second ResearchMatch Email…………………………………..…………54

Appendix C. Facebook Advertisement………………………………………………..... 55

Appendix D. Facebook Page……………………………………………………………..56

Appendix E.. Brochure Outside and Inside……………………………………………...57

Appendix F. Survey…………………………………………………………………...…59

List of Tables

Table 1. Power and sample size estimates by effect size ………………………………. 29

Table 2. Participant (G2) characteristics by maternal age (G1) at the participant’s (G2) birth………………………………………………………………………………………32

Table 3. Twelve-month Infertility in Participant (G2) by Mother’s (G1) Age at Participant’s (G2) Birth…………………………………………………………………..34

Table 4. Unadjusted and adjusted relation between mother’s (G1) age at the participant’s

(G2) birth and the participant’s (G2) 12-month infertility……………………………….35

List of Figures

Figure 1. Live Birth Rates per IVF cycle using fresh nondonor eggs in 2011……………3

Figure 2. Maternal age and the risk for aneuploidy………………………………………4

Figure 3. Schematic of oocyte lifespan from embryologic development to fertilization..12

Chapter 1: Introduction

Infertility

Infertility is defined by the World Health Organization as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse (Zegers-

Hochschild et al, 2009) and affects 9-18% of the population (Aghajanova, 2016).

Polycystic ovary syndrome, endometriosis, underlying genetic factors, hormonal imbalances, and environmental factors are associated with infertility (Hanson et al, 2016).

Individual genetic polymorphisms have also been shown to play a role (Wainer-Katsir et al, 2015). About half of all infertility is thought to be caused in part or in total by male infertility (Skakkebaek et al, 1994; Minai-Tehrani et al, 2015). Although numerous factors have been associated with infertility, increasing maternal age is believed to have the strongest association (American College of Obstetricians and Gynecologists

Committee, 2014). While much is understood about risk factors for infertility, 10%-30% of infertility remains unexplained (Quass & Hansen, 2016).

Trends in childbearing and implications for infertility

More women are having children later in life due to educational and career pursuits despite confirmed risks to pregnancy and infertility (Martin et al, 2015) (Sauer, 2015).

First births in women over 30 increased 6-fold between 1970 and 2002 (Crawford &

Steiner, 2015). The risks for aneuploidy and reduced fertility due to decreased egg

1 quantity and quality associated with advanced maternal age (AMA) are well established in the scientific literature (Sauer, 2015). Infertility rates increase from 6% for women age

20-24 years to 64% for women over ages 40-44 years (Menken et al, 1986). Live birth rates from in vitro fertilization cycles decrease from 40% for women under age 35 years to 5% for women age 43-44 years (Crawford & Steiner, 2015) (Figure 1). This decrease in fecundity is thought to be due in large part to an increased risk for aneuploidy.

Franasiak et al, 2014 found that aneuploidy rates increased for women under age 23 years and over age 26 years in blastocysts obtained from couples undergoing in vitro fertilization (Franasiak et al, 2014) (Figure 2).

2 Figure 1. Live Birth Rates per IVF cycle using fresh nondonor eggs in 2011. (Adapted from Crawford & Steiner, 2015)

50%

40%

30%

20%

10%

0% < 35 years 35-37 years 38-40 years 41-42 years 43-44 years > 44 years

The blue bars represent the percentage of couples undergoing IVF using autologous fresh eggs who had a live born child. The age represents maternal age. This data was amalgamated by Crawford & Steiner from Centers for Disease Control and Prevention, American Society for Reproductive Medicine Society for Assisted Reproductive Technology. 2011 assisted reproductive technology: national summary report. Atlanta (GA): Centers for Disease Control and Prevention; 2013.

3 Figure 2. Maternal age and the risk for aneuploidy (Data from Franasiak et al, 2014)

125%

100%

75%

50%

Percent Embryos Which are are Aneuploid Which Embryos Percent 25%

0% 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44+ Age in Years

The blue bars represent the percentage of embryos that were aneuploid, detected by chromosomal screening of 15,169 trophectoderm biopsies obtained by couples undergoing IVF.

4 The increasing rates of aneuploidy correlated with maternal aging is likely due to the increased probability for nondisjunction during meiotic division; this in turn is thought to be due to changes in the meiotic spindle (American College of Obstetricians and

Gynecologists Committee, 2014). Another age-related contribution to the increased incidence of aneuploidy is a reduced ability of the aged oocyte to repair double-strand breaks, which contributes to meiotic dysfunction (Tarin et al, 2016). The increased risk for aneuploidy as described above is further demonstrated by age-related risks for

Trisomy 21. The risk for Trisomy 21 in women age 30 years and younger is 1 in 1,000; and this risk increases to 1 in 30 by age 45 years (Snijders et al, 1999).

Introduction of Transgenerational Terms

Throughout this document, there will be reference to multiple generations of individuals within the thesis research and the referenced literature. In order to keep interfamilial relationships clear and to draw parallels between background information and individuals within the study the following nomenclature will be used.

G1 – This term will be used to describe the mother of the study participant in our study.

In some literature, this individual is referred to as the mother or the grandmother. Within this document, we will use the term used by the referenced literature as well as G1.

G2 – This term will be used to describe the participant in our study. In the referenced literature, this individual is referred to as the mother or the child. Within this document, we will use the term used by the referenced literature as well as G2.

G3 – This term will be used to describe the offspring of the participants in our study. In some literature, this individual is referred to as the child or grandchild. Within this

5 document, we will use the term used by the referenced literature as well as G3.

Age-related Infertility

Fecundity begins to decline at age 32 years and declines more quickly after age 37 years

(American College of Obstetricians and Gynecologists Committee, 2014). The aging process impacts this decline in many ways. Ovarian reserve decreases from 300,000-

500,000 at puberty to 25,000 at age 37 years to 1,000 at age 51 years via the process of atresia (American College of Obstetricians and Gynecologists Committee, 2014).

Although many factors, both singularly and in combination, play a role in age-related fertility decline, age of the egg is thought to be the most important factor as women of all ages undergoing in vitro fertilization (IVF) have similar success rates when using young donor eggs (American College of Obstetricians and Gynecologists Committee, 2014).

Aging-associated chromosomal nondisjunction is not the only defect that occurs during aging that affects reproductive outcome. Changes to the nuclear genome, mitochondrial genome, telomere lengths of DNA, and epigenetic modifications in oocytes impact their quality during the aging process. (Wainer-Katsir, 2015). Changes in hormone levels as women age also play a role (American College of Obstetricians and Gynecologists

Committee, 2014).

A single egg cell contains a large number of mitochondria and mutations may accumulate in the mitochondria, impairing fertility (Bentov et al, 2011). Women born to older mothers show greater heterogeneity in their mitochondria genome than women born to

6 younger mothers (Rebolledo-Jaramillo et al, 2014). Reactive oxygen species related- damage may also accumulate within an egg cell due to mitochondrial energy production, adding to the aging effects (Wei et al, 2002). Mitochondrial aging may not only impact the G2 offspring, but also play a role in the health of multiple generations of offspring

(G3 and beyond) as mutations are passed to subsequent generations (Wilding, 2015).

Not only are mitochondrial aging-related changes seen in the oocyte, but shortened telomere length in the germ cell has been shown to correlate with aberrant fertilization and abnormal embryonic cleavage in mice (Liu, 2002). Yue et al (2012) demonstrated an epigenetic effect of aging by showing that genome-wide DNA methylation in oocytes and preimplantation embryos from older mice were associated with a lower reproductive potential.

In addition to oocyte aging, hormonal levels that change with age, including increased circulating follicle-stimulating hormone and decreased circulating antimüllerian hormone and inhibin B concentrations, are also thought to be associated with a decline in fertility.

Other health conditions associated with an increase with age, such as leiomyomas, tubal disease, and endometriosis may also impair fertility (American College of Obstetricians and Gynecologists Committee, 2014).

Multiple reproductive structures (ovaries, fallopian tubes, uterus, vagina, cervix) and cyclically released hormones, all of which are affected by the aging process, play critical roles in ovulation and fertilization (Carson, 2014). Paczowski et al (2015) found that

7 uterine tissue of non-pregnant aged mice showed severe dysregulation in transcript abundance. Expression of genes associated with fetal and placental growth, as well as those associated with nutrient supply, were decreased, suggesting that the uterus of an aged mouse (G1) is less able to support embryo implantation and provide the needs of the developing fetus (G2) (Paczowski et al, 2015). If the levels of hormones and other bodily chemicals exert effects on an egg cell over the course of a woman’s life, considering their role in signaling and inducing changes in tissue, it stands to reason that mRNA expression in egg cells from mothers of AMA differs compared to those of younger mothers. This change in mRNA expression may be due to a lifetime of cumulative age- related damage and also to the different environment, structurally and hormonally, that exists in older mothers. Environmental exposures are well known to cause alteration in

DNA methylation which influences gene expression (Ho et al, 2016).

Genetic variants may also contribute to the link between oocyte aging and infertility.

Studies in mice show that variants in the gene Nlrp2 play a role in regulating oocyte quality as maternal age (G1) increases (Kuchmiy et al, 2016). Complex human reproductive behavior that is linked to infertility has been found to have a heritable basis.

In a recent large genome-wide association study (GWAS) project, 12 loci were found to influence age at first birth and number of children ever born, which are both strongly related to fertility. Not only were single nucleotide polymorphisms (SNPs) found to be associated with these behaviors, but so was the level of methylation in these regions

(Barban, 2017). While it is clear that the age of a woman (G1) influences her own fertility, what is not known is whether the age of a woman (G1) at the birth of her

8 daughter (G2) is correlated with the fertility of the daughter (G2).

Gametogenesis to Fertilization: a window of vulnerability for the egg cell

The risk for aneuploidy and decrease in fecundity that is associated with advanced maternal age in the G1 generation is not thought to be a heritable process; therefore, a different mechanism would likely be at play if the fertility of G2 women was associated with the age of her mother (G1). The formation of human egg cells has a well described embryologic course that is summarized here, based on a review by Carlson (2014). This overview is meant to illustrate the numerous processes that occur early in development that impact both the egg cells’ future ability for successful fertilization as well as the developing reproductive system as a possible explanation for transgenerational impact of age (G1) on offspring (G2) fertility.

Female gamete formation starts very early in embryonic development with all egg cells being formed before birth. The primordial germ cells that are to become these egg cells are recognizable at 24 days after fertilization. These cells must undergo a successful migration from the yolk sac to the primitive gonads. The primordial germ cells undergo rounds of mitotic proliferation to increase the number of germ cells to the millions in the embryonic ovary from the second through fifth week of pregnancy. Oogonia develop a complex of follicular cells by birth. It is the interactions between the egg cell and follicular cells that allows the egg cell to remain arrested in the first meiotic division at birth so that is can later complete the division during menstruation. An important non- cellular layer also forms between the egg cell and its follicular cells called the zona

9 pellucida which contains sperm receptors and components necessary for future fertilization. Primary oocytes also form cortical granules, which play a role in preventing penetration by more than one spermatozoa, during the period of meiotic arrest. Crossing over between sister chromatids occurs in utero. A small amount of ribosomal DNA is also amplified at this time which is thought to aid later cleavage of the cell. At birth, a female newborn’s ovaries will contain roughly 2 million primary oocytes, which are arrested in the first meiotic division and will remain so until menarche (Carson, 2014).

Although the egg cell is arrested in the first meiotic division at birth, is not dormant.

After the G2 is born and continues to grow, so do the egg cells that will become the G3 generation. The G3 egg cells continue with a maturation process from birth through ovulation and fertilization. In the years leading up to puberty, different types of cells and membranes form in preparation for an egg cell’s eventual destination to ovulation and a potential fertilized egg (Carson, 2014). When it is time for ovulation, many of the cells, some of which have been present since embryonic development of the G2, aid in making hormones and responding to hormones made in other parts of the body. At this time, the primary oocyte divides into a secondary oocyte and first polar body. This completes the first meiotic division. The secondary oocyte begins the process of the second meiotic division, but cannot complete this division unless there is fertilization by spermatozoa.

This process happens during each menstrual cycle throughout a woman’s reproductive years if a woman is not on hormonal contraception (Ho et al, 2016 or Carson, 2014).

During both the first and second meiotic division which are necessary in order to become a haploid egg cell, nondisjunction can occur resulting in an aneuploid cell

10 (American College of Obstetricians and Gynecologists Committee, 2014).

It is clear that the aging process affects egg cells, but the formative time during reproductive system development is also critical for future fertility. Formation of the gross reproductive structures and female gamete cells are completed during fetal development. Consequently, time in utero is critical to the efficaciousness of the adult reproductive system (Ho, et al, 2016). To restate this in the context of the transgenerational framework, the fetal environment is provided by the G1 which will impact the reproductive system development of the G2 as well as the gametes which will become G3 (Figure 3). The G2 can also be viewed as the G1 and her offspring being G2 or G3 with differing implications depending on the relationship being studied.

11 Figure 3. Schematic of oocyte lifespan from embryologic development to fertilization

(Adapted from images at infobaby.com and storiesfromthebelly.com, 2017) The primordial germ cells that will become G3 can be seen very early in G2 fetal development. The development of G2 occurs within the G1environment. The G3 germ cells develop within the G1 uterine environment and the G2 embryologic environment. The G3 egg cells continue to develop in the G2 from the time G2 is born until she menstruates. Both the in utero environment created by the G1 and G2 can influence G3 oocyte integrity. The G2 will become the G1 and the cycle repeats itself with each generation. Any G3 offspring will result from the environment of both the G1 and the G2.

12 Male Gametogenesis

Just as in early female primordial germ cells (which are developing inside the female fetus (G2)), male primordial germ cells (which are developing inside the male fetus(G2)) undergo a period of mitotic activity to increase their number. In females, the period of mitotic activity is followed by meiosis. Male sex cells do not enter meiosis in utero as female sex cells do, but rather arrest in the G0 phase of mitosis and remain so until puberty. At puberty, mitosis begins again and it is only now the meiosis occurs. This pattern of mitosis and meiosis continues throughout the male lifespan (Carlson, 2014) with the adult male producing more than 40 million spermatozoa each day (Oliveira &

Alves, 2015). The replication process creates spermatozoa and replenishes the progenitor stem cell pool for future spermatogenesis (Oliveira & Alves, 2015). Because sperm cell production involves multiple divisions and involves making copies of copies, sperm cells created later in adult life contain a larger number mutations. This is both because successive clones accumulate mutations and because de novo mutations are more common with increasing age (Kluiver et al, 2016).

Regardless of whether the fetus (G2) is male or female, the host environment will be that of the mother (G1). The effects that advanced maternal age of the G1 has on male sex cell development in G2 is beyond the scope of this thesis. It is highlighted here to illustrate the different developmental process between male and female sex cells and provide foundation for some of the ongoing analyses being done with data obtained from the thesis survey.

13 Associations between disease in G2 and advanced maternal and paternal age in G1

Because a great deal of egg cell development and maturation occurs in utero, developing egg cells are vulnerable to the external forces exerted by the environment of its host mother (G1), which is more likely to be compromised during an AMA pregnancy. The placenta is a conduit for the nutritional, hormonal, and environmental state between the mother and the fetus and this communication impacts fetal programming underlying future adult health, with epigenetic modulation being one mechanism (Rosenfeld, 2015)

(Gabory et al, 2013). These effects are not likely to be seen until the G2 reaches adulthood when effects to the reproductive system are likely to be more obvious or in the next generation (G3) born from those egg cells.

Early in utero development has been shown to impact later adult health (Gluckman et al,

2008) (Gabory et al, 2013), including the quality and function of reproductive structures and egg cells (Aiken et al, 2015) (Ho et al, 2016). The sensitivity of the reproductive system to in utero environmental exposure was shown by Vom Saal & Moyer (1985).

Their studies found that exposure to testosterone in utero affected reproductive potential in mice, with testosterone exposed mice (G2) ceasing to produce litters at younger ages.

As reviewed by Ho et al (2016), epigenetic changes, such as methylation, histone modifications, microRNAs, and chromatin remodeling, are also now known to exert their effects on the developing fetus (G2) and impact future phenotype; meaning exposures during fetal development may impact future reproductive system. These studies suggest maternal age in G1 may be in and of itself an exposure that is characterized by a hormonal, structural, and chemical environment capable of exerting epigenetic changes

14 on the embryonic and fetal genome of G2 and G3.

Because egg cells begin nearly at conception and undergo a maturation process until ovulation and fertilization, there is a large time period during which egg cells are vulnerable to exposures; these effects can manifest in both the mother (G1), her offspring

(G2), and even future generations (G3) (Ho et al, 2016). The lifetime effects on children

(G2) delivered to mothers (G1) of AMA are now being explored with associations found between maternal age and a number of physical and mental health outcomes. A review by

Tarin et al (2016) showed that advanced maternal age (G1) was associated with slight, but significant, increased risk in offspring (G2) for birth defects, childhood-onset type 1 diabetes, childhood cancers, breast cancer, testicular cancer, male infertility, menstrual disorders, autism spectrum disorders, schizophrenia, and bipolar disorder. Their review notes that a number of confounding variables (such as paternal age, parental medical conditions, race, and socioeconomic status) make detecting the effect maternal age difficult. Golding, et al (2010) found an increased prevalence of autism is offspring (G2) when either the mother or father (G1) was 30-34 years of age, compared with parents

(G1) under 30 or over 35 years of age (Figure 1). A review and meta-analysis by Sandin, et al (2012) found that the risk for autism in offspring (G2) increased as the age of the mother (G1) increased.

A number of later health outcomes in G2 have also been associated with advanced paternal age in G1, as reviewed by Andersen & Urhoj (2017) including increased risk for stillbirth, musculo-skeletal syndromes, cleft palate, acute lymphoblastic leukemia,

15 retinoblastoma, autism spectrum disorder, and schizophrenia. Frans, et al (2011) found a link between paternal age (G1) and schizophrenia. In this study, children (G2) with fathers (G1) older than 55 had a 2-fold increase risk of schizophrenia compared to children (G2) whose fathers (G1) were 20-24.

Associations between disease in G3 and advanced grandparental age in G1

In addition to impact of G1 age on G2 phenotype, some studies show association of disease risk with G1 age on G3 phenotype, which supports the hypothesis that G1 age can impact phenotypes in G3. Malini and Ramachandra (2006) reported that each year of advancement of grandmaternal age (G1) was associated with as high as a 30% risk increase of Down syndrome for their daughter’s pregnancies (G3). A similar study by

Malini, et al (2007) reported a 36% increase risk for sex chromosomal aneuploidy in daughter’s pregnancies (G3) with each year of grandmaternal age.

Possible correlations have also been found between grandparental age (G1) and autism spectrum disorder in G3 (Golding, et al, 2010) (Frans, et al, 2013). Golding, et al (2010) found a higher prevalence of autism in grandchildren (G3) of grandparents (G1) who were 35 or older at the birth of the mother (G2). Using Swedish multi-generation and patient registers, Frans, et al (2013) found that both paternal (G2) and paternal G1) age were associated with an increased risk of autism in children (G2) and grandchildren (G3).

A similar study by Frans, et al (2011) found an increase in grandfather (G1) age was found to be associated with an increased risk of schizophrenia in G3, but only on the maternal side.

16 Further evidence that G1 is resonant in G3 offspring, is elucidated by research correlating

G1 age, especially paternal grandfather G1 age, with telomere size (Eisenberg, et al,

2012). This research found that although telomere length shortens with age in tissues, it increases with age in sperm. In this study, children (G3) with older fathers (G2) inherited longer telomeres. Further, they found that telomere length is cumulative across generations with advanced G1 age at birth of the G2 being associated with longer telomere length in the G3 . Telomere size, both shortened and lengthened, has been linked to a number of health outcomes (Murillo-Ortiz, et al, 2017) (Kloda, et al, 2017)

(Gebreab, et al, 2017).

Transgenerational effects of age on fertility

While studies associate G1 age to developmental, psychological, and physiological issues in G2 and G3, animal studies also show a connection between G1 age on fertility of G2 and G3. Research using Drosophila examined both the effects of maternal (G2) and maternal grandmother (G1) age on offspring (G3) fitness. In these insects, maternal age

(G2) was associated with decreased egg hatch rate in G3 and lower larval-to-adult viability. Maternal grandmother (G1) age was associated with a decrease in egg hatch rate in G3. The age effect was also found to be cumulative with offspring (G3) flies of older maternal grandmothers (G1) and older mothers (G2) having the lowest viability.

(Hercus & Hoffman, 2000). Aiken, et al (2015) found that, in rats, maternal grandmother

(G1) diet affected the reproductive potential of not only daughter (G2) rats, but also granddaughter (G3) rats. Their research showed maternal grandmother (G1) rats fed low- protein diets had granddaughters (G3) with decreased ovarian reserve and a larger

17 amount of oxidative stress and mtDNA copy number instability in their ovaries.

Granddaughter (G3) rats in this study were also found to demonstrate accelerated aging of the reproductive tract, which was measured by an increased rate of telomere length decline in ovarian cells (Aiken et al, 2015). In one retrospective study of humans (Smits, et al, 2002), researchers found a decreased fecundity and increased risk of childlessness in women (G2) born to mothers (G1) age 40 and older which supports the basis of my thesis research.

Epigenetics and the Developing Female Reproductive System

The environment of the fetus (G2), including steroid hormones, growth factors, and other transcription factors coordinate the development, differentiation, and maturation of the future adult reproductive system. One way in which this process can be affected is through epigenetic modifications, “heritable changes in gene function that occur without a change in the nucleotide sequence” (Ho et al, 2016). In a review, Ho et al (2016) studied the effects of estrogenic and anti-androgenic endocrine disrupting chemicals, such as the synthetic estrogen once used by mothers (G1) during pregnancy to prevent miscarriage, on embryonic development of the offspring (G2) reproductive tract of mothers (G1) who used the drug. They proposed epigenetic modifications result after these exposures and are damaging on a number of tissue types. Ovarian reserve, female cycling, reduced embryo implantation, adult uterine abnormalities, sperm quality, prostate disease, and mating behavior in offspring (G2) of mother’s (G1) who used estrogenic and anti-androgenic endocrine disrupting chemicals during pregnancy have all been shown to be affected in animal studies. Ho et al (2016) further showed that in

18 human studies, estrogenic and anti-androgenic endocrine disrupting chemical exposure in females (G2) in utero is associated with vaginal clear-cell carcinoma, cervical and vaginal structural anomalies, endometriosis, uterine leiomyoma, and early fibroids. In males (G2), estrogenic and anti-androgenic endocrine disrupting chemical exposure was associated with hypospadias, infertility, testicular problems, reduction of sperm concentration, oligospermia, and abnormal sperm morphology (Ho et al, 2016). Bromer et al (2009) also showed that in utero alteration to methylation patterns in HOXA10 due to EDC exposure has been shown to alter the expression of the gene in the reproductive tract in adulthood (G2). Mouse studies by Paczowski et al (2015) found that ovaries undergo significant alterations in methylation and gene expression over time. This same research further showed that maternal age (G1) negatively affects imprinted gene methylation and expression in both germ cells and somatic cells of the reproductive tract of G2.

Framing their review under the developmental origin of adult diseases hypothesis, Ho et al (2016) asserted that the environment of the fetus (G2) during embryonic development affects not only whole life health outcome of the fetus (G2), but also the outcome of future generations (G3) via epigenetic mechanisms, such as expression of microRNAs and other non-coding RNAs, histone modification, and especially DNA methylation.

Because female egg cells begin nearly at conception and undergo a maturation process until ovulation and fertilization, there is a “wide window of susceptibility to disruption by environmental factors.” (Ho et al, 2016).

In all of these studies, the timing of the exposure was critical in whether epigenetic

19 reprogramming occurred or not. The time before tissue differentiation is complete is more vulnerable, making gestation a particularly sensitive time for epigenetic modification. Tarin et al (2016) suggested that the intrauterine environment of mothers

(G1) of AMA with age-related obstetric complications is an exposure that induces epigenetic DNA reprogramming. They found that fetal growth restriction, preterm labor,

Caesarean delivery, hypertensive complications in pregnancy, and gestational diabetes were associated with a change in DNA methylation in the genes of offspring (G2) of older mothers (G1). These differential methylation patterns are thought to impart a number of diverse late outcomes in these offspring (G2) by affecting gene expression involved in the nitric oxide system, metabolic disease growth, cardiovascular and immune mediated diseases, and others. (Tarin et al, 2016).

While methylation is the most well characterized epigenetic phenomenon in regards to germ cell reprogramming (Ho et al, 2016), histone modifications may also play a role.

Mainpal & Yanowitz (2016) have shown in their studies with Caenorhabditis elegans that xdn-1, which is thought to repress transcription, is deposited into early embryos (G2) by the mother (G1) and then found on chromatin in offspring (G2) germ lineage throughout development and into adulthood. They further show that loss of xnd-1 predisposes the animal (G2) and its offspring (G3) to reduced fecundity due to resulting abnormal gene expression caused by a variety of epigenetic effects including histone modifications and demethylation.

20 In addition to methylation and histone modification, small non-coding RNAs have been shown to enable epigenetic reprogramming in gametes. The untransmitted genotype of male mice (G1) has been shown to influence offspring (G2) phenotype via epigenetic information on other genes carried in his gametes (Chong et al, 2007). Daxinger

&Whitelaw (2012) suggest this may be due to diffusible RNAs. The authors discussed a number of ways various non-coding RNA types may influence epigenetic reprogramming in gametes and influence transgenerational inheritance. Oocytes store a large amount of

RNA that is utilized by the developing zygote. They also suggest because RNAs have been shown to silence transposable elements in adjacent germline cells, small RNAs could be at play during fertilization and affect gene silencing. piRNAs are expressed in reproductive organs and play a role in parental imprinting during reprogramming in primordial germ cells, suggesting that RNAs establish the epigenetic mark and DNA methylation carries the information in the gametes, or, alternatively, piRNAs are carried in the gametes and influence DNA methylation in offspring. Additionally, miRNAs in oocytes may affect the amount of mRNA in the zygotes, which could indirectly change the epigenetic state of the developing embryo. Evidence for this can be seen in wild type mice offspring (G2) with mutant phenotype of their parent (G1), where the mRNA levels, not the genotype, determined the phenotype (Daxinger & Whitelaw, 2012). Studies in mice show the miRNA content in sperm cells is affected by environmental factors such as paternal obesity and stress (Fullston et al, 2013). Further, miRNA differences in sperm have been associated with transgenerational behavioral and metabolic outcome (Ho et al,

2016). In human oocytes, miRNAs may play a role in the regulation between oocyte and

21 cumulus cells (Assou et al, 2013). How this is affected by age or might impact fertility is not yet known.

Epigenetic Modifications as a Mechanism of Inheritance and Transgenerational

Transmission of Epigenetic Alterations across Generations

Epigenetic alterations, including methylation, histone modifications, and non-coding

RNAs, in the egg cells could impact phenotype in multiple generations (Ho et al, 2016).

Epigenetic changes that occur in one generation are typically reset in the next generation during embryonic development when primordial germ cells undergo a process of erasure of DNA methylation followed by a process of re-methylation in sex specific manner

(Allegrucci, 2005). Because DNA needs to be sensitive to natural reprogramming at this time, it is also particularly vulnerable to new methylation patterning (Skinner et al, 2010).

Skinner et al (2010) proposed that permanent epigenetic modification of the germ line can be transmitted across generations. In order for a phenotype to persist across generations, the epigenetic change that originally occurred must escape the normal methylation erasure and resetting that happens during the embryonic period prior to sex determination. Persistence of epigenetic effects in mouse models up to the fourth generation have been demonstrated (Anway et al, 2005; Anway et al, 2006) Some demethylation-resistant loci and other regulatory elements that escape DNA demethylation have been identified (Daxinger & Whitelaw, 2012; Hackett et al, 2013, Ho et al, 2016).

A trend in postponing motherhood brings into focus the importance of understanding the

22 consequences of reproduction at an older age beyond those risks related to pregnancy and perinatal outcome. The connection between in utero exposure or in prior generations to adult health outcome is difficult to document in humans because there is such a large gap of time between the exposure and the outcome of the exposure during adulthood. This not only makes the relationship harder to detect, but also makes it difficult to tease out the many other forces acting on phenotype over time. Long-term effects due to a variety of mechanisms acting on the egg cell from conception to ovulation and fertilization can exert an effect on the health of adult offspring as well as subsequent generations.

Exposures during pregnancy, such as AMA, are associated with a variety of perinatal outcomes, but there is evidence that it may be associated with problems later in life, one of those being infertility in daughters. While a number of possible outcomes and candidate causal mechanisms have been elucidated in the transmission of phenotype across generations, more research is needed to characterize the effect on fertility of daughters (G2) born to older mothers (G1).

Study hypothesis

The goal of this study is to evaluate if a mother’s age (G1) at the birth of her daughter

(G2) is associated with the fertility of the daughter (G2). We hypothesize that the women

(G2) born to mothers (G1) of advanced maternal age are more likely to experience infertility or a longer time to first pregnancy than women (G2) born to younger mothers

(G1). We also hypothesize that there may be a cumulative effect of the woman’s age (G2) and mother’s (G1) age at the time of the woman’s birth that negatively impacts fertility.

23 These hypotheses will be tested using survey data, including fertility history and parental ages, of over 3,000 women.

Results of this thesis are important in the context of fertility management as they may provide a deeper understanding of some of the causes of infertility and the impact that

AMA could have on future generations. As the trend in postponing child bearing continues (Sauer, 2015), a more comprehensive understanding of the effect of having children later in life has important implications not only in the interpersonal sphere of family planning and informed decision making, but also in the broader domain of public health.

Chapter 2: Methods

Participant Recruitment

The study was approved by the Institutional Review Board at The Ohio State University.

Participants were eligible for this study if they were female, age 18 or older as of

September 22, 2016, and had been pregnant or tried to become pregnant. Participants were recruited from three sources: ResearchMatch, Facebook, and Ohio Reproductive

Medicine, a fertility clinic in Columbus, Ohio. Participants provided an online consent prior to starting the survey.

The majority of participants were recruited from ResearchMatch, an online database of research volunteers, from September 22, 2016 through November 28, 2016.

ResearchMatch is NIH sponsored and has had a fully executed agreement with the Ohio

State University since 2009. The IRB of record for ResearchMatch is Vanderbilt

University. At the time of recruitment, the ResearchMatch database contained 105,456 total members. For this study, members were filtered so that only female volunteers age

18 years or older were contacted. 73,662 total volunteers met these criteria and were contacted first by ResearchMatch via email with an invitation to participate in the survey

(Appendix A). Of the 73,662 volunteers contacted, 4,226 agreed to be contacted with more information about the research as well as a link to take the survey (Appendix B).

Because the surveys are taken anonymously, there was not a way to confirm how many

25 participants came from which recruitment source; however, we estimate that about 2700 participants came from ResearchMatch. This estimate was based on how many participants took the survey on the days a specific recruitment method was being utilized.

Participants were also recruited using Facebook advertisements (Appendix C) targeted to women who “liked” various infertility related groups, pages, and businesses. The first

Facebook advertisement ran from September 17, 2016 - September 23, 2016. This advertisement reached 684 people and resulted in 33 “likes” to the study’s page

(Appendix D) and an unknown number of clicks to the online survey. A second advertisement was run November 7, 2016 - November 19, 2016. This advertisement reached 8,294 people and resulted in 253 clicks to the online survey.

Our third recruitment strategy was identification of potential participants from a local reproductive medicine clinic, Ohio Reproductive Medicine. Brochures were created which described the study, provided a link to the survey, and included a QR code allowing women to access the survey from a smart phone. These brochures were placed in the lobby of the clinic during the recruitment period. A copy of the brochure is provided in Appendix E.

Survey Description

An anonymous online survey was administered to female participants age 18 and older from September 22, 2016 through December 16, 2016. The survey was created specifically for this research and was hosted by Qualtrics. Survey questions were

26 designed to assess participant fertility, ages of participants and their parents, participant pregnancy history, birth outcome for each pregnancy, and long-term health outcome of offspring with the aim of assessing the relationship between parental age and fertility, grandparental age and fertility, and long-term effects of both maternal and grandparental age on offspring. A skip logic design was employed to ask participants questions specific to their reproductive history. For example, if a woman had two pregnancies, questions about only those two pregnancies were asked for that participant. For participants who never conceived a pregnancy, questions regarding pregnancy details were not displayed.

Several questions required participants to know their own age at each pregnancy as well as their parents’ ages at their birth. A link to online age calculator was included with each of these questions. This online age calculator can be accessed at: http://www.calculator.net/age-calculator.html

This calculator allowed the participant to input her birthdate along with her children’s or parents’ birthdates to calculate specific ages for questions. The survey was closed on

December 16, 2016. A copy of the survey is provided as Appendix F.

Data Analysis and Statistics

Prior to data collection, calculations were made to determine the minimum number of participants needed to significantly power the study. The Ohio State University College of Public Health statistics department performed an analysis using two reports from the

CDC, “The National Survey of Family Growth/The National Vital Statistics Report” and

“The National Health Statistics” report. The following projection for sample sizes was determined:

27 All calculations assume that the proportion of fertile women in our study who were born to a mother of AMA (35+) is an average of the AMA rates from 1965-1995, which was

7.5%. The number of participants required for four different calculations are provided in

28 Table 1. Power and sample size estimates by effect size

Odds Ratio Power Women needed in Total participants each group needed (fertile/nonfertile) 2.5 80% 193 396

2.5 90% 258 516

2.0 80% 356 712

2.0 90% 476 952

The above table details the total number of participants needed to power the study. These calculations are based on having an equal number of participants defined as fertile or infertile, which is defined as no pregnancy despite 12 months of regular, unprotected intercourse.

29 Based on this analysis, we required a minimum of 396 women participants, but aimed for up to 1000 in order to achieve higher powered statistics and to detect smaller effect sizes.

A total of 3,105 women completed the survey. One hundred and twenty-six surveys were omitted due to insufficient question completion. The survey collected a number of measures of infertility to use as outcome variables. Analyses were done using the outcome variable ‘yes’ or ‘no’ to survey question, “throughout your life has there ever been a period of time during which you were having regular intercourse without contraception for 12 or more months but did not get pregnant?” Comparisons were made between groups of participants defined by age of participant mother: under 20 years old,

20-29 years old, 30-34 years old, 35-39, 40-44 and 45 years and older. Demographic characteristics between groups defined by age of participant mother were compared using descriptive statistics. Differences in the probability of infertility outcomes between groups defined by age of participant mother were assessed via unadjusted chi-square tests and adjusted logistic regression models. All data analyses were conducted using Stata version 14 (StataCorp, 2015).

Chapter 3: Results

Characteristics of Study Participants

A total of 3105 surveys were submitted. Of the 3105 surveys, 2979 (96%) were marked as complete and were used in the final analyses. The average age of participants was 45.9 years and ranged from 18 to 92 years. Of those women that completed the survey, 2833

(95%) answered at least some of the demographic questions. The vast majority were

Caucasian (88%), had at least some college education (96%), and were married (72%).

Participant characteristics are shown in Table 2.

31 Table 2. Participant (G2) characteristics by maternal age (G1) at the participant’s (G2) birth (n=2833) G1 Age G1 Age 35-39 G1 Age 40+ Total

<35 (n=221) (n=81) n= 2833

(n=2531)

Race

Caucasian 2229 (88%) 190 (86%) 69 (85%) 2488 (88%)

African American 146 (6%) 11 (5%) 7 (9%) 164 (6%)

Hispanic 65 (3%) 10 (5%) 4 (5%) 79 (3%)

All Others 91 (4%) 10 (5%) 1 (1%) 102 (4%)

Annual household income

<$25K 160 (7%) 13 (7%) 5 (7%) 178 (7%)

$25-49 421 (18%) 30 (15%) 11 (15%) 462 (17%)

$50-74 506 (21%) 45 (23%) 14 (20%) 565 (21%)

$75-99 489 (20%) 45 (23%) 21 (30%) 555 (21%)

$>100 814 (34%) 66 (33%) 20 (28%) 900 (34%)

Education

HS (or less) 103 (4%) 3 (1%) 2 (1%) 108 (4%)

Some college 680 (27%) 47 (21%) 22 (27%) 749 (26%)

Bachelor’s Degree 794 (31%) 74 (34%) 28 (35%) 896 (32%)

Master’s Degree (or higher) 957 (38%) 95 (44%) 29 (36%) 1081 (38%)

Marital Status

Single 174 (7%) 25 (11%) 8 (10%) 207 (7%)

Married 1834 (73%) 151 (69%) 58 (72%) 2043 (72%)

Widow/Divorced/Separated 517 (21%) 44 (20%) 15 (19%) 576 (20%)

Distribution of participant (G2) demographics, including race, income, education, and marital status, grouped by age of participant mother (G1)

32 Participants (G2) born to teenage mothers (G1) were more likely to experience infertility

Logistic regression was used to compare groups defined by age of participant mother

(G1) with participant (G2) having had or not had infertility defined as no pregnancy despite 12 months of regular unprotected intercourse. Participants (G2) with mothers

(G1) age 20-29 were used as the reference group. For participants (G2) with mothers

(G1) under age 20 at the time of their birth, 132 (49.6%) did not report a period of infertility versus 134 (50.4%) who did report a period of infertility. Analyses showed that women (G2) born to teenage mothers (G1) were 40% more likely to experience infertility than women (G2) whose mothers (G1) were 20-29. The unadjusted odds ratio for infertility in this group was 1.4 (95% CI 1.08-1.81; p-value= 0.01). After adjusting for age of participant (G2), near identical findings were seen with an odds ratio for infertility of 1.4 (95% CI: 1.08-1.82; p-value=0.010). Adjusting for race and education did not affect the model. For participants (G2) with older mothers (G1), no statistically significant effects were found. For participants (G2) with mothers (G1) 45 years or older, a small effect was seen, but this was not statistically significant (adjusted OR 1.07; 95%

CI: 0.51-2.25; p-value=0.85). The odds ratio for infertility in the group with mothers (G1)

45 or older was not sufficiently powered for small effect sizes as only 29 participants fell into this group. It is important to note, only 81 participants (G2) had older mothers (G1).

Consequently, the study may not have been sufficiently powered to detect associations with advanced maternal (G1) age. These results are summarized in Tables 3 and 4.

33 Table 3. Twelve-month Infertility in Participant (G2) by Mother’s (G1) Age at Participant’s (G2) Birth G1 (years) For G2 No For G2 History history of 12- of 12-month month infertility infertility* <20 132 (49.6%) 134 (50.4%) 20-29 1023 (57.9%) 743(42.1%) 30-34 209 (58.1%) 151(41.9% 35-39 132 (58.9%) 92 (41.1%) 40-44 32 (61.5%) 20 (38.5%) 45+ 16 (55.2%) 13 (44.8%) *Defined as no pregnancy despite 12 months of regular unprotected intercourse

Participants (G2) were grouped by who experienced fertility and who did not and were stratified by age of their mother (G1) at the time of their birth. The majority of participants had mothers age 20-29. For each maternal age group, the proportion of participants who did not experience infertility and did experience infertility is noted both numerically and by percentage. In every age group, except the under 20 age group, there was a greater proportion of women who did not experience infertility than those that did experience infertility.

34 Table 4. Unadjusted and adjusted relation between mother’s (G1) age at the participant’s (G2) birth and the participant’s (G2) 12-month infertility* Unadjusted Adjusted** Odds Ratios Odds Ratios (95% CI; p-value) (95% CI; p-value) Mother’s (G1) age at birth of G2 (years) 20-29 REF REF <20 1.4 1.4 (CI:1.08-1.81; (CI:1.08-1.82; p-value=0.01) p-value=0.01) 30-39 0.98 0.99 (CI:0.81-1.19; (CI:0.82-1.2; p-value=0.84 p-value=0.91) 40-44 0.86 0.91 (CI:0.49-1.52; (CI:0.53-1.61; p-value=0.6) p-value=0.75) 45+ 1.12 1.07 (CI:0.53-2.34; (CI:0.51-2.25; p-value=0.77) p-value=0.85) * Defined as no pregnancy despite 12 months of regular unprotected intercourse **Adjusted woman’s (G2) age at interview Participants in each group were compared to participants with mother’s age 20-29. The odds ratio for infertility is noted for each age group. There was little to no difference when the odds ratio was adjusted for participant (G2) age.

Chapter 4: Discussion

This study sought to determine if women (G2) born to older mothers (G1) are at a higher risk for infertility or reduced fecundity. We found that women (G2) in our sample born to older mothers (G1) did not have an increased risk for infertility; however, those (G2) born to teenage mothers (G1) were 1.4 times more likely to experience infertility than women (G2) with mothers age 20-29. Age of a woman’s mother at the time of her birth was associated with infertility, just not in the direction that we expected.

In hindsight, this result is not entirely surprising. Young maternal age (G1) has been linked to a number of adverse pregnancy outcomes including risk for prematurity

(Blomberg et al, 2014), congenital malformations in G2 (Kang et al, 2015), and fetal death in G2 (de Vienne et al, 2005). Although some of the poor outcomes may be attributed to social factors and poor prenatal care (de Vienne et al, 2015), young maternal age in G1 on its own may be a risk factor for poor pregnancy outcome in G2 (Kang et al,

2015; Kaplanoglu et al, 2015). The implications of this finding are that young maternal age in the mother (G1) may be associated with infertility in the adult daughter (G2). Our study was not significantly powered to detect an association with infertility and older mothers (G1).

36 Strengths

To our knowledge, this study is the first research in humans looking at the effect maternal age (G1) has on daughter (G2) fertility. One of the main strengths of this study was the large sample size and diverse group of participants in terms of age and ethnicity. This study also collected a diverse group of late childhood and adult outcome variables in offspring (G3). This will allow for a number of analyses to be done comparing ages of participants (G2), participants’ parents (G1) (individually as well cumulatively) to a number of offspring (G3) outcomes.

Limitations

Much of the study limitations revolved around the study design, the participant demographics, and the accuracy and sensitivity of the study’s infertility outcome measure.

Study Design

The participants were self-selected and the data was self-reported and retrospective.

Participants were asked to provide their parents’ ages at the time of their birth, time it took to achieve each pregnancy, and several other variables which required them to remember and accurately report. We have no way to verify the accuracy of this information. Additionally, not all participants answered all of the survey questions.

Another challenge with this project and all research evaluating multi-factorial adult outcome (in this case daughter infertility) with in utero factors is the large period of time

37 between exposure in G1 (maternal environment) and outcome in G2 (infertility). It is difficult to tease out all of the other factors, both known and unknown, that can occur over time that contribute to outcome and assign causality.

Demographics

The vast majority of survey takers were college educated. Amongst our study participants

69% had a bachelor’s degree or higher compared to 30% in the United States general population (United States Census Bureau, 2015). Over-representation of college educated individuals is a common limitation in scientific research (Allmark, 2004). Consequently, results from this study may not be generalizable to all populations (Britton et al, 1999).

Infertility Outcome Variable

The World Organization defines infertility as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse (Zegers-Hochschild et al, 2009). Our survey collected a number of fertility measures, but used the 12-month infertility definition as our final outcome measure as it is considered the standard in infertility research. Many of our participants were past reproductive age (1402 were 45 years or older) and likely would experience age-related fertility decline. In order to account for this potential bias, we adjusted the analyses by age, which made no significant difference in our results. We also reran the analyses using only participants

(G2) under age 52 years (median age at menopause) with very little effect on the results.

The odds ratio for infertility for these participants (G2) with teenage mothers (G1) was

1.36 (95% CI: 0.99-1.87; p-value + 0.06).

38 Ongoing Studies

The survey collected other fertility measures that were not analyzed which may detect subtle differences in fecundity. Ongoing analyses using “time to first pregnancy” and age as a continuous variable would allow for more subtle differences in fertility to be captured.

There is potential to evaluate several other fertility related questions. Analyses to assess if there is a cumulative effect of mother’s age (G1) and daughter’s age (G2) on daughter’s fertility can be done using the collected dataset. Age related effects of pregnancy outcome may also be evaluated. For example, is age of mother (G1) associated with a risk for miscarriage or preterm birth in the daughter’s (G2) pregnancies?

Current analyses compared fertility outcome to mothers’ (G1) ages in groups (i.e. under

20, 20-29, 30-34, 35-39, 40-44, 45 and older). Future analyses, using age of mother (G1) as a continuous variable may allow for more subtle differences to be detected as well.

Although this primary focus of this study was effect of maternal age (G1) on daughter

(G2) fertility, a number of later childhood and adult outcomes in participants’ children

(G3) were also ascertained by the survey. Investigations whether or not a woman’s age

(G1) at the birth of her daughter (G2) affects her daughter’s risk for having a child (G3) with Down syndrome, autism, developmental delay, nut allergy, celiac disease, cancer, diabetes, or autoimmune disease can be done.

39 The survey also ascertained age of father (G1) at birth of participant (G2). The same set of analyses performed using participants’ maternal age (G1) compared to fertility in G2 and later childhood outcome in G3 can be done using paternal age (G1) of participants

(G2).

Although no statistically significant difference in fertility was found in participants with older mothers, the odds ratio was actually protective in women (G2) with mothers age

(G1) 40-44 (OR 0.91; CI 95% 0.52-1.61; p-value 0.75). Although not a significant difference, it does raise the possibility that women (G1) who are able to conceive a viable pregnancy at 40-44 are more fertile and, consequently, so are their daughters (G2).

Although beyond the scope of this research and not what we expected, future studies examining the possibility of increased fecundity in women (G2) with older mothers (G1) may be worthwhile.

Conclusion

Results of our study did not support the hypothesis that advanced age of mother (G1) at the birth of her daughter (G2) is associated with infertility in the daughter (G2); however, young maternal age (G1) was. Additional studies should be done to confirm these findings.

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Appendix A. Initial ResearchMatch Email

A research team with The Ohio State University in Columbus, OH, believes you might be good match for the following study:

Would you like to help researchers better understand how the age of one’s mother at delivery may affect her daughter’s fertility? You may be eligible for this survey study if you are:

• A woman, age 18 or older, AND

• Have been pregnant or have tried to become pregnant.

The survey for this study will take approximately 20 minutes to complete.

If you are interested in this study and having the research team contact you directly, please select the "Yes, I'm interested" link below. By clicking the "Yes, I'm interested" link, your contact information will be released to the research team. If you select the "No, thanks." link or do not respond to this study message, your contact information will not be released to the research team.

Yes, I'm interested! No, thanks.

You are receiving this email message since you have registered in the ResearchMatch registry. Should you wish to edit your profile or remove your contact information from this registry, please login here.

ResearchMatch Disclaimer

ResearchMatch is a free and secure tool that helps match willing volunteers with eligible

52 researchers and their studies at institutions across the country. ResearchMatch is only providing a tool that allows you to be contacted by researchers about their studies.

ResearchMatch therefore does not endorse any research, research institution, or study.

Any recruitment message that you may receive about a study does not mean that

ResearchMatch has reviewed the study or recommends that you consider participating in this study.

Appendix B. Second ResearchMatch Email

You are invited to fill out a short online survey as part of a research project at the Ohio

State University that is studying reduced fertility risk factors. By clicking this link, you will be taken to a survey. The survey will ask you about the age, health, and pregnancy history of you, your parents and your children. The survey should take 20 minutes or less to complete. You can contact Tamara Reynolds at (614) 293-9099 or email at [email protected] with questions.

Appendix C. Facebook Advertisement

Appendix D. Facebook Page

Appendix E. Brochure Outside

Family History & Fertiliy Research Study

Research Study

Researchers at the Ohio State University are conducting a study, investigating factors that may influence female fertility. We are inviting women to complete an anonymous and confidential online survey.

The survey will ask questions about your fertility history and family. Both women who have Feel free to contact Tamara conceived a pregnancy and those Reynolds by phone at (614) who have not conceived a preg- 293-9099 or email at nancy are invited to participate. [email protected] It takes about 20 minutes to with any questions. complete.

No personal identifying information will be collected. If you would like to contribute your experience to this research, you can visit the weblink below and take the survey from your home computer. You can also access the weblink via the QR code to the right. www.go.osu.edu/fertility

Appendix F. Survey

Fertility Survey

Q151 The Ohio State University Consent to Participate in Research Study title:

Transgenerational effects of advanced maternal age on fertility of offspring

Principal Investigator: Amanda Toland, PhD Sponsor: None This is a consent form

for research participation. It contains information about this study and what to

expect if you participate. Your participation is voluntary. You can stop taking the

survey at any time. Purpose: We are conducting a survey of women over the age of

18 to determine if there is an association between the age of a woman’s parents and

her fertility. Duration: It will take approximately 15-20 minutes to complete the

online survey. You may leave the survey at any time or skip any question. There is

no penalty for stopping the survey. Risks and Benefits: For women who have

experienced fertility issues, there may be a negative emotional response to recalling

poor past pregnancy outcomes. There are no other risks to taking the survey beyond

those that exist in daily life. You may skip/prefer not to answer any question.

Although your participation may not benefit you directly, results of the study may

provide insight into a previously unidentified risk factor for infertility. Incentives:

There are no incentives for taking this survey. Confidentiality: Your participation in

this study is completely confidential. You will not be asked your name or other

personally identifying information. We will not receive you IP address. When 59 downloading survey data, the research team will use SSL encryption for secure data

transmission. All data will be averaged and reported in aggregate. Possible outlets

of dissemination may be academic conference presentations and journal articles.

We will work to make sure that no one sees your survey responses without

approval. But because we are using the internet there is a chance someone could

access your online responses without permission. In rare cases, this information

could be used to identify you. Page 1! of !2 Form date: 02/11/13 CONSENT IRB

Protocol Number: Biomedical Science IRB Approval Date: Version: Participant

Rights: You may refuse to participate in this study without penalty or loss of

benefits to which you are otherwise entitled. If you are student or employee at Ohio

State, your decision will not affect your grades or employment status. If you choose

to participate in the study, you may discontinue participation at any time without

penalty or loss of benefits. By signing this form, you do not give up any personal

legal rights you may have as a participant in this study. An Institutional Review

Board responsible for human subjects research at the Ohio State University

reviewed this project and found it to be acceptable, according to applicable federal

and state regulations and university policies designed to protect the rights and

welfare of participants involved in research. Contact and Questions: For questions,

concerns, or complaints about the study, or feel you have been harmed as a result of

study participation, you may contact Amanda Toland at the Ohio State University at

614-688-1884 or [email protected]. For questions about your rights as a

participant in this study or to discuss other study-related concerns or complaints

with someone who is not part of the research team, you may contact Ms. Sandra

60 Meadows in the Office of Responsible Research Practices at 1-800-678-6251.

Please print a copy of this consent form for your records, if you so desire. WAIVER

OF CONSENT DOCUMENTATION TO PARTICIATE I have read and

understand the above consent form, I certify that I am 18 years old or older and, by

clicking the submit button to enter the survey, I indicate my willingness voluntarily

take part in the study. Otherwise, use the X at the upper right

corner to close this window and disconnect. By clicking the button below to take

this survey, you indicate that you agree with the above statement. Agree (1)

Disagree (2)

Q4 The following set of questions will ask you about your pregnancy history and/or

fertility challenges.

Q1 Have you ever tried to get pregnant? Yes (1) No (2)

Q2 Have you ever been pregnant? (Please include all pregnancies regardless of outcome,

including miscarriages, pregnancy terminations, still births, and live births.) Yes

(1) No (2)

Q56 How many times have you been pregnant? (Please include all pregnancies regardless

of outcome including miscarriages, pregnancy terminations, still births, and live

births.) 0 (1) 1 (2)

2 (3) 3 (4) 4 (5) 5 (6) 6 (7) 7 (8) 8 (9) 9 (10) 10 (11)

Q57 Throughout your life has there ever been a period of time during which you were

having regular intercourse without contraception for 12 or more months but did not

get pregnant? Yes (1) No (2)

61 Q67 The following questions will ask you for some details about your first pregnancy.

Display This Question: If Have you ever been pregnant? (Please include all pregnancies

regardless of outcome, including mis... Yes Is Selected

Q76 What was the outcome of your first pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. Live birth (1)

Miscarriage (2) Termination (3) Still Birth (4) Ectopic pregnancy (5)

Notes (6) ______

Q77 Was your first pregnancy planned? Yes (1) No (2)

Q78 How many months did it take you to get pregnant?

Q79 How old were you at the birth or loss of your first pregnancy? If you need help you

can use the link to the online age calculator.

Q80 Did you undergo fertility treatments to conceive your first pregnancy? Yes (1)

No (2)

Q81 Did your first pregnancy result in a child that has been diagnosed with any of the

following? (Click all that apply.) Down syndrome (1) Autism (2)

Developmental Delay (3) q Nut allergy (4) Celiac's disease (5) Cancer (6)

Diabetes (7) Autoimmune disease (8) Other (9) ______

Q109 The following questions will ask you for some details about your second

pregnancy.

Display This Question: If Was your first pregnancy planned? Yes Is Selected

Q110 What was the outcome of your second pregnancy? If this pregnancy resulted in

more than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. Live birth (1)

Miscarriage (2) Termination (3) Still Birth (4) Ectopic pregnancy (5)

Notes (6) ______

Q111 Was your second pregnancy planned? m Yes (1) m No (2)

Q112 How many months did it take you to get pregnant?

Q113 How old were you at the birth or loss of your second pregnancy? If you need help

you can use the link to the online age calculator.

Q114 Did you undergo fertility treatments to conceive your second pregnancy? m Yes

(1) m No (2)

Q115 Did your second pregnancy result in a child that has been diagnosed with any of the

following? (Click all that apply.) q Down syndrome (1) q Autism (2) q Developmental Delay (3) q Nut allergy (4) q Celiac's disease (5) q Cancer (6) q Diabetes (7) q Autoimmune disease (8) q Other (9) ______

Q116 The following questions will ask you for some details about your third pregnancy.

Display This Question: If Was your second pregnancy planned? Yes Is Selected

63 Q117 What was the outcome of your third pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1) m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q118 Was your third pregnancy planned? m Yes (1) m No (2)

Q119 How many months did it take you to get pregnant?

Q120 How old were you at the birth or loss of your third pregnancy? If you need help

you can use the link to the online age calculator.

Q121 Did you undergo fertility treatments to conceive your third pregnancy? m Yes

(1) m No (2)

Q122 Did your third pregnancy result in a child that has been diagnosed with any of the

Q123 The following questions will ask you for some details about your fourth pregnancy.

Display This Question: If Was your third pregnancy planned? Yes Is Selected

Q124 What was the outcome of your fourth pregnancy? If this pregnancy resulted in

more than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1)

64 m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q125 Was your fourth pregnancy planned? m Yes (1) m No (2)

Q126 How many months did it take you to get pregnant?

Q127 How old were you at the birth or loss of your fourth pregnancy? If you need help

you can use the link to the online age calculator.

Q128 Did you undergo fertility treatments to conceive your fourth pregnancy? m Yes

(1) m No (2)

Q129 Did your fourth pregnancy result in a child that has been diagnosed with any of the

Q130 The following questions will ask you for some details about your fifth pregnancy.

Display This Question: If Was your fourth pregnancy planned? Yes Is Selected

Q131 What was the outcome of your fifth pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1) m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q132 Was your fifth pregnancy planned? m Yes (1) m No (2)

65 Q133 How many months did it take you to get pregnant?

Q134 How old were you at the birth or loss of your fifth pregnancy? If you need help you

can use the link to the online age calculator.

Q135 Did you undergo fertility treatments to conceive your fifth pregnancy? m Yes

(1) m No (2)

Q136 Did your fifth pregnancy result in a child that has been diagnosed with any of the

Q137 The following questions will ask you for some details about your sixth pregnancy.

Display This Question: If Was your fifth pregnancy planned? Yes Is Selected

Q138 What was the outcome of your sixth pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1) m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q139 Was your sixth pregnancy planned? m Yes (1) m No (2)

Q140 How many months did it take you to get pregnant?

Q141 How old were you at the birth or loss of your sixth pregnancy? If you need help

you can use the link to the online age calculator.

66 Q142 Did you undergo fertility treatments to conceive your sixth pregnancy? m Yes

(1) m No (2)

Q143 Did your sixth pregnancy result in a child that has been diagnosed with any of the

Q144 The following questions will ask you for some details about your seventh

pregnancy.

Display This Question: If Was your sixth pregnancy planned? Yes Is Selected

Q145 What was the outcome of your seventh pregnancy? If this pregnancy resulted in

more than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1) m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q146 Was your seventh pregnancy planned? m Yes (1) m No (2)

Q147 How many months did it take you to get pregnant?

Q148 How old were you at the birth or loss of your seventh pregnancy? If you need help

you can use the link to the online age calculator.

Q149 Did you undergo fertility treatments to conceive your seventh pregnancy? m Yes

(1) m No (2)

67 Q150 Did your seventh pregnancy result in a child that has been diagnosed with any of

the following? (Click all that apply.) q Down syndrome (1) q Autism (2) q Developmental Delay (3) q Nut allergy (4) q Celiac's disease (5) q Cancer (6) q Diabetes (7) q Autoimmune disease (8) q Other (9) ______

Q151 The following questions will ask you for some details about your eighth pregnancy.

Display This Question: If Was your seventh pregnancy planned? Yes Is Selected

Q152 What was the outcome of your eighth pregnancy? If this pregnancy resulted in

more than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. Live birth (1)

Miscarriage (2) Termination (3) Still Birth (4) Ectopic pregnancy (5) m

Notes (6) ______

Q153 Was your eighth pregnancy planned? Yes (1) No (2)

Q154 How many months did it take you to get pregnant?

Q155 How old were you at the birth or loss of your eighth pregnancy? If you need help

you can use the link to the online age calculator.

Q156 Did you undergo fertility treatments to conceive your eighth pregnancy? Yes

(1) No (2)

Q157 Did your eighth pregnancy result in a child that has been diagnosed with any of the

following? (Click all that apply.) Down syndrome (1) Autism (2) q Developmental Delay (3) Nut allergy (4) Celiac's disease (5) Cancer (6)

68 q Diabetes (7) Autoimmune disease (8) Other (9) ______

Q158 The following questions will ask you for some details about your ninth pregnancy.

Display This Question: If Was your eighth pregnancy planned? Yes Is Selected

Q159 What was the outcome of your ninth pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. Live birth (1) m Miscarriage (2) Termination (3) Still Birth (4) Ectopic pregnancy (5)

Notes (6) ______

Q160 Was your ninth pregnancy planned? Yes (1) No (2)

Q161 How many months did it take you to get pregnant?

Q162 How old were you at the birth or loss of your ninth pregnancy? If you need help

you can use the link to the online age calculator.

Q163 Did you undergo fertility treatments to conceive your ninth pregnancy?

Yes(1) No (2)

Q164 Did your ninth pregnancy result in a child that has been diagnosed with any of the

following? (Click all that apply.) Down syndrome (1) Autism (2)

Developmental Delay (3) Nut allergy (4) Celiac's disease (5) Cancer (6)

Diabetes (7) Autoimmune disease (8) Other (9) ______

Q165 The following questions will ask you for some details about your tenth pregnancy.

Display This Question: If Was your ninth pregnancy planned? Yes Is Selected

Q166 What was the outcome of your tenth pregnancy? If this pregnancy resulted in more

than one baby (e.g. twins, triplets, etc.), please tell us about your pregnancy

outcome in the notes section. m Live birth (1) m Miscarriage (2) m Termination (3) m Still Birth (4) m Ectopic pregnancy

(5) m Notes (6) ______

Q167 Was your tenth pregnancy planned? m Yes (1) m No (2)

Q168 How many months did it take you to get pregnant?

Q169 How old were you at the birth or loss of your tenth pregnancy? If you need help

you can use the link to the online age calculator.

Q170 Did you undergo fertility treatments to conceive your tenth pregnancy? m Yes

(1) m No (2)

Q171 Did your tenth pregnancy result in a child that has been diagnosed with any of the

Q30 The next set of questions will ask you about your age, your mother's and father's

ages, and your mother's pregnancy history.

Q73 What is your age?

70 Q75 How old was your biological mother at the time of your birth? (If you need help

determining your mother's age at this time, you can click the link to the online age

calculator. Put your

Display This Question: If Was your tenth pregnancy planned? Yes Is Selected mother's birthdate in the "Date of Birth" drop down and put your birthdate in the "Age at

date of" dropdown. Write the calculated age in years in the box below.) If you do

not know, write "I don't know."

Q76 Please click one of the following choices. m I am certain of my biological

mother's age or I called to confirm it. (1) m I am not 100% certain of her age. (2)

Q77 How old was your biological father at the time of your birth? (If you need help

determining your father’s age at this time, you can click the following link to an

online age calculator. Put your father’s birthdate in the “Date of Birth” drop down

and put your birthdate in the “Age at the date of” dropdown. Write the calculated age in years in the box below. "I don't know."

Q78 Please click one of the following choices. m I am certain of my biological father's

age or I called to confirm it. (1) m I am not 100% certain of his age. (2)

Q31 How many biological children does your mother have? m 1 (2) m 2 (3) m 3

(4) m 4 (5) m 5 (6) m 6 (7) m 7 (8) m 8 (9) m 9 (10) m 10+ (11) m I don't

know (12)

If you do not know, write 71 Display This Question: If How many biological children does your mother have? 1 Is

Selected Or How many biological children does your mother have? 2 Is Selected

Or How many biological children does your mother have? 3 Is Selected Or How

many biological children does your mother have? 4 Is Selected Or How many

biological children does your mother have? 5 Is Selected Or How many biological

children does your mother have? 6 Is Selected Or How many biological children

does your mother have? 7 Is Selected Or How many biological children does your

mother have? 8 Is Selected Or How many biological children does your mother

have? 9 Is Selected Or How many biological children does your mother have? 10+

Is Selected

Q32 How old was your mother when her first child was born? (If you need help

determining ages, you can use the online age calculator.)

Display This Question: If How many biological children does your mother have? 2 Is

Selected Or How many biological children does your mother have? 3 Is Selected

Or How many biological children does your mother have? 4 Is Selected Or How

many biological children does your mother have? 5 Is Selected Or How many

biological children does your mother have? 6 Is Selected Or How many biological

children does your mother have? 7 Is Selected Or How many biological children

does your mother have? 8 Is Selected Or How many biological children does your

mother have? 9 Is Selected Or How many biological children does your mother

have? 10+ Is Selected

Q33 How old was your mother when her second child was born?

72 Display This Question: If How many biological children does your mother have? 3 Is

Selected Or How many biological children does your mother have? 4 Is Selected

Or How many biological children does your mother have? 5 Is Selected Or How

many biological children does your mother have? 6 Is Selected Or How many

biological children does your mother have? 7 Is Selected Or How many biological

children does your mother have? 8 Is Selected Or How many biological children

does your mother have? 9 Is Selected Or How many biological children does your

mother have? 10+ Is Selected

Q34 How old was your mother when her third child was born?

Display This Question: If How many biological children does your mother have? 4 Is

Selected Or How many biological children does your mother have? 5 Is Selected

Or How many biological children does your mother have? 6 Is Selected Or How

many biological children does your mother have? 7 Is Selected Or How many

biological children does your mother have? 8 Is Selected Or How many biological

children does your mother have? 9 Is Selected Or How many biological children

does your mother have? 10+ Is Selected

Q35 How old was your mother when her fourth child was born?

Display This Question: If How many biological children does your mother have? 5 Is

Selected Or How many biological children does your mother have? 6 Is Selected

Or How many biological children does your mother have? 7 Is Selected Or How

many biological children does your mother have? 8 Is Selected Or How many

73 biological children does your mother have? 9 Is Selected Or How many biological

children does your mother have? 10+ Is Selected

Q36 How old was your mother when her fifth child was born?

Q37 How old was your mother when her sixth child was born?

Q38 How old was your mother when her seventh child was born?

Q39 How old was your mother when her eighth child was born?

Display This Question: If How many biological children does your mother have? 6 Is

Selected Or How many biological children does your mother have? 7 Is Selected

Or How many biological children does your mother have? 8 Is Selected Or How

many biological children does your mother have? 9 Is Selected Or How many

biological children does your mother have? 10+ Is Selected

Display This Question: If How many biological children does your mother have? 7 Is

Selected Or How many biological children does your mother have? 8 Is Selected

Or How many biological children does your mother have? 9 Is Selected Or How

many biological children does your mother have? 10+ Is Selected

Display This Question: If How many biological children does your mother have? 8 Is

Selected Or How many biological children does your mother have? 9 Is Selected

Or How many biological children does your mother have? 10+ Is Selected

Display This Question: If How many biological children does your mother have? 9 Is

Selected Or How many biological children does your mother have? 10+ Is Selected

Q40 How old was your mother when her ninth child was born?

Q41 How old was your mother when her tenth child was born?

74 Q42 If you have had trouble getting pregnant in the past, the next question will ask if the

reason has been determined.

Q43 Has a physician or fertility specialist told you that you or your partner have any of

the following conditions that may make getting pregnant more difficult? Check all

that apply. q No (1) q Polycystic ovary syndrome (2) q Advanced maternal age (3) q Chromosomal abnormality in you (4) q

Chromosomal abnormality in your partner (5) q Uterine or cervical abnormality

(6) q Fallopian tube damage or blockage (7) q Diminished ovarian

reserve/premature menopause (8) q Pelvic adhesions (9) q Thyroid problems

(10) q Endometriosis (11) q Cancer in you or your partner (12) q Abnormal

sperm production or delivery in your partner (13) q Unknown (14) q Other (15)

______

Q44 The next set of questions will ask about your demographic information. The survey

will end after this set of questions is completed.

Q45 What is your ethnicity/race (optional)? m Caucasian (1) m African American

(2) m Hispanic (3) m Asian (4) m Native American (5) m Other (6) ______

Display This Question: If How many biological children does your mother have? 10+ Is

Selected

75 Q47 What is your marital status (optional)? m Single, never married (1) m Married

or domestic partnership (2) m Widowed (3) m Divorced (4) m Separated (5)

Q46 What is the highest degree or level of school you have completed (optional)? m

No schooling completed (1) m 8th grade (2) m Some high school, no diploma

(3) m High school graduate or equivalent (GED) (4) m Some college credit, no degree

(5) m Trade/technical/vocational training (6) m Associate degree (7) m Bachelor's degree (8) m Master's degree (9) m Professional degree (10) m

Doctorate degree (11)

Q49 What is your household income (optional)? m < $25,000 (1) m $25,000-

$49,999 (2) m $50,000-$74,999 (3) m $75,000-$100,000 (4) m > $100,000 (5)