Menopause and Ovarian ReserveGenetic and Clinical Aspects
Jeroen van Disseldorp
Menopause and Ovarian Reserve: Genetic and Clinical ApectsThesis, Utrecht University, The Netherlands, with a summary in Dutch.Proefschrift, Universiteit Utrecht, met een samenvatting in het Nederlands.
isbn: 978-90-3935228-1Author: Jeroen van DisseldorpCover design: Robert Buijtendijk & Jeroen van DisseldorpLayout: Robert BuijtendijkPrint: HooibergHaasbeek
© 2009 J. van DisseldorpAll rights reserved, no part of this thesis may be reproduced or transmitted in any form or by any means, without permission of the copyright owner.
The author gratefully acknowledges financial support for printing this thesis by the follow-ing companies and organisations:Dit proefschrift werd (mede) mogelijk gemaakt met financiële steun van de volgende bedrijven en organisaties:Division Woman and Baby University Medical Center Utrecht, JE Jurriaanse Stichting, Goodlife Healthcare, Merck Serono, Origio Benelux bv, Gynotec, Schering Plough, bma bv (mosos), Memidis Pharma, Beckman Coulter, Medical Dynamics.
Menopause and Ovarian ReserveGenetic and Clinical Aspects(with a summary in Dutch)
Menopauze en ovariële reservegenetische en klinische aspecten(met een samenvatting in het Nederlands)
proefschrift
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. J.C. Stoof, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op dinsdag 12 januari 2010 des middags te 4.15 uur
door
Jeroen van Disseldorpgeboren op 21 november 1978 te Maastricht
Promotor: Prof.Dr. B.C.J.M. Fauser
Co-promotor: Dr. F.J.M. Broekmans
Gracias a la vida.(Mercedes Sosa)
Contents
Menopause and ovarian reserve (general introduction) 7-13 Testing ovarian reserve to predict age at menopause 15-31 The relationship of serum Anti-Müllerian Hormone concentration to age at menopause 33-41 The association between vascular function related genes and age at natural menopause 43-51 Hypertensive pregnancy complications in poor andnormal responders following in vitro fertilization 53-61 Genomic predictors of ovarian response to stimulationfor in vitro fertilization 63-75 Comparison of inter- and intra-cycle variability of Anti-Müllerian Hormone and antral follicle counts 77-87 Cumulative live birth rates following ivf in 41-43 year old women presenting with favorable ovarian reserve 89-100characteristics General Discussion 101-109 Reference List 111-129Summary (Engelse Samenvatting) 131-135Dutch Summary (Nederlandse Samenvatting) 137-141List of co-authors and their affiliations 143-146List of Publications 147-149About the Author (Over de auteur) 151-153Words of Appreciation (Dankwoord) 155-159
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Menopause and Ovarian Reserve.(General Introduction)
Menopause in human evolutionHumans are virtually the only species in which fertility ends at menopause, far before reach-ing maximum lifespan expectancy. This difference between species has initiated theories concerning the evolutionary origin of menopause. Evolutionary selection is only possible if menopause is at least partially genetically determined, which has been systematically confirmed in sib-pair and twin studies (340;364;367;376). Moreover, the advantages associ-ated with menopause should offset the obvious disadvantages of infertility and menopause associated health risks like osteoporosis and cardiovascular disease.
The advantages of menopause suggested by adaptive theories of menopause are two-fold. Firstly, human offspring depends on protection and provision especially from the mother, for an extended time after birth. Menopause keeps older women from conceiving, when maternal mortality has increased at older age (139;170). This enables them to raise their children until independency. Secondly, it is postulated that post-menopausal women increase the group’s fitness and survival of offspring by intergenerational cooperation in providing protection and provisions (151;152). This theory is referred to as ‘The grand-mother effect’ (151). Recent studies have indeed shown plausible advantages of the close presence of a grandmother in reducing mortality (221;322;323) and increasing fertility in offspring (221).
Contrary to these adaptive theories of menopause, neutral theories suggest that there simply is a genetic constraint of about 50 years on oocyte viability in species that finish oogenesis during fetal development (290). This is also known as Williams’ pleiotropy the-ory, which suggests that features having high adaptive value early in life will be selected even if they result in reduced fitness later in life (419). If evolution favors genes that allow having the most offspring the fastest, enhanced early fertility could be selected for at the cost of reduced fertility and menopause at older age. Human life expectancy centuries ago, however, was so short that the majority of women would never experience menopause. When the maximum lifespan expectancy of humans increased, the size or loss rate of the primordial follicle pool did not adapt concomitantly. Since research into this area is extremely difficult, the question if menopause was selected for during the course of evolu-tion or is just a consequence of increased lifespan expectancy in the past centuries will not easily be answered.
DefinitionsMenopause is defined as the permanent cessation of menstruation resulting from the loss of ovarian follicular activity. Natural menopause has occurred after 12 consecutive months of amenorrhea, for which there is no other obvious pathological or physiological cause (426). In the Western world menopause occurs around age 51, with a normal range of 40-60 years. Menopause is said to occur premature if a woman is under 40 years old.
Infertility is traditionally defined as a failure to conceive after at least one year of unpro-tected intercourse (427). However, a large proportion of these patients will conceive natu-rally within the next 12-36 months (129;339). During the course of female life, infertility is also seen as the equivalent of the last possibility to conceive naturally. In populations where only natural conception is practised, this is also referred to as age at last child.
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Menopause and women healthMenopause marks the end of an individual woman’s reproductive life. Women, however, experience menopause very differently. For some women the menopausal transition repre-sents a major life event, which influences their social and psychological well-being. Others judge menopause as a part of life or a symbol of aging.
Various factors have been identified that influence an individual woman’s view on the menopausal transition. Naturally, the severity of menopause associated symptoms colours the experience of individual women (210). Also, a positive or negative attitude towards menopause has been associated with well-being and the severity of symptoms like flushes and urogenital complaints (80;140;331). On the contrary, since more severe complaints are associated with a worse cardiovascular risk profile (124), these women may experience more frequent illnesses, influencing their attitude towards menopause. The balance between the severity of complaints and coping strategies adopted probably determines an individual woman’s attitude towards menopause. In addition, society’s attitude towards menopause and ageing is also important to stress the important social role of postmenopausal women. In particular, we would need to make an evolutionary plausible paradigm shift from the notion that we need to care for older women to the many cases in which we receive care from older women.
The concept of ovarian ageingWomen are endowed with a number of approximately 6-7 million follicles before birth, which they gradually lose over time (15;30;31;66;108-110;134;150;239;308). This gradual decline of the follicle pool, also known as ovarian ageing, ultimately culminates in a final menstruation, known as menopause. Arbitrarily, the definition of menopause includes a period of amenorrhea of at least 12 months after the final menstruation (426). Menopause, therefore, can only be assessed retrospectively.
During the process of ovarian ageing, not only does the quantity of follicles decline, the quality of follicles and oocytes is also affected. This becomes apparent in increased aneu-ploidy rates, responsible for the increased miscarriage rates observed at older age (362). It is postulated that during the gradual decline of follicles, various stages of ovarian ageing can be identified, like infertility and sterility. The temporal relationship between these events is essential for the individual prediction of menopause, which is further outlined in chapter 2.
Causes of ovarian ageingThere is not a single cause of ovarian ageing. Multiple factors, both genetic, vascular and environmental, are thought to determine female ovarian reserve and ovarian ageing. More-over, genetic and environmental factors possibly act synergistically to determine age at menopause.
Age at menopause is a complex quantitative trait with estimates of heritability rang-ing between 30 and 85% (418). This is also evidenced in the association between meno-pausal age of mothers and daughters and sister pairs (269;364;376). The finding of single gene defects, however, has only been associated with premature ovarian failure (i.e. natural menopause before age 40 years). The genetic mechanisms underlying the normal variation in age at menopause are thought to be more complex and are probably a summation of vari-ous susceptibility loci at interplay with environmental effects. This makes the search for causative genes a laborious project and causes the ovarian ageing process to remain largely unknown.
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Environmental factors have also been associated with age at menopause. Both smoking during the menopausal transition and increased body weight have been shown to predis-pose to earlier vascular damage and menopause (358;389). However, only a small part of the total variation of age at menopause has been estimated to be influenced by lifestyle factors (389).
Since longevity is also considered to be genetically determined (113), it has been postu-lated that general aging and reproductive aging might be subject to the same biological pro-cess of accumulation of oxidative stress leading to dna damage and apoptosis (125;355;379). Apart from direct follicular depletion by apoptosis, oxidative stress also causes vascular damage to the ovaries (143). Patients with compromised vasculature after ovarian surgery, possibly mimicking the effect of vascular ageing, have indeed demonstrated diminished ovarian reserve (161;228;230). Also, poor responders to ivf stimulation, a group with diminished ovarian reserve, were shown to have poorer vascular status (21). The current thesis aims to validate the hypothesis that a vascular pathway is involved in the onset of menopause (chapter 4 and 5).
Ovarian reserve tests: the assessment of ovarian ageing in clinical practiceThe process of ovarian ageing cannot be influenced by current medicine. Clinical practice is therefore mainly concerned with the accurate assessment of ovarian reserve. For this pur-pose, various hormones and function tests have been studied for their ability to accurately reflect ovarian reserve status (42).
Not only ovarian reserve tests, but also female age is an important determinant of ovar-ian reserve, predicting response and pregnancy in ivf. Two recent studies have shown that pregnancy chances are primarily influenced by age and only for a small part by the basal fsh value (169;321). The finding that age is the most important predictor of pregnancy was also confirmed in multivariate analyses by previous authors (55;62;391).
The ultimate goal of ivf treatment is, of course, the birth of a healthy baby. Since ovar-ian reserve tests have no clinical value in the prediction of (ongoing) pregnancy or live birth, the main aim of assessing ovarian reserve is to predict response to gonadotrophins in ivf treatment. On the basis of the current ovarian reserve assessment tests, prediction of response in ivf is, however, still inaccurate (42). A possible explanation for the failing accuracy of these tests is the cycle dependency and intercycle variability of most tests (chap-ter 7). In view of this moderate predictive accuracy, it is surprising to find that in clinical practice the results of these tests are used to refuse ivf treatment. We must ask ourselves if ovarian reserve tests have enough proven clinical value to allow such definitive strategy decisions or that ovarian reserve tests should only be used in certain subgroups, such as older ivf patients ((200), chapter 8).
Although the current selection of ovarian reserve tests seems clinically impractical, new strategies might overcome the current inaccuracy. One option proposed is to consider poor response itself as a predictor. Especially confirmation of “true” poor ovarian reserve status by age or an ovarian reserve test has shown promising predictive capacity (168). Fur-ther treatments in this group of “true” poor responders do not improve pregnancy chances substantially (74;168;201). Moreover, increasing the recfsh starting dosage, changing the treatment regimen or starting various co-treatments do not improve ivf outcome (199;356). Furthermore, these “true” poor responders have also been shown to reach menopause early, confirming their advanced ovarian ageing status (65;236).
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Another option is provided by the relatively new field of pharmacogenomics, which aims to individualise drug dosing on the basis of an individual’s genetic make-up. For ivf treat-ments this option has never been researched, but in other fields it has already proven useful (29;50). The current thesis addresses various aspects of the clinical applicability of ovarian reserve tests (chapter 6-8).
Ovarian reserve tests: predicting future fecundityOnly in the last decade has the focus of ovarian reserve testing shifted from direct clinical applicability to prediction of the decline of ovarian reserve towards the future. Since many couples start having children at a later age, prediction of future ovarian reserve is becoming increasingly important for younger women contemplating to postpone childbearing. As outlined in chapter 2, there is quite some evidence that menopause is preceded by various degrees of ovarian ageing at fixed time intervals. This opens opportunities to predict the end of natural fertility for individual women and estimate fecundity at a certain age.
For accurate prediction, there is a need for an ovarian reserve test that follows the full range of gradual decline of ovarian reserve from puberty to menopause. As outlined in chapter 2, not all ovarian reserve tests are suitable as predictors of menopause. Most tests only provide adequate information from about ten years before the menopausal transition onwards, at which stage most women will have lost their fertility. Moreover, most tests have no linear longitudinal decline, since ovarian reserve seems to decrease more rapidly towards the end of natural fertility, which occurs around age 40 (110;148;319).
To reduce within subject variability and reduce recall bias, high quality studies inves-tigate perimenopausal endocrine and ultrasound changes longitudinally. However, only few studies have done so (75;118;262;264;283;301;303;344;392;394). Moreover, studies are often descriptive, without reporting the accuracy of menopause prediction. Anti-Müllerian hormone is suggested to be the best marker for menopause prediction, since it best reflects the declining number of primary and small antral follicles over time (75;392). The current thesis reviews the predictive accuracy of amh and other factors to predict age at menopause (chapter 2, 3).
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Aims and outline of the thesis
Aims
1. to review and investigate the ability of various ovarian reserve tests to predict age at menopause (part I).
2. to investigate vascular factors related to follicle quantity in relation to the occurrence of menopause (part II).
3. to evaluate how to improve the clinical usefulness of ovarian reserve tests in clinical practice (part III).
OutlineChapter one represents the general introduction.
Part IChapter two reviews the literature regarding the ability of ovarian reserve tests and other factors to predict age at menopause.
Chapter three presents a model for the prediction of age at natural menopause based on a woman’s individual amh level and her chronological age.
Part IIChapter four describes a validation study concerning vascular genetic polymorphisms pre-viously associated with early menopause.
Chapter five describes a case-control study in which we study the incidence of hypertensive pregnancy complications in a pregnancy ensuing a poor response or a normal response after hyperstimulation for ivf.
Part IIIChapter six describes the results of a naïve search for genetic polymorphisms related to ovarian response to hyperstimulation.
Chapter seven describes the intra- and intercycle variability of afc and amh and discusses which ovarian reserve test displays least cycle and measurement fluctuation.
Chapter eight evaluates the application of basal fsh and the antral follicle count in their capability of selecting a favorable group of older ivf patients.
Chapter nine discusses the strengths and weaknesses of current menopause prediction, the possibility of vascular involvement in the onset of menopause and possible improvements of ovarian reserve tests in practice. The chapter includes propositions for future research.
Testing ovarian reserve to predict age at menopause.
C.B. Lambalk, J. van Disseldorp, C.H. de Koning, F.J. Broekmans.
Maturitas. 2009 Aug 20;63(4):280-91.
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1 IntroductionThe menopause is the final menstrual period. The natural menopause can only be ascer-tained in retrospect after 12 consecutive months of spontaneous amenorrhea. The age of the natural menopause has a normal distribution with a mean at approximately 51 years, varying between 40 and 60 years (365). Since the introduction of a definition for prema-ture ovarian failure for women below 40 years of age with a basal fsh of > 40 iu/l (60), we regard normal menopause as occurring from age 40 onwards. Women between 40 and 45 experiencing natural menopause are regarded as reaching menopause relatively early, but are considered as a representation of the lower end of the normal distribution. Whereas for some women menopause may be a relief and the start of yet another phase of life, many experience this event and the associated physical symptoms and psychosocial impact as a burden. The menopause has certain implications. It is the final sign that a woman’s reproduc-tive capacity has become exhausted. Also, early age at menopause has been associated with increased cardiovascular mortality (381), osteoporotic fracture (382) and colorectal cancer (398) as well as respiratory and urogenital disease (266). In modern society being able to predict the age of menopause and occurrence of natu-ral infertility may help women to decide about when they should start attempting to have children. Furthermore, if premature or early menopause could be predicted from tests in young women, strategies could be instigated to reduce the long term health risks of estro-gen deficiency. Remarkably, while the average age at menarche has declined very significantly over the past 100 years, the average age at menopause has remained quite constant. Important determinants of age at menopause are most likely epiphenomena of ovarian oocyte content and genetic factors. This review provides an overview of the physiology of ovarian ageing. Predictive mark-ers of age at menopause and the preceding decline in fertility are evaluated.
2 Female reproductive ageing, infertility and menopauseThe reproductive ageing process is dominated by a gradual decrease in both oocyte quan-tity and quality (357). From the initial 6-7 million primordial follicles present at the fourth month of fetal development (15;30;31;110;134;148;239;308), only 400.000-600.000 primor-dial follicles remain at menarche (31;357). At menopause, the number of remaining follicles has dropped to below 1.000 (108;109;308) (Figure 1 (67;198)).
From the age of 31 years onwards, the declining follicle pool heralds various reproduc-tive events: decreasing fecundity, natural sterility, menstrual cycle irregularity and finally menopause (Figure 2 (33;39;67;79;94;365)).
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Figure 1 The decline in follicle number and the increase in the proportion of poor quality oocytes in relation to reproductive events with increasing female age (redrawn after (67;198))
Figure 2 The distributions of age at the onset of subfertility (cumulative curve 1), at occurrence of natural ste-rility (cumulative curve 2), at transition into cycle irregularity (cumulative curve 3) and at occurrence of menopause (cumulative curve 4). Mean ages for these events are depicted on the X-axis. Curve 4 is based on data by Treloar and Broekmans (39;365), curve 3 and its temporal relation to curve 4 is based on data from den Tonkelaar (79), curve 2 is based on data on last child birth in a 19th century natural fertility population (33) and curve 1 is a hypothetical construct based on the age distribution of related reproductive events as depicted in curve 2,3 and 4 and partially supported by data from Eijkemans (93).
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A fixed temporal relationship, with large interindividual variation, is thought to be pres-ent among the various reproductive events (357). Such a relationship seems highly relevant since accurate prediction of a woman’s menopause may also provide valuable informa-tion regarding her fertility lifespan. While longitudinal data documenting the relation-ship between reproductive events in individual women are limited (246), evidence for this hypothesis primarily stems from cross-sectional observations. In the Balsac natural fertil-ity population study, it has been demonstrated that early loss of natural fertility is preceded by reduced fecundity already before the age of 30 years (figure 3 (94). Moreover, repeated poor response in in vitro fertilization (ivf) is associated with an early occurrence of the menopausal transition (65;236;246;278). Finally, the duration of menstrual cycle irregular-ity preceding menopause appears to be constant and independent of the age at menopause (79;357). In-depth research into this area is extremely difficult in view of the widespread use of hormonal contraception, so that longitudinal estimates of an individual’s fertility at several points over time are not available. Despite cycle regularity remaining unaffected for a period of nearly 30 years, profound changes occur at the oocyte level, causing every woman to pass through the various repro-ductive events mentioned in figure 2. Monthly fecundity dramatically decreases from a mean age of 31 years onwards (366;390). In contemporary population studies it has been demonstrated that the chance of not conceiving a first child within 1 year increases approxi-mately 6-fold when women over 30 years are compared with their younger counterparts (1). With increasing female age this pattern becomes more and more pronounced (105). In recent decades, numerous reports regarding the outcome of Assisted Reproduction Tech-nology (art) treatment have confirmed that the probability of a live birth decreases dis-tinctly after the age of 35 years (51;121;359). It has also been recognised (243) that postpon-ing pregnancy until women are well into their thirties will frequently lead to a permanent loss of reproductive potential, even with art. More liberal and early use of art in infertility patients has been claimed to be the solution for the societal consequences of age related infertility (135). However, the high costs and complications from multiple pregnancies needs to be taken into account (145).
Predicting age at menopause with ovarian reserve tests will allow young women to make informed choices about postponing pregnancy or not. The challenge lies in finding ovarian reserve tests capable of identifying women with a reduced reproductive lifespan early in life. Known tests of ovarian reserve have been developed mainly on the basis of predicting the outcome of art. The antral follicle count (afc), baseline follicle-stimulating hormone (fsh) and anti-Müllerian hormone (amh) have been shown to be able to predict response to ovarian hyperstimulation in ivf/icsi (41-43). These tests mostly relate to the quantita-tive aspects of ovarian reserve and therefore are candidate predictors of menopausal age. In addition to ultrasound and endocrine markers for ovarian ageing, genetic factors are also potential candidates in view of the high heritability of menopausal age.
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Figure 3 Distribution curves for observed age at last child birth (proxy variable for natural sterility, blue line) and age at menopause (black line). Graph for age at last child was redrawn based on the balsac demo-graphic database (balsac project at the University of Quebec in Chicoutimi (n=1040) (33)). Graph for age at menopause was based on data from the Prospect-epic (European Prospective Investigation into Cancer and Nutrition) study (n= 3483) (307).
3 Features of reproductive ageing and predictors of menopauseFactors that are able to predict age at menopause early in life should relate to the decreas-ing pool of follicles over a period of several decades. Factors that change only in the later stages of ovarian ageing in principal cannot be used as early predictors. While identifi-cation of early predictors requires long term follow up, most studies use cross-sectional data. Based on the available cross-sectional data, such a predictive test has not emerged, although some predictive ability has been attributed to the afc, ovarian volume and amh levels (39;386;408). Longitudinal studies have shown an increased incidence of early meno-pause in poor responders in ivf (65;236). In the following sections we will discuss the value of evaluating quantitative ovarian morphology either by taking tissue samples or performing ultrasound scans and the evalu-ation of hormone levels and menstrual cycle changes in the context of their relation to ovar-ian ageing. All factors are assessed on four criteria: biological plausibility, cross-sectional relationships, longitudinal relationships and proven or deduced predictive capacity for age at menopause.
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3.1 Histological assessment of ovarian reserveAll ovarian reserve tests that aim to predict age at menopause do so by direct or indirect assessment of declining follicle numbers. The biological plausibility of histological assess-ment of ovarian reserve is clear, since in theory it is the most direct representation of the follicle pool. Various studies have investigated a cross-sectional relationship with ovarian biopsies and age. A recent model of ovarian follicle depletion predicts a constantly increasing loss-rate which agrees well with observed ages of menopause (148). Overall, the mean number of pri-mordial follicles in the ovaries of regularly menstruating women is 10-fold higher than that in perimenopausal women (308). Follicles are virtually absent in postmenopausal ovaries. Attempts have been made to quantify the total number of primordial, primary and sec-ondary follicles based on small biopsies taken at diagnostic laparoscopy or open tubal sur-gery in 60 infertile women aged 19-45 years (mean 34.4 years), showing a clear age depen-dent decline in follicular density (232). Women over 35 years of age only had a third of the follicular density (number of follicles/mm3) compared with younger women. However, it was questioned whether a biopsy accurately represents the follicle density of the whole ovary (229). Recently, several authors have shown that follicle density varies greatly within the cortex. Ovarian biopsies therefore provide an unreliable estimate of ovarian follicle content (227;297;300;320) and not surprisingly, further studies have not been pursued.
Figure 4 Concepts of the endocrine changes associated with ovarian ageing in relation to ovarian morphology.
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3.2 Endocrine aspects of ovarian reserve
3.2.1 Follicle-stimulating Hormone (FSH) The biological plausibility of the early follicular fsh rise was based on the reduced negative feedback by Inhibin B and estradiol, consequent to the decreased size of the fsh-sensitive follicle pool (196)(figure 4). The first reports on elevated basal fsh levels date from 1976 by Sherman and Korenman (328) and were confirmed by many others in cross-sectional studies (237;241;251;306). The authors noted a striking selective increase in the levels of serum fsh in older regularly cycling women. (237;241;251;306). Elevated levels of fsh are an irrefutable hormonal hallmark of reproductive aging. Unfortunately, longitudinal stud-ies have shown that markedly elevated fsh is a relatively late predictor of the menopausal transition, since increasing values only occur about 10 years before the menopause which is probably also when infertility begins to prevail (392;394). This means that elevated fsh levels cannot be used as an early predictor of reduced fertility (388) as early follicular phase fsh gradually starts to rise about 10 years before the menopause (344). Sequential basal fsh measurements may however be useful as a short term predictor. 3.2.2 Inhibins and activinsInhibins and activins are members of the transforming growth factor-β superfamily. Both inhibin A and inhibin B directly suppress pituitary fsh secretion, while activins selectively stimulate fsh secretion (374;412). Inhibin A is primarily secreted by the mature follicle and corpus luteum (141;311). Its bio-logical plausibility as a predictor is not directly evident. Some cross-sectional studies have shown inhibin A to be lower in older women (63;85;314;414) but at a considerably later stage of the menopausal transition. However, higher inhibin A levels during the luteo-follicular transition (196) and around day 6 of the follicular phase (305) in older cycling women have also been described. This was postulated to be a consequence of stronger fsh stimulation of the granulosa cells and to reflect advanced follicular maturation (see section 3.3)(197). Inhibin B is a product of the smaller non-dominant antral follicles and as such reflects the ovarian follicle pool. Inhibin B is highest in the early follicular phase, falls approaching ovulation, and is low in the luteal phase (141). Cross-sectional studies have shown older women with regular menstrual cycles to have lower serum inhibin B concentrations (63;196;197;271;305;314;317;414), but normal inhibin B concentrations in the dominant follicle (197). Although a decreased day 3 serum inhibin B precedes the early follicular phase rise of fsh in ivf patients with poor response, (324) longitudinal studies have shown that Inhibin B correlates with age only during a relatively short period before the menopausal transition (392). Furthermore, serum Inhibin B levels decline to very low or undetectable levels about 4 years prior to the last menstrual period (344). Not surprisingly, inhibin B is a poor predictor of age at menopause (344;392). Activins are present in many tissues, but its pituitary secretion mediates fsh levels. Two cross-sectional studies (305;314) have reported an increase in serum activin A levels in older ovulatory women and suggested that an increase in activin A could be a factor in the monotropic fsh rise. In subsequent studies no difference in activin A levels in older (40-45 years) ovulatory women has been found (196). Although our current understanding of activin physiology is limited by a lack of available assays for other activin forms (e.g. activin B, activin AB), to date there is little evidence to support an endocrine role for activin in fsh regulation around the menopausal transition.
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In conclusion, a decrease in inhibin B seems the most important and earliest factor that plays a role in the elevation of early follicular phase fsh. Immeasurable inhibin B levels may be used to indicate that menopause is imminent, but its role as an early predictor is limited. The role of activin A and inhibin A as predictors is limited by the lack of consistent evidence and their late change in the menopausal transition.
3.2.3 Anti-Müllerian hormoneAnti-Müllerian hormone (amh) is another member of the transforming growth factor-β family. The biological plausibility of amh as a predictive factor for menopause is 2-fold. In the postnatal female amh, which is produced in the granulosa cells, regulates growth and development of ovarian follicles. In women, amh is expressed in primary and small antral follicles, whose number is related to the size of the primordial follicle pool (133). Several cross-sectional studies have shown that serum amh levels are strongly corre-lated with the antral follicle count, the number of follicles retrieved at ivf, age, inhibin B and fsh (115;119;220;325;406). Longitudinal studies have shown that with the age related decrease in the number of antral follicles, amh production declines (392;394). The useful-ness of serum amh as a marker of ovarian reserve was tested in a group of 41 normal ovula-tory women on two occasions with an average interval of 2.6 ± 1.7 years. amh serum levels significantly decreased between the two time points and a negative correlation was found between age and amh levels. Furthermore, amh showed a strong correlation (r=0.66 and r=0.71 respectively, for first and second sample) with the antral follicle count (afc) (75). Recent studies suggest that amh levels do not vary much throughout the menstrual cycle (58;160;220), and are constant in the same woman from one cycle to the next (116) Thus, serum amh seems to be an easily obtainable marker of ovarian reserve. Longitudinal follow up in premenopausal women has shown that serum amh levels become very low or undetectable 5 years before the final menstrual period after a log linear decline of some 10 years. Thus, amh seems to be a viable possible predictor (344). However, these very low and undetectable amh levels before the final menstrual period limit its use as an accurate predictor of when the menopause will actually occur.
3.2.4 Other reproductive hormonesBiologically plausible relationships of hormones such as estradiol, progesterone and luteinizing hormone (LH) with age at menopause exist but there are no clear associations with age. With regard to estradiol and progesterone, while some have found lower levels (240;251;328), others have described no changes (192;194;195;237;304;306;317) or increased levels (190;197;270;316). A recent longitudinal follow-up study showed a continuing decline of sex steroids with advancing age (344). Most studies, but not all (192;251), show that LH levels also rise with age (70;71;120;237;305;316) as a result of increased pituitary sensitivity to GnRH, independent of estradiol levels (71). This has been suggested to result from limited secretion of gonad-otrophin surge inhibiting factor (GnSIF), a putative ovarian protein that lowers pituitary response to GnRH (70;71). Given the very subtle changes and the many uncertainties with regard to their secretion, these factors are not likely candidates for tests to predict menopause. On the other hand, disturbed ovarian driven fine tuning of pituitary secretion contributes substantially to peri-menopausal menstrual cycle irregularity and further understanding of its physiology and biochemistry could potentially lead to better understanding of the menopausal transition.
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3.3 Menstrual cycle changesSome years before menopause, exhaustion of the ovarian follicle pool becomes already apparent in various cycle characteristics like cycle length, multiple follicle growth and ano-vulation. Both cross-sectional studies and longitudinal studies have found that a key feature of reproductive aging is shortening of menstrual cycle length (366). This change is largely attributable to shortening of the follicular phase. Many authors describe a shorter follicu-lar phase length together with elevated early follicular phase fsh levels (192;193;242;316). Hormone profiles suggest that the growth of the dominant follicle is not accelerated (192;399), but that its selection is advanced (i.e. earlier) (195) and the maximum diameter of the ovulatory follicle reduced (5;120;192;399). Shorter cycles in older women compared to younger women could be a consequence of an earlier and higher fsh rise in the preced-ing luteal phase causing protracted follicle growth and selection at an earlier stage (399). However, also in absence of a preceding luteal phase (after recovery from GnRH agonist desensitization) an earlier and higher fsh rise can be seen in older women, suggesting that this process occurs independently of the traditional hormonal influences in the preceding luteal phase. Rather a subtle altered basic state of feedback associated with declining ovar-ian reserve is probably involved (195;261). Another feature of reproductive aging is the increased chance of natural dizygotic twin-ning for which multiple follicle growth is a prerequisite (49). Older women show sponta-neous multiple follicle growth in up to 25% of cycles whereas this is only 5% in cycles of younger women (23). This was associated with higher fsh levels and because of associated higher estradiol levels, this could explain an earlier lh surge and consequent ovulation. We have published a case report describing these features (225). A perimenopausal woman had fsh levels of 18 iu/l in the previous cycle. The endocrine and follicle dynamics were then extensively monitored. Figure 5 (224) depicts events in the luteo-follicular transition: (1) follicles started to grow during the luteal phase of the preceding cycle; (2) there was ongo-ing multiple follicle growth; (3) growth velocity was normal (±2 mm/day); (4) maximum follicular diameter was 16-17 mm and slightly smaller than normal; (5) ovulation occurred during menstruation (note shifts in basal body temperature and progesterone levels).
Finally, anovulation predominates in the perimenopause and associates with both shorter and longer cycles. Anovulatory cycles may occur with and without estrogen rises and LH surges. Different types of anovulatory cycles may occur in a mixed way in individual women and may even be followed by frank ovulatory cycles which are associated with heavy bleed-ing (397). Thus anovulatory cycles do not unidirectionally (i.e. a time path going from follic-ular growth with ovulation to follicular growth without an LH surge to no follicular growth and no LH surge) preclude menopause (337). Also vasomotor symptoms occur more often in close relation (approximately 2 years prior) to the final menstrual period possibly due to greater variability of estrogen levels (315). Menstrual cycle characteristics have also been researched for their ability to predict age at menopause. During the 9 years before menopause, women with a late age at menopause have a somewhat higher mean menstrual cycle length than women with a younger age at menopause. However, cycle length variability was not statistically significantly different between various categories of age at menopause (79). Nonetheless, cycle irregularity is associated with lower follicle counts in ovarian biopsies (308) and increased cycle length and variability are both associated with a shorter time to the final menstrual period (315).
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Figure 5Patterns of serum fsh, estradiol and progesterone (upper 3 panels) and follicle growth and basal body temperature (lower 2 panels) of a patient who had an fsh concentration of 18 iu/l in a previous cycle, showing typical ovulatory changes as rise in basal body temperature and progesterone production dur-ing menstruation while progressive multiple follicle growth was observed during the luteal phase of the preceding cycle. Please note absence of elevated fsh levels during this particular period of monitoring (from (224)).
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In summary, changes in cycle characteristics occur relatively late and are imprecise predic-tors of menopause. Currently, the occurrence of vasomotor symptoms seems to come clos-est to predicting menopause, but only within a short time span of about 2 years.
3.4 Ultrasound markers for ovarian reserveVarious ultrasound predictors of ovarian reserve have been tested. The most common ultra-sound ovarian reserve tests are the antral follicle count, ovarian volume and stromal blood flow. However, only the afc and ovarian volume have been investigated as a predictor for age at menopause. All ultrasound predictors have high intraobserver and interobserver reproducibility (260). Individual ovarian volume measurement has been shown to be impre-cise and intercycle variation of ovarian volume is larger than for afc (36;174). The following paragraphs attempt to summarize the current status of ultrasound predictors for age at menopause.
3.4.1 Ovarian stromal blood flowOvarian stromal blood flow is necessary for maintaining the ovarian follicle pool and for monthly maturation of the cohort to select the developing mature follicle for ovula-tion (102). It is hypothesised that the amount of stromal blood flow is related to the size of the follicle cohort. Indeed, stromal blood flow indices have consistently been shown to be increased in pcos women (2;4;223;254). The main problem with the current research on stromal blood flow is that different studies use very different flow-derived predictors, mak-ing it difficult to compare studies. The most commonly used flow-derived predictor is the peak systolic velocity (212). Other measurements are obtained with either normal Doppler (pulsatility index (pi) and resistance index (ri)) or 3D power Doppler (vascularisation index (vi), flow index (f1) or vascularisation flow index (vfi)) (103;298). Furthermore, absent stromal blood flow has also been shown to be consistently correlated with poor response to ivf (103;211;212;298;431;432). This suggests that ovarian stromal blood flow may be related to the number of antral follicles and ovarian reserve. While the predictive accuracy of ovarian stromal blood flow indices for age at menopause has not yet been researched, stromal blood flow has been shown to decrease with chronological age in a cross-sectional study (212). Since it has been suggested that stromal blood flow corresponds with ovarian reserve and absent stromal blood flow is predictive of poor response to ivf, we speculate that there might be a possible role for ovarian stromal blood flow indices to be predictors of age at menopause.
3.4.2 Ovarian volumeOvarian volume, like stromal blood flow, is suggested to be related to ovarian reserve, as it may directly relate to the volume of the follicle cohort (233). A large cross-sectional report has found that ovarian volume changes with age, but not before the age of 35 (291). In a recent review, it was pointed out that studies reporting on the predictive value of ovarian vol-ume on ivf outcome often had selection and verification bias (42). This review also showed that ovarian volume has a modest predictive accuracy in predicting poor response to ivf. Moreover, various studies have shown that afc or amh outperform ovarian volume in the prediction of response after ovarian hyperstimulation in ivf (16;166;175;177). Neverthe-less, ovarian volume may play an accessory role in a multivariate model (215;217). Ovarian volume has also been tested for demonstrating menopausal status (122;127). Both studies found similar accuracy of ovarian volume and afc in the prediction of menopausal status.
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Since longitudinal data are lacking, it is unclear from which age onwards, menopause pre-diction on the basis of ovarian volume is possible. As such, we were unable to classify ovar-ian volume as either an early or a late predictor of age at menopause.
3.4.3 Antral follicle count (AFC)The afc reflects the remaining ovarian follicle pool as accurately as possible. In contrast to stromal blood flow and ovarian volume, afc has a clear and well-defined biological and histological relation with ovarian reserve. The number of antral follicles corresponds well with the number of primordial follicles in histological analysis (110;134). Furthermore, with female ageing, declining numbers of primordial follicles parallel the decreasing size of the fsh-sensitive antral follicle cohort (319). Furthermore, it has been shown that it is easy to assess the size of the antral follicle cohort by transvaginal ultrasound (258;285). Also, measuring afc for poor response prediction to ivf can be done with adequate accu-racy (42). Thus afc is a likely candidate for menopause prediction and it has been well tested both in cross-sectional and longitudinal studies (39;127;394). Moreover, the gradual decline of afc from birth onwards, allowing monitoring far before the menopause, makes it a possible early predictor. In a recent study investigating different decline models, the best model showed that the afc decreases faster with increasing age (148). However, due to the amount of individual variation only consistently low afcs are predictive of early age at menopause (39). Nevertheless, compared to the predictive capacity of age alone, the antral follicle count added accuracy to the prediction of menopausal age (39). However, Giacobbe et al. found that ovarian volume, afc and chronological age were all individually predictive of menopausal status, with similar accuracies (127). Van Rooij et al. subsequently found the afc to be predictive of age at menopause only in a univariate analysis (394). In the multivari-ate analysis only amh and age were the best predictors of age at menopause. Although the results are conflicting, the afc is currently the best ultrasound parameter for the prediction of age at menopause.
3.5 Genetic markers for menopauseGenetic factors are thought to be important determinants of the natural menopause. Women with a family history of early menopause are a high-risk group for undergoing early menopause (61;66;357). The rate of ovarian ageing, leading to sterility and ultimately menopause, is highly vari-able. Age at menopause is a complex quantitative trait with high heritability. Association between menopausal age of mothers and daughters and also between sister pairs has been convincingly demonstrated (269;364;376). Estimates of the heritability for age at meno-pause have shown to range from 30 to 85% (418). Although accurate prediction of age at menopause is not yet feasible, genetic studies may help to identify and understand the vari-ous processes that underlie ovarian ageing and its variation, and may also provide tools for prediction of reproductive life span. Single gene defects related to age at menopause have mainly been implicated in pre-mature ovarian failure: Turner syndrome and other syndromic defects like galactosemia, blepharophimosis-ptosis-epicanthus inversus syndrome (bpes) and fmr1 gene premuta-tion carriers (fragile X syndrome) (334;415;421). The natural variation in age at menopause spanning from 40 to 60 years of age has a more complex etiology. Studies on premature ovarian failure have proposed various candidate genes to be involved in the natural varia-tion of ovarian ageing (334). The first group of candidate genes primarily affects follicle
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function by exerting known hormonal effects (fsh, fshr, lh, lhr, cyp17 and cyp19). A second group of candidate genes affects the rate of initial recruitment from the primor-dial follicle pool into growing follicles (bmp15, gdf9, foxl2 and gpr3). Furthermore, a third group of genes include dna binding proteins and transcription factors like nobox and lhx8, and rna binding proteins like nanos. Since these genes are expressed during oogenesis, mutations may lead to various degrees of lack of germ cell formation. Causative mutations have only been identified in a few women with pof (nobox, gdf9, ldx8) and may guide research studying the genetics behind the variation in reproductive ageing (336). Small variations in these genes could determine the viability of the follicle pool and thus influence variation in reproductive lifespan (205). Association studies have also found various candidate genes that may explain the natural variation in ovarian ageing. Heterozygosity for Factor V Leiden and Apolipopro-tein E-2 (apoe-2) has been associated with age at natural menopause (206;208;299). It is hypothesized that genetically determined poor vascular support leading to accumulation of oxidative stress has long term effects on ovarian follicle depletion (125;355;358;379). The estrogen-inactivating cyp1b1-4 polymorphism was also shown to be associated with a reduced age at natural menopause, where women with a homozygous mutation entered menopause about 1.1 year later (409). It is believed that this polymorphism leads to higher levels of estrogens throughout reproductive life. How this would affect ovarian follicular wastage, remains to be elucidated. This finding however, was not confirmed in Dutch and Japanese cohort studies (131;204). Finally, common polymorphisms in the amh-receptor-2 gene have been associated with age at natural menopause. It is hypothesized that decreased amh signaling would lead to faltering inhibition of initial follicle recruitment, resulting in an increased rate of follicle loss (189). Linkage analysis using genome wide scans has shown different regions of interest. In 165 Dutch families, a linkage based genome scan has identified two chromosomal regions with suggestive linkage: 9q21.3 and Xp21.3 (378). Suggestive linkage on the X chromosome is not surprising, because of the discussed widespread involvement in premature ovarian failure (415). One of the genes in the linkage region of chromosome 9 encodes for a member of the bcl2 family, which is involved in apoptosis (172;294). The next step in identifying the underlying genes is by fine mapping of the linked regions. This will necessitate the avail-ability of a large cohort of women with adequate information on the age of natural meno-pause (11). Thus, the variation in age at menopause is a complex heterogeneous trait that may be influenced by several genetic mechanisms. Some genetic factors may follow Men-delian rules of inheritance, but most contributing factors are probably susceptibility vari-ants that increase the risk of developing ovarian dysfunction. Improvement of tests for the identification of women with a reduced ovarian lifespan is likely to come from combined endocrine, imaging and genetic information. Yet, the final predictive relation between such markers may only be derived from large long term follow-up studies.
3.6 Other factors related to diminished oocyte reserveOther, acquired factors include: chemotherapy, radiotherapy, pelvic surgery such as unilat-eral oophorectomy, repeat cesarean section and embolization of fibroids (161;228;230;370) pelvic infections or tubal disease (185;186;326), severe endometriosis (20), and as already mentioned smoking (12;315). Women who smoke 10 cigarettes a day have a 40 % increase in the risk of experiencing an earlier menopause (184). Others reported that in moderate-to-heavy smokers (14 or more cigarettes daily) menopause occurred 2.8 years earlier than in never smokers (191).
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Recently a higher prevalence of premature ovarian failure was described in monozygotic and dizygotic twins (132). An explanation for the latter finding is probably an earlier meno-pause in mothers of dizygotic twins, related to higher fsh levels and as a consequence of multiple follicle growth and ovulation. By genetic inheritance, the daughters will also expe-rience earlier menopause. For monozygotic twinning it is more difficult to find a plausible explanation for an earlier menopause. In families with a history of dizygotic twinning, variants in the growth differentiation factor-9 (gdf9) gene were found more often than in controls (287). gdf9 is an oocyte-derived growth factor essential for follicle growth. Recent papers describe rare variants in both gdf9 and bmp15 contributing to pof (83;222) A rela-tionship between twinning and pof may be found in gdf9 and bmp15 gene variants.
4 DiscussionOver the past two decades much effort has been put into attempts to more precisely forecast the age at menopause for individual women. For many reasons the notion that menopause normally occurs on the average age of 51 but that it may vary from 40 to 60 is unsatisfy-ing. The ability to accurately predict the age of the last menstrual period is useful for two purposes: firstly because of the associated definitive infertility and secondly the associated subfertility starting many years earlier. As mentioned in the introduction, advanced knowl-edge could lead to very important strategy decisions such as planning to attempt concep-tions early or to consider interventions that may improve fertility. For this purpose very early predictors of ovarian ageing are mandatory. However, only a few parameters have the potential to be useful.
With regard to a lifetime risk estimate, it seems that only a family history of age at meno-pause and in particular that of early menopause may have some predictive value (table 1). It is to be expected, that in the near future genomic studies will result in genetic parameters that relate to age at menopause. This is a very important and active area of current research and potentially fruitful given the notion that menopause is predominantly genetically deter-mined. Currently, no precise data are available regarding possible predictive values of amh and the inhibins for early prediction of menopause. It is likely that women with very low and undetectable amh levels and a poor response to ivf will become menopausal within the next 10 years. It is unlikely that normal amh levels and a normal ivf response will be of any use in predicting age at menopause. This is the same for inhibin measurements that also show strong within and between cycle variation dependent on the monthly variation of the size of the available follicle cohort (the antral follicle count). Most of these assumptions need further scientific evaluation. Thus today knowledge of very early predictors of meno-pause is disappointingly small and warrants extensive research efforts. However, some clarity now exists regarding short term predictors. In particular men-strual cycle irregularity, vasomotor symptoms, very high basal fsh and undetectable inhibin B levels have shown to be indicators that menopause will occur within 2 years (table 1). Obviously combinations of these parameters could potentially make the predic-tion stronger. Markers that long in advance may indicate limited ovarian reserve and conse-quently earlier menopause in later life, such as low or immeasurable amh, a poor response to ivf stimulation, some rise of early follicular phase fsh and low antral follicle counts, do not seem to be able to signal imminent menopause. Possible roles for genomic and detailed ultrasound measurements are an area of further research.
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Table 1Qualitative assessment of potential factors involved in long (upper panel) and short term (lower panel) prediction of age at menopause. For each feature it is indicated whether it has a cross-sectional and longitudinal relationship with age and if it is or might be able to predict the onset of menopause. The composition is based on and deduced from existing literature. The strength of a possible relationship is marked by the number of plus-signs. A question mark indicates that no data are available or could be deduced. na indicates not applicable for features that are age-independent.
Cross-sectional
relation with age
Longitudinal
relation with age
Predictive of age at
menopause
Qualitative assessment of potential factors involved in long term prediction of age at menopause
(10-30 years in advance).
Calendar age na na + +
fsh +/– +/– –
Anti Müllerian Hormone + + + + + + +/–
Antral Follicle Count + + + + + + +/–
Inhibin B +/– +/– –
Genetic information
- Family history na na + +
- Candidate SNPs/genes na na +
Qualitative assessment of potential factors involved in short term prediction of age at menopause
(2-10 years in advance).
Cycle characteristics
- Cycle shortening + + ? +
- Cycle lengthening + + ? +
- Cycle irregularity + + ? +
Vasomotor symptoms + + ? + +
fsh + + + + +
Anti Müllerian Hormone + + + +
Antral Follicle Count + +/– +
Inhibin B + + + +
Poor ovarian response in art + + + + +
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Most studies have been undertaken in populations with risk estimates of menopause in association with various possibly related features. In contrast, studies that evaluated their predictive value on an individual basis are virtually absent. Over the past two decades, we have greatly extended our knowledge about the physiol-ogy and pathophysiology of menopause. We have learned much more about endocrinology and follicle dynamics. While many are interesting, none of these factors are useful as “test”. Examples are the changes in estradiol, progesterone, lh, activin, GnSIF, follicle dynamics, changes in gonadotrophin secretory pulse patterns and their intercycle variation. On the other hand, other features may become in the future, within certain limits, part of a panel of tests to predict menopause. Among these are chronological age, family history, low amh, undetectable amh, low antral follicle counts, poor response to ivf, and elevated basal fsh for long term prediction; and cycle shortening and occurrence of vasomotor symptoms for short term prediction. Unfortunately so far, none of the parameters discussed has been shown to be predictive in individual women. Therefore, even today we cannot tell a woman with sufficient reliability and certainty when her final menstrual period will occur. Further results of new and ongoing longitudinal studies will hopefully provide practically useful predictive models for individual use in the near future. In particular, genetic profiles and presence of combination of certain features or their occurrence in a certain sequence are expected to be of particular value.
The relationship of serum Anti-MüllerianHormone concentration to age at menopause.
J. van Disseldorp, M.J. Faddy, A.P.N. Themmen, F.H. de Jong, P.H.M. Peeters, Y.T. van der Schouw, F.J.M. Broekmans.
J Clin Endocrinol Metab. 2008 Jun;93(6):2129-34.
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1 IntroductionAnti-Müllerian Hormone (amh or Müllerian inhibiting substance) is considered a quan-titative marker for ovarian reserve (188;392;394). Animal studies have shown amh to play a role in the primary follicle depletion rate by inhibiting the transition from primordial follicles into primary follicles (88). amh appears to correspond well with antral follicle counts (afc) and ovarian response to hyperstimulation in ivf (219;257;392). Also, amh has been shown to be the marker that best reflects the gradual decline in reproductive capacity with increasing age (119;154;338;392-394). Because of its presumed menstrual cycle inde-pendence, it is valued as a marker for ovarian reserve that may become the test of choice over other tests like those based on afc (58;160;219). The decrease of female reproductive capacity with age is believed to be a consequence of the similar decline in follicle numbers (38;94;357). Antral follicle counts have been consid-ered to reflect reproductive status since they are related to age at menopause and age at birth of last child (39). However, only low afcs provide clinically useful estimates of reproductive status. Moreover, afcs show some cycle to cycle variation and may be prone to observer bias (39;319). amh on the other hand does not vary so much between cycles (116), is easily measurable and is highly correlated with afc (160;392). In this paper we consider whether amh does reflect reproductive status, by modeling its relation to age at menopause.
2 Methods
2.1 Study population
In order to relate age-dependent changes in amh measured in an ovulatory cycle with varia-tion in age at menopause, we combined two sources of information. First, for amh measurement, a group of 144 healthy, regularly cycling, fertile, predomi-nantly Caucasian female volunteers aged 25–46 years was recruited through advertisements in local newspapers (319). Volunteers were enrolled in the study protocol if they met all of the following criteria: (1) regular menstrual cycles, with mean length varying from 21 to 35 days; (2) biphasic basal body temperature; (3) proven natural fertility by having carried at least one pregnancy to term; (4) each achieved pregnancy was established within 1 year after the interruption of contraceptive methods; (5) no evidence of endocrine disease; (6) no history of ovarian surgery; (7) no ovarian abnormalities, as assessed by vaginal ultrasound; and (8) cessation of hormonal contraception at least 2 months before entering the study protocol. From all volunteers an amh blood sample was obtained at cycle day 3. The study was approved by the Institutional Review Board. All participants gave written informed consent and received monetary compensation for participating. Secondly, to estimate the distribution of the age at menopause, a sample of Dutch women participating in the Prospect-epic (European Prospective Investigation into Cancer and Nutrition) study was used (32;307). For the Prospect-epic study, a total of 17,357 women 50 to 70 years of age were recruited from an ongoing nationwide breast cancer screening program conducted in the Netherlands. Data on reproductive history were obtained from a questionnaire. Menopause was defined according to the who classification as a condition of absence of spontaneous menstrual bleeding for more than 12 months. For the current study, a cross-sectional cohort (n = 5449) of women with a natural menopause and who conceived at least one child was selected from the initial prospective cohort, to create a high level of comparability with the other women used in this study. Furthermore, only women
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58 years and older were selected, to prevent under-representation of women who reached menopause late in their life, leaving 3384 women who met all these criteria to be included in the present analysis.
2.2 Hormone Assaysamh concentrations were measured in serum from blood samples stored at –20º C until processed. In all samples, amh levels were estimated using an enzyme-immunometric assay (Diagnostic Systems Laboratories, Webster, tx, usa). Inter- and intra-assay coeffi-cients of variation (cvs) were less than 5% at the level of 3 µg/liter and less than 11% at the level of 13 µg/liter. The detection limit of the assay was 0.026 µg/liter. Repeated freezing and thawing of the samples or storage at 37º C for 1 h did not affect the results of the assay (393). The current assay was compared with the ultrasensitive Immunotech-Coulter assay (Marseilles, France) in a previous publication (160). To be able to compare our results with earlier published data, all results need to be multiplied by a factor 2.0.
2.3 AnalysisThe amh values obtained from the study of normal fertile volunteers were plotted against age, and locally quadratic smoothing (35) was used to estimate the change in mean amh level with age. A smoothed distribution of the residual deviations of the actual amh levels from this estimated mean, was then determined using methods described by Faddy (107). This residual distribution and the estimated change in mean amh with age formed a model for age-related change of amh. Moreover, it was hypothesized that this variation in amh would correspond to variation in the future occurrence of reproductive events, such as menopause. Assuming that menopause is triggered by amh falling below a certain threshold, the model was used to obtain a predictive distribution of age at menopause. This distribu-tion was then matched to the epic data on age at menopause to derive an estimate of the amh menopausal threshold level. Agreement between this predictive distribution and the observed distribution of age at menopause was assessed by a visual comparison of the dis-tributional shapes, and a Quantile-Quantile plot, where quantiles of the observed distribu-tion are plotted against corresponding quantiles of the predictive distribution. For individual women, predictions of age at menopause could then be done using quan-tiles of the predictive distribution. From each woman’s data on amh and age, she was placed in a percentile band (lower 5%, 5%–10%, 10%–25%, 25%–50%, 50%–75%, 75%–90%, 90%–95% or upper 95%) from the model for age-related change of amh, using the estimated residual distribution (for example, a woman aged 33 years would have an amh of 0.6 or less with probability 0.1, and 1.9 or less with probability 0.25, etc.). From the cor-responding quantiles of the predictive distribution of age at menopause, classification into one of eight categories for age at menopause then follows. In this way, predictions of age at menopause can be made on the basis of amh level and age.
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Figure 1Age dependent amh levels ( ), plotted on a logarithmic scale to show more homogeneous variation (n=144). The solid line indicates the smoothed estimate of mean amh level as a function of age.
3 ResultsFigure 1 shows the amh data plotted against the women’s ages, and the smoothed estimate of mean amh levels. Use of a logarithmic transformation of amh here reflects the hetero-geneity in amh variation (high levels showing more variation than low levels) with more homogeneous variation apparent on the logarithmic scale. The distribution of the residual deviations of the data-points about this estimated mean was markedly left-skewed (more points showing less dispersion above the estimated mean than below it in figure 1). This residual distribution and the estimated mean provide a model for age dependent change in amh levels: a woman with a low value at a young age will have depleted her follicle pool at an earlier age than a woman with a high value and the same age. The mean amh only declines after age about 30 (± 3) years, so amh can only be regarded as being correlated with declining ovarian reserve after this age. The epic data set showed a mean age at which women experienced menopause of 50.4 years (sd = 4.1; median = 51). The distribution of age at menopause was left-skewed (figure 2), probably because premature ovarian failure caused menopause to occur early in life in some women, and similar in shape to the amh residual distribution referred to above.
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Figure 2Comparison of the observed distributions of age at menopause from the epic data (bars) and the pre-dicted age at menopause using the amh threshold model (– –), indicating good concordance between the two distributions, particularly between the ages of 41 and 57 years as evidenced by the quantile-quantile plot (inset).
Since there is an age difference between the amh and Prospect-epic cohorts, table 1 displays some environmental and generational characteristics of both cohorts.
Maximum likelihood estimation using the data on age at menopause and a predictive dis-tribution derived from a threshold amh level (measured with the dsl assay) below which women experience menopause (39) resulted in an estimated threshold of 0.086 µg/l (using the Immunotech-Coulter assay the amh threshold level would be 0.172 µg/l). The distribu-tion of menopausal ages so predicted from declining amh levels and this threshold was in good concordance with the epic data distribution, as shown by the plots in Figure 2. Thus, the observed age distribution is well matched by the predictive distribution, except possibly at very low (≤ 41y) and very high (≥ 57y) ages. Estimated percentile categories for amh and age are shown in Figure 3, with amh again on a logarithmic scale (cf. Figure 1), together with the corresponding estimated ages at which menopause would be expected to occur (amp) in the inset (with standard errors in brackets). This figure illustrates that a woman with an amh low for her age (*) is likely to experience menopause at a younger age (between 41 and 44 years) or some 7 (±0.4) to 10 (±0.5) years before the median age of 51 years, which would be the expectation without the additional information provided by amh. Similarly, one with higher amh for her age (#) can expect to become menopausal at a later age (between 51 and 53 years), or up to 2 (±0.1) years after the median age.
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Table 1baseline characteristics of the amh and Prospect-epic cohorts.
amh Prospect-epic
Age at inclusion 37.9 ± 5.5 63.0 ± 3.4 p <0.01b
Offspring:
0
1
2
3
4
5
6
≥ 6
4 ( 3%)a
36 ( 25%)
64 ( 44%)
31 ( 22%)
8 ( 6%)
1 ( 1%)
0 ( 0%)
0 ( 0%)
304 ( 9%)
1099 ( 33%)
918 ( 27%)
568 ( 17%)
246 ( 7%)
139 ( 4%)
110 ( 3%)
p <0.01c
Age at first child 29.6 ± 5.8 26.1 ± 4.0 p <0.01b
Smoking
Past/current
none
54.2%
45.8%
49.8%
50.2%
p =0.30b
bmi 24.2 ± 3.8 26.5 ± 4.0 p <0.01b
a four women carried a pregnancy to term but did not deliver a healthy childb Mann-Whitney-Uc Chi Square
Figure 3The relationship between amh and age presented in terms of the 5th and 95th (——), 10th and 90th (– –) and 25th and 75th (——) percentiles, and the median (– –), with the menopausal threshold for amh indicated by the solid vertical line. Corresponding percentiles (± se) for predicted age at menopause (amp) are shown in the inset. The examples indicated by * and # show that a woman with an amh low for her age (*) is likely to experience menopause at a younger age than the median age of 51 years, which would be the expectation without the additional information provided by amh. Similarly, one with higher amh for her age (#) can expect to become menopausal at a later age.
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4 DiscussionIn this study it has been shown that there was a good level of conformity between the dis-tribution of observed age at menopause and the predicted distribution based on a modeled mean decline of amh with age and the application of a menopausal threshold amh level. This approach assumes that changes in reproductive status and amh levels are related. The close correspondence between actual and predicted distributions of age at menopause supports such a relationship. amh levels of postmenopausal women have been shown to be mostly undetectable (218). However, in women followed into menopause prospectively the finding of one or two antral follicles by ultrasound is not exceptional (122;127). A threshold amh level of 0.086 µg/l not far from the detection limit of the assay of 0.026 µg/l, would therefore seem to be a plausible cut off for the occurrence of menopause. Moreover, in a recent study, amh levels in women with cancelled ivf cycles due to poor response, a condition regarded as close to the onset of the menopausal transition (65), ranged from 0.098 µg/l to 0.63 µg/l, with a mean of 0.175 ± 0.04 µg/l on the Immunotech-Coulter assay (273). As the menopausal transition is considered to precede menopause by some 4-6 years (342) and since the decline in amh in this period is quite slow, the calculated threshold seems to fit quite well between the observed levels for the postmenopausal state and the onset of the menopausal transition (406). The reliability of the epic cohort in providing a distribution for age at onset of menopause has already been addressed (39). The epic distribution of age at menopause is similar to those observed in other epidemiological studies (269). Comparability of the epic and amh cohorts (table 1) can be assessed from the following considerations. Firstly, there has not been identified a consistent set of lifestyle or nutritional factors that influence age at menopause, apart from smoking, which was comparable in both cohorts (table 1) (32;319). Secondly, both cohorts stem from the same female Caucasian soci-ety, albeit that they differ in age at inclusion by some 30 years. Finally, although bmi and age at first child differed between the two cohorts, the first relates to the age at which bmi was determined and the latter to differences in reproductive behavior across generations. There-fore it is unlikely that either genetic or environmental factors would have had a major influ-ence on the results presented. Previous studies addressing the validity of recalling age at menopause showed that 70% of women recall their age at menopause accurately within 1 year, limiting the effect of recall bias (57;78). Considering that the large variation in amh levels in normal fertile volunteers reflects an equally large variation in reproductive status of these women, amh may be regarded as a use-ful predictor of an individual’s reproductive capacity. Moreover, in ivf, amh has been shown to be a good predictor of ovarian response (97;257;272;273;292;393). As a poor response after ovarian hyperstimulation is regarded as a state of diminished ovarian reserve, prediction of this from amh levels underlines its potential as a test for general reproductive status. Recent research has shown that antral follicle counts currently provide the best perfor-mance in predicting poor response in ivf treatment (42), while data on amh have also started to accumulate. amh and afc values have been shown to be highly correlated (75) and levels of amh, which is produced by granulosa cells of small antral follicles in the size range of 2-7 mm (410), are considered to be a reflection of the size of the primordial follicle pool (188;319). In an earlier report modeling of afc and the occurrence of menopause using an afc threshold of 0 or 1 follicle was described (39). This model and the present one using an amh threshold both fitted the available data comparably well, and so the association of age at
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menopause with amh is similar to that with afc. However, a limitation of using afc in the prediction of menopause is its inter-cycle variability (149), which causes only consistently low afc values to be informative and of potential predictive value. amh has been demonstrated to vary only minimally from cycle to cycle (116) and levels appear to be cycle-independent (160;219). Taken together, these findings indicate that amh may be a marker for reproductive status at least as good as antral follicle count. The data presented here suggest that amh is capable of specifying a woman’s reproduc-tive status more realistically than chronological age alone. As menopausal prediction using age and amh level has been done using percentile categories, this prediction cannot be pre-cise. Prediction for younger women may be more problematic since observed amh levels are underrepresented at younger ages, and figure 1 and a recent study in mice show that mean amh levels do not decline at young ages (188). Longitudinal studies starting with women in their twenties, gathering any endocrine, ultrasound and genetic information that may pos-sibly relate to reproductive ageing, and recording of natural menopause many years later, may be the only definitive way to develop testing for expected reproductive life span. Since such studies are unlikely in practice, prediction of reproductive life span depends on cross-sec-tional studies. Recently, several studies have addressed possible relationships between endocrine, ultra-sound and genetic factors, and age at menopause. Van Rooij showed a relationship between amh and inhibin B and cycle irregularity, a proxy variable for the menopausal transition (394). Broekmans reported on the predictive capacity of afc to predict age at last child and onset of menopause (39). Hefler and Tempfer found estrogen metabolising gene polymorphisms and polymorphisms associated with thrombophilia and vascular homeostasis to be associated with earlier onset of menopause (158;358). Further research is necessary to determine which factors are the best predictors of menopause. In conclusion, this study shows that amh levels are related to reproductive events such as age of onset of menopause, at a population level. The results suggest that amh reflects a woman’s reproductive age more realistically than chronological age alone.
AcknowledgementsThe Prospect-epic cohort was funded by “European Commission: Public Health and Con-sumer Protection Directorate 1993-2004; Research Directorate-General 2005-”; the Dutch Ministry of Health; the Dutch Cancer Society; ZonMw the Netherlands Organization for Health Research and Development; World Cancer Research Fund (wcrf).The authors express their thanks to the reviewers of an earlier draft for their many useful comments that have improved the present manuscript.
The association between vascular function related genes and age at natural menopause.
J. van Disseldorp, F.J. Broekmans, P.H. Peeters, B.C. Fauser, Y.T. van der Schouw.
Menopause. 2008 May-Jun;15(3):511-6.
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1 IntroductionThroughout female life both quantity and quality of follicles and oocytes decline. This pro-cess of ovarian ageing influences a woman’s fertility and her menopausal age. It is pos-tulated that menopause occurs when the follicle pool is passing a critical threshold of a thousand or less follicles, a number insufficient to sustain the cyclic hormonal process nec-essary for menstruation (110;357). A large body of evidence exists from twin and sib-pair studies suggesting that age at natural menopause is genetically determined (66;340;364;367;376). However, the causative genes or, more likely, the combination of genes dictating the ovarian ageing process, remain largely unknown. General ageing and reproductive ageing might be subject to the same bio-logical processes, including accumulation of oxidative stress damage leading to follicular depletion as a result of a compromised microcirculation (125;355;379). This theory is sup-ported by the observation that an early menopause is associated with increased chances for cardiovascular disease later in life (10;59;341;381). However, cardiovascular risk factors may also determine age at natural menopause (206). Although common environmental fac-tors are thought to be only of limited significance for the onset of menopause, smoking during the menopausal transition and an increased body weight have been shown to pre-dispose to earlier vascular damage and menopause (358;389). These outcomes support the idea that reproductive ageing and general or vascular ageing are somehow connected. Recent research has targeted different gene products associated with a vascular origin of reproductive ageing. The general hypothesis is that individual variation in snps may affect variation in the vascular ageing process. Ageing of the ovarian vasculature is thought to be one of the mechanisms that influences the individual variation in ovarian capacity to sustain menstrual cycle regularity, leading to individual variation in age at menopause (153;213;313). In this view, recent research has focused on clotting factors II (F2) prothrom-bin G20210A, factor V (F5) Leiden G1691A, plasminogen activator inhibitor1 (pai-1) 4G/5G, angiotensinogen (agt) Met235Thr, endothelial nitric oxide synthase (nos3) T768C and nos3 Glu298Asp, apolipoprotein E-1 (apo E1) Cys112Arg, and apo E2 Arg158Cys (358). However, only for the gene products of factor II, V and VII, as well as for apo E2, a statisti-cally significant relation with age at natural menopause has been found (299;358;377). The current study aims to confirm these recent findings showing that genes related to the coagulation pathway are associated with age at natural menopause (299;358;377). For this purpose single nucleotide polymorphisms (snps) in coagulation factors II, V and VII, and apo E2 genes were studied in a large cross-sectional study of naturally postmenopausal women. The detected associations will help to identify the factors involved in ovarian age-ing and onset of menopause and its underlying biological mechanisms.
2 Subjects and Methods
2.1 Study populationThe study population was selected from the Prospect-epic cohort, which is one of the two Dutch contributions to the European Prospective Investigation into Cancer and Nutrition (epic) (32). This cohort comprises 17,357 Caucasian women aged 49-70, invited through an existing regional breast cancer screening project to participate in the study between 1993 and 1997. At enrollment all women underwent a physical examination and all partici-pants filled out detailed questionnaires about dietary, reproductive and medical history. In addition, women donated a 30-ml non-fasting blood sample, which was fractionated into
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serum, citrated plasma, buffy coat and erythrocyte aliquots of 0.5 ml each. The straws were stored under liquid nitrogen at -196 centigrade for future research. A 10% random sample of 1,736 women was taken for biochemical and genetic analyses. For 36 (2.1%) women, serum, plasma or buffy coat samples were missing. Natural meno-pause was defined according to the definition by the World Health Organization as amen-orrhea for at least 12 consecutive months without other obvious reasons. For the current study population all women with a natural menopause (n = 762) were selected from the original random sample (n = 1736). Women whose age at natural menopause could not (yet) be determined (e.g. surgical menopause (n = 387), hormone replacement therapy use during the menopausal transition (n = 222), or unknown menopausal age (n = 329)) were excluded. From the 762 women with a known age at natural menopause, twenty women were excluded because dna extraction or genotyping failed.
2.2 Questionnaire and anthropometric dataType of menopause (natural or artificial), menopausal age, oral contraceptive use, parity and smoking information were recorded through questionnaire. Oral contraceptive use was defined as ever or never. Smoking was defined as current, past or never. Body height was measured to the nearest 0.5 cm with a wall mounted stadiometer (Lameris, Utrecht, The Netherlands). Body weight was measured in light indoor clothing without shoes to the nearest 0.5 kg with a floor scale (Seca, Atlanta, ga, usa). Body mass index was calculated as weight divided by height squared (kg/m2).
2.3 Single Nucleotide Polymorphisms (SNPs)The single nucleotide polymorphisms in the coagulation factors II, V and VII, and apo E2 genes have shown to influence age at natural menopause in previous studies (299;358;377). The single nucleotide polymorphism in the clotting factor II gene is an exchange of guanine for adenine at position 20210. The heterozygous and homozygous mutations are associated with an increased level of factor II activity and confers a two- to fivefold increase in the risk for venous thrombo-embolism (435). Point mutations in the factor V gene (factor V Leiden) and the prothrombin gene (the sub-stitution of A for G at position 20210) are the most common causes of inherited thrombophilia (73). For factor V a guanine for adenine mutation was studied at the 1691 position leading to an arginine for glutamine transition in the subscription product at position 506, also known as the Factor V Leiden mutation. This mutation leads to thrombophilia due to activated protein C resistance and is prevalent in 2-4% of the Dutch population (28;253). Carriers of both fac-tor V Leiden and the G20210A prothrombin mutation have an increased risk of recurrent deep venous thrombosis after a first episode and are candidates for lifelong anticoagulation (73). For factor VII two polymorphisms were studied. The first mutation concerns a decanu-cleotide insertion/deletion functional polymorphism (-323 0/10-bp) in the promoter region of factor VII, the second an arginine for glutamine transition at position 353 (82;180). Although both minor homozygous genotypes are associated with a 66% and 72% reduc-tion of activated factor VII activity respectively, only the heterozygote genotypes of both the insertion and transition polymorphism are associated with a decreased risk of myocardial infarction among patients with severe coronary atherosclerosis (128). For apolipoprotein E2 (apo E2) a cytosine for thymine mutation was studied which results in an aminoacid change from arginine to cysteine at position 158. This mutation leads to type III hyperlipoproteinemia and its associated spontaneous atherosclerosis in 1-4% of homozy-gotes (101;353).
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2.4 Genotype assessmentsTo detect the single nucleotide polymorphisms as listed, dna was extracted from the buffy coat aliquots with the use of the QIAamp® Blood Kit (Qiagen Inc., Valencia, ca, usa). Genotyping was performed using a multilocus genotyping assay for candidate markers of cardiovascular disease risk (Roche Molecular Systems Inc., Pleasanton, ca, usa) (53). Briefly, for the detection of the polymorphisms each dna sample is amplified using two multiplex polymerase chain reactions, and the alleles are genotyped simultaneously using an array of immobilized, sequence-specific oligonucleotide probes. This array of probes is blotted on plastic strips and, after staining, genotypes can be scored based on blue (posi-tive) and white (negative) bands. Each blue band, representing a specific genotype, was scored by specific software (counting the pixel intensity of each band) and checked manu-ally.
2.5 Data analysisDeviations from Hardy–Weinberg equilibrium were assessed using a goodness-of-fit Chi square-test with one degree of freedom (p > 0.05) (335). To estimate the effect of the differ-ent genotypes on age at menopause we used linear regression analysis with age at meno-pause as the dependent, and dummy variables for the heterozygosity and minor homozy-gosity genotypes as independent variables. The following potential confounders were con-sidered: smoking (current/past or never), oral contraceptive (ever/never) use, parity (nul-liparous/parous), body mass index (bmi, kg/m2). All analyses were performed using spss for Windows version 12.0.1.
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3 ResultsWe studied 742 women with a natural menopause (table 1). The study population had a nor-mal cardiovascular profile, although almost 50% of the population tended to be overweight.
Table 1Population characteristics (n = 742)
Mean ± sd
Age at inclusion (year) 58.2 ± 6.1
Age at menarche (year) 13.5 ± 1.7
Age at natural menopause (year) 50.0 ± 4.2
Recall period (year) 8.7 ± 6.7
Parity 2.7 ± 1.4
bmi- ≤ 25- > 25
398344
(53.6%)(46.4%)
Pulse (bpm) 73.5 ± 11.0
Blood pressure (mmHg)- systolic- diastolic
133.479.1
± 20.0± 10.4
Smoking- current- past- never
147264331
(19.8%)(35.6%)(44.6%)
Table 2 shows that for the tested snps there was a large variation in frequency of the major homozygous genotype. In factor II the wild type variant was present in 97%, while in the dele-tion/insertion mutation of factor VII the wild type variant was found in only 73%. Frequency of minor homozygous genotypes in this population ranged between 0 and 2%. All but one studied mutations were in Hardy-Weinberg equilibrium (hwe) (p > 0.05). Factor II was not in hwe, due to overrepresentation of one minor homozygous case. The heterozygous mutation in factor II was not associated with menopausal age (table 2). The minor homozygous muta-tion was associated with age at menopause: -8.0 years (95% CI -16.2 – 0.14; p=0.05) compared to the major homozygous allele. However, since this finding was based on one observation only and since the variable was not in hwe, we ignored this result. The heterozygous insertion/deletion mutation in factor VII was associated with an increased menopausal age of 0.81 (95% CI 0.12 – 1.50; p=0.02). The minor homozygous mutation was not associated with age at menopause. The finding in the women homozygous for the inser-tion variant was based on 11 observations. The arg353gln mutation in the factor VII gene, nor the factor V Leiden mutation nor the poly-morphism in the apo E2 gene were associated with age at menopause.The covariates current smoking and parity were associated with age at natural menopause (p<0.001 and p=0.006 respectively) in a univariate analysis. However, adding these factors to the regression models with the genotypes did not cause any changes in the effect of the gene
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variants (data not shown). bmi and oral contraceptive use were not related to menopausal age, and thus are not confounders. Therefore, only crude associations of the genotypes with age at menopause are shown.
Table 2Single Nucleotide Polymorphisms Outcomes
n=742 n % Difference in age
at menopause with
major homozygous
genotype
95% CI Significance
(p-value)
Factor II gg 721 97
(G20210A) ga 19 3 -0,4 -2.29-1.50 0,68
aa 1 0 -8,03 -16.2-0.14 0,05
Factor V gg 673 91
(arg506gln) ga 67 9 0,51 -0.55-1.56 0,35
aa 1 0 -1,96 -10.1-6.21 0,64
Factor VII Del/Del 540 73
del/ins Del/Ins 188 25 0,81 0.12-1.50 0,02
Ins/Ins 13 2 0,36 -1.93-2.65 0,76
Factor VII gg 562 76
(arg353gln) ga 168 23 0,53 -0.19-1.25 0,15
aa 11 2 0,3 -2.19-2.79 0,81
apo E2 cc 594 80
(arg158cys) ct 145 20 -0,17 -0.92-0.59 0,66
tt 3 0 -2,4 -7.12-2.33 0,32
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4 DiscussionIn this study we could not confirm earlier findings that a mutant allele in clotting factor II and V (Leiden) or in the apo E2 gene are associated with a reduced age at natural menopause, although we were able to study a larger, more homogeneous cohort of women with a natural menopause than available in previous studies (159;299;309;358;377;424). A validation of these findings would have supported the theory that age at natural menopause is indeed related to cardiovascular risk factors and possibly to a lack of oxygen and nutrients to the ovaries. In the present study, we found a significant relationship for the heterozygous deletion/ insertion polymorphism in clotting factor VII. We could not find a consistent significant cor-relation for the minor homozygous variant. This is consistent with the findings from Girelli et al., who also just found a clinical relevant effect in the heterozygote carriers (128). However, these findings should be validated in other research populations to decrease the probability of chance findings. Validating these results in larger populations will also handle power prob-lems which we encountered with the minor homozygote variants that were present in only a few cases. Although we failed to validate earlier findings with the variation found in our cohort, several findings in the literature suggest a role for vascular ageing in the onset of meno-pause. Firstly, several studies have addressed the influence of smoking during the meno-pausal transition, which has been associated with decreasing oocyte quality and quantity (256;416;420;433). However, it remains unclear how smoking affects the follicle pool. In a study relating carriership of the factor V Leiden mutation and smoking with onset of meno-pause it was suggested that genetic and environmental factors might exacerbate each other’s effect (377). Recently a direct interaction between environmental and genetic factors was identified. It was shown that smoking induces apoptosis in individuals through increase of the pro-apoptotic protein Bax (81;256). A direct toxic effect of smoking cannot be ruled out, but the influence of environmental factors on the onset of menopause has been estimated to be small (389). Notwithstanding, smoking waste products have been found to accumulate in the follicular fluid, possibly leading to decreased ovarian vascularization and increased oxi-dative stress, with disturbed maturation of oocytes and decreasing follicle pool as the results (256;267;289;363). Secondly, poor responders in ivf have a high risk of early menopause (64;65;278) and vas-cular status in poor responders has indeed been shown to be worse than in normal responders (21). It is therefore not surprising that pregnancies after ivf are more likely to occur in those women with a favorable vascular status (259;284). Moreover, hypertensive pregnancy compli-cations like pre-eclampsia occurred more often after poor ovarian response to hyperstimula-tion after ivf as compared to controls (422). From earlier literature an increased incidence of hypertensive disorders in ivf pregnancies has been long known (255;354). The explanation may be found in advanced vascular ageing compared to age matched controls, affecting both vascular quality and ovarian reserve. Finally, cardiovascular risk factors such as hypertension, overweight and hypercholester-olemia are associated with onset of menopause. Every year delay in onset of menopause has been shown to decrease the risk of developing cardiovascular disease by 2% (381). Cardiovas-cular disease risks have repeatedly been shown to be associated with a decreased age at natural menopause and vice versa (206;381). Moreover, disruption of the gonadal-pituitary axis with hypo-estrogenism as the result is associated with a suboptimal cardiovascular status. Estro-gen deficiency causes decreased prostacyclin production in vascular endothelium, resulting in vasoconstriction and increased platelet aggregation, and reduced vascular endothelium regrowth (9).
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Thus, the vascular role in ovarian ageing cannot be discarded and other (vascular) genes than those studied may exert a role in the onset of natural menopause. The fact that we could not confirm earlier associations may be due to the fact that we did not study tagging snps or true functional snps. Larger (genome wide) association studies are warranted to answer the question which genetic variations underlie menopausal age. In this approach a selection of haplotype specific tagsnps per gene are taken and checked for their association with the out-come variable, making genetic variation identification possible without genotyping every snp in a chromosomal region. The major advantage of such a genome wide approach is that it is hypothesis free, which allows one to test the involvement of much more complex mecha-nisms or different gene pathways than those studied. In this study we investigated one or two functional snps per gene with a previously described effect on a subject’s vascular status. It might be that, notwithstanding the evidence for these functional snps, different snps in these genes are involved in the onset of natural menopause. A final explanation for the inconsistency of our results compared with previous research might be that the vascular function related single nucleotide polymorphisms studied only have a small impact on age at natural menopause. Although we studied a larger cohort of natural menopausal women and consequently assumed to have more power to detect associa-tions of similar magnitude as the previously published studies, the associations found were small and the allele frequencies infrequent. We therefore assume that the findings in previous studies are based on chance findings. The inconsistencies shown make it hard to draw firm conclusions about presence or absence of effects. Ultimately, a cumulative effect from the heterozygous to the minor homozygous variant, a sort of genetic dose-response relationship, might add more power to the findings. In conclusion, in this study it was shown that single nucleotide polymorphisms in clotting factors II and V and apolipoprotein E2, which showed positive associations in previous stud-ies, are of little influence in predicting natural menopause. The relation between a deletion/ insertion polymorphism in clotting factor VII still needs to be validated, before we can draw any firm conclusions about its effect. Notwithstanding these findings, there is still a large body of evidence supporting vascular pathways in determining age at natural menopause.
Hypertensive pregnancy complications in poor and normal responders following in vitro fertilization.
J. van Disseldorp, M.J.C. Eijkemans, B.C.J.M. Fauser, F.J.M. Broekmans.
Fertil Steril. 2009 In Press.
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1 IntroductionAs compared to the general population, ivf pregnancies are associated with a 2.7 fold increased risk of pre-eclampsia and with an increased incidence of hypertensive pregnancy complications in general (255;329;354). In these studies the incidence of pregnancy induced hypertension in ivf pregnancies ranged from 6.4-21% and for spontaneous conceptions between 4.0-5.2%. For pre-eclampsia, incidences were 2.4% in spontaneously conceived pregnancies and 4.7% in ivf/icsi pregnancies. This association can be partly explained by factors such as multiple gestation and advanced female age. The advanced female age and associated subfertility reflect a group of women with decreased ovarian reserve, which has also been associated with cardiovascular risk (54;183). Moreover, vascular status in poor responders to ivf stimulation (18;42;95) has indeed been shown to be worse compared to normal responders (21). Finally, pre-eclampsia occurring in pregnancies established after ivf was more common in women requiring higher doses of exogenous fsh for ovarian stimulation, reflecting decreased ovarian reserve (422). It has been described that this higher incidence of pre-eclampsia in women with poor ovarian reserve may be attributed to advanced vascular ageing compared to age matched controls, affecting both vascular quality and ovarian reserve (21;212). Additional evidence suggests that ovarian ageing may be caused by vascular ageing mech-anisms. Cardiovascular risk factors such as hypertension, obesity and hypercholesterolemia are associated with onset of menopause. Every year delay in onset of menopause has been shown to decrease the risk of developing cardiovascular disease by 2% (381). Cardiovascular disease risks have repeatedly been shown to be associated with a decreased age at natural menopause and vice versa (206;381). Since a poor response after ivf stimulation reflects a declining ovarian reserve (42;257), we hypothesize that hypertensive pregnancy complications are more common in ivf poor responders compared to normal responders. An increased frequency of hypertensive preg-nancy disorders after poor response in ivf, would substantiate a possible causative relation between vascular and ovarian ageing (156;422).
2 Methods
2.1 Study population and ivf treatment characteristicsFrom our own ivf database, we selected 150 women who got pregnant after a poor ovar-ian response (≤3 oocytes after follicle aspiration (65)) between the years 2000 and 2004 and compared those with 150 matched control pregnancies after a normal response in ivf (8-12 oocytes after follicle aspiration) (182). Matching was done by hand with exact matching for type of infertility (primary or secondary), pregnancy multiplicity (singleton or twin) and treat-ment received (ivf or icsi) and a nearest match for age at the time of the follicle aspiration and daily dose of recombinant fsh administered. There are no uniform criteria for the definition of poor ovarian response (42). In this analysis, poor response refers to insufficient oocyte yields, defined as less than 4 oocytes after pick-up, which definition has been published ear-lier (16;92;114;165). Other studies regarding poor response have also included cycle cancella-tion and insufficient follicular growth. Since we are interested in pregnancy complications, we obviously could not include cancelled cycles and insufficient follicular growth yielding no oocytes after pick-up. The upper limit of the definition for a normal response of 12 oocytes has also been reported earlier (62;106;302). The lower limit of our normal response group was set at 8 oocytes after pick-up as proposed by the International Society for Mild Approaches in Assisted Reproduction (ismaar) and as reported earlier (182;275).
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Data regarding the established pregnancies were obtained from The Netherlands Peri-natal Registry (360). At the time of matching, no perinatal outcome data were available yet. The inclusion period from 2000-2004 was chosen because pregnancy data from our national registries (The Netherlands Perinatal Registry) are currently only coupled for the years 2000-2004 and provide approximately 95% coverage. We only included women < 41 years of age whose pregnancies were established after fresh ivf or icsi cycles. Women were included only once. Since data were collected retrospectively, no reliable or only incom-plete information regarding smoking and bmi could be obtained. We excluded women with polycystic ovary syndrome, previous ovarian surgery and those who needed less than 150 iu recombinant fsh per day. Previous studies by Yong et al. and Out et al. have shown that ovarian stimulation in women aged 33 years and over and in women aged 30-39 years, respectively, can be considered to be maximal when at least 150 iu/day of recombinant fsh are used (281;430).
All women were treated with a long GnRH agonist suppression protocol, details of which have been published earlier (387). The menstrual cycle was suppressed by starting Leupro-reline (Lucrin®, Abbott, Hoofddorp, The Netherlands) in the midluteal phase of the preced-ing cycle or after at least 10 days of oral contraception use. After the onset of subsequent menses or the onset of oral contraception withdrawal bleeding, ovarian hyperstimulation was started with follitropin (Puregon®, Organon, Oss, The Netherlands; or Gonal-F®, Serono Benelux, The Hague, The Netherlands) with a daily dose of at least 150 iu. If one or more dominant follicles of at least 18 mm diameter were observed at ultrasound, 10.000 iu of hCG (Pregnyl®, Organon, Oss, The Netherlands) was administered and 36 hours later transvaginal oocyte retrieval was performed. After in vitro fertilization and 3-4 days after the follicle aspiration, 2-3 embryos were transferred if available, if not, a lower number of embryos was transferred. In general, women aged 38 years or older could choose to have a maximum of three embryos replaced. Women 37 years of age or younger were allowed a maximum of two embryo’s to be transferred. Remaining embryos were cryopreserved for transfer in a later natural cycle. The luteal phase was supported with 3 doses of 5000 iu hCG (Pregnyl®). 18 days after oocyte retrieval a pregnancy test was done. If this test was positive, subsequent ultrasound examinations were scheduled at a gestational age of 7 and 11 weeks.
2.2 Power calculationA power calculation for a 2-group comparison of proportions showed we have 80% power (alpha 0.05) to detect a difference in the incidence of pregnancy related hypertensive disor-ders of 10% with a total number of 150 subjects in both groups, assuming a 5% incidence in the normal response group.
2.3 Data analysisPrimary endpoints included the birth weight of the neonate and the incidence of pregnancy related hypertensive disorders: pre-eclampsia and pregnancy induced hypertension. Preg-nancy induced hypertension was defined according to the International Society for the Study of Hypertension in Pregnancy (isshp) criteria as a systolic blood pressure ≥140mmHg and/or a diastolic blood pressure ≥90 mmHg after 20 weeks of gestation (46). Pre-pregnancy blood pressure should have been normal and blood pressure should be normalized within 3 months after delivery. Pre-eclampsia was defined as pregnancy induced hypertension with
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proteinuria ≥ 300mg/ 24h. Secondary endpoints were the duration of pregnancy, type of delivery and live birth of the neonate. All patient information was entered into the statisti-cal program spss (Statistical Package for Social Sciences) version 12.01. The baseline data of the paired poor and normal responders were compared using Mc Nemar tests for cat-egorical data and Wilcoxon Signed Ranks tests for continuous variables. A p-value of 0.05 was considered statistically significant. Institutional Review Board approval was not sought, since cases were collected retro-spectively and anonymously. Perinatal registry data were also acquired and used anony-mously. We report no commercial or financial conflicts of interest.
3 ResultsWe studied 1,359 poor response cycles in women aged 41 years or less between 2000 and 2004, which resulted in 235 pregnancies of which 180 were ongoing (ongoing pregnancy rate per ovum pick-up 13,2%). From these 180 cycles resulting in an ongoing pregnancy we excluded 30 cycles for using a GnRH antagonist protocol, for performing ovarian stimula-tion with less than 150 iu/d recombinant fsh or when it concerned oocyte donation cycles. Of the remaining 150 cycles, 123 (82,0%) were ivf cycles and 27 (18,0%) were icsi cycles. We matched 150 pregnancies achieved after a normal response (8-12 oocytes after fol-licle aspiration) (182) to IVF stimulation, based on age at the time of the follicle aspiration, type of infertility (primary or secondary), treatment received (ivf or icsi), singleton or twin pregnancy and daily iu/l of recombinant fsh administered. The ongoing pregnancy rate per ovum pick-up in the normal response group over the years 2000-2004 was 32,1%. A comparison of baseline data between both groups is given in table 1.
We were able to obtain perinatal data from 267 of the 300 cases (89%) from The Neth-erlands Perinatal Registry, without significant differences between the poor and normal response groups. Matching criteria were similar in both poor and normal responders. Poor responders needed a significantly higher total amount of fsh.
The ivf and icsi treatments resulted in 128 singleton, 21 twin and 1 triplet pregnancies in both groups. In case of a twin or triplet pregnancy, only data from the firstborn child were taken into account. Poor and normal responders did not differ significantly in their primary outcomes: inci-dence in pregnancy related hypertensive disorders and birth weight (Figure 1). Moreover, secondary outcomes such as duration of pregnancy, percentage of spontaneous deliveries and live birth ratios were similar in both poor and normal responders (Table 2).
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Table 1baseline characteristics of poor and normal responders to ovarian stimulation for ivf resulting in an Ongoing pregnancy (means ± sd or numbers + %).
Pregnant after poor
response (n=150)
Pregnant after normal
response (n=150)
Age at follicle aspiration (year) 35,4 ± 3,5 35,4 ± 3,4a
Primary infertility 84 (56%) 84 (56%)a
Secondary infertility 66 (44%) 66 (44%)a
Parity 0,52 ± 1,1 0,59 ± 0,7
ivf 123 (82%) 123 (82%)a
icsi 27 (18%) 27 (18%)a
Retrieved oocytes 2,5 ± 0,6 9,7 ± 1,4
Daily fsh dose 233 iu ± 83 222 iu ± 74a
Total fsh dose 2835 iu ± 1219 2538 iu ± 949b
Pregnancy multiplicityc Twin 21Triplet 1
14%0.7%
Twin 21Triplet 1
14%0.7%
Cause subfertility- idiopathic- tubal- male- endometriosis
51 46 48 5
34% 31% 32% 3%
59 35 54 2
39% 23% 36% 1%
a matching criteriab p < 0.01c all fraternal multiple pregnancies
Figure 1Incidence of pregnancy-related hypertensive disorders and birth weight.
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Table 2Outcomes for poor and normal responders to ovarian stimulation for ivf who achieved an ongoing pregnancy (% or means ± sd).
Pregnant after poor
response (n=150)
Pregnant after normal
response (n=150)
p-value
Pregnancy induced hypertension 14,7% 12,2% 0,29a
Pre-eclampsia 8,8% 6,1% 0,27a
Birth weight (gram) 3068 ± 937 3190 ± 785 0,40b
Duration of pregnancy (weeks) 37,9 ± 4,6 38,3 ± 4,4 0,18b
Spontaneous delivery 63,4% 65,1% 0,62a
Live birth 96,4% 95,5% 1,0a
a Mc Nemar testb Wilcoxon Signed Ranks test
4 DiscussionIn the current matched controlled study we were unable to confirm our hypothesis that pregnancies resulting after a poor response in ivf present with more hypertensive preg-nancy complications and a more adverse perinatal outcome compared to pregnancies after normal response in ivf. These results suggest that either the vascular status in poor responders that do get pregnant is not significantly affected or that the alleged influence of vascular status on pregnancy complications is small. ivf pregnancies have been shown to be associated with increased incidences of hyper-tensive pregnancy disorders in previous studies (255;329;354), but not in others (209;405). Possible etiologic factors for the increased number of hypertensive pregnancy complica-tions after ivf are the ivf treatment per se, the ovarian stimulation or parental factors like infertility (209). Unfortunately, research studying why ivf pregnancies do worse than spontaneously conceived pregnancies, even after correction for age, is scarce. Using a mul-tivariate continuous approach with the number of oocytes as outcome parameter, Wol-dringh and colleagues have recently drawn attention to the influence of ovarian reserve on the incidence of hypertensive pregnancy complications after ivf (422). They concluded that women who develop pre-eclampsia in a pregnancy established after ivf/icsi more often have indicators of decreased ovarian reserve, in terms of oocyte yield. The current manu-script tests if women pregnant after a poor response, reflecting poor ovarian reserve, are indeed at increased risk of developing pre-eclampsia. Similarly, pregnancies established after oocyte donation, a treatment often offered after poor or absent response in ivf, are known to suffer from hypertensive pregnancy complications (187;286). These data suggest that the group of poor responders might be responsible for the compromised outcomes of ivf/icsi pregnancies, possibly because poor responders have a poorer vascular status (21). However, in our matched control study, we were unable to confirm this hypothesis. A pos-sible explanation might be that our poor response group is too heterogeneous. Vascular status might not be equally affected in all poor responders and other causes such as the presence of fsh receptor polymorphisms and/or fsh antibodies might have been respon-sible for the poor response (117;147;247). Unfortunately, we do not have data available measuring vascular status or ovarian reserve status before the start of the ivf treatment.
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However, ovarian response to hyperstimulation is considered superior in reflecting ovar-ian reserve as compared to basal functions such as fsh (42;231). Moreover, subanalyses in two subpopulations of primary infertile poor responders and poor responders that needed more than 225 iu recombinant fsh, who were considered to represent a more strictly defined poor response phenotype, did not show a trend towards significant differences in pregnancy induced hypertension and pre-eclampsia (p = 1,0 and p = 0,32, respectively). Another possibility might be that poor responders that become pregnant after ivf/icsi are a relatively favorable group, possibly with better vascular status, than those poor respond-ers that do not become pregnant. The development of a vital pregnancy could have selected against an unfavorable vascular status, smoking habit or high bmi, which are all associated with reduced implantation rates and higher abortion rates (245). Since data on bmi and prior vascular status were not available, we were unable to judge the influence of possible heterogeneity in the poor response group. Another explanation might be that the normal response group is a disadvantaged cohort, since the incidences of hypertensive pregnancy disorders in table 2 are higher than expected in this group. Since pre-existent hypertension was not present in both groups, a possible explanation might be that our population is older than some ivf populations previously reported (255;329). However, Tallo et al. reported a high incidence of 21% of pregnancy related hypertensive disorders in a relatively young population, thus age is not the only factor to explain this difference (354). The existence of endometriosis could also be a confounder, since it could be argued that severe endometrio-sis may cause ovarian scarring leading to a poor response. Furthermore, a recent paper found endometriosis to be inversely correlated to the risk to develop pre-eclampsia (45). We only identified few cases with severe endometriosis in our population and analysis showed no influence of endometriosis on the results presented. We showed that the birth weight of the neonates did not differ between the two groups of ovarian responders. This is in concordance with recent literature (138), which showed that neither the amount of gonadotrophins, nor the number of oocytes retrieved influenced birth weight of neonates born from subsequent pregnancies. Our secondary endpoints duration of pregnancy, type of delivery and live birth were also similar in both groups. As far as we know this is the first study relating ovarian response to these pregnancy charac-teristics, thus a comparison with previous studies is not possible. When comparing ivf pregnancies with spontaneously conceived pregnancies, the results found were contradic-tory (209;329). Overall, ivf pregnancies have been found to do worse, but virtually nothing is known about the etiology. A recent Norwegian population based cohort study is the first comparing assisted fertilization conceptions and spontaneous conceptions in the same mother. They found that birth weight and gestational age did not differ between these preg-nancies and hypothesized that adverse outcomes could be attributable to the factors leading to infertility, rather than to the reproductive technology (312). In this sibling-relationship comparison still birth was more likely to occur in spontaneous conceptions prior to art treatment, while in the general population crude associations for still birth were in favor of the spontaneously conceived pregnancies. An early ageing vascular system might lead to poor ovarian reserve through both dimin-ished oocyte quantity and quality (21;212). The diminished oocyte quantity often results in a poor response. Diminished oocyte quality is reflected in the associated lower pregnancy rates and higher pregnancy loss rates (179;244;400), possibly due to higher rates of fetal aneuploidy (276). The observed pregnancy rate of 17,3% and observed pregnancy loss rate of 23,4% are reasonable compared to other studies (48;123;244;372), possibly reflecting the selection of a relatively favorable poor response group.
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Further studies must validate our findings that pregnancies after a poor response do not have an increased risk for hypertensive pregnancy complications or low birth weight. Future studies focusing on the association between a poor response in ivf and diminished pregnancy rates and increased pregnancy loss rates, should consider looking into preg-nancy complications and outcome as well. Such studies should aim for a stricter phenotype by analyzing pregnancies in poor responders with additional evidence for advanced ovar-ian ageing, such as abnormal reserve testing or repeated poor response. Furthermore, in a larger population, other confounders like smoking behavior and bmi should be taken into account, although we hypothesize that the influence of these factors is small, because ivf populations have been found to be healthier than women conceiving spontaneously, pre-senting with lower bmi and lower number of smokers (329). Moreover, because smoking and obese women present with lower implantation rates and higher miscarriage rates, the incidence of smoking and overweight women in ongoing pregnancies after ivf might be lower than in the general ivf population, limiting its importance. In conclusion, in this matched control study we were unable to show that pregnancies after a poor response in ivf are associated with an increase in hypertensive pregnancy com-plications, compared to pregnancies after a normal response in ivf. This suggests that the hypothetically responsible vascular status in poor responders was not significantly differ-ent than that of normal responders; possibly through selection by establishing an ongo-ing pregnancy or that the suspected influence of vascular status on hypertensive pregnancy complications is small.
Genomic predictors of ovarian response to stimulation for in vitro fertilization.
J. van Disseldorp, L. Franke, M.J.C. Eijkemans, F.J. Broekmans, N.S. Macklon, C. Wijmenga, B.C. Fauser.
Submitted.
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1 IntroductionCurrently employed clinical screening parameters provide only limited information to pre-dict the individual response to ovarian stimulation for ivf (43;117). Chronological age of the woman represents a reasonable ovarian response predictor (404), and other patient charac-teristics associated with ovarian response, such as body weight and smoking have also been identified (25;298). Recently, a prospective multi-center study demonstrated the limited effec-tiveness of a dosing algorithm involving age, body mass index (bmi), antral follicle count (afc) and follicle-stimulation hormone (fsh) concentrations as compared to a standard fsh dose (280). Moreover, certain polymorphisms have been shown to affect the individual ovar-ian responses to hormone stimulation (238;375). Although a shift towards the individualization of stimulation protocols can be observed in recent years, applying current clinical and endocrine parameters has not yet resulted in the successful implementation of a more patient tailored approach. Extreme responses, both in terms of hyporesponse (often resulting in cycle cancellation and no pregnancy chances) or hyperresponse (resulting in decreased pregnancy chances (380), and increased patient dis-comfort and higher chances for ovarian hyperstimulation syndrome [ohss]) may occur fol-lowing standard stimulation regimens (117). Even following mild ovarian stimulation a wide range of ovarian responses is observed ranging from no response (i.e. cancellation; (402)), up to over 20 oocytes being retrieved (401). Moreover, a recent meta-analysis involving all published randomized controlled trials com-paring different gonadotrophin doses demonstrated no clinical benefit of higher doses even in women of more advanced reproductive age (347). The development of more individualized ovarian stimulation protocols seems the only way forward towards further improving the bal-ance between success and risks of ivf treatment. Pharmacogenomics, leading to more patient tailored drug dosing on the basis of an indi-vidual’s genetic make-up, may provide a powerful novel tool to tailor treatments (29;50). For instance, a genome wide pharmacogenomic approach has recently been shown to be useful to predict response of multiple sclerosis patients to interferon beta treatment (50). Further-more, it has already been suggested that pharmacogenomics may aid in the tailoring of ivf treatments to the individual patient (136;238;333). The fsh receptor in particular has been studied extensively in this respect, both in terms of inactivating and activating mutations and polymorphisms (181;247;250;332). In this proof-of-principle study in a relatively small, but homogenous ivf population we applied a genome-wide pharmacogenomic approach aiming to identify a correlation between single-nucleotide polymorphisms (snp) and ovarian response to standard stimulation with recombinant fsh. 2 Methods
2.1 Subjects and sample collectionBetween October 2006 and July 2008, 102 patients were recruited from our standardized preconceptional screening program prior to starting ivf treatment. Approval was obtained from the local ethics review board, and written informed consent was provided by all par-ticipants. In order to limit the influence of non-genetic, clinical characteristics on ovarian response, inclusion criteria were; 1) healthy Caucasian, 2) non-smoking, 3) regularly cycling, 4) aged 38 years or less, and 5) bmi below 30 kg/m2. Blood serum and lymphocytes for dna analysis were obtained from all individuals. For most individuals fsh and amh measure-ments were available from their fertility work-up.
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All women were treated with a standard ovarian stimulation protocol applying 150 iu/day recombinant fsh (Puregon®, Schering Plough, Oss, The Netherlands; or Gonal-F®, Merck Serono, The Hague, The Netherlands), starting on cycle day 2 using GnRH antagonist co-treatment (25 µg/day of Orgalutran®, Schering Plough, or Cetrotide®, Merck Serono), start-ing on cycle day 6. Dose adjustments were allowed after cycle day 6. If three or more fol-licles of at least 17 mm diameter were observed by ultrasound, 10.000 iu of hCG (Pregnyl®, Schering Plough) was administered and 36 hours later transvaginal oocyte retrieval was performed. After fertilization in vitro and 4 days after aspiration of the follicles, 1-2 good quality embryos were transferred (preferably 1). Remaining embryos were cryopreserved for transfer in a subsequent natural cycle. The luteal phase was supported with progesterone vaginal pessaries for 12 days starting on the evening of oocyte pick up (Utrogestan®, Good-life, The Hague, The Netherlands). A pregnancy test was performed 18 days after oocyte retrieval. If this test was positive, a subsequent ultrasound examination was scheduled at a gestational age of 9 weeks.
2.2 Data analysisIllumina genome wide technology (Illumina, San Diego, ca, us) was used, as described ear-lier (203;383). All cases were genotyped using Illumina Infinium II Human610-Quad Bead-Chips v.1_B October/November 2008 (Illumina, San Diego, ca, us). All experiments were carried out at the Division of Medical Genetics in the umc Groningen according to the man-ufacturer’s protocol. In short, 750 nanogram of dna per sample was whole-genome ampli-fied, fragmented, precipitated and resuspended in the appropriate hybridization buffer. Denaturized samples were then hybridized on Illumina BeadChips at 48°C for a minimum of 16 hours. After hybridization, the BeadChips were processed for single base extension reac-tion and stained. Chips were then imaged using the Illumina Bead Array Reader. After genotyping all samples, the following quality control procedures were employed. Beadstudio version 3.0 was used to call genotypes for each sample using normalized bead intensity data. Samples with overall call rates below 95% were removed. Furthermore, snps with call rates less than 95%, minor allele frequencies (maf) below 5%, or deviations from Hardy-Weinberg equilibrium (hwe) (Exact hwe P-Value <0.001) were removed from subse-quent analyses. To test for a quantitative association, Plink software was used. Significance of associa-tion was determined by using likelihood ratio tests and Wald tests. As over 600,000 tests were performed, correction for multiple testing was done by determining what nominal single snp p-value would correspond to a p = 0.05. A commonly used threshold for deeming a snp association genome-wide significant is p <5 x 10-7 (413), corresponding to a genome-wide significance of p = 0.05 on the Human610-Quad BeadChip. Since this is a preliminary study which is only able to investigate large effects of individual snps, the data were ana-lyzed using both naïve and intelligent approaches. In the naïve approach, likelihood ratio tests were performed with plink software, including adaptive permutation analysis and analysis with adjustment for female age, fsh and amh. Because of a relatively small sample size and consequent ability to detect relatively large contributions of individual snps only, additional analysis was also performed after a dichotomization of the primary outcome at the median of 8 oocytes and by comparing the lower tertile (6 or fewer oocytes obtained) with the upper tertile (11 or more oocytes obtained).
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In the intelligent approach, the influence of gene-gene interactions of known polymor-phisms on the associations found were assessed for the fshr polymorphism Asn680Ser (rs6166) and amh polymorphism (rs10407022). Since signal intensities in the process of correction for multiple testing might be lost in this small sample size, only the influence of certain pathways described on the ovarian kaleidoscope database was assessed (26). To study pathways involved in granulosa cell function, cell cycle regulation and apoptosis, only snps relevant to these pathways within 25kB of genes of interest were included (for a summary of genes, see appendix A).
Table 1Patient characteristics
Patient characteristic Mean ± sd Relation to nr of oocytes (p-value)
Age at screening (years) 33,8 ± 2,7 0.07a
Primary subfertility (%) 57 0.76a
Cause subfertility (%)- Idiopathic- Mild male factor- Tubal pathology- Other
5723137
0.83b
bmi (kg/m2) 22,4 ± 2.7 0.54a
fsh 7,99 ± 2,9 0.02a
amh 2,68 ± 1,9 <0.001a
Duration of stimulation (days) 8,8 ± 1.5 0.90a
Total dose (iu) 1326 ± 219 0.90a
Number of oocytes (n) 8,9 ± 5.4
Number of embryos (n) 3,9 ± 3.2
Pregnancy rate (%) 29
a Spearmann correlationb anova
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3 ResultsIn total, 102 ivf patients were genotyped using Illumina Human610-Quad BeadChips. This population was selected from a total of about 800 women with an indication for ivf. Patient characteristics from the study population are summarized in Table 1. In this homogeneous population, ovarian response ranged from cancelled cycles due to mono-follicular growth or no oocytes being obtained on one end of the spectrum to 24 oocytes being retrieved or cancelled cycles due to hyperresponse (>30 follicles) on the other end (See Figure 1). As expected, the variables used for selecting this homogenous population were not significantly associated with the number of oocytes obtained (see Table 1). Since oocyte yield is known to be influenced by female age, we corrected for this variable in subse-quent analyses. Since fsh and amh were related to oocyte yield, we also corrected for amh and fsh.
Figure 1Poisson distribution of the number of oocytes retrieved with normal curve.
Sample quality control resulted in the exclusion of ten cases from analysis because their call rates fell below 95%. These 10 cases did not significantly differ from the total group. After comparing the remaining 92 cases, no related individuals were identified. A total of 454,102 snps passed our quality control (Exact hwe p-value >0.001, maf >0.05, call rates >95%). After correction for multiple testing, no snps were observed to be significantly correlated to ovarian response in the naïve approach (Figure 2). Permutation analysis, dichotomization of the outcome and adjustment for age, fsh and amh did not change the results (data not shown). The few snps showing a possible trend towards significance are shown in Table 2. In the intelligent approach, the influence of gene-gene interactions and selection of snps involved in pathways regulating granulosa cell function, cell cycle regulation or apoptosis did not yield statistically significant associations with the number of oocytes obtained.
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Table 2
Possible SNPs involved in ovarian response
snp Chromosome p-value* b/ Odds Ratio se/number of permutations
Nearest gene
rs1885678 6 1,00e-06 17.93 1 000 000 rab32: GTPase: mitochondrial dynamics
rs9403799 6 2,00e-06 17.76 1 000 000 rab32
rs4499783 5 2.62e-006 3.724 0.7457 mast4: protein phosphorylation
rs8025763 15 2,00e-06 25.62 1 000 000 arrdc4: unknown
rs2271463 10 7,00e-06 24.3 1 000 000 cubn: Intrinsic
Factor-Cobala-
min Receptor
Candidate SNPs involved in ovarian response
rs6166 2 0,75 -0.2673 0.8191 fshr
rs2234693 na esr1
rs928554 6 0,78 -0.2825 1.019 esr2
rs10407022 19 0,007 -3.342 1.211 amh
rs2002555 na amhr
* p-values > 5*10-7 are not statistically significant.na: not available on the Illumina Infinium II Human610-Quad BeadChip.
Figure 2Quantile-Quantile plot of observed versus expected p-values of the 10.000 most significant SNPs, with refer-ence line.
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4 Discussion In this preliminary study in a homogeneous Caucasian population, we were unable to identify snps significantly associated with oocyte yield after ovarian stimulation for ivf. We have con-firmed that ovarian response to stimulation varies widely, even in this relatively homogenous and young patient group. The finding that no single snp is clearly associated with oocyte yield in the current preliminary study suggests that individual genetic variation may not represent a major determinant for the variability in ovarian response to stimulation. To our knowledge, this is the first genome wide approach to study pharmacogenomic influences in ovarian stimulation. These results might suggest that a combination of smaller gene effects (regulating uptake, metabolism and response to the various hormones) could be involved. The limited sample size only permits the identification of genes with strong effects. Moreover, an individual’s ovarian reserve status may also be affected by impaired gametogenesis during early fetal development leading to fewer primordial follicles, inappro-priate follicular atresia or dysfunctional follicular recruitment and maturation (104). Also, gene-gene interactions may further complicate the search for snps possibly associated with ovarian response to stimulation. We suggest that the current study should be repeated in a (much) larger cohort of ivf patients. Previous studies have focused on one or a few polymorphisms in genes known to be involved in ovarian stimulation for ovulation induction or ivf, including fshr, esr1 and esr2, cyp19 aromatase, amh and amhr (6;8;24;68;69;111;137;181;189;234;247;293;352). However, the current study does not support a major role for these biological candidate genes even if we would apply a less stringent p value cut-off. It should be realized that due to cor-rection for multiple testing in a genome wide approach a distinctly higher significance level compared to single gene studies is required (96). The role of genetic factors in ovarian response may be questioned. No data concerning ovarian response are available in twin studies and heritability measurements are unknown. Yet, the role of genetic factors in ovarian response remains extensively studied. The fsh recep-tor gene is by far the most studied gene in relation to ovarian stimulation (137;293). Particular interest has been given to two polymorphisms at codon 307 and 680. Although various stud-ies show significant differences in hormonal markers of ovarian response, a direct relation between the three genotypes and response in terms of oocyte yield has not been established (248). The distribution of fsh receptor polymorphisms has been suggested to be different in women with who type ii anovulatory infertility and a general infertility population, when compared to normal fertile controls (111;234). Recent studies have shown that ovulatory response to treatment in pcos women is associated with polymorphisms in both the fsh receptor and stk11 gene (238;282). Furthermore, the estrogen receptor genes esr1 and esr2 may interact with the fsh receptor gene and when combined in a model, these three genes may partly predict poor response in ivf (68). Next to fsh receptor variants, amh and amh type ii receptor polymorphisms, which are thought to be involved in regulating fsh sensitiv-ity (87), were shown to influence follicular phase estradiol levels in normo-ovulatory women (189). These findings, however, need to be validated in other studies. In conclusion, although evidence suggests that ovarian response is mediated by various polymorphisms, it is unlikely that gene effects represent a significant factor underlying the observed individual variability in ovarian response to stimulation for ivf. Since a genome wide approach requires correction for multiple testing, it may obscure significant findings of genes of smaller effects that would be more readily discriminated through e.g. pathway analyses. To assess more subtle genetic effects, a larger sample size study is warranted to further investigate which genes and pathways could be involved.
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AcknowledgementsWe would like to thank Carolien Boomsma and all other collaborators involved in the pre-conceptional screening of the ivf population at the University Medical Center Utrecht and the Genomics facility of the University Medical Center Groningen for their work in preparing and analyzing the Illumina chips.
No external funding was provided for this study.
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Appendix A
okd# Gene Product Chromosome locationsignal transduction genes in granulosa cells 197 Serum/glucocorticoid-regulated Kinase 6q23 405 erk1, Mitogen-activated Protein Kinase 3 16p11.2 406 erk2, Mitogen-activated Protein Kinase 1 22q11.2 483 Guanylate Cyclase 1, Soluble, Beta-3 4q32 599 Adenylate Cyclase 5 3q13.2-q21 642 Mothers Against Decapentaplegic, Drosophila, Homolog Of, 4 18q21.1 825 Glycogen Synthase Kinase 3-beta 3q13.3
873 Protein kinase A, rii-alpha subunit 3p21.3-p21.2 874 Protein kinase A regulatory, type I 17q23-q24 920 Protein Kinase C, Delta 3p 948 smad3; Mothers Against Decapentaplegic, Drosophila, Homolog Of, 3 15q21-q22 949 Smad-2 18q21 999 V-akt Murine Thymoma Viral Oncogene Homolog 1 14q32.3 1030 Insulin Receptor Substrate 1 2q36 1043 V-jun Avian Sarcoma Virus 17 Oncogene Homolog 1p32-p31 1048 Arrestin, Beta, 1 11q13 1062 Regulator Of G Protein Signaling 2 1q31 1186 Sprouty, Drosophila, Homolog Of, 2 13q31.1 1202 Adenylate Cyclase 7 16q12-q13 1203 Adenylate Cyclase 1 7p13-p12 1204 Adenylate Cyclase 3 2p24-p22 1320 Suppressor of cytokine signaling 2 12q21.3-q23 1338 Protein kinase c, zeta form; prkcz 1p36 1793 pten, Phosphatase And Tensin Homolog 10q23.3 2396 Phosphatidylinositol 3-kinase, catalytic, gamma; pik3cg; pi3 kinase 7q22 2404 Hypoxia-inducible Factor 1, Alpha Subunit 14q21-q24 2514 Protein Kinase, Cgmp-dependent, Type Ii 4q13.1-q21.1 2752 Signal Transducer And Activator Of Transcription 3 17q21
Granulosa cell genes, mutations causing infertility phenotypes 1 Luteinizing Hormone/choriogonadotropin Receptor; lh receptor; lhr 2p21 2 Follicle-stimulating Hormone Receptor 2p21-p16 74 Estrogen Receptor 2 14q22-q24 75 Cytochrome p450, Subfamily Xix 15q21.1 145 Inhibin, Alpha 2q33-q36 154 Prostaglandin E Receptor 2, Ep2 Subtype 14q22 190 Follistatin 5q11.2 209 Frizzled, Drosophila, Homolog Of, 4 11q14-q21 294 Nuclear Receptor Subfamily 0, Group B, Member 1 Xp21.3-p21.2 316 Steroidogenic Acute Regulatory Protein 8p11.2 333 Progesterone Receptor; pgr 11q22 356 sf1, Nuclear Receptor Subfamily 5, Group A, Member 1 9q33 369 Peroxisome Proliferator-activated Receptor-gamma 3p25 969 Bone Morphogenetic Protein Receptor, Type Ib 4q23-q24 971 Activin A Receptor, Type I 2q23-q24 1085 lrh1, Nuclear Receptor Subfamily 5, Group A, Member 2 1q32.1 1297 taf4b rna Polymerase II, Tata Box-Binding Protein- Associated Factor, 105-Kd; taf4b 18q11
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1540 Indian Hedgehog 2q33-q35 1644 Inhibin, Beta A 7p15-p13 2412 Cytochrome p450 Reductase 7q11.2
Apoptosis 999 V-Akt Murine Thymoma Viral Oncogene Homolog 1 akt1 14q32.3 1065 V-Akt Murine Thymoma Viral Oncogene Homolog 2 akt2 19q13.1-q13.2 907 Apoptotic Peptidase Activating Factor 1 apaf1 12q23 106 Ataxia Telangiectasia Mutated atm 11q22-q2 9 bcl2-associated agonist of cell death bad 11q13.1 905 bcl2-associated X protein bax 19q13.3-q13.4
1071 B-cell cll/lymphoma 2 bcl2 18q21.3 166 B-cell cll/lymphoma BclX 20q11 311 Baculoviral iap Repeat-Containing 2 birc2 11q22-q23 1734 Baculoviral iap Repeat-Containing 3 birc3 11q22-q23 307 Baculoviral iap Repeat-Containing 4 birc4 Xq25 661 Calpain 1 capn1 11q13 3268 Calpain 2 capn2 1q41-q42 3267 Caspase 10 casp10 2q33-q34 783 Caspase 6 casp6 4q25-q25 2336 Caspase 7 casp7 10q25.1-q25.2 3266 Caspase 8 casp8 2q33-q34 915 Caspase 9 casp9 1p36 302 Caspase-3 4q35 1432 casp8 and fadd-like apoptosis regulator cflar 2q33-q34 1332 Cytochrome C 317 dna fragmentation factor dffb 1p36.3 3668 Fas (tnfrsf6)-associated via death domain fadd 11q13.3 303 Interleukin 1a il1a 2q14 304 Interleukin 1b il1b 2q14 319 Interleukin 1 receptor il1r1 2q12 3351 Interleukin 3 receptor il3ra Xp22.3 or Yp11.3 2562 Interleukin-1 Receptor-Associated Kinase 1 irak1 Xq28 3784 Interleukin-1 Receptor-Associated Kinase 4 irak4 12q12 3542 Myeloid Differentiation Primary Response Gene myd88 3p22-p21.3 1572 Nuclear Factor Of Kappa Light Polypeptide Gene Enhancer In B-Cells Inhibitor, Alpha nfkbia 14q13 1153 Nerve Growth Factor ngfb 1p13.1 1365 Neurotrophic Tyrosine Kinase, Receptor ntrk1 1q21-q 1566 Nuclear Factor Kappa-B, Subunit 1; nfkb1 4q23-q24 3976 pdcd8 Xq25-q26 2396 phosphatidylinositol 3-Kinase, Catalytic, Gamma; P 7q22 1207 Phosphoinositide-3-Kinase pik3ca, pi3kc 3q26.3 3819 Phosphoinositide-3-Kinase pik3cb 3q22.3 3250 Phosphoinositide-3-Kinase pik3r2 19q13.2-q13.4 3436 Protein Kinase, Camp-Dependent, prkar2b 7q22 874 Protein kinase A regulatory, type I 17q23-q24 873 Protein kinase A, rii-alpha subunit 3p21.3-p21.2 1567 rela; Nuclear Factor Kappa-B, Subunit 3; 11q12-q13 2351 tnf 6p21.3 1711 tnf Receptor-Associated Factor 2; traf2 9q34 2397 Tumor Necrosis Factor Receptor Superfamily tnfrsf10a 8p21
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1462 Tumor Necrosis Factor Receptor Superfamily tnfrsf10b 8p22-p21 4049 Tumor Necrosis Factor Receptor Superfamily tnfrsf10d 8p21 2183 Tumor Necrosis Factor Receptor Superfamily tnfrsf1a 12p13.2 299 Tumor Necrosis Factor Receptor Superfamily tnfrsf6 10q24.1 2398 Tumor Necrosis Factor Superfamily tnfsf10 3q26 312 Tumor Necrosis Factor Superfamily tnfsf6 1q23 31 Tumor Protein p53 tp53 17p13.1 1664 tnfrsf1a-associated via death domain tradd 16q22 1543 Tumor Necrosis Factor Receptor Superfamily, Member 8p22-p21
Cell Cycle 2111 Abelson Murine Leukemia Viral Oncogene Homolog 1; 9q34.1 3058 Anaphase Promoting Complex anapc1 2q12.1 3059 Anaphase Promoting Complex anapc10 4q31 106 Ataxia Telangiectasia Mutated atm 11q22-q23 3865 Ataxia Telangiectasia And Rad3 Related atr 3q22-q24 3061 Budding Uninhibited By Benzimidazoles bub1 2q14 2519 Budding Uninhibited By Benzimidazoles bub1b 15q15 689 Budding Uninhibited By Benzimidazoles bub3 10q26 3525 Cyclin a1 ccna1 13q12.3-q13 934 Cyclin b1 ccnb1 5q12 2316 Cyclin b2 ccnb2 15q22 53 Cyclin d1 ccnd1 11q13 52 Cyclin d2 ccnd2 12p13 54 Cyclin d3 ccnd3 6p21 3215 Cyclin H ccnh 5q13.3-q14 4037 Cell Division Cycle 14 Homolog A cdc14a 1p21 3858 Cell Division Cycle 14 Homolog B cdc14b 9q22.33 1322 Cell Division Cycle 2 cdc2 10q21.1 3093 Cell Division Cycle 20 cdc20 1p34.1 726 Cell Division Cycle 25A cdc25a 3p21 170 Cyclin-Dependent Kinase 4 cdk4 12q14 2126 Cyclin-Dependent Kinase 6 cdk6 7q21-q22 3217 Cyclin-Dependent Kinase 7 cdk7 5q12.1 56 Cyclin-Dependent Kinase Inhibitor 1A cdkn1a 6p21.2 55 Cyclin-Dependent Kinase Inhibitor 1B cdkn1b 12p13 1634 Cell Division Cycle 18, S. Pombe, Homolog-Like; Cd 17q21.3 1470 cell Division Cycle 25b; cdc25b 20p13 1333 cell Division Cycle 25c; cdc25c 5q31 3600 Checkpoint Homolog chek1 11q24-q24 1810 cyclin A2; ccna2 4q27 1323 cyclin-Dependent Kinase 2; cdk2 12q13 1623 cyclin-Dependent Kinase Inhibitor 2a; cdkn2a 9p21 2462 e1a Binding Protein p300 ep300 22q13 2758 Extra Spindle Pole Bodies Homolog 1 espl1 12q 3823 Fizzy/Cell Division Cycle 20 Related 1 fzr1 19p13.3 659 Growth Arrest And dna-Damage-Inducible gadd45 1p34-p12 3761 Growth Arrest And dna-Damage-Inducible gadd45b 19p13.3 3133 Growth Arrest And dna-Damage-Inducible gadd45g 9q22.1-q22.2 2133 Glycogen Synthase Kinase gsk3a 19q13 825 Glycogen Synthase Kinase gsk3b 3q13.3 1172 Histone Deacetylase 1 hdac1 1p34.1
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1173 Histone Deacetylase 2 hdac2 6q21 1739 mad1 Mitotic Arrest Deficient-Like mad1l1 7p22 1035 mad1 Mitotic Arrest Deficient-Like mad2l1 4q27 948 mad1 Mitotic Arrest Deficient-Like madh3 15q21-q22 642 mad1 Mitotic Arrest Deficient-Like madh4 18q21.1 3625 Minichromosome Maintenance Complex mcm2 3q21 3105 Minichromosome Maintenance Complex mcm3 6p12 3647 Minichromosome Maintenance Complex mcm4 8q11.2 3275 Minichromosome Maintenance Complex mcm6 2q21 3599 Minichromosome Maintenance Complex mcm7 7q21.3-q22.1 217 p53 Binding Protein Homolog mdm2 12q14.3-q15
3597 Origin Recognition Complex orc1l 1p32 3644 Origin Recognition Complex orc6l 16q12 1850 Proliferating Cell Nuclear Antigen pcna 20p12 820 Polo-Like Kinase plk 16p12 3637 Polo-Like Kinase plk1 16p12.1 3280 Protein Kinase, dna-Activated, Catalytic Polypeptide prkdc 8q11 387 Pituitary Tumor-Transforming 1 pttg1 5q33 26 Retinoblastoma 1 rb1 13q14.1-q14. 1164 Retinoblastoma-Like 1 rbl1 20q11.2 2013 Ring-Box 1 rbx1 3q21 2753 Stratifin sfn 1p36 949 smad, mothers against dpp Smad-2 18q21 3758 Structural Maintenance Of Chromosomes smc1b 22q13.31 2956 Structural Maintenance Of Chromosomes 1b smc1l1 Xp11.22-p11.21 76 Transforming Growth Factor, Beta 1 tgfb1 19q13.1 3366 Transforming Growth Factor, Beta 2 tgfb2 1q41 2121 Transforming Growth Factor, Beta 3 tgfb3 14q24 31 Tumor Protein p53 tp53 17p13.1 1053 wee1 homolog wee1 11p15.3-p15.1 22 Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein ywhab 20q13.1 3141 Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein ywhaq 2p25.1 3380 Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein ywhaz 8q23.1
okd# Ovarian Kaleidoscope Database number
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Comparison of inter- and intra-cycle variability of Anti-Müllerian Hormone and antral follicle counts.
J. van Disseldorp, C.B. Lambalk, J. Kwee, C.W.N. Looman, M.J.C. Eijkemans, B.C. Fauser, F.J. Broekmans.
Hum Reprod. 2009 In Press.
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1 IntroductionOvarian reserve tests aim to predict outcome of in vitro fertilization (ivf) treatment in terms of poor response and pregnancy. A recent meta-analysis has shown that multivariate mod-els are comparable to single tests like the antral follicle count (afc) in its capacity to predict ovarian response to stimulation for ivf (404). It was concluded that the antral follicle count may be considered the test of first choice when assessing diminished ovarian reserve. Recent research reports that anti-Müllerian hormone (amh) might be at least as good as the afc in predicting response to controlled ovarian hyperstimulation in ivf (44;98;217;219;257;392). A similar accuracy of amh and afc in predicting ovarian response to hyperstimulation in ivf is not surprising, since amh is produced by antral follicles up to the size of 6 mm (410). This follicle size class may well be associated with the antral follicle count (2-5 or 2-10 mm in diameter) on ultrasound. Because of its production already in pre-antral follicle stages, amh is suggested to represent the cohort of primordial follicles better (89;188). With respect to age related antral follicle decline in humans, amh was shown to be a better marker for the change in reproductive status over time, when compared to the afc (392). As a laboratory test, amh may have further advantages since assay variation is well doc-umented, in contrast to the afc (37). Moreover, most studies consider amh to be cycle inde-pendent (160;220;368), although others challenge this (58;428). The afc might be more prone to observer bias and show more variance between cycles in patients (116;318;392). The ideal ovarian reserve test would only need one, preferably cycle independent, mea-surement to represent the ovarian reserve status. This study compares the afc with amh both as to their intercycle variability across four subsequent cycles as well as their stability across a full cycle. For this purpose, the interclass and intraclass correlation coefficients are calculated, which provide an estimate of the variation present both within the same woman and between different women.
2 Subjects and Methods
2.1 Intercycle variationTo assess the intercycle variation for afc and amh we used a study population previously described by Kwee et al. (216). In brief, this study population is part of a prospective ran-domized study on the determination of ovarian reserve conducted at the vu University Med-ical Center, Amsterdam, The Netherlands. In the original study, patients were randomized to undergo a clomiphene citrate challenge test (ccct) or an exogenous fsh ovarian reserve test (efort) in the early follicular phase of four menstrual cycles. From June 1997 to May 1999, 85 patients aged 18-39 years who were eligible for intra-uterine insemination (iui) entered the study. From the original 85 subjects, 77 completed 2 or more cycles which made them eligible for the current analysis. Their infertility was either idiopathic for > 3 years and/or due to a male factor and/or cervical hostility (nega-tive well-timed postcoital test). Patients had to have two ovaries and regular menstrual cycles (between 21 and 35 days with the next cycle predictable within 7 days). Excluded were patients with either polycystic ovary syndrome diagnosed according to the Rotterdam consensus criteria (361) or a severe male factor. Severe male factor was defined as (i) <1x106 motile spermatozoa after Percoll centrifugation (gradient 40/90); and/or (ii) >20% anti-bodies present on the spermatozoa after processing with Percoll centrifugation (gradient 40/90); and/or (iii) >50% of the spermatozoa without an acrosome. Other exclusion criteria
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were untreated or insufficiently corrected endocrinopathies, clinically relevant systemic diseases or a body mass index > 28 kg/m2. During the first three cycles, patients were treated with iui; in the fourth cycle patients underwent an ivf treatment. The ivf treatment followed the first iui treatment within one year. Patients did not use contraceptive pills before iui or ivf treatment. As an integral part of the study, afcs were performed on cycle day 3 of every treatment cycle, before initiating ovarian reserve testing and treatment. All antral follicles of 2-10 mm diameter present in both ovaries were measured by calculating the mean of two perpendicular measurements on an Aloka ssd-1700 with 5.0 MHz probe and counted by the same author (jk) as described previously (215). All data were recorded in the patient file, using a standard form. Also blood was drawn and serum frozen at -80 °C for subsequent per batch measurements of serum amh using an enzyme-immunometric assay (Diagnostic Systems Laboratories, Webster, tx, usa). Inter- and intra-assay coefficients of variation (cvs) were less than 5% at the level of 3 ng/ml and less than 11% at the level of 13 ng/ml. Repeated freezing and thawing of the sam-ples or storage at 37º C for 1 h have been shown not to affect the results of the assay (393). The study protocol was approved by the Ethics Committee of research involving human subjects of the vu University Medical Center, Amsterdam, The Netherlands. Informed con-sent was signed by all the couples participating in the study.
2.2 Intracycle variationTo assess the intracycle variation of the afc, we used a study population previously described by Scheffer et al. (319). A study describing the intracycle variation of amh was recently published using data from the same study population (160). The amh data from this publication were used for comparison purposes only. Briefly, the study was conducted at the University Medical Center Utrecht, The Netherlands. A group of 44 healthy, regularly cycling, fertile, Caucasian female volunteers aged 25–46 years was recruited through adver-tisements in local newspapers. Volunteers were enrolled in the study protocol if they met all of the following criteria: (1) regular menstrual cycles, with mean length varying from 21 to 35 days; (2) biphasic basal body temperature; (3) proven natural fertility by having carried at least one pregnancy to term; (4) each of the pregnancies established within 1 year after the interruption of contraceptive methods; (5) no evidence of endocrine disease; (6) no history of ovarian surgery; (7) no ovarian abnormalities, as assessed by vaginal ultrasound; and (8) cessation of hormonal contraception 2 months before entering the study protocol. Serial transvaginal ultrasound scans were performed by the same observer with a 7.5-MHz transvaginal probe on a Toshiba Capasee ssa-220a (Toshiba Medical Systems Europe bv, Zoetermeer, the Netherlands) as described previously (319). Measuring and counting follicles 2-10 mm was started in the midluteal phase of the first study cycle. The luteal phase was assumed to have started when a temperature rise on the bbt chart, based on classical criteria (425), had been observed. From the seventh day after the temperature shift onward, the volunteers visited the clinic every 2 or 3 days for antral follicle measurement and blood sampling until the occurrence of the subsequent ovulation. Ovulation was registered by daily ultrasound scans for at least 4 days when the dominant follicle had reached a mean diameter of at least 14 mm. Ovulation day was defined as the day at which a complete disap-pearance of the follicle or a reduction of its mean diameter by at least 5 mm was observed (52;173). The Institutional Review Board approved the study, and written informed consent was obtained from all participants. The volunteers received monetary compensation for participating. Inter- and intra-observer variation of the afc were found to be low with inter- and intraclass coefficients between 0.98-0.99, indicating high reproducibility (318).
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2.3 Statistical analysis For the analysis of intercycle fluctuations, we visualized the available amh and afc values per cycle in boxplots (Figure 1) and analyzed if they differed significantly using repeated measures anova. To assess within-subject reproducibility of the afc and amh results we calculated the intraclass correlation coefficient (icc) and its 95% confidence intervals (330). We chose to calculate intraclass correlation coefficients, since they distinguish between variation within the same woman and between individual women. We adjusted the icc for woman’s age using bootstrap procedure with 2000 replications, since amh and afc decline are age-dependent. For the analysis of intracycle fluctuations, we defined seven cycle phases as a range of days counted from either menstruation (M) or from the ultrasound assessed ovulation (O) day in cycle 2 as described earlier (160). The seven cycle phases were defined as follows: mid luteal: M-9 to M-5; late luteal: M-4 to M-1; early follicular: M to M+4; mid follicular: O-9 to O-6; late follicular: O-5 to O-2; peri-ovulation: O-1 to O+1; and early luteal O+2 to O+4. To visualize the intracycle variability of amh and afc, all available amh and afc values per cycle phase were averaged and box plots were constructed using these data (Figure 2). We again calculated the age-adjusted intraclass correlation coefficient (icc) and its 95% confidence intervals to assess within-subject reproducibility of the afc and amh results. The relationship between two different continuous variables was assessed by correlation coefficient. As the size classes of follicles may affect their clinical significance (144;296), all intra-cycle variability analyses were done for afc 2-5 mm as well as for afc 2-10 mm. For the intracycle variability, we evaluated the size of the effect of intra-individual fluc-tuations by classifying values of the afc in five quintiles and registering how often two paired measurements of an individual were located in the same quintile, in adjacent quin-tiles or in non-adjacent quintiles. The cut-off levels used for the analysis of quintile catego-ries for afc 2-5 mm were 0.5, 2, 4, 6, 9 and 37 respectively (corresponding to 0%, 20%, 40%, 60%, 80%, 100%). Cut-off levels for afc 2-10 mm were 0.5, 3, 6, 9, 12 and 43. Statistical analyses were performed by using the linear mixed-effects model in SPlus (version 6.0; Mathsoft Inc., Seattle, wa) and with spss version 15.1 (spss Inc., Chicago, IL, usa).
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3 Results The 77 women eligible for the intercycle variation analysis had a median age of 33 (24-40) years and completed on average 3.73 cycles (83% completed 3 cycles, 77% completed 4 cycles). The afc and amh levels in the population varied between 0 and 25 follicles (median 10 follicles) and between 0.3 and 27.1 ng/ml (median 4.64 ng/ml), respectively. The mean amh and afc levels per cycle were not statistically different over the four consecutive cycles and ranged between 5.7 – 6.0 ng/ml and 9.1 – 10.4 follicles, respectively (Figure 1).
Figure 1Boxplots depicting distribution of Anti-Müllerian Hormone levels (in ng/ml) and antral follicle counts (of all follicles 2-10mm in both ovaries) in the early follicular phase across four cycles.
amh correlated positively and significantly with the afc with correlation coefficients of 0.70, 0.66, 0.68 and 0.63 in each of the four respective cycles (p<0.001 for all 4 cycles). The age adjusted intraclass correlation coefficients (icc) for amh and afc across 4 cycles were 0.89 (95%CI 0.84-0.94) and 0.71 (95%CI 0.63-0.77), respectively. The difference was shown to be statistically significant (iccdiff 0.18 (95% CI 0.12-0.27). In other words, since 89% (95%CI 84-94%) of the variation in amh can be attributed to between subject variation, only 11% (95%CI 6-16%) is true individual cycle fluctuation. For the afc, 71% (95%CI 63-77%) of the variation can be attributed to between subject variation and 29% (95%CI 23-37%) is individual cycle variation. The 44 females volunteering in the intracycle variation analysis had a median age of 38.3 year (25.6-46.2 year), a median afc 2-5 mm of 5 follicles, (range 0 – 37 follicles) and a median afc 2-10 mm of 7 follicles (range 0 – 43 follicles). On average, 9.4 (range 5-16) antral follicle counts were made per volunteer. The correlation of the afcs between the dif-ferent cycle phases was modest, with a correlation coefficient of 0.43 and 0.58 for afc 2-5 and 2-10 mm, respectively (See Figure 2).
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Figure 2Distribution of antral follicle counts 2-5 mm and antral follicle counts 2-10 mm with corre-lation coefficients across the 7 cycle phases of 0.43 and 0.58, respectively. Cycle phases are depicted as (1) early follicular (n=58), (2) midfollicular (n=32), (3) late follicular (n=81), (4) peri-ovulatory (n=106), (5) early luteal (n=8), (6) midluteal (n=42) and (7) late luteal (n=66). Mean afcs did not differ significantly between cycle phases. Please note that the variation in the early luteal phase (5) is quite large due to few observations, with over-representation of younger volunteers. The amh distribution previously published is pre-sented for comparison (Reprint with permis-sion from Hehenkamp (160)).
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The age adjusted intraclass correlation coefficients for afc across the seven cycle phases were 0.66 (95%CI 0.41-0.82) and 0.69 (95%CI 0.46-0.82) for afc 2-5 and afc 2-10 mm, respectively. In other words, respectively 66% (95%CI 41-82%) and 69% (95%CI 46-82%) of afc variation can be attributed to between subject variation. Within the same subject afcs varied 34% (95%CI 18-59%) and 31% (95%CI 18-54%), for afc 2-5 mm and afc 2-10 mm respectively. In the analysis of the clinical effect of intra-individual cycle fluctuations it appeared that (Table 1) the paired afc’s were located in the same quintile in 41% and 45% of the cases for the afc 2-5 mm and afc 2-10 mm, respectively. Paired measurements of afcs 2-5 mm and 2-10 mm appeared divided over two or more quintiles in 21% and 16% of the cases, respec-tively. To compare the intracycle variability of afc with amh, we have added the previously published boxplot depicting amh levels over the seven cycle phases in figure 2 (160). From these data, we have calculated the intraclass correlation coefficient for amh (ICC 0.87 (95% CI 0.82-0.91), demonstrating 13% (95%CI 9-18%) within-subject variation. The difference in icc was shown to be statistically significant both for the afc 2-5mm (iccdiff 0.21 (95% ci 0.046-0.47)), as well as for the afc 2-10mm (iccdiff 0.18 (95% ci 0.034-0.42)) Further-more, for amh, intra-individual fluctuations were shown to fall in the same quintile in 72% of the cases and to skip 2 quintiles only in 1% of the cases (160).
Table 1Intra-individual fluctuations of the afc, showing the percentage of random paired measurements that fall in the same (0), adjacent (1) or non-adjacent (2-4) quintile categories.
Quintile 0 1 2 3 4
afc 2-5 mm 41.2% 38.3% 13.0% 5.5% 2.0%
afc 2-10 mm 45.5% 38.7% 12.3% 2.6% 1.0%
4 Discussion This study describes both the intra- and interindividual variability of amh and the afc across a number of up to four consecutive cycles. Although both amh and afc on group level did not differ significantly between the various cycles, the intra-individual variation in amh levels appeared significantly smaller than for the afc. These findings are consis-tent with a previous comparative study (116), where intraclass correlation coefficients for amh and afc across 3 cycles were 0.89 (95%CI: 0.83 – 0.94) and 0.73 (95%CI: 0.66 – 0.86), respectively. Our validation across four cycles renders these results even more robust. Oth-ers (257;350) studied the intercycle variability of amh across two cycles and concluded inde-pendently that amh varies little between cycles. Intercycle variability of afc alone has also been studied previously and was shown to be moderate in most studies (17;100;149;319). During the four menstrual cycles studied, amh levels showed a positive correlation with afc levels, indicating that a substantial proportion of antral follicles must contrib-ute to amh serum levels (75;115;393). Moreover, coefficients of correlation were reasonably constant over the four cycles ranging from 0.63-0.70, which is in concordance with the cur-rent concept that amh is produced by a steady pool of small (pre)antral follicles (14;410). Not all patients completed 4 cycles. All patients that dropped out after cycle 2 or 3 (18
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out of 77) were pregnant. Age, amh and afc were not significantly different between those that dropped out or completed 4 cycles. Because of this, we believe that the influence on the final result is small. A possible explanation for the higher variability of afc is the reproducibility and stan-dardization of the afc itself (37). Jayaprakasan et al. have shown that the 2D real-time ultra-sound method does not generate significant differences in antral follicle counts compared to 3D ultrasound post hoc processing. However, 3D ultrasound may allow for improvement in measurement reproducibility (176;178). Moreover, intra- and interclass correlation coef-ficients found for intra- and interobserver variation varied between 0.96-0.99 for 2D and 3D afc measurements (176;318). However, limits of agreement in intra- and interobserver vari-ation were wide, exceeding the variation commonly found in amh assays, as reported in the methods section. Since all afc measurements were made by the same experienced observer on the same machine in both studies, observer variation in the present data thereby has been minimized. With expected increase in variation among different observers the varia-tion in the afc may further increase. The higher stability of amh measurements may also be explained by assuming that amh levels are also determined by a cohort of invisible pre-antral or small antral follicles, while the number of larger and visible antral follicles, expressed by the afc, may be more prone to short term variation. Possible explanations for a varying cohort of antral follicles might be cyclic differences in decay or growth rate which may depend on the presence of larger follicles in the early follicular phase (142;150).
For the whole study population, the antral follicle count did not differ significantly between the various cycle phases. However, intra-individual variation appeared to be moderate as demonstrated by the intraclass correlation coefficient, determining individual intracycle consistency of afc. Compared to the intra-individual, intracycle variation of amh, calcu-lated from data previously published (160), the afc was indeed less consistent. This dem-onstrates that amh has superior performance compared to both afc size classes, when individual cycle consistency is concerned. The intracycle stability of amh has already been described in several studies (58;160;219;368;428). Most of these studies found very stable levels of amh throughout the cycle, except two studies, who found a late follicular or peri-ovulatory rise in amh (58;428). An obvious explanation for this difference, like study setup or patient age could not justify this difference. Our relative (non-significant) afc and amh rise (figure 2) in the early luteal phase is caused by relatively few measurements in young individuals (160). The intracycle variability of afc has not been extensively studied before (285). For the first time this feature has now been analyzed for two size classes of afc over seven cycle phases. The boxplots in Figure 2 show modest intracycle differences in mean afc and intraclass correlation coef-ficients demonstrate that this variation is primarily based on inter-individual differences. In agreement with the variation depicted in Figure 2, multilevel analysis of variance failed to show large differences between the seven cycle phases (unpublished data). This suggests that the intracycle fluctuations of afc may be of limited biological importance and may be considered chance findings or caused by observer bias, as discussed earlier. For the purpose of clinical application as a test for ovarian reserve, we analyzed the amount of quintile transgression. Only 41.2% and 45.5% of afcs 2-5 and 2-10 mm, respec-tively, remained in the same quintile, while 21%, respectively 16% crossed 2 or more quin-tiles. In a recent study, amh was shown to cross 2 or more quintiles in only 1% of the cases
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(160). This quintile transgression is probably due to the large deviations from the mean afc per cycle phase, caused by high amounts of variation in patients, particularly at low afcs. This difference in quintile transgression between amh and afc, together with the lower intra-individual intracycle and intercycle variability of amh, indicates that amh may be the better cycle independent test for prediction of ovarian reserve. Assessed irrespective of the cycle phases, misclassification in relevant diagnostic categories for amh seems unlikely. This has already been shown in clinical studies in art populations (219). Obviously, this study was not designed to study the performance of amh and afc to predict poor response or pregnancy. Future prospective studies specifically designed for this purpose are needed to test this hypothesis. As for the afc, a switch towards random chosen moments of ultra-sound based counting of antral follicles seems not justified, especially in view of the moder-ate consistency even at lower counts. When comparing intercycle with intracycle variation of amh, a recent study found the intercycle variability of amh with 28% (95%CI -23.2%-80.3%) to be larger (350) than the amplitude of intracycle amh fluctuations from previous studies, which all fell below this 28% (160;349;428). This suggests that in clinical practice, amh can be measured cycle independently, as shown by La Marca et al. (219). However, caution seems justified when amh is used for estimating ovarian reserve status in young women, as fluctuations may be more considerable and misclassifications more likely (160). Since our two populations may have different profiles regarding their intrinsic fertility level, we refrained from compar-ing intracycle and intercycle variation. Also, amh levels may vary according to the cause of subfertility, especially for endometriosis and pcos cases (7;112;295). However, whether the cycle variability of amh and afc differs for the various causes of subfertility is cur-rently unknown. Our conclusions regarding intracycle variation in a fertile population can be extrapolated to an infertility population, because we corrected for female age. It is likely that in infertility populations ovarian reserve may be more often compromised, leading to lower follicle counts and amh levels within similar age classes. No obvious reasoning would support the idea that low counts in subfertile women would have different variation compared to low counts or levels in normal fertile counterparts. There is still substantial variation in literature concerning the size of antral follicles to be measured and used for clinical decision making. Current data indicate that the afc 2-5 mm shows more fluctuation than the afc 2-10 mm. Moreover, the afc 2-5 mm showed substantially more quintile transgression than afc 2-10 mm. A possible explanation might be that the accuracy of follicular measurements has been shown to increase with follicular diameter (285). Therefore, we advocate the use of the afc 2-10 mm over the afc 2-5 mm. Earlier studies regarding size classes of follicles have shown some conflicting results. In one study the follicle size class 2-6 mm appeared clearly related to female age, while the follicle size class 7-10 mm did not (144). In another study the follicle size class 6-10 mm correlated very clearly with oocyte yield after ivf which is highly correlated with age, while the follicle size class 2-5 mm did not (296). Future research should focus on improving reliable measurement techniques like oper-ator independent automated afcs (77). Furthermore, the performance of random afcs to classify expected poor responders or hyperresponders should also be investigated. Finally, daily measurements of amh and afc, aligned with the lh peak, could give more insight in subtle fluctuations within the menstrual cycle. This might show that possibly only younger women exhibit relevant cycle variation. In conclusion, although afc and amh do not seem to differ between and within cycles
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at first sight (figure 1 and 2), we demonstrated that amh displays less intra-individual fluc-tuation than the afc both within and between cycles. This suggests amh to be the better cycle-independent parameter to assess ovarian reserve, but future prospective studies spe-cifically designed for this purpose are needed to confirm this hypothesis. If he afc is used for screening ovarian reserve, it is advocated to count follicles in the size range 2-10 mm, in view of better cycle stability. Previous studies showed intra- and interobserver variations to be small for afc, but with wide limits of agreement, exceeding the variation commonly found in amh assays.
Cumulative live birth rates following IVF in 41-43 year old women presenting with favorable ovarian reserve characteristics.
J. van Disseldorp, M.J.C. Eijkemans, E.R. Klinkert, E.R. te Velde, B.C. Fauser, F.J.M. Broekmans.
Reprod Biomed Online. 2007 Apr;14(4):455-63.
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1 IntroductionThe average female age at first childbirth in the Netherlands, like other western countries, has increased rapidly over the last decades (373). This postponed desire to conceive has resulted in a rise in age specific infertility rates, as it has been shown that fertility decreases notably after a woman’s 31st birthday (369). Decreasing fertility is thought to result from ovarian ageing. Ovarian ageing is mediated through a decrease in quantity and quality of oocytes, which reduces the monthly chance of conception and leads to an increased rate of miscarriage (3). Ovarian ageing ultimately leads to a complete loss of fertility at a mean age of 41, but shows a major variability between women (357). Hence, female age has a major impact on the management of infertile couples, as with increasing age success rates of art decline (277). Therefore, ivf/icsi treatment in most centers in the Netherlands has been restricted to couples of which the woman is aged under 41 (90). However, female age may not always accurately represent a woman’s reproductive capacity due to the inter-individ-ual variability in the ovarian ageing process. Among women beyond 40 years of age, cases with acceptable chances for pregnancy may be identified. In our hospital infertile couples of which the woman is 41-43 years of age are admitted for ivf when they are expected to respond normally to ovarian hyperstimulation and have reasonable chances for pregnancy. Cases selected upon initial screening by means of the antral follicle count (afc) and basal fsh are expected to make a series of ivf cycles worthwhile (40;200). Chances that women of 44 years and over conceive have been estimated to be almost zero, and therefore these couples are excluded from ivf (40). In this retrospective study we aimed to evaluate our current policy of allowing women in the age group of 41-43 years to partake in ivf, selected on the basis of the afc and basal fsh. We performed an analysis of the cumulative live birth rate in a series of three cycles and the associated medical costs.
2 Materials and Methods
2.1 PatientsIn our university hospital all women aged 41 up to and including 43 years of age with regu-lar menstrual cycles and with an ivf/icsi indication are screened for ovarian reserve status by performing an afc and an assessment of early follicular phase fsh. Previous research has shown that an antral follicle count below 5 is an adequate single predictor of poor ivf/icsi outcome (16;17;212). A cut-off of 15 iu/l was used for the fsh assessment, as published previously (16;19;244). The menstrual cycle was considered regular when the mean cycle length varied between 21-35 days and the next cycle was predictable within 7 days (394). A total motile sperm count below 20 million was judged as male factor indication for ivf. Tubal pathology was diagnosed by means of history taking, Chlamydia antibodies and the application of hysterosalpingography and/or laparoscopy. For the setup of the study group, information on all women who underwent an afc prior to initiation of ivf treatment between March 2003 and August 2005 (n = 211) was obtained from their medical files. Cases undergoing ivf in other hospitals (n=5), with female age outside the range of 41 - 43 (n=24), referred for second opinions (n=2), with duration of infertility < 1 year (n=33), with unknown birth date or selection parameters (n=5), without partner (n=1), with a polycystic ovary syndrome (pcos) (n=1) or with oocyte donation (n=2) were excluded (total of n=67). This leaves 144 patients who could serve as the study group to answer our research question (figure 1).
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Figure 1Patient flow diagram
Only cases with an afc of 5 or more, basal fsh <15 iu/l and regular cycles were admitted for ivf/icsi, as they were expected to respond normally to ovarian hyperstimulation (number of expected oocytes ≥4) and to have a favorable prognosis for ongoing pregnancy and live birth, as shown by Ciray et al (56). If during the first ivf/icsi cycle a poor ovarian response to ovarian hyperstimulation was observed (≤2 growing follicles, or ≤3 oocytes after follicle aspiration), they were refrained from initiating a second cycle (16). Previous studies have shown that manifest poor responders are likely to exhibit signs of ovarian ageing, reach menopause early and have a poor prognosis for pregnancy (22;65;236). Cases that did not fulfill the admission criteria were not allowed to enter the ivf/icsi program, but baseline data were recorded for comparison.
2.2 Treatment protocol for ivf/icsi In our hospital all women were treated with a long-suppression protocol, details of which have been published earlier (387). The menstrual cycle was suppressed by starting Leupro-reline (Lucrin®) in the midluteal phase of the preceding cycle or after at least 10 days of oral contraception use. After the onset of subsequent menses or the onset of oral contraception withdrawal bleeding, ovarian hyperstimulation was started with follitropin (Puregon®, Organon, Oss, The Netherlands; or Gonal-F®, Serono Benelux, The Hague, The Nether-lands) with a daily dose of at least 100 or 150 iu, respectively. If one or more dominant follicles of at least 18 mm diameter were observed at ultrasound, 10.000 iu of hCG (Preg-nyl®, Organon) was administered and 36 hours later transvaginal oocyte retrieval was per-formed. After in vitro fertilization and 3-4 days after the follicle aspiration, 3 embryos were transferred if available, if not, a lower number of embryos was transferred. If more than 3 embryos were available for transfer, the remaining were cryopreserved for transfer in a later natural cycle. The luteal phase was supported with 3 doses of 5000 iu hCG (Pregnyl®). 18 days after oocyte retrieval a pregnancy test was done. If this test was positive, subsequent ultrasound examinations were scheduled at a gestational age of 7 and 11 weeks.
All afc recordings (n = 211)
Immediate drop-outs (n = 67)- ivf in other hospital- age <41 or >44 year- duration of infert. <1 year- 2nd opinion/pco syndrome- not retraceable/no partner- oocyte donation- unknown selection parameters
Admitted for ivf (n = 89)- afc ≥5- and fsh <15 iu/l - and reg. menstrual cycle- infertility >12 months- infertility work-up complete
Not admitted for ivf (n = 55)- afc <5- and/or fsh ≥15 iu/l- and/or irreg. menstrual cycle- spont. pregnancy (3)
ivf treatment not started (n = 23)- reason unknown (19)- spont. pregnancy (4)
ivf treatment started: (n = 66)- 20 pregnancies - ivf live birth (10) - ivf miscarriage (8) - spont. pregnancy (2)
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2.3 Ovarian reserve testsTransvaginal sonography of the ovaries was carried out on either cycle day 2, 3 or 4. All sonography measurements were performed using the 7.5 MHz transvaginal probe on a Volu-son 530D (Kretz Technik, Zipf, Austria). Images were stored and printed in case recounts were necessary. Examination of the ovary was established by scanning from the outer to the inner margin (285;395). All visible follicles were measured and counted in each ovary. Follicle size was calculated from 2 perpendicular measurements. The sum of all antral fol-licles measuring 2-5 mm was the antral follicle count as addressed in earlier research from our fertility centre (17;42;167). Basal fsh level was measured from blood samples obtained on cycle day 2, 3 or 4, using the automated immunometric fsh assay by Chiron Diagnostics (Tarrytown, ny) on the automated acs 180 immunoassay platform (Bayer, Tarrytown, ny). The inter-assay varia-tion was 3.9% at both 5.5 iu/l and 26 iu/l. The assay is calibrated according to the who Second International Reference Preparation for human fsh. Clinical pregnancies were recorded if a gestational sac was visible at the first ultrasound at 7 weeks of gestational age. We recorded live births when the pregnancies resulted in live born children.
2.4 Statistical AnalysisAll patient information was entered into the statistical program spss (Statistical Package for Social Sciences) version 12.01. The baseline data of the group admitted and the group denied ivf, as well as the data from other subgroups, were compared using chi square tests for categorical data and Mann-Whitney U tests for continuous variables. A p-value of 0.05 was considered statistically significant. Cumulative pregnancy rates were calculated using an adapted Kaplan-Meier method that gives a more accurate estimate of the cumulative pregnancy rate (201). This adapted Kaplan-Meier survival analysis uses a pessimistic and optimistic calculation. The pessi-mistic approach assumes all drop-outs to have a zero chance of becoming pregnant. The optimistic approach assumes that all drop-outs have the same chances of becoming preg-nant as those that completed treatment. Drop-outs are those patients that did not become pregnant (either spontaneous or through ivf) in the previous cycle and did not participate in the next cycle. The true cumulative pregnancy rate will lie somewhere in between these values. In practice, however, the true rate will be close to the pessimistic approach, since it has been shown that women in this age group who stop treatment even for other than medical reasons do not have equal chances of becoming pregnant as those who complete treatment (327). In our hospital the medical costs per ivf cycle are about €3500. This figure is based on a structured cost analysis as published elsewhere (162). It includes all visits, technical pro-cedures, laboratory results and medication. Other associated costs like genetic testing and costs of delivery or complications were not taken into account. Using the cumulative live birth rate, the total treatment costs per child delivered were calculated.
3 ResultsWe studied 144 couples with a female age of 41-43 years and an afc performed as part of their ovarian reserve status assessment before the ivf/icsi procedure. Eighty-nine had an afc of 5 or more, a normal basal fsh (<15 iu/l) and a regular menstrual cycle and were consequently admitted. Fifty-five patients were excluded based on their afc or basal fsh
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result or both, or because of cycle irregularity (see flow chart in Figure 1). Table 1 shows that the baseline characteristics of those admitted were quite similar to those not admitted. However, the patients admitted for ivf had a slightly different distribution of indications for ivf (p = 0.03). Women with explaining causes for their infertility (tubal pathology and male factor) were favored for admittance.
Table 1Baseline characteristics of the study population (n=144)
Admitted for ivf (n = 89) Not admitted for ivf (n = 55) p-value
Age at entry (year) 42.2 +/- 0.8 42.3 +/- 0.8 0.19a
Duration of infertility (months) 38.3 +/- 31.1 29.7 +/- 23.2 0.07a
Parity 0.5 +/- 1.0 0.6 +/- 1.0 0.57a
Infertility:*
- primary
- secondary
41 ( 46.1%)
48 ( 53.9%)
17 ( 31.5%)
37 ( 68.5%)
0.09b
IVF indication:*
- Tubal pathology
- Idiopathic
- Male subfertility
- Other
10 ( 11.2%)
46 ( 51.7%)
30 ( 33.7%)
3 ( 3.3%)
3 ( 5.6%)
32 ( 59.3%)
11 ( 20.4%)
8 ( 14.8%)
0.03b
Cycle-length 27.8 +/- 2.7 26.8 +/- 2.5 0.11a
fsh on day 3 (iu/l) 7.6 +/- 2.3 12.5 +/- 8.5 n/a
afc 8.1 +/- 4.1 2.7 +/- 1.5 n/a
Age male 41.9 +/- 6.2 41.3 +/- 6.5 0.67a
tmc (*106)** 75.5 +/- 90.2 89.4 +/- 99.0 0.84a
Drop-out reason***
- Low response 1st cycle
- unknown
- afc <5
- fsh >15
- oligo-menorrhoea
14 ( 43.7%)
18 ( 56.3%)
-
-
51 ( 92.7%)
14 ( 25.5%)
4 ( 7.3%)
Follow-up (year) 1.50 +/- 0.78 -
values are means (+/- standard deviation), or numbers (percentages)a Mann-Whitney U; b Chi square testn/a not applicable, selection parameters* in the not-admitted group, information from 1 patient is missing** tmc: total motile count: volume*concentration*progressive motility*** those who started ivf, but did not complete 3 cycles or became pregnant (n = 32) or those not allowed treatment (n = 55).
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From the people admitted to ivf treatment (n = 89), 23 did not start treatment for largely unknown reasons, while 4 got pregnant spontaneously (Figure 1). Couples that were admit-ted and actually started (n=66) were followed up for an average period of 1.5 year after their entry into the program. A total of 107 ivf cycles, 18 icsi cycles and 11 cryocycles were performed in this group. Since the cryocycles did not yield clinical pregnancies or live born children, they were left out of further calculations to yield a more homogeneous group where one follicular aspiration resulted in one embryotransfer. In this group of 66, two patients achieved a spontaneous clinical pregnancy between ivf cycles. Thirty-two women did not complete the maximum of 3 cycles, for instance because they showed a poor response in their first cycle (n = 14; 21% of the started group) and subsequently were excluded according to protocol. The 125 treatment cycles resulted in 18 clinical pregnancies (14.4% per cycle) (table 2). From these 18 pregnancies, 10 (8.0% per cycle) were live births and 8 were miscarriages (44.4% of all pregnancies). Table 2 shows that most live births were achieved in cycle 1 and 2; pregnancy numbers in the third cycle were lower.
Table 2Listing of number of cases at risk of pregnancy in three consecutive treatment cycles of ivf and the num-ber of pregnancies and drop outs after every treatment cycle. Numbers and percentages are projected according to a pessimistic and optimistic scenario of cumulative pregnancy.
Cycle 1 Cycle 2 Cycle 3
No. at risk
- Optimistica
- Pessimistic
66
66
40
60
19
56
No. of live births 6 4 0
No. of clinical (+ spontaneousb) pregnancies 8 ( +1) 5 ( +1) 5
No. of miscarriages 3 ( 33%) 2 ( 33%) 5 (100%)
No. of drop outsc 17 15 -
lbr/cycle (%)
- Optimistic
- Pessimistic
9.1
9.1
10.0
6.7
0
0
clbr (%, 95%ci)
- Optimistic
- Pessimistic
9.1 ( 2-16)
9.1 ( 2-16)
18.4 ( 8-29)
15.4 ( 6-24)
18.4 ( 8-29)
15.4 ( 6-24)
lbr/cycle (%): live birth rate per cycle (observed) clbr: cumulative live birth rate (predicted), values in parentheses are 95% ci.a The pessimistic approach assumes all drop-outs to have a zero chance of becoming pregnant. The optimistic approach assumes that all drop-outs have the same chances of becoming pregnant as those that completed treatment (348).b The two spontaneous pregnancies were not regarded as drop-outs and are not included in the live birth analysis.c The number of patients that did not become pregnant in the previous cycle and did not participate in the next cycle (n = 32).
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Analysis showed no significant baseline differences between those that dropped out and those that completed treatment. However, drop-out analysis showed that there were sig-nificant differences in ivf treatment outcomes (number of oocytes, embryos) between those that dropped out and those that completed treatment (table 3). These differences were expected since the majority of the drop outs had a poor response in their first cycle. The percentage of poor responders that was denied further treatment is consistent with current literature (40). Women who delivered live born children appeared to have a better antral fol-licle count and longer cycle length compared to their counterparts who did not deliver live born children and to produce a significantly higher number of dividing embryos (table 3).
Table 3Subgroup comparisons for drop out and prognosis analysis.
Drop-out analysis after cycle 1 Pregnancy outcome analysis
cycle 1
Drop-outsa
n = 17
Started 2nd
cycleb
n = 40
Live birthc
n = 7
No live birth
n = 59
Age (year) 42.3 +/- 0.9 42.1 +/- 0.9 42.6 +/- 1.0 42.2 +/- 0.8
Duration of infertility (months) 29.9 +/- 30.3 42.4 +/- 33.3 36.4 +/- 27.6 38.3 +/- 30.4
Parity (n) 0.5 +/- 1.7 0.5 +/- 0.8 0.4 +/- 0.8 0.5 +/- 1.2
Cycle length (days) 27.6 +/- 2.7 27.2 +/- 2.0 29.1* +/- 2.0 27.4* +/- 2.2
fsh day 3 (iu/l) 7.7 +/- 5.2 7.7 +/- 2.4 6.9 +/- 1.8 7.6 +/- 2.3
afc (n) 7.6 +/- 6.0 7.6 +/- 2.4 11.1* +/- 5.4 7.8* +/- 3.1
tmc (n*106)d 63.4 +/- 98.5 87.6 +/- 113.7 79.4 +/- 100.6 84.8 +/- 101.7
Age male (year) 42.2 +/- 7.0 42.1 +/- 5.1 42.9 +/- 7.3 41.6 +/- 5.3
Indication
- Primary
- Secondary
47%
53%
47%
53%
43%
57%
49%
51%
fsh dose (iu/l) 253.1 +/- 74.4 222.3 +/- 86.3 196.4 +/- 79.6 232.4 +/- 81.4
E2 max (pmol/l) 3524.3*
+/- 3151.4
4818.3*
+/- 2680.9
6007.8
+/- 3415.5
4315.4
+/- 2816.0
Oocytes (n) 3.7* +/- 3.5 7.0* +/- 3.8 6.1 +/- 2.8 5.8 +/- 3.8
Embryos (n) 1.6* +/- 2.3 3.1* +/- 2.1 4.4* +/- 2.4 2.6* +/- 2.1
Embryos transferred (n) 1.1* +/- 0.7 1.7* +/- 1.1 2.0 +/- 0.0 1.5 +/- 1.0
a those women that started their first ivf cycle, but did not participate in the second and did not become pregnant.b those women participating in the next cycle.c pregnancies after ivf treatment (6) and spontaneous pregnancies (1)d total motile count: volume*concentration*motility* p < 0.05
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Kaplan Meier survival analysis was used to calculate the pessimistic and optimistic cumu-lative pregnancy rate, where dropouts were believed to have either zero or similar chances to conceive, respectively, in comparison to those who completed treatment. The pessimistic approach for the cumulative clinical pregnancy rate after three cycles was 25.8%. With the optimistic approach this was 38.7%. Cumulative live birth rates after three cycles ranged between 15.4% and 18.4%, for a pessimistic and optimistic approach, respectively (table 2). When the results of the first cycle were applied as a selection parameter, where further treatment was denied to those with a poor response (n = 14), we found the cumulative clini-cal pregnancy rate for normal responders in the first cycle (n = 52) to lie between 14.0% and 21.5% for a pessimistic and optimistic approach, respectively. The cumulative live birth rate for this group after the first cycle was 6.8% and 7.7% for the pessimistic and optimistic method respectively. For the subgroup of admitted patients that actually started treatment the total medical costs for 125 cycles was €437.500. The cost per child born (10 children) then comes down to about €44.000. If all 66 women, including an assumed equal number of poor responders (n=14), would have completed their three, respectively, one cycles ((66 – 14) * 3 cycles + 14 poor response cycles = 170 cycles), and taking the Kaplan Meier prediction of a live birth rate of around 17.0% (11 children), the cost per child would increase to about €54.000.
4 DiscussionIn this retrospective study it was shown that in ivf indicated women in the age group 41 up to and including 43 years about two-thirds are to be considered as having sufficient ovarian reserve and an acceptable chance of live birth. The selection of the favorable prognosis cases was based on the afc and basal fsh. A recent meta-analysis shows that the afc is superior in predicting the occurrence of poor ovarian response in ivf (167). For the women selected by this procedure, the probability of a live birth in a series of three cycles approaches 17%, which is disappointing in view of earlier reports that were the basis of our selection policy in these older women. In these reports couples with a predicted normal ovarian response reached a ~ 20% rate of success in their first ivf cycle, while a theoretical 49% cumulative ongoing pregnancy rate was foreseen after three treatment cycles (40;200). Furthermore, when comparing our live birth rate of 9% in the first cycle with the ongoing pregnancy rate per cycle of approximately 7% as found in a comprehensive review on women over 40, our results in this selected population imply only a marginal improvement (40). We also showed that a group of 23, who were allowed for ivf but did not start, real-ized 4 spontaneous clinical pregnancies (17%), whereas the group that was admitted and actually started ivf realized 2 spontaneous clinical pregnancies and 18 ivf/icsi clinical pregnancies (30%) . From recent literature it has appeared that for all age-groups on the waiting list for ivf, the spontaneous pregnancy rate is 18%, of which 11% conceives within 1 year (171). This means that for older women indicated for ivf there is still a promising residual chance to become pregnant without any intervention. To what extent the applica-tion of art procedures will raise the probability of pregnancy over expectant management or iui treatment has not been clearly demonstrated (288). However, the aforementioned numbers indicate that in this age group the added value of three cycles of ivf (13% increase in clinical pregnancy rate) must be placed in an appropriate context with the disadvantages of ivf treatment. With regard to poor response management, Ubaldi et al. advocate the use of natural cycle ivf, which is thought to facilitate both patient compliance and cost
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effectiveness (371). However, they showed that results with natural cycle ivf are probably equally poor, since only 7% of cycles resulted in a clinical pregnancy. Obviously, from the present study no definite answer as to the effectiveness of ivf/icsi in this selected group of couples can be given. Given the low rate of cumulative live births it may be argued that ivf treatment applied according to the policy described indeed has limited value for the over 41 age group. All the couples presented here were managed based on the discriminative value of the afc as shown in an earlier study from our center by Klinkert et al., who showed that couples with a predicted normal ovarian response reached a ~ 20% rate of success in their first ivf cycle. The discrepancy in ongoing pregnancy/ live birth rates between the current study and this earlier study urges us to provide an explanation (200). First, the findings in the study by Klinkert, which were established in a different population, were based on the discrimina-tive capacity of the afc for both the occurrence of normal response of the ovaries to hyper-stimulation and clinical pregnancy. In general, predictive tests or models applied to other populations tend to fail to produce the high abilities that were found in the development population (348). The high predictive value of the afc therefore may not be confirmed if the test is applied in another population. In the current, consecutive study the accuracy of a normal afc to predict normal response and live birth indeed appeared to be lower than found in the study by Klinkert et al. Second, the percentages of normal test results for the current study and the study by Klinkert et al. were similar: 65% and 70% respectively. However, the predictive values for normal response and live birth (ongoing pregnancy in the Klinkert study) differed consider-ably: 46% and 8%, respectively, for the current study and 80% and 24%, respectively, for the Klinkert study. A possible explanation for the difference in response and outcome predic-tion is that the subpopulation of Klinkert et al., with an afc ≥5 and fsh <15, that matches our population in age, may be considered more fertile if judged upon markers like the afc, number of oocytes retrieved and clinical pregnancy (table 4). However, in this situation, one would expect the percentage of normal test results to be lower. It might be though that more cases with borderline counts of antral follicles in this population have been catego-rized as normal by the ultrasound operator who was not blinded for the purpose of the test, thus decreasing the predictive value and increasing the number of normal tests. From the lower mean afc and afc distribution, it can indeed be postulated that inflation of the test value has taken place. By raising the cut-off for the afc to for example 7, more fertile women are selected and the probability of test inflation is accounted for in the test. Conclu-sions concerning this raised cut-off should be drawn cautiously (n=24), but an increase in cumulative clinical pregnancy and live birth rates from ~30% to ~38% and from ~17% to ~23%, respectively, can be calculated. Third, in the period covering this research our embryo transfer policy appeared to have changed from transferring a maximum of three to a maximum of two embryos in this age group. The mean difference in the number of embryo’s transferred between the study by Klinkert et al. and this study is significant at the 0.001 level. Thus it may be that this policy change has influenced pregnancy outcomes significantly. From a randomized study in older women where a two versus three embryo transfer strategy was compared, it was shown that in the two embryo group pregnancies accumulated more slowly across a series of treatment cycles (163;359). For comparison purposes, the (ongoing) implantation rates between the present and Klinkert study were calculated using the Huber-White method which allows correction for clustering of embryos within the same woman (417) (table 4) (Software: S-PLUS® 7.0 for Windows (2005 Insightful Corp)). However, although the dif-
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ference in implantation rate does not reach significance, we cannot exclude the possibility that the population of the study by Klinkert et al. had a higher a priori fecundity when tak-ing the significant differences in oocytes harvested, embryos developed and clinical preg-nancy rate between the two studies into consideration (table 4). This implies that next to the change in embryo transfer policy also the inflation of the afc selection variable is likely to be responsible for these poor results.
Table 4Comparison of the populations (aged 41-43, with fsh <15 and afc ≥5) between the present study and the study by Klinkert et al (200).
Current studya
(n = 89)
Klinkert studyb
(n = 52)
p-value
Age at entry (year) 42.2 +/- 0.8 42.2 +/- 0.8 0.74
Duration of infertility (months) 38.3 +/- 31.1 42.0 +/- 39.7 0.49
Infertility:
- primary
- secondary
41 (46.1%)
48 (53.9%)
21 (40.4%)
31 (59.6%)
0.48c
ivf indication:
- Tubal pathology
- Idiopathic
- Male subfertility
- Other
10 (11.2%)
46 (51.7%)
30 (33.7%)
3 (3.3%)
7 (13.5%)
22 (42.3%)
23 (44.2%)
0.23c
Cycle-length 27.8 +/- 2.7 27.9 +/- 2.3 0.74
fsh on day 3 (iu/l) 7.6 +/- 2.3 7.7 +/- 2.7 0.72
afc 8.1 +/- 4.1 8.9 +/- 4.0 0.02
Age male 41.9 +/- 6.2 -
tmc (*106)d 75.5 +/- 90.2 110.5 +/- 166.4 0.46
Mean number of oocytes 6.0 6.6 <0.001
Mean number of embryos 2.8 4.5 0.001
Mean number of embryos transferred 1.6 2.2 <0.001
Clinical pregnancy 8 (12.1%) 20 (38.5%) <0.001c
Live birth/ ongoing pregnancye 6 (9.1%) 13 (25.0%) 0.06c
Implantation rate 11.6% 18.4% 0.15f
Ongoing implantation rate 7.4% 12.3% 0.23f
a For comparison purposes only first cycle characteristics are compared using Mann-Whitney-U tests, unless stated otherwise.b The subpopulation aged 41-43 with an afc ≥5 and an fsh <15 iu/lc Chi square testsd tmc: total motile count: volume*concentration*progressive motilitye The study by Klinkert at al. focuses on ongoing pregnancy instead of live birthf Huber-White method with correction for clustering of embryos within the same woman.
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Recent research has shown that serum anti-Müllerian hormone (amh) is a promising new test that may be incorporated in the selection process. amh levels have a high degree of cor-relation with the afc. Moreover, amh levels have shown to have potential as a menstrual cycle independent marker (115;154;160;220;394). Moreover, since it is a laboratory test, it is operator independent and thus test inflation is unlikely to occur. Although one-third of the ivf/icsi indicated patients was not allowed ivf based on the results of the ovarian reserve tests, the costs appeared considerably high. With the live birth rate calculated in this study, the actual cost per child conceived comes down to €44.000, but increases to €54.000 if all women, except the poor responders, would have finished three cycles. This difference is due to a decreasing pregnancy rate over subsequent cycles which has been described in literature before (72;359). It has become clear that in general costs per child born are three times higher in this age group compared to younger women (40). It is hard to define a maximum limit for the cost per child conceived that society is willing to pay for couples with fertility problems. Expert opinion may be necessary to decide to what level costs may rise before treatment can be denied from a regulatory point of view (cost-benefit analysis). Moreover, the patient as an expert herself may be asked to participate in such an inventory of minimal cost-effectiveness (willingness to pay analysis). It needs to be stressed that costs for absence due to illness or clinic visits, travelling and medical complications during treatment and in pregnancy have not been incorporated in the calculations above. Also, additional costs related to the occurrence of multiple gestations were not counted in. As the chance of a twin pregnancy will certainly be much higher in a younger age group it is needless to say that this is to the benefit of the cost effectiveness of treatment in the older age group (99). Still, although replacing several embryos in older women is often advised (56), it remains common sense to strive for singleton pregnancies. Even in older women the use of a modest number of embryo’s per transfer will minimize the rate of twinning, with-out reducing the overall prospects of becoming pregnant (163;263;403). Since complications due to the ovarian hyperstimulation syndrome and multiple preg-nancies are low in this age group, the most important determining factor for starting treat-ment from a patient’s perspective is patient preference balancing treatment stress and dis-comfort against the low success rates found in this study (164;351). Recently it has been shown that patient distress towards failed ivf treatments is far lower than expected by phy-sicians, thus favoring ivf treatment from a patient perspective even in cases with expected low success rates (265). In conclusion, selection towards favorable ovarian reserve status in the female age group 41-43 years yielded disappointing results in terms of cumulative live birth rates after ivf. In order to maintain a selection policy and thereby decrease the considerable costs associated with ivf treatment in this group further adjustments in management may be considered. One may argue that applying a more rigorous cut off for the afc may limit the number of cycles applied by preventing cases with less favorable prospects to enter the program. Effi-cacy of such an approach and the search for more effective selection parameters may be the subject of further research.
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General Discussion.
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Ovarian reserve tests in the prediction of age at menopauseCurrent menopause prediction is very inaccurate, since clinicians can only indicate that menopause will occur at a mean age of 51 years, varying between 40 and 60 years. Especially for fertility prediction, this inaccuracy is very unsatisfying, forming the basis for research conducted in this field in the past decade. The main aim of current research on menopause prediction is the ability to inform young women about their reproductive potential many years later. This knowledge could then be used to direct women in their family planning or to consider interventions to advance fer-tility. However, the current believe that menopause is preceded by earlier signs of declin-ing ovarian reserve, is not based on extensive evidence, as outlined in chapter two. Espe-cially longitudinal studies are lacking, since they need to span an enormous period of time. Cross-sectional and longitudinal research does consistently show, however, that women with diminished ovarian reserve can be identified at any age (39;202;386) and the relation-ship between diminished ovarian reserve, as evidenced for example by poor response after hyperstimulation for ivf, has been consistently linked with early menopause (65;236;278). Although longitudinal studies are scarce, accurate prediction can only be validated with such studies. Current large prospective cohort studies like the Lifelines project in Groningen should be able to shed light on the accuracy of menopause prediction. The Lifelines project follows three generations for 30 years aiming to get more insight in the development of diseases. The Lifelines project would therefore be suitable to follow women in their early twenties until the onset of menopause. Next to providing prognostic information, predictors may also elucidate the mechanisms of ovarian ageing. Consistently higher fsh levels for example may give rise to increased dizygotic twinning rate and may possibly lead to earlier diminished ovarian reserve (226). Also, variation in granulosa cell function of CYP19A1 aromatase (also known as estrogen synthetase), important for follicle and oocyte maturation, is responsible for the pcos pheno-type and influences the antral follicle count (8;429). These two examples illustrate that both endocrine and genetic studies may provide information on ovarian function. Future research should incorporate both to investigate the underlying mechanisms of ovarian ageing.
Requirements of good menopause predictorsTimely and accurate prediction of menopause depends on the identification of early signs of ovarian ageing. Therefore, a longitudinal or at least cross-sectional relationship between the predictor and age at menopause should exist. A linear relationship spanning the period from puberty to menopause would be most convenient, but other types of association like power functions or exponential decline are possible (39;148). As shown in chapter two, the current inventory of ovarian reserve parameters includes only few factors that have the potential to be useful as menopause predictors. A family history of early age at menopause seems currently the best indicator of early menopause (61). In chapter 3 we demonstrated data that suggest that amh is capable of specifying a woman’s reproductive status more realistically than chronological age alone, as was shown previously for the antral follicle count (39). Both these models are based on the presumption that menopause occurs when the AMH level or antral follicle count drop below a certain value. For amh, this has recently been challenged by Sowers et al. (344), who showed amh values to become undetectable about 5 years before the onset of menopause, making accurate prediction more problem-atic. Future prospective studies should take these requirements into account when model-ing ovarian ageing and menopause prediction.
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Genetic prediction of age at menopauseOvarian reserve tests are merely a reflection of the quantitative ovarian reserve present at a certain time. Since menopause is predominantly genetically determined, it is the individual balance between genes advancing and delaying age at menopause in interaction with envi-ronmental factors that determines its actual onset. It is therefore not surprising that current technological advancement has lead to genomic studies searching for the genetic basis of ovarian ageing and onset of menopause. However, the genetic pathways determining onset of menopause and their relation with environmental factors are still unclear. It is unknown whether an individual woman’s ovarian reserve at a certain age is determined by (i) gameto-genesis determining the initial amount of follicles, (ii) the rate of follicular atresia before or after puberty or (iii) differences in follicular recruitment and maturation (104). However, in a cross-sectional mouse model involving mrna and protein expression profiles, Zimon et al. demonstrated that the onset of menopause is not so much determined by steroidogenic failure, but by upregulation of pro-inflammatory and oxidative stress pathways (434). If these pathways are also involved in human onset of menopause and if they determine ovar-ian reserve at any age requires further research. We do know that onset of menopause is a complex genetic trait which probably involves a summation of various susceptibility loci and environmental effects. However, genetic prediction of such a complex trait might be very difficult as shown by a recent study by Aulchenko et al. (13). They investigated the prediction of body height by comparing a method with 54 genetic loci of interest with a method averaging the height of both parents. They showed that the genomic profile method, explaining 4-6% of the variance, was surpassed by the method averaging phenotypic information of related individuals, which explained 40% of the variance. Since there has not been a single gene identified consistently explain-ing a large part of the variance of menopausal age, genetic prediction of age at menopause might be similarly problematic as prediction of body height. In view of the complexity of the mechanisms that underlie the variation in the ovarian ageing, a lot of work remains to be done before the genetic mechanisms underlying the variation in onset of menopause are unraveled. As discussed previously, menopause predic-tion based on tests reflecting ovarian reserve, without knowing the actual causative mecha-nism, also warrants extensive prospective, longitudinal research before accuracy can be definitely assessed. It is therefore not easy to assess which approach would yield the most accurate results most quickly.
Vascular factors related to follicle quantity in the occurrence of menopause.Circumstantial evidence suggests a role for vascular factors in the onset of menopause. Firstly, smoking during the menopausal transition has been associated with decreasing oocyte quality and quantity (256;416;420;433). It has been shown that smoking waste prod-ucts accumulate in the follicular fluid, possibly leading to decreased ovarian vasculariza-tion and increased oxidative stress, causing disturbed oocyte maturation and follicular atresia (256;267;289;363). How smoking affects the follicle pool remains largely unknown, but smoking was shown to increase the pro-apoptotic protein Bax, suggesting that genetic and environmental factors might exacerbate each other’s effect (81;256). Secondly, poor responders in ivf have been shown to have a poorer vascular status and to reach menopause earlier (21;64;65;278). Various vascular genes have been implicated in age at onset of menopause, although not consistently (157;358;377;384;409). Moreover, it has been suggested that pregnancies after ivf are more likely to occur in those women
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with a favorable vascular status (259;284). Correspondingly, pre-eclampsia was found to be more common in women who displayed characteristics of a poor ovarian reserve (422). Unfortunately we were unable to replicate this finding in a case-control setting (385). Finally, cardiovascular risk factors such as hypertension, obesity and hypercholester-olemia have repeatedly been shown to be associated with a decreased age at natural meno-pause (381). However, the reverse is also true: (peri)-menopausal disruption of the gonadal-pituitary axis leading to hypo-estrogenism is associated with a suboptimal cardiovascular status (206). Poor vascular status thus seems to cause early menopause and early menopause causes poor vascular status, suggesting an analogy between general and ovarian ageing. In view of this scientific background, vascular ageing determining ovarian reserve seems plausible. The underlying mechanisms of vascular ageing, however, remain unclear and may be the expression of other causes of ageing like dysregulation of glucose homeostasis or inflammation (47;86;126). Research into this area is difficult because of the absence of a universal definition of a poor vascular status or stages of (vascular) ageing. In view of these unclarities future research should be performed with caution.
Possible vascular factors involved in onset of menopauseResearch investigating a possible vascular pathway underlying the onset of menopause has identified various factors. Polymorphisms in estrogen metabolising genes, clotting factors and Apolipoprotein E have been the main focus of recent research (157;345;358;377;409). Their possible hypoxic effects are thought to be mediated by atherosclerosis, dyslipidemia and coagulation disorders. When comparing previous studies investigating vascular involvement in the onset of menopause, it becomes clear that the effects and significance levels described vary per study. For the estrogen receptor alpha (esr1), for example, Weel et al. found a significant decrease in age at menopause, but Dvornyk et al. and Kok et al. found no statistical differ-ence (91;204;409). The same discrepancy was shown for the apoe-2 Arg158Cys polymor-phism where Tempfer et al. showed a significant increase in age at menopause, but He et al., Koochmeshgi et al. and our group were not able to replicate this finding (155;208;358;384). We were also unable to validate earlier associations between age at natural menopause and single nucleotide polymorphisms in the genes encoding for coagulation factors II, V and VII (358;377). Further research in a well phenotyped population is necessary before definite conclusions can be drawn concerning the involvement of these vascular factors in the onset of menopause. Since we were unable to validate earlier findings of vascular factors involved in onset of menopause and because earlier findings find different magnitudes of effect, we hypothesize that the overall effect of vascular genes on onset of menopause may be limited. Possibly, these factors are only related to age at menopause when co-expressed with other factors. Zimon et al showed that the genes involved in the onset of menopause and early changes in the ovary are not related to steroidogenic failure, but to increased pro-inflammatory and oxidative stress pathways (434). Vascular damage may just be an expression of this pro-inflammatory and oxidative stress response and not the actual cause. Moreover, ovarian reserve might also be determined by other factors controlling follicle endowment, loss rate and maturation. We hypothesize that vascular function related genes are possibly just one of the many susceptibility factors involved in the determination of age at menopause. In view of the lack of a universal definition of vascular ageing and in view of the contradictory findings, we suggest that future research into this field should be performed cautiously.
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Improving the clinical usefulness of ovarian reserve tests in practice.The study by Muasher et al. was the first to report on basal fsh to predict ovarian response to stimulation (268). Since then, ovarian reserve tests have been extensively studied and used in everyday ivf practice. A recent systematic review assessed all existing ovarian reserve tests for their capacity to predict poor response and pregnancy (42). They concluded that the antral follicle count is currently the best ovarian reserve test to predict poor response with moderate accuracy. In a separate study the same investigators reviewed the antral follicle count with anti-Müllerian hormone and concluded that they had equal predictive accuracy for poor response (44). Moreover, it was concluded that prediction of pregnancy is currently not feasible. The accuracy of ovarian reserve tests depends on both within subject variability and between subject variation. Between subject variation is primarily determined by the accu-racy of the test method, by differences in body composition and genetic polymorphisms (136;238;247;333;396). Within subject variation could be related to the timing of a test rel-ative to the menstrual cycle or circadian rhythm (160). We will shortly address the mea-surement reliability of ovarian reserve tests in general and discuss the influence of genetic variation and timing of ovarian reserve tests relative to the menstrual cycle. The influence of other factors on the variability of test results falls beyond the scope of this thesis.
Obviously, measurement reliability is an important factor in determining the accuracy of a test method. Assay variation of endocrine values is therefore well documented in individ-ual studies (7;160). Comparing different assays, however, is often problematic since direct comparisons between assays are scarce and because of the lack of a reference assay. Compared to endocrine ovarian reserve tests, ultrasound ovarian reserve tests like the afc are more prone to observer bias and depend on the experience of the ultrasonographer. The introduction of 3D ultrasound machines, automated follicle recognition and post-hoc processing, however, have improved measurement reliability considerably (77;176). In the evaluation of ovarian reserve tests, measurement reliability should be taken into account and related to other measures of variability.
Most research concerning the influence of genetic variation on ovarian reserve test results has focused on fsh receptor polymorphisms (250). Various studies found the basal fsh values to correspond with fsh receptor genotype at position 680 (137;181;293;352;375). Moreover, activating mutations leading to increased susceptibility to ohss have also been identified (250;310). fsh sensitivity was also shown to be influenced by polymorphisms in the amh and amhr2 genes which were associated with follicular phase estradiol levels (189). Ovarian volume and the antral follicle count were also shown to be susceptible to genetic polymorphisms. Altmae et al. suggested that variation in the cyp19a1 gene (also known as estrogen synthetase) is associated with smaller ovaries and fewer antral follicles on days 3-5 of the menstrual cycle (8). De Castro et al. showed in an oligogenic model that variation in the estrogen receptors esr1 and esr2 and the fsh receptor are associated with poor response (68). This also suggests that these genes are involved in determining follicle numbers. Since ovarian response to hyperstimulation reflects ovarian reserve, it might also be judged as a functional ovarian reserve test (42). Genetic prediction of response may not only inform us of an individual woman’s ovarian reserve status, but may also open the way to individ-
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ualize treatment strategies, as was already shown in other fields of medicine (29;50). We were the first to explore the association between genetic variation and the ovarian response obtained (chapter 6), by adopting a genome wide approach. We were unable to demonstrate that variation in ovarian response could be traced back to single nucleotide polymorphisms. Since this was a preliminary study with limited sample size, this only suggests that the number of oocytes generated by a standard fsh dose is not determined by a single gene effect. As ovarian reserve and thus ovarian response are thought to be a complex genetic trait, it is likely that many factors are involved in determining ovarian reserve. Future larger studies are needed to investigate if genetic variation determines the full range of ovarian responses observed and if it is predictive of ovarian reserve and age at menopause.
Although measurement reliability is important, the intra- and intercycle variation of most ovarian reserve tests exceeds the variation of various test methods (116;160;349). This may provide a possible explanation for the moderate accuracy of ovarian reserve tests, since a single measurement might easily result in misclassification of an individual woman’s expected response (chapter 7). Previous studies showed that the efort, afc and amh showed least intercycle fluctua-tion (116;174;216). Also ovarian response was shown to be stable over various treatment cycles (84;207), suggesting it could be used as a functional ovarian reserve test. Most ovarian reserve tests display large within cycle fluctuation, since they follow the pattern of follicle maturation. fsh, for example, rises in the early follicular phase and decreases in the late follicular phase. It was therefore decided that fsh is to be measured on cycle days 1-4 to make individual values comparable to a reference value. This implicates, however, that for an individual woman it should be clear when her period started. Common complaints of spotting or metrorrhagia may complicate this and make measurements less reliable. Moreover, there is evidence that in advanced stages of ovarian ageing fsh already starts to rise in the luteal phase of the preceding cycle (399), making it difficult to assess standardized basal values on cycle day 3. Identification of ovarian reserve tests that do not fluctuate within and between cycles may lead to more reliable predictors of ovarian reserve. Anti-Müllerian hormone, which regulates growth and development of ovarian follicles, is considered by most studies to be cycle independent (160;220;368), although others challenge this (58;428). Moreover, we demonstrated that the variation in amh levels attributed to the individual woman appeared significantly smaller than for the afc (Chapter 7). This suggests that a single measure-ment of amh in an individual woman is more reliable than a single antral follicle count. Small prospective studies studying the performance of amh in prediction of poor and hyperresponders in a clinical setting have already shown the benefit of amh measurements (130;274).
The clinical relevance of poor response predictionOvarian reserve tests predict poor response with reasonable accuracy (42). In clinical practice this accuracy is too inadequate to refuse patients solely on the outcome of these tests. Moreover, the ultimate outcome of a healthy baby cannot be predicted, suggesting that the prediction of poor response is not the same as predicting sterility. On the other hand, it has been demonstrated that once a poor response has been confirmed, women have decreased pregnancy chances (179;244;400) and a higher chance of reaching meno-pause earlier (64;65;236). Moreover, previous research has convincingly demonstrated that
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there are few treatment options to counter a poor response in a following ivf treatment (199;356). Expected poor responders, based on age or an abnormal ovarian reserve test, do not improve pregnancy chances substantially in further treatments (74;168;201). This evi-dence suggests that ovarian reserve tests could possibly be used to confirm poor prognosis cases after the occurrence of a poor response. Before starting treatment, however, ovarian reserve tests can only be applied to inform couples about a possible disappointing result.
The influence of age on the prediction of pregnancy by ovarian reserve testsOvarian ageing leads to sterility about ten years prior to the onset of menopause, at a mean age of 41 years. Like menopause, the onset of sterility occurs with quite some variation (357). When a woman has reached a stage of reduced fecundity, artificial reproductive tech-niques are not able to compensate for this loss (243). Moreover, with increasing age success rates of art decline (277). Female age has therefore a major impact on the management of infertile couples. Since pregnancy chances in women over 41 years of age are low (243), ivf/icsi treatment in most centers in the Netherlands has been restricted to couples of which the woman is aged under 41 (90). Moreover, virtually no pregnancies are reported after the age of 43 (243). Female age may not always accurately represent a woman’s reproductive capacity due to the inter-individual variability in the ovarian ageing process (243;357). Among women beyond 40 years of age, cases with acceptable chances for pregnancy may be identified (200). However, our validation in chapter eight showed disappointingly low live birth rates of 17% after three ivf cycles instead of the anticipated ~20% success rate per cycle (200). Further studies are therefore necessary to investigate if ovarian reserve tests can identify a good prognosis group a priori. Future studies might incorporate likely predictive factors such as amh, familial age at menopause and cycle regularity to identify those cases with an adequate ovarian reserve. Since ovarian reserve declines with age, age itself is also an easy to obtain ovarian reserve test. Declining pregnancy chances with increasing age are probably due to decreas-ing oocyte quality and quantity, which lead to increased aneuploidy rates and increased miscarriage rates (214). The influence of age on pregnancy prediction was recently dem-onstrated in two studies showing that pregnancy chances are primarily influenced by age and only for a small part by the basal fsh value (169;321). The finding that age is the most important predictor of pregnancy was also confirmed in multivariate analyses by previous authors (55;62;391). We advocate that future studies investigating the predictive value of ovarian reserve tests should always compare the predictive value of these tests with age.
ConclusionAlthough recent research has greatly extended our knowledge about the physiology and pathophysiology of menopause, most of the assumptions made in chapters 2 until 5 about menopause prediction need further scientific evaluation. However, from all ovarian reserve tests studied for their ability to predict menopause, amh seems currently the best option in view of its correlation with age and the antral follicle count, its cycle stability and longitu-dinal decline with age. We suggest that the involvement of vascular factors should be inter-preted with caution, because of contradictory findings and lack of a clear definition. For reliable prediction, current knowledge of very early genetic, ultrasound and endocrine pre-dictors of menopause is insufficient, warranting extensive research efforts. Future results of new and ongoing longitudinal studies will hopefully provide practically useful predictive models of menopause for individual application.
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For the prediction of ivf outcome, we recommend that future research takes into account the predictive effect of age and poor response. Genetic prediction of response was shown to be very complex, warranting larger studies to investigate the pathways involved. Identification of good responders among older women seeking ivf treatment seems pos-sible with strict ovarian reserve test cut-offs. Moreover we suggest the addition of amh, familial age at menopause and cycle regularity to future prediction models.
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Summary.
Chapter 1: General IntroductionIn the general introduction the concept of ovarian ageing and the role of ovarian reserve tests in the prediction of infertility are discussed. Furthermore, the outline and aims of the current thesis are sketched. In the fourth month of fetal development, there is a maximum of about 6-7 million fol-licles present in the ovaries. Thereafter, follicle numbers decline slowly. The progressive loss of quality and quantity of oocytes is called ovarian ageing. During the process of ovar-ian ageing, different phases can be discerned with about 10 year intervals: optimal fecun-dity, infertility, sterility and menopause. Consequently, if menopause occurs early in life, a woman’s fecundity has been compromised decades before. Therefore, accurate prediction of the rate of ovarian ageing and the associated fecundity is desirable. This thesis discusses the potential influence of genetic, vascular and other factors in the prediction of meno-pause and the clinical applicability of ovarian reserve tests is evaluated.
1 Part 1Part 1 discusses (chapter 2) and investigates (chapter 3) the ability of various ovarian reserve tests to predict age at menopause.
1.1 Chapter 2This chapter further outlines the concept of ovarian ageing and provides a literature review of the predictive ability of the current ovarian reserve tests for age at menopause. There is a wide array of endocrine, ultrasound and genetic tests available that try to esti-mate the ovarian reserve present. Besides ovarian reserve tests, there are a few characteris-tics that are informative of age at menopause. These characteristics include the association between mother’s and daughter’s age at menopause and the association between cycle dis-turbances preceding the onset of menopause. Currently, there is no single ovarian reserve test able to predict age at menopause with enough certainty in individual women. Future research should focus on combining various ovarian reserve tests to improve the predictive accuracy.
1.2 Chapter 3This chapter describes the development of a mathematical model of the relationship between amh and age at menopause. This model is constructed by combining information from two populations. From a group of fertile women, the mean decline of amh with age and individual deviations from this mean were calculated. We hypothesized that individual variation in amh level correlated with the individual variation in ovarian reserve and ulti-mately age at menopause. With the use of an amh cut-of below which menopause occurs, a distribution of predicted age at menopause is constructed. This distribution of predicted age at menopause was compared and found to be comparable to the distribution of observed ages at menopause from a different population. This suggests that amh levels are related to age at onset of menopause at a population level and that amh reflects a woman’s reproduc-tive age more realistically than chronological age alone.
2 Part 2Part two describes our research (Chapter 4 and 5) involving a possible vascular factor in the determination of age at menopause.
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2.1 Chapter 4This chapter describes a validation study of the relationship between age at menopause and polymorphisms in the genes coding for coagulation factor 2, 5 and 7 and the apoe2 gene. Polymorphisms are small dna variations that are present in over 1% of the general pop-ulation. These dna variations are probably responsible for the variation around us. The polymorphisms studied in coagulation factors 2, 5, 7 and apoe2 were previously associated with age at menopause. Heterozygosity for a deletion/insertion polymorphism in the pro-moter region of factor 7 was associated with age at menopause. We were unable to confirm previous findings in the other polymorphisms studied. Previous finding should therefore be interpreted with caution. 2.2 Chapter 5In this chapter the difference in incidence of hypertensive pregnancy complications is described after poor and normal response to ovarian hyperstimulation for ivf. We hypoth-esized that a poor response to ovarian hyperstimulation might be caused by diminished blood supply to the ovaries. A poor response could be a first signal of vascular damage, that in a subsequent pregnancy could lead to pregnancy induced hypertension or pre-eclampsia. To test this hypothesis, women with a poor response were matched to women with a normal response based on several characteristics. However, no significant differences in hyperten-sive pregnancy complications were found. Therefore, vascular damage does not seem to play a role in the occurrence of poor response. Another explanation might be that women pregnant after a poor response are a relatively favorable group, selected through the occur-rence of their pregnancy.
3 Part 3 In part 3 we evaluate how to improve the clinical applicability of various ovarian reserve tests (chapter 6-8).
3.1 Chapter 6This chapter investigates if response to ovarian hyperstimulation for ivf is dependent on certain polymorphisms. Although patient tailored stimulation protocols become more popular, both hypo- and hyperresponse do occur. The notion that response to medication is genetically determined has already been proven in other fields. The current proof-of-principle study investigates a homogenous ivf population for relevant polymorphisms in a genome wide fashion. We were unable to identify polymorphisms associated with oocyte yield. Possibly, oocyte yield is determined by a summation of smaller effects than we were able to study.
3.2 Chapter 7Chapter 7 describes the intra- and intercycle variation of amh and afc. Cycle variation of ovarian reserve tests influences their clinical applicability and predic-tive value. Both amh and afc are good predictors of oocyte yield, but their individual cycle variation may determine their value as an ovarian reserve test. Cycle variation was calcu-lated with the intraclass correlation coefficient, which shows both the variation within and between individual women. Both intra- and intercycle variation were found to be lower for amh. Future studies should investigate if the better cycle stability of amh leads to more accurate predictions of ivf outcome and ovarian reserve.
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3.3 Chapter 8Chapter 8 describes the efficacy of fsh and afc in selecting a 41-43 year old ivf population with acceptable pregnancy chances. Pregnancy chances decline on average between age 31 and 44 years, when pregnancy chances are almost zero. Because there is a wide variation in individual female fecundity, women with adequate pregnancy chances at age 41-43 can be identified. These women may not be offered regular ivf treatment because the treatment is not offered to women over 41 years. The current study could not confirm previous positive findings that fsh and afc are capable of selecting a favorable group of older ivf patients. Possible explanations are regression to the mean, the occurrence of test-inflation and a reduction in the number of transferred embryos.
4 Chapter 9: General discussion.This chapter discusses the conclusions that can be drawn from this thesis. The current thesis underlines the complexity of ovarian ageing and the difficulties that prediction of menopause entails. Based on its relationship with age and the antral follicle count, its cycle stability and longitudinal decline with age, amh seems currently the best ovarian reserve test to predict age at menopause. The plausibility of vascular factors determining age at menopause is based primarily on indirect evidence. Since there is no consensus about the definition and mode of measure-ment of vascular ageing, future research should be interpreted cautiously. Diverse ovarian reserve tests are integrated in fertility work-up worldwide, although their capacity to predict pregnancy is poor. Moreover, current research involving ovarian reserve tests often ignores female age and past treatment outcomes as predictive factors of pregnancy. Identification of women with an adequate prognosis seems possible based on strict test cut-offs. Future research should focus on improving the accuracy of prediction models, possibly by incorporating amh, familial age at menopause and cycle regularity into these models.
Dutch Summary.(Nederlandse Samenvatting)
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Hoofdstuk 1: IntroductieIn de inleiding van dit proefschrift wordt het concept van ovariële veroudering en de rol van ovariële reserve testen in de voorspelling van vruchtbaarheid behandeld. Daarnaast worden de opzet en doelen van dit proefschrift besproken. In de vierde maand van de foetale ontwikkeling is een maximaal aantal van 6-7 miljoen follikels aangelegd, die daarna langzaam verloren gaan. Dit langzame verlies van kwantiteit en kwaliteit van follikels wordt ovariële veroudering genoemd. Gedurende het proces van ovariële veroudering zijn verschillende fasen te onderscheiden met tussenpozen van onge-veer 10 jaar: optimale vruchtbaarheid, infertiliteit, steriliteit en uiteindelijk menopauze. De consequentie hiervan is dat een uiteindelijke vroege menopauze de vruchtbaarheid van een vrouw al decennia daarvoor heeft beïnvloed. Correcte voorspelling van het tempo van ova-riële veroudering en de hieraan gekoppelde vruchtbaarheid op een bepaald moment is dan ook wenselijk. In dit proefschrift wordt de rol van genetische, vasculaire en andere factoren onderzocht in de voorspelling van menopauze en wordt de klinische toepasbaarheid van de huidige ovariële reserve testen geëvalueerd.
1 Deel 1
Deel 1 betreft een literatuuronderzoek (hoofdstuk 2) en een studie (hoofdstuk 3) naar de capaciteit van verschillende ovariële reserve testen om menopauzeleeftijd te voorspellen.
1.1 Hoofdstuk 2In dit hoofdstuk wordt het concept ovariële veroudering verder toegelicht en wordt een overzicht van de literatuur gegeven over het voorspellend vermogen van de huidige ovariële reserve testen op de leeftijd van de menopauze. Er is een breed scala aan endocriene, echografische en genetische testen beschikbaar die de ovariële reserve op een bepaald moment proberen in te schatten. Daarnaast zijn er enkele kenmerken die informatie kunnen verschaffen over de leeftijd waarop de menopauze optreed, zoals de relatie tussen de menopauzeleeftijden van moeders en dochters en de rela-tie tussen cyclusveranderingen voorafgaand aan het optreden van de menopauze. Op dit moment is er echter geen ovariële reserve test die met voldoende zekerheid de menopauze van individuele vrouwen kan voorspellen. Toekomstig onderzoek zal zich moeten richten op het combineren van verschillende beschikbare testen om de voorspellende waarde te verbeteren.
1.2 Hoofdstuk 3Dit hoofdstuk beschrijft de ontwikkeling van een wiskundig model van de relatie tussen amh (anti-Müllerian hormone) en de leeftijd waarop de menopauze optreedt.Voor de studie werden twee populaties gebruikt. Van een groep vruchtbare vrouwen werd de gemiddelde afname van amh met de leeftijd bepaald en de individuele afwijkingen van dit gemiddelde. Onze hypothese was dat de individuele variatie in amh, die correleert met de follikelvoorraad, waarschijnlijk een weerspiegeling is van de variatie in de uiteindelijke menopauzeleeftijd. Op basis van een amh grenswaarde waaronder de menopauze optreedt, is met dit model een verdeling gemaakt van de voorspelde menopauzeleeftijd. Deze verde-ling bleek in overeenstemming met de verdeling van menopauzeleeftijd van een andere, ver-gelijkbare populatie. amh is dus geassocieerd met menopauzeleeftijd op populatieniveau. Voor individuele vrouwen lijkt amh menopauzeleeftijd beter te kunnen voorspellen dan chronologische leeftijd alleen.
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2 Deel 2Deel 2 beschrijft het onderzoek (hoofdstuk 4 en 5) naar mogelijke vasculaire factoren die betrokken zijn bij het bepalen van de menopauzeleeftijd.
2.1 Hoofdstuk 4In dit hoofdstuk wordt de relatie onderzocht tussen menopauzeleeftijd en polymorfismen in de genen van stollingsfactoren 2, 5 en 7 en het apo-e2 gen. Polymorfismen zijn kleine dna variaties die in meer dan 1% van de bevolking voorko-men. Deze variaties zijn mede verantwoordelijk voor de variëteit om ons heen. Van de onder-zochte stollingsfactoren 2 (F2 G20210A), 5 (F5 arg506gln: F5 Leiden), 7 (F7 -323 0/10-bp en F7 arg353gln) en apo-e2 (arg158cys) is eerder beschreven dat zij de menopauzeleeftijd beïnvloedden. Heterozygotie voor het deletie/insertie polymorfisme in factor 7 (-323 0/10-bp) liet als enige een significant verband met menopauzeleeftijd zien; de overige polymor-fismen in deze studie waren niet geassocieerd met menopauzeleeftijd. De eerdere bevindin-gen moeten dan ook voorzichtig geïnterpreteerd worden.
2.2 Hoofdstuk 5Hoofdstuk 5 beschrijft een onderzoek naar de verschillen in incidentie van pre-eclampsie en hypertensie in zwangerschappen ontstaan na een normale en lage response in een voor-afgaande ivf behandeling. Een lage response (lage hoeveelheid eicellen) in een voorafgaande ivf behandeling zou veroorzaakt kunnen worden door verminderde doorbloeding van de eierstokken en een teken kunnen zijn van vasculaire schade. Deze vaatschade zou in een aansluitende zwan-gerschap kunnen leiden tot het ontstaan van hypertensie (hoge bloeddruk) of pre-eclamp-sie (zwangerschapsvergiftiging). Na het op een aantal kenmerken matchen van 150 vrou-wen met een lage opbrengst met 150 vrouwen met een normale opbrengst, konden geen verschillen in hypertensie of pre-eclampsie incidentie worden gevonden. Vaatschade lijkt dus niet vaker voor te komen bij een lage eicelopbrengst. Een andere verklaring kan zijn dat vrouwen die zwanger worden na een lage eicelopbrengst al zijn uitgeselecteerd op basis van afwezige vaatschade.
3 Deel 3In deel 3 wordt geëvalueerd hoe de klinische toepasbaarheid van ovariële reserve testen ver-beterd kan worden (hoofdstuk 6-8).
3.1 Hoofdstuk 6In dit hoofdstuk is onderzocht of de response (eicelopbrengst) van vrouwen op stimulatie van de eierstokken afhangt van bepaalde genetische polymorfismen. Ondanks het ontstaan van geïndividualiseerde stimulatieprotocollen, blijft eicelop-brengst moeilijk te voorspellen aangezien slechte response en hyperresponse blijven voor-komen. Dat de response op therapie afhankelijk kan zijn van genetische variaties, is voor andere medicijnen al vastgesteld. In deze proof-of-principle studie zijn we in een homogene populatie op zoek gegaan naar verklarende polymorfismen in alle chromosomen (genome wide). Het bleek niet mogelijk polymorfismen te identificeren die eicelopbrengst voorspel-len. Mogelijk wordt eicelopbrengst bepaald door een optelsom van kleinere effecten, die in de huidige beperkte dataset onopgemerkt zijn gebleven.
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3.2 Hoofdstuk 7In dit hoofdstuk wordt de variatie in amh (anti-Müllerian hormone) en afc (antral follikel telling) zowel binnen als tussen menstruatiecycli beschreven.Cyclusvariatie van ovariële reserve testen beïnvloedt hun bruikbaarheid en predictieve waarde negatief. Zowel amh als afc zijn goede voorspellers van eicelopbrengst, maar hun cyclusvariatie bepaalt onder andere hun waarde als ovariële reserve test. De cyclusvariatie is berekend met de intraclass correlatiecoëfficiënt die zowel de variatie binnen 1 vrouw (de echte cyclusvariatie) als tussen vrouwen schat. Zowel tussen als binnen menstruatiecycli bleek dat amh minder variatie vertoonde dan de afc. Toekomstige studies zullen moeten uitwijzen of deze betere cyclusstabiliteit van amh ook leidt tot een betere voorspelling van ivf uitkomsten.
3.3 Hoofdstuk 8Dit hoofdstuk behandelt de effectiviteit van afc en fsh in de selectie van een ivf populatie tussen de 41 en 43 jaar met goede zwangerschapskansen. Zwangerschapskansen nemen af vanaf gemiddeld het 31ste jaar tot op 44 jarige leeftijd, wanneer de kans op zwangerschap bijna 0 is. Vanwege de grote variatie in vruchtbaarheid zijn er vrouwen tussen de 41 en 43 jaar met acceptabele zwangerschapskansen die normaal niet meer in aanmerking komen voor een ivf behandeling omdat die veelal boven de 41 jaar niet aangeboden wordt. De huidige studie is een validatie van een eerdere studie waarin vrouwen tussen de 41 en 43 jaar met gunstige afc en fsh goede zwangerschapscijfers lie-ten zien. Helaas kon dit resultaat niet gevalideerd worden. Mogelijke oorzaken zijn regres-sie naar het gemiddelde, test-inflatie van de selectietesten en veranderingen in het embryo-transferbeleid.
4 Hoofdstuk 9: DiscussieIn dit hoofdstuk worden de conclusies die uit de studies in dit proefschrift getrokken kon-den worden bediscussieerd. Dit proefschrift onderstreept de complexiteit van ovariële ver-oudering en de problemen die voorspelling van menopauze met zich meebrengt. Op basis van de relatie met leeftijd en met de afc, de cyclusstabiliteit en longitudinale daling met de leeftijd, lijkt amh op dit moment de beste ovariële reserve test om de menopauze te voor-spellen. De plausibiliteit van vasculaire factoren die menopauze bepalen is gebaseerd op veel indirect bewijs. Aangezien er geen overeenstemming is over een definitie van vascu-laire veroudering en over de juiste meetmethode, moet toekomstig onderzoek in dit veld met de nodige omzichtigheid betracht worden. Ovariële reserve testen worden veelvuldig toegepast, maar zijn slechte voorspellers van zwangerschap. Bovendien worden de leeftijd van de vrouw en de uitkomst van eerdere ivf behandelingen als predictieve factoren in de voorspelling van zwangerschap vaak vergeten. Identificatie van vrouwen met een goede prognose lijkt mogelijk op basis van strikte test-waarden. Toekomstig onderzoek moet uitwijzen of de accuratesse van predictiemodellen vergroot kan worden door amh, familiaire menopauzeleeftijd en regelmatigheid van de cyclus toe te voegen aan deze modellen.
List of co-authors and their affiliations.
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F.J. Broekmans MD PhDDepartment of Reproductive Medicine and GynaecologyUniversity Medical Center UtrechtUtrecht, The Netherlands
M.J.C. Eijkemans PhDJulius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrecht, The Netherlands
M.J. Faddy DPhilQueensland University of TechnologyBrisbane, Australia
L. Franke PhDDepartment of Medical GeneticsUniversity Medical Center GroningenGroningen, The Netherlands
B.C. Fauser MD PhDDepartment of Reproductive Medicine and GynaecologyUniversity Medical Center UtrechtUtrecht, The Netherlands
F.H. de Jong PhDDepartment of Internal MedicineErasmus Medical CenterRotterdam, The Netherlands
E.R. Klinkert MD PhDDepartment of Obstetrics and GynaecologyUniversity Medical Center GroningenGroningen, The Netherlands
C.H. de Koning MD PhDCenter for Reproductive MedicineAcademic Medical Center, University of AmsterdamAmsterdam, The Netherlands
J. Kwee MD PhDDepartment of Obstetrics and GynaecologySint Lucas Andreas ZiekenhuisAmsterdam, The Netherlands
C.B. Lambalk MD PhDDepartment of Obstetrics and GynaecologyVU University Medical CenterAmsterdam, The Netherlands
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C.W.N. Looman PhDDepartment of Public HealthErasmus Medical CenterRotterdam, The Netherlands.
N.S. Macklon MD PhDDivision of Developmental Origins of Health and DiseaseUniversity of Southampton, Princess Anne HospitalSouthampton, UK.
P.H.M. Peeters PhDJulius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrecht, The Netherlands
Y.T. van der Schouw PhDJulius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrecht, The Netherlands
A.P.N. Themmen PhDDepartment of Internal MedicineErasmus Medical CenterRotterdam, The Netherlands
E.R. te Velde MD PhDDepartment of Reproductive Medicine and GynaecologyUniversity Medical Center UtrechtUtrecht, The Netherlands
C. Wijmenga MD PhDDepartment of Medical GeneticsUniversity Medical Center GroningenGroningen, The Netherlands
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List of publications.
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van Disseldorp J, Slingenberg EJ, Matute A, Delgado E, Hak E, Hoepelman IM. Application of guidelines on pre-operative antibiotic prophylaxis in León, Nicaragua. Neth J Med. 2006;64:411-6.
van Disseldorp J, Broekmans FJ, Peeters PH, Fauser BC, van der Schouw YT. Cumulative live birth rates following ivf in 41-43 year old women presenting with favour-able ovarian ageing characteristics. Reprod Biomed Online. 2007;14:455-63.
van Disseldorp J, Broekmans FJ, Peeters PH, Fauser BC, van der Schouw YT. The association between vascular function-related genes and age at natural menopause. Menopause. 2008;15:511-6.
van Disseldorp J, Faddy MJ, Themmen AP, de Jong FH, Peeters PH, van der Schouw YT, Broekmans FJ. Relationship of serum antimüllerian hormone concentration to age at menopause. J Clin Endocrinol Metab. 2008;93:2129-34.
Van Disseldorp J, Eijkemans MJC, Fauser BC, Broekmans FJ. Hypertensive pregnancy complications in poor and normal responders following in vitro fertilization. Fertility and Sterility. 2009 In Press.
Lambalk CB, van Disseldorp J, de Koning CH, Broekmans FJ. Testing ovarian reserve to predict age at menopause. Maturitas. 2009;63:280-91.
van Disseldorp J, Lambalk CB, Kwee J, Looman CWN, Eijkemans MJC, Fauser BC, Broekmans FJ.Comparison of inter- and intra-cycle variability of Anti-Müllerian Hormone and antral fol-licle counts. Hum Reprod. 2009 In Press.
Fatemi HM, Kasius JC, Timmermans A, van Disseldorp J, Fauser BC, Devroey P, Broekmans FJ.Prevalence of minor uterine cavity abnormalities diagnosed by office hysteroscopy prior to in vitro fertilization. Submitted.
van Disseldorp J, Franke L, Eijkemans MJC, Broekmans FJ, Macklon NS, Wijmenga C, Fauser BC. Genomic predictors of ovarian response to stimulation for in vitro fertilization. Submitted.
About the Author.(Over de auteur)
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Jeroen van Disseldorp is geboren op 21 November 1978 te Maastricht. In 1997 heeft hij eind-examen gedaan op het Stedelijk Gymnasium te Apeldoorn. Van 1997 tot 2001 studeerde hij aan het University College te Utrecht, waar hij cum laude zijn Bachelor Life Sciences behaalde. Gedurende deze periode heeft hij zich een jaar ingezet voor de ucsa (University College Student Association) als treasurer van het bestuur. Hij studeerde Geneeskunde aan de Universiteit Utrecht van 2001-2006. Gedurende zijn studie heeft hij in Léon Nicaragua onderzoek gedaan naar naleving van preoperatieve antibiotica profylaxe, onder leiding van Prof.Dr. I.M. Hoepelman. Na zijn terugkomst in Nederland is hij gestart met onderzoek naar ‘ivf performance van 41-43 jarige patiënten na selectie op basis van afc en fsh’, onder leiding van Dr. F.J.M. Broekmans. Dit was de eerste stap op weg naar zijn proefschrift. Na het beha-len van zijn studie Geneeskunde is Jeroen gaan werken als fertiliteitarts in het Universitair Medisch Centrum Utrecht. Gedurende deze periode heeft hij onderzoek gedaan naar voor-spelling van de menopauze en ovariële reserve, begeleid door promotor Prof.Dr. B.C. Fauser en co-promotor Dr. F.J.M. Broekmans. In augustus 2009 is Jeroen begonnen aan de oplei-ding tot gynaecoloog in het TweeSteden Ziekenhuis te Tilburg (opleider Dr. H.J.H.M. Van Dessel). Hij zal zijn verdere opleiding tot gynaecoloog voortzetten in het cluster Utrecht. Jeroen is in 2008 getrouwd met Marlous van Disseldorp-Voorendt, momenteel wonen zij in Utrecht.
Simone BroerMonique Sterrenburg(paranimfen)
Words of Appreciation.(Dankwoord)
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Het boek is uit; voor u ligt het resultaat van ruim 3 jaar schrijven en herschrijven. Ik vond het leuk en een uitdaging om het beschreven onderzoek te verrichten en op te schrijven, maar naast mij zijn vele personen belangrijk geweest bij het tot stand komen van dit uiteindelijke resultaat. Graag wil ik hen hieronder daarvoor bedanken.
Beste Frank, jij bent de afgelopen 3 jaren mijn coach geweest en degene die mij deze promo-tieplek toevertrouwde. De afgelopen jaren heb ik versteld gestaan van jou onuitputtelijke stroom nieuwe ideeën. Ik wil je graag bedanken voor het gestelde vertrouwen, de discussies over de interpretatie van de uitkomsten en de manier waarop jij promovendi persoonlijk weet te begeleiden en steunen.
Beste Bart, bedankt voor de ruimte en zelfstandigheid die je ons geeft. Ik vind het een eer deel uit te maken van jouw grote onderzoeksfamilie. Daarnaast weet jij als geen ander over-zicht te houden en de vinger precies op de zere plek te leggen ten einde manuscripten verder te verbeteren.
Beste Nick, dank voor de begeleiding van mijn ivf-traject. Je bent de grondlegger van de preconceptionele zorg in Nederland. Dankzij jouw goede zorg heeft het verzamelen van gegevens vlak voor je vertrek tot een manscript geleid.
Prof. Dr. E.P.J.G. Cuppen, Prof. Dr. B.J. Prakken, Prof. Dr. F. van Bel, Prof. Dr. Y.T. van der Schouw en Dr. C.B. Lambalk wil ik graag bedanken voor het zitting nemen in mijn leescommissie.
Beste Rene, dank voor je onmisbare statistische ondersteuning, ook als resultaten net niet meer significant waren. Je bent een kei in het inzichtelijk maken van die ondoorzichtige statistiek. Beste Yvonne, Nils en Lude, dank voor jullie actieve begeleiding als co-auteur bij de ver-schillende hoofdstukken. Jullie hebben alle drie het geduld gehad om mij moeilijke dingen simpel uit te leggen.
Dear Malcolm, although I would like to visit Australia in the future, the distance caused us to never meet in person. Nevertheless, I would like to thank you for your helpfull mathema-tical modelling in chapter 3.
Lieve collega fertiliteitartsen Frederika, Anna, Nijske, Lieneke, Marieke, Marianne, Moni-que, Yvonne en Ouijdane, ik heb mij als haantje in jullie kippenhok erg thuis gevoeld; dank voor de prettige samenwerking. Onderzoek combineren met ivf werkzaamheden is niet altijd makkelijk geweest, maar ik had het nooit willen missen.
Lieve ivf verpleegkundigen dank voor jullie plezierige ondersteuning. Jullie maakten de diensten erg gezellig. Marjolein, roomie, dank voor de leuke tijd in Barcelona. Sif, dank voor het opvangen en opvoeden van de kuikens op de voorkant. Marian, dank voor je vrie-zerkunde en ondersteuning bij het onderzoeken.
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Ellis en Annet, zonder jullie hulp stond ik nu nog steeds in het archief te ploeteren. Geen lijstje met patiëntgegevens was jullie ooit te veel. Dank voor al jullie gezelligheid en steun. Tessa, Ingid en Ellis dank voor de gezelligheid, tips, benodigde adressen en afspraken met de bazen.
Sjerp, Peter, Dagmar, Esther, dank voor het beantwoorden van alle labtechnische ivf vraag-jes en het meedenken over het verkrijgen van onderzoeksgegevens. Lieve analisten, Renata, Erna, Truus, Linde, Michael, Margot, Joke, Els, Caroline, Jolanda, Marleen dank voor jullie hulp en de prettige tijd tijdens het IVF werk.
Inge Maitimu, Eef Lentjes, Hilda Compagner, dank voor jullie hulp bij het verwerken en opslaan van monsters voor het cola en pc spreekuur. Dank voor het meewerken bij de verschillende nabepalingen.
Frank, Angelique, Piet, Marjan, Ron en Michelle dank voor de inwijding in de voortplan-tingsgeneeskunde. Angelique bedankt voor de prettige samenwerking rondom het cola spreekuur en het leerzaamste uurtje in de week: de cola-b bespreking. Frank bedankt voor het vertrouwen (en agenda-technische noodzaak) in het zelfstandig hysteroscopieren.
Lieve collega uitvinders van het azu en de “overkant”, we hebben gelachen en geklaagd. Ik zal de broodjes van de week, de kroketten, de rbm pruik, de lijst en de discussie over kamertje 1 missen. Gelukkig worden we de komende jaren allemaal opnieuw collega’s.
Ellen Klinkert, Ilse van Rooij, Janet Kwee en Gabrielle Scheffer, dank voor het verrichte voorwerk en vergaarde gegevens. Ik kon dankzij jullie op een solide basis verder bouwen.
Collega’s in het TweeSteden Ziekenhuis, dank voor de opvang van deze groene aios en het bieden van een veilige leeromgeving.
Sebas, Maas, Roger en Chief, ons contact sinds uc is wat minder frequent geworden de laat-ste jaren. Last-man-standing tijdens ons onregelmatige werk is toch minder aantrekkelijk geworden. Gelukkig blijven we de tijd vinden elkaar regelmatig te zien. Three words…
Monique en Simone, ik ben heel gelukkig dat twee zulke sterke en bijzondere vrouwelijke collega’s achter me staan tijdens mijn verdediging. Monique, ik waardeer jouw karakter en persoonlijkheid enorm. Ik heb er van genoten om met mijn fietsmaatje steeds stoom te kunnen afblazen. Simone, je hebt als co-assistent met het grootste gemak een plek veroverd tussen de vaste onderzoekers. Ik bewonder je nu als collega om je openheid en gezellig-heid.
Lieve familie Voorendt, ik voel me thuis in jullie hechte gezin en kijk telkens weer uit naar jullie ideeën over nieuw te ondernemen uitstapjes of vakanties.
Lieve Pa, Ma, Rob en Janneke en Inge, bedankt voor alles. Door het uit huis gaan van ons allen, zijn de laatste jaren niet makkelijk geweest. Pap, het is bijzonder met een specialisatie bezig te zijn die jij ook ambieerde, maar niet hebt kunnen doen. Inge, jij geeft dit boekje pas écht gestalte.
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Lieve Marlous, jij bent de beste. Jouw steun en flexibiliteit waren onmisbaar voor het maken van dit boekje. Samen kunnen we de wereld aan, dus ik ben benieuwd naar ons volgende avontuur.
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