1 Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Marmara University School of Medicine, Istanbul, Turkey
2 To whom correspondence should be addressed at: Kuyubasi S.Fenik Apt. No: 20/17, 34724 Feneryolu, Istanbul, Turkey. Email: korayelter{at}marmara.edu.tr
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Abstract |
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Key words: age/antral follicle/FSH/ovarian reserve/ovarian volume
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Introduction |
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Many women in developed countries delay childbearing. Since age is not a good predictor of reproductive potential (Bukman and Heineman, 2001), use of OR tests for an accurate assessment of the reproductive potential in these older women may help clinicians during counselling. The determination of OR may help clinicians in making the decision regarding prophylactic oophorectomy in selected women (Barnes-Kedar and Plon, 2002
). Younger women can be screened for early ovarian ageing, especially those in high-risk groups, as has been proposed recently for the prevention of subfertility (Nikolaou and Templeton, 2003
). Detection of early ovarian ageing is not only important for infertility, but is also of interest in relation to general somatic ageing (Kirkwood, 1998
). Therefore, the establishment of normal curves for OR tests is important. A normal curve for OV may also help to define abnormal values at different ages in screening for early ovarian cancer (DePriest et al., 1997
).
It has been suggested that the normal rate of oocyte depletion follows a biphasic pattern, accelerating below a number of 25 000 at a mean age of 3738 years based on histological analyses of ovaries (Faddy et al., 1992
). This suggested midlife acceleration in oocyte loss is of great interest. Although the OR tests have been studied in infertile women, there are few studies addressing the effects of age on OR tests in women without infertility (Ruess et al., 1996
; Scheffer et al., 1999
; Ng et al., 2003
). Furthermore, these studies have not analysed an optimum curve for the relationship between OR and age. Some authors have suggested a monophasic linear relationship, and others have suggested a biphasic pattern without analysing a comparison between different models (Ruess et al., 1996
; Ng et al., 2003
).
Therefore, in this prospective study, we aimed to find an optimum curve that might define the relationship between different OR tests and age, by comparing the predictive power of monophasic and biphasic regression lines with different cut-off values of age.
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Materials and methods |
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On cycle day 3 of spontaneous bleeding, baseline vaginal ultrasonography was performed for bilateral AF count and OV determinations, and venous blood samples were withdrawn for serum FSH and E2 determinations.
Assays and ultrasonographic measurements
All blood samples were centrifuged within 2 h after withdrawal and stored at 20°C until assayed. Serum FSH and E2 concentrations were determined using the Immulite immunoassay system (Diagnostic Products Corporation, Los Angeles, CA). This assay is standardized to the World Health Organization Second International Reference Preparation 78/549. The inter- and intra-assay coefficients of variation were 6.6 and 5.4% for FSH, and 5.4 and 4.4% for E2, respectively.
Transvaginal ultrasound was performed by the same physician (E.T.), using a GE Logiq 200 Pro (GE Medical Systems, Milwaukee, WI) with a 6.5 MHz vaginal transducer. Round or oval echo-free structures were regarded as follicles, and all ovarian follicles measuring 210 mm on both ovaries were counted on cycle day 3. Ovarian volume subsequently was computed using the ellipsoid formula: OV = D1 x D2 x D3x/6, where D1, D2 and D3 are the maximal perpendicular diameters of each ovary. The volumes and AF counts of both ovaries were added, and the total number of follicles and total OV per patient were used for calculations.
Statistical analysis
Correlations between OR tests and age were analysed by using the Pearson's correlation test. For the significantly correlated parameters, curve estimation was performed to determine the optimal relationship between age and OR tests. For the curve estimation procedure, curve estimation regression statistics were performed for 11 different regression models, including linear, logarithmic, inverse, quadratic, cubic, power, compound, S-curve, logistic, growth, and exponential models. The model with the highest coefficient of determination (r2) was accepted as the optimal model for the relationship. The coefficient of determination is the amount of the scatter in one variable that can be explained by another. In the present study, it is the rate (e.g. 35% when r2=0.35) of variation in the values of the OR test which can be accounted for by knowing the age through using the relevant model.
Standard deviations for the OR test in different age groups, i.e. 2125, 2630, 3135, 3640 and 4145 years, were determined. The predictive power of the model with the highest r2 value was analysed by determining the number of observed values in the mean as determined by the regression equation±2 SDs range. Any observed value below or above the±2 SDs of the predicted value was accepted as the outlying value. The number of outlying values was determined and the ratio of outlying values to the total number of values (n=81) was used for the statistical analysis.
Linear regression equations were also determined. The regression coefficient and the y-intercept were determined for the regression formula: OR test =
x age + y-intercept. The number of observed values below and above the±2 SDs of the predicted values was determined. Following determination of the monophasic line for the significantly correlated OR tests, the predictive power of biphasic lines was analysed. Each of the cut-offs between 30 and 38 for the age was analysed separately, and the number and ratio of outlying values to the total number of values (n=81) were determined for each biphasic line. The ratio of outlying values was compared by using the
2 or Fisher's exact tests, where appropriate. SPSS, Release 11.5 (SPSS, Inc, Chicago, IL) was used for the statistical analysis, and a P-value of <0.05 was considered significant.
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Results |
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Discussion |
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It has been suggested that oocyte depletion accelerates toward the onset of menopause, and this acceleration is characterized by a bend in the scatter of points on a log-linear plot of follicle number by age (Richardson et al., 1987; Faddy et al., 1992
; Gougeon et al., 1994
). This abrupt change in the rate of follicular loss has been suggested to occur at the age of 37.5 years (Faddy et al., 1992
). Thus, follicular atresia has been interpreted as biphasic. A linear relationship after logarithmic transformation indicates that the rate of percentage change is constant. However, what happens at some critical age to make the rate of follicular depletion increase abruptly? Recently, this biphasic pattern has been questioned by Leidy et al. (1998)
. They have tested four models with the use of data drawn from published studies and histological analyses of ovaries (Leidy et al., 1998
). These models did not include a quadratic model. Leidy et al. (1998)
concluded that follicular atresia was not linear on either original measurement or log-linear scales. They also suggested that the data on follicular atresia did not support the notion that an abrupt change in the exponential rate of decline exists, and concluded that a biphasic pattern was most likely to be an artefact (Leidy et al., 1998
). It is more acceptable to hypothesize the existence of an increase in the decline rate of follicles through the years instead of an abrupt change at a certain age.
Faddy and Gosden also revised their original model, and suggested a gradual change in follicle depletion (Faddy and Gosden, 1996, 2000
; Faddy et al., 1992
). In their model, the follicles were classified into three stages of growth: stage I, primordial follicles; stage II, early growing forms; and stage III, more advanced stages. Following re-analysis of ovaries in their initial study (Faddy et al., 1992
), they suggested a model that shows a gradual increase in follicle depletion based on either growth of follicles between these three compartments or atresia. Their model suggested that a proportional number of primordial follicles enter the growth phase. This model suggests that the AF count can reliably reflect the primordial follicle pool, which is the actual OR. This is consistent with the results of clinical studies, which showed that AF count is a reliable OR test. Since their model was based on histological findings and a three compartment model, we avoided detailed comparison between their model and that in the present study, which was based on OR tests. However, the similarity of the graph in the present study to that in their model supports the proportional relationship between the primordial follicle cohort and growing follicles (Faddy and Gosden, 2000
).
Clinical experience and demographic data obtained from natural populations suggest that the period of optimal fertility only lasts until age 3031 and decreases thereafter (van Noord-Zaadstra et al., 1991; te Velde et al., 1998
). The suggested model for the AF count and OV in the present study indicates almost a constant OR between ages 21 and 30. The OV decreases by 1.7% and the AF count decreases by 7.7% during that time. Thereafter, although the amount of decrease per year is constant according to the model, the percentage change increases with age. Therefore, the apparent acceleration in decline is merely due to a constant depletion and a decreasing denominator; percentage change = amount of difference (which is constant)/previous amount (which decreases with age). The percentage change in the serum FSH level increases with age through all ages between 21 and 43. However, this increase is slight for ages between 21 and 30, i.e. 23% in 9 years.
These models may also help to analyse the mechanism for ovarian ageing. It has been suggested previously that the accelerated increase of oocyte depletion might be due to a rising serum FSH level (Gosden and Faddy, 1994; Gougeon, 1996
). In the present study, we observed that the serum FSH level increases slightly at the third decade of age, but AF count and OV appear to decline slightly in the same period. The increase in serum FSH level was slightly faster than the decline in AF count. In the model, serum FSH level increased 60% between ages 25 and 40. The AF count decreased 51% during the same period.. These findings suggest that increasing serum FSH level may initiate the accelerated loss. However, it should be mentioned that the evidence that the FSH receptor is not expressed in follicles until they have reached a certain stage argues against this FSH-dependent hypothesis (Oktay et al., 1997
). The underlying mechanism for the accelerated loss of oocytes remains unclear. However, the results of egg donation programmes suggest that the oocyte itself is responsible for the declining fertility in humans. It appears that ageing has some negative effects on the quiescent oocytes. This may be a result of either damage to DNA, decreasing density of oocytes or changes in growth factor tone (Faddy and Gosden, 2000
).
In the present study, the quadratic model for the AF count was superior to any biphasic linear relationship for predicting the observed values. This model indicates a slower increase in the decline rate instead of an abrupt change. This relationship between age and AF count is similar to fertility change by age in different populations (Menken et al., 1986). Compared with women aged 2024, fecundity is reduced on average by 6% for women aged 2529, 14% for those aged 3034, and 31% for women aged 3539 (Menken et al., 1986
). The corresponding values were 3, 14 and 34%, respectively, when the model in the present study for the AF count was used.
The model for the AF count in the present study means that the existence of an FSH-sensitive cohort will have ended at the age of 46.7 years. This seems biologically unlikely in view of the fact that the vast majority of women will still have cycles at this age, albeit irregular. Although this value is consistent with mean age at menopause in Turkey (47 years) (Neslihan Carda et al., 1998), it should also be mentioned that models in the present study are based on the data of women between the ages of 20 and 43 years, and therefore extrapolating these results to older ages may not be correct.
The AF count had the highest correlation with age among the OR tests in the present study. Scheffer et al. (2003) also showed that the number of AFs correlated much better with the age of the women evaluated in their study than other presumed basal markers for reproductive age, including FSH, inhibin B, E2 and OV. Therefore, results in the present study also support the hypothesis that the AF count is superior to other static measures of reproductive ageing.
Although nomograms for OV in post-menopausal women have been studied (Goswamy et al., 1983, 1988
), data in premenopausal women are limited (van Nagell et al., 1990
). Surprisingly, there are reports that used a constant cut-off value for the OV while studying the effectiveness of transvaginal sonography in screening for ovarian cancer in premenopausal women (DePriest et al., 1997
). The present study showed that OV decreases with age, especially after the age of 30 years. Therefore, using a constant cut-off value for the OV in premenopausal women may not be appropriate for screening purposes.
In conclusion, the pattern of reproductive ageing as evidenced by hormonal and ultrasonographic OR tests does not show an abrupt change at a certain age, but follows a continuously increasing rate of decline in the third decade of life. The change in serum FSH level and ultrasonographic OR tests with age follows a quadratic model in regularly menstruating premenopausal women.
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Submitted on March 26, 2004; accepted on July 13, 2004.