Predictors of antral follicle count during the reproductive years

Predictors of antral follicle count during the reproductiveyears

J.Kline1,2,3,7, A.Kinney4, A.Kelly5, M.L.Reuss6 and B.Levin3

1Epidemiology of Developmental Brain Disorders Department, New York State Psychiatric Institute, New York, NY 10032,2Gertrude H. Sergievsky Center and 3Mailman School of Public Health, Columbia University, 4Research Foundation for Mental

Hygiene, New York State Psychiatric Institute, New York, NY and Graduate School of Arts and Sciences, Columbia University,5Department of Obstetrics and Gynecology, Columbia University, New York, NY 10032 and 6Southwest Women’s Sonography,

Albuquerque, NM 87109, USA

7To whom correspondence should be addressed at: Psychiatric Institute, Epidemiology, 722 West 168th Street, Room 1607,

New York, NY 10032. E-mail: [email protected]

BACKGROUND: We sought to identify indicators of antral follicle count which would be serviceable to cliniciansseeking to estimate the number of ovarian follicles without relying on sonographic counts. METHODS: We exam-ined the relations of chronological age and four potential indicators of ovarian age—ovarian volume, FSH, dimericinhibin B and estradiol—to antral follicle count in 176 recently pregnant women. We identified the regressionmodels which best predict low antral follicle count (#10 follicles). RESULTS: Chronological age, ovarian volume,FSH and inhibin B were each significantly associated with antral follicle count. Fifty-three (30.1%) women had#10 antral follicles. In the total sample, at the cutpoint corresponding to 80% sensitivity, the positive predictivevalue for a regression model with all four variables was 60%. All regression models performed less well in women<35 years (13.9% with low count) than in women $35 years (52.0% with low count). In older women, the positivepredictive value for the model with all four variables was 79%, compared with 60% for a model with chronologi-cal age alone. CONCLUSIONS: Our models provide a basis for advising women aged $35 years who are eithertrying to conceive or wish to learn whether they may postpone childbearing.

Key words: age/epidemiology/FSH/inhibin B/ovarian follicles

Introduction

Many women defer childbearing to the later years, raising

their risk of adverse pregnancy outcomes associated with

ovarian ageing. In particular, rates of fecundity (the physio-

logical capacity to have a live birth) and fertility (the number

of actual live births) begin to decline rapidly when women

reach the mid–late 30s (Howe et al., 1985; Menken et al.,

1986). While social and medical factors (e.g. contraception,

voluntary sterility, coital frequency) influence these rates,

some of the decline is biologically determined. Data suggest

that follicular and ovarian, rather than uterine, factors are the

primary determinants of decreasing fecundability (the physio-

logical capacity to conceive) during a woman’s 30s and 40s.

For example, although implantation and clinical pregnancy

rates for women receiving donor ova do not decline until

about age 50 years (CDC, 2000; Toner et al., 2002), preg-

nancy rates for women attempting assisted reproduction with

their own ova start to decline when they are in their mid-30s

(CDC, 2000).

Studies of autopsy and surgical specimens show that the

germ cell population, which is largest during fetal develop-

ment (,7 £ 106), decreases approximately exponentially

with chronological age (Block, 1952; Baker, 1963;

Thomford et al., 1987; Faddy et al., 1992; Leidy et al.,

1998; Westhoff et al., 2000). A similar decline is evident

for antral follicles, the small fraction (,0.5%) of the total

pool that develops and enlarges during each menstrual

cycle. Sonographic studies confirm that antral follicle count

declines with chronological age in women of reproductive

age (Reuss et al., 1996; Scheffer et al., 1999; Broekmans

et al., 2004; Kline et al., 2004). Moreover, a detectable

decline in follicle count may precede detectable changes in

hormone levels (Scheffer et al., 1999; Kline et al., 2004). It

is reasonable to infer that the decline in fecundity with

chronological age reflects, at least in part, the diminishing

supply of oocytes. Among infertile women, indicators of the

size of the oocyte pool supplement chronological age as

predictors of the response to assisted reproductive technol-

ogies. While findings are not entirely consistent (Bukman

and Heineman, 2001; Bancsi et al., 2003), antral folli-

cle count, ovarian volume and pretreatment levels of FSH

and inhibin B have each been associated with ovarian

response to hormonal stimulation and clinical pregnancy

rates.

Human Reproduction Vol.20, No.8 pp. 2179–2189, 2005 doi:10.1093/humrep/dei048

Advance Access publication April 28, 2005

q The Author 2005. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.For Permissions, please email: [email protected]

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In this paper we assume that antral follicle count is the

best indicator of the size of the underlying follicle pool.

Obtaining high quality data is, however, labour intense.

While follicles can be counted with moderate to high

reliability in research settings (Scheffer et al., 2002; Kline

et al., 2004), similar results are less likely in clinical practice

(Hansen et al., 2003), at least until specialized (potentially

automated) three-dimensional applications are developed and

widely available. We therefore sought to identify indicators

other than antral follicle count which would be serviceable

to clinicians seeking to estimate the size of the underlying

follicle pool.

We draw on data from a sample of recently pregnant

women to extend observations on the natural history of ovar-

ian ageing. First, we describe the relations between chrono-

logical age and several potential indicators of ovarian age:

the number of antral follicles, the diameter of the largest fol-

licle, the surface area of the follicles, ovarian volume, and

levels of FSH, inhibin B and estradiol (E2). We also describe

associations between indicators. Next, from among the indi-

cators that can be easily obtained (age, ovarian volume and

levels of FSH, inhibin B and E2) we identify the combination

that best predicts antral follicle count. We also consider the

utility of inhibin B level since, in clinical practice, measure-

ment of this hormone is less common than measurement of

FSH and E2.

Materials and methods

Participants

Selection criteria and the protocol for this study are described in

detail elsewhere (Kline et al., 2004). The sample derives from a

study designed to test the hypothesis that the association between

advancing chronological age and trisomic pregnancy reflects an

association between the size of the oocyte pool and trisomy, with

risk higher in women with fewer oocytes. The data did not support

the hypothesis.

The protocol

Briefly, from September 1998 to April 2001, we identified women

aged $18 years with singleton prefetal losses (developmental age

,9 weeks) whose products of conception were submitted to the

Pathology Department of a hospital in New York State. We asked

for permission to karyotype the abortus. If a woman’s loss was suc-

cessfully karyotyped, we asked her to complete a short telephone

interview to determine her eligibility. Eligible women who con-

sented to the protocol completed a more extensive telephone inter-

view and made two visits to the study hospital during the first week

of their second or later menstrual cycle, the first on day 1–4 for a

blood sample and the second on day 5–7 for transvaginal

sonography.

To obtain valid ovarian age measures, we required: no pituitary

disorder or hormonal disorder related to ovarian function, no

oophorectomy, no hormonal medication, no pregnancy at the time

of ultrasound, no breastfeeding or breastfeeding no more than once

per day during the menstrual cycle preceding the study assessments.

We required that any diagnosis be current, the report of the diagnos-

tic work-up informative and the clinical symptoms and treatment

consistent with the diagnosis. The study reproductive endocrinolo-

gist (A.C.K.) reviewed the interview data to determine whether or

not a potential participant currently had a condition associated with

altered hormone levels.

Women with spontaneous abortions

Of the 244 women with karyotyped losses, 127 (52%) completed

the protocol (Table I). The principal reasons for not completing the

protocol were: refusal (23%) and ineligibility (25%), primarily due

to use of hormonal contraceptives or pregnancy soon after the index

loss. Six women were excluded because of hormonal conditions and

another six were excluded due to use of fertility drugs, although

only two had experienced conception delay .1 year prior to the

study pregnancy.

Women who completed the protocol were on average older than

women who did not. The age difference arose chiefly because

younger women were more likely to begin hormonal contraception

immediately after their loss. Among the 205 women who completed

the eligibility interview, adjusting for age, the odds of completing

the protocol did not differ with education, parity or number of prior

induced abortions; completion rates were significantly higher for

women with one or more prior spontaneous abortions than for

women with no prior losses (74 versus 56%).

Table I. Number of women identified/selected, declining the study orineligible, or completing the protocol

Prefetalloss

Livebirth

Identified/selected 435 219Tissue culture set up 269 NAKaryotyped 244 NAMoved or not located 1 7Declined eligibility interview 36 56Eligible, declined study or lost during follow-up 19 11Ineligiblea 61 80

Hormonal contraceptiveb 25 38Pregnantc 18 12Breastfeeding 3 23Oophorectomy 1 3Other condition affecting ovaries or hormonesd 6 2Fertility medication 6 0Other hormonal medicatione 1 1Otherf 1 1

Completed protocol 127 65Analytical exclusions

Repeat entranceg 4 0Only one informative ovary 6 6

Analytical sample 117 59

aIncludes 14 women (two with prefetal losses, 12 with live births) who pro-vided information on their eligibility status but declined the eligibilityinterview.bSixty-one women were taking oral contraceptives; two live birth controlshad intrauterine devices that included a hormonal component. Two live birthcontrols were also breastfeeding.cOne live birth control was also breastfeeding.dFive women (four with prefetal losses, one live birth control) had beendiagnosed with polycystic ovary disease, two women (one with a prefetalloss, one live birth control) with hyperprolactaemia, and one woman with aprefetal loss had been diagnosed with anovulation.eOne woman with a prefetal loss was taking oral contraceptives to regulateher periods (oligomenorrhoea, possible polycystic ovary disease). One livebirth control was taking progesterone ‘for a possible cyst’.fOne woman with a prefetal loss did not complete the protocol within 8months of her spontaneous abortion. One live birth control had had a pre-vious trisomic conception.gTo maintain the independence of observations, the analysis excludes repeatstudy entrances of four women.

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Women with live births

For each woman with a trisomic loss who completed the study, we

selected an age-matched control with a chromosomally and anatomi-

cally normal live birth $1800 g, no pregnancy loss since the index

pregnancy and no known trisomic pregnancy. They were selected

from the hospital delivery log of women who delivered during the

7–13 months preceding the date of selection. Live birth controls

were matched to trisomy cases for projected age (^6 months) at the

sonography visit. If a selected control was ineligible for the study or

refused to participate, we replaced her. The protocol for women

with live births was identical to the protocol for women with prefe-

tal losses.

In total, we selected 219 women with live births, 65 of whom

(30%) completed the protocol (Table I). The principal reasons for

not completing the protocol were refusal (31%) and ineligibility

(37%), primarily due to use of hormonal contraceptives or breast-

feeding. Two women were excluded because of hormonal

conditions.

Women who completed the protocol tended to be older, though

not significantly so, than women who did not. Among the 144

women who completed the eligibility interview, the odds of com-

pleting the protocol did not differ with educational attainment,

parity, number of prior induced abortions or number of prior spon-

taneous abortions.

Analytical sample

Analyses exclude repeat study entrances of four women (all with

pregnancy losses) to maintain the independence of observations.

They also exclude 12 women: eight women for whom we were able

to scan only one ovary—even though, based on history and transab-

dominal scans, each woman was known to have two ovaries—and

four women who had conditions—three a cyst and one an endome-

trioma—which might have obscured the follicle count. Thus the

analytical sample includes 176 women with both ovaries scanned

and unobstructed.

Characteristics of the sample (Table II)

Among the 176 women, 117 had an index pregnancy ending in

spontaneous abortion and 59 an index pregnancy ending in live

birth. Average age at ultrasound was 34 years (range 22–48). The

majority were white and had completed high school. Ninety-five per

cent completed the blood and sonography protocols after the second

or third menstrual period following the index loss or, for women

with live births, the introductory letter.

Indicators of ovarian age

Antral follicles

We used four transducers over the course of the study: the first two,

Acuson EXP 128, operated at a frequency of 7 MHz; the second

two, Acuson Sequoia (8–10 MHz) were usually used at 10 MHz.

The sonography technician scanned each ovary in transverse and

longitudinal views at a subjectively constant velocity, repeating

scans, as needed, until she obtained an optimal scan.

We videotaped the sonography scan. The scans were counted in

four randomized batches. The randomization procedure was

unknown to the sonographer.

The sonographer (M.L.R.) identified the optimal scan, converted

it to a digitized format and exported it to Matrox Inspector

(Matrox Electronic Systems Ltd, Dorval, Quebec, Canada 2005),

an interactive imaging software. We used the software to: (i) follow

each sonolucency interpreted as a follicle through the scan, frame

by frame, to identify its maximum diameter; and (ii) measure

the maximum diameter of each follicle in the vertical plane by cali-

brating measurements to the centimetre scale generated by the ultra-

sound machine. Diameters ranged from 1.2 to 24.1 mm, with 99.3%

of follicles ,12 mm in diameter.

To assess the reliability of the counting procedure, we counted

again the follicles in 40 ovaries (Kline et al., 2004). The intra-class

correlation coefficient was 0.92. Analyses use the second count

for the 20 women whose scans were counted twice. Counts ranged

from 2 to 70 (median ¼ 15, mean ¼ 18.7, SD ¼ 12.8). The mean

difference between counts in the left and right ovaries was

0.2 (SD ¼ 5.4, range ¼ 2 15 to 28); the correlation between them

was 0.70 (P , 0.0001).

We also examined associations with three alternative measures:

(i) diameter of the largest follicle, because some data suggest that

advancing age is associated with earlier emergence of the dominant

follicle (Klein et al., 1996a, 2000); (ii) the sum of the antral follicle

surface areas, to examine whether surface area is more closely

related to inhibin B level than to follicle count; (iii) total ovarian

volume, as a possible indicator, easily obtained, of the number of

antral follicles. We assumed that antral follicles are spherical, esti-

mating the surface area of each by 4pr 2, where r is the radius. We

assumed that each ovary is ellipsoidal, estimating the volume of

each by D1 £ D2 £ D3 £ p/6, where D1, D2 and D3 are the three

dimensions of the ovary. For the sum of the surface areas, the mean

difference between left and right ovaries was 255.1 mm2

(SD ¼ 511.2, range 21710.6 to 1315.2); the correlation between

them was 0.40 (P , 0.0001). For ovarian volume, the mean

Table II. Selected characteristics of women who completed the study

No. of women 176Outcome of the index pregnancy

Spontaneous abortiona 117Live birth 59

Age at ultrasound, mean (SD) 34.0 (5.4)No. of live births, mean (SD) 1.3 (1.1)No. of spontaneous abortionsb, mean (SD) 1.2 (1.1)No. of induced abortions, mean (SD) 0.2 (0.5)Percentage distributionsEducation (%)#high school 19.3Some college 29.5College graduate 31.3Postgraduate degree 19.9

Ethnicity (%)White, non-Hispanic 94.3

Menstrual period preceding study protocol (%)Period 2 74.4Period 3c 20.5Period 4–7 5.1

Day of blood samplingDay 1 9.1Day 2 31.3Day 3 33.0Day 4 26.7

Day of sonographyDay 5 33.5Day 6 32.4Day 7 34.1

aKaryotypes of the abortus: trisomy (n ¼ 52), other chromosomally abnormal(n ¼ 22), confirmed chromosomally normal (n ¼ 21), unconfirmed chromo-somally normal female (n ¼ 22). We classify as ‘unconfirmed chromoso-mally normally female’ those losses in which the karyotype was 46,XX andconfirmatory DNA analysis was either not possible because of inadequatespecimens (n ¼ 17) or DNA analysis indicated inadvertent karyotyping ofmaternal cells (n ¼ 5).bExcludes one woman with unknown number of spontaneous abortions.cIncludes two women where the menstrual period preceding the studyprotocol may have been either period 2 or period 3.

Predictors of antral follicle count

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difference between left and right ovaries was 20.8 cm3 (SD ¼ 4.5,

range ¼ 2 16.8 to 16.1); the correlation between them was 0.25

(P ¼ 0.25).

Serum hormone levels

Blood samples were processed in a refrigerated centrifuge and, after

separation, sera were frozen at 2258C at the study hospital; they

were shipped to New York City and stored at 2208C. FSH and E2

were measured by solid-phase chemiluminescent enzyme immuno-

assays (Diagnostic Products Co. Los Angeles, CA) (Immulite);

dimeric inhibin B was measured by radioimmunoassay (Oxford Bio-

Innovation Ltd, Upper Heyford, Oxfordshire, England). For FSH,

sensitivity (the minimum detection limit) was 0.1 mIU/ml; intra- and

inter-assay coefficients of variation (CV) were 9.3 and 10.5%

respectively. For inhibin B, sensitivity was 20 pg/ml; intra- and

inter-assay CV were 5.1 and 6.2%, respectively. For E2, sensitivity

was 20 pg/ml; intra- and inter-assay CV were 1.9 and 5%

respectively.

Table III shows summary statistics on each of the ovarian age

indicators. Follicles were counted and hormones assayed without

knowledge of any subject characteristics.

Statistical analysis

We used a logarithmic transformation for all ovarian age indicators

to meet the normal error assumption of least squares regression. For

antral follicle count, follicle maximum diameter and follicle surface

area we used the transformation ln(1þmeasure) since, in theory, a

woman might have no antral follicles.

We first describe the magnitude of associations: (i) between

chronological age and each ovarian age indicator; and (ii) between

the ovarian age indicators, adjusting for chronological age. For each

indicator we illustrate the pattern of change with chronological age

by showing medians and the least squares regression equation that

describes change with age. We fit models with first-, second- and

third-order terms for age, but retained a higher-order term only if it

significantly improved the proportion of variance explained and

visual inspection of the data suggested that the association with age

was not log-linear. The descriptive regression equations are based

on 168 women; they exclude eight women with one or two ovarian

age indicators that were .3 SD from the value predicted, given

age.

Second, drawing on the entire sample of 176, we used ordinary

least squares regression analysis to predict follicle count using

chronological age and readily obtained indicators of ovarian age—

namely, ovarian volume and hormone levels.

We evaluated the accuracy of eight regression models to discrimi-

nate between women with low antral follicle count (‘older’ ovarian

age) and high antral follicle count using Receiver Operating Charac-

teristic (ROC) curves (McNeil and Hanley, 1984). We defined ‘low

count’ as #10 antral follicles. We chose #10 follicles for scientific

and practical reasons: it corresponds to the median count at 39

years, an age at which infecundity rates increase steeply (Schwartz

and Mayaux, 1982; Menken et al., 1986); it demarcates the lowest

30% of the sample, providing a sufficient number of women with

‘low count’ for analysis.

The larger the area under the ROC curve, the better the discrimi-

natory capability of the model. The two models with the best ROCs

include measures taken at two different points during the menstrual

cycle, on day 1–4 for hormones and on day 5–7 for ovarian

volume. Five models, including the model with age only, which are

based on data from a single time-point, might be more practicable

for clinicians. For each model, we determined the cutpoint that cor-

responds to sensitivity of $80% and set out the corresponding

specificity, positive predictive value and negative predictive value.

Because predictive value is a function of the proportion with low

count, we also describe the characteristics of five of the models

separately for women aged ,35 and $35 years.

We repeated all regression analyses adjusting for the outcome of

the index pregnancy (spontaneous abortion versus live birth).

Results were unchanged (data not shown). For our primary

regression model predicting follicle count, we also examined

whether the addition of other covariates (i.e. obstetric variables,

menstrual characteristics, body mass index, education, smoking)

added significantly to the proportion of variance in follicle count

explained by the model; they did not.

Results

Associations with chronological age

All ovarian age indicators are significantly associated with

chronological age in the expected direction (Table IV). The

correlation is strongest with ln(1þantral follicle count)

(r ¼ 20.52). Correlations were moderate for ln(1þ follicle

maximum diameter), ln(1þ follicle surface area), ln(ovarian

Table III. Summary statistics for antral follicle count, maximum follicle diameter, surface area of the follicles, ovarian volume, inhibin B, FSH and estradiol(E2) in 176 women

Median Mean (SD) Geometric meana Range Percentage belowassay detection level

FolliclesCount 15.0 18.7 (12.8) 15.3 2–70Maximum diameter (mm) 8.5 9.2 (2.8) 8.3 5.1–24.1Surface area (mm2) 1459.2 1583.8 (781.3) 1376.0 155.6–4016.6

OvaryTotal volume (cm3) 12.1 13.1 (5.8) 12.2 2.7–33.1

HormonesFSH (mIU/ml) 4.4 4.8 (2.6) 4.2 0.7–16.3 0.0Inhibin B (pg/ml) 80.5 89.5 (53.8) 72.9 20–272 9.1E2 (pg/ml) 35.0 40.4 (23.0) 36.6 20–206 5.7

aTo meet the normality assumptions of ordinary least squares regression, we used natural logarithmic transformations for each ovarian age indicator. For folliclecount, follicle maximum diameter and follicle surface area, where values of zero are theoretically possible, we used the transformation ln(1 þ follicle measure).For other ovarian age indicators we used ln(other measure). Values are reported in the original scale using the inverse transformation [exp(mean) 2 1 forfollicle measures and exp(mean) for other measures].

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volume) and ln(FSH), ranging from 20.26 to 0.35. Corre-

lations were weak for ln(inhibin B) and ln(E2).

Figure 1 illustrates the shape of the relations between

chronological age and each of the ovarian age indicators. For

antral follicle count, we illustrate two models—one with only

a linear term for age and one with both linear and quadratic

age terms. The quadratic age term improved the R2 of the

regression model by 1.6% (P ¼ 0.053). Follicle surface area

and ovarian volume decrease approximately exponentially

with age. Maximum follicle diameter and E2 increase

approximately exponentially with age. For FSH, the model

includes a quadratic term; levels begin to rise steeply around

age 40 years. The pattern of results for inhibin B suggests

that levels are either constant or increasing until the late 30s

and low after age 40 years; the curve drawn here does not fit

the data well.

Follicle surface area is a function of the number of fol-

licles and their size. We used least squares regression to

examine whether age was associated with follicle surface

area independently of its association with follicle count.

Without adjustment for follicle count, age accounts for

17.0% of the variance in ln(1 þ follicle surface area); with

adjustment, the corresponding proportion is 0.2% (P ¼ 0.33).

Age-adjusted correlations between ovarian age indicators(Table IV)

Ln(1þantral follicle count) is most strongly related to

Ln(1þ follicle surface area) (r ¼ 0.76) and significantly

associated with ovarian volume (r ¼ 0.48), Ln(FSH)

(r ¼ 20.34) and Ln(inhibin B) (r ¼ 0.24). Among

the remaining measures, the pattern of associations is, for

the most part, consistent with expectation. For example,

Ln(1þ follicle surface area) is positively associated with

Ln(inhibin B) (r ¼ 0.34) and with Ln(1 þ follicle maximum

diameter) (r ¼ 0.47). Ln(FSH) is inversely related to Ln(E2)

(r ¼ 20.23).

Ln(FSH) is not correlated with Ln(inhibin B) (r ¼ 20.03).

Because inhibin B levels were lower on day 1

(median ¼ 33.0 pg/ml, n ¼ 16) than on days 2–4

(median ¼ 86.5 pg/ml, n ¼ 160), we repeated the analysis,

limiting the sample to women with blood samples taken on

days 2–4. Ln(FSH) and Ln(inhibin B) are weakly correlated

(r ¼ 20.15, P ¼ 0.053). The correlations between inhibin B

and other ovarian indicators are similar to those in the entire

sample.

Predicting antral follicle count (Table V)

Using only age and four readily available measures (ovarian

volume, FSH, inhibin B, E2), we sought the regression model

which best predicts Ln(1þantral follicle count). Only E2 is

not independently and significantly related to follicle count

(R2 ¼ 0.005, adjusting for chronological age, P ¼ 0.28).

Thus, we did not consider E2 further in the regression ana-

lyses.

The model which includes, in addition to age, ovarian

volume, FSH and inhibin B explains 49.6% of the variance

in follicle count (Table V, model A). Omitting inhibin B

from the model decreases the proportion of variance

explained to 48.3% (model B). The standardized b values

indicate that the magnitude of change, per SD unit increase,

is about the same for age and Ln(1 þ ovarian volume) (stan-

dardized b ¼ 20.33 and 0.35 respectively, model A), lower

for Ln(FSH) (standardized b ¼ 20.23) and lower still for

Ln(inhibin B) (standardized b ¼ 0.12).

Receiver operating characteristics

Fifty-three (30.1%) women had #10 antral follicles. We

assessed the performance of all models to distinguish women

with #10 follicles from women with .10 (Table VI). The

areas under all ROC curves are good, especially for models

A (age, ovarian volume, FSH, inhibin B) and B (age, ovarian

volume, FSH). For comparison, we show the model which

includes only a linear term for age; the addition of a quadra-

tic term for age did not improve the ROC curve. Using the

cutpoint which produces a sensitivity of 80%, models A

and B increase the positive predictive value to 58–60%,

Table IV. Correlations between chronological age and ovarian age indicatorsa with adjustment for chronological age in 176 women

Age(years)

Adjusted for chronological age

Folliclecount

Follicle maximumdiameter

Folliclesurface area

Total ovarianvolume

FSH(mIU/ml)

Inhibin B(pg/ml)

E2

(pg/ml)

Follicle count 20.52{ 1.00 20.06 0.76{ 0.48{ 20.34{ 0.24† 0.08Follicle maximum diameter 0.26§ 1.00 0.47{ 0.37{ 0.14 0.21** 0.06Follicle surface area 20.34{ 1.00 0.67{ 20.29§ 0.34{ 0.14Total ovarian volume 20.26§ 1.00 20.23† 0.24† 0.18*FSH (mIU/ml) 0.35{ 1.00 20.03 20.23†Inhibin B (pg/ml) 2 0.16* 1.00 0.06E2 (pg/ml) 0.18* 1.00

aTo meet the normality assumptions of ordinary least squares regression, we used natural logarithmic transformations for each ovarian age indicator. For folliclecount, follicle maximum diameter and follicle surface area, where values of zero are theoretically possible, we used the transformation ln(1þ indicator). Forovarian volume and hormones, we used ln(indicator).

*P , 0.05.

**P , 0.01.†P , 0.005.§P , 0.0005.{P , 0.0001.


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Figure 1. Chronological age and indicators of ovarian age: Medians and fitted regression equations. Medians are based on 176 women. Theregression equations are based on 168 women; they exclude eight women with one or two ovarian age indicators that were .3 SD from thatpredicted, given age.

Age (years) 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44–48n 2 3 2 5 1 11 4 11 19 14 10 12 7 10 10 15 9 5 4 9 7 2 4

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compared with 42% for a model which includes only age. Of

the three models which include age plus a single ovarian age

indicator, at the cutpoint corresponding to a sensitivity of

80%, the positive predictive value is highest (55%) for the

model with ovarian volume.

We evaluated the performance of five models in younger

(,35 years) and older ($35 years) women separately (Table

VII). We chose the two models with the best performance

overall (models A and B), the model based on results from a

blood test only (model D), the model based on measurement

of the ovary only (model E) and the model based simply

on age (model H). At the cutpoint which corresponds to

a sensitivity of $80%, all models perform better among

older women than among younger women. For example,

Table V. Regression models predicting antral follicle count using chronological age and three ovarian age indicators in 176 women

Model b SE Standardized b P R2

(A) Age, volume, FSH, inhibin B 0.496Intercept 1.702 0.308Age230a 20.038 0.007 20.329 ,0.0001Ln(ovarian volume) 20.487 0.083 0.350 ,0.0001Ln(FSH) 20.301 0.079 20.228 0.0002Ln(inhibin B) 0.109 0.052 0.120 0.0360

(B) Age, volume, FSH 0.483Intercept 2.070 0.257Age230 20.040 0.007 20.342 ,0.0001Ln(ovarian volume) 20.529 0.081 0.379 ,0.0001Ln(FSH) 20.297 0.079 20.225 0.0002

(C) Age, volume, inhibin B 0.453Intercept 1.139 0.281Age230 20.046 0.007 20.396 ,0.0001Ln(ovarian volume) 0.559 0.084 0.401 ,0.0001Ln(inhibin B) 0.105 0.054 0.115 0.0520

(D) Age, FSH, inhibin B 0.394Intercept 2.776 0.271Age230 20.044 0.007 20.378 ,0.0001Ln(FSH) 20.406 0.084 20.307 ,0.0001Ln(inhibin B) 0.181 0.055 0.199 0.0012

(E) Age, volume 0.441Intercept 1.499 0.215Age230 20.047 0.007 20.408 ,0.0001Ln(ovarian volume) 20.598 0.082 0.429 ,0.0001

(F) Age, FSH 0.356Intercept 3.582 0.123Age230 20.047 0.008 20.408 ,0.0001Ln(FSH) 20.415 0.086 20.314 ,0.0001

(G) Age, inhibin B 0.312Intercept 2.201 0.260Age230 20.056 0.007 20.485 ,0.0001Ln(inhibin B) 0.190 0.058 0.208 0.0013

(H) Age 0.269Intercept 3.033 0.050Age230 20.060 0.008 20.529 ,0.0001

aWe centered the age variable by subtracting 30 from each woman’s age in years at ultrasound.

Table VI. Summary data on selected regression modelsa

Model Area under theROC curve

Regression equationcutpoint which yields80% sensitivity

Corresponding to sensitivity of 80%

Specificity Positive predictivevalueb

Negativepredictive valueb

(A) Age, ovarian volume, FSH, inhibin B 0.86 #14.85 0.77 0.60 0.90(B) Age, ovarian volume, FSH 0.85 #14.30 0.76 0.58 0.89(C) Age, ovarian volume, inhibin B 0.83 #15.85 0.68 0.52 0.89(D) Age, FSH, inhibin B 0.84 #15.35 0.72 0.57 0.90(E) Age, ovarian volume 0.82 #14.95 0.73 0.55 0.89(F) Age, FSH 0.82 #15.35 0.66 0.51 0.89(G) Age, inhibin B 0.80 #15.87 0.63 0.48 0.89(H) Age 0.77 #17.10 0.51 0.42 0.86

a(i) Each model’s ability to discriminate between women with low (#10 follicles) and high (.10 follicles) antral follicle count [the area under the receiveroperating characteristic (ROC) curve]; (ii) specificity and predictive values at the regression equation cutpoint yielding 80% sensitivity.bIn the sample, 30.1% (53/176) of women had #10 antral follicles.


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using model A, among older women, at 80% sensitivity,

specificity is 0.77, positive predictive value 0.79 and negative

predictive value 0.78. The analogous values in younger

women are specificity 0.39, positive predictive value 0.17

and negative predictive value 0.92. The disparities arise from

the fact that predictive values are sensitive to the proportion

of women with low antral count (13.9% in the 101 young

women, 52.0% in the 75 older women). In both age strata,

use of the best model (model B for young women, model A

for older women) increases the ability to identify women

with low antral count by ,50%. For example, among older

women, without information other than age we correctly

identify 52% of women with low antral count; using model

A we correctly identify 79%.

Discussion

Our study adds to a small body of evidence on antral follicle

count among women of presumed fecundability (Reuss et al.,

1996; Schipper et al., 1998) or demonstrated fertility (Erdem

et al., 2002; Scheffer et al., 2003).

The strengths of the study are several. First, all participants

were of demonstrated fecundability and 71% had at least one

live birth, avoiding the potential biases of assisted repro-

duction samples. Second, all ovarian age indicators were

assessed without knowledge of maternal characteristics, elim-

inating the possibility of bias. Third, reliability was excellent:

the procedure for counting antral follicles was repeatable;

the intra- and inter-assay CV for the hormone assays were

high. Fourth, ovaries were imaged on day 5–7 rather than, as

in previous studies, during the early follicular phase, maxi-

mizing the ability to detect developing follicles. (Earlier,

follicles are often too small to see; later, the emerging domi-

nant follicle may obscure smaller follicles.)

Three aspects of the study design are of possible concern.

First, the sample over-represents women with spontaneous

abortions. We do not believe this feature limits generalizability

since spontaneous abortion is unrelated to these ovarian age

indicators (Kline et al., 2004) and all results persisted when

analyses were adjusted for the index pregnancy. Moreover,

as noted above, nearly all women had at least one previous

live birth. Second, because all women had conceived

recently (0.3–2.5 years prior to the study sonogram, mean

0.9 years), at older ages (the late 30s and 40s) our sample

may over-represent women of high fecundability. This

over-representation may alter the patterns of change

observed at older ages. Third, we measured hormones and

counted follicles at different times during the follicular

phase, an approach which may limit the clinical utility of

regression equations which draw on measures from two

different days.

In our sample, as expected, ln(1þantral follicle count)

showed the strongest association with chronological age

(r ¼ 20.52). Age was modestly associated with ln(FSH)

(r ¼ 0.35), ln(1þ follicle maximum diameter) (r ¼ 0.26)

and ln(ovarian volume) (r ¼ 20.26). Age was statistically

significantly associated with ln(E2) (r ¼ 0.18) and ln(inhibin

B) (r ¼ 2 0.16), although both correlations were small. The

association of age with follicle surface area was entirely

explained by follicle count.

For comparison, we consider the two largest of the afore-

mentioned studies. Scheffer et al. (2003), in the Netherlands,

recruited 162 regularly menstruating volunteers, 25–46 years;

they obtained ovarian age measures on day 1–4 of the cycle.

Erdem et al. (2002), in Turkey, drew on a sample of 108 men-

struating women, 35–50 years, with minor gynaecological

problems; all measures were made on day 3 of the cycle. The

two studies differ from ours in several respects. First, both

obtained follicular and ovarian measures on a single day

during the early follicular phase. The range of follicle sizes in

both studies was small to moderate (2–10 mm in the Nether-

lands, ,10 mm in Turkey), whereas we counted all follicles

(of which 96% were 2–10 mm). The difference in follicle

size is likely a function of the timing of the measures. Second,

Table VII. Summary data on the performance of five regression models in younger (,35 years) and older ($35 years) womena

Model Area underthe ROC curve

Regression equation cutpointwhich yields 80% sensitivity

Corresponding to sensitivity of 80%

Specificity Positivepredictive value

Negativepredictive value

Women ,35 yearsb

(A) Age, ovarian volume, FSH, inhibin B 0.75 #22.05 0.39 0.17 0.92(B) Age, ovarian volume, FSH 0.77 #20.05 0.48 0.21 0.95(D) Age, FSH, inhibin B 0.64 #20.55 0.42 0.18 0.93(E) Age, ovarian volume 0.74 #21.30 0.43 0.18 0.93(H) Age 0.51 #22.30 0.25 0.14 0.88

Women $35 yearsc

(A) Age, ovarian volume, FSH, inhibin B 0.85 #11.55 0.77 0.79 0.78(B) Age, ovarian volume, FSH 0.82 #11.55 0.69 0.74 0.76(D) Age, FSH, inhibin B 0.85 #12.80 0.66 0.71 0.77(E) Age, ovarian volume 0.77 #11.93 0.57 0.65 0.69(H) Age 0.72 #12.40 0.42 0.60 0.68

a(i)Each model’s ability to discriminate between women with low (#10 follicles) and high (.10 follicles) antral follicle count [the area under the receiveroperating characteristic (ROC) curve]; (ii) specificity and predictive values at the regression equation cutpoint yielding 80% sensitivity.bAmong women ,35 years, 13.9% (14/101) had #10 antral follicles.cAmong women $35 years, 52.0% (39/75) had #10 antral follicles.

J.Kline et al.

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in both studies, follicular and ovarian measures were taken

during the scan, leaving open the potential for bias related to

awareness of the woman’s age. Data from the Netherlands

(Scheffer et al., 2002), however, show both high inter-obser-

ver agreement for counts obtained during a scan and high

intra-observer agreement between real-time counts and sub-

sequent counts from stored scans. Third, both studies analysed

ovarian age indicators without logarithmic transformation.

Average follicle count (our computations from published

data) is ,3.3 per woman in the Turkish sample, about nine

in the Netherlands sample and 18.7 in our sample. The differ-

ences most likely reflect the higher proportions of older

women and the earlier time-period of scanning in the two

previous studies, when a smaller proportion of developing

follicles are large enough to detect by sonography.

Observations in the Netherlands sample are roughly com-

patible with ours—chronological age is more strongly associ-

ated with follicle count than with FSH, inhibin B, E2 or with

measures of the ovary or follicles (Scheffer et al., 2003). As

in our data, chronological age is modestly associated with

ovarian volume and FSH, but the correlation of age with E2

is stronger. The higher proportion of women aged .40 years

in the Netherlands sample probably accounts for the some-

what higher correlations of chronological age with follicle

count and E2.

In the Turkish sample (Erdem et al., 2002), chronological

age is more strongly associated with FSH than with follicle

count, ovarian volume or E2. Moreover, the association with

E2 is inverse, rather than positive as in our study and the

Netherlands study. This pattern of results probably reflects

the age distribution of the sample. The inverse correlation of

age with E2 was confined to women aged 45–50 years,

suggesting that many, though menstruating, were close to

their final menstrual periods.

There is little doubt that antral follicle count declines

monotonically with age, but various models describe the

shape. In our sample, the association of chronological age

with ln(1þantral follicle count) is well fitted by either of two

models—a simple exponential model (R2 ¼ 0.30) or a model

which includes both linear and quadratic terms (R2 ¼ 0.32).

While the quadratic age term is marginally statistically sig-

nificant, it accounts for only 1.6% of the variance in count,

leaving uncertain which model most fairly represents the

data. The latter equation suggests that follicle count declines

more steeply after about age 27 years than before. One intui-

tively appealing feature of the equation is the suggestion of a

natural upper limit to antral follicle count. Our models con-

trast with those fitted by Scheffer and colleagues, who also

fitted two models—a biphasic model in which ln(count)

decreases more steeply beginning at age 38 years than before

(Scheffer et al., 1999) and a linear model relating age to

count until age 46 years (Broekmans et al., 2004). (It is

beyond the scope of this paper to combine the evidence from

these studies with autopsy and hysterectomy data to model

the relation of age to antral follicle count.)

Age explained only a small proportion of the variance in

ovarian volume and FSH in our sample. Nevertheless, our

data are consistent with previous observations. Data from a

large series of women enrolled in a cancer screening project

show that average ovarian volume declines between ages 25

and 91 years (Pavlik et al., 2000). Like previous studies

(Reyes et al., 1977; Metcalf and Livesey, 1985; Lee et al.,

1988; Lenton et al., 1988; Cramer et al., 1994; Broekmans

et al., 1998), our data indicate that FSH levels increase

beginning around age 40 years.

With respect to inhibin B, our data indicate that the associ-

ation with age is not monotonic; rather, inhibin B levels are

constant or increasing until about age 40 years and then

decrease. These results are compatible with two studies

which show that inhibin B levels are lower in older regularly

cycling women than in younger women (Klein et al., 1996b;

Welt et al., 1999); at later ages (i.e. 39–52 years in one

cross-sectional study), levels appear to decline linearly with

age (Danforth et al., 1998).

In our sample, adjusting for age, ln(inhibin B) is modestly

correlated with ln(1þ follicle count) (r ¼ 0.24) and slightly

more strongly correlated with ln(1þ follicle surface area)

(r ¼ 0.34), results consistent with those from the Netherlands

sample (Scheffer et al., 2003). Taken together, the two

studies suggest that inhibin B measured on day 1–4 is not a

good indicator of the number of developing follicles. Since

inhibin B is produced by the developing cohort of antral fol-

licles (Groome et al., 1996; Burger, 2000), measurements

later in the follicular phase may more accurately reflect the

size and quality of the developing cohort.

Our data show only a weak positive relation between age

and E2. This result joins an already inconsistent body of evi-

dence, among menstruating women, showing a positive

association (Welt et al., 1999; Scheffer et al., 2003), no

association (Klein et al., 1996b) or an inverse association

(Erdem et al., 2002). This inconsistency is probably

explained by methodological disparities.

Clinical application

From among the easily obtained indicators—age, ovarian

volume and levels of FSH, inhibin B and E2—we identified

the combination that best predicts ln(1þantral follicle count).

Other informative hormones might also be easy to obtain and

useful. For example, two recent studies—one in presumably

fecund women (de Vet et al., 2002) and the other in infertile

women (van Rooij et al., 2002)—suggest that anti-Mullerian

hormone levels may be a useful predictor of antral follicle

count.

Of the five indicators examined, only E2 was not signifi-

cantly related to ln(1þ follicle count). The regression model

which included age, ovarian volume, FSH and inhibin B

explained 49.6% of the variance in count. Age and ovarian

volume were the strongest predictors, followed by FSH.

The association between ovarian volume and count is not

surprising, since the ovary enlarges to accommodate the

developing follicles. Inhibin B, though significantly associ-

ated with count, was responsible for only a small proportion

of its variance. Removing this term from the equation

decreased the proportion of variance explained only trivially,

to 48.3%.


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To predict which women have #10 antral follicles, models

that include ovarian age indicators as well as chronological

age improve markedly on the model that includes chronologi-

cal age alone. Two models—A (chronological age, ovarian

volume, FSH and inhibin B) and B (chronological age, ovar-

ian volume and FSH)—are virtually indistinguishable in their

ability to discriminate between women with low and high

count. For the clinician, these models have the disadvantage

that they require measures of both ovarian volume and hor-

mones. Moreover, our regression equation derives from ovar-

ian and hormone data which were collected on two different

days of the cycle. Model D, which includes only chronologi-

cal age and hormone levels, performs nearly as well as

models A and B.

All models performed less well in younger (,35 years)

women than in older ($35 years) women, as expected given

the prevalence (13.9%) of low antral follicle count among

younger women. For a presumably fecund young woman

who wants to defer childbearing, our models do not improve

upon knowledge of her age alone for predicting whether or

not she will encounter problems when she later tries to con-

ceive. For an older woman who wants to know how long she

can postpone childbearing or who is trying to conceive and

wants to know whether to expect difficulties, our models

improve upon prediction based on chronological age alone.

In our sample, in which 52% of older women had low count,

the best model has a positive predictive value of 79%, a

marked improvement over the positive predictive value

(60%) of the model based only on chronological age. Thus,

our models help to identify women who might benefit from

expedited evaluation (for example, actually counting fol-

licles).

Three caveats are in order. First, our predictive regression

equations, which derive from a single sample, need replica-

tion to determine validity. Second, strictly speaking our

equations apply to women of demonstrated (or presumed)

fecundability; it remains to test whether or not they are useful

to women seeking treatment for infertility. Third, an under-

lying assumption for this work is that antral follicle count

predicts fecundability. While it is generally thought that low

count is associated with low fecundability, data are limited to

women seeking care for infertility and their response to

assisted reproduction treatment. These data do not readily

translate into predictions about conception for women of

known or presumed fecundability, nor are they generalizable.

It remains to test this assumption in samples not selected for

difficulty conceiving.

Finally, one aspect of the analytical approach merits elab-

oration. Although we defined low count as #10 antral fol-

licles, the choice was not guided by empirical evidence; the

biologically relevant cutpoint may be lower. In our sample,

antral follicle count of 10 corresponds to the median at age

39 years—when the risk of infecundity is $20% (Menken

et al., 1986) and the proportion of women taking .1 year to

conceive may be on the order of 50% (Schwartz and

Mayaux, 1982). If our equations are valid, they will improve

the prediction of time to conception in older women beyond

that provided by chronological age alone.

Acknowledgements

We acknowledge our co-investigators Drs Dorothy Warburton andMichel Ferin who collaborated in the design and implementation ofthe study. We thank Dr Grace Jorgensen who welcomed and facili-tated this study; we thank her and her colleagues for their help inproviding access to their patients. We acknowledge Maria Bautista,Jennifer Cassin, Terry Fox, the late Kris Keough, and Donna Westwho facilitated our work at the study hospital; Rebecca Russell andJeannie Small-Fish who obtained the sonography scans. We thankMegan Meldrum who carried out the fieldwork of the study andRenee Davenport who assisted in data processing and checking.This research would not be possible without the help of the womenwho participated in it to further understanding of the causes ofreproductive loss. This work was supported by a grant from theNational Institutes on Aging (R01 AG 15386).

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Submitted on February 14, 2005; resubmitted on March 22, 2005; acceptedon April 1, 2005


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