Validation of a reference ELISA for estrone glucuronide using urine samples normalized by dilution to a constant rate of urine production

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Validation of a reference ELISA for estrone glucuronide using urine samples normalized by dilution to a constant rate of urine production Delwyn G. Cooke a,1 , Jan E. Binnie a , Leonard F. Blackwell b,∗ a b

Institute of Molecular Biosciences, Massey University, Palmerston North, New Zealand Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand

a r t i c l e

i n f o

a b s t r a c t

Article history:

A direct enzyme linked immunosorbent assay (ELISA) system has been optimized as a

Received 24 October 2006

reference method for the measurement of first statistically significant rises in estrone glu-

Received in revised form

curonide excretion rates in human urine by analysing samples pre-diluted at the time of the

12 February 2007

collection by the women subjects to a constant urine production rate of 150 mL/h. Validation

Accepted 20 March 2007

was achieved by correlation of the individual menstrual cycle profiles with the correspond-

Published on line 30 March 2007

ing estrone glucuronide excretion rates determined by radioimmunoassay (RIA) on the same urine samples for a total of 221 samples from nine cycles. The pre-dilution proce-

Keywords:

dure removed random variations due to fluctuations in the daily rate of urine excretion and

Enzyme linked immunosorbent

minimized between sample matrix effects. When the ELISA data were correlated with the

assay

RIA data, Deming regression gave a slope of 1.20 ± 0.03 and an intercept of 4.6 ± 1.8 nmol/24 h

Estrone glucuronide

(r = 0.944) and a random experimental error of 14.2 nmol/24 h. The major difference in the

Matrix

measurements was a proportional error of 20%, which was present in either the ELISA or

Menstrual cycle

RIA methods or in both. Comparison of the standard normal variate transformation of the

Method comparison

ELISA and RIA data gave hormonal profiles of the individual menstrual cycles (N = 9) that

Urine

overlapped almost perfectly. Statistically significant rises or falls in the magnitude of the excretion rate in one profile were mirrored faithfully in the other. © 2007 Elsevier Inc. All rights reserved.

1.

Introduction

During a woman’s menstrual cycle there is a fertile window when pregnancy can occur which in the highly fertile couple is about 6 days [1–3]. The length is determined by a combination of sperm and ovum survival times [4]. Ovum survival time is relatively constant (about 12 h), but sperm survival is determined by a combination of factors including the ovarian activity at the time and the quality of the cervical mucus

∗

[5–7]. The central problem for fertility prediction in a particular cycle is to locate the beginning and end of the fertile window prospectively since it varies in position and length from woman to woman and from cycle to cycle [3,5,8]. The transition from a follicular androgen environment to an estrogen one [9] marks a key step in the life cycle of a growing follicle. Thus a rise in plasma estradiol, urinary estrone glucuronide (E1G) [2,8,10–22] or the ratio of estrone glucuronide to pregnanediol glucuronide (E1G/PdG) [11] may be used to locate the

Corresponding author. Tel.: +64 6 3569099/3584; fax: +64 6 3505682. E-mail address: [email protected] (L.F. Blackwell). Abbreviations: ELISA, enzyme linked immunosorbent assay; E1G, estrone glucuronide; LC–ESMS, liquid chromatography–electrospray mass spectrometry; NMR, nuclear magnetic resonance spectroscopy; PdG, pregnanediol glucuronide; RIA, radioimmunoassay 1 . Present address: Manawatu Biotech Investments Limited, c/o Ecology Building, Massey University, Palmerston North, New Zealand. 0039-128X/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.steroids.2007.03.009

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start of the fertile window [8]. Ovarian activity can change abruptly within a 24 h period with profound consequences thus daily monitoring of hormone levels is needed at certain times to define the beginning of the fertile window [23] accurately. Daily monitoring is more feasible with a non-invasive urinary or salivary-based hormonal assay and, compared to blood, analysis of urinary metabolites does not suffer from a number of technical issues such as pulsatile secretion [24–26] and serum matrix effects which arise with direct serum assays for ovarian steroids [27–31]. A urinary assay also has the advantage that the concentrations of ovarian steroid metabolites in urine are higher than the concentrations of ovarian steroid in serum [8,26]. Although there is a slight lag in the excretion of the ovarian steroids into urine, the overall correlation between the blood and urine profile is extremely high [8,16,32,33]. Algorithms based on urinary ovarian metabolite measurements have been developed to identify the day of ovulation [11,34,35], the ovulatory status of the cycle [35], and the beginning [2,8,11,13–16,19–21,36] and the end of the fertile window [16,35,37,38]. A major aim of our research is to provide nonlaboratory (point-of-care) urinary hormone assay methods [2,12–15,37,39] that are simple, but laboratory accurate and precise enough to allow women to locate their fertile window prospectively in individual cycles. Accuracy and precision allow statistically significant rises and falls [2,12] in urinary E1G excretion rate to be distinguished reliably from assay noise and thus recognition of when a potentially fertilizable follicle is present. The Ovarian Monitor measures the first statistically significant rise in the E1G excretion rate [12] as the marker for the beginning of the fertile window and a rise in PdG excretion rate to exceed a threshold (7 ␮mol/24 h) to mark the end of the fertile window. However, despite its success as an analytical tool [12] it has not proven to be acceptable for general use in the home largely because of its long incubation and assay times. Nevertheless, the Monitor’s ability to identify the fertile window using a urinary hormonal assay justifies the development of new, simpler point-of-care methods that maintain its accuracy. The Ovarian Monitor, which uses urine specimens diluted to a volume of 150 mL/h of collection (time-dilution) [12,14], has been validated against the total urinary estrogen method of Brown et al. [40] that measures the total urinary estrogen excretion rate per 24 h. This latter method employed hydrolysis of the glucuronides, and after extraction of the free steroids, it measured the sum of estradiol, estrone and estriol by fluorescence [40]. The procedure is not subject to matrix effects and was the only chemical method ever developed with inbuilt tests for specificity and reliability that was capable of giving valid values over the entire hormonal continuum. However, the method [40] is labor intensive, requires a high level of technical expertise [41], and is no longer readily available as most of the laboratories that used it have been disestablished. An alternative reference assay linked to the Brown method is therefore needed against which new non-laboratory methods may be compared for their ability to produce valid daily urinary E1G excretion rate profiles in individual cycles. In our experience with the Ovarian Monitor [12,15], dilution to a constant rate of urine production before assay is needed to reduce variability by normalizing the effect of variable

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hydration status and minimizing inter-sample matrix effects [42–44]. Thus, we have chosen a direct enzyme immunoassay for E1G [45] as the replacement reference assay, and optimized it for use with a single volume of urine time-diluted to 150 mL/h [13]. It is validated by comparison of the normalized E1G ELISA excretion rate profiles obtained with it and the corresponding profiles obtained previously from an E1G radioimmunoassay method. The RIA method was used in a multi-center trial of the Ovarian Monitor using the same timediluted urine samples [12] and is thus linked to the Brown chemical method [40]. We wanted a cheap and simple enzyme immunoassay to be our new reference method as it eliminated the problems of equipment, compliance costs and disposal associated with the use of radioactive isotopes. The normalization and comparison procedure between RIA and ELISA outlined in this paper can be applied to other pairs of urinary E1G and PdG assays.

2.

Experimental

2.1.

Reagents

17-Oxoestra-1,3,5(10)-triene-3-yl-␤-d-glucopyranosiduronic acid (estrone glucuronide or E1G) was synthesized according to Conrow and Bernstein [46] and the purity of the product was confirmed by LC–ESMS and NMR spectroscopy. The synthetic E1G was used for the E1G standards, generating the antibodies and conjugating to horseradish peroxidase. Horseradish peroxidase (grade VI) and gelatine were obtained from Sigma Chemical Company (St. Louis, MO, USA), the o-phenylenediamine was from Merck-Schuchardt (Darmstadt, Germany) and the hydrogen peroxide (30%, w/v) from AJAX (Auburn, NSW, Australia). All other chemicals were of analytical or reagent grade and water was of Milli-Q quality. Polyclonal E1G antisera were raised in four sheep to thyroglobulin-E1G conjugates (Dr. Keith Henderson, AgResearch, Wallaceville Animal Research Centre, Upper Hutt, New Zealand). Immunization followed by four booster shots was carried out over 20 months and antiserum 243-4 (bleed 4 from sheep 243) was chosen. Active ester horseradish peroxidase-E1G conjugates were prepared [45] using a molar ratio of enzyme to steroid of 1:78. ELISA plate absorbance readings at 490 nm were measured using an eight channel single-beam microplate absorption photometer (Anthos Labtec Instruments, Salzberg, Austria), and polystyrene 96 well ELISA microtitre plates (Maxisorp) were supplied by Nalge Nunc International (Kamstrup, Roskilde, Denmark).

2.2.

Urine samples

Urine samples were collected during a multi-center trial of the Ovarian Monitor [12], and all the nine cycles chosen from the Palmerston North center for RIA analysis in London were available for further analysis by ELISA. The details of the urine collections and the characteristics of the participants have been described previously [12]. The participants, who were experienced users of natural family planning, collected urine samples on a daily basis for six consecutive menstrual cycles. The urine could be collected overnight or at any convenient

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time during the day, but the collection period had to be for a minimum of 3 h, which could normally be managed as a single void. The samples were collected directly into a 2 L plastic jug calibrated in quarter hours with each hour interval equal to a volume of 150 mL. The urine was then diluted with tap water to the mark corresponding to the hours of collection (to the nearest quarter hour) so that it represented a constant urine excretion rate of 150 mL/h (≡3600 mL/24 h). Samples collected as described are referred to as time-diluted samples. On the rare occasion when the volume passed exceeded 150 mL/h, the urine was diluted to twice the number of hours over which it was passed and twice the volume of diluted urine was used in the assay. The urine samples were preserved in boric acid (ca. 10 mg per 6 mL) at the time of collection and stored frozen in small, stoppered plastic tubes. The results were compared with those obtained previously independently by the WHO reference laboratory in London [12] using the same time-diluted urine samples, but analyzed with their radioimmunoassay for E1G as part of study #90905 on the Ovarian Monitor [12]. Ethical permission was obtained from Massey University’s ethics committee and written informed consent was obtained from each participant. The ‘blank’ sample of urine obtained from a prepubertal male (aged 5 years) was pre-diluted to 150 mL/h of collection.

2.3.

E1G ELISA protocol

The 3 day ELISA protocol was optimized for use with timediluted urines by adaptation of the procedure of Henderson et al. [45]. Unless stated otherwise the volumes and buffers were those reported by Henderson et al. [45]. For the urinary unknowns, the diluted enzyme conjugate (100 ␮L) and the sample (5 or 10 ␮L of the time-diluted urine freshly diluted with assay buffer to a total volume of 50 ␮L) were added simultaneously with a dual dispensing autopipette. The same protocol was used for the E1G standards except that the urine was replaced by the equivalent volume of standard in buffer. For the blank urine studies, the blank urine (10, 20 or 30 ␮L) replaced the equivalent volume of assay buffer used to make the standard up to 50 ␮L. During the development phase of the study, the plates were incubated for 2 h at room temperature. However, subsequent experience showed that incubation at 4 ◦ C overnight (16 h) was essential to eliminate urine matrix effects (non-parallelism). Substrate solution was prepared by dissolving ophenylenediamine (40 mg) in 100 mL substrate buffer (24.4 mM citric acid, 51.4 mM disodium hydrogen orthophosphate, pH 5.0) approximately 5 min before required; the solution was then stored wrapped in aluminum foil before adding hydrogen peroxide (30% commercial solution, 40 ␮L) immediately before use. The plates were checked intermittently for color development and the reaction stopped between 15 min and 1 h by the addition of 50 ␮L of 2.5 M sulfuric acid. The development of color varied from day to day, but control experiments showed that the shape of the curve was independent of the development time. Hence, the plate was allowed to develop until a subjectively acceptable level of color was obtained, and the mean relative absorbance values recorded (Abs/Abs0 where Abs0 is the absorbance for the zero standard).

Table 1 – Cross-reaction of antiserum 243-4 raised against estrone glucuronide Steroid

Cross-reaction relative to E1G (%)

Estrone glucuronide Estrone sulfate Estrone Androstenedione Progesterone Estradiol Testosterone Pregnanediol glucuronide Pregnanediol

100.00 14.28 1.90 0.05 0.05 0.05 0.01 0.01 Nil

Each plate contained a standard curve covering the range of 0–2000 nmol E1G/24 h (i.e. 0–278 fmole/well), which was performed under identical conditions to the cycle unknowns. The excretion rates of E1G for each cycle day were calculated from the standard curve for the same plate and hence are expressed as amount per 24 h. The urinary samples and standards were assayed in duplicate and the menstrual cycles are referred to using the numbering system of Blackwell et al. [12] where the subject code is used together with the cycle number for that subject. An off-peak and peak E1G excretion rate day were chosen from two cycles from different women and used as low and high quality control samples. For the cross-reactivity studies, a series of standards were prepared with available steroids (Table 1) and an appropriate standard curve generated. The cross-reactivity was calculated by comparing the relative amounts of E1G and cross-reactor required to produce an absorbance 50% that of the zero standard for the control and test curves. The cross-reactivity was expressed as a percentage relative to E1G.

2.4.

Radioimmunoassay

The RIA methods for E1G and PdG were developed by the London laboratory for the WHO Special Programme for Research in Human Reproduction. The assays utilize antisera and tritiated labels produced by Dr P. Samarajeewa (Department of Biochemistry, University College, London). The assays have been used in all earlier WHO sponsored studies on the Ovarian Monitor and have been described previously [12,47]. The urine samples were diluted a further 100 fold for both assays and the results analyzed using the WHO RIA data analysis program and expressed in nM. The amount per liter was converted to amount per 24 h by multiplying by 3.6 (0.15 L/h time-diluted urine × 24 h). All RIAs were performed in duplicate in London, and contained 3 quality control samples at the start and finish of each assay. The interassay coefficient of variation (CV) was 8.7% at 133 nM (478 nmol/24 h; N = 36) [12].

2.5.

Method comparison

ELISA standard curves were fitted to a sigmoidal dose response curve with a variable slope and the readings of the unknowns were calculated from the data analysis and graphics program PRISM (Graphpad Software, San Diego, CA). The correlation between the E1G excretion rate data calculated by the ELISA and RIA methods was performed for each separate cycle using

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the Deming least squares regression [48–50] package of PRISM. The combined cycle data for the nine cycles were also correlated against the corresponding combined RIA data using the Deming regression method. This analysis returned the slope (m), intercept (b), the correlation coefficient (r) and the standard deviation in the y direction (Sy/x ). If m differs from 1, proportional errors are present in the data, and if b is not equal to zero, a constant error is present [51] while Sy/x gives the random error. If the E1G measurements delivered by the two assay methods are linearly related and the random error in the linear relationship is small, it can be shown that the variance (ELISA) = m2 variance (RIA) [52]. Hence the standard normal variate of the ELISA and RIA data sets should be the same. The standard normal variate of the ELISA data is: (Ei − E) √ var(E) Thus, each cycle was normalized by calculating the mean √ (E) and standard deviation ( var(E)) for all the daily data for the cycle and then subtracting this cycle mean from each individual day’s value and dividing the result by the standard deviation of the mean. The RIA data were transformed in an analogous manner. Only daily data for which paired values were available (ELISA and RIA) were analyzed using this transformation. The differences (D) between the ELISA and RIA values for each cycle day were calculated and analyzed by the BlandAltman [53] package of PRISM which returned the mean difference (bias) and standard deviation of the mean (S.D.d ).

2.6.

Measurement of first rises in E1G excretion rate

The first statistically significant rises in E1G levels were calculated from the departure of the daily data from the mean baseline period which was taken as the first 6 days of the cycle. The first 6 days were chosen since these days do not typically show the continuous increases in the rate of estrogen excretion characteristic of ovarian activity [2]. The tracking signal parameters were calculated using a computer program written essentially according to the procedure outlined previously [2,12].

3.

Results

3.1. ELISA E1G assay standard curves and the effect of the urine matrix E1G–HRP conjugate (26 fmole per well) and a dilution of the E1G antiserum 243-4 of 1/2,500 gave a standard curve suitable for use with time-diluted urine samples. The same volume (10 ␮L) of E1G standards was used for all samples covering the excretion rate range 0–1000 nmol/24 h. E1G standard curves were then generated using a 2 h incubation period at room temperature with the above protocol, in both buffer and in the presence of varying amounts of the pre-diluted ‘blank’ urine sample (Fig. 1A). The urine spiked E1G standard curves became increasingly non-parallel and the zero standard A490 value decreased as the volume of the time-diluted ‘blank’ urine was increased (10, 20 and 30 ␮L in the incubation mixture). The absorbance values for the lowest E1G standard (20 nmol/24 h)

Fig. 1 – Panel A. E1G standard curve showing comparison of buffer curve with the addition of increasing amounts of time-diluted “blank” urine to the assay using a 2 h incubation. Control curve (); 10 ␮L urine (); 20 ␮L urine (䊉); 30 ␮L urine (), error bars as S.D. Panel B. Standard curves replotted against the total amount of E1G (E1G from the standards plus the apparent amount read from the control standard curve) as explained in the text and using a 2 hour incubation step. Control curve (); 10 ␮L urine (); 20 ␮L urine (䊉); 30 ␮L urine (), error bars as S.D. Panel C. A repeat of (A) but with an overnight incubation step. Control curve (); 10 ␮L urine (), error bars as S.D.

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for each of the spiked standard curves gave apparent total E1G excretion rates of 47, 70 and 93 nmol/24 h, respectively, when read from the control buffer standard curve (Fig. 1A). These values corresponded to apparent urinary E1G excretion rates of 27, 25 and 24 nmol/24 h per 10 ␮L of the time-diluted ‘blank’ urine sample respectively. A replot of the urine-spiked standard curve data against the total amount of E1G (the apparent amount from the time-diluted ‘blank’ urine plus the amount added from the standard) was similar to the control standard curve (Fig. 1B). However, when the experiment was repeated using an overnight incubation (16 h) at 4 ◦ C as opposed to a 2 h incubation at room temperature, the 10 ␮L time-diluted ‘blank’ urine spiked E1G standard curve was now identical to the control standard curve within experimental error (Fig. 1C). The low and high quality control urine samples from two cycles from different women using an overnight incubation and 10 ␮L of the time-diluted urine sample gave an intraassay CV (N = 12 for all quality control urine samples) of 2.8% at a mean E1G concentration of 97 nmol/24 h (S.D. ± 2.7), 2.4% at 229 nmol/24 h (S.D. ± 5.5), 1.8% at 76 nmol/24 h (S.D. ± 1.4) and 9.3% at 380 nmol/24 h (S.D. ± 35.5). The cross-reactivity of the antiserum under these assay conditions with a range of available competing steroids is shown in Table 1. The only significant cross-reactions (>0.1%) were with estrone and estrone sulfate. The calculated recoveries of duplicate E1G samples spiked with blank urine (10 ␮L) and using overnight incubations at excretion rates of 10, 100 and 1000 nmol/24 h were 98.6%, 101.4% and 105.5%, respectively.

181.1 ± 1.2 (Fig. 1C) to 39.1 ± 1.1 nmol/24 h and the correlation coefficient was 0.999 for the fit to a sigmoidal equation with variable slope. At an E1G standard concentration of 5 nmol/24 h the coefficient of variation was 3.7% (N = 8) and at 267 nmol/24 h it was 12.1% (N = 8). The minimum detectable amount of the assay, calculated as 2 standard deviations from the zero standard, was 2.4 pg E1G per well (3.9 nmol/24 h).

3.2. Characteristics of the optimized E1G assay method

3.3. Application of the E1G ELISA to time-diluted menstrual cycle urine samples

The amount of conjugate was increased from 26 to 51 fmole per well and the anti-E1G antiserum further diluted from 1/2,500 to 1/20,000. The volume of time-diluted urine added to the assay was also decreased to 5 ␮L. The interassay performance was assessed from the means of duplicate standard curves from eight different plates on three different days during the analysis of the menstrual cycles (Fig. 2). The mid-point of this mean optimized curve (EC50 ± S.E.M.) was reduced from

Fig. 3A shows the daily urinary E1G excretion rate profile for cycle 2F-C2 as measured by the optimized E1G ELISA method with an overnight incubation at 4 ◦ C. The mean coefficient of variation (CV) for all the duplicate daily data (N = 23 days) was 2.7 ± 1.9% (min 0.2%, max 8.5%). Fig. 3A also shows the corresponding profile obtained independently by RIA in London with the same time-diluted urine samples. A plot of the ELISA data against the RIA data was

Fig. 2 – Repeatability of the optimized standard curves. Standard curves from eight different ELISA plates under optimum conditions as explained in the text and using 5 ␮L of time-diluted urine sample; error bars as S.E.M.

Table 2 – Statistical summary of the RIA versus ELISA menstrual cycle data Cycle

Na

Deming regression Slope

2F-C2 3J-C2 9D-C2 14X-C1 14X-C2 20K-C1 21R-C2 23B-C1 23B-C2 All cycles a b c

22 24 28 23 25 24 27 25 23 221

N: number of data pairs. nmol E1G 24 h−1 . R: correlation coefficient.

1.32 1.14 1.42 0.92 1.25 1.44 1.05 1.22 1.12 1.20

± ± ± ± ± ± ± ± ± ±

0.08 0.09 0.10 0.03 0.05 0.08 0.06 0.09 0.09 0.03

Bland-Altman

Interceptb

Sy/x b

Rc

± ± ± ± ± ± ± ± ± ±

8.5 14.5 5.9 6.8 8.0 17.9 6.4 12.8 8.1 14.2

0.969 0.945 0.940 0.990 0.983 0.966 0.961 0.940 0.811 0.944

3.1 2.7 7.3 12.8 −5.7 −7.1 13.4 9.6 −2.2 4.6

3.9 5.6 2.7 2.4 3.6 7.4 3.0 5.8 4.9 1.8

Biasb

S.D.d b

17.9 10.5 17.4 7.6 11.0 27.3 15.7 21.4 7.2 15.2

10.7 14.2 6.7 7.5 10.9 24.8 6.2 12.7 15.8 14.5

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Fig. 3 – Panel A. Menstrual cycle profiles for subject 2F-C2 determined by ELISA (䊉) and RIA (). Panel B. Menstrual cycle profiles for subject 2F-C2 determined by ELISA (䊉) and RIA () after normalization as discussed in the text.

Fig. 4 – Panel A. Menstrual cycle profiles for subject 21R-C2 determined by ELISA (䊉) and RIA (). Panel B. Menstrual cycle profiles for subject 21R-C2 determined by ELISA (䊉) and RIA () after normalization as discussed in the text.

linear with a slope of 1.32 ± 0.08 (Table 2), a correlation coefficient (r) of 0.969 and an intercept of 3.1 ± 3.9 nmol/24 h. The Sy/x value was 8.5 nmol/24 h and the Bland-Altman bias was 17.9 ± 10.7 nmol/24 h. The general profiles were obviously the same, but the ELISA excretion rates were consistently higher than the corresponding RIA values. The mean for the daily ELISA data was 63.8 ± 33.2 nmol/24 h and for the corresponding RIA data it was 45.9 ± 25.3 nmol/24 h. Using these values the ELISA and RIA data were normalized and the resulting normalized profiles are shown in Fig. 3B. The two profiles now overlapped almost completely and gave the mid-cycle peak in E1G excretion as day 13 for both methods. The menstrual cycle profiles obtained from the urine samples from cycle 21R-C2 by ELISA and RIA are also shown (Fig. 4A). The mean CV for all the duplicate ELISA daily data (N = 28 days) was 2.6 ± 1.8% (min 0%, max 6.5%). Again the RIA and ELISA profiles were essentially parallel being separated by an almost constant difference. This cycle showed a double peak by both assays where two high E1G excretion days (days

13 and 15) were separated by an intervening lower value. There was also a small, early E1G peak [54] on day 10. The ELISA and RIA data for this cycle were correlated to give a slope of 1.05 ± 0.06, an intercept of 13.4 ± 3.0 nmol/24 h and a correlation coefficient of 0.961 (Table 2). The standard deviation in the y direction (Sy/x ) was 6.4 nmol/24 h and the bias calculated from the Bland-Altman analysis was 15.7 ± 6.2 nmol/24 h (Table 2). Normalization of the two datasets gave profiles that again almost completely overlapped (Fig. 4B). A similar comparison was carried out for the other seven cycles and the results of the statistical analysis of the correlations for the individual cycles are shown in Table 2. With one exception (cycle 23B-C2), all of the correlation coefficients for the individual cycles were ≥0.940 and the normalized cycle profiles by ELISA and RIA for each cycle were identical within experimental error. The overall correlation for the combined data obtained for the nine cycles gave a slope of 1.20 ± 0.03, an intercept of 4.6 ± 1.8 nmol/24 h (r = 0.944) (Table 2) and an overall random error (Sy/x ) of 14.2 nmol/24 h. The mean cycle

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reported direct urinary E1G ELISA assays [16,28,45,55] are similar to the E1G assay reported here, none have been optimized for use with urine samples time-diluted before measurement for analysis of individual cycles or are linked to our original reference system [40].

4.1. Correction for hydration status of the subject using time-diluted urine samples

Fig. 5 – Panel A. Mean cycle data for nine cycles determined by ELISA (䊉) and RIA (); error bars as S.D. Panel B. Mean cycle data for nine cycles determined by ELISA (䊉) and RIA () after normalization; error bars as S.D, up bars for ELISA, down bars for RIA.

profiles, referenced to the peak day of E1G excretion as day 0, for the ELISA and RIA methods are shown in Fig. 5A and the normalized mean profiles in Fig. 5B. These mean profiles (N = 9) showed exceptionally high correlation against each other with linear regression giving a correlation coefficient of 0.988.

4.

Discussion

For practical reasons a point-of-care assay requires a minimum of manipulations of the urine sample and data. Consequently, it is desirable to add the same volume of urine to the assay system for all samples. In addition, a new nonlaboratory assay for E1G should be simpler to operate than the Monitor assay, but retain its accuracy and precision. To ensure this, the results from a new test must be compared on a cycle-by-cycle basis with the corresponding excretion rate profiles from a validated reference assay. Although other

The daily increase in excretion rate of urinary estrogen metabolites marks the beginning of the fertile window [8,12,16,18–22,36], not only because it marks unequivocally the development of a potentially ovulatory follicle [2,9], but also because the increased circulating levels of serum estradiol, which change in parallel [18,32,33], are responsible for the conversion of the cervical mucus into a form essential for sperm survival [5,56,57]. Once a follicle enters its rapid growth phase, the daily increase in urinary estrogen excretion rate is on average only 40% per day [2]. Thus, a functional E1G test for detecting the beginning of the fertile window must have a coefficient of variation of less than about 10% if rises of this magnitude are to be distinguished reliably from assay noise. However, even with a precise E1G assay, recognition of the day of the first statistically significant increase in the E1G excretion rate [2] can be obscured by variations in the rate of urine production. This variation may be up to ten fold even with the use of overnight urine samples. For example, the two most recent cycles processed in our laboratory had a rate of overnight urine production that ranged from 14 mL/h to 93 mL/h and from 22 mL/h to 90 mL/h, respectively. The overnight variation in rates of urine production explains the poor correlation seen between 24 h urine collections and overnight urines [25]. Clearly, such obfuscating variations in the rate of urine production must be corrected for otherwise there is no consistent relationship between the true excretion rate that parallels follicle growth and the concentration of E1G that is actually measured in the urine samples. The gold standard for correcting for hydration status in the daily measurement of urinary metabolites has been to measure the total metabolite output of a complete 24 h urine collection and to express the results as a 24 h excretion rate [58]. However, the inconvenience and lack of appeal of collecting and processing 24 h urine samples to both the subject and the investigator has led to a wide variety of alternative methods. Such methods have been to use overnight urines (also known as early morning urine) [17,26], creatinine measurement [59], specific gravity [55], and measurement of the E1G/PdG ratio [11]—approaches which all have their limitations for our purpose. The E1G/PdG ratio can potentially mitigate the effects of variable urine status [8,11]. However, there are problems using it to detect the first statistically significant rise in urinary E1G excretion rate since the PdG excretion rate throughout the follicular phase is not necessarily constant [37]. Several studies [17,59–62] have concluded that creatinine correction offers little advantage over unadjusted results. Specific gravity correction is useful, but may be inaccurate if the urine specimen is very dilute or very concentrated [61]. A recent study showed that combining specific gravity to correct for urine volume variation with a statistical correction to deal with standard curve non-parallelism is a

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useful approach. However, possible limitations of this are the uncertainty of knowing which standard dilution will yield the correct concentration and the relatively large standard deviation of the corrected concentrations [63]. Our preferred alternative is to utilize time-diluted samples [12] as E1G excretion rates obtained from time-diluted urines correlate well with the 24 h urine total estrogen method (r = 0.89, N = 1012) [14]. Time-dilution involves the dilution of a urinary void (with single or consecutive voidings over the chosen time period of at least 3 h) to a constant volume per hour [2,12,37]. The woman can time-dilute the urine sample at the time of collection easily and conveniently by means of the calibrated collection vessel [12] and no further manipulation is necessary. In practice, the use of overnight collections (usually 6–8 h) subsequently diluted to 150 mL/h of collection time (900–1200 mL) with tap water represent a very simple and convenient method. Since the Ovarian Monitor assay shows no effect of the presence of tap water [12–15,39,64], it is unlikely that it constitutes a problem for the ELISA results where the dilution of the sample is much greater. It should be noted that the collection period is never 24 h and can be as short as 3 h. The minimum time was selected as shorter intervals result in noisier data since the integrating effect of a urine collection [26] on the pulsatile secretion patterns in serum is less effective. Frequent blood sampling has shown the estradiol levels can vary by a factor of two between the peak and troughs of a pulse [24]. To take this pulsing into account it has been recommended that a minimum of four blood samples should be taken over a period of an hour [25]. This integrating effect of urine is an often overlooked advantage of urine over blood. Apart from the advantages of reducing the urine collection time to a minimum of 3 h, the shorter time interval enables a more up-to-date assessment of ovarian activity and also allows sampling at any time of day as well as multiple sampling across the day.

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with the fact that the 5 ␮L of blank urine obtained from a 5 year old male should have contributed an E1G excretion rate of less than a twentieth of that of the mean follicular phase baseline [66], suggests that the effects shown in Fig. 1A are artifacts of the direct assay system and short incubation time. A similar urine dependent matrix effect has been observed with use of the Ovarian Monitor [13] and has been explained on the basis of a low affinity material (or materials) that bind rapidly to the antiserum, but also dissociate rapidly. Thus, in the present work, a 2 h incubation time may not be sufficient to allow equilibrium to become established [67,68]. The 16 h incubation at 4 ◦ C gave 100% recovery across the standard curve within experimental error since there was no matrix effect. However, if more than 10 ␮L of time-diluted ‘blank’ urine was added, there was still reduced binding of the enzyme label. This is an important finding since it implies that there is a dose dependent matrix effect even with the time-diluted urine samples and long incubation times. The fact that the matrix effect appears to be dose dependent suggests that the strategy of performing immunoassay procedures on non-diluted urine samples with point-of-care tests and then correcting the values obtained by a second measurement on the urine sample to correct for hydration status may not produce valid menstrual cycle profiles. To ensure that matrix effects were not a factor in the menstrual cycle data, we re-optimized the ELISA assay. The standard curve for the optimized assay spanned the range of excretion rates encountered in the normal menstrual cycle with the volume of time-diluted urine in all assays decreased to 5 ␮L. Under these conditions, the optimized procedure showed no matrix effect and as shown in Fig. 2 the standard curves were highly reproducible with low inter-assay variation. The optimized standard curve showed acceptable sensitivity and precision for the range of E1G excretion rates experienced over the crucial transition period between the baseline and the beginning of the potentially fertile window of the menstrual cycle [2].

4.2. Removal of a matrix effect of time-diluted urine on the E1G standard curves

4.3.

The antiserum used in the ELISA had a high degree of specificity towards E1G (Table 1) although some of the important potential cross-reactors were not available including 2-hydroxyestrone glucuronide, estriol-16-glucuronide, estriol3-glucuronide, estradiol-17-glucuronide and any androgen glucuronides. High specificity is one pre-requisite for a reliable assay. However, minimal matrix effects is another. Potentially all direct urinary immunoassays for steroid glucuronides and blood assays for steroids are subject to matrix effects particularly at low analyte concentrations [27,31,65]. An additional advantage of time-dilution of the urine samples to a constant rate of urine production before analysis is that it can also normalize matrix interference between samples. The increasingly non-parallel behavior (Fig. 1A) with increasing urine volume is expected if urine components are interfering with the antibody binding reaction [44]. It could be concluded from Fig. 1A that the so-called ‘blank’ urine sample contained E1G. However, this cannot be the explanation of the interference since when the same assay was carried out with an overnight incubation at 4 ◦ C the matrix effect of the blank urine sample disappeared completely (Fig. 1C). This, combined

The validity of the optimized E1G ELISA is important to our research into development of new point-of-care (nonlaboratory) systems for the detection of the beginning of the potentially fertile window of the human menstrual cycle [2,12]. Valid in this context means that the menstrual cycle excretion rate profiles obtained from the assay are due to the underlying biochemical changes signaling ovarian events. In particular, it is essential that the transition to an estrogenic environment as follicle development continues is easily recognizable. Any other valid E1G assay should show the same relative daily changes. The fact that the slope of the Deming regression plots for the individual cycles differed from 1 [51] (with high correlation coefficients for the individual cycles) indicated that a proportional error was present and this error differed from woman to woman (Table 2) and from cycle to cycle for the same woman. Since a proportional error was present, an estimate of its actual magnitude could not be made from the Bland-Altman parameters (bias and S.D.d ) [53,69,70] as discussed by Westgard and Hunt [51] and Dewitte et al. [71]. The Deming regression parameters (Table 2) were used therefore to give estimates of the nature and magnitude of

Assessment of errors in the assays

588

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the excretion rate differences between the E1G ELISA and RIA methods. Only for one cycle (14X-C1) was m < 1 and for another m ≈ 1 (21R-C2), but in both these cases a measurable intercept effect was present also. However, apart from these two cycles there was not a constant error since all of the intercepts included zero in their 95% confidence interval ranges. As a result, the E1G excretion rates given by the two assay methods differed mainly in absolute magnitude because of the proportional error, with the E1G excretion rates given by ELISA being on average 20% greater than those given by RIA (Table 2). The imprecision of the ELISA and RIA assays were similar, both having coefficients of variation about 5–10% and thus contributing an experimental error of between 7.5–15 nmol/24 h at an excretion rate of 150 nmol/24 h. The magnitude of the imprecision (Sy/x ) from the Deming regression agreed with this estimate and was generally between 6–9 nmol/24 h except for cycles 3J–C2, 23B–C1 and 20K–C1 where the error was 12–18 nmol/24 h. Why the random error should be cycle specific is unclear. The important result is that the error is much less (5–10 fold) than either the mean of the differences between the daily E1G values given by ELISA and RIA, or the mean of the daily E1G values for a complete cycle. Hence the assumption that the random errors are small compared with the differences made in calculating the standard normal variate for the ELISA and RIA data is justified (S. Brown, personal communication).

4.4.

Method comparison

From a clinical perspective, the major question is whether the same information regarding changes in ovarian activity is imprinted on the individual menstrual cycle profiles derived from the ELISA and RIA excretion rate data. This was examined by comparing the normalized menstrual cycle patterns. The standard normal variate transformation corrects for m = 1 (slope) and b = 0 (intercept) without having to decide which of the two methods is notionally correct, or knowing the true values of m and b [72]. In effect, the normalization procedure transforms the data so that the proportional and absolute differences between the two methods are removed. Then, if the patterns given by the ELISA and RIA methods agree, both sets of data are valid with respect to the menstrual cycle profiles. In all nine cycles there was virtually no difference in the individual menstrual cycle profiles determined by ELISA and RIA with each pair of profiles for a cycle overlapping almost completely apart from small fluctuations due to the assay noise. Hence, the raw (non-normalized) ELISA profiles (see Figs. 3 and 4, for example) are valid and were in effect slightly scaled versions of the RIA profiles and vice versa. The important conclusion is that statistically significant rises [2] or falls in the magnitude of the excretion rate in one profile were mirrored almost exactly in the other and the same information regarding the beginning of the fertile window from the first rise in E1G excretion rates was obtained. For example, the mean warning of ovulation given by the first statistically significant E1G rises calculated by the Trigg’s tracking signal [73] was 6.8 ± 2.2 days (N = 9) in agreement with the value of 6.5 ± 1.4 days (N = 142) derived from our reference database of 24 h total estrogen excretion rates [2]. In one cycle, the first E1G rise occurred a day before

the rise in the RIA profile, and in a second it was one day behind. The remainder occurred on the same day. All of the peak E1G excretion rate days were identical. These results are similar to those reported for the RIA in the Ovarian Monitor study [12]. Questions have been raised about the possibility that the E1G excretion rates in urine lag behind the corresponding serum estradiol values [32,55] thus potentially causing an error in detection of the beginning of the fertile window when using urinary parameters. However, in a comprehensive study where plasma estrone, estradiol, and estriol and all their equivalent sulphate and glucuronide plasma conjugates were measured concurrently along with 24 h total urinary estrogens, and 24 h total urinary estrone, estradiol and estriol, for seven cycles from six women, no lags were found [33]. There was a close correlation in the profiles between plasma estradiol, estrone sulphate and total urinary estrogens. In an additional study, there was also a close correlation between 24 h total urinary estrogens and 24 h urinary estrone glucuronide [74] for 22 clomiphene stimulated cycles (r = 0.87). Hence, it is unlikely that serious lags exist between the urinary E1G first rise parameters expressed here and any corresponding serum estradiol values. The mean cycle profiles as customarily used in the literature [54,55] can be misleading and conceal important differences in follicular activity between individual menstrual cycles. By pooling cycle data, as is commonly the practice for population studies [8,75], it is always possible to get better correlations. For example, the correlation coefficient for all the data pairs from the nine cycles was 0.944 compared with a correlation coefficient of 0.988 for the mean profiles aligned to the day of the E1G peak (Fig. 5B). It must be emphasized again that the important consideration for monitoring individual cycles is the correlation of an individual cycle with a reference method. The fact that high correlation coefficients (>0.9) were obtained here for the nine individual cycles from seven different women (Table 2) demonstrates the beneficial effect on the data of pre-diluting the urine samples to a constant rate of urine production before measurement. The one correlation coefficient <0.9 was due to a missing peak day value for the RIA data. It should be noted also that the mean profile of the nine cycles (Fig. 5A) might suggest that cycle 21R-C2 (raw data Fig. 4A) was abnormal or that the assay was noisy. In fact, it is a normal cycle variant [37,54] and can be interpreted as both an early rise in E1G excretion rate due to an early developing follicle [76] which presumably underwent atresia, and a split peak at mid-cycle [54]. These are real changes and are not due to random errors in the assays. Both are common experiences found by daily monitoring of menstrual cycles [2] and underline the need to analyze cycles individually and daily for a complete understanding of menstrual cycle dynamics. The daily use of an accurate E1G assay to detect the first rise in estrogen excretion rates provides a prospective method for detecting the beginning of the fertile window which requires no previous cycle history or information and makes no assumptions concerning the expected variability of its location [3]. The E1G assay as described here using timediluted urine samples serves as a suitable reference assay against which the first statistically significant rises in E1G

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excretion rates from new point-of-care assays may be compared.

Acknowledgements We thank Professor Emeritus James Brown (University of Melbourne, Australia) and Dr. Maria Alliende (Departamento de Obstetricia y Ginecolog´ıa, Universidad de Chile, Santiago, Chile) for helpful comments and suggestions for improving this manuscript. We also acknowledge the support of the World Health Organisation project #90905 in allowing the use of the urine samples. Our thanks are due to Dr. Simon Brown (School of Human Life Sciences, University of Tasmania) for his contribution towards the statistical analysis used here.

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