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From the * Center for Advanced Research in Human
Reproduction, Infertility, and Sexual Function, Urological. Institute and
Department of Obstetrics-Gynecology, and the
Department of Biostatistics and Epidemiology,
The Cleveland Clinic Foundation, Cleveland, Ohio.
| Correspondence to: Dr Ashok Agarwal, Director, Center for Advanced Research in Human Reproduction, Infertility, and Sexual Function, Urological Institute, The Cleveland Clinic Foundation, 9500 Euclid Ave, Desk A19.1, Cleveland, OH 44195 (e-mail: agarwaa{at}ccf.org). |
| Received for publication September 13, 2002; accepted for publication November 12, 2002. |
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Abstract |
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Key words: Computer-assisted semen analysis, infertility, spermatozoa, morphology
The present study was performed with the goal of providing a more efficient way of summarizing SQ and also predicting the outcome of natural conception and assisted conception techniques utilizing a 2-factor scoring system based on the results of a routine semen analysis. This was accomplished by applying principal component analysis to the original semen variables obtained by both CASA and conventional or manual semen analysis. The resultant scores may provide the clinicians with a more reliable and efficient method to predict SQ and the outcome of a specific assisted reproductive technology (ART) procedure.
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Materials and Methods |
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Assessment of Semen Variables
Sperm samples were analyzed on a CASA instrument (IVOS, V10.7s, Hamilton
Thorne Research, Beverly, Mass) as described in the European Society of Human
Reproduction and Embryology (ESHRE) Andrology Special Interest Group
guidelines (1998). For each
measurement, a 5-µL sample aliquot from either a control or infertile
patient sample was loaded on a MicroCell slide (Conception Technologies, La
Jolla, Calif). Sperm motion kinetics measured by CASA included the
following:
In addition to the computerized results, manual results were also calculated for sperm concentration and motility.
Standardization, strict quality control, and quality assurance are critical and are strictly enforced in our program. The calibration setup of the CASA was as follows: 2 wells, 20 µm, duration of data capture (frames); 15 (whole semen); minimum motile speed (micrometers per second): 2 (raw); maximum burst speed (micrometers per second): 600 (raw); distance scale factor (micrometers per pixel): 0.9457; centroid cell size minimum (pixels): 2; centroid cell size maximum (pixels): 8; number of cells to find per well: 200; and minimum number of fields per sample: 3. The results of CASA were manually verified. If the difference between the CASA and manual readings was within 20%, the CASA results were deemed valid; however, the manual results were used if the difference was greater than 20%. A high degree of correlation was seen between CASA and manual sperm counts (r2 = 1, slope = 1) and motility (r2 = 0.97, slope = 0.97) (n = 111) and established the accuracy of CASA measurements. The reproducibility of the semen analyzer was determined by a videotape recording for calibration. An intra-assay variation of less than 10% was seen in CASA sperm counts and motility. Rejection criteria were greater than 2 standard deviations. The sperm count was 38.3–42.4 x 106/mL (30 frames/s), and sperm motility was 60.6%–75.0%.
Measurement of Sperm Morphology
For morphological evaluation, seminal smears were stained with Giemsa stain
(Diff-Quik, Baxter Scientific Products, McGraw Park, Ill). Sperm morphology
was assessed by World Health Organization (WHO) guidelines
(1999) and Tygerberg strict
criteria (Mortimer and Menkveld,
2001).
Calculation of the Semen Scores and Statistical Analyses
A principal component analysis model can be employed to calculate overall
semen scores that accounts for most of variability observed among the battery
of interrelated semen variables. Based on the fact that semen variables are
interrelated, the relationships among the semen parameters and the feasibility
of composite scores that represent most of the overall variability of the
semen parameters were tested. The semen scores were calculated as follows:
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The newly constructed principal component scores were scaled on the basis of a sample of 25 normal, healthy donors to be converted to semen scores that would average 100 with a standard deviation of 10 among this donor group, such as that provided for intelligence quotient (IQ) scores.
Similarly, the same procedure was undertaken with the 4 non-CASA characteristics: concentration, motility, and 2 morphology measures. Pearson product-moment correlations between the 9-characteristic and 4-characteristic semen scores were calculated. The broken-stick test was used to determine whether the principal component analysis provided variables that accounted for more overall variation than chance alone. Student's t tests were used to compare semen scores of various diagnoses. Calculations were performed with SAS version 8.1 software (SAS Institute Inc, Cary, NC).
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Results |
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Principal component analysis indicated that the 2 semen scores or components account for 80.3% of the variability observed among the 9 semen parameters, which was significantly better than expected by chance alone (P < .001). The first principal component was termed the "SQ" score to represent "overall SQ." This was a weighted sum of all semen characteristics and accounted for 64.8% or almost two thirds of the overall variability observed among the 9 variables. The SQ score weighs each semen parameter on the basis of the values listed in Table 2. For example, the variable with the greatest weight was sperm concentration (+0.81). All of the other scores also had positive weights. Therefore, the SQ score sums up all the semen characteristics with varying weights given to each score.
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The second component was termed an "RQ" score since it represents "relative quality." It assigns a negative weight for concentration and positive weights for all other variables. Therefore, an individual with relatively low concentrations but above-average values for other characteristics would have a high RQ score compared to what would be expected on the basis of other characteristics. In contrast, low scores on this scale will have poor motility, poor morphology, or low scores on other characteristics relative to their concentration. Therefore, the 2 new scores together accounted for more than 80% of the variability observed among the 9 semen characteristics. Because these 2 scores are produced by principal component analysis, SQ and RQ are not correlated.
Subsequently, the SQ and RQ scores were applied to samples from normal, healthy donors such that these 2 scores could be scaled to a mean of 100 and a standard deviation of 10. This involved both addition and multiplication of the values by a number of constants to derive the standardized scale. A mean of 100 was chosen, simply because it provided a level of recognition greater than 100 and had a quality better than the average male, and vice versa for scores less than 100, similar to the IQ scores, which are also standardized to a mean of 100. The standard deviation of 10 was chosen because it provided an easily computable difference from 100 or any other value of interest. For example, a population of normal individuals can often fall into a range of +2 standard deviations from the mean; therefore, the semen score would generally be expected to fall within a range of 80–120 for a group of donors. The steps involved in the calculation of the scores are illustrated in Figure 1. Examples of the semen characteristics that produce the 5 highest and the 5 lowest SQ and RQ scores are shown in Table 3. The 5 lowest SQ scores represent poor SQ. The highest and lowest SQ scores are more obvious in interpretation. The sample with the highest SQ score had a concentration of 414 x 106/mL, a motility of 95%, and a normal morphology (by WHO guidelines) of 40%, whereas the sample with the lowest SQ score had a concentration of 1.02 x 106/mL, a motility of 6%, and a WHO morphology of 11%. In addition, the 5 highest values for RQ scores showed that the concentration was lower than expected on the basis of the other above-average semen parameters. The RQ score is not as obvious, because it is based on the relationship of 8 parameters compared to their concentration. For instance, the highest RQ score was a sample with a concentration of only 0.56 x 106/mL but with a motility of 39% and a normal sperm morphology of 48%. In contrast, the individual with the lowest RQ score had a concentration of 24.3 x 106/mL, with a motility of 36% and a normal sperm morphology of 1%. Among the entire group of 250 patient samples, the average SQ score (median and 25% and 75% interquartile values) was 89.9, 25.1, and 130.4, and the average RQ score was 106.1, 45.2, and 165.9, respectively. The distribution of the 250 patients and 25 donors for both SQ and RQ scores is illustrated in Figure 2.
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Patients are often classified as oligozoospermic, asthenozoospermic, or teratozoospermic on the basis of concentration, motility, and morphology or any of these combinations. To illustrate how these classifications are related to the semen scores, the 250 patients in our study were classified into 8 groups (Figure 3). Patients without any abnormal semen parameters had average SQ and RQ scores greater than 100. Patients that were either asthenospermic or teratozoospermic had an average SQ score of only 90. Since concentration is the parameter that is assigned the maximum weight in defining the SQ score, patients who were oligospermic had an average SQ score that was only slightly greater than 80, similar to the score in asthenoteratozoospermic patients. When patients with oligozoospermia were combined with those patients that were either asthenozoospermic or teratozoospermic, SQ values averaged between 70 and 80. Patients with all 3 combinations (oligoasthenoteratozoospermic) had an average SQ score of less than 60. Therefore, SQ scores decrease as the number of abnormal characteristics increases. This plot also helps in interpreting perhaps the less intuitive RQ score, which also is a measure of asthenoteratospermia, because it examines the quality of the non-concentration characteristics. Patients identified as asthenoteratozoospermic had an average RQ score of less than 90 compared to oligozoospermic patients, who had an average RQ score of 130. The latter patients' RQ score is high, in the sense that their sperm concentration is poor, but other motility and morphology values are in the normal range. As in normal patients, the RQ score in oligoasthenoteratozoospermic patients was close to 100, because their motility and morphology were, as expected, based on their poor sperm concentration.
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As part of the validation process, the 2 SQ scores were applied to patients with various clinical diagnoses and were also compared with the semen scores in donors. Nonsignificant differences in SQ would indicate a lack of validity of the scores, whereas significant differences would provide initial evidence of their applicability. All 4 clinical diagnoses had significantly lower SQ scores than the donor control group (P < .03) (Table 4). These groups averaged SQ values 15–22 points below donors. Using multivariate logistic regression analysis to determine the ability of the semen scores to discriminate between donors and patients, the SQ score had an odds ratio of 2.53 (95% CI, 1.36–4.72; P < .004); the RQ score had an odds ratio of 0.86 (95% CI, 0.51–1.46; P < .58).
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We next calculated the conventional semen score. In this score only (conventional or manual), semen characteristics (ie, sperm concentration, percent motility, and percent normal forms according to both WHO morphology and Tygerberg strict criteria) were used. The principal component results for these 4 variables are shown in Table 3. A highly significant positive correlation was seen between the CASA SQ score and the conventional SQ score (r = 0.99). Similarly, a very strong correlation was seen for the RQ score between the CASA RQ score and the conventional RQ score (4 variables) (r = 0.96).
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Discussion |
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Sperm motility characteristics are important semen characteristics, which, before the 1980s, could only be evaluated subjectively (Aitken et al, 1982; Mortimer et al, 1986; Wichmann et al, 1994). The introduction of CASA made it possible to measure motility characteristics of individual spermatozoa. CASA is widely accepted as providing a rapid and objective measurement of individual "classical" sperm characteristics such as sperm count and sperm movement. CASA also increases the accuracy and reproducibility of sperm count and motility (Keel and Webster, 1990) and allows the determination of sperm motion characteristics, termed "kinematics," which cannot be obtained by microscopic observation, and may be important in determining the fertility potential of sperm (Seber, 1984). A comparison of the measurements obtained by CASA with those obtained by conventional means has shown the potential for discrepancy due to the methodology used (ESHRE, 2000). These sperm motion characteristics particularly have been correlated with the sperm penetration of cervical mucus, the sperm penetration of human oocytes, and the results of in vitro fertilization (Aitken et al, 1982). It is often useful to make comparisons of semen parameters prior to and after treatments such as varicocelectomy or medical/hormonal treatment of the infertile male. It may also be useful to compare sperm characteristics and response to sperm treatments (such as sperm washing methodology) over lengthy time periods of inseminations or treatments.
Perhaps the most widely utilized semen characteristic is sperm count. Men with less than 20 x 106 spermatozoa/mL are typically deemed subfertile, and men with counts less than 5 x 106 spermatozoa/mL are often considered infertile. Semen samples containing less than 14% normal forms by Tygerberg strict criteria are reported as subfertile, and those containing less than 5% normal forms are considered severely impaired, causing some centers to recommend couples discontinue treatment and move on to donor insemination. However, like its predecessors, strict sperm morphology is not absolutely accurate in predicting fertility. In recent years, numerous authors have realized the deficiencies in using these measures of semen parameters (Huszar et al, 1988a,b; Aitken et al, 1992; Davis and Katz, 1993; Mortimer, 1994; Clements et al, 1995). The reported predictive value of spermatozoa concentration, for example, has been criticized because of the natural day-to-day variations that occur in spermatozoa concentration (Huszar et al, 1988a,b). The use of CASA technology has been criticized because of difficulties in achieving optimum set-up procedures (Davis and Katz, 1993; Mortimer, 1994; Clements et al, 1995) and because of the operational difficulties of using routine CASA technology (Boone et al, 2000; Carrell, 2000; Oehninger et al, 2000). Many spermatozoa function tests today are included as part of a conventional semen analysis designed to detect deficiencies of the fertilizing potential of mature spermatozoa that otherwise may show optimal motility, morphology, and concentration values (Cross et al, 1986; Barratt et al, 1992, 1993; Marshburn et al, 1992; Benoff et al, 1993; Coddington et al, 1994; Irvine et al, 1994; Krause, 1995; Macleod and Irvine, 1995; Moutaffian and Parinaud, 1995). It is a basic principle of statistical modeling that a set of predictive characteristics must be tested on another set of data to determine if the analysis is valid and hence useful.
In this study, we examined a statistical model utilizing the principal components to narrow down the elaborate measures of semen analysis to 2 main components or scores. The results demonstrate that SQ and RQ scores were able to account for the majority of the variability of 9 individual semen characteristics. A majority of the variables demonstrated positive correlation with these scores. Among the patients, the average SQ score (median and 25% and 75% interquartile value) was 89.9, 25.1, and 130.4, respectively. In addition, using different subsets of infertile patients with various clinical diagnoses, we were able to measure the SQ and RQ scores. Therefore, SQ scores decrease as the number of abnormal parameters increases. A low SQ score reflects low concentration, motility, and morphology, whereas a high SQ score represents high concentration and relatively good motility and morphology.
Significantly lower SQ scores were seen in men with varicocele and in patients following vasectomy reversal than in controls. Therefore, SQ scores were significantly poor in all clinical diagnoses, but they did not differ among themselves. No significant differences were observed between donors and the clinical diagnosis groups for the RQ score, indicating that their concentration levels were not significantly different from that expected on the basis of their other characteristics. From our results, we can conclude that the SQ score can significantly discriminate between the normal control donors and patients, whereas RQ was not a good discriminator of SQ in these patient groups; this also confirms our univariate findings.
Our next objective was to examine how this score could be utilized effectively in those programs for which CASA is not available and, secondly, how much information is really provided by the sperm kinematics associated with CASA. As seen from our findings, very little information will be lost if the CASA characteristics are excluded and only conventional or manual scores are used. Therefore, programs that are not equipped with more sophisticated computer analysis systems for semen analysis can still have an effective means of interpreting the results of semen analysis. However, it is imperative that these programs have high standards of quality control and efficient and well-trained technical staff to minimize within-laboratory variations in semen analysis results (Clements et al, 1995; Cooper et al, 1999; Auger et al, 2000; Keel et al, 2000).
The semen score can provide important information on the SQ and the likelihood of establishing a pregnancy. Also, semen scores provide more meaningful information than the individual semen characteristics. However, it is important to understand the underlying cell biology of the spermatozoon. The ejaculate is composed of subpopulations of spermatozoa that differ in maturity, physiological status with respect to the ability to undergo capacitation and acrosome reaction, and fertilizing ability (Huszar et al, 1988a; Aitken et al, 1992).
What are the clinical applications of our findings? The utility of SQ and RQ scores for clinical practice may differ depending on the clinical situation. Because all semen characteristics were included in the derivation of the SQ score, this score may have greater utility in assessing the male fertility status for natural conception. On the other hand, the RQ score is derived from the measurement of morphology, motility, and motion parameters after adjusting for concentration. Therefore, the RQ score may be more helpful in artificial reproduction methods, where concentration may not be as essential as the other parameters.
In conclusion, we have demonstrated that semen characteristics can be reduced to 2 SQ scores, which account for more than 80% of the variability expressed by all of the semen characteristics individually. We believe that reducing the 9 semen characteristics to 2 scores will be more efficient by allowing quick comparisons of SQ. In addition, the semen scores may provide improved assessment of male fertility. Patient scores less than 80 are below the expected normal range of donors. Similar information is obtained using either CASA or conventional or manual semen analysis variables. Given the outcome of our studies, SQ scores may provide a more reliable alternative to the prediction of ART outcome, particularly in couples with male-factor or idiopathic infertility. This can be accomplished by examining a database that includes 2 sets of patients undergoing IUI, one that resulted in successful pregnancies and the second that failed to establish pregnancies. In the accompanying article, we are extending our semen score model in establishing the clinical usefulness of these new measures of SQ compared to simple routine variables of semen analysis. The clinical value of this analysis, such as optimum criteria that can maximize the clinical outcome of predicting in vivo and in vitro conception, is discussed.
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Acknowledgments |
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Footnotes |
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