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Cap‐Score™ prospectively predicts probability of pregnancy

Semen analysis (SA) poorly predicts male fertility, because it does not assess sperm fertilizing ability. The percentage of capacitated sperm determined by GM1 localization (“Cap‐Score™”), differs between cohorts of fertile and potentially infertile men, and retrospectively, between men conceiving or failing to conceive by intrauterine insemination (IUI). Here, we prospectively tested whether Cap‐Score can predict male fertility with the outcome being clinical pregnancy within ≤3 IUI cycles. Cap‐Score and SA were performed (n = 208) with outcomes initially available for 91 men. Men were predicted to have either low (n = 47) or high (n = 44) chance of generating pregnancy using previously‐defined Cap‐Score reference ranges. Absolute and cumulative pregnancy rates were reduced in men predicted to have low pregnancy rates versus high ([absolute: 10.6% vs. 29.5%; p = 0.04]; [cumulative: 4.3% vs. 18.2%, 9.9% vs. 29.1%, and 14.0% vs. 32.8% for cycles 1–3; n = 91, 64, and 41; p = 0.02]). Only Cap‐Score, not male/female age or SA results, differed significantly between outcome groups. Logistic regression evaluated Cap‐Score and SA results relative to the probability of generating pregnancy (PGP) for men who were successful in, or completed, three IUI cycles (n = 57). Cap‐Score was significantly related to PGP (p = 0.01). The model fit was then tested with 67 additional patients (n = 124; five clinics); the equation changed minimally, but fit improved (p < 0.001; margin of error: 4%). The Akaike Information Criterion found the best model used Cap‐Score as the only predictor. These data show that Cap‐Score provides a practical, predictive assessment of male fertility, with applications in assisted reproduction and treatment of male infertility.

Traditional semen analysis has remained the primary diagnostic tool, despite the fact that it fails to diagnose the vast majority of cases of male infertility (Guzick et al., 2001;Ombelet et al., 1997;van der Steeg et al., 2011), and has not changed appreciably in decades. Although effective at identifying descriptive parameters, traditional semen analysis fails to identify defects in sperm function. Put simply, it does not provide information about whether a man's sperm can fertilize an egg, and the probability of that man generating a pregnancy.
Being able to assess the fertilizing capacity of a man's sperm could play multiple important roles. First, particularly in couples who have delayed attempting to have children until their 30s or later, there is increasing recognition of reduction in conception rates (Dunson, Baird, & Colombo, 2004;Navot et al., 1991) and of increasing risk of genetic abnormalities (Hansen, 1986;Reichenberg et al., 2006;Wyrobek et al., 2006), associated with increasing age in both women and men.
Knowledge of the male partner's fertilizing ability might help them decide whether to try at home or to pursue fertility examinations more quickly than guidelines currently suggest. Second, should an intervention or treatment be necessary, knowledge of sperm fertilizing ability could be used to personalize fertility treatment plans (Oehninger, Franken, & Ombelet, 2014;Palermo, Neri, & Rosenwaks, 2015). For example, men whose sperm have normal function and a high probability of fertilization could be guided by clinicians to approaches such as intrauterine insemination (IUI). Conversely, those with poorly functioning sperm might be able to avoid the emotional, physical, and financial costs associated with repeated IUI attempts that have little chance of success, by being guided to procedures such as intracytoplasmic sperm injection (ICSI). However, as has been pointed out (Barratt et al., 2018), approaches such as ICSI are not treatments of poor male fertility; they instead seek to overcome it. Therefore, the third application for a test that quantifies male fertilization competence would be as a metric for urologists attempting to treat underlying factors contributing to male infertility.
For example, this test could enable an evidence-based approach to the treatment of male infertility, assessing the response of an individual man to interventions such as surgical repair of a varicocele (Seaman & Aly, 2018), or changes in medications, nutritional supplements, or lifestyle (see Hayden, Flannigan, and Schlegel (2018) for a review of the impacts of factors such as diet and exercise). Pharmaceutical companies could assess potential off-target impacts of drugs as well as on-target impacts of male contraceptives being developed. From a public health perspective, it is becoming appreciated that semen parameters can provide insights into overall male health (Glazer et al., 2017;Hanson, Eisenberg, & Hotaling, 2018), and sperm function might prove to be an even more sensitive indicator than reduction in sperm production.
Together, these pressing needs have led to numerous calls for the development of an assay of male fertility that focuses on the fertilizing ability of sperm (e.g., Lamb, 2010;Oehninger et al., 2014;Wang & Swerdloff, 2014). Much attention has focused on a process known as "capacitation," through which sperm acquire the ability to fertilize in response to stimuli within the female reproductive tract (Austin, 1952;Chang, 1951). We showed recently that localization patterns of the ganglioside monosialotetrahexosylganglioside (G M1 ) within the plasma membrane of human sperm can identify capacitation status (Supporting Information Figure S1A; Moody et al., 2017).
Importantly, the percentage of capacitated sperm determined by G M1 localization ("Cap-Score™"), differed between cohorts of fertile men and men questioning their fertility, and retrospectively, between men having success at, or failing, IUI . Cap-Scores were highly reproducible among ejaculates within a man and had no relationship with traditional semen analysis parameters . In addition, we showed at the level of single sperm, that those cells having the "capacitated" patterns were indeed those cells that could undergo acrosome exocytosis in response to ionophore (A23187; Moody et al., 2017) and progesterone (Ostermeier et al., 2018). Using repeated Cap-Score values over time, we also found that the timing of capacitation differed among men but was consistent within a man (Ostermeier et al., 2018).
Here, we build on those data and present the results of a prospective test of Cap-Score to predict a man's ability to generate a clinical pregnancy using data from IUI cycles as a well-controlled study design. We then had independent statisticians test whether a combination of Cap-Score and one or more semen analysis parameters might convey even more information, finding instead that Cap-Score alone had the best fit with clinical outcomes. The model fit on these original data was then further tested with additional data, originating from a total of five clinics, to yield a model that transformed Cap-Score results into the probability of generating a pregnancy.

| RESULTS
We prospectively tested Cap-Score with the outcome measure being clinical pregnancy within three or fewer cycles of IUI. We chose IUI as a rigorous study model because it offered control over the timing and number of inseminations relative to a given ovulation, as opposed to natural/spontaneous conceptions. To separate men into groups predicted to have a low versus high chance of generating pregnancy, we used a cut-off of one standard deviation below the previously identified mean of Cap-Scores in a cohort of fertile men based on comparison of their distribution with results from a cohort of men questioning their fertility (Supporting Information Figure S1B; Cardona et al., 2017). Cap-Score and semen analysis were performed (n = 208), with clinical outcomes available for 91 couples at the time of analysis.
Survival analysis was used to compare the abilities of men with low (Table 1)  greater (29.5% vs. 10.6%; P = 0.04) for men prospectively predicted by Cap-Score to have a high chance of pregnancy (n = 44 men) versus those predicted to have a low chance (n = 47 men). To determine when and how these differences were established and to take into consideration that not every couple made three attempts, cumulative pregnancy rates were calculated for each round of IUI. Men predicted to have a high chance of fertility due to their Cap-Score were 4.23-, 2.94-, and 2.34-fold more likely to generate a pregnancy than men in the low group following 1, 2, and 3 IUI attempts, respectively (p = 0.02; cumulative predicted pregnancy for low vs. high predictions: 4.3 vs. 18.2, 9.9 vs. 29.1, and 14.0 vs. 32.8%). These results were confirmed by independent statisticians given access to all raw data.
In addition to this prospective test of Cap-Score, traditional semen analysis measures were compared retrospectively between those men who were and were not successful in generating a pregnancy (Table 3). Only Cap-Score differed significantly between the pregnant and not-pregnant groups (p < 0.01). These observations support our previous findings that Cap-Score differs between fertile and subfertile men and does not correlate with traditional semen analysis parameters . In addition, these results are consistent with the observation of others, that traditional semen analysis measures have significant overlap between fertile and infertile men (Guzick et al., 2001;van der Steeg et al., 2011).
The relationship between Cap-Score and the probability of generating a pregnancy was defined by the independent statisticians using logistic regression analysis for those men generating a pregnancy within or completing 3 IUI attempts (Figure 1a; n = 57 men). A To determine if inclusion of one or more traditional semen analysis parameters could improve fit even further, logistic regression models were calculated using Cap-Score and semen analysis measures alone and in combination for both the single-(one clinic; n = 57) and multiclinic (five clinics; n = 124) data sets (Table 4). The deviance and Akaike Information Criterion (AIC; Akaike, 1974) were then used to test the relative quality of the models. In general, the smaller the deviance, the better the model fits. However, as more parameters are added, the fit will appear to improve in large part because of increased complexity. To guard against the model being tailored to the random noise of the sample, rather than representing the overall population, the AIC was used. The AIC penalizes increasing model complexity without a reciprocal increase in fit. Those models with smaller AIC are considered most appropriate. For both the single-and multiclinic data sets, Cap-Score alone was deemed most appropriate. These observations are consistent with Cap-Score being the best predictor of male fertility.

| DISCUSSION
It is widely appreciated that our reliance on semen analysis-either to diagnose male infertility or to assess a functional response to treatment of male infertility-is inadequate (Barratt et al., 2018).
Assays proposed over the years to fill these needs have not demonstrated efficacy at prospectively predicting the clinical outcome of generating a pregnancy (Barratt et al., 2018), and are often found to correlate with the traditional semen analysis parameters (Aitken, 2002;Giwercman et al., 2003;Hazary, Chaudhuri, & Wishart, 2001;Zini et al., 2009). Prospective data presented here strongly demonstrate that the Cap-Score, a measure quantifying the percentage of sperm that can capacitate and undergo acrosome exocytosis Moody et al., 2017;Ostermeier et al., 2018), successfully predicted low or high success at achieving pregnancy in three or fewer attempts at IUI. Yet from a clinical perspective, we know that male fertility does not simply switch between a binary "infertile" to "fertile" categorization at a single cut-off; rather, it is complex, multifactorial, and exists along a continuum with men having different probabilities of fertilizing. Importantly, logistic regression analysis of the raw data performed by independent statisticians showed that Cap-Score provided a straightforward means of identifying the probability of a man to generate a pregnancy, given a female partner who was eligible for IUI.
These data support the findings of prior retrospective and cohort comparisons, that Cap-Score can be used as a measure of male fertility . In the current investigation, a previously determined Cap-Score reference range  was used to separate individuals prospectively into groups having either a low or high chance of generating a pregnancy. A survival analysis then prospectively showed that the absolute pregnancy rate of couples with men in the high Cap-Score group was 278% that of men predicted to have difficulty conceiving due to low Cap-Score. In contrast to the predictive power of Cap-Score, traditional semen analysis parameters and male and female age were not related to clinical outcome, which also supported a previous finding that Cap-Score does not correlate with traditional semen analysis parameters .
T A B L E 3 Semen analysis measures for men who were and were not successful in generating pregnancy Note. Individuals completed at least one round of IUI. Men were placed in the pregnant (Preg) category if their partner conceived in ≤3 rounds of IUI. Otherwise, they were placed in the not pregnant (NP) group. p-value (Preg vs. NP): p-value from two-tailed t test. N: number of observations. Conc: sperm concentration. Cap-Score™: proportion of sperm having G M1 localization patterns consistent with capacitation.
F I G U R E 1 Cap-Score™ and its association with the probability to generate pregnancy. Logistic regression defined the relationship between Cap-Score and a man's PGP. Panel A shows data from a single clinic (n = 57; p = .01) with the PGP ranging from 5% to 69%. In Panel B, the singleclinic model was tested by adding 67 additional data points obtained from this and four other clinics (n = 124; p < 0.001; PGP range: 5-78%). The green and blue points respectively show those men generating (Preg) and not generating (NP) a pregnancy. The light and dark grey points respectively show the lower (LL) and upper (UL) limits for the 95%CI. CI: confidence intervals; NP: not pregnant; PGP: probability of generating a pregnancy SCHINFELD ET AL.

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Of interest, the proportion of pregnancies generated for each of three IUI attempts for men in the low group was consistent (4.3%, 5.9%, and 4.5%), with an average of 4.9%. In contrast, success in the first IUI attempt for men in the high group was 18.2% or 423% that of the low group. Success for the high group declined to 13.3% in the second attempt, and 5.3% in the third, still yielding cumulative pregnancy rates that were 294% and 234% those of the men predicted to have a low chance of conceiving. Although requiring further investigation beyond the scope of this report, the decline in success in the third cycle of men with normal-range Cap-Scores is suggestive that the IUI protocol being used was not in some way optimized for the sperm function of this subpopulation. That is, their success was no better than that of men whose sperm had low capacitation ability. One possible explanation is offered by our recent finding that the timing of capacitation differs among men, but is reproducible within repeated ejaculates of a given man (Ostermeier et al., 2018). In that study, 44% of men were shown to have sperm that took significantly longer to capacitate than those of their peers (Ostermeier et al., 2018). Currently, insemination is typically performed 24-36 hr after human chorionic gonadotropin (hCG) injection. Ovulation has been reported to occur over a range of 26-46 hr after the hCG injection with an average of 38 hr (Andersen, Als-Nielsen, Hornnes, & Franch Andersen, 1995;Testart, Thebault, Souderes, & Frydman, 1982). Because oocytes are believed to be fertilization competent for less than 24 hr (Lash & Whittaker, 1974;Miao, Kikuchi, Sun, & Schatten, 2009) (Amann, 1989;Amann & Hammerstedt, 1993). Men producing sperm within the normal Cap-Score range and failing to generate pregnancy within two IUI cycles might be producing sperm that lack the ability to initiate embryo development (Swann, Saunders, Rogers, & Lai, 2006;Yoon et al., 2008). As yet another alternative, recent evidence suggests that although there is little association between the presence of seminal human papillomavirus and semen analysis parameters (Luttmer et al., 2016), men with this infection have reduced ability to generate pregnancy (Garolla et al., 2016). The infected sperm are still able to gain access to oocytes and fertilize (Foresta et al., 2011), but appear to have an early reduction in embryo viability beyond that event. If either of these alternatives is correct as opposed to the issue of capacitation timing, then the data would support attempting only two IUI cycles even in couples in which men are producing sperm within the normal Cap-Score range. Of course, it is possible that all three alternatives contribute to the reduced success in the third IUI attempt.
There are several reasons to evaluate male fertility. These range from counseling couples on the best technology of assisted reproduction to generate pregnancy (Palermo et al., 2015), to determining the effects of interventions designed to improve male fertility, such as changes in diet and exercise (Hayden et al., 2018), nutritional supplements (Walczak-Jedrzejowska, Wolski, & Slowikowska-Hilczer, 2013), administering or withdrawing various medications (Brezina, Yunus, & Zhao, 2012), surgical repair of varicoceles (Comhaire & Kunnen, 1985), vasectomy reversal, and so forth. One could also evaluate the efficacy of sperm cryopreservation (Bailey, Blodeau, & Cormier, 2000) or other protocols to extend or preserve male fertility. This latter application could have use beyond human medicine in various animal agriculture industries such as dairy or turkey production, in which artificial insemination is almost universally practiced. Indeed, before our tests of human fertility, the relationship of G M1 localization and capacitation status was first identified in nonhuman animals such as mice and bulls (Selvaraj et al., 2007). One might question why this test of capacitation should prove to be such a strong indicator of male fertility. Unlike genetic analyses that look for specific mutations, or screens that cover a panel of genes that have unclear contributions to male fertility (and do not preclude the possibility that other gene products might compensate for the reduction in function of any given gene), Cap-Score is a test that reflects the end product of the interactions of hundreds or possibly thousands of proteins and lipids in multiple pathways and developmental processes. For example, to capacitate and contribute toward a normal-range Cap-Score, a sperm must successfully complete germ cell development and differentiation within the testis, mature within the epididymis to achieve both normal membrane architecture and be able to capacitate, and interact first with seminal plasma and then respond appropriately to stimuli for capacitation.
The complexity of spermatogenesis and the signaling pathways involved in sperm functional maturation offer a vast number of potential opportunities for sperm function to be deranged. Although traditional semen analysis can correctly identify profound problems that would result in reduced or abnormal sperm production or impaired motility, the literature repeatedly finds that more subtle defects in function are responsible for the majority of male factor infertility. By providing a downstream assessment of the final product-the percentage of sperm that can fertilize-the single measure of the Cap-Score has proven capable of identifying a wide range of these problems.
In the absence of a test such as Cap-Score, these defects in sperm function are identified only after repeated failure with natural conception and IUI (Aboulghar et al., 2001;Tournaye, 2012).
Accordingly, many couples being treated for infertility are faced with substantial emotional, physical and monetary burdens. When these burdens become too great, it is not uncommon for couples to drop out of treatment before attaining pregnancy (Domar, Smith, Conboy, Iannone, & Alper, 2010). Our data suggest that Cap-Score results could be used by clinicians to inform their patients of that man's ability to generate pregnancy and help guide a treatment pathway. The impact provided by such an approach was previously modeled and shown to not only increase clinical pregnancy rates but also to greatly reduce medical costs, in an age-dependent fashion (Babigumira, Sharara, & Garrison, 2018).

| Specimen collection and processing
Semen samples were collected by manual masturbation as part of a standard male fertility evaluation exam. Any sample having fewer than 10 × 10 6 motile sperm on initial count was excluded. The samples were collected and processed using Cap-Score™ Male 21.4 mM sodium lactate; 10 µg/ml gentamicin; 5 mg/L phenol red]).
The sperm were resuspended in mHTF with (CAP) and without (NonCap) 2-hydroxypropyl-β-cyclodextrin (Sigma; St. Louis, MO; catalog #C0926) to promote capacitation. Following incubation, the samples were fixed, packaged and shipped overnight to Androvia for the test to be performed and Cap-Score™ determined.
All samples were sent in this way to Androvia LifeSciences, with the exception of 37 samples provided by Weill Cornell Medical Center, which were processed and scored at Weill Medical Center before the formation of Androvia LifeSciences. The processing and scoring of these samples have been detailed previously ; these samples were not part of the prospective study but contributed to the additional 67 data points that were used to test the model generated by the logistic regression analysis.
For all 124 patients considered in this study, semen analysis was performed in conjunction with the Cap-Score. All semen analyses were performed by licensed technicians following WHO guidelines (WHO, 2010).
After ten minutes, 5 µl of the labeled sperm were placed on a microscope slide, overlaid with a coverslip (50 mm no. 1), and moved to an imaging station.

| Cap-Score determination
Readers were trained to identify G M1 localization patterns associated with both non-capacitated and capacitated human sperm . All readers passed proficiency testing and daily quality assurance testing as described . The proportion of sperm within a sample having undergone capacitation was determined and reported as the Cap-Score (# of sperm with patterns associated with capacitation/(# of sperm with patterns associated with capacitation + number of sperm with other patterns)). All readings were performed according to validated methods , and consistent with CLIA-, CAP-and Clinical Laboratory Evaluation Program (CLEP)approved best practices for quality control and assurance. Briefly, if sufficient sperm were present in the sample, at least 150 total patterns were determined for each condition. If insufficient cells were available, a minimum of 100 patterns was necessary to compute Cap-Score.
Otherwise, the samples were rejected.

| Patients
Cap-Scores were performed on men whose fertility was being assessed for purpose of trying to conceive. The initial exclusion criterion for men was having fewer than 10 × 10 6 motile sperm on initial count. The fertility of female partners was examined, but findings of female factor (e.g., polycystic ovary syndrome, diminished ovarian reserve, repeated pregnancy loss, amenorrhea, myoma, anovulation, endometriosis, etc.) that did not preclude attempts at IUI were not considered grounds for exclusion so that the test population could most accurately reflect the patient population pursuing IUI. Only couples that pursued IUI were included in the study because that approach afforded the most rigorous control of the number and timing of insemination(s) relative to a given ovulation. Therefore, couples that were advised to pursue a course of expectant management and/or achieved natural/spontaneous conception, were excluded, as were couples who pursued classical IVF or ICSI. Differences among clinics reflecting their patient base, practices, and the components of the study to which they contributed are described below.

| Abington Reproductive Medicine
All patients were offered the Cap-Score™ Male Fertility Assay (n = 208 performed). Patients were included in the survival analysis if outcomes were available from at least one IUI attempt at the time of initial data analysis (n = 91). The data set used for this initial logistic regression analysis was from those couples who generated a pregnancy within, and (or) completed, at least three rounds of IUI at the time of data analysis (n = 57). Data were collected between 11/ 2016 and 1/2018.

| IVF1
Data from 13 consecutive couples looking for initial fertility treatment who generated a pregnancy within and (or) completed at least three rounds of IUI were included in the test of the original model generated by logistic regression. Data were collected between 3/2017 and 2/2018.

| Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine and Infertility, Weill Cornell Medicine
Men scheduled for semen analysis of any age that had normal semen parameters (WHO, 2010) and a female partner equal to or less than 35 years old were considered eligible. Data from four consecutive couples who generated a pregnancy within and (or) completed at least three rounds of IUI were included in the test of the original model generated by logistic regression. Data were collected between 3/2017 and 2/2018. As described above, data previously collected and described  from 37 couples that fell under the current criteria for participation were also included in the test of the original model generated by logistic regression.

| Virginia Center for Reproductive Medicine
Data from nine consecutive couples looking for initial fertility treatment who generated a pregnancy within and (

| Virginia Center for Reproductive Medicine
Two weeks after insemination a urine test was performed; if positive, hCG and progesterone blood levels were determined. Blood tests were repeated every two days, to make sure that hCG was doubling every 48 hr. Once hCG blood levels of 1,000 U/ml were detected, ultrasonography was performed.

| Statistical analyses
Initial statistical analyses (Kaplan-Meier survival analysis and two sample t test) were carried out in XLSTAT Version 19.03.45087.
Following the best practice of having analyses performed by independent statisticians, Singular Value Consulting (Houston, TX) was contracted and given Androvia's complete raw data set related to this study including updated clinical outcomes as they came in. First, the survival analysis and two sample t test results were confirmed.
Second, logistic regression was used to evaluate Cap-Score and traditional semen analysis results alone and in combination in relation to the PGP for men who were successful in, or completed, three rounds of IUI (n = 57). The model fit on these data was then tested using data from 67 additional patients (five total clinics). The AIC was used to assess model quality when the Cap-Score was combined with traditional semen analysis parameters. Confirmational statistics and logistic regression analysis were carried out in R (Team, 2013) and SciPy (Jones, Oliphant, & Peterson, 2001).

ACKNOWLEDGMENTS
Performance of Cap-Scores and independent statistical analysis were funded by Androvia LifeSciences, LLC. Data reported previously  were obtained with support from the New York City