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Obstetrics & Gynecology 2005;105:788-793
© 2005 by The American College of Obstetricians and Gynecologists
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ORIGINAL RESEARCH

Reduced Fertilization Rates in Older Men When Cervical Mucus Is Suboptimal

David B. Dunson, PhD*, Jamie L. Bigelow, MS*{dagger} and Bernardo Colombo, MS{ddagger}

From the *National Institute of Environmental Health Sciences, U.S. National Institutes of Health, Research Triangle Park, North Carolina; {dagger}Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; and {ddagger}Department of Statistics, University of Padua, Padua, Italy.


   ABSTRACT
TOP
ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
OBJECTIVE: Cervical mucus is vital in the regulation of sperm survival and transport through the reproductive tract. The goal of this study is to assess whether the lowered fertility for men in their late 30s and early 40s is related to the nature of cervical mucus on the day of intercourse.

METHODS: In a prospective study of 7 European family planning centers, 782 couples not using birth control recorded daily observations of intercourse and the nature of cervical mucus. Using data from 1,459 menstrual cycles, 342 ending in pregnancy, we estimate day-specific conception probabilities in relation to mucus and male and female age.

RESULTS: On days where cervical mucus was not evident, intercourse for men in their late 30s and early 40s was 50% less likely to result in a clinical pregnancy, adjusting for intercourse timing and female age. As secretions become more conducive to sperm transport, the effect of male age diminishes steadily from 21% on days with damp secretions, to 11% on days with thick mucus, to only 4% on days with most fertile-type mucus.

CONCLUSION: The effect of male age on fecundability can be minimized by timing intercourse on days with optimal secretions.

LEVEL OF EVIDENCE: II-2

A recent study reported that fecundity begins to decline in the late 20s for women and in the late 30s for men.1 This report generated considerable attention in the press, and many couples are concerned that they may have trouble conceiving if they start attempting pregnancy too late. The decline in male fecundity starting at age 35 generated particular interest, because a gradual decrease in female fecundity between age 20 and the early 30s has been reported previously.2 Although data suggest lower semen quality among men older than 50 compared with men younger than 30, the evidence of declines with age in the 30s and 40s is inconsistent.3 Sperm motility appears to be the factor most related to age, and one study of frozen semen reported reduced postthaw motility for men in their late 30s.4 However, data on motility are inconclusive and causes of declines in male fecundity from the early to late 30s remain unknown.

In assessing effects of male aging on the probability of conception, it is important to consider the role of cervical mucus in regulating sperm survival and transport. About 5–6 days before ovulation at the start of the fertile interval of the menstrual cycle,5,6 a rise in estrogen stimulates the increased secretion of estrogenic cervical mucus. Nonestrogenic mucus is viscous and tends to block passage of human spermatozoa into the uterus, while estrogenic mucus facilitates progressive motility of sperm. Observation of mucus discharge can identify the most fertile days of the cycle, and these correspond closely with hormonally identified fertile days.10

We hypothesize that sperm capable of progressive motility in optimal mucus conditions may vary in their ability to survive, travel, and fertilize the ovum if intercourse occurs when mucus has suboptimal biophysical properties. Thus, a man could potentially have clinically normal sperm motility, but have fecundity that varies from normal to low depending on the type and amount of cervical mucus, factors that vary substantially among women and cycles within women.8,11


   MATERIALS AND METHODS
TOP
ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Data were drawn from the European Study of Daily Fecundability, a prospective cohort study of the day-specific probabilities of conception within the menstrual cycle for outwardly healthy women in their reproductive years.12 The research protocol was reviewed and approved by the institutional review boards of Fondazione Lanza (Padua, Italy) and Georgetown University (Washington, DC). Colombo and Massarotto12 have published a detailed description of the study and initial results. From 1992 to 1996, 782 couples were recruited from 7 European centers providing services on fertility awareness and natural family planning. Neither partner had a history of fertility problems, and women were between 18 and 40 years of age, had at least 1 menses after the most recent cessation of breastfeeding or delivery, and were not taking hormonal medication or drugs affecting fertility. Women kept daily records of basal body temperature (BBT), sexual intercourse, menstrual bleeding, and vaginal observations of cervical mucus during the course of the study. Mucus was classified as 1) dry and nothing seen; 2) damp and nothing seen; 3) damp and mucus is thick, creamy, sticky, and not stretchy; and 4) wet, slippery, and smooth-feeling with transparent and stretchy mucus. Higher scores indicate better conditions for progressive sperm movement, and we refer to a score of 4 as most fertile-type mucus.

The daily BBT measurements were used retrospectively to identify the last day of hypothermia before the postovulatory rise in BBT. Following previous authors,2 the rise in BBT was estimated for each cycle using the 3-over-6 rule, which defines the BBT rise to occur when 3 consecutive temperatures are higher than all of the previous 6 measurements. Cycles were excluded from the analysis if there was insufficient BBT data to estimate the ovulation day, if there were no reported intercourse acts during the predicted 6-day fertile interval, if the male partner's age was not available, or if mucus records were missing on a potentially fertile day on which intercourse occurred. Out of 7,017 menstrual cycles of data, 6,329 had sufficient daily records of BBT. Of these, 6,098 had a detectable BBT shift using the 3-over-6 rule. Because many of the couples under study were attempting to avoid conception by intentionally timing intercourse outside of a prospectively identified fertile interval, only 1,849 cycles had at least 1 reported intercourse act in the 6-day fertile interval. Excluding cycles with missing mucus records resulted in 1,473 cycles out of which 14 were missing male age information, yielding 1,459 cycles for analysis. Pregnancies were detected in 342 of these cycles. Summary statistics of the number of conception cycles and couples in the different male and female age categories are presented in Table 1.


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  		Table 1. Age and Conception Status for Couples and Menstrual Cycles Under Study

 

To adjust for confounding effects of timing and frequency of intercourse, we focus on the day-specific probabilities of pregnancy according to the timing of intercourse within the predicted 6-day fertile interval. In cycles with multiple intercourse acts occurring during the potentially fertile days, it is not possible to determine which act resulted in a pregnancy.5 To avoid discarding valuable information and potentially biasing our results, we follow earlier authors,1,5,6,13,14 in using a statistical model that allows for the incorporation of information from cycles where multiple intercourse acts occurred.

The analyses presented in this article are based on the methods of Dunson and Stanford (Dunson DB, Stanford JB. Bayesian inferences on predictors of conception probabilities. Biometrics, in press), which have been applied previously in studying the effects of cervical mucus on day-specific conception probabilities in the menstrual cycle.14 Conception probabilities are allowed to depend on timing of intercourse relative to ovulation, mucus score on that day, female age, male age, and the interaction between mucus score and male age. Based on our biological hypothesis, our main statistical hypothesis was that there would be a significant interaction between mucus score and male age in predicting the probability of conception.

In particular, we were interested in 1-sided alternatives pertaining to the main effect of mucus score, the main effects of male and female age, and the interactions between the mucus score and age. For the main effect of mucus score, we tested the null hypothesis of no change in the conception probabilities against the alternative that the conception probabilities are nondecreasing (with at least 1 strict increase) as the mucus score increases. Similarly, for the main effects of male and female age, we consider null hypotheses of no change against alternatives of nonincreasing orders. Finally, for the interaction between male age and mucus, we considered the null of no effect compared with the alternative of an increasing age effect as the mucus score decreases.

Technical details pertaining to the statistical methods for incorporating cycles with multiple intercourse acts, for accounting for within-woman dependency, and for model fitting have been presented elsewhere (Dunson and Stanford. Biometrics, in press). For simplicity, we focus on the form of the model for the probability of pregnancy in the simple case in which there is a single intercourse act on day k of cycle j from couple i:

Pr(pregnancy in cycle j from couple i) = pijk



{17MMU1}

(1)

The term {xi}i is a multiplier accounting for unmeasured predictors of the fecundity of the couple that may lead to within-couple dependency. Accounting for dependency in this manner is often referred to as frailty15 or random-effects modeling.16 Day-specific parameters, {lambda}k, allow changes across the fertile interval. The variable aij = 1 indicates that the man is aged 35 or younger at the start of the cycle (aij = 0, otherwise), so that {tau} quantifies the main effect of male age. The term involving the product of {gamma}h quantifies the main effect of mucus score, with mijk denoting the 1–4 score.

Similarly, the effect of female age is quantified by {delta}l, l = 1, 2, 3 (wij is the 1–4 woman age category). The interaction between male age and mucus is characterized by {rho}h, h = 1, 2, 3. If any of the {rho}h != 1, then mucus affects the pregnancy probability differently for younger and older men.

Inferences were conducted within a Bayesian statistical framework based on posterior model probabilities. For each scientific question of interest, including assessment of mucus and age main effects as well as interactions, we assigned equal prior probability to the null and alternative hypotheses. Using a Markov chain Monte Carlo approach, we can estimate summaries of the posterior distribution. These summaries include not only the hypothesis probabilities but also estimates of the day-specific conception probabilities in relation to age and mucus scores on the day of intercourse. Posterior mean estimates are presented in figures and tables. Statements about statistical significance were based on using a .05 cutoff for the posterior probability of the null hypothesis, a quantity often used as a Bayesian alternative to the P value. Performance of the approach was validated using simulation studies (Dunson and Stanford. Biometrics, in press).


   RESULTS
TOP
ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The frequencies of occurrence of the different mucus types depend on timing relative to a BBT-based estimate of ovulation day but not on age of the woman (Table 2). Without adjusting for male age, the probability that a single act of sexual intercourse during the fertile interval results in a clinical pregnancy depends on timing of intercourse relative to ovulation, the type of mucus present on the day of intercourse, and the woman's age (Fig. 1). The probability of pregnancy increases with each unit increase in the mucus score and decreases with age of the woman. However, mucus and woman's age have essentially independent effects on the probability of conception, and the estimated decline with age in female fecundity remains the same whether or not the analysis accounts for mucus.


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  		Table 2. Medians and 10th and 90th Percentiles of Mucus Score on the Days in the Fertile Interval by Female Age Group

 


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  		Fig. 1. Probability of conception according to day of intercourse relative to ovulation, female age, and mucus score. For simplicity, only 2 female age groups are shown. The light gray lines represent females younger than 27 years, and the solid black lines represent females older than 35 years. Women aged 27–35 years have estimated pregnancy probabilities that fall between these 2 extremes.

Dunson. Low Conception Rates in Suboptimal Mucus. Obstet Gynecol 2005.

 

Incorporating data on male age, we find that the magnitude of the decline in fecundity for men in their late 30s and early 40s relative to younger men is entirely dependent on the type of mucus observed on the day of intercourse (P = .02). In particular, adjusting for the effects of intercourse timing and age of the female partner, men over 35 had equivalent pregnancy probabilities to younger men if most fertile-type mucus was present on the day of intercourse. If most fertile-type mucus was not present, then men over 35 had significantly lower pregnancy probabilities with the magnitude of the difference increasing with each unit decrease in the mucus score (Fig. 2).



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  		Fig. 2. Probability of conception according to male age, mucus score, and female age, assuming intercourse 3 days before ovulation on the most fertile day. The relationship was similar for females between the ages of 27 and 35 years, with pregnancy probabilities falling between the oldest and youngest age groups.

Dunson. Low Conception Rates in Suboptimal Mucus. Obstet Gynecol 2005.

 

Assuming that the female partner is aged 35 years and sexual intercourse occurs 2 days before ovulation, the probability of conceiving a clinical pregnancy for a man younger than 35 is .14, .14, .18, or .25, depending on whether the mucus score is 1, 2, 3, or 4, respectively, on the day of intercourse. For an older man, these probabilities are, instead, .07, .11, .16, and .24. Thus, if intercourse occurs on a day with no secretions so that the mucus score is 1, there is a 50% reduction in the per menstrual cycle conception probability attributable to male aging in the late 30s and early 40s. This aging effect is not apparent if mucus conditions are optimal.


   DISCUSSION
TOP
ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The less the mucus resembled ideal estrogenic mucus and the worse the conditions were for progressive sperm motility, the greater the gap in conception probability between younger and older men. The reduced fertility in older men could involve such mechanisms as poor progressive motility of sperm in suboptimal mucus conditions or reduced ability of the semen to sustain survival of sperm until cervical mucus conditions improve. The differential semen effectiveness from older men results in clinically important differences in fecundability, the probability of conception per menstrual cycle.

Couples who fail to conceive within a year of regular intercourse without contraception are classified as clinically infertile, and may be treated with assisted reproduction. This diagnosis and treatment have an associated psychological and financial burden, and children conceived under assisted reproduction have a higher incidence of preterm birth.17 Studies suggest that a high proportion of these clinically infertile couples would eventually conceive naturally if continuing the attempt. To shorten time to pregnancy and avoid being diagnosed as clinically infertile, couples can intentionally time intercourse to occur during the most fertile days of the cycle. However, timing based on luteinizing hormone surge or other markers of ovulation can miss the most fertile days of the cycle,21 and regular intercourse can be just as effective.22 Our results suggest that per menstrual cycle pregnancy probabilities are much higher with intercourse on days with high mucus scores, particularly for men in their late 30s. Thus, timing of intercourse on days with fertile-type mucus, which can easily be identified by examination of vaginal secretions at the vulva, may result in a reduced time to pregnancy on average.

Although BBT data cannot be used to reliably predict the fertile interval of the menstrual cycle prospectively,21 the retrospectively identified last day of hypothermia is a useful marker of the end of the fertile interval. Martinez et al23 concluded that the last day of hypothermia occurred within 1 day of the urinary luteinizing hormone surge in 75% of cycles and within 2 days in 90% of cycles. Guida et al24 found that the day before the BBT rise occurred within 2 days of ovulation documented by transvaginal ultrasonography in 94% of cycles. In addition, using data from different studies, Dunson et al1,6 found that nearly all pregnancies occurred from intercourse that took place in the 6-day window ending with the BBT-estimated day of ovulation, and day-specific conception probabilities estimated for days outside this interval were close to zero. For this reason, we predict the fertile days of the cycle using this 6-day interval, and do not consider intercourse or cervical mucus data collected outside of this interval. Some degree of measurement error in predicting the fertile days of the cycle is unavoidable, even for hormonal markers of ovulation. Such measurement error will tend to attenuate (flatten out) the estimated day-specific conception probabilities within the fertile interval,25 and may result in a slight decrease in power to detect effects of cervical mucus and other factors.


   Footnotes
 
The authors thank Drs. Masarotto, Barbato, Girotto, Ecochard, Gnoth, Frank-Hermann, and Pyper and the other principal investigators of the European Study of Daily Fecundability for providing the data.

Address reprint requests to: David B. Dunson, PhD, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709; e-mail: dunson1{at}niehs.nih.gov.

Received September 18, 2004. Received in revised form November 17, 2004. Accepted December 8, 2004.

doi:10.1097/01.AOG.0000154155.20366.ee


   REFERENCES
TOP
ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
1. Dunson DB, Colombo B, Baird DD. Changes with age in the level and duration of fertility in the menstrual cycle. Hum Reprod 2002;17:1399–1403.[Abstract/Free Full Text]

2. Schwartz D, Mayaux MJ. Female fecundity as a function of age: results of artificial-insemination in 2193 nulliparous women with azoospermic husbands. N Engl J Med 1982;306:404–6.[Medline]

3. Kidd SA, Eskenazi B, Wyrobek AJ. Effects of male age on semen quality and fertility: a review of the literature. Fertil Steril 2001;75:237–48.[Medline]

4. Schwartz D, Mayaux MJ, Spira A, Moscato ML, Jouannet P, Czyglik F, et al. Semen characteristics as a function of age in 833 fertile men. Fertil Steril 1983;39:530–5.[Medline]

5. Wilcox AJ, Weinberg CR, Baird DD. Timing of sexual intercourse in relation to ovulation-effects on the probability of conception, survival of the pregnancy, and sex of the baby. N Engl J Med 1995;333:1517–21.[Abstract/Free Full Text]

6. Dunson DB, Baird DB, Wilcox AJ, Weinberg CR. Day-specific probabilities of clinical pregnancy based on two studies with imperfect measures of ovulation. Hum Reprod 1999;14:1835–9.[Abstract/Free Full Text]

7. Eriksen GV, Carlstedt I, Uldbjerg N, Ernst E. Cervical mucins affect the motility of human spermatozoa in vitro. Fertil Steril 1998;70:350–4.[Medline]

8. Katz DF. Human cervical-mucus-research update. Am J Obstet Gynecol 1991;165:1984–6.[Medline]

9. Katz DF, Slade DA, Nakajima ST. Analysis of pre-ovulatory changes in cervical mucus hydration and sperm penetrability. Adv Contracept 1997;13:143–51.[Medline]

10. Fehring RJ, Raviele K, Schneider M. A comparison of the fertile phase as determined by the Clearplan Easy Fertility Monitor and self-assessment of cervical mucus. Contraception 2004;69:9–14.[Medline]

11. Dunson DB, Colombo B. Bayesian modeling of markers of day-specific fertility. J Am Stat Assoc 2003;98:28–37.

12. Colombo B, Masarotto G. Daily fecundability: first results from a new data base. Demogr Res 2000;3:[39] p.

13. Stanford JB, Smith KR, Dunson DB. Vulvar mucus observations and the probability of pregnancy. Obstet Gynecol 2003;101:1285–93.[Abstract/Free Full Text]

14. Bigelow JL, Dunson DB, Stanford JB, Ecochard R, Gnoth C, Colombo B. Mucus observations in the fertile window: a better predictor of conception than timing of intercourse. Hum Reprod 2004;19:889–92.[Abstract/Free Full Text]

15. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 1979;16:439–54.[Medline]

16. Hogan JW, Blazar AS. Hierarchical logistic regressions models for clustered binary outcomes in studies of IVF-ET. Fertil Steril 2000;73:575–81.[Medline]

17. Helmerhorst FM, Perquin DAM, Donker D, Keirse MJNC. Perinatal outcome of singletons and twins after assisted conception: a systematic review. BMJ 2004;328:261–4.[Abstract/Free Full Text]

18. Trussell J, Wilson C. Sterility in a population with natural fertility. Popul Stud 1985;39:269–86.

19. Collins JA, Wrixon W, Janes LB, Wilson EH. Treatment-independent pregnancy among infertile couples. N Engl J Med 1983;309:1201–6.[Abstract]

20. Menken J, Trussell J, Larsen U. Age and infertility. Science 1986;233:1389–94.[Abstract/Free Full Text]

21. Stanford JB, White GL, Hatasaka H. Timing intercourse to achieve pregnancy: current evidence. Obstet Gynecol 2002;100:1333–41.[Abstract/Free Full Text]

22. Agarwal SK, Haney AF. Does recommending timed intercourse really help the infertile couple? Obstet Gynecol 1994;84:307–10.[Abstract/Free Full Text]

23. Martinez AR, Bernardus RF, Vermeiden JP, Schoemaker J. Reliability of home urinary LH tests for timing of insemination: a consumer's study. Hum Reprod 1992;7:751–3.[Abstract/Free Full Text]

24. Guida M, Tommaselli GA, Palomba S, Pellicano M, Moccia G, DiCarlo C, et al. Efficacy of methods for determining ovulation in a natural family planning program. Fertil Steril 1999;72:900–4.[Medline]

25. Dunson DB, Weinberg CR. Modeling human fertility in the presence of measurement error. Biometrics 2000;56:288–92.[Medline]





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