The National Institute of Environmental Health Sciences National Institutes of Health
SHANNA H. SWAN, PH.D., CHARLENE BRAZIL, B.S., ERMA Z. DROBNIS, PH.D., FAN LIU, M.S., ROBIN L. KRUSE, PH.D., MAUREEN HATCH, PH.D., J. BRUCE REDMON, M.D., CHRISTINA WANG, M.D., JAMES W. OVERSTREET, M.D., PH.D. AND THE STUDY FOR FUTURE FAMILIES RESEARCH GROUP
Corresponding author: Shanna H. Swan, Department of Family and Community Medicine, MA306 Medical Sciences Building, University of Missouri-Columbia, Columbia MO 65212. SwanS@health.missouri.edu
KEY WORDS: sperm concentration, sperm motility, semen quality, geography, sperm morphology, agriculture
We acknowledge the Study for Future Families Research Group; the physicians, midwives and staff of University Physicians Clinic, Columbia, MO; Fairview Riverside Women’s Clinic, Minneapolis, MN; Harbor-UCLA Medical Center, Torrance, CA; Cedars-Sinai Medical Center, Los Angeles, CA; Mt. Sinai Medical Center, New York, NY; and the study participants. This work was supported by the following grants from the National Institutes of Health: R01- ES09916 to the University of Missouri from the National Institute for Environmental Health Sciences; MO1-RR00400 to the University of Minnesota General Clinical Research Center, and MO1-RR0425 to the Research and Education Institute at Harbor-UCLA Medical Center and the Cedars-Sinai Research Institute from the National Center for Research Resources.
ABBREVIATIONS:
ACC Andrology Coordinating Center BMI Body mass index CA California CV Coefficient of variation IA Iowa MN Minnesota MO Missouri NY New York QC Quality control SFF Study for Future Families STD Sexually transmitted disease TC Total sperm count TMC Total motile count TTP Time to pregnancy US United States WHO World Health Organization
ABSTRACT
While geographic variation in semen quality has been reported, this is the first study in the US to compare semen quality among study centers using standardized methods and strict quality control. We evaluated semen specimens from partners of 512 pregnant women recruited through prenatal clinics in four US cities during 1999-2001; 91% of men provided two specimens. Sperm concentration, semen volume and motility were determined at the centers and morphology was assessed at a central laboratory. Study protocols were identical across centers and quality control was rigorously maintained. Sperm concentration was significantly lower in Columbia MO than in New York NY, Minneapolis MN and Los Angeles CA; mean counts were 58.7, 102.9, 98.6 and 80.8×106 per milliliter (medians 53.5, 88.5, 81.8 and 64.8×106 per milliliter) in MO, NY, MN and CA, respectively. The total number of motile sperm was also lower in MO than in other centers; 113, 162, 201 and 196×106 in MO, NY, MN and CA, respectively. Semen volume and the percent morphologically normal sperm did not differ appreciably among centers. These between-center differences remained significant in multivariate models that controlled for abstinence time, semen analysis time, age, race, smoking, history of sexually transmitted disease and recent fever (all p-values <0.01). Confounding factors and differences in study methods are unlikely to account for the lower semen quality seen in this mid-Missouri population. These data suggest that sperm concentration and motility may be reduced in semi-rural and agricultural areas relative to more urban and less agriculturally exposed areas.
INTRODUCTION
Historically, semen parameter studies have included highly selected and nonrepresentative subgroups, such as compensated sperm donors, pre-vasectomy patients or infertility clinic populations. Moreover, measures of semen quality are very sensitive to the methods of semen collection (including abstinence time) and analysis, which vary significantly among study sites. Further, most analyses of temporal trends and geographic variation in semen parameters have been retrospective and subject to confounding by factors such as smoking or recent high fever that cannot be well controlled retrospectively. These studies have been conducted, almost exclusively, at andrology centers, which are usually located in urban areas, primarily in Western Europe and North America.
Nonetheless, over the past decade several authors have reported large geographic differences between cities in mean sperm concentration. For example, an international study of testosterone-induced azoospermia found that mean pre-treatment sperm concentrations of normal men in nine countries ranged from 52.1×106 per milliliter in Bangkok to 103.5×106 per milliliter in Melbourne (WHO Task Force on Methods of Regulation of Male Fertility 1996). A wide range of sperm concentration was also reported in eight cities in France (Auger and Jouannet 1997). Several recent studies suggest that wide variation is also present among cities in the US. Wittmaack and Shapiro (Wittmaack and Shapiro 1992) examined sperm concentration between 1978 and 1987 in Madison, Wisconsin; mean sperm concentration during this time was approximately 80 x106/ml., while Paulsen et al. (Paulsen et al. 1996) reported a geometric mean of about 50 x106/ml in Seattle, Washington during 1972-1993. A recent study in California (Swan et al. 1997), found a median sperm concentration of 64x106/ml. Fisch et al. (Fisch and Goluboff 1996) reported large differences in mean sperm concentration in pre-vasectomy patients from Los Angeles, Minneapolis and New York City, with low concentration in Los Angeles compared to Minneapolis and New York City (72.7 vs. 100.8 and 131.5x106/ml, respectively). Because these retrospective studies utilized data collected under a variety of protocols, differences in population selection or methods of semen analysis may have contributed to these differences. Recent multi-center studies have sought to eliminate many limitations of earlier studies by standardizing methods and populations. Recognizing that carefully controlled, prospective studies of semen parameters are needed, several multi-center national and international studies have been underway since 1997. The International Study of Semen Quality in Partners of Pregnant Women was recently completed in Europe (Jorgenson 2001). This study found significant differences in mean sperm count and other semen parameters between fertile men recruited in Copenhagen, Paris, Edinburgh and Turku (Finland). For example, sperm concentration in Copenhagen was only 74% that in Turku. The observed differences were not changed appreciably by adjustment for age, abstinence time and season.
The ongoing Study for Future Families (SFF), described below, was designed in collaboration with the European study, so that meaningful comparisons can be made between US and European centers. SFF examines semen quality and other reproductive parameters of fertile couples recruited at prenatal clinics in four cities in the North, East, West and South-Central US, using methods for clinical examination, data collection and semen analysis that are identical across US centers and consistent with those used in the European study.
METHODS
Study subjects
In SFF, a four-year study funded by the National Institute of Environmental Health Sciences, we have been recruiting women at prenatal clinics affiliated with university hospitals in Los Angeles CA (Harbor-UCLA and Cedars-Sinai), Minneapolis MN (University of Minnesota Health Center), Columbia MO (University Physicians) and New York NY (Mt. Sinai School of Medicine) since September 1999. We use a standardized recruitment protocol at each center to minimize between-center differences. Any woman who keeps her prenatal appointment at a study clinic during a recruitment session is a potential subject and the outcome of every potential subject (eligibility and level of participation, if eligible) is determined and recorded in the potential subject database. The couple is eligible unless: the woman or her partner are <18 years old; the pregnancy was medically assisted; either partner does not read and speak Spanish or English; the father is unavailable or unknown; the couple does not plan to stay in the area (because couples planning to move out of area would be unlikely to complete their study participation); the pregnancy is medically threatened, or either partner is incompetent or a prisoner. We ask eligible women to take home study information and a recruitment video to review with their partners. If the couple agrees to participate, the man completes a questionnaire, receives a physical examination, and gives a blood sample, a urine sample and two semen samples. The woman completes a questionnaire and gives blood and urine samples. All study instruments (including questionnaires, mini-questionnaires, letters, and instructions for the man) were translated into Spanish and back translated for accuracy. The instructional video was also produced in both English and Spanish. Subjects are offered monetary compensation, the amount reflecting the cost of living in the study area. In this communication we report semen analysis results from the 512 men who completed participation by November 15, 2001.
Recruiters ask eligible couples who refuse to participate to answer a very brief miniquestionnaire that includes demographics, history of infertility and time to pregnancy (TTP). They also ask a sample of study participants to answer the same questions. We compared responses between refusals and study subjects to examine selection bias. This issue was also examined by comparing questionnaires of subjects who gave a semen sample with those of men who agreed to participate in the study but preferred not to give a semen sample.
The number of subjects varied by center, and was particularly low in NY, where the closure of the Mt. Sinai andrology center in the second study year resulted in a shortened period of recruitment. However, because NY results on study-wide QC samples were in close agreement with other centers, and coefficients of variation for NY technicians were low, NY data could be meaningfully compared to other centers, despite small numbers. We also conducted an analysis that examined the impact of excluding New York subjects on the estimates of differences in semen quality among the remaining center (see Results: Sensitivity Analyses).
Semen collection and analysis
We request that subjects observe a 2-5 day abstinence period before providing a semen sample. Prior to each of the two visits, which are approximately three weeks apart, we mail instructions regarding specimen collection, including a schedule to assist the subject in timing his last ejaculation prior to the visit. At the time of the visit we stress the importance of accurately reporting the actual abstinence period and assure men that their sample will not be rejected if they deviated from the recommended protocol. At the study visit men collect semen samples by masturbation at the clinic and these are analyzed within 45 minutes of collection.
We determine sperm concentration for each of the two samples by µ-Cell (a disposable counting chamber, Conception Technologies, San Diego CA), and, for the first sample only, also by hemacytometer (Improved Neubauer, Hauser Scientific Inc., Horsham PA).
Regardless of the counting method, sperm concentration is estimated for each sample as the mean of two readings, unless these differ by more than 10%, in which case a third reading is taken and it is estimated by the median of the three counts. Ejaculate volumes are estimated by specimen weight, assuming a semen density of 1.0 g/ml. For this calculation each container is pre-weighed and the weight (written on the container) is subtracted from the weight of the container plus sample.
In this analysis the percent motile sperm was counted in a µ-Cell chamber (Overstreet and Brazil 1997) and refers to the percentage of sperm with any flagellar movement, whether twitching or progressive. We calculate the total motile count (TMC) by multiplying the sperm concentration by the semen volume; values obtained by each of the two sperm counting methods were used to calculate TMC. Determinations of motility by the methods recommended by WHO 1999 (World Health Organization 1999) were made on the first sample only. These data are not discussed here.
Seminal smears are prepared at the clinical centers and shipped to the Andrology Coordinating Center (ACC) at the University of California, Davis, for Papanicalou staining, analysis, and storage. Sperm morphology is assessed by a single technician using the Strict Morphology method (World Health Organization 1999) and by a second technician using more traditional 1987-WHO criteria (World Health Organization 1987). For each determination, 100 consecutive sperm are scored in each of two randomly selected areas of the slide and the percentage with normal morphology is determined. Under strict criteria for assessing morphology, the only method reported here and that recommended by WHO (World Health Organization 1999; Guzick et al. 2001), only sperm with absolutely no defects are classified as normal.
In addition to the primary measures of semen quality (sperm concentration, volume, percent morphologically normal sperm and percent motile sperm) we analyzed two derived semen parameters; total count (sperm concentration x volume, or TC); and total motile count (TC x percent motile, or TMC). TC and TMC were calculated using both µ- Cell and hemacytometer estimates of concentration.
Technicians from each study site attended a weeklong training session at the ACC and had to be certified by passing a proficiency test before conducting any semen analyses for this study. The ACC also conducts ongoing quarterly QC testing. Test results are reported to the ACC where within- and between-technician variability are assessed. A coefficient of variation (CV) is calculated for each technician based on the average of four blind readings of each ejaculate, and these are averaged to obtain the intratechnician CV for each technique. Throughout the course of this study all andrology technicians achieved CVs of 15% or less. The technicians’ average values were within 15% of standard values for all semen parameters throughout the course of the study, except for hemacytometer counts, which were within 17% of standard values.
Statistical analysis
The primary outcomes of interest in these analyses were between-center differences in semen parameters, which we estimated in two ways. First, we calculated simple (untransformed and unadjusted) means, since these are easy to interpret and to compare to published studies. We report unadjusted sperm counts based on one sample per man, obtained by hemacytometer, the most frequently used method for counting sperm (Brouwer et al. 1998). We also include counts obtained using the µ-Cell chamber for comparison.
Because sperm concentration, semen volume, TC and TMC follow markedly skewed (non-normal) distributions, they must be transformed before analysis. We transformed the data using logarithm (base 10), which is generally recommended (Berman et al. 1996) for transformation of skewed semen parameters. We then used multivariate models to adjust for covariates of semen quality that appeared to confound these between-center comparisons. Finally, we back-transformed the regression coefficients for logarithmically transformed variables for ease of interpretation.
Because most men (85%) provided two specimens and because of the expected correlation between semen samples, mixed models that account for repeated measures were fit (Zeger and Liang 1986; Laird and Ware 1982; SAS Institute, Inc. 2001) assuming a compound symmetry covariance structure (equivalent to assuming that all samples within a man are equally correlated). We used these models to analyze all semen characteristics that were determined on both the first and second samples. Concentrations by hemacytometer (and TC and TMC based on hemacytometer), which are only available for the man’s first sample, were analyzed using a general linear model (SAS Institute, Inc. 2001). We then compared the between-center differences in semen quality based on simple (unadjusted) means to the adjusted estimates obtained from these multivariate models.
We compared a number of self-reported variables across study centers and examined their relationships to semen characteristics. These include; age, race, smoking, education, body mass index (BMI), fever in the three months prior to study entry, use of steroids, history of infertility, history of STD, cryptorchidism and other genital problems. Characteristics of the semen sample and analysis that were examined include; abstinence time, season (January-March, April-June, July-September, and October- December), time from sample collection to start of semen evaluation and time to perform the semen evaluation. We excluded samples with missing or unknown abstinence times, or with reported abstinence of less than two hours or greater than ten days. Selection of covariates for the final model was based on their importance in the literature, biological plausibility, sufficient numbers within strata and evidence of some effect on between-center comparisons.
RESULTS
At the time the data set was created for this analysis, we had identified 4,825 potential subjects, of whom 33% were ineligible. Primary reasons for ineligibility include; more than 36 weeks pregnant (38%), partner not available (18%), conception medically assisted (10%), not literate in English or Spanish (8%), either partner under 18 years of age (7%) or not pregnant (7%). Among eligible subjects, 55% refused participation (49%, 63%, 46% and 60% in MO, CA, MN and NY, respectively) and 12% of subjects were lost-to-follow-up. Among eligible subjects who refused participation or were lost to follow-up, 40% completed a mini-questionnaire (45%, 29%, 53% and 34% in MO, CA, MN and NY, respectively). At the time of this analysis an additional 11% of subjects had expressed interest in the study, or begun, but not yet completed, participation. We compared questionnaire responses of subjects who would only participate if they were not required to provide a semen sample (19% of completed subjects) with the 512 men who provided one (48) or two (464) semen samples and had completed participation by November 15, 2001. From these 512 men we excluded 19 because of missing or outof- range abstinence times. As discussed below (see Results: Sensitivity Analyses), these exclusions did not affect study conclusions.
Univariate analyses
After exclusions, 493 men were available for analysis, of whom 410 provided two semen samples an average of 24 days apart. The abstinence time-adjusted mean sperm concentration for these two samples did not differ (p=0.36) and results of both semen evaluations are included in these analyses.
Several population characteristics varied considerably; study populations and sample characteristics at the four centers are summarized in Table 1. Race varied by center; in CA only 23% of subjects were Caucasian (non-Hispanic) compared to 86% in MN and MO. Subjects in CA were also less educated (25% graduated college or technical school compared to 75% in MN and 74% in NY). Age differed among centers, though less markedly; subjects were slightly younger in CA (mean 30 years) and somewhat older in NY (mean 36 years). The proportion of men who smoked at least 10 cigarettes per day also varied somewhat by center and ranged from 3% in NY to 13% in MO. History of an STD (gonorrhea, chlamydia or genital warts) was reported by 13% of men and 3.6% reported a fever (101o F or more) in the three months prior to semen collection.
Mean abstinence time was within six hours of the study average (78 hours) at all centers. Time from specimen collection to start of semen analysis was also similar across centers and averaged 30 minutes. The time to conduct the semen evaluation varied somewhat more across centers. This time was shorter for the second semen evaluation (average 62 and 41 minutes for first and second sample, respectively) because the second evaluation did not include determination of concentration by hemacytometer or evaluation of motility using WHO methods.
As can be seen in Table 2, which contains unadjusted semen parameters from each center, mean sperm concentration in MO was lower than at all other centers. Mean (hemacytometer) concentration was 38% higher in CA than MO. Greater differences were seen comparing MO to NY and MN, which were 75% and 67% higher than MO, respectively. In this unadjusted comparison the percent motile sperm was 8% to 17% higher in other centers relative to MO. Mean TMC was higher in all centers, but particularly in NY and MN; compared to MO, NY and MN were 74% and 77% higher, respectively. Semen volume and the percent morphologically normal sperm differed little among centers. Between-center differences in total sperm count (TC) were similar to those seen for sperm concentration and are not presented here. In Table 2 we include sperm characteristics determined both by the µ-Cell chamber and hemacytometer to allow for comparisons between estimates obtained by these two methods.
Table 2 also contains the crude (unadjusted) relationships between covariates and semen parameters. However, these relationships may be somewhat misleading since they are unadjusted for confounding, which can be appreciable. For example, based on these unadjusted estimates, it would appear that semen volume increases with age. In fact, after adjustment for abstinence time and other covariates, semen volume is seen to decrease at older ages (Table 3).
Multivariate models
Of the subject characteristics examined, race, age, smoking, recent fever and history of STD were retained in final models, as were abstinence time, time from specimen collection to start of semen analysis and time to conduct the semen evaluation. Genital infections other than STDs, education and BMI did not confound these analyses and were not retained in final models. Since steroid use was reported by only 12 men, and a history of infertility by four, these variables could not be examined further. Figure 1 contains (back-transformed) adjusted estimates of center-specific estimates of semen quality. The differences between MO and other centers based on these adjusted data are similar to those based on unadjusted means, as can be seen by comparing results from Table 4 with those from Table 1. For example, percent motile sperm was 17% higher in NY than MO using unadjusted data, compared to 21% after adjustment. Differences (both adjusted and unadjusted) between MO and others centers were somewhat greater when based on hemacytometer counts than on µ-Cell counts. The (adjusted) sperm concentration in MN, for example, was 62% higher than that in MO when based on hemacytometer, compared to 45% higher when based on µ-Cell concentration. Thus, the (crude) unadjusted estimates based on µ-Cell concentrations provide somewhat conservative estimates of between-center differences.
Table 3 shows regression coefficients for all covariates in relation to semen parameters. Age was not related to concentration, morphology or motility but a strong non-linear (quadratic) relationship was seen between volume and age (p-value <0.001 for both age and age-squared). Non-Caucasians had significantly lower semen volume than Caucasians. Smoking more than ten cigarettes per day was associated with decreased semen volume, but had little effect on concentration, motility or morphology. Fever within the prior three months significantly decreased sperm concentration and motility, but not morphology or semen volume. The percent morphologically normal sperm was reduced among men who reported a history of a STD. TMC was significantly associated with all of these covariates, reflecting their relationship to sperm concentration, percent motile sperm and semen volume, from which TMC is calculated. Similarly, significant associations were seen between total count and race, age (and age-squared), smoking, fever and history of STDs, reflecting associations between these covariates and sperm concentration and semen volume (data not shown).
Abstinence time (restricted to 2-240 hours) was strongly and linearly related to sperm concentration and semen volume and TMC (all p-values <0.001). Increasing time from sample collection to start of semen analysis and increasing time to complete the semen analysis were each associated with reduced motility.
Little or no association was seen between any semen parameter and use of steroids, self-reported urogenital abnormalities or history of infertility, all of which were quite rare in this population. No consistent pattern was found between season and any semen parameter, either overall, or within each center.
Sensitivity analyses
For the semen analysis we had excluded 12 men on the basis of abstinence times that were missing (n=5), less than 10 minutes (n=2), or more than 10 days (n=5), as well as seven men whose sperm concentration was more than 3 SD from predicted by the modelled relationship between concentration and abstinence time. To test the sensitivity of the results to these exclusions, we reran the model including the 11 men with an abstinence time between 30 minutes and 2,000 hours. Their inclusion did not alter the study’s conclusions. In fact, the contrasts between MO and both NY and CA were somewhat stronger, and p-values were unchanged or reduced slightly for all between-center contrasts and all semen parameters.
Because the number of subjects in NY was small, we also reran the model excluding these subjects. The effect was to slightly (2%-5%) increase the contrasts between MN and MO for all semen parameters, so that the model including NY subjects presented here slightly underestimates these differences.
Analyses to examine selection bias
We examined selection bias in two ways. One of these was to compare participants (N=514) who gave semen samples with those who did not (N=107) with respect to characteristics related to semen quality and fertility (race, age, education, smoking, recent fever, infertility, STD history, and TTP). There were no statistically significant differences between these groups for any of these factors. We also examined selection bias by comparing responses about fertility (whether either partner ever saw a doctor for infertility and TTP) from a sample of study subjects who completed the miniquestionnaire (N=338) and from potential subjects who refused participation (N=956).
These fertility-related responses did not differ significantly between groups, although non-participants appeared to have somewhat longer TTP. Together these analyses argue against significant selection bias in this data set.
DISCUSSION
Our study found significantly lower sperm concentration and TMC in fertile men from mid-Missouri relative to those from New York, Minnesota and California. The percent of sperm that were motile also varied significantly among centers. Differences in semen volume and percent normal sperm (by strict morphology) were small and nonsignificant.
The National Cooperative Reproductive Medicine Network, using methods similar to those employed here (Guzick et al. 2001), classified men into three categories: fertile, subfertile, and of uncertain fertility. Men classified as fertile were those with sperm concentration (using µ-Cell) that exceeded 48×106 per milliliter with more than 63% motile sperm and more than 12% morphologically normal sperm. In our study of fertile men there were significantly fewer samples from men living in mid-Missouri that met all three of these criteria compared to men in the three urban centers (1.1% compared to 8.5%, p<0.001).
We examined three contrasts among the four centers (MO-MN, MO-CA and MN-NY). Of these, the MO-MN contrast is least likely to be affected by confounding and selection bias. The recruitment rates at these two centers were comparable, and their study populations were quite similar demographically. It is reassuring, therefore, that differences in semen parameters between MO and MN were large and highly significant.
While some confounding may remain uncontrolled, we feel this is unlikely to explain the between-ceneter differences we report here. We examined methodological variables (abstinence time, time to start and complete semen analysis) and adjusted for these. Several personal characteristics of the men were related to semen quality and varied across centers (age, race, smoking, history of STD and recent fever). After statistical adjustment for these factors estimates of between-center differences were similar to (or slightly greater than) unadjusted estimates.
Nor are these findings likely to be due to differences in study methods at the four study centers. Common protocols and study instruments were used at all centers. All andrology technicians were centrally trained, and equipment and supplies were standardized across centers. Moreover, strict QC procedures were implemented and quarterly testing was conducted throughout the period of the study.
Our study was designed in collaboration with the International Study of Semen Quality in Partners of Pregnant Women (Jorgenson 2001), and protocols and QC samples were shared between the two studies. How do results of these two studies compare? Differences in concentration in the European study were somewhat less marked than those we report here (Jorgenson 2001). For example, sperm concentration (by hemacytometer) in Copenhagen (whether using means or medians, adjusted or unadjusted) was 74% of that in Turku. In comparison, sperm concentration (by hemacytometer) in MO was 57% that of NY and 60% that of MN. Between-center difference in the European study increased somewhat after statistical adjustment, while semen volume and sperm morphology varied little among study locations. The four European centers were in urban areas (Copenhagen, Denmark; Paris France and Edinburgh, Scotland, Turku, Finland). While use of agricultural chemicals may differ among these urban centers, these agents have not yet been examined in relation to semen quality.
Using data from pre-vasectomy males, Fisch et al. reported mean sperm concentrations of 132, 101 and 73×106 per milliliter in NY, MN and CA, respectively (Fisch and Goluboff 1996). In our study the urban centers also differed somewhat amongst themselves, but less than each differed from mid-Missouri. We saw lower sperm concentration in CA than NY and MN, as did Fisch et al, but, unlike that study, saw little or no difference in semen quality between MN and NY.
Most studies of semen quality have been conducted in large metropolitan areas and it is difficult to find comparable studies from semi-rural areas. Among the 61 studies analyzed by Carlsen et al. in a much-cited meta-analysis (Carlsen et al. 1992), 27 were conducted in the US. Of these, only one, in Iowa City IA, was conducted in a county of fewer than 250,000 residents. In this IA population the mean (hemacytometer) sperm concentration in pre-vasectomy patients was 48×106 per milliliter, which is lower than the concentration reported here for Columbia MO (Nelson and Bunge 1974). We compared population density, proportion of land in farms and use of agricultural chemicals for the four centers in the current study as well as Iowa City IA (US Census Bureau 2001). Acres in farmland ranged from 288,139 in Johnson County (where Iowa City is located) and 249,849 in Boone County (where Columbia Mo is located) to 69,128 in MN and 0 in New York City. Agricultural chemicals (fertilizers, pesticides or herbicides) were applied to all (or most) of this farmland. A recent US Geologic Survey report on water quality noted that extensive herbicide use in agricultural areas (accounting for about 70 percent of total national use of pesticides) has resulted in widespread occurrence of herbicides in agricultural streams and shallow ground water in those areas (United States Geological Survey 2001). We are examining urinary metabolite levels in relation to semen quality in a subset of the population in a separate analysis, and hope to obtain funding to obtain biomarkers of pesticide exposure on the entire study population. When data from the entire cohort has been collected we will examine semen quality with respect to self-reported pesticide exposures as well. This study has a number of strengths but also some weaknesses. Among its strengths are its prospective design and strict adherence to protocol to ensure comparability across centers. The exacting QC demands, for all aspects of the study, have produced semen analysis results of extremely high precision.
However, as with all studies of semen quality, low participation rates and potential selection bias are of concern. In studies of partners of pregnant women recruitment is particularly difficult, since a woman must give permission before her partner can be contacted, unless he is present at the prenatal visit. To examine selection bias we compared questionnaire data on TTP and history of infertility, as well as demographics, of study subjects and non-participants, and of men who did and did not give semen samples. Reassuringly, there was little evidence that these populations differed. However, the limited number of non-Caucasian subjects, and few subjects from NY limited our ability to examine this question within ethnic groups and at all centers. The current analysis is not able to explain the between-center difference in semen quality demonstrated. However, the extensive data (questionnaire and biological samples) available on these men will permit us to examine a range of hypotheses in future analyses.
The current study finds considerably reduced semen quality in Columbia MO compared to NY, MN and CA. While there may well be multiple factors on which MO differs from the other centers, MO is unusual among sites for semen studies because of its proximity to intensive agriculture. The limited availability of semen quality data from semi-rural, agricultural communities, the historically low concentrations in IA, and the low sperm concentration and percent motile sperm reported here for Columbia MO suggest the need for further study in such communities.
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TABLE 1. CHARACTERISTICS OF THE STUDY POPULATION AND SEMEN SAMPLES BY CENTER.a
MISSOURI CALIFORNIA MINNESOTA NEW YORK TOTAL Population Characteristics Number of participants 176 124 155 38 493 Mean age (yr) 30.7 29.8 32.2 36.1 31.3 Education Less than college 42.5 75.2 25.2 26.3 43.8 College/technical 57.5 24.8 74.8 73.7 56.2 Non-Caucasian race 14.2 77.4 14.2 31.6 31.4 Smoking status Non-smoking 79.5 70.5 85.8 81.6 79.4 <10/day 7.4 26.2 10.3 15.8 13.7 >10/day 13.1 3.3 3.9 2.6 6.9 Recent fever 4.0 2.4 4.5 2.6 3.6 Steroid use 2.3 4.1 1.9 0.0 2.4 History of sexually 11.4 12.9 13.6 15.8 12.8 transmitted disease(STD) History of genital 10.2 8.9 9.0 5.3 9.1 disease other than STD History of infertility 0.6 0.0 1.3 2.6 0.8 Sample Characteristics b Mean ejaculation 78 84 72 84 78 abstinence time (hr) Mean time to start of 26 28 30 34 28 semen analysis (min) Mean time to conduct 45 52 61 47 52 semen analysis (min) a Reported figures are percents unless otherwise indicated b Average time for the first and second samples
TABLE 2. MEAN SEMEN CHARACTERISTICS (UNADJUSTED, UNTRANSFORMED) BY CENTER AND COVARIATES.
SPERM CONCENTRATION(106/ML) VOLUME PERCENT TOTAL MOTILE COUNT(106) PERCENT HEMACYTOMETER µ-CELL (GM) MOTILE HEMACYTOMETER µ-CELL NORMAL Number of samples 472 901 901 903 471 899 887 Center Missouri 58.7 53.4 3.9 48.2 113.0 101.0 10.8 California 80.8 69.0 3.6 54.5 162.2 137.5 12.2 Minnesota 98.6 74.6 3.9 52.1 200.9 152.9 11.4 New York 102.9 75.5 3.3 56.4 196.4 149.7 10.9 Covariate Age <25 70.1 60.4 3.4 52.8 127.5 109.1 11.5 25-34 81.7 67.0 3.8 52.4 170.6 138.8 11.4 35+ 82.0 65.8 3.8 49.3 151.9 123.8 11.2 Education < College 72.5 61.0 3.5 51.8 138.9 114.3 11.5 College/Tech 86.2 69.4 4.0 51.4 175.4 141.8 11.2 Covariate Caucasian 79.5 65.4 3.9 51.1 160.5 130.8 11.1 Non-Caucasian 81.2 66.2 3.5 52.9 156.2 128.6 11.9 Smoking Non-smoking 82.1 67.9 3.9 51.3 165.4 136.2 11.2 <10/day 77.2 60.3 3.5 53.9 147.8 116.2 12.2 >10/day 59.7 49.3 3.2 50.8 111.8 87.2 11.0 Recent fever Yes 68.4 51.5 3.6 43.3 120.4 95.8 9.8 No 80.4 66.2 3.8 52.0 160.6 131.5 11.4 STDs Yes 73.0 57.7 3.7 50.0 140.5 111.2 9.8 No 81.1 66.8 3.8 51.9 162.0 132.9 11.6
TABLE 3. SUMMARY OF ADJUSTED SEMEN CHARACTERISTICS BY COVARIATES.a
SPERM CONCENTRATION(106/ML) VOLUME PERCENT TOTAL MOTILE COUNT(106) PERCENT HEMACYTOMETER µ-CELL (GM) MOTILE HEMACYTOMETER µ-CELL NORMAL Covariate Age b .029(.18) .018(.32) .040(<.001) .31(.58) .054(.05) .060(.01) .023(.94) Age2 b -.0036(.27) -.00023(.40) -.00057(<.001) -.0075(.38) -.0007(.08) -.00087(.01) .00015(.97) Non-Caucasian -.013(.75) -.036(.31) -.063(.005) -1.40(.21) -.077(.15) -.11(.02) .0051(.99) Smoking < 10/day -.0085(.86) -.033(.42) -.038(.14) 1.93(.13) -.024(.70) -.057(.28) .94(.16) > 10/day -.064(.31) -.064(.23) -.082(.01) 1.23(.47) -.14(.08) -.14(.05) .063(.94) Recent fever -.17(.04) -.15(.03) -.021(.64) -7.04(.001) -.23(.03) -.25(.005) -1.45(.21) History of STD -.088(.06) -.074(.06) -.026(.29) -2.12(.09) -.16(.006) -.12(.02) -1.62(.01) Ejaculation .0024(<.001) .0024(<.001) .0011(<.001) .0078(.42) .0039(<.001) .0036(<.001) -.0050(.12) abstinence time(hr) Time to start .082(.32) .0050(.91) .022(.37) -4.20(.005) .068(.52) -.0034(.95) -.43(.38) of semen analysis(hr) Time to conduct .064(.35) -.016(.53) -.0064(.66) -2.5(.006) -.030(.73) -.049(.15) -.51(.08) semen analysis(hr) a, b Using mean values for all other variables, the adjusted TMC (using µ-cell) for a Caucasian man in MN is 113 x 106 at age 25 vs.. 108 x 106 at age 45
TABLE 4. SUMMARY OF ADJUSTED SEMEN CHARACTERISTICS BY CENTER.a
SPERM CONCENTRATION(106/ML) VOLUME PERCENT TOTAL MOTILE COUNT(106) PERCENT HEMACYTOMETER µ-CELL (GM) MOTILE HEMACYTOMETER µ-CELL NORMAL Number of samples b 466 890 889 891 465 888 876 Center Missouri (Reference) 35.0 30.8 2.9 43.9 45 37.7 9.8 California 43.4(0.060) 40.3(0.005) 3.1(0.39) 50.9(<0.001) 64.6(0.02) 59.8(<0.001) 10.9(0.11) Minnesota 54.9(<0.001) 45.7(<0.001) 3.0(0.43) 48.9(<0.001) 83.2(<0.001) 64.5(<0.001) 10.3(0.35) New York 58.2(0.001) 46.0(0.002) 2.5(0.07) 53.7(<0.001) 77.0(0.007) 59.6(0.005) 9.6(0.84) a From mixed models including multiple samples per man (except hemacytometer). All analyses included covariates listed in Table 3. Analysis was restricted to samples with abstinence times between 2 and 240 hours. Concentration, volume, and total motile counts were log-transformed for analysis; estimates are the back-transformed means. P-values compare center to Missouri. b Samples with one or more covariates missing were excluded.
doi:10.1289/ehp.5927 Online 11 November 2002 Journal of the National Institute of Environmental Health Sciences www.ehponline.org
SHANNA H. SWAN, PH.D., CHARLENE BRAZIL, B.S., ERMA Z. DROBNIS, PH.D., FAN LIU, M.S., ROBIN L. KRUSE, PH.D., MAUREEN HATCH, PH.D., J. BRUCE REDMON, M.D., CHRISTINA WANG, M.D., JAMES W. OVERSTREET, M.D., PH.D. AND THE STUDY FOR FUTURE FAMILIES RESEARCH GROUP FROM THE DEPARTMENT OF FAMILY AND COMMUNITY MEDICINE, UNIVERSITY OF MISSOURICOLUMBIA SCHOOL OF MEDICINE (S.H.S., F.L., R.L.K.); THE DEPARTMENT OF OBSTETRICS AND GYNECOLOGY, UNIVERSITY OF MISSOURI-COLUMBIA (E.Z.D.); THE HARBOR-UCLA MEDICAL CENTER AND RESEARCH AND EDUCATION INSTITUTE (C.W.); THE UNIVERSITY OF CALIFORNIA, DAVIS (C.B., J.W.O.); THE SCHOOL OF MEDICINE, MT SINAI, NEW YORK (M.H.); THE DEPARTMENTS OF MEDICINE AND UROLOGIC SURGERY, UNIVERSITY OF MINNESOTA (J.B.R.).
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