Occupational Exposures and Male Infertility

Clarisa R. Gracia1,2, Mary D. Sammel2, Christos Coutifaris1, David S. Guzick3 and Kurt T. Barnhart1,2

1 Division of Reproductive Endocrinology and Infertility, School of Medicine, University of Pennsylvania, Philadelphia, PA
2 Center for Clinical Epidemiology and Biostatistics, School of Medicine, University of Pennsylvania, Philadelphia, PA
3 Division of Reproductive Endocrinology and Infertility, School of Medicine and Dentistry, University of Rochester, Rochester, NY

Correspondence to Dr. Clarisa R. Gracia, Penn Fertility Care, 3701 Market Street, Suite 800, Philadelphia, PA 19104 (e-mail: cgracia{at}obgyn.upenn.edu).

Received for publication July 21, 2004. Accepted for publication May 11, 2005.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
The purpose of this study was to determine the association between male occupational exposures and infertility. A retrospective case-control study was performed using data collected between 1991 and 1997 at nine US clinical sites as part of a previously conducted large multicenter trial. Cases were defined as infertile males whose partner had an infertility evaluation with normal results, and controls were defined as fertile males whose partner became pregnant within 2 years. Exposures were assessed by means of self-report questionnaires. Bivariate, stratified, and multivariable analyses were performed. A total of 650 infertile cases and 698 fertile controls were compared. In the final model, a protective association with infertility was observed for occupational exposures to radiation (odds ratio = 0.21, 95% confidence interval: 0.06, 0.77) and video display terminals (odds ratio = 0.30, 95% confidence interval: 0.13, 0.68). No significant associations were noted between infertility and exposure to shift work, metal fumes, electromagnetic fields, solvents, lead, paint, pesticides, work-related stress, or vibration. Overall, no clear, clinically important associations between occupational exposures and male infertility could be identified in this study.

case-control studies; environment; infertility; infertility, male; occupational exposure


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Infertility, defined as the inability to conceive after 12 months of unprotected intercourse, affects 10–15 percent of all couples (1Go). In roughly half of cases, a male factor is identified, while an occult male factor may be involved in 15–24 percent of cases in which no etiology is uncovered ("unexplained" infertility) (2Go, 3Go). A variety of occupational exposures have been linked to impaired male fertility (4Go). However, studies have been limited by inadequate sample sizes, inappropriate study designs, and/or selection bias (5Go–9Go). Additionally, the use of semen measures as surrogates for male fertility has been problematic, since there is considerable intraindividual variability, substantial overlap between infertile men and fertile men, and poor correlation between fertility and decrements in semen measures within the "normal" range (10Go–12Go).

We performed a large case-control study to assess the association between occupational exposures and male infertility and to examine the strength of the associations between specific exposures and male-factor infertility as compared with unexplained infertility.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
We performed a retrospective case-control study using data collected between 1991 and 1997 as part of a large multicenter trial of 932 infertile couples and 698 fertile couples who were recruited from university-based infertility and prenatal clinics (11Go, 13Go). While 10 sites were originally involved in patient recruitment, only nine sites contributed data to this particular study (the University of Alabama (Birmingham, Alabama), Baylor College of Medicine (Houston, Texas), Columbia University (New York, New York), the University of California at Davis (Davis, California), Harvard University (Cambridge, Massachusetts), Kaiser Permanente–Sacramento (Sacramento, California), the University of Pennsylvania (Philadelphia, Pennsylvania), the University of Pittsburgh (Pittsburgh, Pennsylvania), and the University of Rochester (Rochester, New York)). Cases were defined as infertile males aged 20–55 years whose partners had no identifiable cause of infertility. Inclusion criteria consisted of at least 12 months of infertility, the presence of motile sperm upon semen analysis, and a detailed fertility evaluation of the female partner with normal results (11Go, 13Go). Controls were fertile males of similar age whose partner had a documented pregnancy or delivery within 2 years of enrollment. Female partners were 20–40 years of age. Exclusion criteria consisted of previous infertility treatment, a history of chemotherapy or radiotherapy, previous surgery, or a medical condition related to infertility.

Semen specimens were collected from all subjects and evaluated using a standard protocol (11Go). The first sample was used to categorize subjects according to infertility type, since collection generally occurred within 30 days of exposure assessment. Male-factor infertility was defined in two ways: according to the Guzick et al. (11Go) criteria (sperm concentration <13.5 million/ml, motility <32 percent, or <9 percent normal forms by the Kruger criteria) and according to the World Health Organization criteria (14Go) (sperm concentration ≤20 million/ml, motility ≤50 percent, or ≤30 percent normal morphology).

Exposure assessment
The subjects completed extensive self-report questionnaires on socioeconomic, medical, and environmental factors. They were asked about the presence and duration of occupational exposures incurred within the past month, including exposures to solvents, lead, paint, pesticides, metal fumes, excess heat, vibration, radiation, video display terminals, and electromagnetic fields. Any reported exposure was considered positive exposure. Men who reported work-related stress fairly often or very often were considered stressed, and men working either rotating shifts or night shifts for their primary employment were considered shift-workers. The questionnaire used is posted on the Journal's website (www.aje.oxfordjournals.org).

Data analysis
De-identified data from the previous study (11Go, 13Go) were made available to investigators at the study sites, including the University of Pennsylvania. For this analysis, all data management and analyses were performed using Stata, version 8 (Stata Corporation, College Station, Texas). Institutional review board approval was obtained.

Of the 932 infertile cases recruited for this study, 282 (30 percent) did not complete the exposure questionnaire. These subjects were excluded from the analysis, leaving 650 infertile cases. Complete data were available for all 698 recruited fertile controls. Comparison of the baseline characteristics of excluded subjects with those of retained subjects revealed no significant differences with respect to age, partner's age, race, education, or employment.

Baseline demographic information for cases and controls was compared. Next, bivariate analyses were performed to determine the association between fertility status and exposures based on a priori hypotheses. In general, t tests were used for continuous variables and the Pearson chi-squared test was used for categorical and dichotomous variables. Stratified analyses were then performed for assessment of confounding. Next, a multivariable logistic regression model was employed using a backwards elimination strategy. Candidate exposure variables had a bivariate association with fertility of p ≤ 0.20. We also considered potential confounders identified a priori on the basis of biologic plausibility or previous reports, including the woman's age (in 5-year intervals), race, education, employment, cigarette smoking, alcohol drinking, marijuana use, heat exposures, and consumption of caffeinated beverages (15Go–22Go). In addition, clinical site was explored as a potential confounder, since semen parameters can vary by geographic site and this study comprised nine clinical sites located in various geographic areas of the United States (23Go, 24Go). A variable was classified as a confounder if inclusion of the variable induced a change in the odds ratio of 15 percent or more (25Go). Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test. A two-tailed p value less than 0.05 was considered statistically significant.

To indirectly evaluate the mechanism by which the exposures affect fertility, we conducted a multinomial logistic regression analysis in which the outcome variable was categorized into three levels: fertile males, infertile males with male-factor infertility, and infertile males with unexplained infertility. We also performed separate analyses comparing subjects with each definition of male-factor infertility with fertile males.

A priori calculations indicated that an odds ratio of 1.8 could be detected assuming a fixed sample size of 700 infertile cases, an {alpha} value of 0.05, power of 0.80, and a 1:1 case:control ratio, given a 5 percent exposure prevalence among controls.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
A comparison of the baseline characteristics of infertile and fertile men revealed that infertile men were more likely than fertile men to be Caucasian, employed in blue-collar jobs, and less educated. Cases and controls were not evenly distributed among the clinical sites, and significant overall differences remained even when sites were categorized by geographic location (table 1). Among infertile subjects, socioeconomic characteristics were similar for persons with male-factor infertility and persons with unexplained infertility.


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TABLE 1. Baseline sociodemographic characteristics of infertile male cases and fertile male controls in a retrospective case-control study using data collected between 1991 and 1997 at nine US clinical sites

 
After adjustment for biologically plausible risk factors, infertile men were less likely to report exposure to radiation and video display terminals than were fertile men (table 2). There appeared to be no association between fertility status and exposure to metal fumes, electromagnetic fields, solvents, lead, paint, pesticides, work-related stress, shift work, or vibration. In the model that separated the two types of infertility, the directions of the associations were similar when exposures for fertile controls and cases were compared, regardless of infertility type.


View this table:
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TABLE 2. Distribution of occupational exposures among infertile male cases and fertile male controls in a retrospective case-control study using data collected between 1991 and 1997 at nine US clinical sites*

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
We used data previously collected as part of a large multicenter trial (11Go, 13Go) to conduct an evaluation that was, to our knowledge, the largest and most rigorous case-control study to date assessing the association between occupational exposures and male infertility. After adjusting the results for a variety of factors related to infertility, we found that men with infertility were slightly less likely to be exposed to occupational radiation and computer terminals than were fertile men. We believe that these findings are consistent with reports indicating that these exposures are not clearly associated with male infertility or decreased semen parameters (26Go, 27Go). No significant associations were noted between fertility status and exposure to shift work, metal fumes, electromagnetic fields, solvents, lead, paint, pesticides, work-related stress, or vibration.

Several limitations must be kept in mind when interpreting the results of this study. While substantial effort was made to recruit cases and controls from the same underlying population (men served by academic centers who were attempting to conceive), a comparison of the distributions of baseline socioeconomic factors between fertile and infertile participants revealed that the cases and controls were slightly, though significantly, different. The selection process differed somewhat for cases and controls, since infertile couples were recruited to participate in a clinical trial of superovulation and intrauterine insemination, while fertile couples were not enrolled in a trial (11Go, 13Go). It is possible that this selection process may have biased the results if these differences were related to differences in occupational exposure. In addition, there is evidence that studies requiring semen collection have low participation rates, potentially introducing selection bias. An assessment of the magnitude of bias would be possible by comparing exposure information between semen donors and nondonors (8Go), but such information was not collected.

For assessment of occupational exposures, patients were asked whether they had incurred any exposure during the prior month and, if so, the duration of exposure. This limited amount of exposure data might have led to attenuation of the effects, since misclassification would probably be nondifferential with respect to outcome. More detailed questions on the type, intensity, and duration of the exposures would be useful for further evaluation of their effects on fertility. Additionally, the retrospective nature of the data collection may have introduced bias due to behavior modification and recall. It is possible that infertile subjects, knowing about the well-publicized associations between certain occupational exposures (such as pesticides) and infertility, eliminated these behaviors prior to the study in hopes of achieving pregnancy. Such behavior modification could change the direction of an association. We were unable to examine whether such bias existed, since behavioral changes were not assessed.

Despite the limitations, this study had several strengths. To our knowledge, ours was the largest sample studied to date in which occupational exposures and fertility status, a clearly defined, clinically relevant outcome, were evaluated. Subjects underwent extensive medical evaluation for identification of the specific causes of infertility. In addition, the case-control design is appropriate, since it is an efficient method of exploring associations between multiple exposures and a relatively rare outcome. Nonetheless, this study highlights many of the challenges involved in studying environmental exposures and fertility. Indeed, these challenges probably account for the disparate findings in the literature. Given the limitations discussed, it appears that a better study design for evaluating the relation between environmental exposures and infertility would be a prospective cohort study of couples attempting pregnancy or retrospective assessment of time to pregnancy in pregnant patients (7Go, 18Go). Overall, it appears that no clear, clinically important associations between occupational exposures and male infertility could be identified in this case-control study.


    ACKNOWLEDGMENTS
 
This research was supported by cooperative agreements U10-HD27049 (C. C.), U10-HD27039 (D. S. G.), U01-HD 27006 (R. E. C), U10-HD26981 (J. W. O.), U10-HD27011 (S. A. C.), U10-HD33172 (M. P. S.), and U10-HD33173 (J. A. H.) with the National Institute of Child Health and Human Development. Data analysis for this particular study was supported by a Reproductive Epidemiology Training Grant (T32-HD07440-07).

The authors thank Dr. Russell Hauser from the Harvard School of Public Health and Russell Localio and Rachel Weinstein from the Division of Biostatistics at the University of Pennsylvania for their invaluable assistance and advice in data management and analysis.

The authors acknowledge all investigators and staff who participated in the National Cooperative Reproductive Medicine Network while at the following institutions: University of California, Davis, California—Drs. J. W. Overstreet and S. T. Nakajima; Columbia University, New York, New York—Drs. R. E. Canfield and P. Factor-Litvak; Baylor College of Medicine, Houston, Texas—Drs. S. A. Carson and J. Buster; University of Alabama, Birmingham, Alabama—Dr. M. P. Steinkampf; Brigham and Women's Hospital, Boston, Massachusetts—Dr. J. A. Hill; University of Pennsylvania, Philadelphia, Pennsylvania—Dr. L. Mastroianni; and National Institutes of Health, Bethesda, Maryland—Dr. D. L. Vogel.

Conflict of interest: none declared.


    References
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 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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