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Relationship between physical occupational exposures and health on semen quality: data from the Longitudinal Investigation of Fertility and the Environment (LIFE) Study

      Objective

      To study the relationship among occupation, health, and semen quality in a cohort of men attempting to conceive.

      Design

      Observational prospective cohort.

      Setting

      Not applicable.

      Patient(s)

      A total of 501 couples discontinuing contraception were followed for 1 year while trying to conceive; 473 men (94%) provided one semen sample, and 80% provided a second sample.

      Intervention(s)

      None.

      Main Outcome Measure(s)

      Semen data obtained through at-home semen collection with next-day analysis/quantification.

      Result(s)

      In all, complete data were available for 456 men, with a mean age of 31.8 years. Work-related heavy exertion was consistently associated with lower semen concentration and total sperm count. Thirteen percent of men who reported heavy exertion displayed oligospermia, compared with 6% who did not report workplace exertion. Shift work, night work, vibration, noise, heat, and prolonged sitting were not associated with semen quality. Men with high blood pressure had significantly lower strict morphology scores compared with normotensive men (17% vs. 21%). In contrast, hyperlipidemia, diabetes, and composite of total comorbidities were not associated with semen quality. The number of medications a man was taking as a proxy of health status was associated with semen quality. There was a negative association between number of medications and sperm count.

      Conclusion(s)

      A negative relationship among occupational exertion, hypertension, and the number of medications with semen quality was identified. As these are potentially modifiable factors, further research should determine whether treatment or cessation may improve male fecundity.

      Key Words

      Discuss: You can discuss this article with its authors and with other ASRM members at http://fertstertforum.com/eisenbergm-physical-occupational-exposures-semen/
      Up to 15% of all couples are unable to conceive after a year of trying and are labeled infertile (
      • Louis J.F.
      • Thoma M.E.
      • Sorensen D.N.
      • McLain A.C.
      • King R.B.
      • Sundaram R.
      • et al.
      The prevalence of couple infertility in the United States from a male perspective: evidence from a nationally representative sample.
      ,
      • Thoma M.E.
      • McLain A.C.
      • Louis J.F.
      • King R.B.
      • Trumble A.C.
      • Sundaram R.
      • et al.
      Prevalence of infertility in the United States as estimated by the current duration approach and a traditional constructed approach.
      ). Among them, up to half have a male factor to explain the etiology (
      • Thonneau P.
      • Marchand S.
      • Tallec A.
      • Ferial M.L.
      • Ducot B.
      • Lansac J.
      • et al.
      Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988–1989).
      ). Given the sensitivity of spermatogenesis to extrinsic and intrinsic factors, a man's environment may have a profound impact on semen quality. A potential source of adverse exposure may be a man's occupation, and past investigators have examined chemical, physical, and psychological factors (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ,
      • Buck Louis G.M.
      • Sundaram R.
      • Schisterman E.F.
      • Sweeney A.M.
      • Lynch C.D.
      • Gore-Langton R.E.
      • et al.
      Persistent environmental pollutants and couple fecundity: the LIFE study.
      ,
      • Buck Louis G.M.
      • Sundaram R.
      • Schisterman E.F.
      • Sweeney A.M.
      • Lynch C.D.
      • Gore-Langton R.E.
      • et al.
      Heavy metals and couple fecundity, the LIFE Study.
      ). Much of the existing literature has examined the role of chemical exposure, with limited data on physical exposures such as exertion, heat, shift work, vibration, noise, and sedentary positions in an occupational context. As such, conflicting data exist regarding the harms of work-related physical exposures, and applicability to the general population is uncertain.
      While heat exposure is known to negatively impact spermatogenesis, whether occupational amounts have severe impacts is uncertain. Several studies demonstrate that excess heat can impair fertility, although other studies have not found an association (
      • El-Helaly M.
      • Awadalla N.
      • Mansour M.
      • El-Biomy Y.
      Workplace exposures and male infertility—a case-control study.
      ,
      • Oliva A.
      • Spira A.
      • Multigner L.
      Contribution of environmental factors to the risk of male infertility.
      ). Studies have suggested impairments to fertility in men with occupations with significant exposure to vibration (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ). Moreover, shift work and heavy exertion have also demonstrated a negative impact on fertility (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ,
      • El-Helaly M.
      • Awadalla N.
      • Mansour M.
      • El-Biomy Y.
      Workplace exposures and male infertility—a case-control study.
      ). However, it is difficult to isolate a particular occupational exposure given that hazards tend to coexist (e.g., noise and vibration) and are also related to other confounding factors such as smoking, age, and socioeconomic status (
      • Claman P.
      Men at risk: occupation and male infertility.
      ). Much of the available literature has relied on cross-sectional, case-control, or retrospective designs that have not prospectively measured the time required for couples to become pregnant.
      In addition, health-related factors may be related to occupation, which may mediate an impact on sperm production. Indeed, exposure to occupational noise is known to increase the risk for cardiovascular disease and mortality (
      • Gopinath B.
      • Thiagalingam A.
      • Teber E.
      • Mitchell P.
      Exposure to workplace noise and the risk of cardiovascular disease events and mortality among older adults.
      ). In addition, obesity can impact health and semen quality and may be directly impacted by occupational activity (
      • Sermondade N.
      • Faure C.
      • Fezeu L.
      • Shayeb A.G.
      • Bonde J.P.
      • Jensen T.K.
      • et al.
      BMI in relation to sperm count: an updated systematic review and collaborative meta-analysis.
      ,
      • Luckhaupt S.E.
      • Cohen M.A.
      • Li J.
      • Calvert G.M.
      Prevalence of obesity among U.S. workers and associations with occupational factors.
      ). Given the complex interplay between occupational environment, health, and fertility, we sought to determine their separate and combined effects on a man's fertility.

      Materials and methods

       Study Population

      We used data from the Longitudinal Investigation of Fertility and the Environment (LIFE) Study that have been described elsewhere (
      • Buck Louis G.M.
      • Schisterman E.F.
      • Sweeney A.M.
      • Wilcosky T.C.
      • Gore-Langton R.E.
      • Lynch C.D.
      • et al.
      Designing prospective cohort studies for assessing reproductive and developmental toxicity during sensitive windows of human reproduction and development—the LIFE Study.
      ). Briefly, the LIFE Study is a prospective cohort of 501 couples attempting to conceive in two geographic areas (Texas and Michigan) in 2005–2009. Couples planning pregnancy were recruited from four counties in Michigan and 12 counties in Texas to ensure a range of environmental exposures and lifestyle characteristics. Minimal eligibility criteria were required: female ages 18–44 years and male ages 18+ years, in a committed relationship, ability to communicate in English or Spanish, menstrual cycles between 21 and 42 days, no hormonal contraception injections during past year, and no sterilization procedures or physician-diagnosed infertility. The study cohort comprised 501 (42%) screened eligible couples; a complete description is presented elsewhere (
      • Buck Louis G.M.
      • Schisterman E.F.
      • Sweeney A.M.
      • Wilcosky T.C.
      • Gore-Langton R.E.
      • Lynch C.D.
      • et al.
      Designing prospective cohort studies for assessing reproductive and developmental toxicity during sensitive windows of human reproduction and development—the LIFE Study.
      ). The most frequent reasons for ineligibility included age (27%), not interested in pregnancy (19%), not in a committed relationship (19%), and moving outside study area (16%). Full human subjects' approval was granted before obtaining informed consent from all couples.

       Data Collection and Operational Definitions

      All participants completed baseline interviews by trained research assistants, usually at the patient's home, that queried men about their medical and reproductive history, lifestyle, and occupational activity. For study purposes, occupational exposures were defined by men's responses to the following question:“Does your current job involve any of the following? (Yes/No)—Night work, rotating shifts, whole body vibration, noise, extreme heat, heavy exertion or lifting, prolonged sitting.” (Important: separate questions were asked for each occupational exposure.)
      Medical history was defined by men's responses to the following question:“Have you ever been told by a doctor that you have any of the following health conditions? (Yes/No)—Hypothyroid (under-active thyroid), Hyperthyroid (over-active thyroid), high blood pressure, high cholesterol, diabetes.” (Important: separate questions were asked for each occupational exposure.)
      Men were also queried as to whether they were taking medications for any health conditions:“(If yes) Are you currently receiving medical treatment for this condition? (Yes/No)”
      Research nurses performed the standardized anthropometric assessment using the methodology adapted from the National Health and Nutrition Examination Survey (NHANES) III survey (
      ). Specifically, all men were weighed after removing shoes and excessive clothing using the digital self-calibrating Health-O-Meter scale. The nurse was instructed to take two measurements and record weight to the nearest pound. If the measurements differed by more than 1 pound, a third measurement was taken. The scale is reported to be accurate up to 330 pounds. For men with weights in excess, we relied upon self-reported weight.
      Height was measured using a standardized cloth tape measure. The male was asked to remove shoes, stand erect with his back to the wall and shoulders relaxed at the sides, and look straight ahead. The nurse took two measurements rounded to the nearest 1/2 inch and a third if the difference was more than 1/2 inch. Multiple measurements were averaged and converted to kilograms and meters to calculate body mass index (BMI).

       Biospecimen Collection and Analysis

      Semen samples were collected via masturbation without the use of any lubricant after 2 days of abstinence using home collection kits that comprised an insulated shipping container (Hamilton Research) for maintaining sperm integrity. An aliquot of semen was placed in a 20 μm deep chamber slide (Leja), and sperm motility was assessed using the HTM-IVOS (Hamilton Thorne Biosciences) computer-assisted semen analysis system. Sperm concentration was also measured using the IVOS system and the IDENT stain. Microscope slides were prepared for sperm morphometry and morphology assessment and completed by Fertility Solutions. An aliquot of the whole semen was diluted in tris NaCl EDTA (TNE) buffer with glycerol and frozen for the sperm chromatin stability assay analysis (
      • Evenson D.P.
      • Larson K.L.
      • Jost L.K.
      Sperm chromatin structure assay: its clinical use for detecting sperm DNA fragmentation in male infertility and comparisons with other techniques.
      ).
      To ensure integrity of the next-day analysis, steps taken to ensure the quality of the semen parameters. A thermometer was attached to all collection jars to ensure the temperature of the sample was within acceptable limits as was the integrity of the sample as judged by laboratory personnel. All samples were found to be acceptable. Motility endpoints are most sensitive to next-day analysis and were excluded from our analysis. Other investigators have successfully used similar at-home semen collection approaches (
      • Luben T.J.
      • Olshan A.F.
      • Herring A.H.
      • Jeffay S.
      • Strader L.
      • Buus R.M.
      • et al.
      The healthy men study: an evaluation of exposure to disinfection by-products in tap water and sperm quality.
      ,
      • Royster M.O.
      • Lobdell D.T.
      • Mendola P.
      • Perreault S.D.
      • Selevan S.G.
      • Rothmann S.A.
      • et al.
      Evaluation of a container for collection and shipment of semen with potential uses in population-based, clinical, and occupational settings.
      ).

       Statistical Analysis

      We conducted descriptive analyses to assess distributions of semen outcomes, occupational, and health factors, with means (SD) or medians (interquartile range [IQR]) for continuous measures and valid n (%) for categorical measures. For association between semen outcomes and occupational and health factors, we used linear (for continuous) and logistic (for dichotomized) regressions. When two semen samples were available, we used the generalized estimating equations approach to account for the induced correlation. All regression models were adjusted for a set of a priori selected covariates: age (continuous, years), race (white [reference], others), BMI (continuous, kg/m2), and smoking status (yes, no [reference]). When dichotomizing semen outcomes, abnormal semen parameters were defined based on the World Health Organisation (WHO) fifth edition of the manual on semen analyses (
      • Cooper T.G.
      • Noonan E.
      • von Eckardstein S.
      • Auger J.
      • Baker H.W.
      • Behre H.M.
      • et al.
      World Health Organization reference values for human semen characteristics.
      ). We assumed that data were missing at random, which enables us to conduct likelihood-based estimation and inference without resorting to multiple imputations. Confidence intervals that excluded 1.0 or P<.05 were considered statistically significant. All analyses were implemented using SAS 9.3 (SAS Institute).

      Results

      A description of the study cohort is provided in Table 1 and reflects that the cohort comprised men who mostly reported being white (77%) and college educated (91%) and who had never fathered a pregnancy (52%). In all, complete data were available for 456 (91%) men. Many men described physical exposures at work including noise (27%), heat (21%), and exertion (32%). In addition, night work (23%) or shift work (16%) was also reported. Hyperlipidemia was the most common medical condition in the cohort, present in 16% of men. The median sperm concentration for the men in the cohort was 61.8 million/mL, with 8% having oligospermia (<15 million/mL).
      Table 1Baseline characteristics of men, LIFE Study (n = 456).
      CharacteristicMean (SD)n (%)
      Demographics
       Age (y), mean (SD)31.8 (4.9)
       BMI (kg/m2), mean (SD)29.8 (5.6)
       White384 (77)
       College educated454 (91)
       Prior paternity225 (48)
       Smoker70 (15)
      Occupational
       Night work106 (23)
       Rotating shifts73 (16)
       Vibration105 (23)
       Noise124 (27)
       Extreme heat98 (21)
       Heavy exertion145 (32)
       Prolonged sitting224 (49)
      Health
       High cholesterol76 (16)
       Diabetes14 (3)
       High blood pressure49 (10)
      Semen parameters
       Volume (mL), median (IQR)3.25 (2.2, 4.25)
       Volume <1.5 mL43 (9)
       Concentration (million/mL), median (IQR)61.75 (36.7, 102.5)
       Concentration <15 million/mL40 (8)
       Total sperm count (million), median (IQR)195.3 (108.05, 309.8)
       Total sperm count <39 million41 (9)
       Morphology (% WHO normal), median (IQR)30.5 (21.75, 39)
       WHO normal <30%208 (48)
       Morphology (% strict criteria), median (IQR)20 (13, 27)
       Strict criteria <4%18 (4)
       DNA (% fragmentation index), median (IQR)12.44 (8.5, 19.27)
       Fragmentation index ≥30%34 (7)
      Semen parameters were analyzed on a continuous scale and after stratifying based on abnormal parameters as defined by the WHO fifth edition (
      • Cooper T.G.
      • Noonan E.
      • von Eckardstein S.
      • Auger J.
      • Baker H.W.
      • Behre H.M.
      • et al.
      World Health Organization reference values for human semen characteristics.
      ). Work-related heavy exertion was consistently associated with lower semen concentration and total sperm count. Thirteen percent of men who reported heavy exertion displayed oligospermia, compared with 6% who did not report workplace exertion. Moreover, the average sperm counts were 16% lower in men who reported workplace exertion. Such associations persisted after adjusting for age, BMI, race, and smoking. In contrast, no other work place exposure showed consistent detriments or improvements in semen quality (Table 2, Table 3).
      Table 2Linear regression results for the association between baseline occupational and health factors and semen quality.
      FactorVolume (mL)Concentration (M/mL)Total count (M)WHO morphology (%)Strict morphology (%)DFI (%)
      Night work
       Yes3.4 (1.6)82.4 (66.5)250.4 (199)29.9 (13.9)19.8 (11)14.5 (9.5)
       No3.3 (1.5)72.4 (51)230.1 (179.3)30.7 (11.9)20.3 (9.6)15.5 (10.7)
      Rotating shifts
       Yes3.2 (1.5)82.3 (67.4)236.3 (189.8)29.7 (13.5)19.3 (10.8)15.4 (9.8)
       No3.4 (1.5)73.2 (52.4)234.1 (183.1)30.6 (12.1)20.3 (9.7)15.3 (10.6)
      Whole body vibration
       Yes3.1 (1.4)78.6 (63.6)227.7 (181)30.8 (12.6)20.2 (10)13.5 (10)
       No3.4 (1.5)73.5 (52.2)236.9 (184.8)30.4 (12.3)20.1 (9.9)15.8 (10.5)
      Noise
       Yes3.5 (1.6)69.2 (58.9)226.9 (193.3)29.5 (12.2)19.4 (9.7)13.9 (9.3)
       No3.3 (1.5)76.9 (53.4)238.3 (180.3)30.9 (12.4)20.4 (10)15.8 (10.8)
      Extreme heat
       Yes3.6 (1.6)78.8 (67)248.3 (198.1)31 (13)20.3 (10)14.1 (9)
       No3.3 (1.5)73.6 (51.3)231 (179.8)30.4 (12.2)20.1 (9.9)15.6 (10.8)
      Heavy exertion or lifting
       Yes3.3 (1.6)68.1 (59.4)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      207.5 (179.2)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      30.1 (12.1)19.7 (9.8)14.9 (11.6)
       No3.4 (1.5)77.8 (52.7)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      247.4 (184.8)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      30.7 (12.5)20.4 (10)15.5 (9.9)
      Prolonged sitting
       Yes3.3 (1.4)76.4 (51.4)241.5 (174.2)31 (12.8)20.7 (10.4)15.7 (11.1)
       No3.4 (1.6)73 (58.3)228.3 (192.8)30.1 (11.9)19.6 (9.5)15 (9.8)
      High cholesterol
       Yes3.1 (1.3)79.9 (62.6)239.1 (210.3)29.2 (13.1)19 (10.3)16.7 (9.7)
       No3.4 (1.6)73.2 (52.9)230.6 (176.4)30.7 (12.3)20.3 (9.9)15 (10.5)
      Diabetes
       Yes2.6 (1.4)72.9 (57.6)159.2 (116.9)29.8 (10)20 (8)10.1 (5)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
       No3.4 (1.5)74.1 (54.3)233.7 (182.5)30.4 (12.5)20.1 (10.1)15.5 (10.4)
      P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      High blood pressure
       Yes3 (1.5)69.4 (57.8)190.4 (193.7)26.6 (13.1)17.1 (10.2)14.1 (7.9)
       No3.4 (1.5)75 (54.3)236.9 (180.3)30.9 (12.3)20.5 (9.9)15.5 (10.6)
      Comorbidities (high blood pressure, high cholesterol, diabetes mellitus)
       03.4 (1.6)73.2 (51.7)234.4 (172.5)31 (12.4)20.6 (10)15.4 (10.8)
       13.1 (1.5)80.2 (66)237.7 (224.2)28.8 (12.1)18.6 (9.6)15.1 (8.8)
       2+2.9 (1.1)69.4 (48.3)176.1 (128.6)27.9 (14.6)18.5 (11.4)15.4 (9.7)
      Medications
       03.3 (1.4)78.8 (61.2)253.1 (213.7)29.7 (12.2)19.7 (9.8)15.9 (9.4)
       1+3.4 (1.6)72.2 (51)222.5 (163.7)30.9 (12.6)20.3 (10.1)15 (10.8)
      Note: Data presented as mean (SD). DFI = DNA fragmentation index.
      a P<.05 for linear regression model adjusted for age, race, BMI, and smoking status.
      Table 3Linear regression results for the association between baseline occupational and health factors and abnormal semen quality parameters.
      FactornVolume <1.5 mLConcentration <15 M/mLTotal count <39 MWHO morphology <30%Strict morphology <4%DFI (≥30%)
      Night work
       Yes10613 (12)7 (7)8 (8)50 (50)6 (6)8 (8)
       No35028 (8)31 (9)30 (9)150 (47)10 (3)25 (7)
      Rotating shifts
       Yes7310 (14)6 (8)8 (11)34 (48)4 (6)6 (8)
       No38331 (8)32 (8)30 (8)166 (48)12 (3)27 (7)
      Whole body vibration
       Yes10512 (11)12 (11)13 (12)43 (44)3 (3)4 (4)
       No35229 (8)26 (7)25 (7)157 (48)13 (4)29 (8)
      Noise
       Yes1248 (6)13 (10)14 (11)57 (51)5 (5)8 (7)
       No33233 (10)24 (7)23 (7)143 (46)11 (4)25 (8)
      Extreme heat
       Yes986 (6)6 (6)6 (6)42 (45)4 (4)5 (5)
       No35935 (10)32 (9)32 (9)158 (48)12 (4)28 (8)
      Heavy exertion or lifting
       Yes14514 (10)19 (13)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      19 (13)66 (51)4 (3)9 (6)
       No31227 (9)19 (6)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      19 (6)134 (46)12 (4)24 (8)
      Prolonged sitting
       Yes22417 (8)13 (6)12 (5)102 (49)7 (3)18 (8)
       No23324 (10)25 (11)26 (11)98 (46)9 (4)15 (7)
      High cholesterol
       Yes767 (9)8 (11)8 (11)36 (51)3 (4)8 (11)
       No39636 (9)32 (8)33 (8)171 (47)15 (4)26 (7)
      Diabetes
       Yes142 (14)3 (21)4 (29)6 (46)0 (0)0 (0)
       No45841 (9)37 (8)37 (8)202 (48)18 (4)34 (8)
      High blood pressure
       Yes499 (18)9 (18)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      9 (18)26 (58)5 (11)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      3 (6)
       No42334 (8)31 (7)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      32 (8)181 (46)13 (3)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      31 (8)
      Comorbidities (high blood pressure, high cholesterol, diabetes mellitus)
       036030 (8)26 (7)26 (7)153 (46)11 (3)25 (7)
       1919 (10)9 (10)11 (12)44 (52)6 (7)7 (8)
       2+224 (18)5 (23)4 (18)11 (55)1 (5)2 (10)
      Medications
       031630 (9)25 (8)22 (7)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      140 (47)13 (4)20 (7)
       1+15613 (8)15 (10)19 (12)
      P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      67 (48)5 (4)14 (9)
      Note: Data presented as n (%). DFI = DNA fragmentation index.
      a P<.05 for logistic regression model adjusted for age, race, BMI, and smoking status.
      When examining men with hypertension, diabetes, and hyperlipidemia, only hypertension was consistently associated with semen quality. High blood pressure led to a lower WHO and strict morphology scores compared with normotensive men (WHO, 27% vs. 31%; strict, 17% vs. 21%). Moreover, a trend of lower total sperm counts was also identified, although it failed to reach statistical significance (P<.1). In contrast, hyperlipidemia, diabetes, and a composite of total comorbidities did not impact semen quality (Table 2, Table 3).
      The number of medications a man reported taking at baseline appeared to influence semen quality (Table 4). The more medications a man took, the higher the risk of low sperm count (P value for trend <.05). For example, 7% of men who were not taking medications had a total sperm count <39 million compared with 15% of men who reported taking two or more medications. In addition, when examining all men with sperm counts <39 million, the average number of medications per man is 1.2. In contrast, a man with normal sperm counts took on average 0.6 medications.
      Table 4Logistic regression results for the association between number of medications and abnormal semen parameters.
      Semen parameterCut pointn (%)Medications, mean (SD)P value
      Volume<1.5 mL43 (9)0.9 (1.5).90
      ≥1.5 mL430 (91)0.6 (1.2)
      Concentration<15 M/mL40 (8)0.9 (1.5).97
      ≥15 M/mL433 (92)0.6 (1.2)
      Total count<39 M41 (9)1.2 (1.8).02
      ≥39 M432 (91)0.6 (1.2)
      WHO morphology<30%208 (48)0.7 (1.2).94
      ≥30%228 (52)0.6 (1.2)
      Strict morphology<4%18 (4)0.6 (1).72
      ≥4%418 (96)0.6 (1.2)
      DFI≥30%34 (7)1.2 (1.9).03
      <30%425 (93)0.6 (1.2)
      Note: P values represent logistic regression model adjusted for age, race, BMI, and smoking status. M = millions.

      Discussion

      To our knowledge, the LIFE Study is the first prospective cohort study with preconception enrollment of couples to examine the relationship between physical occupational reproductive hazards while accounting for health and socioeconomic factors. We identified a negative relationship between strenuous occupational activity and sperm counts. Interestingly, no other work exposure impacted semen quality. When examining somatic health, hypertension was associated with impaired sperm morphology. While no relationship was identified between total medical comorbidities and semen quality, a positive association was identified between number of medications and sperm count.
      Similar to the findings of the current report, El-Helaly and colleagues demonstrated a relationship between male factor infertility and workplace exertion (
      • El-Helaly M.
      • Awadalla N.
      • Mansour M.
      • El-Biomy Y.
      Workplace exposures and male infertility—a case-control study.
      ). In contrast, the literature does suggest that exercise and activity may improve semen parameters and hypothalamic-pituitary-gonadal hormone levels (
      • Hakonsen L.B.
      • Thulstrup A.M.
      • Aggerholm A.S.
      • Olsen J.
      • Bonde J.P.
      • Andersen C.Y.
      • et al.
      Does weight loss improve semen quality and reproductive hormones? Results from a cohort of severely obese men.
      ,
      • Safarinejad M.R.
      • Azma K.
      • Kolahi A.A.
      The effects of intensive, long-term treadmill running on reproductive hormones, hypothalamus-pituitary-testis axis, and semen quality: a randomized controlled study.
      ,
      • Bobbert T.
      • Mai K.
      • Brechtel L.
      • Schulte H.M.
      • Weger B.
      • Pfeiffer A.F.
      • et al.
      Leptin and endocrine parameters in marathon runners.
      ,
      • Gaskins A.J.
      • Mendiola J.
      • Afeiche M.
      • Jorgensen N.
      • Swan S.H.
      • Chavarro J.E.
      Physical activity and television watching in relation to semen quality in young men.
      ). Other studies have failed to demonstrate a relationship between physical activity and semen quality (
      • Eisenberg M.L.
      • Kim S.
      • Chen Z.
      • Sundaram R.
      • Schisterman E.F.
      • Buck Louis G.M.
      The relationship between male BMI and waist circumference on semen quality: data from the LIFE study.
      ). However, similar to those who exercise to exhaustion (e.g., marathon runners or cyclists), it is conceivable that a threshold of exertion can be reached that may negatively impact fertility (
      • Safarinejad M.R.
      • Azma K.
      • Kolahi A.A.
      The effects of intensive, long-term treadmill running on reproductive hormones, hypothalamus-pituitary-testis axis, and semen quality: a randomized controlled study.
      ).
      Other studies have examined the relationship between physical workplace exposures and semen quality. While heat exposure in certain occupations such as for welders is associated with impaired semen quality, other examinations have not demonstrated a detriment to semen production from excessive workplace heat. In two separate case-control studies, investigators failed to identify a higher risk of male factor infertility due to self-reported occupational heat exposure (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ,
      • El-Helaly M.
      • Awadalla N.
      • Mansour M.
      • El-Biomy Y.
      Workplace exposures and male infertility—a case-control study.
      ). It is possible that many levels of occupational heat are of insufficient intensity or duration to impact spermatogenesis. As the data are self-reported and not quantified, it is also possible that methods of self-reporting are inadequate to capture relevant exposures. Moreover, we did not examine a job exposure matrix to attempt to identify relevant exposures.
      Shift work has also been shown to impact fertility in some studies but not in others, which suggests an unclear relationship (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ,
      • El-Helaly M.
      • Awadalla N.
      • Mansour M.
      • El-Biomy Y.
      Workplace exposures and male infertility—a case-control study.
      ). While the hypothalamic-pituitary-gonadal axis can be altered by such work conditions, the reproductive impact is uncertain (
      • Axelsson J.
      • Akerstedt T.
      • Kecklund G.
      • Lindqvist A.
      • Attefors R.
      Hormonal changes in satisfied and dissatisfied shift workers across a shift cycle.
      ). Similar to our findings, sedentary behavior has not been clearly associated with male reproductive harm in the literature (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ,
      • Hjollund N.H.
      • Storgaard L.
      • Ernst E.
      • Bonde J.P.
      • Olsen J.
      Impact of diurnal scrotal temperature on semen quality.
      ,
      • Stoy J.
      • Hjollund N.H.
      • Mortensen J.T.
      • Burr H.
      • Bonde J.P.
      Semen quality and sedentary work position.
      ). However, Italian taxi drivers have higher rates of abnormal morphology, suggesting it may be vehicle noise, vibration, or exhaust that contributes rather than sitting itself (
      • Figa-Talamanca I.
      • Cini C.
      • Varricchio G.C.
      • Dondero F.
      • Gandini L.
      • Lenzi A.
      • et al.
      Effects of prolonged autovehicle driving on male reproduction function: a study among taxi drivers.
      ).
      Prior reports have identified possible links between somatic health and male fertility (
      • Eisenberg M.L.
      • Li S.
      • Behr B.
      • Pera R.R.
      • Cullen M.R.
      Relationship between semen production and medical comorbidity.
      ). For example, diabetes has been linked to impaired sperm production (
      • Dinulovic D.
      • Radonjic G.
      Diabetes mellitus/male infertility.
      ,
      • Garcia-Diez L.C.
      • Corrales Hernandez J.J.
      • Hernandez-Diaz J.
      • Pedraz M.J.
      • Miralles J.M.
      Semen characteristics and diabetes mellitus: significance of insulin in male infertility.
      ). In addition, investigators have demonstrated a relationship among hyperlipidemia, lipid levels, and male fertility (
      • Ramirez-Torres M.A.
      • Carrera A.
      • Zambrana M.
      High incidence of hyperestrogenemia and dyslipidemia in a group of infertile men.
      ,
      • Schisterman E.F.
      • Mumford S.L.
      • Browne R.W.
      • Barr D.B.
      • Chen Z.
      • Louis G.M.
      Lipid concentrations and couple fecundity: the LIFE study.
      ,
      • Schisterman E.F.
      • Mumford S.L.
      • Chen Z.
      • Browne R.W.
      • Boyd Barr D.
      • Kim S.
      • et al.
      Lipid concentrations and semen quality: the LIFE study.
      ). Salonia et al. suggested higher medical comorbidities in infertile men (
      • Salonia A.
      • Matloob R.
      • Gallina A.
      • Abdollah F.
      • Sacca A.
      • Briganti A.
      • et al.
      Are infertile men less healthy than fertile men? Results of a prospective case-control survey.
      ). Indeed, men with impaired semen quality have a higher rate of overall mortality compared with men with normal sperm production, suggesting that semen quality may represent a biomarker of overall health (
      • Jensen T.K.
      • Jacobsen R.
      • Christensen K.
      • Nielsen N.C.
      • Bostofte E.
      Good semen quality and life expectancy: a cohort study of 43,277 men.
      ,
      • Eisenberg M.L.
      • Li S.
      • Behr B.
      • Cullen M.R.
      • Galusha D.
      • Lamb D.J.
      • et al.
      Semen quality, infertility and mortality in the USA.
      ). In the current report, hypertension was associated with impaired sperm morphology. However, other comorbidities were not associated with semen quality. Importantly, the total number of comorbidities was not associated with semen quality, which suggests that medical problems may interact with reproductive health in varied ways. In contrast to Sheiner and colleagues, we identified a relationship between the number of medications taken and sperm count (
      • Sheiner E.K.
      • Sheiner E.
      • Carel R.
      • Potashnik G.
      • Shoham-Vardi I.
      Potential association between male infertility and occupational psychological stress.
      ). Moreover, when stratifying by semen quality, men with impaired parameters were more likely to take more medications. Given the absence of a complete medication history including clinical indication, we cannot determine whether this association is a proxy for health status or indicative of pharmacotoxic effects. While our numbers were inadequate to determine whether the comorbidity or treatment may explain this relationship, it merits further research.
      Our study findings are strengthened by our population-based sampling framework, rather than by a reliance on a convenient or clinically based sample, and by our in-depth semen analysis. Still, the findings require cautious interpretation given that our exposures are subject to possible reporting errors, although we are unaware of any potential biases that may have been introduced, as men were unaware of their semen quality at the time of reporting. Similarly, we were unable to verify self-reported medical diagnoses, so reporting errors may be possible. Another important limitation is the use of home semen collection. As such, our semen analysis may not be directly comparable to clinical analyses. Given the strong relationship between sociodemographic factors and many of our occupational and health exposures, even after adjustment we may be unable to isolate the workplace contribution. Next, while several semen parameters are incorporated into a clinical evaluation, only certain factors were identified as related to occupational or work exposures in the current analysis, leading to uncertain clinical significance. Lastly, we cannot rule out residual confounding or chance findings in light of the multiple comparisons made in the analysis.
      Nevertheless, the current report demonstrates a relationship among occupational exertion, hypertension, and medications with semen quality. As these are potentially modifiable factors, further research should determine whether treatment or cessation may improve male fecundity.

      Acknowledgments

      The authors thank the Reproductive Health Assessment Team, Biomonitoring and Health Assessment Branch, National Institute of Occupational Safety and Health, for conducting the semen analyses through a Memo of Understanding with the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

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