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. Author manuscript; available in PMC: 2022 Nov 14.
Published in final edited form as: Int J Hyg Environ Health. 2022 May 6;243:113977. doi: 10.1016/j.ijheh.2022.113977

Urinary phthalate metabolite concentrations during four windows spanning puberty (prepuberty through sexual maturity) and association with semen quality among young Russian men

Lidia Mínguez-Alarcón 1,2,*, Jane Burns 2, Paige L Williams 3,4, Susan A Korrick 1,2, Mary M Lee 5, Jemar R Bather 4, Sergey V Kovalev 6, Sergey A Sokolov 6, Albert T Lebedev 6, Luidmila Smigulina 7,8, Ramy Abou Ghayda 9, Holger M Koch 10, Oleg Sergeyev 7,8,**, Russ Hauser 2,3,11,**
PMCID: PMC9662187  NIHMSID: NIHMS1846068  PMID: 35533529

Abstract

Aim:

To prospectively investigate the associations of urinary phthalate metabolite concentrations measured at four time points spanning pubertal development with semen parameters in Russian men.

Design:

516 boys were enrolled at ages 8-9 years (2003-2005) and followed annually.

Methods:

Urine samples were collected annually and pooled into four exposure windows [prepuberty, early puberty, late puberty and sexual maturity] based on physician assessed Tanner genitalia stages and testicular volume. Fifteen phthalate metabolites were quantified using isotope dilution HPLC-MS/MS at Moscow State University. We calculated molar sums (∑) of di-2-ethylhexyl phthalate (DEHP), di-isononyl phthalate (DiNP), di-isodecyl phthalate (DiDP) and anti-androgenic phthalate (AAP) metabolites. At sexual maturity (ages 18-19 years), the men provided 1-2 semen samples for analysis. We estimated the associations of quintiles of urinary ∑phthalate metabolites as well as mono-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), and mono-benzyl phthalate (MBzP) at each pubertal window, with semen parameters by fitting generalized linear mixed models with random intercepts and adjusting for confounders.

Results:

A total of 223 men who provided semen samples had phthalates measured at one or more pubertal windows. Higher urinary concentrations of ∑DiNP metabolites during late puberty were related to poorer semen quality (men with the highest quintile of urinary ∑DiNP had 30% lower sperm concentration, 32% lower count and 30% lower progressive motile count, compared to men in the lowest quintile). Also, young men with higher urinary concentrations of MiBP metabolites in early puberty tended to have poorer semen quality. No associations were observed for ∑DEHP metabolites, ∑DiDP metabolites, ∑AAP, MBzP or MnBP metabolites with semen quality parameters.

Conclusions:

∑DiNP metabolites measured during late puberty and MiBP metabolites at early puberty were related to poorer semen quality, highlighting the importance of considering specific windows of exposure when investigating chemical exposures in relation to measures of reproductive health in men.

Introduction

Some phthalates are endocrine disrupting chemicals used in a multitude of consumer products, leading to widespread general population exposure (CDC 2018; Hauser and Calafat 2005; Koch et al. 2017; Silva et al. 2004; Zota et al. 2014). Exposure to phthalates occurs through ingestion, inhalation, and dermal absorption (Cirillo et al. 2013; Duty et al. 2005; Just et al. 2010; Langer et al. 2014; Rudel et al. 2003; Rudel et al. 2011; Wilson et al. 2003; Wormuth et al. 2006). After entering the body, phthalates are quickly hydrolyzed to their respective biologically active monoester metabolites and excreted in urine. Although phthalates do not persist in the body and have short biological half-lives (<24 hours), there is repeated, episodic, and chronic exposure to phthalates (Kao et al. 2012; Koch et al. 2006; Koch et al. 2012). Urine serves as a non-invasive and convenient medium for biological monitoring and has been shown to be the optimal matrix for measuring phthalates because of their short serum half-lives and non-persistent nature (Calafat et al. 2015). Di-2-ethylhexyl phthalate (DEHP) is commonly added to plastics to increase flexibility and can be found in consumer products, flooring and wall coverings, food contact applications, and medical devices. Metabolites of DEHP include mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxy-hexyl phthalate (MEHHP), mono(2-ethyl-5-oxo-hexyl) phthalate (MEOHP), and mono(2-ethyl-5-carboxy-pentyl) phthalate (MECPP). Other phthalate metabolites such as mono-butyl phthalate (MnBP), mono-isobutyl phthalate (MiBP), and mono-benzyl phthalate (MBzP) can be found in personal care products and other consumer products. Di-isononyl phthalate (DiNP) is added to plastic consumer products including some polyvinyl chloride flooring, materials used in automobile interiors, wire and cable insulation, gloves, tubing, garden hoses, and shoes (Braun et al. 2014; Hauser and Calafat 2005).

As a result of their anti-androgenic activity, some phthalates have been associated with male reproductive tract abnormalities and lower circulating testosterone levels in experimental animal studies (Howdeshell et al. 2008; Johnson et al. 2012). In a recent systematic review and meta-analysis of epidemiology literature on phthalates and male reproductive health, the authors concluded that there was robust evidence of a negative association of DEHP and di-n-butyl phthalate (DnBP) with semen quality, and moderate evidence for associations of di-isononyl phthalate (DiNP), and butyl-benzyl phthalate (BBzP) with poorer semen quality (Radke et al. 2018). However, most of these epidemiologic studies of semen quality were cross-sectional with exposure biomarkers measured at the time of semen collection and thus precluded the ability to study specific windows of exposure during spermatogenesis or exposures earlier in life during pubertal development. In addition, urinary phthalate metabolites were quantified in a single urine sample leading to potential misclassification of the exposure.

Given the robust cross-sectional associations and the lack of studies on earlier exposure windows, such as during puberty, we leveraged our long-standing Russian Children’s Study cohort to investigate the longitudinal associations of phthalate metabolite concentrations collected during four pubertal windows with semen parameters measured in young adulthood. Although fetal and neonatal periods involve biological processes which are highly sensitive to chemical exposures that can impact male reproductive tract development and subsequent pubertal timing, puberty is also considered a critical developmental stage with exposure sensitivity (Woodruff et al. 2010). During puberty, development and activation of the male reproductive system occurs, characterized by maturation of the supporting Sertoli cells, differentiation of the testosterone producing Leydig cells, and spermatogenesis. Therefore, with these critical developmental processes, the pubertal period may also be vulnerable to chemical exposures.

Methods

Study population

Our study population consists of a subset of 223 of the 516 boys residing in Chapaevsk, Russia, who were enrolled at 8 and 9 years old between 2003 and 2005 in the Russian Children’s Study, and followed until time of semen collection approximately 10 years later (Supplemental Figure 1). At enrollment, each boy underwent a complete physical exam and their adult guardian completed health, dietary, and lifestyle surveys. This initial assessment was followed by yearly physical exams and questionnaires as previously described (Burns et al. 2020; Sergeyev et al. 2017; Williams et al. 2019) as well as annual urine sample collections and biennial blood sample collections. Throughout the study follow-up, a standardized anthropometric examination was performed by a single trained research nurse and pubertal staging was performed by a single physician. Genitalia (G) and pubic hair (P) staging, graded as 1 (immature) to 5 (sexually mature) were assessed by visual inspection following standard procedures (Tanner and Whitehouse 1976). Testicular volume (TV) was measured using a Prader orchidometer.

Of the 516 boys initially enrolled in the cohort between 2003 and 2005, 139 (26%) were lost to follow up by the time they were eligible for semen collection either due to death (n = 6) or no longer residing in the study area (n = 129), and 4 were not invited to participate due to severe cognitive impairment (Supplemental Figure 1). Of the 377 remaining boys, 152 declined to participate in the semen study. Thus, 225 (44%) young men provided 1 or 2 semen samples between 2012 and 2018 and contributed urine samples during at least one pubertal window. Additionally, 1 participant who was diagnosed with severe chronic disease and 1 azoospermic young man were excluded. Thus, for this analysis, we included the remaining 223 men with semen samples. A total of 134 boys provided urine samples for phthalate metabolite quantification during the prepubertal period defined as TV=1 or 2 cc and G=1 or 2, or TV=3 cc and G=1; 219 boys provided urine samples during early puberty defined as TV=3 cc and G=2 or 3, or TV=4 to 8 cc and G=1 to 3, or TV=10 cc and G=1; 203 boys provided urine during the late pubertal period defined as TV=10 to 15 cc and G=2 to 5, or TV=20 cc and G=2 to 3; and 213 boys provided urine at sexual maturity defined as TV≥20 cc and G=4 or 5. Additionally, for those young men who contributed a semen sample, the sexual maturity pool was limited to urines collected 90 days or more before semen sample collection.

The study was approved by the Human Studies Institutional Review Boards of the Chapaevsk Medical Association (Chapaevsk, Russia,), Harvard T.H. Chan School of Public Health, Brigham and Women’s Hospital (Boston, MA, USA), and Nemours Children’s Health (Wilmington, DE, USA). During the baseline assessment, the adult guardian/parent signed an informed consent, and each participant signed an assent before participation. When participants reached 18 years of age or older, they signed a consent form, including a separate consent for providing semen samples.

Urinary phthalate metabolite assessment

At enrollment and annually, spot urines collected in clean polypropylene containers were aliquoted into 15 ml sterile glass containers and stored at −35°C. An aliquot of urine from each annual exam was defrosted, vortexed, and pooled for each boy into one of four pubertal categories (prepuberty, early puberty, late puberty, or sexual maturity) using pre-specified G and TV criteria described above. The pooled urine was thoroughly vortexed before measuring specific gravity (sg), then aliquoted into 1.8 ml polypropylene cryovials for storage at −35°C. Urine samples collected during the first ten months of enrollment, n=216, were stored at the Harvard T.H. Chan School of Public Health in Boston, Massachusetts, U.S.A. and unavailable for pooling because of Russian restrictions on shipping human biospecimens into the country.

The frozen pooled urine samples were transported in dry ice from Chapaevsk to Moscow State University (MSU) laboratory, Moscow, Russia, for analysis according to the methods of Koch et al. (Koch et al. 2017). Phthalate metabolite concentrations were measured using online liquid chromatography tandem mass spectrometry (LC-MS/MS) (Koch et al. 2017). Urinary phthalate metabolites measured and included in analyses were: the metabolite of di-isobutyl phthalate (DiBP), MiBP; the metabolite of BBzP, MBzP; the metabolite of DnBP, MnBP; the metabolites of di-2-ethylhexyl phthalate (DEHP), MEHP, MEHHP, MEOHP, and MECPP; the metabolites of di-isononyl phthalate (DiNP), mono-hydroxy-iso-nonyl phthalate (MHiNP), mono-oxo-iso-nonyl phthalate (MOiNP), and mono-carboxy-iso-octyl phthalate (MCOP); and the metabolites of di-isodecyl phthalate (DiDP), mono-(hydroxy-iso-decyl) phthalate (MHiDP), mono-(oxo-iso-decyl) phthalate (MOiDP), and mono-(carboxy-iso-nonyl) phthalate (MCNP); and mono-(3-carboxypropyl) phthalate (MCPP), which is a metabolite of multiple parent phthalates, e.g. DnBP, DiNP, DiDP, among others. Calibrations were performed using commercial reference standards from LGC (MiBP, MnBP, MBzP, MEHP; Teddington, UK), Biozol (MEHHP, MEOHP, MECPP; Eching, Germany), custom synthesized standards provided by Koch/IPA (MCPP, MHiNP, MOiNP, MCOP, MHiDP, MOiDP, MCNP; Bochum, Germany) and isotopically labelled internal standards such as LGC (MCPP, MEHP, MEOHP; Teddington, UK) and custom synthesized by Koch/IPA (MiBP, MnBP, MBzP, MEHHP, MECPP, MHiNP, MOiNP, MCOP, MHiDP, MOiDP, MCNP; Bochum, Germany). Analyses were performed in 34 batches of 50 samples each including two randomly selected participants’ samples analyzed in duplicate, two quality control (QC) samples (from designated discard urines) with known low and high concentrations of each metabolite (1 QClow and 1 QChigh), and 1 field blank. For each boy, pooled urines from each pubertal stage were analyzed in the same batch. When a peak was absent or indeterminate, a concentration of zero was assigned. Limits of detection (LOD) were defined as a signal-to-noise (S/N) ratio of 3 in a urine matrix assessed as part of method validation. External reference standards were not available for analysis, but the MSU laboratory’s reproducibility measures were generally excellent. Specifically, the inter-assay relative SD percent (RSD%: (SD/mean)*100) across the 34 batches for QChigh ranged from 1.5-20.0%, except for MEHP (29.1%), whereas RSD% for QClow were ≤20%, except for MEHP (50.8%) and DiDP metabolites (23.6 – 57.8%), one of which had 23% of values below the LOD. High RSD% are seen with both MEHP (it can be a contaminant in reagents) and DiDP metabolites (they are a complex mixture of isomers that do not have as well resolved analytic peaks as other phthalates).

Semen quality assessment

Each young man in the study was asked to contribute two semen samples after reaching 18 years of age. They were asked to abstain from ejaculation for 2 to 4 days prior to semen sample collection and each provided up to 2 semen samples (approximately one week apart) by masturbation inside a dedicated room next to the Andrology Lab. The physician recorded information regarding any viral/bacterial illness or fever in the months prior to the semen collection and date/time of last recalled ejaculation to calculate abstinence time. Three subjects reported their first ejaculation at the study visit so their values were included in the highest category of the abstinence time covariate. The samples were stored inside an incubator at 37°C and analyzed within one hour after sample collection. Most samples (99%), however, were analyzed within a half hour of the collection. All samples were assessed by a single andrology technician (LS) according to criteria updated by the Nordic Association for Andrology (NAFA) and European Society of Human Reproduction and Embryology–Special Interest Group in Andrology (ESHRE-SIGA) (Björndahl et al. 2010). All semen samples were analyzed by the technician prior to measurements of urinary phthalate metabolite concentrations, thus the technician and study staff were blinded to the exposure data.

Semen volume was measured using a one, five, or ten mL disposable pipette. Sperm motility was evaluated by microscopic examination of the semen sample in duplicate at 400 times magnification , using at least 200 spermatozoa and at least 5 microscope fields in each duplicate count. Results were reported according to the 1999 WHO manual for the examination and processing of human semen. Specifically, at least 200 sperm per duplicate were classified into one of 4 categories: Class A: rapidly progressive motile; Class B: slowly progressive motile; Class C: locally motile and Class D: immotile. The percent progressive motile sperm was calculated by summing the individual percentages of the WHO classes A and B of each sample (WHO 2010). Sperm concentration was quantified using standard dilutions (1:50, 1:20, or 1:10 by sperm immobilization solution), two aliquots and Improved Neubauer Chamber Hemacytometer (INCH) at 200 times magnification under a phase contrast microscope with typically at least 200 spermatozoa in each sample. Duplicates for sperm concentration and motility were assessed and compared for agreement. Differences between the duplicates did not exceed the corresponding acceptance limit in any of the 438 semen samples for both sperm concentration and motility and average values were used for statistical analysis. Within-observer mean coefficients of variation (CV) in duplicates were 6.4% for sperm concentration and 4.9% for progressive motility and Lin’s concordance correlation coefficients (Lin 1989) reflected high agreement, rho=0.97 for sperm concentration and rho=0.92 for progressive motility.

Statistical analysis

We calculated medians and interquartile ranges (IQR) for participant demographics, dietary and parental characteristics that were continuous variables, and number and percentages for categorical variables. Semen parameters were reported as means (SD) and medians (IQR). We reported distribution of urinary phthalate metabolite concentrations as medians (IQR) for each pubertal period. Using their corresponding molar weights, we calculated the molar sum (μmol/L) of the ∑DEHP = ( MEHP + MEHHP + MEOHP + MECPP ), ∑DiNP = ( MHiNP + MOiNP + MCOP ), ∑DiDP = ( MHiDP + MOiDP + MCNP ), ∑ anti-androgenic (AAP) = ( MEHP + MEHHP + MEOHP + MECPP + MnBP + MiBP + MBzP + MHiNP + MOiNP + MCOP) with DiNP metabolites weighted 0.43 because of its weaker relative impact on fetal testosterone production (Hannas et al. 2011). Within each pubertal window, molar sums of the different urinary phthalate metabolites as well as MnBP, MiBP, and MBzP, were divided into quintiles, and the first (lowest) quintile was used as the reference group. Total sperm count (volume x sperm concentration) and total progressive motile sperm count (total sperm count x % progressive motile sperm) were calculated. We calculated Spearman correlations between the first and the second semen sample for the measured semen parameters. Total sperm count, sperm concentration and total progressive motile sperm count were log-transformed to approximate a normal distribution. Multivariable linear regression models with random subject effects to account for repeated measurements within the same man were used to examine the relation between the urinary phthalate metabolites, in quintiles, and semen parameters. We compared semen parameters (semen volume, total sperm count, sperm concentration, % progressive motile sperm, and total progressive motile sperm count) among men with higher quintiles of urinary concentrations to those in the lowest quintile. Predicted marginal means for these semen parameters were estimated as least square means (Searle et al. 1980) (adjusted for confounders at the mean level for continuous variables and for categorical variables weighted according to their frequencies) and were back-transformed to allow presentation of results in the original scale. Tests for linear trends were conducted using quintiles of urinary phthalate metabolite concentrations as ordinal levels. Inverse probability of censoring weights (IPCW) were used to account for potential selection bias, based on fitting a logistic regression model to obtain predicted probabilities of having a semen sample available among men with urinary phthalate data. Covariates included in the IPCW model were urinary DEHP metabolite concentrations at early puberty (larger sample size for phthalate quantification), birthweight, breastfeeding weeks, body mass index (BMI), household income and intakes of total calories, fats and carbohydrates. We collected BMI data from the most recent physical examination. Diet intakes, neonatal and household characteristics were collected from the parent or guardian when the boys were age 8-9 years. Mean (range) of censoring weights were 2.02 (1.18, 3.91). Intakes of total calories at study entry as well as urinary DEHP metabolite concentrations were the most predictive variables in the IPCW model.

To supplement our primary analyses, we applied a multiple informant model (MIM) (Horton et al. 1999; Pepe et al. 1999) to compare associations of urinary phthalate metabolite concentrations across pubertal windows with semen quality parameters only for those phthalates in which our primary analysis demonstrated associations with semen quality. For these MIM analyses, we used the urinary phthalate metabolites as continuous exposure variables and we included one continuous outcome per man by calculating the geometric mean of each semen parameter for participants who contributed two semen samples. In each model, we included a pubertal variable with four levels, an interaction term for the urinary phthalate metabolites and the pubertal window variable as well as the confounders. Results from this model allowed evaluation of whether associations of semen quality with urinary phthalate metabolite concentrations differed across the four time periods (prepuberty, early puberty, late puberty, and sexual maturity). Results are presented as differences in β estimates (SE) with their corresponding p-value for each pairwise comparison of the three earlier time periods versus sexual maturity (as the reference group), along with the p-value for interaction for the overall comparison across the four pubertal periods. We also estimated specific associations for each semen parameter among samples collected in each pubertal window. IPCW was used in the MIM to account for potential selection bias, as described above. Lastly, we repeated our main models using log-transformed continuous exposures rather than quintiles, and tested for non-linearity by including a quadratic term of the log-transformed exposure concentration in the models.

Covariates which could be potential confounders were included in the primary models for semen parameters, and were selected based on a priori evidence from the literature and supported empirically by associations with one or more of the semen parameters or urinary phthalate metabolite concentrations. In addition, we included abstinence time (days) in the models regardless of statistical significance because this is a well-known predictor of most semen quality parameters, and thus can improve the precision of the exposure estimates in the model (Schisterman et al. 2009). Based on these criteria, statistical models were adjusted for urine dilution (SG), current BMI (kg/m2), abstinence time (<2 days, 2-5 days, ≥ 5 days), and smoking (yes/no) as well as beer consumption (yes/no) both during the year of their most recent study questionnaire (up to 3 years prior to semen collection). For example, smoking status (yes/no), was collected using the question: “Have you smoked a cigarette, even a few puffs, within the past year?” . We analyzed the data using SAS (version 9.4; SAS Institute Inc., Cary, NC, USA).

Results

A total of 223 men had semen samples and contributed urines for phthalates for at least one pubertal window covering the four windows (Table 1). The study participants were all white and had a median (IQR) age at time of semen collection of 18.5 years (18.1, 19.1) and a median (IQR) BMI of 20.9 (19.0-23.1) kg/m2. More than half the men reported smoking (51%) or beer consumption (63%) during the year of their most recent study questionnaire. Four (2%), 7 (3%) and 1 (1%) of the participants had been previously diagnosed with cryptorchidism, severe varicocele (grade III or after surgery) and orchiditis, respectively. Twenty-eight (14%) of the young men’s parents reported household smoking during pregnancy.

Table 1.

Demographic, dietary, reproductive and parental characteristics among 223 participants in the Russian Children’s Study with semen samples and urine samples for at least one pubertal window for this analysis.

Median (IQR) or N (%)
Demographics and dietary characteristics
Age, years 18.5 (18.1, 19.1)
BMI, kg/m2 20.9 (19.0, 23.1)
Smokinga, n (%) 113 (51)
Beer intakea, n (%) 139 (63)
Other alcohol intakea, n (%) 70 (32)
At entry (8-9 years old)
Total calorie intakea (kcal/day) 2777 (2139, 3385)
Carbohydratesa (% calories) 53.9 (49.6, 58.1)
Fata (% calories) 34.5 (30.9, 37.6)
Proteina (% calories) 11.4 (10.5, 12.5)
Reproductive characteristics
Cryptorchidism, n (%) 4 (2)
Varicocele, n (%) 7 (3)
Orchiditis, n (%) 1 (1)
Parental and residential characteristics
Any household smoking during pregnancya,n (%) 31 (14)
Parental educationa, n (%)
   High school or less 11 (5)
   College degree 131 (60)
   Graduate degree or more 76 (35)
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We collected BMI data from the most recent physical examination. All cases of cryptorchidism were verified after investigation of medical history and follow-up. Smoking status was based on the response to the question: “Have you smoked a cigarette, even a few puffs, within the past year?”). The questionnaire to collect smoking information was completed up to 3 years before the semen sample was collected. Diet and parental/residential characteristics were collected at age 8-9 years.

a

These variables had missing data.

Overall, urinary concentrations of the DEHP metabolites were higher during the prepubertal and the early pubertal period, compared to those measured in urine samples collected in late puberty and at sexual maturity (Table 2). In contrast, urinary concentrations of the DiNP and the DiDP metabolites were higher at sexual maturity compared to the other three windows. Similar distributions of urinary phthalate metabolite concentrations were observed among a subset of 110 men who contributed to all four peripubertal windows (Supplemental Table 1). Participants in the Russian Children’s Study had, overall, higher urinary phthalate metabolite concentrations compared to children in the U.S. NHANES (CDC 2019) and Germany (Kasper-Sonnenberg et al. 2012), except for MBzP which was comparable to a German cohort and lower than NHANES children, and MCNP which was higher in NHANES children. Mean (SD) and median (IQR) values for semen parameters are reported in Table 3. Median (IQR) abstinence time was 2.71 days (1.88, 3.96). Correlations between the first and the second semen sample for the measured semen parameters were moderate-to-high (r=0.49 for total sperm count and 0.74 for sperm concentration) among 215 men who contributed 2 semen samples. Young men in the Russian Children’s Study had similar median sperm concentrations compared to young men in Sweden (53.5 million/mL and 56 million /mL, respectively) (Axelsson et al. 2015), Finland (54 million /mL) and Estonia (57 million /mL) (Jørgensen et al. 2002). However, they had higher semen concentrations than men in Murcia, Spain (44 million /mL) (Mendiola et al. 2013), Australia (45 million /mL) (Hart et al. 2015), Norway and Denmark (41 million /mL) (Jørgensen et al. 2002), and worse than a young Spanish cohort in Almeria (62 million /mL) (Fernandez et al. 2012). Urinary concentrations of MBzP were moderately correlated in prepubertal and early pubertal samples (r=0.63) as well as late pubertal and sexual maturity samples (r=0.50) (Supplemental Table 2). We found weak-to-moderate correlations for the other phthalate metabolites across pubertal samples (r=0.01-0.44).

Table 2.

Distribution (median, IQR) of urinary phthalate metabolite concentrations (ng/mL) by pubertal window among participants in the Russian Children’s Study.

Prepuberty
134 men
338 urines
Median=2
Range=1-6		 Early Puberty
219 men
578 urines
Median=2
Range=1-6		 Late Puberty
203 men
283 urines
Median=1
Range=1-6		 Sexual Maturity
213 men
891 urines
Median=4
Range=1-7
Mono-n-butyl phthalate (MnBP) 205 (118, 290) 183 (114, 281) 127 (68.3, 220) 107 (75.0, 161)
Mono-isobutyl phthalate (MiBP) 54.4 (33.7, 84.2) 68.9 (43.8, 115) 58.7 (36.4, 96.8) 66.7 (43.5, 112)
Monobenzyl phthalate (MBzP) 6.26 (3.01, 13.1) 7.69 (4.18, 20.8) 6.94 (3.63, 14.0) 7.27 (3.86, 15.6)
Di-2-ethylhexyl phthalate (DEHP) metabolites
  Mono-2-ethylhexyl phthalate (MEHP) 12.8 (6.60, 21.5) 15.4 (7.53, 26.2) 10.8 (6.40, 20.4) 11.1 (6.98, 19.8)
  Mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) 76.7 (46.4, 107) 77.6 (44.5, 136) 53.6 (30.8, 97.2) 50.4 (32.2, 80.7)
  Mono-2-ethyl-5-oxohexyl phthalate (MEOHP) 61.5 (39.2, 94.2) 68.2 (37.2, 110) 46.2 (26.1, 82.3) 40.7 (26.6, 67.1)
  Mono-2-ethyl-5-carboxypentyl phthalate (MECPP) 150 (98.2, 245) 165 (95.1, 259) 117 (69.0, 197) 105 (61.3, 160)
Di-isononyl phthalate (DiNP) metabolites
  Mono-hydroxy-iso-nonyl phthalate (MHiNP) 8.21 (4.93, 14.7) 10.0 (6.01, 18.3) 10.0 (5.39, 18.0) 19.5 (11.2, 41.5)
  Mono-oxo-iso-nonyl phthalate (MOiNP) 2.88 (1.80, 5.77) 3.92 (2.04, 7.61) 3.97 (1.94, 7.27) 6.76 (3.96, 14.0)
  Mono-carboxy-iso-octyl phthalate (MCOP) 5.93 (3.39, 12.7) 7.81 (4.59, 14.2) 7.45 (3.65, 15.0) 15.3 (8.24, 34.2)
Di-isodecyl phthalate (DiDP) metabolites
  Mono-(hydroxy-iso-decyl) phthalate (MHiDP) 3.79 (2.20, 7.07) 3.86 (2.18, 8.01) 3.73 (1.90, 7.55) 4.60 (2.57, 10.2)
  Mono-(oxo-iso-decyl) phthalate (MOiDP) 0.41 (0.13, 0.68) 0.43 (0.17, 0.84) 0.41 (0.14, 0.93) 0.86 (0.42, 1.51)
Mono-(carboxy-iso-nonyl) phthalate (MCNP) 0.82 (0.49, 1.37) 0.94 (0.50, 1.53) 0.75 (0.47, 1.36) 1.18 (0.74, 1.78)
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Table 3.

Semen parameters and reproductive characteristics among 223 men contributing 438 semen samples in the Russian Children’s Study who also contributed urine biomarkers of phthalate exposure to at least to one pubertal window for this analysis.

Mean (SD) Median (IQR) Spearman correlation between the two semen samples (N=215) N (%) samples within normal NAFA-ESHRE rangesa
Ejaculated volume (mL) 2.76 (1.63) 2.40 (1.60, 3.50) 0.72 288 (66)
Sperm concentration (million/mL) 67.8 (58.1) 53.5 (28.3, 88.5) 0.74 378 (86)
Total sperm count (million/ejaculate) 174 (165) 128 (63.0, 230) 0.49 295 (67)
Progressive sperm motility (%) 53.1 (10.2) 55.0 (48.0, 60.0) 0.65 306 (70)
Total progressive motile count (million/ejaculate) 96.3 (92.4) 70.9 (31.4, 131) 0.50 -
Abstinence time (days) 4.92 (10.9) 2.71 (1.88, 3.96) - -
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a

Ejaculated volume ≥2 mL; sperm concentration ≥20million/mL; total sperm count ≥80 million; progressive sperm motility ≥50%.

When we investigated the associations of phthalate metabolite concentrations measured in pooled urine samples collected during the four pubertal windows with semen parameters, we found that young men with higher urinary concentrations of ∑DiNP metabolites in late puberty had poorer semen quality, on average, as measured by lower sperm concentration (p-trend=0.09), count (p-trend=0.07) and progressive motile count (p-trend=0.12) (Table 4) than men with lower urinary concentrations. Specifically, men in the highest versus lowest quintile of urinary ∑DiNP had 30% lower sperm concentration, 32% lower count and 30% lower progressive motile count. Of note, ∑DiNP-associated differences in semen quality were most evident among men in the highest quintile of exposure supporting a potential non-linear relationship. Also, young men with higher urinary concentrations of MiBP metabolites in the early pubertal samples tended to have poorer semen quality, on average, as measured by lower ejaculated volume, sperm count, progressive motility and progressive motile count (Supplemental Table 4). We observed no evidence of consistent associations with semen quality parameters of urinary concentrations of ∑DiNP and MiBP measured in the other three pubertal windows (Supplemental Tables 3, 4 & 5) nor of ∑DEHP, ∑AA and ∑DiDP, MBzP and MnBP in the four periods (Table 4, Supplemental Tables 3, 4 & 5). However, isolated associations between higher urinary MBzP and MnBP at sexual maturity and decreased percent of progressive motility were found. The MIM models showed no meaningful associations between ∑DiNP and semen parameters for any of the windows. In addition, there were no differences in measures of association in relation to semen parameters when comparing prepuberty, early puberty and late puberty to sexual maturity (Supplemental Table 6). When repeating the primary analyses evaluating urinary ∑DiNP with respect to semen parameters using a continuous measure (log-transformed) rather than quintiles, no associations were observed for the late puberty period, consistent with the MIM model results (Supplemental Table 7). However, when a continuous measure of ∑DiNP was modelled, including a quadratic term, results supported potential non-linear associations between urinary ∑DiNP in late pubertal samples with sperm concentration, count and progressive motility consistent with findings in models of exposure quintiles (Supplemental Table 7). ”

Table 4.

Adjusted semen parameters by quintiles of urinary phthalates (measured during the late pubertal window) among 203 men contributing 398 semen samples in the Russian Children’s Study.

Urinary phthalates (range in μmol/L or ng/mL) Ejaculate volume (mL) Sperm concentration (million /mL) Total sperm count (million /ejaculate) Progressive motility

(%)		 Total progressive motile count (million /ejaculate)
∑DEHP
Q1 (0.09-0.40) 2.34 (1.91, 2.76) 48.2 (33.3, 69.8) 95.1 (63.6, 142) 50.7 (47.2, 54.3) 46.5 (29.7, 73.0)
Q2 (0.41-0.64) 2.89 (2.41, 3.35) 46.2 (37.3, 57.1) 113 (86.6, 146) 53.6 (50.9, 56.3) 59.1 (44.3, 78.9)
Q3 (0.65-0.89) 2.86 (2.37, 3.34) 54.0 (43.1, 67.5) 124 (90.7, 171) 54.0 (51.6, 56.5) 66.2 (46.9, 93.4)
Q4 (0.90-1.43) 2.71 (2.27, 3.14) 48.2 (36.8, 63.1) 109 (81.8, 144) 53.2 (50.6, 55.9) 56.9 (41.7, 77.6)
Q5 (1.46-64.6) 2.66 (2.20, 3.11) 40.7 (29.5, 56.2) 89.3 (63.4, 126) 52.0 (49.2, 55.0) 45.3 (31.1, 65.9)
P-trend 0.75 0.60 0.70 0.79 0.78
∑DiNP
Q1 (0.004-0.03) 2.60 (2.16, 3.05) 52.3 (38.5, 71.0) 112 (81.4, 155) 51.9 (48.9, 55.3) 56.5 (39.3, 81.2)
Q2 (0.04-0.05) 2.96 (2.51, 3.40) 48.9 (38.6, 62.1) 127 (94.6, 169) 54.1 (51.2, 57.0) 67.4 (48.6, 93.5)
Q3 (0.06-0.08) 2.62 (2.11, 3.11) 55.1 (42.3, 71.8) 109 (77.3, 154) 51.8 (49.0, 54.6) 55.5 (37.8, 81.5)
Q4 (0.09-0.16) 2.88 (2.40, 3.36) 46.2 (34.1, 62.6) 111 (79.6, 155) 52.7 (50.0, 55.4) 57.2 (39.9, 82.1)
Q5 (0.17-4.72) 2.39 (2.03, 2.76) 36.4 (26.6, 49.8) 75.7 (54.4, 105) 53.3 (50.5, 56.1) 39.4 (27.1, 57.2)
P-trend 0.41 0.09 0.07 0.76 0.12
∑DiDP
Q1 (<LOD-0.007) 2.74 (2.28, 3.20) 42.0 (30.7, 57.4) 98.7 (69.5, 140) 52.5 (49.1, 56.0) 50.3 (33.7, 75.1)
Q2 (0.008-0.013) 2.88 (2.40, 3.36) 50.6 (39.8, 64.4) 117 (89.6, 153) 52.3 (49.5, 55.0) 59.9 (44.5, 80.5)
Q3 (0.014-0.018) 2.24 (1.94, 2.53)ǂ 61.4 (51.2, 73.7) 120 (91.4, 157) 53.7 (51.3, 56.1) 63.4 (47.5, 84.7)
Q4 (0.019-0.042) 2.71 (2.38, 3.04) 42.1 (28.7, 61.6) 102 (69.2, 150) 52.8 (49.9, 55.7) 52.6 (3.3, 80.6)
Q5 (0.044-1.20) 2.88 (2.32, 3.45) 42.6 (32.6, 55.7) 92.4 (66.1, 129) 52.3 (49.3, 55.3) 47.2 (32.4, 68.9)
P-trend 0.83 0.63 0.53 0.99 0.58
∑AA
Q1 (0.23-1.04) 2.43 (1.99, 2.85) 50.8 (35.7, 72.3) 104 (70.4, 155) 53.1 (49.4, 56.7) 53.9 (34.7, 83.7)
Q2 (1.05-1.51) 2.92 (2.3, 3.47) 47.4 (37.3, 60.4) 113 (84.4, 152) 53.2 (50.3, 56.1) 59.1 (42.5, 82.1)
Q3 (1.52-2.25) 2.71 (2.31, 3.12) 52.1 (41.2, 66.0) 118 (89.0, 159) 51.9 (49.1, 54.7) 60.5 (43.9, 83.4)
Q4 (2.26-3.28) 2.62 (2.18, 3.06) 36.9 (28.4, 48.1) 81.7 (59.9, 111) 51.6 (48.9, 54.3) 41.4 (29.2, 58.6)
Q5 (3.29-69.9) 2.78 (2.25, 3.31) 51.0 (37.2, 69.9) 114 (81.4, 160) 53.9 (51.1, 56.8) 59.9 (41.5, 86.3)
P-trend 0.75 0.67 0.78 0.98 0.80
MBzP
Q1 (0.10-2.81) 2.31 (1.88, 2.71) 55.7 (41.1, 75.4) 110 (77.7, 156) 54.3 (51.2, 57.5) 58.7 (39.6, 87.1)
Q2 (2.82-5.49) 3.00 (2.54, 3.46)ǂ 47.9 (38.0, 60.5) 126 (97.9, 161) 53.7 (50.6, 56.8) 66.0 (50.0, 87.2)
Q3 (5.52-8.18) 2.85 (2.38, 3.32)ǂ 50.4 (39.2, 64.9) 117 (87.9, 156) 51.2 (48.1, 54.3) 58.6 (42.2, 81.3)
Q4 (8.31-18.4) 2.34 (1.92, 2.75) 44.3 (32.7, 60.0) 85.2 (59.0, 123) 50.9 (48.3, 53.6) 42.3 (28.3, 63.2)
Q5 (18.8-243) 2.89 (2.48, 3.46)ǂ 39.9 (28.9, 55.0) 95.6 (66.9, 137) 53.6 (51.0, 56.3) 50.1 (33.7, 74.6)
P-trend 0.53 0.14 0.19 0.44 0.20
MnBP
Q1 (15.9-60.8) 2.43 (1.99, 2.87) 48.5 (34.4, 68.5) 96.8 (66.1, 142) 52.5 (49.1, 55.8) 49.5 (32.4, 75.7)
Q2 (61.1-106) 2.72 (2.31, 3.13) 53.4 (41.6, 68.6) 125 (90.2, 172) 52.8 (50.0, 55.7) 64.5 (44.9, 92.6)
Q3 (107-159) 2.81 (2.26, 3.37) 49.8 (39.2, 63.3) 113 (86.8, 146) 52.9 (50.0, 55.8) 58.3 (43.8, 77.7)
Q4 (159-242) 2.45 (2.10, 2.80) 40.4 (30.2, 54.1) 86.2 (62.0, 120) 52.1 (48.9, 55.30 43.8 (29.9, 64.1)
Q5 (244-1459) 3.06 (2.51, 3.61) 45.3 (35.5, 57.9) 112 (82.2, 153) 53.4 (50.8, 56.1) 58.8 (42.0, 82.4)
P-trend 0.29 0.37 0.88 0.84 0.89
MiBP
Q1 (12.2-31.7) 2.83 (2.38, 3.28) 50.6 (39.7, 64.5) 123 (91.3, 166) 55.3 (52.4, 58.2) 66.7 (47.6, 93.5)
Q2 (32.9-50.8) 2.82 (2.31, 3.35) 43.7 (32.3, 59.0) 98.7 (70.7, 138) 52.4 (49.3, 55.6) 50.5 (34.6, 73.7)
Q3 (51.2-73.9) 2.82 (2.38, 3.260 50.5 (39.6, 64.4) 121 (91.5, 161) 52.2 (49.4, 55.0) 62.2 (45.8, 84.4)
Q4 (76.1-105) 2.48 (2.02, 2.94) 42.1 (31.8, 55.6) 85.2 (63.5, 114)ǂ 52.0 (49.5, 54.5)ǂ 43.4 (31.6, 59.6)ǂ
Q5 (108-1019) 2.48 (2.02, 2.96) 50.4 (37.6, 67.6) 103 (71.4, 148) 51.8 (48.9, 54.7)ǂ 51.8 (34.6, 77.6)
P-trend 0.25 0.98 0.43 0.11 0.31
Open in a new tab

Models adjusted for specific gravity, abstinence time, BMI, smoking, alcohol intake and household income.

*

p-value <0.05 when compared that quartile with the lowest quintile of exposure.

ǂ

p-value <0.10 when compared that quartile with the lowest quintile of exposure.

∑DEHP = (MEHP + MEHHP + MEOHP + MECPP). ∑DiNP = (MHiNP + MOiNP + MCOP). ∑DiDP = (MHiDP + MOiDP + MCNP). ∑ anti-androgenic (AAP) = (MEHP + MEHHP + MEOHP + MECPP + MnBP + MiBP + MBzP + MHiNP + MOiNP + MCOP).

Discussion

To our knowledge, this is the first study to prospectively investigate prepubertal and pubertal urinary phthalate metabolite concentrations and subsequent semen quality in young adults. Urine samples to quantify phthalate metabolite concentrations were collected annually and pooled for each boy from four windows spanning puberty: prepuberty, early puberty, late puberty and sexual maturity. Our primary finding was that, on average, men with higher urinary concentrations of ∑DiNP metabolites in late puberty had poorer semen quality than those with lower urinary ∑DiNP metabolite concentrations. We additionally found marginal associations with lower semen quality in men with higher, as compared to lower, MiBP metabolite concentrations in early puberty. However, we did not find any evidence of associations between ∑DiNP or MiBP measured during the other three pubertal windows or of ∑DEHP, ∑DiDP or ∑AAP in any of the four exposure windows and semen quality. These results highlight the importance of considering different windows of exposure during pubertal development when investigating chemical exposures in relation to semen quality in men.

Despite the fact that urinary concentrations of DiNP metabolites were highest at sexual maturity in the Russian Children’s Study participants, in our primary analysis we only found associations of ∑DiNP metabolites at late puberty with poorer semen quality. We defined “late” puberty in our cohort as TV=10 to 15 cc and G=2 to 5, or TV=20 cc and G=2 to 3. Potential explanations are that late puberty may be more sensitive to chemical exposures than other time windows or that the exposure impact on spermatogenesis during this late pubertal stage immediately preceding sexual maturity is critical for subsequent semen quality. During late puberty, serum concentrations of LH, FSH and testosterone are highest and AMH concentrations decline. Progression of spermatogenesis from spermatogonial differentiation to generation of mature spermatozoa occurs during late puberty with the full spermatogenic cycle taking 64 days (Rey 2021). Therefore disruption of this process may impair spermatogenesis and affect semen quality.

Another potential explanation is based on varying epigenetic susceptibility of the germ cells and the somatic Sertoli and Leydig cells involved in the regulation and progression of spermatogenesis across the different pubertal windows. Sperm epigenetic modifications may include clonal expansion of undifferentiated spermatogonia through mitosis starting in early puberty, and sperm-specific changes in DNA methylation, histone modifications, and histone to protamine exchanges in mature sperm during late puberty (Marcho et al. 2020; Pilsner et al. 2017). Marcho and colleagues suggested that mitotic divisions of undifferentiated spermatogonia occur before the blood-testis barrier is formed, and that the epigenome may be more susceptible to environmental conditions during spermatocytogenesis, the initial stage of spermatogenesis (Marcho et al. 2020).

Therefore, specific physiological changes in the reproductive organs, cells, hormone profiles, and epigenetic programming of both somatic and germ cells, together with the anti-androgenic effects of the DiNP may explain the negative associations found between ∑DiNP metabolites in the late pubertal samples with semen parameters. The MIM model findings suggested no association between urinary ∑DiNP metabolite concentrations in late puberty and semen parameters, and also identified no differences in associations of ∑DiNP with any semen parameter when comparing prepuberty, early puberty, and late puberty with sexual maturity. These findings were corroborated when using ∑DiNP metabolite concentrations as a log-transformed continuous exposure variables in the main models. However, the null findings observed when a continuous exposure measure was assessed in a linear regression model may be attributable to evidence of non-linear associations between ∑DiNP metabolite concentrations in late pubertal samples with sperm concentration, count and progressive motility – supporting the greater sensitivity of detecting associations in the main models based on exposure quintiles. It should be noted that some differences between the primary (time-specific) analyses and the MIMs remained, since we were able to include up to two semen parameters (outcomes) as repeated measures for each study participant in our primary analyses, whereas the MIM method is limited to a single outcome per subject, which we modeled as the geometric mean for men who contributed two semen samples. Yet another limitation of the MIM approach is that non-linear associations, which we observed in sensitivity analyses described above, are not easily incorporated.

Young men in our study with higher urinary MiBP metabolite concentrations showed a trend toward poorer semen quality, possibly because of the higher concentrations found in the Russian boys compared to boys in NHANES (CDC 2019) and in Germany (Kasper-Sonnenberg et al. 2012). However, urinary DEHP and other phthalate metabolites with anti-androgenic activity were not consistently associated with semen quality in this study despite previous cross-sectional epidemiologic evidence in adult men (Radke et al. 2018) and the high concentrations compared to the aforementioned studies. It is important to note that young men participating in this study (RCS) are generally healthy and have good semen quality as demonstrated by the high proportion of semen samples above the normal NAFA-EHSRE ranges for semen quality. Similar studies in other cohorts that assess urinary phthalate metabolite concentrations across different pubertal periods in relation to semen quality are needed to confirm these results.

Cross-sectional epidemiological studies in Chinese (Pan et al. 2015), Polish (Jurewicz et al. 2013), Swedish (Axelsson et al. 2015), and other European (Specht et al. 2014) adult men of reproductive age, have reported altered semen quality with increased urinary DiNP metabolite concentrations (MCiOP or MINP). Specifically, urinary DiNP metabolite concentrations were associated with poorer morphology (Axelsson et al. 2015; Jurewicz et al. 2013; Pan et al. 2015), lower motility (Axelsson et al. 2015; Pan et al. 2015), and lower sperm concentration (Pan et al. 2015; Specht et al. 2014). Since studies suggest that semen quality has generally declined during the past few decades in Western Countries (Levine et al. 2017; Minguez-Alarcon et al. 2018) and has been associated with higher risk of common chronic diseases and mortality (Choy and Eisenberg 2018; Eisenberg et al. 2014; Eisenberg et al. 2016; Jensen et al. 2009; Latif et al. 2017), identifying potentially modifiable contributory factors, such as environmental exposures, is critical given the public health relevance beyond fertility and reproduction.

Limitations of this study include lack of information on parents’ exposure to phthalates, including maternal prenatal exposure, which may have transgenerational epigenetic effects on spermatogenesis, and the father’s reproductive health, including semen parameters (Soubry et al. 2014; Stuppia et al. 2015). Other potential limitations are that the urinary phthalate metabolite concentrations among participants in the Russian Children’s Study were higher than other populations slightly reducing generalizability to other populations, the potential lack of power to detect associations in the prepubertal window due to a small sample size, and the nonconcurrent time frame (>1 year) between urines collected at sexual maturity and semen sample collection. Lastly, we did not have information regarding exposure to other environmental chemicals that might impact semen quality for each of our four exposure windows so, if other chemical measures are correlated with phthalate metabolites, unmeasured confounding could impact our results. The main strengths of this study are the collection of annual urine samples during the course of the study minimizing the risk of misclassification of the exposure for these chemicals with short half-lives, and its prospective design, limiting the possibility of reverse causation, specifically for the prepubertal, early and late pubertal analysis. Other strengths include the analysis of the semen samples by a single technician who was blinded to the urinary phthalate concentrations and the collection of two semen samples for most of the young men. We also used sophisticated statistical methods such as inverse probability weights to account for censoring, in order to reduce concerns regarding selection bias.

In conclusion, we found that young men with higher concentrations of ∑DiNP metabolites quantified in urine samples collected during late puberty had poorer semen quality at sexual maturity. Also, those with higher MiBP metabolites in early puberty tended to have poorer semen quality. We did not find any evidence of consistent associations with semen quality for ∑DiNP or MiBP measured at other pubertal windows or of urinary ∑DEHP, ∑DiDP and ∑AAP at any pubertal period. These results highlight the importance of considering different windows of exposure when investigating chemical exposures in relation to semen quality in men. Further studies are needed to confirm these results.

Supplementary Material

sup tables
1

Funding and Acknowledgments:

Funding was provided through grants R01ES0014370 and P30ES000002 from the National Institutes of Health/National Institute of Environmental Health Sciences, grant R82943701 from the U.S. Environmental Protection Agency, and grant 18-15-00202 from the Russian Science Foundation (O.S.). The authors gratefully acknowledge all of the children and adults who participated in this study. We also acknowledge the Chapaevsk government, and the Chapaevsk Medical Association and Chapaevsk Central Hospital staff. We also thank our colleagues Larisa Altshul and Boris Revich for their input in the Russian Children’s Study.

Footnotes

Competing financial interests: None of the authors has any conflicts of interest to declare.

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