Exposure to heavy metals (lead, cadmium and mercury) and its effect on the outcome of in-vitro fertilization treatment

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Int. J. Hyg. Environ. Health 211 (2008) 560–579 www.elsevier.de/ijheh

Exposure to heavy metals (lead, cadmium and mercury) and its effect on the outcome of in-vitro fertilization treatment Iman Al-Saleha,, Serdar Coskunb, Abdullah Mashhoura, Neptune Shinwaria, Inaam El-Dousha, Grisellhi Billedoa, Kamal Jaroudic, Abdulaziz Al-Shahranic, Maya Al-Kabrab, Gamal El Din Mohamedd a

Biological and Medical Research Department, King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia b Pathology and Laboratory Medicine Department, King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia c Obstetrics and Gynecology Department, King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia d Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia Received 25 November 2006; received in revised form 18 July 2007; accepted 11 September 2007

Abstract We investigated the effect of lead, cadmium and mercury exposure on pregnancy and fertilization rate outcome among 619 Saudi women (age 19–50 years) who sought in-vitro fertilization (IVF) treatment between 2002 and 2003. The concentrations of lead, cadmium and mercury were measured in both blood and follicular fluids. At levels well below the current US occupational exposure limit guidelines (40 mg/dL) and even less than the current Centers for Disease Control and Prevention level of concern for preventing lead poisoning in children (10 mg/dL), blood lead level was negatively associated with fertilization outcome in both adjusted and unadjusted logistic regression models. We found that among various demographic, socioeconomic and environmental factors, fish consumption was positively associated with blood lead levels. These results support the hypothesis that a raised blood lead level affects infertility and intervention to reduce the lead exposure might be needed for women of reproductive age. The present results also revealed unexpected finding – the positive relationship between follicular cadmium levels and fertilization outcome, which points to the necessity for further investigation. Though adverse effect of mercury on pregnancy outcome or fertilization rate was not evident in this study, mercuryX5.8 mg/L (EPA safety limit) was found in the blood and follicular fluid of 18.7% and 8.3% of the women, respectively. Concerns about its possible adverse effects on the physiology of reproduction or fetal development cannot be ruled out. It should be noted that skin-lightening creams and dental amalgam were important contributors to mercury exposure. Such finding is alarming and priority for further studies are, urgently, needed. r 2007 Elsevier GmbH. All rights reserved. Keywords: In-vitro fertilization; Saudi Arabia; Pregnancy outcome; Fertilization rate; Lead; Cadmium; Mercury

Corresponding author. Tel.: +966 1 442 4772; fax: +966 1 442 7858.

E-mail address: [email protected] (I. Al-Saleh). 1438-4639/$ - see front matter r 2007 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijheh.2007.09.005

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Introduction Although the overall contribution of environmental exposures to infertility is unknown, the available literature suggests that exposure to various environmental factors, both in utero and neonatally, could dramatically affect adult fertility. Studies of various contaminant-exposed wildlife populations suggest that multiple mechanisms contribute to changes in gonadal development, maturation of germ cells, fertilization and pregnancy, specifically, the endocrine processes supporting these events (Guillette and Moore, 2006). Heavy metals exposure has been identified as a factor affecting human fertility (Sharara et al., 1998). They may induce hormonal disorders, preventing ovulation and pregnancies (Gerhard and Runnebaum, 1992; Choi et al., 2004) as well as abnormalities in sperm production (Sinawat, 2000). Lead has an extensive history as a reproductive toxin, which exerts its effect either directly on the developing fetus, after gestation begins, or indirectly on paternal or maternal physiology before and during the reproduction process (Silbergeld, 1991). High levels of paternal lead exposure (440 or 425 mg/dL for a period of years) appear to reduce fertility and to increase the risks of spontaneous abortion and reduced fetal growth (preterm delivery, low birth weight). Maternal blood lead levels of approximately 10 mg/dL have been linked to increased risks of pregnancy hypertension, spontaneous abortion and reduced offspring neurobehavioral development (Bellinger, 2005). Pillai et al. (2002) found that lead may affect pituitary membrane function and cause alterations in receptor binding and secretory mechanism(s) of pituitary hormones. This may be an important factor in the pathogenesis of infertility. The main sources of lead exposure are paints, water, food, dust, soil, kitchen utensils and leaded gasoline (CDC, 1991). Lead poisoning due to the use of cosmetics and traditional remedies such as by Kohl, Negd and Saoot have been reported in different countries. Although lead poisoning cases are rare nowadays, chronic lead exposure remains a major public health problem worldwide. Even though there are no available literature on the effect of lead on reproduction in Saudi Arabia, few studies have shown that lead can cross the placenta and have a negative effect on birth weight (Al-Saleh et al., 1995). Another study revealed that low lead exposure could induce high blood pressure among Saudi non-occupationally menopausal women (Al-Saleh et al., 2005). Cadmium is highly toxic, and one of the most important environmental pollutants in industrialized countries. It accumulates in the human body during lifetime and can induce renal dysfunction (Bernard, 2004). Recent study by Nampoothini and Gupta (2006) has shown that cadmium and lead can cause significant

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reduction in gonadotropin binding, which altered the steroidogenic enzyme activity of granulosa cells, and thus dysfunction in the production of hormones, leading to infertility. For the general population, the two main sources of exposure are diet and tobacco smoking (Satarug and Moore, 2004). Smokers have higher levels of cadmium accumulation than non-smokers. Zenzes et al. (1995) found that cadmium accumulates in that oocytes of smokers in a dose-dependent manner, affecting the quality of oocytes. Mlynarcikova et al. (2005) reported that harmful effects of cigarette smoke components (cadmium, nicotine and cotinine) can disturb the intrafollicular process, leading to infertility. Blood cadmium levels were found significantly higher among Saudi cigarette smokers (Al-Saleh and Coate, 1993). There are also other smoking waterpipe devices such as sheesha, maasal and jurak that are widely used in Saudi Arabia. Our previous study revealed that smoking sheesha was another source for cadmium exposure and we found that urinary cotinine was significantly higher among cigarette and sheesha smokers (Al-Saleh et al., 2000). According to a study by Jarallah et al. (1999), the overall prevalence of smoking in Saudi Arabia was 21.1% for males and 0.9% for females. The latter figure for female population might be underestimated due to cultural reasons. In this study, urinary cotinine was measured as an indicator of tobacco-smoke exposure (Haufroid and Lison, 1998). Mercury is a naturally occurring metal that has several forms: (1) metallic or elemental and is commonly used in dental filling and thermometers; (2) inorganic compounds are used in skin care and medicinal products; and (3) organic compounds that are used in fungicides, paints, diet (contaminated fish), etc. The general population is primarily exposed to mercury through diet and dental amalgam (WHO, 1991). Few studies have reported women’s use of skin-lightening creams and soaps (McRill et al., 2000; Al-Saleh and Shinwari, 1997) and its association with mercury exposure. Mercury and its compounds have a wide spectrum of toxicities, depending on the chemical forms and modes of exposure (Satoh, 2003). There have been numerous studies on the effects of mercury on the immune system, renal system, cardiovascular, reproductive system and the central nervous system (Zahir et al., 2005). According to a number of review articles, mercury resulted in reproductive problems, such as spontaneous abortion, stillbirths, congenital malformations, infertility, disturbances in the menstrual cycle, inhibition of the ovulation and behaviourial effects of the offspring (Gardella and Hill, 2000; Schuurs, 1999; Yoshida, 2003). A study by Shen et al. (2000) indicated that mercury chloride could affect the meiotic maturation of mouse oocyte, obviously block in-vitro fertilization (IVF) and injure or reduce the reproductive capacity of mouse. Davis et al. (2001) showed that

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exposure to metallic mercury altered estrous cyclicity, but had no significant effect on ovulation, implantation or maintenance of first pregnancy during exposure of short duration in female rats. Two studies were conducted on the impact of mercury on IVF outcome and both confirmed its association with poor outcome (Choy et al., 2002; Fateh et al., 2005). Shortcomings still exist in the present knowledge of heavy metal’s effect on the reproductive system. More research is needed, in particular regarding its disturbances of the menstrual cycle, reduced female and male fertility, and behavioral effects of prenatal exposure. The goal of this study was to evaluate the influence of exposure to lead, cadmium and mercury from different sources on IVF treatment outcomes such as pregnancy and fertilization rate.

Materials and methods Selection of subjects Blood and follicular fluid samples were collected from 619 women undergoing IVF treatment at the IVFembryo transfer unit, King Faisal Specialist Hospital and Research during the period between 12/01/2002 and 16/10/2003. Entry criteria for this study were women aged 19–50 years with tubal or cervical factors, endometriosis, polycystic ovaries, male factors or unexplained infertility undergoing IVF. Of these women, 65.7% had male factor, 14% had tubal factor, 9.1% had miscellaneous problems and 11.2% had unexplained infertility. The mean age of these women was 31.7675.12 years. Each participant signed an informed consent form approved by the Research Ethic Committee of King Faisal Specialist Hospital and Research Centre. Infertility specialists interviewed each participant for their medical history and physical examination. A detailed questionnaire containing demography weight (Kg), height (cm), menstrual history; number of treatment cycle, duration of infertility; previous pregnancy history, smoking habits, occupational, residential history, use of traditional cosmetics and remedies; use of pesticides; source of drinking water; consumption of fish; history of medical illness and use of medications was filled in for each participant by a trained data collector.

Sample collection Ovarian stimulation, oocyte retrieval, IVF/ICSI procedures and embryo grading were performed as previously described (Coskun et al., 2000). Follicular aspirates without major blood contamination were obtained from each woman who was treated in the

IVF clinic. The follicles were aspirated and all the cumulus–oocyte complexes were collected from follicular fluid. About 50 mL of the remaining follicular fluid were pooled for each patient and centrifuged to separate cells. Supernatant was immediately transferred to plastic tubes, and stored at 20 1C for analyses of lead, cadmium and mercury. Venous blood sample (5 mL) was drawn from each participant into Vacutainer tubes containing 10.5 mg of tripotassium-ethylene diaminetetraacetic acid (K3-EDTA) as an anticoagulant and stored at 4 1C for analyses of lead, cadmium and mercury. Eggs harvested from follicular aspirates were either inseminated or injected with husband’s sperm. Fertilization was checked after 16–18 h and defined as having two pronuclei and two polar bodies. Two to three embryos obtained from fertilized eggs were transferred to the uterus and the establishment of pregnancy was confirmed with urine pregnancy test and blood beta-hCG (b-hCG) levels 14 days and by ultrasound 5 weeks after embryo transfer. Patients with positive b-hCG were considered as pregnant. Pregnancy was subdivided into biochemical (positive b-hCG, negative ultrasound), abortion or ongoing.

Sample analyses Lead and cadmium analyses Lead and cadmium analyses in blood and follicular fluids were performed using a Varian AA-880 Zeeman atomic absorption spectrophotometer coupled to a GTA-100 electrothermal atomizer and a programmable sample dispenser (Varian Techtron Pty. Ltd., Australia). Lead and cadmium in blood samples were analyzed according to the recommended methods by Al-Saleh et al. (2005) and Al-Saleh and Coate (1993), respectively. One volume of whole blood or follicular fluid (usually 100 mL) was mixed with two volumes of aqueous 2% (v/v) Triton X-100 solution in an Eppendrof microcentrifuge tube and mixed for 30 s. They were then centrifuged at 10,000 rpm for 4 min. The clear supernatant was transferred to the vial. Total volume dispensed was 6 mL, which includes 3 mL of NH4H2PO4 modifier (1% w/v). Calibration standards was prepared daily using a manual standard addition procedure. Fresh venous blood samples (obtained from the Hospital Blood Bank) and pooled follicular fluid sample were divided into six equal portions. Known amounts of aqueous lead or cadmium solutions were added to these to give final concentrations in the range 0.63–10.0 mg/dL for lead and 0.25–4.0 mg/L for cadmium. Quadruplicate determinations were made on all samples. Detection limits (DL) of lead and cadmium in this study were 0.66 and 0.25 mg/L, respectively.

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A quality assurance program was incorporated to check the accuracy of lead and cadmium measurements in blood and follicular fluids and to evaluate the performance of the method. Two sets of blood controls with known lead or cadmium concentrations determined by a reference laboratory were obtained from Kaulson Laboratories (CONTOX Heavy Metal Blood ControlA, W. Caldwell, NJ, USA) to verify the accuracy of the method. There was an excellent agreement between the experimental and the certified recommended values for both controls. The values found for levels I and II were 17.7072.64 and 46.4377.64 mg/dL, respectively, while the recommended values for lead were 14.0–22.0 and 39.0–51.0 mg/dL, respectively. The values found for levels I and II cadmium were 10.6271.51 and 16.8172.43 mg/L, respectively, while the recommended values for cadmium were 4.0–12.0 and 11–19 mg/L, respectively. The accuracy of the method was also determined by measuring the recovery of lead or cadmium added to blood and follicular fluid samples. These spiked blood and follicular fluid samples were run with the test samples using the same analytical procedure. The analytical recovery for blood and follicular fluid samples spiked with various lead concentrations (5.0–40.0 mg/dL) were 93–101% and 103–107%, respectively. The recovery of spiked blood and follicular fluid samples with 0.5–10.0 mg/L cadmium was 100–105% and 97–105%, respectively.

Mercury Whole blood or follicular fluid sample of 1 mL was digested in Teflon vessels with 3 mL concentrated trace metal grade nitric acid for 2 h at room temperature and then at 85 1C for 14 h. After digestion, the samples were allowed to cool to room temperature. The clear supernatant was transferred to polypropylene tubes and diluted to 10 mL with demonized water. Analysis was performed using a Varian AA-880 Zeeman atomic absorption spectrophotometer, coupled to a Vapor Generation Accessory VGA-76 and sample preparation system (SPS 5) from Varian Techtron Pty. Ltd., Australia. The gas used was argon. The reductant channel of the VGA-76 contained 25% stannous chloride in 20% hydrochloric acid (Fisher Scientific Co.). The acid channel contained 5 M hydrochloric acid. In this study, the flow rates were about 8 mL/min for the sample and 1.5 mL/min for the stannous chloride solution and hydrochloric acid. The detection limit for mercury was 0.28 mg/L. Calibration standards for blood and follicular fluid samples were prepared each day using a manual standard addition procedure in the range 0.25–4.0 mg/L for mercury. Quadruplicate determinations were made on all samples. The recovery of spiked blood and

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follicular fluid samples with 5.0–20 mg/L was 93–107% and 97–107%, respectively. Cotinine analysis A modified method of Urakawa et al. (1994) was used in this study. Before extraction, 1 mL of serum or follicular fluid sample was spiked with 10 mL of 5 mg/mL cotinine-d3, vortexed for 15–20 s and equilibrated for another 30 min at room temperature. Methyl-tetra butyl ether (2 mL) was added and followed by 8 mL of dichloromethane, then vortexed for 30 s and shaken vertically for 5 min. The sample was loaded into Beckman GPR and centrifuged for 15 min at 2500 rpm (25 1C). The upper solvent layer was removed into another tube and evaporated by a gentle stream of nitrogen at 37 1C and the residues were reconstituted into 100 mL toluene, vortexed and transferred to 0.1 mL crimp seal vials and placed in the auto sampler for analysis. Analysis of cotinine in serum or follicular fluid samples was done using Hewlett Packard gas chromatograph Model 5890 series II with an automatic injector and a 7673 autosampler, coupled with a Hewlett Packard quadrupole mass spectrometer detector Model 5972 series MSD. A Hewlett Packard Vectra 466/33N and Hewlett Packard MS Chemstation software controlled this system. A cross-linked methyl silicone J & W model DB-5MS capillary column (30 m  0.25 mm i.d.  0.25 mm thickness) was used with a column head pressure of 15 psi in the splitless injection mode. Ultrahigh-purity (99.9999%) helium was used as a carrier gas. The GC temperature program was as follows: initial oven temperature was 80 1C held for 0.5 min, increased from 9 to 280 1C/min for 2 min. The total run time was 24.72 min. Injection port pressures were maintained with vacuum compensation and programmed as follows: initial pressure was 3 psi and held for 0.1 min, increased to 10 psi at a rate of 99 psi/min for 16 min and then decreased to 3 psi by 99 psi/min for 0.43 min. Injection port temperature was 280 1C and injection volume was 1 mL. The mass spectrometer was operated at 70 eV in the electron impact (EI) ionization mode. Data acquisition and analysis software was Hewlett Packard MS Chemstation Model G1034 Revision C.01.05. All samples were analyzed in selected ion monitoring (SIM) data acquisition modes. Deuterated continine (d-cotinine) was added to each serum and follicular fluid sample, working standards and spiked samples as an internal standard at a final concentration of 50 mg/L. The determination of cotinine in serum and follicular fluid samples was achieved by GC/MS using SIM. Ions used for quantification were 101 for d-cotinine and 98 for cotinine. The scanning mode was used to monitor 179 and 121 for d-cotinine and 118 for cotinine. The calibration curve was constructed using a quadratic description forcing the

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plot through zero with the following concentrations of cotinine: 5, 10, 20, 40, 80 and 160 mg/L. The mass spectrometer produced a linear calibration curve up to 160 mg/L. The analytical recovery for serum samples spiked with 5.0, 20.0 and 160.0 mg/L cotinine was 100.60712.97%, 98.0876.30% and 101.6477.23%, respectively, while for spiked follicular fluid with 5.0, 20.0 and 160.0 mg/L cotinine was 97.54710.62%, 96.2876.97% and 100.8877.96%, respectively.

Data management and statistical analyses An SIR computer database application was developed for the entry of these data for all subjects. However, the study design was based on the pregnancy outcome. We also included fertilization rate as a second outcome. Women with successful pregnancy or fertilization outcome constitute controls and the rest as cases. The fertilization rate was defined as the percentage of the fertilized eggs to the number of eggs. All lead, cadmium and mercury measurements in blood and follicular fluids were subjected to log transformation to obtain approximate normality of their distribution. Data are given as mean7SD or percentage values. In this study, we thought it is more appropriate to include zero values in the statistical analyses to avoid overestimation of exposure risk, especially there is no defined source of exposure. A number of demographic, socioeconomic, environmental and health-related variables were used as potential confounding variables (Table 1). Due to the limited number of smokers, we combined different smoking habits (cigarettes, sheesha and muaasal) for our IVF participants and husbands into one variable each. The region of living variable was created to reflect the location of the participants. Saudi Arabia is divided into five provinces: Central, Northern, Southern, Western and Eastern. Due to the limited sample size, we had to group our participants into two groups: Central province and other provinces. In order to identify predicator variables of pregnancy or fertilization outcome, standards w2-test and Student’s t-tests were used to compare cases and controls for dichotomous and continuous variables, respectively. The w2-test was replaced by Fisher’s Exact test (in 2  2 tables where the expected value of one of the four cells was o5). The association between pregnancy outcome or fertilization outcome as a binary outcome variable and the different independent variables was assessed by binary logistic regression models to examine the marginal or the joint effect of different exposures after controlling for confounding variables. In addition to potential confounders, lead, cadmium and mercury in blood and follicular fluids were included in turn as

continuous variables. Some variables were excluded from the model due to insufficient cases such as number of miscarriages, number of birth defects, women’s kind of job, husband’s kind of jobs, use of contraceptive and all health conditions. The crude odds ratios (OR) with 95% confidence intervals (CI) were used to measure the magnitude of association between the outcome and heavy metals levels and other confounding variables. Cox and Snell R-squared (R2) was used to assess binary logistic regression model fitness. The closer the values of R2 to 1, the better the fit of the model. Correlation coefficients were computed to establish relationships between continuous variables. The Pearson product correlation coefficient was used for variables that demonstrate linear relationships, while spearman rank correlation was used for non-linear, nonnormal variables. The correlations between lead, cadmium and mercury in blood and follicular fluids were evaluated by Pearson’s correlation test. Due to the high skewness of most reproductive parameters, Spearman rank correlation coefficients were calculated to examine its association with blood or follicular lead, cadmium and mercury levels. Separate multiple linear regression models with a backward elimination procedure were applied to identify predicators of lead, cadmium and mercury in blood and follicular fluids after controlling for environmental, socioeconomic and demographic factors. Selected variables used were traditional cosmetic, herbal remedies, source of drinking water, source of cooking water, use of pottery, use of canned food, regional location, proximity to traffic and industrial areas, family income, attended school, educational level, work status, age and BMI. These variables were entered into the model as independent variables whereas blood and follicular lead, cadmium and mercury as dependent variables. All linear regression models were evaluated for collinearity between the variables using a condition index of o30. Statistical parameters presented are b (standardized regression coefficient) and adjusted R2 (coefficient of multiple determination). Due to the insufficient number of cases, some variables such as use of saaot, use of traditional cosmetics, smoking behavior, and health problems were not included. Dummy variables can take only certain fixed values, with no intermediate values in between. They were used to quantify the qualitative variable for regression analysis. In this study, a number of dummy variables were created for the regression analysis, such as source of drinking water had four categories and was quantified by the use of four dummy variables; area of residence and family income had only three categories and was quantified by the use of two dummy variables. Each dummy variable can take only the values 1 or 0. When we use dummy coding, one of the groups becomes the reference group and all the other groups are compared to it.

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Table 1.

List of confounding variables

Continuous variables

Categorial variables

Demographic variables

Environmental

Demographic/socioeconomic/health-related variables

Women’s age (years)

Use of skin-lightening creams (1: Yes; 2: No) Use of Kohl (1: Yes; 2: No)

Use of oral contraceptives (1: Yes; 2: No)

Women’s weight (Kg) Women’s height (cm) BMI (Kg/m2) Husband’s age (years) Age started menstruation in years Days of menstrual cycle Number of live children Number of miscarriages Number of stillbirths

Number of birth defects Blood lead levels (mg/dL) Follicular lead levels (mg/dL) Blood cadmium levels (mg/L)

Follicular cadmium levels (mg/L) Blood mercury levels (mg/L) Follicular mercury levels (mg/L) Duration of living in the current province in years Duration of living in the former province in years Duration Duration Duration Duration

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of of of of

women smoking cigarette in months women smoking sheesha in months women smoking muassal in months using skin-lightening creams in months

Duration of using henna in months Duration of using hair dye in months Duration of Kohl use (years) Duration of Negd use in months Duration of Saoot in months Duration of using herbal treatment in months Number of dental amalgams Number of good embryo Number of fair embryo Number of bad embryo Total number of embryos Number of eggs Number of transferred embryo Number of implanted embryo Number of pregnant Number biochemical pregnancy Number of abortion

Use of Neqd (1: Yes; 2: No) Use of Saoot (1: Yes; 2: No) Use of traditional cosmetics (1: Yes; 2: No) Use of herbal treatment (1: Yes; 2: No) Use of hair dye (1: Yes; 2: No) Use of henna (1: Yes; 2: No) Have tattoo (1: Yes; 2: No) Current province of living (1: central province; 2: other provinces) Former province of living (1: central; 2: other provinces) Area of residence (1: busy; 2: residential; 3: various) Recent house paint (1: Yes; 2: No) Source of drinking water (1: tap water; 2: filtered; 3: bottled water; 4: combined) Use of pottery utensils (1: Yes; 2: No) Eating canned food (1: Yes; 2: No) Drinking coffee (1: Yes; 2: No) Drinking tea (1: Yes; 2: No) Drinking caffeine soft drinks (1: Yes; 2: No) Eating fish (1: Yes; 2: No)

Duration of contraceptives in years (1: o1; 2: X1–p5; 3: 45–p10; 4: 410) Women smoking cigarette (1: Yes; 2: No) Women smoking sheesha (1: Yes; 2: No) Women smoking muaasal (1: Yes; 2: No) Husband smoking cigarette (1: Yes; 2: No) Husband smoking sheesha (1: Yes; 2: No) Husband smoking muaasal (1: Yes; 2: No) Suffering from asthma (1: Yes; 2: No) Suffering from neurological problems (1: Yes; 2: No) Suffering from psychiatric problems (1: Yes; 2: No) Suffering from persistent headaches (1: Yes; 2: No) Suffering from convulsions or loss of consciousness (1: Yes; 2: No) Suffering from heart diseases (1: Yes; 2: No)

Suffering from liver diseases (1: Yes; 2: No) Suffering from iron deficiency (1: Yes; 2: No) Suffering from any blood disorders (1: Yes; 2: No) Suffering from thyroid problems (1: Yes; 2: No) Suffering from osteoporosis (1: Yes; 2: No) Suffering from rheumatism (1: Yes; 2: No) Suffering from other illnesses (1: Yes; 2: No) Had cancer (1: Yes; 2: No) Cause of infertility (1: male factor; 2: tubal problems; 3: anovulation; 4: unexplained; 5: combined) Women attended school (1: Yes; 2: No) Women’s levels of education (1: high school or less; 2: more than high school) Women’s working status (1: Yes; 2: No) Husband’s educational level education (1: high school or less; 2: more than high school) Monthly family income in Saudi Riyals (1: p5000; 2: 5001–7500; 3: 47500)

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All statistical analyses were performed using SPSS version 10.0 for Windows (SPSS Inc., Chicago, IL, USA). A P-value of less than 0.05 was considered significant.

Results Description of the study population During the period from January 2002 to October 2003, blood and follicular samples were collected from 640 Saudi IVF recipients. Of these, only 619 participants were included and the remaining were excluded because of missing information. For pregnancy outcome, there were 321 cases who did not achieve pregnancy and 203 controls who had successful pregnancy. While 63 women did not achieve fertilization (cases) and 556 controls produced fertilized eggs. The mean number of embryos transferred was 1.74 (median ¼ 2). The overall pregnancy rate in the study was 38.7%. Reproductive characteristics of the studied women are presented in Table 2. Univariate analyses The demographic and clinical characteristics of the IVF patients in our study as classified by pregnancy and fertilization outcomes were compared using Student’s t-test and w2-test as displayed in Tables 3 and 4, respectively. Cases and controls of the pregnancy outcome were significantly different with regard to follicular mercury levels, women’s age, husband’s age, number of miscarriages, duration of living in former province, former province of living and women attended school (Po0.1). On the other hand, the blood lead levels, follicular cadmium levels, husband’s age and number of birth defects were statistically different between cases and controls of the fertilization outcome (Po0.1). Table 2. Characteristics of the Saudi IVF participants (n ¼ 619) Variables

Mean7SD

Range

Number of eggs Number of fertilized eggs Percent of fertilization Total number of embryo Number of good embryo Number of fair embryo Number of poor embryo Number of transferred embryo Number of implanted embryo Number of biochemical pregnancy Number of abortion

9.5076.86 5.1574.25 52.34728.60 3.5773.06 1.5872.16 0.7171.03 1.3171.79 1.7470.96 0.1770.45 0.0170.12 0.0270.14

0–41 0–27 0–100 0–20 0–25 0–7 0–11 0–5 0–2 0–1 0–1

Multiple logistic regression analyses Binary logistic regression analysis predicating pregnancy or fertilization outcome was used to consider simultaneously the influence of lead, cadmium and mercury in blood and follicular fluid after controlling for other confounding variables. In the pregnancy and fertilization outcome model, we included the following variables: women’s age, husband’s age, BMI, age when menstruation started, days of menstrual cycle, duration of living in the current province, duration of living in the former province, current province of living, former province of living, women’s education, husband’s education, women’s working status, total family income, husband’s smoking status and drinking caffeine soft drinks. Tea and coffee drinking status were entered only in the pregnancy outcome model due to limited number of cases. Initially, we calculated the unadjusted OR for each metal and the outcome was calculated as shown in Table 5. With respect to pregnancy outcome, a positive association with mercury in follicular fluid was only noted but with borderline significance (unadjusted OR 1.17, 95% CI 0.98–1.40). A decrease in the fertilization outcome was observed in association with blood lead levels (unadjusted OR 0.57, 95% CI 0.34–0.93). On the other hand, there was unexpected increase in fertilization outcome with elevated blood cadmium levels (unadjusted OR 1.66, 95% CI 1.29–2.15). The results of adjusted logistic regression analyses (full and backward elimination models) of the pregnancy outcome are presented in Table 6. Only duration of living in former province the final, women’s working status and drinking coffee remained significant predicators of pregnancy outcome with P-value of 0.02 for all. The overall adjusted model was significant (w2 ¼ 21.24, P ¼ 0.002) with a Cox and Snell R2 value of 0.17. In the case of fertilization outcome, after controlling for several confounding variables, the relationship between blood lead levels and fertilization outcome remained related but with borderline significance (b ¼ 0.98, P ¼ 0.05) and OR ¼ 0.38 (95% CI 0.14–0.99). The same was seen with follicular fluid cadmium levels. The fertilization rate was 1.77-fold higher among women with elevated follicular cadmium levels (b ¼ 0.57, P ¼ 0.02). Though the variance in that adjusted model accounted for was small (Cox and Snell R2 ¼ 0.08), the overall model was significant (w2 ¼ 11.52, P ¼ 0.003), suggesting that the retained variables predict fertilization outcome. The results of the full and backward elimination models of the data are presented in Table 7. To evaluate the influence of follicular fluid cadmium on fertilization outcome, an additional logistic regression model with two blocks was applied. The first block contained all confounding variables without follicular fluid cadmium. The second block contained follicular fluid cadmium in order to adjust cadmium to all the confounding variables in block 1 that might be influential. Both

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Table 3.

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Main demographic and characteristic of the Saudi IVF recipients as classified by pregnancy and fertilization outcomes

Characteristics

Mean7SD, range and (n) Pregnancy outcome Cases (0)

Fertilization outcome Controls (1)

Cases (0)

Controls (1)

Blood lead levels (mg/dL)

3.3672.09 0.49–12.87 (n ¼ 256)

3.2571.94 0.51–12.81 (n ¼ 153) P ¼ 0.66

4.1173.68 0.80–23.78 (n ¼ 48)

3.2672.01 0.49–12.87 (n ¼ 434) P ¼ 0.03

Follicular lead levels (mg/dL)

0.6171.17 oDL–16.33 (n ¼ 319)

0.8372.73 oDL–24.73 (n ¼ 202) P ¼ 0.38

0.5570.59 oDL–2.96 (n ¼ 61)

0.7071.90 oDL–24.73 (n ¼ 554) P ¼ 0.40

Blood cadmium levels (mg/L)

0.6070.50 oDL–6.26 (n ¼ 253)

0.6470.71 oDL–5.76 (n ¼ 153) P ¼ 0.22

0.6270.23 oDL–1.27 (n ¼ 47)

0.6270.61 oDL–6.26 (n ¼ 431) P ¼ 0.29

Follicular cadmium levels (mg/L)

0.2970.30 oDL–2.90 (n ¼ 319)

0.3770.76 oDL–9.45 (n ¼ 202) P ¼ 0.34

0.2470.23 oDL–0.90 (n ¼ 61)

0.3470.55 oDL–9.45 (n ¼ 553) P ¼ 0.0

Blood mercury levels (mg/L)

3.6273.08 oDL–23.90 (n ¼ 254)

3.8274.18 oDL–30.35 (n ¼ 155) P ¼ 0.73

3.8073.59 oDL–17.45 (n ¼ 48)

3.6973.55 oDL–30.35 (n ¼ 434) P ¼ 0.78

Follicular mercury levels (mg/L)

2.1272.47 oDL–16.73 (n ¼ 316)

2.6573.54 oDL–38.30 (n ¼ 201) P ¼ 0.08

2.3872.92 oDL–16.73 (n ¼ 62)

2.4974.88 oDL–92.29 (n ¼ 550) P ¼ 0.79

Serum cotinine (mg/L)

3.08724.36 2.13717.03 oDL–250.33 oDL–171.00 (n ¼ 172) (n ¼ 105) P ¼ 0.18

8.17736.80 2.96724.61 oDL–192.94 oDL–286.45 (n ¼ 28) (n ¼ 292) P ¼ 0.58

Follicular fluid cotinine (mg/L)

2.68718.95 1.72712.18 oDL–242.00 oDL–154.44 (n ¼ 255) (n ¼ 167) P ¼ 0.43

4.40724.30 2.40717.82 oDL–165.39 oDL–242.00 (n ¼ 46) (n ¼ 451) P ¼ 0.70

Women’s age (years)

32.2175.20 19–50 (n ¼ 321)

30.5874.56 20–44 (n ¼ 203) P ¼ 0.0

32.4974.74 22–42 (n ¼ 63)

Women’s BMI (Kg/m2)

29.2475.23 17.38–61.04 (n ¼ 321)

28.6075.02 16.99–51.14 (n ¼ 203) P ¼ 0.16 37.7478.24 36.4676.69 24–78 24–68 (n ¼ 291) (n ¼ 190) P ¼ 0.07

28.5275.05 17.75–44.17 (n ¼ 63)

29.1175.11 16.99–61.04 (n ¼ 556) P ¼ 0.39 39.8778.31 37.3977.80 24–62 24–78 (n ¼ 55) (n ¼ 513) P ¼ 0.03

12.8371.47 9–21 (n ¼ 315)

12.8271.40 9–16 (n ¼ 62)

Husband’s age (years)

Age when menstruation started (years)

12.8371.37 9–18 (n ¼ 203) P ¼ 0.99

31.6875.16 19–50 (n ¼ 556) P ¼ 0.23

12.8671.44 9–21 (n ¼ 550) P ¼ 0.85

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Table 3. (continued ) Characteristics

Mean7SD, range and (n) Pregnancy outcome Cases (0)

Fertilization outcome Controls (1)

Cases (0)

Controls (1)

Days of menstrual cycle

5.7571.21 2–11 (n ¼ 319)

5.7671.44 3–15 (n ¼ 203) P ¼ 0.97

5.6171.27 1–9 (n ¼ 63)

5.7571.32 2–15 (n ¼ 554) P ¼ 0.41

Number of live children

1.0770.34 1–3 (n ¼ 43)

1.2370.68 1–4 (n ¼ 30) P ¼ 0.18

1.2970.83 1–4 (n ¼ 14)

1.1170.39 1–3 (n ¼ 79) P ¼ 0.22

Number of miscarriages

1.9071.69 1–11 (n ¼ 80)

1.4570.85 1–4 (n ¼ 53) P ¼ 0.08

2.2071.99 1–7 (n ¼ 10)

1.7071.36 1–11 (n ¼ 143) P ¼ 0.28

Number of birth defects

1.9870.14 1–2 (n ¼ 54)

1.9770.17 1–2 (n ¼ 33) P ¼ 0.73

1.8670.36 1–2 (n ¼ 14)

1.9970.1 1–2 (n ¼ 93) P ¼ 0.005

Duration of living in the current province (years)

11.4375.38 1–35 (n ¼ 321)

10.9675.80 1–38 (n ¼ 203) P ¼ 0.34

11.8776.93 1–30 (n ¼ 63)

11.2275.50 1–38 (n ¼ 556) P ¼ 0.40

Duration of living in the former province (years)

20.3875.18 2–40 (n ¼ 320)

18.7475.04 2–35 (n ¼ 202) P ¼ 0.0

20.4075.74 2–32 (n ¼ 63)

19.7775.28 2–40 (n ¼ 554) P ¼ 0.38

blocks of the second logistic regression model were built using a backward elimination procedure. Once again the model gave similar scenario for follicular fluid cadmium. We also tested the association between pregnancy outcome and coffee intake using the same approach. Coffee intake was put in the second block of the model, while the other confounding variables were entered in the first block of the model. It seems that the effect of coffee intake on pregnancy outcome after adjusting for other variables was not large (OR ¼ 0.2, 95% CI 0.03–1.33) and statistically not significant (P ¼ 0.1). Among the variables in block 1, only women’s working status remained significant (P ¼ 0.013). Heavy metal levels Eight women (1.7%) in the study had blood lead levels below the detection limit for lead, 69 (14.4%) had levels below the detection limit for cadmium and 57 (11.8%) had levels below the detection limit for mercury. On the other hand, follicular lead, cadmium and mercury levels below the detection limit for each of

these metals were found in 482 (70.1%), 289 (47.1%) and 126 (20.6%) women, respectively. As shown in Table 8, the observed mean values of lead, cadmium and mercury in blood were far below than current acceptable levels used by environmental or occupational regulatory agencies. For instance, only eight women (1.7%) had blood lead levels 410 mg/dL, the Centers for Disease Control and Prevention criterion for elevated blood levels in children and pregnant women (CDC, 2002). Similarly, only three participants had blood cadmium levels o5 mg/L, the OSHA Safety Standard for cadmium (OSHA, 2003), but if we use 1 mg/L as the threshold limit of clinical importance, there were 46 women (9.6%). Ninety women (18.7%) had blood mercury levels X5.8 mg/L (the EPA safety limit) which is assumed to be without appreciable harm (CDC, 2004). Looking at the prevalence of elevated heavy metals in follicular fluid, we used the same safety limits set for heavy metals in blood because there is none for follicular fluids. There were six women (0.98%) with lead levels X10 mg/dL and one woman with cadmium levels X5 mg/L, while 51 (8.3%) with mercury levels X5.8 mg/L.

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Table 4.

569

Univariate predicators of pregnancy and fertilization outcomes as evaluated by w2-test

Variable

Pregnancy outcome Cases

Current living in Central province Other provinces Former living in Central province Other provinces Women attended school Yes No Women’s level of education High school or less More than high school Women’s working status Yes No Women’s kind of job Office Non-office Teacher Husband: attended school Yes No Husband: level of education High school or less More than high school Husband’s kind of job Technical Non-office Office Retired Family income in SR p5000 5001–7500 47500 Pregnancy history Yes No

Fertilization outcome Controls

Cases

Controls

153 (48.0%) 86 (42.6%) 166 (52.0%) 116 (57.4%) 1.45 (P ¼ 0.23)

32 (51.6%) 30 (48.4%)

247 (44.7%) 306 (55.3%) 1.09 (P ¼ 0.30)

124 (40.8%) 64 (33.0%) 180 (59.2%) 130 (67.0%) 3.07 (P ¼ 0.08)

19 (33.9%) 37 (66.1%)

193 (36.4%) 337 (63.6%) 0.14 (P ¼ 0.71)

287 (89.4%) 192 (94.6%) 34 (10.6%) 11 (5.4%) 4.24 (P ¼ 0.04)

56 (88.9% 7 (11.1%)

510 (91.7%) 46 (8.3%) 0.58 (P ¼ 0.45)

194 (67.6%) 123 (64.1%) 93 (32.4%) 69 (35.9%) 0.64 (P ¼ 0.42)

35 (62.5% 21 (37.5%)

341 (66.9%) 169 (33.1%) 0.43 (P ¼ 0.51)

79 (24.6%) 51 (25.1%) 242 (75.4%) 152 (74.9%) 0.02 (P ¼ 0.90)

14 (22.2%) 49 (77.8%)

138 (24.8%) 418 (75.2%) 0.21 (P ¼ 0.65)

19 (24.7%) 12 (24.0%) 4 (5.2%) 6 (12.0%) 54 (70.1%) 32 (64.0%) 1.96 (P ¼ 0.38)

3 (21.4%) 1 (7.1%) 10 (71.4%)

35 (26.1%) 9 (6.7%) 90 (67.2%)

295 (91.9%) 193 (95.1%) 26 (8.1%) 10 (4.9%) 1.96 (P ¼ 0.16)

56 (88.9%) 7 (11.1%)

519 (93.3%) 37 (6.7%) 1.70 (P ¼ 0.19)

199 (67.5%) 133 (68.9%) 96 (32.5%) 60 (31.1%) 0.11 (P ¼ 0.74)

40 (71.4%) 16 (28.6%)

356 (68.6%) 163 (31.4%) 0.19 (P ¼ 0.66)

10 102 195 14

1 20 38 4

18 192 323 21 1.60 (P ¼ 0.66)

(3.1%) 5 (31.8%) 79 (60.7%) 109 (4.4%) 8 3.13 (P ¼ 0.37)

(2.5%) (39.3%) (54.2%) (4.0%)

0.15 (P ¼ 0.93)

(1.6%) (31.7%) (60.3%) (6.3%)

(3.2%) (34.7%) (58.3%) (3.8%)

117 (42.4) 78 (42.6%) 60 (21.7%) 39 (21.3%) 99 (35.9%) 86 (36.1%) 0.01 (P ¼ 0.99)

26 (45.6%) 14 (24.6%) 17 (29.8%)

206 (42.6%) 106 (21.9%) 172 (35.5%) 0.75 (P ¼ 0.69)

118 (36.8%) 71 (35.0%) 203 (63.2%) 132 (65.0%) 0.17 (P ¼ 0.68)

20 (31.7%) 43 (68.3%)

353 (63.5%) 203 (36.5%) 0.56 (P ¼ 0.46)

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Table 4. (continued ) Variable

Pregnancy outcome Cases

Use of contraceptive Yes No Health problems Yes No Cause of infertility Male factor Tubal factor Unexplained factor Combined Women’s smoking status Yes No Husband’s smoking status Yes No Use of herbal treatment Yes No Coffee drinking status Yes No Tea drinking status Yes No Caffeine soft drinks status Yes No a

Fertilization outcome Controls

Cases

Controls

12 (3.7%) 4 (2.0%) 309 (96.3%) 199 (98.0%) 1.31 (P ¼ 0.31)a

4 (6.3%) 18 (3.2%) 59 (93.7%) 538 (96.8%) 1.60 (P ¼ 0.27)a

35 (10.9%) 19 (9.4%) 286 (89.1%) 184 (90.6%) 0.32 (P ¼ 0.57)

7 (11.1%) 56 (88.9%)

59 (10.6%) 497 (89.4%) 0.02 (P ¼ 0.90)

212 50 30 28

48 7 4 4

357 79 65 52 3.63 (P ¼ 0.31)

(66.3%) 129 (15.6%) 27 (9.4%) 26 (8.8%) 20 2.13 (P ¼ 0.55)

(63.9%) (13.4%) (12.9%) (9.9%)

(76.2%) (11.1%) (6.3%) (6.3%)

(64.6%) (14.3%) (11.8%) (9.4%)

5 (1.6%) 1 (0.5%) 316 (98.4%) 202 (99.5%) 1.25 (P ¼ 0.41)a

0 63 (100%)

7 (1.3%) 549 (98.7%) 0.80 (P ¼ 1.00)a

83 (25.9%) 59 (29.1%) 238 (74.1%) 144 (70.9%) 0.65 (P ¼ 0.42)

18 (28.6%) 45 (71.4%)

149 (26.8%) 407 (73.2%) 0.09 (P ¼ 0.76)

88 (27.4%) 52 (25.6%) 233 (72.6%) 151 (74.4%) 0.21 (P ¼ 0.65)

13 (20.6%) 50 (79.4%)

150 (27.0%) 406 (73.0%) 1.17 (P ¼ 0.28)

290 (90.3%) 185 (91.1%) 31 (9.7%) 18 (8.9%) 0.09 (P ¼ 0.76)

61 (96.8%) 506 (91%) 2 (3.2%) 50 (9%) 0.15 (P ¼ 0.08)a

298 (92.8%) 188 (92.6%) 23 (7.2%) 15 (7.4%) 0.009 (P ¼ 0.92)

61 (96.8%) 2 (3.2%)

516 (92.8%) 40 (7.2%) 0.30 (P ¼ 0.18)

147 (45.8%) 96 (47.3%) 174 (54.2%) 107 (52.7%) 0.11 (P ¼ 0.74)

30 (47.6%) 33 (52.4%)

250 (45%) 306 (55%) 0.16 (P ¼ 0.69)

Fisher’s exact test.

Correlation analysis Pearson correlation tests carried out on all the subjects showed statistically significant negative associations between cadmium in blood and follicular fluid (r ¼ 0.19, P ¼ 0), and positive relationship between mercury in blood and follicular fluid (r ¼ 0.25, P ¼ 0). Lead in blood and follicular fluid showed no correlation (r ¼ 0.07, P ¼ 0.15). Blood lead levels were correlated positively with blood cadmium (r ¼ 0.28, P ¼ 0)

and blood mercury (r ¼ 0.12, P ¼ 0.01). On the other hand, the relationships between follicular lead levels and follicular cadmium or mercury levels were nonsignificant (r ¼ 0.06, P ¼ 0.21 and r ¼ 0.08, P ¼ 0.1, respectively). A negative relationship was observed between follicular cadmium and mercury levels (r ¼ 0.22, P ¼ 0). To address the potential effect of lead, cadmium and mercury in blood or follicular fluid on a number of reproductive parameters

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571

Table 5. Results of unadjusted logistic regression analyses of pregnancy and fertilization outcome on lead, cadmium and mercury levels in blood and follicular fluid samples Metals

Pregnancy outcome b (P)

Blood lead levels (mg/dL) Follicular fluid lead levels (mg/dL) Blood cadmium levels (mg/L) Follicular fluid cadmium levels (mg/L) Blood mercury levels (mg/L) Follicular fluid mercury levels (mg/L)

0.07 0.07 0.20 0.10 0.04 0.16

(0.66) (0.38) (0.22) (0.34) (0.73) (0.08)

Fertilization outcome OR (95% CI)

b (P)

0.93 0.93 1.22 1.11 0.96 1.17

0.57 0.12 0.25 0.51 0.04 0.04

(0.68–1.28) (0.79–1.09) (0.89–1.67) (0.9–1.36) (0.77–1.20) (0.98–1.40)

OR (95% CI) (0.03) (0.39) (0.29) (0) (0.78) (0.79)

0.57 0.89 0.78 1.66 1.05 0.97

(0.34–0.93) (0.67–1.17) (0.48–1.24) (1.29–2.15) (0.77–1.43) (0.75–1.25)

Table 6. Relation of pregnancy outcome with the levels of lead, cadmium and mercury in blood and follicular fluids after controlling for confounding variables Predicator variables (a) Full regression model Women’s age (years) Husband’s age (years) Women’s BMI (Kg/m2) Location of the current province (central province vs. other provinces) Duration of living in the current province (years) Location of former province (central province vs. other provinces) Duration of living in the former province (years) Age when menstruation started (years) Days of menstrual cycle Women’s level of education (high school or less vs. more than high school) Women’s work status (Yes vs. No) Husband’s level of education (high school or less vs. more than high school) Family income in SRa 5001–7500 47500 Husband’s smoking status (Yes vs. No) Drinking coffee (Yes vs. No) Drinking tea (Yes vs. No) Drinking caffeine soft drinks (Yes vs. No) Blood lead levels (mg/dL) Follicular lead levels (mg/dL) Blood cadmium levels (mg/L) Follicular cadmium levels (mg/L) Blood mercury levels (mg/L) Follicular mercury levels (mg/L) Follicular cotinine levels (mg/L) (b) Backward elimination procedure Husband’s age Location of former province (central province vs. other provinces) Duration of living in the former province (years) Women’s working status (Yes vs. No) Husband’s smoking status (Yes vs. No) Drinking coffee (Yes vs. No)

b

P

OR (95% CI)

0.03 0.07 0.05 0.65 0.006 0.88 0.14 0.36 0.04 0.96 2.47 0.81

0.64 0.10 0.29 0.29 0.94 0.16 0.05 0.06 0.82 0.23 0.006 0.20

1.03 0.93 0.95 1.91 0.99 0.41 0.87 0.70 0.96 2.62 11.86 0.44

(0.90–1.19) (0.85–1.02) (0.86–1.05) (0.58–6.29) (0.86–1.15) (0.12–1.41) (0.75–1.00) (0.48–1.02) (0.68–1.36) (0.55–12.40) (2.00–70.26) (0.13–1.56)

0.70 0.45 1.25 2.22 0.05 0.39 0.60 0.31 0.54 0.13 0.06 0.38 0.03

0.38 0.54 0.03 0.06 0.99 0.44 0.18 0.13 0.21 0.59 0.85 0.27 0.78

2.02 1.57 3.50 0.11 0.99 0.68 0.55 1.36 1.72 0.88 0.94 1.46 0.98

(0.42–9.78) (0.37–6.62) (1.11–11.01) (0.01–1.07) (0.15–6.44) (0.25–1.81) (0.23–1.31) (0.91–2.02) (0.74–4.04) (0.54–1.42) (0.53–1.70) (0.75–2.86) (0.82–1.17)

0.05 0.91 0.13 1.35 0.81 2.02

0.10 0.05 0.02 0.02 0.08 0.02

0.95 0.40 0.88 3.85 2.52 0.13

(0.90–1.01) (0.16–1.00) (0.79–0.98) (1.29–11.50) (0.92–5.52) (0.03–0.69)

Binary logistic regression analysis of pregnancy outcome on heavy metals (lead, cadmium and mercury) in blood and follicular fluids after controlling for various confounding variables. Cox and Snell R2 ¼ 0.17. a Reference category (p5000); Cox and Snell R2 ¼ 0.27.

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Table 7. Binary logistic regression analysis of fertilization outcome on heavy metals (lead, cadmium and mercury) in blood and follicular fluids after controlling for various confounding variables Predicator variables

b

P

OR (95% CI)

(a) Full model Women’s age (years) Husband’s age (years) Women’s BMI (Kg/m2) Location of current province (central province vs. other provinces) Duration of living in the current province (years) Location of the former province (central province vs. other provinces) Duration of living in the former province (years) Age when started menstruating in years Days of menstrual cycle Women’s level of education (high school or less vs. more than high school) Husband’s level of education (high school or less vs. more than high school) Women’s work status (Yes vs. No) Family income in SRa 5001–7500 47500 Husband’s smoking status (Yes vs. No) Blood lead levels (mg/dL) Follicular lead levels (mg/dL) Blood cadmium levels (mg/L) Follicular cadmium levels (mg/L) Blood mercury levels (mg/L) Follicular mercury levels (mg/L) Follicular cotinine levels (mg/L)

0.02 0.10 0.16 0.96 0.11 0.26 0.02 0.46 0.36 0.02 1.22 0.47

0.99 0.16 0.09 0.30 0.39 0.80 0.89 0.09 0.20 0.99 0.31 0.61

0.99 0.90 1.17 0.38 1.11 1.30 0.99 1.58 1.43 1.02 0.30 0.63

(0.78–1.25) (0.78–1.04) (0.98–1.40) (0.06–2.31) (0.87–1.42) (0.17–9.81) (0.79–1.22) (0.93–2.69) (0.83–2.45) (0.10–10.36) (0.03–3.03) (0.10–3.74)

0.93 0.11 0.42 1.22 0.37 0.81 0.63 0.36 0.59 0.005

0.46 0.93 0.65 0.06 0.33 0.38 0.09 0.59 0.17 0.99

0.40 0.89 1.52 0.30 1.45 0.45 1.87 0.70 1.81 1.00

(0.04–4.53) (0.08–10.13) (0.25–9.08) (0.08–1.03) (0.69–3.02) (0.07–2.70) (0.91–3.82) (0.19–2.57) (0.78–4.21) (0.57–1.75)

(b) Backward elimination procedure Blood lead levels (mg/dL) Follicular cadmium levels (mg/L)

0.7 0.57

0.05 0.02

0.38 (0.14–0.990) 1.77 (1.11–2.83)

Cox and Snell R2 ¼ 0.08. a Reference category (p5000). Cox and Snell R2 ¼ 0.18.

Table 8.

Descriptive statistics of lead, cadmium and mercury in blood and follicular fluids of women undergoing IVF procedure

Analytes

N

Mean7SD

Median

Min–max

Current acceptable limit

Blood lead levels (mg/dL) Follicular lead levels (mg/dL) Blood cadmium levels (mg/L) Follicular cadmium levels (mg/L) Blood mercury levels (mg/L) Follicular mercury levels (mg/L)

482 615 478 614 482 612

3.3472.24 0.6871.82 0.6270.58 0.3370.52 3.7073.55 2.4874.72

3.01 0.37 0.54 0.27 3.19 1.58

0.49–23.78 oDL–24.74 oDL–6.26 oDL–9.45 oDL–30.35 oDL–92.29

10 mg/dL

listed in Table 1, Spearman rank correlation analysis was examined. Significant but weak correlation coefficients were found between follicular lead levels and the number of biochemical pregnancy (r ¼ 0.10, P ¼ 0.02), blood cadmium levels and the number of poor embryo (r ¼ 0.09, P ¼ 0.04), follicular cadmium levels with the number of implanted embryo (r ¼ 0.10, P ¼ 0.01) and finally between blood mercury levels and the number of eggs (r ¼ 0.09, P ¼ 0.03).

5 mg/L 5.8 mg/L

Predicators of exposure to heavy metals Separate multiple linear regression models using the backward elimination procedure were constructed in order to identify potential predicators of the levels of lead, cadmium and mercury in blood and follicular fluid separately after controlling for a number of confounding demographic, socioeconomic and environmental variables. These variables were women’s age, women’s BMI, duration of living in the current province (years), duration of living in the former province (years), current

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province of living, former province of living, location of residence, women attending school, levels of women’s education, women’s working status, total family income, use of skin-lightening creams, use of henna, use of herbal treatment, having tattoo, use of Kohl, use of Negd, use of hair dye, eating seafood, using pottery utensils, recent house’s paint, using canned food, source of drinking water, drinking coffee, drinking tea, drinking caffeine soft drinks, number of dental amalgams and parity. Eating fish was found to be the only positive predictor of blood lead levels (b ¼ 0.30, P ¼ 0.04). The adjusted square correlation coefficient (R2) indicates that only 7.2% of the observed variability in blood lead levels is accounted for by the model, even after taking into account the number of predictor variables in the model. The model was significant with an F-value of 4.71 (P ¼ 0.04). On the other hand, the former province of living, number of live children, husband’s smoking, having tattoos, BMI, drinking filtered water and drinking combined types of water were significantly and negatively related to the levels of lead in follicular fluids with b-values of 0.34 (P ¼ 0.03), 0.40 (P ¼ 0.02), 0.45 (P ¼ 0.007), 0.32 (P ¼ 0.03), 0.31 (P ¼ 0.04), 0.40 (P ¼ 0.05) and 0.66 (P ¼ 0.004), respectively. The duration of living in the former province showed a positive relationship with follicular lead levels (b ¼ 0.39, P ¼ 0.04) The model accounted for only 21.5% of the observed variation (adjusted R2) in the follicular lead levels and it was significant (F ¼ 2.40, P ¼ 0.03). Consumption of fish was positively associated with blood cadmium levels (b ¼ 0.27, P ¼ 0.04), while drinking caffeine soft drinks was negatively related (b ¼ 0.46, P ¼ 0.001). The model variation (adjusted R2) was 27.1% with a significant overall regression of F ¼ 6.45 (P ¼ 0.001). When a multiple linear regression model for the change in follicular cadmium levels was fitted with the backward elimination approach to remove non-significant variables from the model, the final model included women’s age (b ¼ 0.30, P ¼ 0.006), use of Kohl (b ¼ 0.27, P ¼ 0.04), number of dental amalgams (b ¼ 0.33, P ¼ 0.02) drinking tap water (b ¼ 0.39, P ¼ 0.006), and drinking tea (b ¼ 0.42, P ¼ 0.003). The overall regression F-value was 4.43 (P ¼ 0.002) with an adjusted R2 of 31.4%. Using a backward elimination approach to the multiple regression model of blood mercury, women’s working status (b ¼ 0.37, P ¼ 0.01), use of skinlightening creams (b ¼ 0.46, P ¼ 0.002) and BMI (b ¼ 0.46, P ¼ 0.001) were the only predicators of blood mercury levels. The model explains 33.8% (adjusted R2) of the variability in the blood mercury levels with an overall regression of F ¼ 6.11 (P ¼ 0.001). In the final regression model, follicular mercury levels were positively associated with the number of dental amalgams (b ¼ 0.27, P ¼ 0.03), recent painting of the house

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(b ¼ 0.38, P ¼ 0.005) and drinking coffee (b ¼ 0.53, P ¼ 0.0), while the relationship was negative with women’s working status (b ¼ 0.28, P ¼ 0.04). Variation in the follicular mercury model explained by all the confounding variables was 33.1% (adjusted R2) with an overall regression value of 4.87 (P ¼ 0.001).

Discussion Our findings revealed an inverse relationship between blood lead levels and fertilization outcome after controlling for potential cofounders. The mean blood lead level in women with no fertilization was 4.11 mg/dL. All our participants had blood lead levels less than the US occupational blood lead exposure limits (40 mg/dL), while 1.7% exceeded the current Centers for Disease Control and Prevention level of concern for preventing lead poisoning in children (10 mg/dL). Our study confirmed previous findings that lead may be harmful even at low blood concentrations. Bellinger (2005) has linked maternal blood lead levels of 10 mg/dL to increased risks of pregnancy hypertension, spontaneous abortion and reduced offspring neurobehavioral development. On the other hand, he related paternal lead exposure to 440 or 425 mg/dL for a period of years to infertility and to increased risks of spontaneous abortion and reduced fetal growth (preterm delivery, low birth weight). Kosnett et al. (2007) advise pregnant women to avoid occupational or avocational lead exposure that would result in blood lead concentrations 45 mg/dL. Though, none of our women was occupationally exposed to lead, 14.9% of them had blood lead levels in the range 5.03–23.78 mg/dL. Unlike lead in blood levels, women who failed to fertilize had lower follicular lead levels (0.5570.59 mg/dL) than those who succeed (0.771.90 mg/dL), but the difference was not significant (P ¼ 0.4). Such levels are much lower than those measured in blood samples in order to exert an effect on fertilization outcome. A recent study by Silberstein et al. (2006) showed that lead could concentrate in ovarian tissue and adversely affect IVF outcome. Surprisingly, the follicular cadmium levels were positively related to fertilization outcome. The follicular cadmium levels in women who achieved fertilization was higher (0.34 mg/L in the range of oDL–9.45 mg/L) than those who did not (0.24 mg/L, range oDL–0.90 mg/L). It is known that smoking is one of the major sources of cadmium exposure (Ja¨rup et al., 1998). Only seven women who achieved fertilization was smokers while none who failed fertilization was smokers. According to Zenzes et al. (1995), cadmium accumulates in oocytes of smokers in a dose-dependent manner, affecting the quality of oocytes. Though our observations are hard to interpret, it might support the findings of Leoni et al.

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(2002) who found that in-vitro exposure to cadmium at low concentrations decreases sperm viability, oocyte maturation, and fertilization, and gives rise to sperm acrosome reaction, activation, and abnormal fertilization of matured oocytes. Henson and Chedrese (2004) suggested that cadmium might have paradoxical effect on steroidogenesis. It exerts significant effects on ovarian and reproductive tract morphology, with extremely low dosages reported to stimulate ovarian luteal progesterone biosynthesis and high dosages inhibiting it. In this study, male factor was the highest contributor to infertility among our couples. The strong deleterious effect of cadmium on male infertility was on spermatogenesis due to the systemic and cellular toxicity (Pant et al., 2003; Akinloye et al., 2006). It is more likely due to the negative effect of cadmium in tobacco smoke (Benoff et al., 2000). High prevalence of smoking (89.2%) was found among husbands of women who achieved fertilization. Hughes et al. (1992) found higher fertilization rate in heavy smokers compared to nonsmokers. Further studies are needed to evaluate the effect of cadmium from smoking on male infertility. The levels of heavy metals in blood or/and follicular fluid did not adversely affect pregnancy outcome. The lower levels of metals in our study population may explain this apparent lack of association. In this study, the only predicators of pregnancy outcome were the duration of living in the former province, women’s working status and drinking coffee. Pregnancy outcome was 0.88 less in women who lived longer in the former province and 0.13 less in those who drink coffee. Pregnancy outcome was 3.85 higher in working women. In this study, there were only 25% working women who achieved pregnancy. None of them were occupationally exposed and they were teachers (64%), non-office workers (12%) and office workers (24%). The current study also shows that pregnancy outcome was 0.13 less in women who drink coffee. To examine whether our result is stable, we applied the logistic regression model with two blocks as explained in the Results section. The effect disappeared. In this study, the observed mean values of lead, cadmium and mercury in blood were far below than current acceptable levels used by environmental or occupational regulatory agencies. For instance, only eight women (1.7%) had blood lead levels 410 mg/dL, the Centers for Disease Control and Prevention criterion for elevated blood levels in children and pregnant women (CDC, 2002). Similarly, only three participants had blood cadmium levels o5 mg/L, the OSHA Safety Standard for cadmium (OSHA, 2003), but if we use 1 mg/L as the threshold limit of clinical importance, there were 46 women (9.6%). Ninety women (18.7%) had blood mercury levels X5.8 mg/L (the EPA safety limit), which is assumed to be without appreciable harm (CDC, 2004). In this study, we also detected lead, cadmium and

mercury in the follicular fluid of 86.5%, 85.2% and 84.6%, respectively, of all IVF women. Looking at the prevalence of elevated heavy metals in follicular fluid, we used the same safety limits set for heavy metals in blood because there is none for follicular fluids. There were six women (0.98%) with lead levels X10 mg/dL and one woman with cadmium levels X5 mg/L, while 51 (8.3%) with mercury levels X5.8 mg/L. Results are presented in Table 8. Chronic exposure to heavy metals might lead to their accumulation in various organs and body tissues. The follicle basement membrane is very permeable to low- and high-molecular substances that can diffuse into follicular fluid in a matter of minutes (Edwards, 1974). There are very few published studies on the amount of lead in follicular fluid. The concentrations of lead in the follicular fluid of women who failed to generate fertilized eggs (0.55 mg/dL) were much lower than those reported by Paksy et al. (2001) of 1.13 mg/dL. Though the follicular fluid lead levels in our participants were low, the contamination of the fluid by these chemicals indicates that the reproductive organs and the fetuses have been exposed to these substances, and that the germ cells and early embryos may be affected. In an experimental study by Junaid et al. (1997), it was found that lead affects the follicular development and maturation, if mice are exposed to sufficiently high concentrations of metal through the oral route. Taupeau et al. (2001) examined the ovaries of mouse exposed chronically to lead and found that low lead concentration in the ovary caused dysfunction of folliculogenesis, with fewer primordial follicles and an increase in atretic antral follicles. A recent experimental study by Avazeri et al. (2006) revealed that lead salt at low concentrations (p10 pM) can affect in vitro the control of meiosis in mouse oocytes. Authors hypothesized that the observed deleterious effects on oogenesis in vivo have been induced via the same calcium-dependent protein kinase C (PKC) pathway disturbance. In contrast to Fiala et al.’s (2001) study, we found an inverse correlation between cadmium in blood and follicular fluid. As noted in Table 8, the levels of cadmium were lower in follicular fluid than those in blood samples. Previous work has shown that metallothionein gene expression and metallothionein protein concentrations are increased in specific tissues of female mice and rats during pregnancy and lactation as a result of normal physiological changes that occur during those periods (Shimada et al., 1997; Solaiman et al., 2001). Espey et al. (2003) have also found a substantial increase of metallothionein-1 (MT1) mRNA expression in the ovaries of immature Wistar rats, which might have an important role in protecting the ovarian tissues from oxidative stress generated by ovarian inflammatory events during the ovulatory process and luteinization. The presence of metallothionein in the ovary might help to reduce the transfer of cadmium to follicular fluid. On the other hand, a

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positive relationship between blood mercury levels and follicular fluid mercury levels was seen in this study. This suggests that once mercury is introduced to the body through food or vapor, it is rapidly absorbed and accumulates in several tissues. There are a number of studies on the reproductive effects of mercury in humans and animals following exposure, for example, spontaneous abortion, stillbirths, congenital malformations and irregular, painful or hemorrhagic menstrual disorders (Barlow and Sullivan, 1982; Schuurs, 1999; Silkorski et al., 1987). There was a non-significant correlation between blood and follicular fluid lead, suggesting that follicular fluid level is little influenced by its blood level. The positive correlation between heavy metals in blood may reflect the fact that these pollutants are produced by the same sources. In contrast, no association was found among heavy metals in follicular fluid. It seems that cadmium, mercury and lead are metabolized differently from that naturally absorbed and vary somewhat with respect to primary sites of deposition (Quig, 1998). Although significant correlations between heavy metals in blood or follicular fluid and a number of reproductive parameters have been shown to exist, its relevance remains unclear and one can expect that these findings are random rather than real. In this study, we attempted to relate the variation of heavy metals in the blood and follicular fluid levels of IVF women to a number of demographic, socioeconomic and environmental factors. It should be noted here that the final multiple regression models did not explain most of the variation in blood or follicular lead, cadmium or mercury. This suggests that there were other important sources of exposure for these women that were not included in the study. Furthermore, a number of variables were important predicators in the final models of lead, cadmium and mercury in either blood or follicular fluids, which we cannot adequately explain. It is possible that examination of a large number of variables could have created spurious chance. Therefore, in the subsequent section, we shall discuss only significant findings. Fish consumption was a predicator of lead and cadmium levels in blood samples. Fish and mussels are known to contain heavy metals (Marcotrigiano and Storelli, 2003). In our study, many of the participants reported regular consumption of fish (88.4%). Results from previous study (Al-Saleh and Shinwari, 2002) revealed that the concentration of lead and cadmium in four different species collected from the Gulf coast of Saudi Arabia was below the maximum allowed limit by the Saudi and international legislations for fish human consumption permissible limit of 2 and 0.5 mg/g, respectively. The potential health effects of lead and cadmium exposure due to fish consumption should be evaluated.

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Interestingly, the blood mercury levels are linked to the use of skin-lightening creams. Of the women in our study, 14% were using skin-lightening creams and their mean values of blood mercury were 4.447 mg/L. Though our mean value was lower than the value 5.8 mg/L that is considered to be safe (Rice et al., 2003), 16 women (23.9%) had blood mercury levels in the range 6.25–23.90 mg/L. The duration of applying skin-lightening creams was 21.13732.19 months in the range of 1–108 months. No significant correlations were found between the levels of mercury in blood or follicular fluid and the duration of applying skin-lightening creams (P40.05). Skin-lightening creams are widely available in Saudi markets. Results from previous study (Al-Saleh and Al-Doush, 1997) revealed that about 45% of the tested skin-lightening cream samples that are commonly used in Saudi Arabia had mercury in the range of 1.18–5260 PPM, well above FDA’s permissible limit of 1 PPM (FDA, 1992). There were 17.8% of the women who did not use skin-lightening creams and had blood mercury levels in the range of 5.83–30.35 mg/L. This reveals other possible sources of mercury contamination (Zahir et al., 2005). In this study, no mercury speciation was done; therefore, the contribution of organic or inorganic mercury to the total mercury in blood is unknown. We also found that blood mercury levels were less in housewives than in working women. In contrast to the Ortiz-Roque and Lo´pez-Rivera (2004) study, our study found positive association between blood mercury values and BMI. Our findings emphasize that preventive measure should be taken to restrict exposure to mercury due to its serious health effect on the physiology of the reproductive system or have consequences on fetal development (Bhan and Sarkar, 2005). The final results of the multiple regression analyses, after adjusting for a number of confounding variables, revealed that a number of variables were important predicators of lead, cadmium and mercury in follicular fluids, which we cannot adequately explain. It is possible that examination of a large number of variables could have created spurious chance. Therefore, we shall discuss only significant findings. As shown in the final follicular lead model, the longer the women lived in the former province, the higher the follicular lead levels. Follicular lead levels decreased significantly with increasing parity. Previous studies revealed that parity is an additional factor that may influence circulating lead levels due to its possible influence on calcium metabolism and bone turnover in pregnancy and lactation (Prentice, 1994; Pires et al., 2001). Therefore, it is possible that lead stored in the bone had been partially depleted by prior pregnancies and lactation. In the case of the follicular fluid cadmium model, more substantial amounts of variability in the levels of cadmium in follicular fluid could be explained by age, use of Kohl, number of dental amalgam, drinking tap

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water and drinking tea. Cadmium increases with age until a maximum level is reached at about age. Its accumulation occurs in various tissues and organs, with the most extensive accumulation occuring in the kidney cortex (IPCS, 1992; Ja¨rup et al., 1998; WHO, 1989). In fact, our study shows that the follicles allow cadmium to accumulate within time. Women who were applying Kohl had higher follicular fluid cadmium levels than those who did not. Kohl is a traditional eye cosmetic of Middle Eastern, Asian and North African societies. Though lead seems to be the main hazardous metal in Kohl (Al-Saleh, 1998), other elements such as aluminum, calcium, magnesium, potassium, silica, iron, zinc, antimony and nickel as sulfides or oxides were also found (Al-Ashban et al., 2004). The positive association between drinking tap water and cadmium in follicular fluid suggests that water might be consider as source of cadmium exposure. No information is available to support this observation. It was also found that drinking tea was linked positively to follicular fluid cadmium levels. The use of phosphatic fertilizer, sewage and cadmium containing bactericide can also cause significant cadmium accumulation in the soil (Piscator, 1985) and subsequently contaminating crops. In the present study, the number of dental amalgam was one of the determinants of follicular fluid mercury levels, while it had no influence on blood mercury levels. This might be due to the fact that mercury levels in blood reflect inorganic and organic mercury compounds, while follicular fluid mercury levels mainly indicate the inorganic portion of mercury. The total number of amalgam fillings in the studied population was 3.3272.27 in the range of 1–16. Our study measured total mercury, which reflects organic, inorganic and metallic mercury. Dental amalgam filling comprises about 50% metallic mercury, with the remainder principally silver, plus small amounts of copper, tin or zinc (Fredin, 1994). All forms of mercury cause toxic effects in a number of tissues and organs, depending on the chemical form of mercury, the level of exposure, the duration of exposure and the route of exposure (Zalups, 2000). However, differences in the mechanisms involved in the transport and metabolism of inorganic and organic forms of mercury in the various compartments of the body are likely responsible for the disparity in their distribution in tissues and organs, pattern of biological effect, and toxicity (Zalups and Lash, 1994). Our data cannot allow us to distinguish differential effects, if any, of organic and inorganic mercury. A number of studies have shown that the concentration of mercury in urine or blood of women was mainly dependent on the presence of amalgam fillings (Vahter et al., 2000; Bjo¨rnberg et al., 2005). Despite the very widespread use of dental amalgam fillings, epidemiological data to establish their safety are inadequate (Bates, 2006).

Recent house painting was positively associated with mercury levels in follicular fluid. Phenylmercury has been used in the past in paints, and dialkyl mercurials are still used in some industrial processes (Risher et al., 2002). We also noted a strong association between follicular fluid mercury concentrations and coffee intake. It is difficult to evaluate how these findings conformed to the literature, since there are currently no comparison studies. Similar to blood mercury values, working women were found to have higher mercury levels in their follicular fluid than housewives. In conclusion, this study shows that lead reduces fertilization outcome at levels less than the US occupational blood lead exposure limits (40 mg/dL) and even less than the current Centers for Disease Control and Prevention level of concern for preventing lead poisoning in children (10 mg/dL). We found that among various demographic, socioeconomic and environmental factors, fish consumption was positively associated with blood lead levels. These results support the hypothesis that a raised blood lead level affects infertility and intervention to reduce the lead exposure might be needed for women of the reproductive age. The present results also revealed an unexpected finding – the positive relationship between follicular cadmium levels and fertilization outcome, which points to the necessity for further investigation. Though adverse effect of mercury on pregnancy outcome or fertilization rate was not evident in this study, mercury X5.8 mg/L (EPA safety limit) was found in the blood and follicular fluid of 18.7% and 8.3% of the women, respectively. Concerns about its possible adverse effects on the physiology of reproduction or fetal development cannot be ruled out. It should be noted that skin-lightening creams and dental amalgam were important contributors to mercury exposure. Such finding is alarming and priority for further studies are, urgently, needed.

Acknowledgments The authors are thankful to the staff of the IVF clinic and laboratory and participants for their cooperation during the study. This study (RAC#2010 006) was supported by King Faisal Specialist Hospital and Research Centre. Approval was obtained from the relevant research ethics and clinical committees.

References Akinloye, O., Arowojolu, A.O., Shittu, O.B., Anetor, J.I., 2006. Cadmium toxicity: a possible cause of male infertility in Nigeria. Reprod. Biol. 6, 17–30.

ARTICLE IN PRESS I. Al-Saleh et al. / Int. J. Hyg. Environ. Health 211 (2008) 560–579

Al-Ashban, R.M., Aslam, M., Shah, A.H., 2004. Kohl (surma): a toxic traditional eye cosmetic study in Saudi Arabia. Public Health 118, 292–298. Al-Saleh, I., 1998. Lead sources in Saudi Arabia: a review. J. Environ. Pathol. Toxicol. Oncol. 17, 17–35. Al-Saleh, I., Al-Doush, I., 1997. Mercury content in skin lightening creams and the potential hazards to the health of Saudi female. J. Environ. Health Toxicol. 51, 123–130. Al-Saleh, I., Coate, L., 1993. Cadmium exposure in Saudi Arabia and its relationship to smoking. Trace Elem. Med. 10, 129–133. Al-Saleh, I., Shinwari, N., 1997. Urinary mercury levels in females: influence of skin-lightening creams, and dental amalgam fillings. BioMetals 10, 315–323. Al-Saleh, I., Shinwari, N., 2002. Preliminary report on the levels of heavy metals in fish from the Arabian Gulf of Saudi Arabia. Chemosphere 48, 749–755. Al-Saleh, I., Khalil, M., Taylor, A., 1995. Lead, erythrocyte protopophyrin and haematological parameters in normal maternal and umbilical cord blood from subjects of the Riyadh region. Arch. Environ. Health 50, 66–73. Al-Saleh, I., Shinwari, N., Basile, P., Al-Dgaither, S., AlMutairi, M., 2000. Exposure to cadmium among sheesha smokers and how do they compare to cigarette smokers. J. Trace Elem. Exp. Med. 13, 381–388. Al-Saleh, I., Shinwari, N., Mashhour, A., Gamal, M., Abu Ghosh, M., Shammasi, Z., Al-Nasser, A., 2005. Is lead considered as a risk factor for high blood pressure during menopause period among Saudi women? Int. J. Hyg. Environ. Health 208, 341–356. Avazeri, N., Denys, A., Lefe`vre, B., 2006. Lead cations affect the control of both meiosis arrest and meiosis resumption of the mouse oocyte in vitro at least via the PKC pathway. Biochimie 88, 1823–1829. Barlow, S.M., Sullivan, F.M., 1982. Reproductive Hazards of Industrial Chemicals: An Evaluation of Animal and Human Data. Academic Press, New York. Bates, M., 2006. Mercury amalgam dental fillings: An epidemiologic assessment Int. J. Hyg. Environ. Health 209, 309–316. Bellinger, D.C., 2005. Teratogen update: lead and pregnancy. Birth Defects Res. A Clin. Mol. Teratol. 73, 409–420. Benoff, S., Cooper, G.W., Centola, G.M., Jacob, A., Hershlag, A., Hurley, I.R., 2000. Metal ions and human sperm mannose receptors. Andrologia 32, 317–329. Bernard, A., 2004. Renal dysfunction induced by cadmium: biomarkers of critical effects. BioMetals 17, 519–523. Bhan, A., Sarkar, N.N., 2005. Mercury in the environment: effect on health and reproduction. Rev. Environ. Health 20, 39–56. Bjo¨rnberg, K.A.B., Vahter, M., Grawe´, K.P., Berglund, M., 2005. Methyl mercury exposure in Swedish women with high fish consumption. Sci. Total Environ. 341, 45–51. CDC, Center for Disease Control, 1991. Preventing Lead Poisoning in Young Children: A statement by the Center for Disease Control. US Department of Health and Human Service/Public Health Service/Center for Disease Control, Atlanta. CDC, Centres for Disease Control and Prevention, 2002. Managing Elevated Blood Lead Levels Among Young

577

Children: Recommendations From the Advisory Committee on Childhood Lead Poisoning Prevention. Centers for Disease Control and Prevention, Atlanta, GA. CDC Centers for Disease Control and Prevention, 2004. Blood mercury levels in young children and childbearing-aged women – United States, 1999–2002. MMWR 53, 1018–1020. Choi, S.M., Yoo, S.D., Lee, B.M., 2004. Toxicological characteristics of endocrine-disrupting chemicals: developmental toxicity, carcinogenicity, and mutagenicity. J. Toxicol. Environ. Health B 7, 1–24. Choy, C.M.Y., Lam, C.W.L., Cheung, L.T.F., Briton-Jones, C.M., Cheung, L.P., Haines, C.J., 2002. Infertility, blood mercury concentrations and dietary seafood consumption: a case–control study. BJOG 109, 1125–1211. Coskun, S., Hollanders, J., Al-Hassan, S., Al-Sufyan, H., AlMayman, H., Jaroudi, K., 2000. Day 5 versus day 3 embryo transfer: a controlled randomized trial. Hum. Reprod. 15, 1947–1952. Davis, B.J., Price, H.C., O’Connor, R.W., Fernando, R., Rowland, A.S., Morgan, D.L., 2001. Mercury vapor and female reproductive toxicity. Toxicol. Sci. 59, 291–296. Edwards, R.G., 1974. Follicular fluid. J. Reprod. Fertil. 37, 189–219. Espey, L.L., Ujioka, T., Okamura, H., Richards, J.S., 2003. Metallothionein-1 messenger RNA transcription in steroidsecreting cells of the rat ovary during the periovulatory period. Biol. Reprod. 68, 1895–1902. Fateh, M., Sultan, K., Obasaju, M., Abeyawardene, S., Chan, M., Khan, S., 2005. Elevated blood mercury levels are associated with poor outcome in IVF cycles. Fertil. Steril. 84, S87. FDA, Food and Drug Administration, 1992. FDA’s Cosmetics Handbook. US Department of Health and Human Services, Public Health Service, Food and Drug Administration. Fiala, J., Hruba´, D., Crha, I., Re´zl, P., Totu sek, J., 2001. Is environmental cadmium a serious hazard to Czech population? Int. J. Occup. Med. Environ. Health 14, 185–188. Fredin, B., 1994. Mercury release from dental amalgam fillings. Int. J. Risk Saf. Med. 4, 197–208. Gardella, J.R., Hill 3rd, J.A., 2000. Environmental toxins associated with recurrent pregnancy loss. Sem Reprod Med 18, 407–424. Gerhard, I., Runnebaum, B., 1992. The limits of hormone substitution in pollutant exposure and fertility disorders. Z. Gynakologie 114, 593–602. Guillette, L.J., Moore, B.C., 2006. Environmental contaminants, fertility, and multioocytic follicles: a lesson from wildlife? Semin. Reprod. Med. 24, 134–141. Haufroid, V., Lison, D., 1998. Urinary cotinine as a tobaccosmoke exposure index: a minireview. Int. Arch. Occup. Environ. Health 71, 162–168. Henson, M.C., Chedrese, P.J., 2004. Endocrine disruption by cadmium, a common environmental toxicant with paradoxical effects on reproduction. Exp. Biol. Med. 229, 383–392. Hughes, E.G., YoungLai, E.V., ward, S.M., 1992. Cigarette smoking and outcomes of in-vitro fertilization and embryo transfer: a prospective cohort study. Hum Reprod 7, 358–361.

ARTICLE IN PRESS 578

I. Al-Saleh et al. / Int. J. Hyg. Environ. Health 211 (2008) 560–579

IPCS (International Programme on Chemical Safety), 1992. Cadmium. Environmental Health Criteria 134 Geneva: World Health Organization. Jarallah, J.S., Al-Rubeaan, K.A., Al-Nuaim, A.A., AlRuhaily, A.A., Kalantan, K.A., 1999. Prevalence and determinants of smoking in three regions of Saudi Arabia. Tob Control 8, 53–56. Ja¨rup, L., Berglund, M., Elinder, C.G., Nordberg, G., Vahter, M., 1998. Health effects of cadmium exposure – a review of the literature and a risk estimate. Scand. J. Work Environ. Health 24, 1–52. Junaid, M., Chowdhuri, D.K., Narayan, R., Shanker, R., Saxena, D.K., 1997. Lead-induced changes in ovarian follicular development and maturation in mice. J. Toxicol. Environ. Health 50, 31–40. Kosnett, M.J., Wedeen, R.P., Rothenberg, S.J., Hipkins, K.L., Materna, B.L., Schwartz, S., Hu, H., Woolf, A., 2007. Recommendations for medical management of adult lead exposure. Environ. Health Perspect. 115, 463–471. Leoni, G., Bogliolo, L., Deiana, G., Berlinguer, F., Rosati, I., Pintus, P., Ledda, S., Naitana, S., 2002. Influence of cadmium exposure on in-vitro ovine gamete dysfunction. Reprod. Toxicol. 16, 371–377. Marcotrigiano, G.O., Storelli, M.M., 2003. Heavy metal, polychlorinated biphenyl and organochlorine pesticide residues in marine organisms: risk evaluation for consumers. Vet. res. Commun. 27, 183–195. McRill, C., Boyer, L.V., Flood, T.J., Ortega, L., 2000. Mercury toxicity due to use of a cosmetic cream. J. Occup. Environ. Med. 42, 4–7. Mlynarcikova, A., Fickova, M., Scsukova, S., 2005. Ovarian intrafollicular processes as a target for cigarette smoke components and selected environmental reproductive disruptors. Endocr. Regul. 39, 21–32. Nampoothini, L.P., Gupta, S., 2006. Simultaneous effect of lead and cadmium on granulosa cells: a cellular model for ovarian toxicity. Reprod. Toxicol. 21, 179–185. Ortiz-Roque, C., Lo´pez-Rivera, Y., 2004. Mercury contamination in reproductive age women in a Caribbean island: Vieques. J. Epidemiol. Comm. Health 58, 756–757. OSHA, Occupational Safety and Health Administration DoL, 2003. Occupational safety and health standards: toxic and hazardous substances: cadmium. Code of Federal Regulations 29, 135–229 CFR 1910.1027. Paksy, K., Gati, I., Naray, M., Rajczy, K., 2001. Lead accumulation in human ovarian follicular fluid, and in vitro effect of lead on progesterone production by cultured human ovarian granulosa cells. J. Toxicol. Environ. Health A. 62, 359–366. Pant, N., Banerjee, A.K., Pandey, S., Mathur, N., Saxena, D.K., Srivastava, S.P., 2003. Correlation of lead and cadmium in human seminal plasma with seminal vesicle and prostatic markers. Hum. Exp. Toxicol. 22, 125–128. Pillai, A., Laxmi Pryiya, P.N., Gupta, S., 2002. Effects of combined exposure to lead and cadmium on pituitary membrane of female rats. Arch. Toxicol. 76, 671–675. Pires, J.B., Bezerra, F.F., Laboissiere, F.P., Miekeley, N., Donangelo, C.M., 2001. Lead levels in erythrocytes and biomarkers of bone turnover in pregnant and lactating

women with marginal calcium intakes. Nutr. Res. 21, 831–841. Piscator, M., 1985. Dietary exposure to cadmium and health effect: impact of environmental changes. Environ. Health Perspect. 63, 127–132. Prentice, A., 1994. Maternal calcium requirements during pregnancy and lactation. Am. J. Clin. Nutr. 59, 477S–483S. Quig, D., 1998. Cysteine metabolism and metal toxicity. Altern. Med. Rev. 3, 262–270. Rice, D.E., Schoeny, R., Mahaffey, K., 2003. Methods and rationale for derivation of a reference dose for methylmercury by the US Environmental Protection Agency. Risk Anal 23, 107–115. Risher, J.F., Murray, H.E., Prince, G.R., 2002. Organic mercury compounds: human exposure and its relevance to public health. Toxicol. Ind. Health 18, 109–160. Satarug, S., Moore, M.R., 2004. Adverse health effects of chronic exposure to low-level cadmium in foodstuffs and cigarette smoke. Environ. Health Perspect. 112, 1099–1103. Satoh, H., 2003. Behavioral teratology of mercury and its compounds. Tohoku J. Exp. Med. 201, 1–9. Schuurs, A.H., 1999. Reproductive toxicity of occupational mercury. A review of the literature. J. Dent. 27, 249–256. Sharara, F.I., Seifer, D.B., Flaws, J.A., 1998. Environmental toxicants and female reproduction. Fertil. Steril. 70, 613–622. Shen, W., Chen, Y., Li, C., Ji, Q., 2000. Effect of mercury chloride on the reproductive function and visceral organ of female mouse. Wei Sheng Yan Jiu 29, 75–77. Shimada, H., Hochadel, J.F., Waalkes, M.P., 1997. Progesterone pretreatment enhances cellular sensitivity to cadmium despite a marked activation of the metallothionein gene. Toxicol. Appl. Pharmacol. 142, 178–185. Silbergeld, E.K., 1991. Lead in bone: implications for toxicology during pregnancy and lactation. Environ. Health Perspect. 91, 63–70. Silberstein, T., Saphier, O., Paz-Tal, O., Trimarchi, J.R., Gonzalez, L., Keefe, D.L., 2006. Lead concentrates in ovarian follicle compromises pregnancy. J. Trace Elem. Med. Biol. 20, 205–207. Silkorski, R., Juszkiewicz, T., Paszkowski, T., SzprengierJuszkiewick, 1987. Women in dental surgeries: reproductive hazards in occupational exposure to metallic mercury. Int. Arch. Occup. Environ. Health 59, 551–557. Sinawat, S., 2000. The environmental impact on male fertility. J. Med. Assoc. Thailand 83, 880–885. Solaiman, D., Jonah, M.M., Miyazaki, W., Ho, G., Bhattacharyya, M.H., 2001. Increased metallothionein in mouse liver, kidneys, and duodenum during lactation. Toxicol. Sci. 60, 184–192. Taupeau, C., Poupon, J., Nome´, F., Lefe`vre, B., 2001. Lead accumulation in the mouse ovary after treatment-induced follicular atresia. Reprod. Toxicol . 15, 385–391. Urakawa, N., Nagata, J., Kudo, K., Kimura, K., Imamura, T., 1994. Simultaneous determination of nicotine and cotinine in various human tissues using capillary gas chromatography/mass spectrometry. Int. J. Legal Med. 106, 232–236. ( Vahter, M., Akesson, A., Lind, B., Bjo¨rs, U., Schu¨tz, A., Berglund, M., 2000. Longitudinal study of methylmercury

ARTICLE IN PRESS I. Al-Saleh et al. / Int. J. Hyg. Environ. Health 211 (2008) 560–579

and inorganic mercury in blood and urine of pregnant and lactating women, as well as in umbilical cord blood. Environ. Res. 84, 186–194. WHO, World Health Organization, 1989. Evaluation of Certain Food Additives and Contaminants (Thirty-third Report of the Joint FAO/WHO Expert Committee on Food Additives). WHO Technical Report Series No. 776. Geneva:World Health Organization. WHO, World Health Organization, 1991. Environmental Health Criteria 118: Inorganic Mercury. WHO, Geneva. Yoshida, M., 2003. Placental to fetal transfer of mercury and fetotoxicity. Tohoku J. Exp. Med. 201, 1–9.

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Zahir, F., Rizwi, S.J., Haq, S.K., Khan, R.H., 2005. Low dose mercury toxicity and human health. Environ. Toxicol. Pharmacol. 20, 351–360. Zalups, R.K., 2000. Molecular interactions with mercury in the kidney. Pharmacol. Rev. 52, 113–143. Zalups, R.K., Lash, L.H., 1994. Advances in understanding the renal transport and toxicity of mercury. J. Toxicol. Environ. Health 42, 1–44. Zenzes, M.T., Krishnan, S., Krishnan, B., Zhang, H., Casper, R.F., 1995. Cadmium accumulation in follicular fluid of women in in vitro fertilization-embryo transfer is higher in smokers. Fert. Steril. 64, 599–603.