Investigation of Serum Heavy Metal Levels in Women Diagnosed with Unexplained Infertility: A Case-Control Study

Investigation of Serum Heavy Metal Levels in Women Diagnosed with Unexplained Infertility: A Case-Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Serum Heavy Metal Levels in Women Diagnosed with Unexplained Infertility: A Case-Control Study Tansu Saltan¹, Hale Cetin Arslan², Pınar Yalcın Bahat³ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7794488/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Unexplained infertility is a diagnosis of exclusion in couples in whom no organic cause is found despite a basic infertility evaluation. Its etiology remains unclear, and oxidative stress and environmental factors are implicated in its development. This study aimed to compare serum cadmium, lead, mercury, and aluminum levels in women with unexplained infertility with those of a fertile control group and to investigate the potential role of heavy metals in the etiology of infertility. Methods This prospective case-control study included women aged 20–45 who presented to an infertility clinic between January and June 2023. Thirty patients diagnosed with unexplained infertility were selected as the case group. At the same time, 30 fertile women with routine examinations at the exact center who presented for IUD insertion served as the control group. Cadmium, lead, mercury, and aluminum levels were measured in venous blood samples from the participants using ICP-MS. Results No statistically significant difference was found between the case and control groups in terms of serum heavy metal levels (p > 0.05). However, a negative correlation was found between lead levels and AMH values in the infertile group (r = -0.42; p = 0.05). Furthermore, when the entire population was examined, a significant association was found between smoking and cadmium levels (p = 0.025). Conclusion No significant association was found between serum heavy metal levels and unexplained infertility. However, the negative association between lead and AMH suggests that heavy metals may have potential effects on reproductive health. Further studies with larger patient series and tissue samples are needed. Unexplained infertility heavy metal cadmium lead mercury aluminum oxidative stress Figures Figure 1 Figure 2 Figure 3 Introduction Heavy metals, despite their widespread occurrence in nature, are environmental pollutants that can cause serious toxic effects on living organisms. Industrial development and increasing environmental contamination have led to increased exposure, particularly through drinking water and food, over the last 50 years [ 1 ]. High concentrations of these elements cause cellular damage through oxidative stress mechanisms and can have adverse effects on reproductive health. Lead, mercury, cadmium, and aluminum, in particular, have no physiological benefits for human health, and their accumulation can lead to toxic consequences [ 2 ]. The World Health Organization defines infertility as the inability to achieve pregnancy despite at least 12 months of regular, unprotected intercourse. Female infertility accounts for approximately 37% of all infertile cases, with anovulation, male factor, and tubal pathologies being the most common causes [ 3 ]. However, in approximately 15% of infertile couples, no organic cause can be identified, and this group is defined as unexplained infertility [ 4 ]. Unexplained infertility remains a significant clinical problem due to its heterogeneous nature, low treatment success rates, and high cost. In recent years, it has been suggested that oxidative stress may play a role in the etiology of unexplained infertility. It has been suggested that heavy metal exposure may impair reproductive functions by increasing reactive oxygen species, negatively affecting folliculogenesis, ovulation, embryo implantation, and endocrine balance [ 5 , 6 ]. This study aimed to assess serum levels of cadmium, lead, mercury, and aluminum in women diagnosed with unexplained infertility and to investigate the possible role of these elements in the etiology of unexplained infertility. Furthermore, we believe that the findings may contribute to increasing pregnancy success rates in the future management of infertility through lifestyle modifications such as chelation therapy, specific dietary choices, regular exercise, and cost-effective measures to reduce toxic exposures. Materials and methods Study design and participants This is a prospective case-control study conducted at the Infertility Outpatient Clinic of the Obstetrics and Gynecology Clinic of Istanbul Kanuni Sultan Süleyman Training and Research Hospital between January 2023 and June 2023. Approval for the study was received from the Ethics Committee of the Hamidiye Faculty of Medicine, University of Health Sciences, on January 2, 2023, with decision number 2022.12.239. Written informed consent was obtained from all participants. The study included 30 women diagnosed with unexplained infertility (case group) and 30 fertile women with at least one live birth (control group). Sample size was calculated using G*Power 3.1 software, assuming an effect size of 0.4, an alpha error of 0.05, and a power of 0.80. The study was completed with a total of 60 patients. Inclusion and exclusion criteria The case group included women aged 20–45 years who had failed to conceive despite at least 12 months of regular, unprotected intercourse and were diagnosed with unexplained infertility following a basic infertility evaluation. These patients had regular gynecological examinations and hormone profiles, demonstrated bilateral tubal patency by hysterosalpingography (HSG), and partner semen analyses met World Health Organization criteria. The control group consisted of women of the same age, with at least one live birth history, who were fertile and had normal physical examination findings. These patients were selected from among those presenting for intrauterine device (IUD) insertion at the exact center. Exclusion criteria included polycystic ovary syndrome (PCOS), endometriosis, uterine or tubal pathology, thyroid dysfunction, significant male factor infertility, history of chronic systemic disease, and previous chemotherapy or radiotherapy. Patients who refused to participate were also excluded from the study. Data collection and sample selection A detailed medical history was obtained from all participants, and age, obstetric history, menstrual cycle, previous surgeries, smoking and alcohol use, previous infertility treatments, and current medical conditions were recorded. Physical examinations and gynecological evaluations were performed. The uterus and adnexa were examined using transvaginal ultrasonography, and antral follicle count (AFC) was determined. Ovarian reserve was assessed by measuring FSH, LH, estradiol, prolactin, thyroid-stimulating hormone (TSH), and anti-Müllerian hormone (AMH) levels in blood samples taken on days 2–4 of menstruation. Male factor infertility was excluded according to World Health Organization criteria by examining the partner's semen analysis. Venous blood samples were collected from both groups. K2/K3-EDTA tubes were used for cadmium, lead, and mercury measurements, and sodium-EDTA tubes were used for aluminum measurements. Samples were centrifuged under appropriate conditions, the serum was separated, and stored at − 20°C until analysis. All heavy metal levels were measured using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700, Acıbadem Labmed, Istanbul). The reliability of the measurements was ensured by calibration curves and commercial quality control samples used in each analysis series. Statistical analysis Data obtained in the study were analyzed using R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria). The gtsummary (v1.6.0) and rstatix ​​(v0.7.0) packages were generally used for sustainable assessments. Normal distribution of continuous variables was assessed using the Shapiro–Wilk test, Q–Q plots, and histograms. Normally distributed continuous variables were expressed as mean ± standard deviation, and non-normally distributed variables were expressed as median (minimum–maximum) values. Categorical variables are presented as numbers and percentages (%). In intergroup comparisons, the independent samples t-test was used for normally distributed continuous data. The Welch t-test was used when variance homogeneity was not achieved. The Mann–Whitney U test was used for non-normally distributed continuous data. In comparing categorical variables, the Pearson chi-square test was used when the number of observations was sufficient, and the Fisher's exact test was used when the number of observations was insufficient. Relationships between continuous variables were assessed using Spearman correlation analysis. A p-value of < 0.05 was considered statistically significant in all analyses. Results A total of 60 women were included in the study, with 30 in the unexplained infertility group and 30 in the control group (Fig. 1 ). The mean age of all patients was 30.0 ± 5.7 years. The mean age was 32.3 ± 6.4 years in the control group and 27.7 ± 3.7 years in the unexplained infertility group, with a statistically significant difference (p = 0.001). When the body mass indices of the groups were examined, the mean values were 27.3 ± 5.2 kg/m² in the control group and 23.9 ± 3.2 kg/m² in the unexplained infertility group, with a significant difference between the two groups (p = 0.003). No significant differences were observed between the two groups in terms of smoking and alcohol use, regular medication use, or sociodemographic characteristics (p > 0.05 for all). Gravida and parity means were found to be significantly higher in the control group (p < 0.001) (Table 1 ). Table 1 Comparison of anthropometric, clinical, and demographic characteristics of patient groups. Features All population, n = 60 Control, n = 30 Infertile, n = 30 P -value Age 0.001 1 Median (min–max) 29.0 (20.0–45.0) 32.0 (20.0–45.0) 28.0 (21.0–35.0) Mean ± SD 30.0 ± 5.7 32.3 ± 6.4 27.7 ± 3.7 BMI, kg/m 2 0.003 1 Median (min–max) 25.5 (15.9–39.5) 26.7 (17.9–39.5) 23.8 (15.9–29.7) Mean ± SD 25.6 ± 4.6 27.3 ± 5.2 23.9 ± 3.2 Known comorbidities, n (%) 2 (3.3) 0 (0) 2 (6.7) 0.492 2 Current medications, n (%) 2 (5.0) 0 (0) 2 (10) 0.237 2 History of gynecologic surgery, n (%) 2 (3.3) 0 (0) 2 (6.7) 0.492 2 Gravida < 0.001 3 Median (min–max) 1.0 (0.0–8.0) 3.0 (1.0–8.0) 0.0 (0.0–1.0) Mean ± SD 1.6 ± 1.9 3.2 ± 1.5 0.1 ± 0.3 Parity 0.999 2 Smoker, n (%) 16 (27) 8 (27) 8 (27) > 0.999 4 Spousal smoking, n (%) 35 (58) 17 (57) 18 (60) 0.793 4 Alcohol use, n (%) 7 (12) 5 (17) 2 (6.7) 0.424 3 Occupational exposure, n (%) 4 (6.7) 1 (3.3) 3 (10) 0.612 3 Above poverty line, n (%) 10 (17) 6 (20) 4 (13) 0.488 4 High school or above, n (%) 30 (50) 11 (37) 19 (63) 0.039 4 Place of residence, n (%) > 0.999 2 Rural area 6 (10) 3 (10) 3 (10) Urban area 54 (90) 27 (90) 27 (90) Fish consumption, n (%) > 0.999 4 At least once a week 16 (27) 8 (27) 8 (27) Less than once a week 44 (73) 22 (73) 22 (73) Regular physical activity, n (%) 3 (5.0) 2 (6.7) 1 (3.3) > 0.999 2 1 Welch t-testi, 2 Fisher’in kesin testi, 3 Mann Whitney U testi, 4 Pearson Ki-kare testi When the serum heavy metal levels of the patients were examined, the cadmium level was found to be 0.442 ± 0.521 ng/L in the control group and 0.242 ± 0.108 ng/L in the unexplained infertility group; however, this difference was not statistically significant (p = 0.095). Lead level was 1.1 ± 0.6 µg/dL in the control group and 0.9 ± 0.5 µg/dL in the unexplained infertility group, and no significant difference was found between the groups (p = 0.447). Mercury levels were 0.32 ± 0.61 µg/L in the control group and 0.31 ± 0.30 µg/L in the unexplained infertility group (p = 0.489). Aluminum levels were measured as 6.02 ± 3.88 µg/L in the control group and 7.11 ± 3.84 µg/L in the unexplained infertility group. The difference between the two groups was not statistically significant (p = 0.228) (Table 2 ). Table 2 Comparison of serum heavy metal levels of patient groups Patient groups All population, n = 60 Control, n = 30 Infertile, n = 30 P -value Cadmium, ng/L 0.095 1 Median (min–max) 0.251 (0.100–2.840) 0.293 (0.105–2.840) 0.220 (0.100–0.490) Mean ± SD 0.342 ± 0.386 0.442 ± 0.521 0.242 ± 0.108 Lead, µg/dL 0.447 1 Median (min–max) 0.9 (0.1–3.2) 1.0 (0.2–3.2) 0.9 (0.1–2.3) Mean ± SD 1.0 ± 0.5 1.1 ± 0.6 0.9 ± 0.5 Mercury, µg/L 0.489 1 Median (min–max) 0.18 (0.10–3.46) 0.16 (0.10–3.46) 0.19 (0.10–1.33) Mean ± SD 0.32 ± 0.48 0.32 ± 0.61 0.31 ± 0.30 Aluminum, µg/L 0.228 1 Median (min–max) 5.35 (1.62–17.40) 4.66 (1.62–17.40) 6.30 (2.05-16.00) Mean ± SD 6.56 ± 3.87 6.02 ± 3.88 7.11 ± 3.84 1 Mann Whitney U test When all participants were evaluated, serum cadmium and aluminum levels were significantly higher in smokers (p = 0.025 and p = 0.042, respectively). In contrast, no statistically significant association was found between body mass index (BMI), occupational heavy metal exposure, residence, or fish consumption and cadmium, lead, or aluminum levels (p > 0.05). A significant association was observed between fish consumption and serum mercury levels, with higher mercury levels in individuals who consumed fish (p = 0.032). Similar trends were observed in the unexplained infertility group; however, the differences in this group were not statistically significant (Table 3 ). Table 3 Comparison of serum heavy metal concentrations between the unexplained infertility and control groups Parameter Cadmium (Cd) Median (min–max) p Lead (Pb) Median (min–max) p Mercury (Hg) Median (min–max) p Aluminum (Al) Median (min–max) p Smoking status Non-smoker 0.22 (0.10–0.98) 0.90 (0.10–3.20) 0.16 (0.10–3.46) 4.8 (1.6–16.9) Smoker 0.33 (0.12–2.84) 0.025* 0.90 (0.30–1.40) 0.827 0.23 (0.10–0.61) 0.620 7.4 (2.6–17.4) 0.042* BMI > 25 (kg/m²) 0.29 (0.10-1.00) 0.807 0.90 (0.10–3.20) 0.666 0.19 (0.10–1.05) 0.671 4.8 (1.6–17.4) 0.081 Occupational exposure to heavy metals 0.29 (0.11–0.39) 0.965 1.15 (0.60–1.50) 0.339 0.21 (0.10–1.05) 0.964 5.2 (3.8–6.8) 0.756 Place of residence(city) 0.25 (0.10–2.84) 0.666 0.90 (0.10–3.20) 0.511 0.18 (0.10–3.46) 0.511 5.3 (1.6–17.4) 0.912 Fish consumption (at least once a week) 0.25 (0.11–0.98) 0.575 0.95 (0.10–2.40) 0.736 0.29 (0.10–3.46) 0.032* 7.4 (1.9–16.9) 0.385 When the relationships between heavy metal levels and age, body mass index (BMI), FSH, AMH, and LH levels were examined in the unexplained infertility group, no significant correlation was found between cadmium and aluminum levels and the aforementioned parameters. However, a weak negative correlation was found between lead levels and AMH (r = − 0.42, p < 0.05). A moderate positive correlation was also found between mercury levels and age (r = 0.54, p < 0.05) (Table 4) (Figs. 2 and 3 ). Table 4. The relationship between blood parameters in patients with unexplained infertility Parameter Cadmium Lead Mercur Aluminum Age -0.064 -0.14 0.54* 0.19 BMI -0.21 -0.22 0.002 -0.16 FSH 0.25 0.21 0.004 0.22 AMH 0.015 -0.42* 0.13 -0.016 LH 0.36 -0.067 0.21 0.13 Spearman Correlation (< 0.25 very weak relationship; 0.26–0.49 weak relationship; 0.50–0.69 moderate relationship; 0.70–0.89 high relationship; 0.90-1.0 very high relationship) *p < 0.05 Discussion In the treatment of unexplained infertility, serum heavy metal levels were evaluated and compared with those of the control group. In our study, no significant difference was found between the two groups in terms of cadmium, lead, mercury, and aluminum levels. Smoking increased cadmium and aluminum levels, while fish consumption was found to accumulate mercury. Furthermore, a notable finding was the negative correlation between lead levels and AMH, as well as a positive correlation between mercury levels and age in the list of unexplained infertility cases. Larger-scale studies in American women have highlighted the potential association between heavy metals and infertility. Lin et al., in a cross-sectional analysis of 838 women aged 20–44 using the 2013–2018 NHANES data, found that urinary cadmium and arsenic were significantly higher in women experiencing infertility. They also noted that blood and urinary lead levels were positively correlated with the risk of infertility in women with a BMI ≥ 25 [ 7 ]. These results support the clinical significance of the negative association observed in our study between lead levels and ovarian reserve parameters. A study by Lee et al. in a Korean population examined the relationship between blood cadmium and lead levels and infertility. No significant association was found between cadmium levels and infertility, but blood lead levels were reported to be significantly associated with infertility [ 8 ]. This result is another important finding supporting the negative correlation found between lead and AMH in our study. Similarly, in a study conducted by Lei et al. in Taiwan on an infertile population (n = 310) in which PCOS was excluded by clinical examination and symptoms, no statistical association was found between blood cadmium levels and infertility; however, a significant association was reported between infertility and blood lead levels [ 9 ]. These results suggest that lead may be a potential risk factor for reproductive health. A study conducted in the USA using NHANES 2013–2016 data examined the relationship between blood mercury levels and infertility in women and found a nonlinear relationship between mercury levels and infertility [ 10 ]. In our study, no statistically significant association was found between serum mercury levels and unexplained infertility. However, an analysis of fish consumption found a significant association with serum mercury levels in the entire population, independent of infertility. This finding is consistent with seafood consumption, one of the primary sources of mercury exposure. Indeed, a case-control study conducted by Choy et al. in Hong Kong revealed significantly higher blood mercury levels in infertile women, which were associated with a more frequent consumption of seafood [ 11 ]. Differences in regional culture, geographic characteristics, lifestyle habits, and population size may explain the discrepancy between our findings and this study. Furthermore, our study found a moderate positive correlation between age and serum mercury levels, but no significant association with BMI. Studies conducted in larger populations will reveal more clearly the effects of age and lifestyle factors, in particular, on mercury exposure and infertility. Adverse effects of heavy metal exposure on reproductive outcomes have also been reported in women undergoing in vitro fertilization (IVF). A 2008 study by Al-Saleh et al. evaluated the relationship between lead, cadmium, and mercury levels and the success of IVF treatment. It was shown that fertilization rates and embryo quality were negatively affected in women with high heavy metal levels, and that lead exposure reduced the number of primordial follicles [ 12 ]. The negative correlation of AMH values ​​with lead in our study supports this information. Rzymski et al., in their review examining the effects of heavy metals on the female reproductive system, similarly stated that metals such as lead, cadmium, mercury, and arsenic can have disruptive effects on folliculogenesis, ovulation, embryo implantation, and hormonal balance [ 13 ]. Similarly, a study in the literature has shown that concentrations of heavy metals and trace elements in blood and follicular fluid affect ART outcomes [ 14 ]. These findings show that heavy metals can have significant effects not only on natural conception but also on assisted reproductive techniques, and support the notion that they are an environmental factor that should be taken into consideration in the etiology of infertility. In clinical practice, the most commonly used biological samples to assess heavy metal exposure are blood and urine. Blood tests reflect the short-term presence of heavy metals in the circulatory system. In contrast, urine tests can better indicate the amount of heavy metals excreted from the body after exposure and the current burden. However, some studies have shown that examining heavy metal levels in tissue rather than serum can more accurately reflect the biological effects of exposure. The fact that Tanrıkut et al. found significant cadmium presence in endometrial tissue in patients with unexplained infertility, while our study found no significant correlation with serum levels, suggests differences in the ability of different biological samples to reflect exposure [ 15 ]. This difference may be explained by the rapid transfer of cadmium from serum to tissues and its accumulation. Due to its ability to displace + 2-valent ions, cadmium can be taken into cells through L-type calcium channels and is rapidly eliminated from the blood. This characteristic also suggests that serum cadmium levels may be insufficient to reflect exposure. This view is also supported by the demonstration by Nasiadek and colleagues of a relationship between serum and myometrial cadmium in patients with uterine myoma [ 16 ]. Therefore, further studies evaluating both serum and endometrial cadmium levels in the same population are needed. It is a well-known finding in the literature that cadmium exposure increases with smoking [ 16 , 17 ]. In our study, a statistically significant association was found between smoking and serum cadmium levels in the total patient population. Our literature review of aluminum revealed no studies directly assessing its effects on the human population, and most of the available data were based on animal experiments, with a focus on male infertility. Experimental studies on female reproductive health have reported that exposure to aluminum in the aquatic ecosystem has adverse effects on the reproductive system of fish, demonstrating aluminum accumulation in tissues, which leads to a decrease in relative fecundity, decreased serum 17-alpha-hydroxyprogesterone and cortisol levels, and an antisteroidogenic effect [ 18 ]. It has also been suggested that aluminum exposure, resulting in the depletion of essential elements such as zinc, copper, and iron, disrupts antioxidant mechanisms, ovarian energy metabolism, and ATPase activity, and decreases the number of FSH-LH receptors in ovarian tissue, potentially leading to follicular atresia and anovulation [ 19 , 20 ]. Although our study found no statistically significant association between serum aluminum levels and infertility in the unexplained infertility group, a significant association was found between aluminum levels and smoking and passive smoking. This finding is consistent with studies showing that aluminum burden increases with smoking [ 21 , 22 ]. However, the significant association found only with passive smoking, while no association with smoking was observed in the unexplained infertility group, may be explained by the limited sample size. Therefore, multicenter, prospective studies with larger populations are needed to elucidate the potential role of aluminum in female infertility. Strengths and Limitations of the Study One of the strengths of this study is that it meticulously excluded causes of infertility and only evaluated women diagnosed with unexplained infertility. This created a more homogeneous patient group rather than a heterogeneous infertile population, allowing the results to be explicitly interpreted for this subgroup. Furthermore, the uniqueness of our study lies in the combined analysis of heavy metals known to have potential effects on reproductive health, such as cadmium, lead, mercury, and aluminum. The collection and storage of serum samples under standardized conditions, along with their analysis using ICP-MS, are other strengths that enhance the reliability of our findings. However, our study has several limitations. First, our sample size was relatively small, making it challenging to statistically demonstrate weak associations. Furthermore, assessing only serum heavy metal levels led to the underestimation of metal accumulation in tissues (e.g., endometrial or ovarian tissue). The tendency of some metals, such as cadmium, to rapidly transfer from serum to tissues may prevent blood levels from fully reflecting exposure. Because our study was cross-sectional, it is not possible to establish a causal relationship between heavy metal levels and infertility. Finally, environmental and lifestyle factors (diet, occupational exposure, geographic location) were assessed through questionnaires, and the reliance on self-reporting of this information carries a risk of bias. Conclusion This study examined serum heavy metal levels in women diagnosed with unexplained infertility and compared them with those of controls. While no significant differences were found between the groups in terms of cadmium, lead, mercury, and aluminum levels, the negative correlation between lead levels and AMH, an indicator of ovarian reserve, and the positive correlation between mercury levels and age are noteworthy findings. Furthermore, smoking has been shown to increase cadmium and aluminum levels, and fish consumption is associated with mercury levels. The findings suggest that lead and mercury, in particular, may have potentially adverse effects on women's reproductive health. However, due to the limited sample size and the assessment of serum levels only, the results need to be confirmed with larger populations and prospective studies. Therefore, prospective, multicenter studies are needed to understand better the role of heavy metal exposure in the etiology of infertility. Declarations Funding : This research received no external funding. Data availability : The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request. Ethics Approval : This study was approved by the Ethics Committee of the Istanbul Kanuni Sultan Süleyman Training and Research Hospital, University of Health Sciences (KAEK/2022.12.239, date: 02.01.2023). Clinical Trial Registration : Not applicable. Clinical trial number: Not applicable. Human Ethics and Consent to Participate : All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee. Written informed consent was obtained from all individual participants included in the study. References Fu Z, Xi S. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7794488","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542413466,"identity":"70b15277-1e46-462a-872f-9f11c2d104ae","order_by":0,"name":"Tansu Saltan¹","email":"","orcid":"","institution":"Osmancık State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tansu","middleName":"","lastName":"Saltan¹","suffix":""},{"id":542413467,"identity":"b4cb0d63-23b5-4be8-9297-04fc75685174","order_by":1,"name":"Hale Cetin Arslan²","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACAwbmBhDNw8De2PgAxOAjrIURpMWAh4Hn8GEDkBY2YrUwMEikpUmARAhqMWc/2PjgR8UfGXOJHLPKrzl2MmwMzA8f3cCjxbInsdmw54wBj2XPG7PbstuSgQ5jMzbOweewA4lt0oxtBjwGx3PMbktuYwZq4WGTxqvl/MP234z/gFoO5JgVS26rJ0LLjcQ2ZsYGoJYTaWmMH7cdJkbLw2bJnmPGPAZnDh+WZtx2nIeNmZBfzicf/PCjRs7e4Hhj48ef26rt+dmbHz7GpwUFMPOASWKVgwDjD1JUj4JRMApGwYgBADD3SFt1Pbf1AAAAAElFTkSuQmCC","orcid":"","institution":"University of Health Sciences, Kanuni Sultan Süleyman Training and Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hale","middleName":"Cetin","lastName":"Arslan²","suffix":""},{"id":542413468,"identity":"64c0c92a-5a87-4767-b599-efbdf70a33e1","order_by":2,"name":"Pınar Yalcın Bahat³","email":"","orcid":"","institution":"Faculty of Medicine Istanbul Yeni Yüzyıl 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16:47:20","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103997,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7794488/v1/dff541c9ec11858feac2c8b1.html"},{"id":95665786,"identity":"746fa01e-c706-4c87-b712-c6172b09cc1a","added_by":"auto","created_at":"2025-11-11 16:47:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59425,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7794488/v1/c9e570fdd8c442eb87f0f4bf.png"},{"id":95665784,"identity":"ee6cf40a-2747-40fa-885c-a02868283c4d","added_by":"auto","created_at":"2025-11-11 16:47:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53524,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Mercury and Age levels in patients with unexplained infertility\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7794488/v1/746a9fdc95d64d954a4adc53.png"},{"id":95665785,"identity":"4f50240f-c05b-43b2-bb0c-4e05cf1beac3","added_by":"auto","created_at":"2025-11-11 16:47:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50248,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Lead and AMH values ​​in patients with unexplained infertility\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7794488/v1/d3c3e3cbd90eeeb91b93b231.png"},{"id":95804549,"identity":"c0f2e174-579c-44bb-9b29-a63e52782ffe","added_by":"auto","created_at":"2025-11-13 08:38:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1089256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7794488/v1/f955250f-cf01-4e80-893c-2535bbd9228d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Serum Heavy Metal Levels in Women Diagnosed with Unexplained Infertility: A Case-Control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeavy metals, despite their widespread occurrence in nature, are environmental pollutants that can cause serious toxic effects on living organisms. Industrial development and increasing environmental contamination have led to increased exposure, particularly through drinking water and food, over the last 50 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. High concentrations of these elements cause cellular damage through oxidative stress mechanisms and can have adverse effects on reproductive health. Lead, mercury, cadmium, and aluminum, in particular, have no physiological benefits for human health, and their accumulation can lead to toxic consequences [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe World Health Organization defines infertility as the inability to achieve pregnancy despite at least 12 months of regular, unprotected intercourse. Female infertility accounts for approximately 37% of all infertile cases, with anovulation, male factor, and tubal pathologies being the most common causes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, in approximately 15% of infertile couples, no organic cause can be identified, and this group is defined as unexplained infertility [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Unexplained infertility remains a significant clinical problem due to its heterogeneous nature, low treatment success rates, and high cost.\u003c/p\u003e\u003cp\u003eIn recent years, it has been suggested that oxidative stress may play a role in the etiology of unexplained infertility. It has been suggested that heavy metal exposure may impair reproductive functions by increasing reactive oxygen species, negatively affecting folliculogenesis, ovulation, embryo implantation, and endocrine balance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to assess serum levels of cadmium, lead, mercury, and aluminum in women diagnosed with unexplained infertility and to investigate the possible role of these elements in the etiology of unexplained infertility. Furthermore, we believe that the findings may contribute to increasing pregnancy success rates in the future management of infertility through lifestyle modifications such as chelation therapy, specific dietary choices, regular exercise, and cost-effective measures to reduce toxic exposures.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and participants\u003c/h2\u003e\u003cp\u003eThis is a prospective case-control study conducted at the Infertility Outpatient Clinic of the Obstetrics and Gynecology Clinic of Istanbul Kanuni Sultan S\u0026uuml;leyman Training and Research Hospital between January 2023 and June 2023. Approval for the study was received from the Ethics Committee of the Hamidiye Faculty of Medicine, University of Health Sciences, on January 2, 2023, with decision number 2022.12.239. Written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eThe study included 30 women diagnosed with unexplained infertility (case group) and 30 fertile women with at least one live birth (control group). Sample size was calculated using G*Power 3.1 software, assuming an effect size of 0.4, an alpha error of 0.05, and a power of 0.80. The study was completed with a total of 60 patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe case group included women aged 20\u0026ndash;45 years who had failed to conceive despite at least 12 months of regular, unprotected intercourse and were diagnosed with unexplained infertility following a basic infertility evaluation. These patients had regular gynecological examinations and hormone profiles, demonstrated bilateral tubal patency by hysterosalpingography (HSG), and partner semen analyses met World Health Organization criteria. The control group consisted of women of the same age, with at least one live birth history, who were fertile and had normal physical examination findings. These patients were selected from among those presenting for intrauterine device (IUD) insertion at the exact center.\u003c/p\u003e\u003cp\u003eExclusion criteria included polycystic ovary syndrome (PCOS), endometriosis, uterine or tubal pathology, thyroid dysfunction, significant male factor infertility, history of chronic systemic disease, and previous chemotherapy or radiotherapy. Patients who refused to participate were also excluded from the study.\u003c/p\u003e\n\u003ch3\u003eData collection and sample selection\u003c/h3\u003e\n\u003cp\u003e A detailed medical history was obtained from all participants, and age, obstetric history, menstrual cycle, previous surgeries, smoking and alcohol use, previous infertility treatments, and current medical conditions were recorded. Physical examinations and gynecological evaluations were performed. The uterus and adnexa were examined using transvaginal ultrasonography, and antral follicle count (AFC) was determined. Ovarian reserve was assessed by measuring FSH, LH, estradiol, prolactin, thyroid-stimulating hormone (TSH), and anti-M\u0026uuml;llerian hormone (AMH) levels in blood samples taken on days 2\u0026ndash;4 of menstruation. Male factor infertility was excluded according to World Health Organization criteria by examining the partner's semen analysis.\u003c/p\u003e\u003cp\u003eVenous blood samples were collected from both groups. K2/K3-EDTA tubes were used for cadmium, lead, and mercury measurements, and sodium-EDTA tubes were used for aluminum measurements. Samples were centrifuged under appropriate conditions, the serum was separated, and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis. All heavy metal levels were measured using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7700, Acıbadem Labmed, Istanbul). The reliability of the measurements was ensured by calibration curves and commercial quality control samples used in each analysis series.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData obtained in the study were analyzed using R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria). The gtsummary (v1.6.0) and rstatix ​​(v0.7.0) packages were generally used for sustainable assessments. Normal distribution of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test, Q\u0026ndash;Q plots, and histograms. Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and non-normally distributed variables were expressed as median (minimum\u0026ndash;maximum) values. Categorical variables are presented as numbers and percentages (%).\u003c/p\u003e\u003cp\u003eIn intergroup comparisons, the independent samples t-test was used for normally distributed continuous data. The Welch t-test was used when variance homogeneity was not achieved. The Mann\u0026ndash;Whitney U test was used for non-normally distributed continuous data. In comparing categorical variables, the Pearson chi-square test was used when the number of observations was sufficient, and the Fisher's exact test was used when the number of observations was insufficient. Relationships between continuous variables were assessed using Spearman correlation analysis. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant in all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 60 women were included in the study, with 30 in the unexplained infertility group and 30 in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of all patients was 30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 years. The mean age was 32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 years in the control group and 27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 years in the unexplained infertility group, with a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.001). When the body mass indices of the groups were examined, the mean values were 27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 kg/m\u0026sup2; in the control group and 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 kg/m\u0026sup2; in the unexplained infertility group, with a significant difference between the two groups (p\u0026thinsp;=\u0026thinsp;0.003). No significant differences were observed between the two groups in terms of smoking and alcohol use, regular medication use, or sociodemographic characteristics (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). Gravida and parity means were found to be significantly higher in the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of anthropometric, clinical, and demographic characteristics of patient groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeatures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll population, n\u0026thinsp;=\u0026thinsp;60\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl, n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInfertile, n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.0 (20.0\u0026ndash;45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.0 (20.0\u0026ndash;45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0 (21.0\u0026ndash;35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.5 (15.9\u0026ndash;39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.7 (17.9\u0026ndash;39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.8 (15.9\u0026ndash;29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKnown comorbidities, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.492\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent medications, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.237\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory of gynecologic surgery, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.492\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGravida\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0 (1.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0 (0.0\u0026ndash;1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0 (0.0\u0026ndash;1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbortion, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoker, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpousal smoking, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.793\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol use, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.424\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupational exposure, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.612\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbove poverty line, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.488\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh school or above, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFish consumption, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least once a week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than once a week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegular physical activity, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eWelch t-testi, \u003csup\u003e2\u003c/sup\u003eFisher\u0026rsquo;in kesin testi, \u003csup\u003e3\u003c/sup\u003eMann Whitney U testi, \u003csup\u003e4\u003c/sup\u003ePearson Ki-kare testi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen the serum heavy metal levels of the patients were examined, the cadmium level was found to be 0.442\u0026thinsp;\u0026plusmn;\u0026thinsp;0.521 ng/L in the control group and 0.242\u0026thinsp;\u0026plusmn;\u0026thinsp;0.108 ng/L in the unexplained infertility group; however, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.095). Lead level was 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 \u0026micro;g/dL in the control group and 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 \u0026micro;g/dL in the unexplained infertility group, and no significant difference was found between the groups (p\u0026thinsp;=\u0026thinsp;0.447). Mercury levels were 0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 \u0026micro;g/L in the control group and 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 \u0026micro;g/L in the unexplained infertility group (p\u0026thinsp;=\u0026thinsp;0.489). Aluminum levels were measured as 6.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88 \u0026micro;g/L in the control group and 7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84 \u0026micro;g/L in the unexplained infertility group. The difference between the two groups was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.228) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of serum heavy metal levels of patient groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePatient groups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll population, n\u0026thinsp;=\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl, n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInfertile, n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCadmium, ng/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.251 (0.100\u0026ndash;2.840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.293 (0.105\u0026ndash;2.840)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.220 (0.100\u0026ndash;0.490)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.342\u0026thinsp;\u0026plusmn;\u0026thinsp;0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.442\u0026thinsp;\u0026plusmn;\u0026thinsp;0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.242\u0026thinsp;\u0026plusmn;\u0026thinsp;0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLead, \u0026micro;g/dL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.447\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9 (0.1\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (0.2\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.1\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMercury, \u0026micro;g/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.489\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18 (0.10\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0.10\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19 (0.10\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAluminum, \u0026micro;g/L\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.228\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.35 (1.62\u0026ndash;17.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.66 (1.62\u0026ndash;17.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.30 (2.05-16.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Mann Whitney U test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen all participants were evaluated, serum cadmium and aluminum levels were significantly higher in smokers (p\u0026thinsp;=\u0026thinsp;0.025 and p\u0026thinsp;=\u0026thinsp;0.042, respectively). In contrast, no statistically significant association was found between body mass index (BMI), occupational heavy metal exposure, residence, or fish consumption and cadmium, lead, or aluminum levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A significant association was observed between fish consumption and serum mercury levels, with higher mercury levels in individuals who consumed fish (p\u0026thinsp;=\u0026thinsp;0.032). Similar trends were observed in the unexplained infertility group; however, the differences in this group were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of serum heavy metal concentrations between the unexplained infertility and control groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCadmium (Cd)\u003c/p\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLead (Pb)\u003c/p\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMercury (Hg)\u003c/p\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAluminum (Al)\u003c/p\u003e\u003cp\u003eMedian (min\u0026ndash;max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.10\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.10\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16 (0.10\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.8 (1.6\u0026ndash;16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33 (0.12\u0026ndash;2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.025*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.30\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23 (0.10\u0026ndash;0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.4 (2.6\u0026ndash;17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.042*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;25 (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 (0.10-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.10\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19 (0.10\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.8 (1.6\u0026ndash;17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational exposure to heavy metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 (0.11\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.60\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21 (0.10\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.2 (3.8\u0026ndash;6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of residence(city)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.10\u0026ndash;2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.10\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18 (0.10\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.3 (1.6\u0026ndash;17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFish consumption (at least once a week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.11\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.10\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29 (0.10\u0026ndash;3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.032*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.4 (1.9\u0026ndash;16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen the relationships between heavy metal levels and age, body mass index (BMI), FSH, AMH, and LH levels were examined in the unexplained infertility group, no significant correlation was found between cadmium and aluminum levels and the aforementioned parameters. However, a weak negative correlation was found between lead levels and AMH\u003c/p\u003e\u003cp\u003e(r = \u0026minus;\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A moderate positive correlation was also found between mercury levels and age (r\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;4) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e The relationship between blood parameters in patients with unexplained infertility\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMercur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAluminum\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.54*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.42*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSpearman Correlation (\u0026lt;\u0026thinsp;0.25 very weak relationship; 0.26\u0026ndash;0.49 weak relationship; 0.50\u0026ndash;0.69 moderate relationship; 0.70\u0026ndash;0.89 high relationship; 0.90-1.0 very high relationship)\u003c/p\u003e\n \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the treatment of unexplained infertility, serum heavy metal levels were evaluated and compared with those of the control group. In our study, no significant difference was found between the two groups in terms of cadmium, lead, mercury, and aluminum levels. Smoking increased cadmium and aluminum levels, while fish consumption was found to accumulate mercury. Furthermore, a notable finding was the negative correlation between lead levels and AMH, as well as a positive correlation between mercury levels and age in the list of unexplained infertility cases.\u003c/p\u003e\u003cp\u003eLarger-scale studies in American women have highlighted the potential association between heavy metals and infertility. Lin et al., in a cross-sectional analysis of 838 women aged 20\u0026ndash;44 using the 2013\u0026ndash;2018 NHANES data, found that urinary cadmium and arsenic were significantly higher in women experiencing infertility. They also noted that blood and urinary lead levels were positively correlated with the risk of infertility in women with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These results support the clinical significance of the negative association observed in our study between lead levels and ovarian reserve parameters. A study by Lee et al. in a Korean population examined the relationship between blood cadmium and lead levels and infertility. No significant association was found between cadmium levels and infertility, but blood lead levels were reported to be significantly associated with infertility [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This result is another important finding supporting the negative correlation found between lead and AMH in our study. Similarly, in a study conducted by Lei et al. in Taiwan on an infertile population (n\u0026thinsp;=\u0026thinsp;310) in which PCOS was excluded by clinical examination and symptoms, no statistical association was found between blood cadmium levels and infertility; however, a significant association was reported between infertility and blood lead levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These results suggest that lead may be a potential risk factor for reproductive health.\u003c/p\u003e\u003cp\u003eA study conducted in the USA using NHANES 2013\u0026ndash;2016 data examined the relationship between blood mercury levels and infertility in women and found a nonlinear relationship between mercury levels and infertility [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In our study, no statistically significant association was found between serum mercury levels and unexplained infertility. However, an analysis of fish consumption found a significant association with serum mercury levels in the entire population, independent of infertility. This finding is consistent with seafood consumption, one of the primary sources of mercury exposure. Indeed, a case-control study conducted by Choy et al. in Hong Kong revealed significantly higher blood mercury levels in infertile women, which were associated with a more frequent consumption of seafood [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Differences in regional culture, geographic characteristics, lifestyle habits, and population size may explain the discrepancy between our findings and this study. Furthermore, our study found a moderate positive correlation between age and serum mercury levels, but no significant association with BMI. Studies conducted in larger populations will reveal more clearly the effects of age and lifestyle factors, in particular, on mercury exposure and infertility.\u003c/p\u003e\u003cp\u003eAdverse effects of heavy metal exposure on reproductive outcomes have also been reported in women undergoing in vitro fertilization (IVF). A 2008 study by Al-Saleh et al. evaluated the relationship between lead, cadmium, and mercury levels and the success of IVF treatment. It was shown that fertilization rates and embryo quality were negatively affected in women with high heavy metal levels, and that lead exposure reduced the number of primordial follicles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The negative correlation of AMH values ​​with lead in our study supports this information. Rzymski et al., in their review examining the effects of heavy metals on the female reproductive system, similarly stated that metals such as lead, cadmium, mercury, and arsenic can have disruptive effects on folliculogenesis, ovulation, embryo implantation, and hormonal balance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, a study in the literature has shown that concentrations of heavy metals and trace elements in blood and follicular fluid affect ART outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings show that heavy metals can have significant effects not only on natural conception but also on assisted reproductive techniques, and support the notion that they are an environmental factor that should be taken into consideration in the etiology of infertility.\u003c/p\u003e\u003cp\u003eIn clinical practice, the most commonly used biological samples to assess heavy metal exposure are blood and urine. Blood tests reflect the short-term presence of heavy metals in the circulatory system. In contrast, urine tests can better indicate the amount of heavy metals excreted from the body after exposure and the current burden. However, some studies have shown that examining heavy metal levels in tissue rather than serum can more accurately reflect the biological effects of exposure. The fact that Tanrıkut et al. found significant cadmium presence in endometrial tissue in patients with unexplained infertility, while our study found no significant correlation with serum levels, suggests differences in the ability of different biological samples to reflect exposure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This difference may be explained by the rapid transfer of cadmium from serum to tissues and its accumulation. Due to its ability to displace\u0026thinsp;+\u0026thinsp;2-valent ions, cadmium can be taken into cells through L-type calcium channels and is rapidly eliminated from the blood. This characteristic also suggests that serum cadmium levels may be insufficient to reflect exposure. This view is also supported by the demonstration by Nasiadek and colleagues of a relationship between serum and myometrial cadmium in patients with uterine myoma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, further studies evaluating both serum and endometrial cadmium levels in the same population are needed.\u003c/p\u003e\u003cp\u003eIt is a well-known finding in the literature that cadmium exposure increases with smoking [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In our study, a statistically significant association was found between smoking and serum cadmium levels in the total patient population.\u003c/p\u003e\u003cp\u003eOur literature review of aluminum revealed no studies directly assessing its effects on the human population, and most of the available data were based on animal experiments, with a focus on male infertility. Experimental studies on female reproductive health have reported that exposure to aluminum in the aquatic ecosystem has adverse effects on the reproductive system of fish, demonstrating aluminum accumulation in tissues, which leads to a decrease in relative fecundity, decreased serum 17-alpha-hydroxyprogesterone and cortisol levels, and an antisteroidogenic effect [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It has also been suggested that aluminum exposure, resulting in the depletion of essential elements such as zinc, copper, and iron, disrupts antioxidant mechanisms, ovarian energy metabolism, and ATPase activity, and decreases the number of FSH-LH receptors in ovarian tissue, potentially leading to follicular atresia and anovulation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although our study found no statistically significant association between serum aluminum levels and infertility in the unexplained infertility group, a significant association was found between aluminum levels and smoking and passive smoking. This finding is consistent with studies showing that aluminum burden increases with smoking [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the significant association found only with passive smoking, while no association with smoking was observed in the unexplained infertility group, may be explained by the limited sample size. Therefore, multicenter, prospective studies with larger populations are needed to elucidate the potential role of aluminum in female infertility.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations of the Study\u003c/h3\u003e\n\u003cp\u003eOne of the strengths of this study is that it meticulously excluded causes of infertility and only evaluated women diagnosed with unexplained infertility. This created a more homogeneous patient group rather than a heterogeneous infertile population, allowing the results to be explicitly interpreted for this subgroup. Furthermore, the uniqueness of our study lies in the combined analysis of heavy metals known to have potential effects on reproductive health, such as cadmium, lead, mercury, and aluminum. The collection and storage of serum samples under standardized conditions, along with their analysis using ICP-MS, are other strengths that enhance the reliability of our findings.\u003c/p\u003e\u003cp\u003eHowever, our study has several limitations. First, our sample size was relatively small, making it challenging to statistically demonstrate weak associations. Furthermore, assessing only serum heavy metal levels led to the underestimation of metal accumulation in tissues (e.g., endometrial or ovarian tissue). The tendency of some metals, such as cadmium, to rapidly transfer from serum to tissues may prevent blood levels from fully reflecting exposure. Because our study was cross-sectional, it is not possible to establish a causal relationship between heavy metal levels and infertility. Finally, environmental and lifestyle factors (diet, occupational exposure, geographic location) were assessed through questionnaires, and the reliance on self-reporting of this information carries a risk of bias.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined serum heavy metal levels in women diagnosed with unexplained infertility and compared them with those of controls. While no significant differences were found between the groups in terms of cadmium, lead, mercury, and aluminum levels, the negative correlation between lead levels and AMH, an indicator of ovarian reserve, and the positive correlation between mercury levels and age are noteworthy findings. Furthermore, smoking has been shown to increase cadmium and aluminum levels, and fish consumption is associated with mercury levels.\u003c/p\u003e\u003cp\u003eThe findings suggest that lead and mercury, in particular, may have potentially adverse effects on women's reproductive health. However, due to the limited sample size and the assessment of serum levels only, the results need to be confirmed with larger populations and prospective studies. Therefore, prospective, multicenter studies are needed to understand better the role of heavy metal exposure in the etiology of infertility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: This study was approved by the Ethics Committee of the Istanbul Kanuni Sultan S\u0026uuml;leyman Training and Research Hospital, University of Health Sciences (KAEK/2022.12.239, date: 02.01.2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e: All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFu Z, Xi S. The effects of heavy metals on human metabolism. Toxicol Mech Methods. 2020;30(3):167\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan Z, Gong T, Liang P. Heavy Metal Exposure and Cardiovascular Disease. Circ Res. 2024;134(9):1160\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker MH, Tobler KJ. Female Infertility. In: StatPearls. Treasure Island (FL): StatPearls Publishing; December 19, 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. JAMA. 2021;326(1):65\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Tang J, Wang L, et al. Oxidative stress in oocyte aging and female reproduction. J Cell Physiol. 2021;236(12):7966\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalaivani M, Pavithra B, DeepakN, et al. Oxidative stress and female reproductive disorder: A review. Asian Pac J Reprod. 2022;11(3):107\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin J, Lin X, Qiu J, You X, Xu J. Association between heavy metals exposure and infertility among American women aged 20\u0026ndash;44 years: A cross-sectional analysis from 2013 to 2018 NHANES data. Front Public Health. 2023;11:1122183.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee S, Min JY, Min KB. Female Infertility Associated with Blood Lead and Cadmium Levels. Int J Environ Res Public Health. 2020;17(5):1794.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei HL, Wei HJ, Ho HY, Liao KW, Chien LC. Relationship between risk factors for infertility in women and lead, cadmium, and arsenic blood levels: a cross-sectional study from Taiwan. BMC Public Health. 2015;15:1220.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu F, Chen C, Zhang Y, et al. Elevated blood mercury level has a non-linear association with infertility in U.S. women: Data from the NHANES 2013\u0026ndash;2016. Reprod Toxicol. 2020;91:53\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoy CM, Lam CW, Cheung LT, Briton-Jones CM, Cheung LP, Haines CJ. Infertility, blood mercury concentrations and dietary seafood consumption: a case-control study. BJOG. 2002;109(10):1121\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Saleh I, Coskun S, Mashhour A, et al. Exposure to heavy metals (lead, cadmium and mercury) and its effect on the outcome of in-vitro fertilization treatment. Int J Hyg Environ Health. 2008;211(5\u0026ndash;6):560\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRzymski P, Tomczyk K, Rzymski P, Poniedziałek B, Opala T, Wilczak M. Impact of heavy metals on the female reproductive system. Ann Agric Environ Med. 2015;22(2):259\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTolunay HE, Ş\u0026uuml;k\u0026uuml;r YE, Ozkavukcu S, et al. Heavy metal and trace element concentrations in blood and follicular fluid affect ART outcome. Eur J Obstet Gynecol Reprod Biol. 2016;198:73\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTanrıkut E, Karaer A, Celik O, et al. Role of endometrial concentrations of heavy metals (cadmium, lead, mercury and arsenic) in the aetiology of unexplained infertility. Eur J Obstet Gynecol Reprod Biol. 2014;179:187\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNasiadek M, Swiatkowska E, Nowinska A, Krawczyk T, Wilczynski JR, Sapota A. The effect of cadmium on steroid hormones and their receptors in women with uterine myomas. Arch Environ Contam Toxicol. 2011;60(4):734\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRzymski P, Rzymski P, Tomczyk K, et al. Metal status in human endometrium: relation to cigarette smoking and histological lesions. Environ Res. 2014;132:328\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorreia TG, Vieira VARO, de Moraes Narcizo A, Zampieri RA, Floeter-Winter LM, Moreira RG. Endocrine disruption caused by the aquatic exposure to aluminum and manganese in Astyanax altiparanae (Teleostei: Characidae) females during the final ovarian maturation. Comp Biochem Physiol C Toxicol Pharmacol. 2021;249:109132.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu Y, Jia FB, Wang J, et al. Effects of sub-chronic aluminum chloride exposure on rat ovaries. Life Sci. 2014;100(1):61\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang N, She Y, Zhu Y, et al. Effects of subchronic aluminum exposure on the reproductive function in female rats. Biol Trace Elem Res. 2012;145(3):382\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVallyathan V, Hahn LH. Cigarette smoking and inorganic dust in human lungs. Arch Environ Health. 1985;40(2):69\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eExley C, Begum A, Woolley MP, Bloor RN. Aluminum in tobacco and cannabis and smoking-related disease. Am J Med. 2006;119(3):e2769\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Unexplained infertility, heavy metal, cadmium, lead, mercury, aluminum, oxidative stress","lastPublishedDoi":"10.21203/rs.3.rs-7794488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7794488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eUnexplained infertility is a diagnosis of exclusion in couples in whom no organic cause is found despite a basic infertility evaluation. Its etiology remains unclear, and oxidative stress and environmental factors are implicated in its development. This study aimed to compare serum cadmium, lead, mercury, and aluminum levels in women with unexplained infertility with those of a fertile control group and to investigate the potential role of heavy metals in the etiology of infertility.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective case-control study included women aged 20\u0026ndash;45 who presented to an infertility clinic between January and June 2023. Thirty patients diagnosed with unexplained infertility were selected as the case group. At the same time, 30 fertile women with routine examinations at the exact center who presented for IUD insertion served as the control group. Cadmium, lead, mercury, and aluminum levels were measured in venous blood samples from the participants using ICP-MS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNo statistically significant difference was found between the case and control groups in terms of serum heavy metal levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, a negative correlation was found between lead levels and AMH values in the infertile group (r = -0.42; p\u0026thinsp;=\u0026thinsp;0.05). Furthermore, when the entire population was examined, a significant association was found between smoking and cadmium levels (p\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eNo significant association was found between serum heavy metal levels and unexplained infertility. However, the negative association between lead and AMH suggests that heavy metals may have potential effects on reproductive health. Further studies with larger patient series and tissue samples are needed.\u003c/p\u003e","manuscriptTitle":"Investigation of Serum Heavy Metal Levels in Women Diagnosed with Unexplained Infertility: A Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:47:15","doi":"10.21203/rs.3.rs-7794488/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-16T19:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2405757098664219452539761083269089393","date":"2025-11-09T13:01:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T05:52:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T15:19:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T23:49:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T23:48:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-10-06T22:06:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ed230d4-9834-4422-9bd6-19751ebe1ddd","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T16:47:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:47:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7794488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7794488","identity":"rs-7794488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}