Environmental heavy metal exposure and altering anti-Mullerian hormone levels in women

Abstract

Background

Serum levels of anti-Mullerian hormone (AMH), a key indicator of ovarian aging, decrease with age. This decline may be accelerated by genetic and environmental factors. Accordingly, the present study investigates the relationship between serum AMH concentrations and exposure to heavy metals.

Methods

This cohort study was conducted on 220 women with a median age of 42 years (Range: 37–45). Participants were reproductive-age women from the Tehran Lipid and Glucose Study (TLGS) that met our inclusion criteria. Serum concentration of heavy metals — including lead (Pb), cadmium (Cd), copper (Cu), aluminum (Al) and chromium (Cr) — as well as AMH levels, were measured using stored samples from the second and fifth follow-up visits with a time interval of approximately 10 years. A multivariate linear regression model was used to assess the relationship between AMH and heavy metals, adjusting for age, body mass index (BMI), smoking, physical activity, age at menarche, education, marital status and parity.

Results

The results indicated that serum AMH concentration in women classified within the fourth and third quartiles of Cu was reduced by -0.43 (95%CI: -0.73, -0.13) ng/ml and − 0.34 (95%CI: -0.65, -0.03) ng/ml, respectively. No statistically significant associations were observed between AMH levels and other heavy metals, including Pb, Al, and Cr (P > 0.05).

Conclusion

These findings suggest a possible link between elevated Cu levels and diminished AMH concentrations in reproductive-age women. However, further research is needed to confirm these findings and to elucidate the underlying factors, particularly in younger age groups.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-025-03952-4.

Introduction

Anti-Mullerian hormone (AMH) is secreted by the granulosa cells of developing ovarian follicles and is known as a marker of ovarian reserve. At birth, its concentration in the blood is minimal, peaks at approximately 25 years of age [1], and then gradually declines to undetectable levels after menopause [2]. Unlike other ovarian reserve markers, such as follicle-stimulating hormone (FSH), AMH are not influenced by fluctuations during the menstrual cycle, allowing for consistent clinical measurement across the spectrum of reproductive stages [3]. This stability has firmly established AMH’s role in predicting ovarian reserve [4]. In recent years, numerous studies have highlighted the significance of AMH as a marker of both ovarian follicular reserve and ovarian aging [5, 6]. A growing body of research indicates that daily exposure to environmental pollutants accelerates the reduction of ovarian reserve [7–9].

Increasing evidence suggests that environmental pollutants, including heavy metals, pose a significant threat to reproductive health [10, 11]. Metals such as lead (Pb), cadmium (Cd), copper (Cu), aluminum (Al), and chromium (Cr) enter the body through inhalation, ingestion, or dermal contact [12]. The toxicity of these substances is attributed to mechanisms such as oxidative stress, endocrine disruption, and chronic inflammation, which can lead to organ dysfunction, including impairment of the reproductive system [13]. Notably, Pb and Cd have been detected in follicular fluid and ovarian tissue, indicating potential direct effects on fertility [14–18]. Epidemiological studies have also demonstrated an inverse relationship between blood metal concentrations and AMH levels, implicating these pollutants in decreased ovarian reserve [19, 20]. For example, exposure to Cd has been associated with a marked decline in AMH levels among premenopausal women, particularly between the ages of 30 to 35 years [19].

Despite considerable interest in the environmental determinants of reproductive health, research specifically investigating the association between heavy metal exposure and AMH levels in population-based cohorts remains limited. To address this gap, the present study examines the relationships between serum concentrations of Pb, Cd, Cu, Al, and Cr and AMH levels in reproductive-age women participating in the Tehran Lipid and Glucose Study (TLGS). The TLGS is a large-scale, community-based cohort initiated in 1998, designed to investigate the incidence of noncommunicable diseases and their associated risk factors in a representative urban population of Tehran.

The present study aimed to investigate the association between serum AMH concentrations and heavy metal exposure, adjusting for key confounding variables, within a population-based cohort study.

Methods

Study design and participants

This cohort study was utilized data from the TLGS cohort, which was initiated in 1998. The TLGS cohort has undergone seven follow-up visits at three-year intervals. Blood samples were collected at each visit as part of the study protocol. AMH levels were measured at baseline. as well as during the 2^nd and 5^th follow-up visits. Due to the limited blood samples volume available at baseline, heavy metal measurements were performed only on samples from the 2^nd and 5^th follow-up visits.

Inclusion and exclusion criteria

Women aged 25 to 40 years at the study’s initiation were included if they reported regular and predictable menstrual cycles and had not reached menopause by at least the 5^th follow-up visit. Eligible participants demonstrated proven natural fertility, defined as having completed at least one full-term pregnancy and having conceived within one year of discontinuing contraceptive use. Exclusion criteria included a history of polycystic ovary syndrome (PCOS), endocrine disorders, hysterectomy, oophorectomy, or any other kind of ovarian surgery. Additionally, participants had no documented history of hormonal contraceptive use or hormone replacement therapy for menopausal symptoms. Adequate blood samples volume was also required to permit heavy metals measurement at the 2^nd and 5^th follow-up visits.

Data collection and sample selection

Participants underwent physical examination every three years. Follow-up assessments included a general physical exam and an interview during which the date of the last menstrual cycle was recorded. Overnight fasting blood samples were collected and stored for future analysis. For this study, stored serum samples from the 2^nd and 5^th follow-up visits were used for AMH and heavy metal measurements.

The study was a continuation of the work by Namvar et al. [21]. A total of 806 samples from TLGS participants, whose AMH levels were measured at the 2^nd and 5^th follow-ups and who met inclusion criteria, were available. Due to financial constraints related to heavy metal measurements, the sample size was determined based on previous relevant studies [22]. Consequently, 220 samples were randomly selected for analysis.

Confounding factors

Confounding factors were identified through a comprehensive review of the literature and focusing on variables known or plausibly associated with both heavy metal exposure and ovarian reserve markers. They were included age, BMI, smoking, physical activity, menarche age, educational level, marital status, and parity. We controlled for factors through statistical adjustments were made for these confounders to ensure that the observed associations accurately reflect the relationships between heavy metals and AMH levels. We excluded smokers from our study due to the well-documented effects of smoking on ovarian reserve.

AMH measurement

All AMH measurements were performed in the same laboratory at the Research Institute for Endocrine Sciences. AMH was analyzed using the Gen II assay kit (Beckman Coulter, Inc, Fullerton, California, USA) and the Sunrise ELISA reader (Tecan Co, Salzburg, Austria) by a single experienced technician. Gen II controls (A79766) at two concentration levels were used to monitor assays accuracy. Intra-assay and inter-assay coefficients of variation were 1.9% and 2.0%, respectively.

A possible issue is that AMH levels for phase 5 participants were measured within one year after blood collection, whereas those for phase 2 participants were measured up to six years post-collection. However, multiple studies have demonstrated that the long-term storage of serum samples at −80°C does not significantly affect AMH stability or concentration [23]. These findings support the reliability of our measurements and the feasibility of longitudinal studies using stored samples.

Heavy metals measurement

Serum concentrations of Cu, Al, Cd, Pb, and Cr were measured using Agilent 4210 Microwave Plasma Atomic Emission Spectrometry (MP-AES) (USA). For each sample, 0.1 mL of serum was placed in a Pyrex vial, followed by the addition of 3 mL of a freshly prepared mixture of concentrated nitric acid and hydrogen peroxide (2:1 V/V). The vials were capped with Teflon lids and left to stand for 10 min before digestion in a water bath at 70°C for 2 h. Digested samples were then heated in an LH-25 A Intelligent Multi-Parameter Digestion Instrument (Beijing, China) at approximately 120°C until a clear solution was obtained. Excess acid was evaporated to dryness, after which the samples were cooled, and diluted with 1% nitric acid. The final solution was transferred to a 10 mL tube and diluted with triple-distilled water to a final volume of 3 mL. A blank extraction was performed using triple-distilled water following the same procedure.

Statistical analysis

Participants characteristics were summarized using descriptive statistics: mean ± standard deviation (SD) for normally distributed variables, median (interquartile range) for non-normally distributed variables, and number (%) for categorical variables. Participants were categorized into quartiles based on serum concentration of each heavy metal. Women in the 3^rd and 4^th quartiles were compared to those in the 1^st quartile.

Associations between AMH levels and heavy metals quartiles were examined using multiple linear regression models, both unadjusted and adjusting for baseline age, BMI, smoking, physical activity, menarche age, educational level, marital status, and parity. Collinearity among variables was checked prior to regression analysis. Box plots were used to visually compare the serum AMH levels across heavy metal quartiles. We have expanded our analysis to include the relation between BMI categories (lower 25, 25 to 29.99, and equal or over 30 (kg/m²)) and heavy metals concentrations (µg/l) expressed as continuous variables Furthermore, we assessed the interaction effects between BMI (kg/m²) and each pollutant (µg/l) on AMH levels at both 2^nd and 5^th follow-ups in both unadjusted and adjusted interaction models. Data analysis was conducted using R version 4.1.1 and IBM SPSS Statistics version 21 (IBM Corp., Armonk, NY, USA). P-value ≤ 0.05 was assumed to be significant statistically.

Results

Table 1 presents the characteristics of participants at the in 2^nd and 5^th follow-up visits, along with the median (IQR) concentration of heavy metals at those time points. Our results indicate a significant increase in mean levels of Al and Cr corresponding to higher BMI categories at the 5^th follow-up (Table S1). Table 2 shows the limit of quantification (LOQ, µg/l), limit of detection (LOD, µg/l), and recovery percentage for each heavy metals measured by MP-AES. For Cd, these value were 4.4 µg/l (LOQ), 1.4 µg/l (LOD) and %96 recovery; for Cu, they were 1.6 µg/l (LOQ), 0.5 µg/l (LOD) and %99 recovery, respectively.

Table 1.

Descriptive statistics of predictors and characteristics of participants in 2^nd and 5^th follow-up visits

Variables		2nd follow-up visit (n = 220)	5th follow-up visit (n = 220)
Age, years, Median (IQR)		42 (37–45)	51 (47–55)
BMI, kg/m2, mean ± SD		28.16 ± 4.45	28.20 ± 4.60
Educational level, years, N (%)	< 6	0 (0)	0 (0)
	6 to 12	183 (83.2)	181 (82.3)
	> 12	37 (16.8)	39 (17.7)
Marital status, N (%)	single	13 (5.9)	12 (5.4)
	married	207 (94.1)	208 (94.6)
Menarche age, Median (IQR)		13 (13–14)	13 (13–14)
Physical activity*	low	166 (75.5)	166 (75.5)
	high	54 (24.5)	54 (24.5)
Parity, Median (IQR)		2 (2–3)	2 (2–3)
Smoking, N (%)	never	210 (95.5)	210 (95.5)
	ever	10 (4.5)	10 (4.5)
AMH (ng/ml)		0.68 (0.22–1.35)	0.06 (0.03–0.45)
Heavy metals (µg/l), Median (IQR):
Cu		1045 (797.80-1344.40)	1485 (1031–1920)
Al		14.88 (12.52–19.12)	16.08 (12.24–21.88)
Pb		25.24 (44.24–33.70)	13.12 (9.62–21.16)
Cr		1.96 (1.33–3.04)	3.24 (2.07–4.29)

*Physical activity: low (less than 600 MET minutes per week), high (at least 600 MET minutes per week and above)

Table 2.

LOQ (µg/l), LOD (µg/l) and recovery percentage of elements measured by MP-AES

Heavy metals	Wavelength (nm)	LOD (µg/l)	LOQ (µg/l)	Recovery (n = 5)
Cd	228.802	1.4	4.4	96
Cu	324.754	0.5	1.6	99
Al	396.152	0.6	1.9	108
Pb	405.781	2.2	7.5	98
Cr	425.433	0.4	1.4	102

Table 3 summarized the results of both unadjusted and adjusted multiple linier regression analyses (by age, smoking status, BMI, physical activity, menarche age, educational level, marital status and parity) examining AMH values at 2^nd and 5^th follow-up visits according to quartiles of each heavy metal. Our findings showed that AMH levels at the 5^th follow-up visit decreased by −0.35 ng/ml (95% CI:−0.60 - −0.10) among those with exposure to Cu at fourth quartile level compared to the first quartile; it remained statistically significant after adjustment − 0.43 ng/ml (95%CI: −0.73 - −0.13). Additionally, in adjusted model we found that exposure to the third quartile of Cu compared to the first quartile reduces the AMH level at 5^th follow-up visit by −0.34 ng/ml (95%CI: −0.65 - −0.03). No significant associations were observed between AMH levels and other heavy metals at either the 2^ndand 5^th follow-up visits in both unadjusted and adjusted models. No significant associations were identified in any of interaction models (unadjusted and adjusted) between BMI (kg/m²) and each pollutant (µg/l) on AMH levels at both 2^nd and 5^th follow-ups (Table S2).

Table 3.

Multiple linier regression model for the effect of quartiles of heavy metals on AMH (ng/ml)

Heavy metals	Response	AMH in 2nd follow-up visit				AMH in 5th follow-up visit
	quartiles*	Model 1		Model 2		Model 1		Model 2
		β	95% CI	β	95% CI	β	95% CI	β	95% CI
Cu	1	-	-	-	-	-	-	-	-
	2	0.25	−0.02-0.48	0.29	−0.01-0.56	−0.17	−0.42-0.08	−0.07	−0.38–0.22
	3	0.12	−0.10-0.35	0.14	−0.13-0.41	−0.30	−0.56 - −0.05	−0.34	−0.65 - −0.03
	4	0.12	−0.10-0.34	0.11	−0.15-0.39	−0.35	−0.60 - −0.10	−0.43	−0.73 - −0.13
Al	1	-	-	-	-	-	-	-	-
	2	0.04	−0.18-0.27	0.08	−0.18-0.35	0.15	−0.10-0.40	0.20	−0.10-0.52
	3	−0.07	−0.30-0.15	−0.03	−0.31-0.24	0.20	−0.05-0.45	0.21	−0.09-0.52
	4	−0.03	−0.27-0.19	−0.01	−0.27-0.26	0.25	−0.01-0.50	0.26	−0.03-0.57
Pb	1	-	-	-	-	-	-	-	-
	2	−0.04	−0.27-0.17	−0.05	−0.31-0.20	−0.10	−0.36-0.14	−0.17	−0.48-0.13
	3	−0.09	−0.32-0.14	−0.11	−0.39-0.15	−0.09	−0.35-0.16	−0.17	−0.49-0.13
	4	−0.02	−0.25-0.20	−0.02	−0.29-0.25	−0.10	−0.36-0.15	−0.14	−0.47-0.17
Cr	1	-	-	-	-	-	-	-	-
	2	0.15	−0.07-0.38	0.15	−0.11-0.42	0.01	−0.25-0.25	−0.06	−0.36-0.23
	3	0.06	−0.15-0.28	0.13	−0.12-0.39	−0.10	−0.35-0.15	−0.08	−0.40-0.23
	4	0.13	−0.09-0.36	0.18	−0.09-0.45	−0.14	−0.39-0.10	−0.22	−0.53-0.08

Model 1: adjusted for enter age

Model 2: adjusted for enter age, smoking status, BMI, physical activity, menarche age, educational level, marital status and parity

*First level of quartiles are given as reference level

Figure 1 displays the box plots comparing AMH levels across quartiles of the four studied pollutants at the 2^nd and 5^th follow-ups. As expected, means AMH levels at the 5^th follow-up were lower than those at the 2^nd follow-up, likely due to the increase in average age. Notably, at the 5^th follow-up, higher quartiles of Cr appeared to be associated with reduced AMH levels, consistent with the regression models results.

Fig. 1.

Box and whisker plots of the distributions of Cu, Al, Pb, and Cr in the samples from the A 2^nd and B 5^th follow-up visits

To evaluate the potential dose-response relationship between Cu and AMH at the 5^th follow-up, a linear regression analysis was performed using numeric Cu quartiles. The model indicated a negative trend in AMH levels from the first to the fourth quartile, with a slope of −0.02 (p = 0.467). However, this association was not statistically significant, suggesting no consistent dose-response relationship. Figure 2 illustrates AMH concentrations across the four Cu quartiles, showing a slight decrease from the first to the fourth quartile, with reduced variability in the third and fourth quartiles, supporting the presence of a potential weak trend.

Fig. 2.

Trend of AMH (ng/ml) by quartiles of Cu at 5^th follow-up

Discussion

In this population-based cohort study of Iranian women, we investigated the association between quartiles of various heavy metals and serum AMH levels. Our results suggest that increased Cu exposure may lead to decreased AMH levels.

Heavy metals have long been recognized as toxic, and clinical consequences that vary widely. Being non-degradable, they can adversely affect health through ingestion, inhalation, or dermal contact. These metals can bind to vital enzymes or replace other elements in biochemical reactions, leading to toxic effects [10]. There is evidence that some heavy metals are harmful to reproductive health [24–27].

Numerous experimental studies suggest several plausible mechanisms by which Cu may influence ovarian reserve. Fluctuations in Cu levels have been shown to impact the functions of neutrophils, monocytes, and lymphocytes within the immune system [20]. Elevated Cu levels may contribute to increased oxidative stress, inflammation, and immune disruption, which can damage cellular structures and impair normal ovarian function. This oxidative stress may lead to follicular atresia, reduced estrogen production, and ultimately premature ovarian insufficiency. For exampple, Kebapcilar et al. [20] conducted a study in Turkey showing that women with premature ovarian failure had higher blood serum Cu concentration compared to women with normal menstrual cycle. Experimental studies have also demonstrated that Cu can induce apoptosis in ovarian cells through pathways involving mitochondrial dysfunction and activation of apoptotic proteins, such as caspase 3, via the MAPK14-Nrf2 signaling pathway [28–30]. Additionally, Cu may interact with the hypothalamic-pituitary-gonadal axis, influencing the secretion of reproductive hormones and further impacting ovarian reserve [31, 32].

In our study, no significant association was found between Pb exposure and AMH levels. This aligns with findings from Christensen et al. [22], who reported no significant association between plasma Pb levels and AMH in women with primary ovarian failure. Similarly, Wafa et al. [33] in Egypt, found no significant association between plasma Pb levels and AMH in this population. Paksy et al. [34] also reported that Pb levels in the ovarian follicle fluid did not significantly affect progesterone secretion, an indicator of ovarian reserve. Furthermore, another study examining serum AMH and urinary Pb levels found no association between elevated Pb concentrations and decreased AMH [35].

Al is a ubiquitous in the environment [36], with the primary route of exposure being through the diet, particularly as food additives [37]. In our study, no statistically significant relationship was observed between Al exposure and AMH levels. Similarly, Ozal et al. [10] in Turkey reported higher serum Al levels in women with primary ovarian failure compared to controls, but the difference was not statistically significant.

Regarding Cr, some studies have reported significant associations between Cr exposure and diminished ovarian reserve, as well as potential benefits of Cr supplementation in improving metabolic dysfunction and lipid and carbohydrate metabolism in women with polycystic ovarian syndrome.

Although Cr (VI) and Cr (0) are commonly produced by industrial processes and can cause tissue damage by inducing oxidative stress and cellular damage. Hexavalent Cr leads to increased formation of reactive oxygen species (ROS) such as superoxide anions, hydroxyl radical and nitric oxide, decreased cell viability, increased cellular genomic DNA fragmentation, membrane damage, apoptotic cell death and necrosis [38]. Our study found no significant association between Cr concentration and AMH levels.

In our study, Cd concentration in serum samples were below the detection limit, preventing meaningful analysis. This contrasts with findings of Christensen et al. [22], who observed a positive correlation between Cd and serum AMH. Lee et al. [19] in South Korea reported that environmental Cd exposure (mean 0.97 µg/l) was associated with decreased AMH levels (mean 3.2 ng/ml) in premenopausal women aged 30 and 35. The discrepancy in the relationship between Cr concentration and AMH levels may be partly attributed to the differences in the age of the participants. In our study, the participants were older (42 and 51 years old at the 2nd and 5th visits) and had lower AMH values (0.68 and 0.06 ng/ml) compared to the participants in Lee et al.‘s study (30–35 years old, AMH 0.97 ng/ml).

Variations findings across studies may stem from differences in environmental or occupational exposure levels, age distribution, reproductive status, health conditions, biological matrices used (serum vs. urine), and timing of sample collection [39–41]. Some evidence suggests that the association between Cd and AMH may be more pronounced in specific age groups, such as women aged 30–35 [19]. In our study, the lack of significant associations with Cd or Pb may reflect lower exposure levels, differences in participant characteristics, or the specific biomarkers and methods used.

We implemented rigorous measures, including standardized AMH assays, well-defined inclusion/exclusion criteria to control for factors affecting ovarian reserve, and multivariate adjustments for confounders, to minimize bias. However, unmeasured confounders—such as occupational heavy metal exposure, endocrine-disrupting chemicals, other environmental pollutants, and dietary factors—were not accounted for and could influence both heavy metal and AMH levels.

Regarding the strengths of this study, it is based on a well-established population-based cohort, minimizing intra-assay and inter-assay variability in AMH measurement since as all assays were conducted in the same laboratory by an experienced technician. Furthermore, data were collected at two time points over a 10-year follow-up period within this cohort, enhancing the study’s longitudinal rigor. Rigorous inclusion and exclusion criteria were applied to ensure that AMH measurements were not be confounded by conditions or interventions known to affect hormonal balance or the ovarian aging. This approach strengthens the validity of our findings on the relationship between heavy metal exposure and ovarian reserve. Additionally, statistical adjustments were made to control for potential confounders, ensuring the robustness of the results. Although a slight decrease in AMH levels was observed at higher Cu quartiles, the absence of statistical significance in the trend analysis calls for cautious interpretation.

The present study is not without its limitations. While our findings suggest an association between higher Cu levels and lower AMH, causality cannot be definitively established due to the observational nature of our research. While repeated samples were indeed available at the second and fifth follow-ups, both heavy metal and AMH measurements were collected concurrently at same time, reflecting simultaneous exposure and outcome assessments. Given the potential for short-term fluctuations in metal concentrations and hormone levels, we prioritized cross-sectional analyses to more accurately characterize these concurrent associations. Despite our efforts to adjust for a comprehensive set of known confounders in the multivariate analyses, we acknowledge that unmeasured factors, such as exposure to endocrine-disrupting chemicals and other environmental pollutants, may have influenced the observed associations. The dataset did not include detailed data on dietary intake, occupational exposure, and other environmental sources. It is recommended that subsequent studies take these factors into consideration to more effectively control for confounding variables. We excluded smokers from our study due to the well-established impact of smoking on ovarian reserve, as measured by AMH levels. Multiple studies have shown that smoking significantly reduces AMH levels, indicating diminished ovarian reserve [42–44]. This reduction is likely caused by toxic substances in cigarette smoke, which induce oxidative stress and damage ovarian follicular cells [45]. Furthermore, smoking disrupts endocrine function, altering AMH and other reproductive hormone levels [46]. Including smokers in our study could introduce confounding variability unrelated to the primary factors being investigated, thus affecting the accuracy of AMH as an indicator of ovarian reserve. However, this exclusion limits the generalizability of our findings to non-smoking populations. Our study population is drawn exclusively from the TLGS cohort, which may limit the external validity due to specific cultural, environmental, and genetic characteristics unique to this group.

Another limitation is the relatively advanced age of participants (mean of 42 and 51 years at two follow-ups), which may obscure the observed subtle effects of heavy metals on ovarian reserve, particularly in younger women. Additionally, AMH was the sole ovarian reserve marker used; inclusion of complementary markers such as antral follicle count (AFC) or FSH levels could provide a more comprehensive assessment. we did not employed the most sensitive assay for AMH measurement (pico AMH), which detects very low ovarian reserves [47], though Gen II and pico AMH assays correlate highly [48]. Samples were stored and not collected on specific menstrual cycle days; However, evidence suggests that AMH values remain stable during long-term storage and is unaffected by menstrual cycle [49]. Serum Cu levels may not fully capture long-term or cumulative exposure compared to urinary measurements.

Several factors may explain the of lack of significant association between AMH and Pb, Cd, Al, and Cr: the modest sample size and geographic restriction to a specific Tehran population may have limited exposure variability. Such homogeneity in environmental and lifestyle factors can constrain the ability to detect significant associations. Exposure measurement error is a recognized challenge in environmental epidemiology and may attenuate the observed associations, particularly when exposures are low or fluctuate over time. Although we employed validated bio monitoring methods, some degree of measurement imprecision is inevitable and could have limited our statistical power to detect subtle effects. Also the sample size and statistical power may have been insufficient to reveal associations for metals with lower exposures or weaker effects on ovarian reserve. We also did not explicitly model interactions or assess multicollinearity among the metals. These issues could affect the observed associations and may mask or exaggerate the effects of individual metals.

Conclusions

Our study identified a statistically significant association between elevated Cu levels and a modest reduction in AMH concentrations (0.34 to 0.43 ng/mL), suggesting a potential adverse effect of environmental Cu exposure on ovarian reserve. While the observed decline in AMH is modest, even small reductions may indicate compromised ovarian follicle quantity, which, in the context of age and reproductive history, could impact fertility potential. It is important to note, however, that AMH alone does not comprehensively predict fertility outcomes, which are multifactorial and complex. Thus, cautious interpretation is warranted, and further longitudinal clinical investigations are needed to clarify the implications of these changes for ovarian aging and reproductive function.

Clinically, these findings underscore the value of considering environmental heavy metal exposure in the assessment of patients presenting with low ovarian reserve. Screening for such exposures may enhance fertility counseling and management strategies. From a public health perspective, efforts should focus on minimizing environmental contamination through strict regulatory controls on industrial emissions and other sources of heavy metals to safeguard reproductive health in the broader population.

We recommend well-designed longitudinal cohort studies incorporating repeated biomarker measurements to capture temporal dynamics of ovarian reserve. Advanced exposure assessment techniques and multi-pollutant statistical models are critical to disentangle the independent and combined effects of co-exposures on ovarian function. Expanding the biomarker panel beyond AMH to include AFC, FSH, inhibin B, and employing highly sensitive assays such as picoAMH will improve the detection of subtle ovarian changes, particularly in women with low baseline reserve. Moreover, the application of sophisticated statistical approaches, including interaction analyses within multi-pollutant frameworks, will elucidate complex exposure interactions affecting ovarian health.

Authors’ contributions

Author contributions Zahra Namvar: Design, Conceptualization, Writing - original draft. Anoushiravan Mohseni-Bandpei: Conceptualization, Project administration, Writing- Reviewing and Editing. Abbas Shahsavani: Conceptualization, Supervision, Writing- Reviewing and Editing. Akbar Eslami: Writing- Reviewing and Editing. Maryam Mousavi: Formal analysis, Writing- Reviewing and Editing. Fatemeh Shokri: Performing experiments. Philip K. Hopke: Methodology, Writing- Reviewing and Editing. Fereydoun Azizi: Data collection, Writing- Reviewing and Editing. Fahimeh Ramezani Tehrani: Design, Conceptualization, Writing- Reviewing and Editing.

Funding

This study was funded by Shahid Beheshti University of Medical Sciences, Tehran, Iran (grant number#32607).

Data availability

All data generated or analyzed during this study are included in this published article and datasets used in this research are available upon request from the corresponding author.

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the principles of the Declaration of Helsinki, with careful consideration given to the health and interests of participants in the TLGS cohort. Measures were implemented to ensure the protection of personal data, and written informed consent was obtained from each participant or their guardian or legal representative. The study was approved by the ethics committee of Shahid Beheshti University of Medical Sciences (ethics code: IR.SBMU.ENDOCRINE.REC.1401.014).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.