Abstract
Hypertension is a prevalent condition that contributes significantly to the global disease burden. Recent research endeavors have been investigating the potential causal link between metal exposure and the development of hypertension, yet consensus remains elusive. Nevertheless, studies examining the interplay among metal exposure, hypertension, and oxidative stress are relatively limited. This study utilized data from a cross-sectional survey conducted in southern Jiangxi Province, China. We evaluated urinary concentrations of 19 metals, including aluminum and manganese, in conjunction with measurements of systolic and diastolic blood pressures, as well as three oxidative stress biomarkers: glutathione peroxidase (GSH), superoxide dismutase (SOD), and malondialdehyde (MDA). In the monometallic model, chromium, iron, manganese, and molybdenum exhibited positive correlations with blood pressure. These findings were consistent in the mixed exposure model. Conversely, all the aforementioned metals exhibited a negative correlation with GSH and SOD, while demonstrating a positive correlation with MDA. Mediation effect analysis revealed that GSH and SOD mediated the relationships between urinary concentrations of aluminum, iron, manganese, and antimony and blood pressure. In contrast, MDA mediated the associations between urinary silver and antimony and blood pressure. Furthermore, GSH and SOD were identified as mediators in part of the relationship between mixed metal exposure and blood pressure, with mediation rates of 19.09% for GSH and 27.36% for SOD. The results of this study suggest that exposure to both individual and combined metals effects blood pressure levels, which are further associated with changes in oxidative stress levels. Moreover, oxidative stress levels may modulate the changes in blood pressure related to metal exposure, providing a basis for further investigation into the health risks associated with these metal exposures.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-23078-4.
Keywords: Urinary metals, Blood pressure, Oxidative stress, BKMR, Mediation analysis
Introduction
Hypertension is a medical condition defined by blood pressure levels surpassing a designated threshold, serving as a significant risk factor for the development of cardiovascular diseases [1]. As reported in the World Health Organization's Global Hypertension Report 2023, the prevalence of hypertension has increased dramatically since 1990, escalating to 1.3 billion cases by 2019, which is double the 650 million cases recorded 29 years prior [2]. This upward trend is similarly observed in China, where the incidence of hypertension continues to rise [3]. The mechanisms underlying the onset of hypertension remain poorly understood. Traditional risk factors, such as age, sex, body mass index (BMI), and adverse lifestyle behaviors, provide only limited understanding of the progression of this condition [4]. Additional factors, including environmental influences (such as air pollution, noise, and stress), genetic predispositions, intestinal microbiota, and other as-yet-unidentified elements, may play a significant role in the development of hypertension.
Over the past decade, there has been a growing focus among researchers on exploring the potential association between heavy metal exposure and the prevalence of hypertension, leading to an accumulation of epidemiological evidence across various countries and regions. An analysis of NHANES data from 2011 to 2014 revealed a negative correlation between urinary manganese levels and blood pressure [5]. Furthermore, a study conducted across four Asian countries demonstrated that lead continues to exert toxic effects on human blood pressure even at low urinary lead concentrations, while selenium was identified as having a protective effect [1]. Lead exposure was also found to cause hypertension in a cross-sectional study of human biomonitoring in China [6]. Several studies have reported either a negative correlation or no significant association between selenium levels and blood pressure [7–9]. The evidence concerning the relationship between cadmium exposure and blood pressure is similarly inconsistent. A large-scale study within the general population of the United States identified a positive association between cadmium exposure and hypertension [10]. Conversely, a cross-sectional survey conducted in Canada demonstrated a negative association between cadmium exposure levels and blood pressure readings [11]. In studies of children and adolescents in the United States, urinary barium has been identified as a major contributor to elevated blood pressure in urinary metal mixtures [12]. The association between various metals, including arsenic, cobalt, and nickel, and hypertension has been documented in the literature [13, 14]. However, it is crucial to recognize that humans are concurrently exposed to a wide range of metals in real-world scenarios, which may lead to interactions among these metals. Notably, some researchers have identified that iron within the human body influences the absorption of arsenic, aluminum, and cadmium [15]. Inconsistencies observed in study results may be attributable to various factors, including the study design, the characteristics of the study population, the methods employed for exposure assessment, and the control of confounding variables such as age, sex, and lifestyle. Furthermore, interactions among different metallic elements, which may manifest as synergistic or antagonistic effects, could also play a significant role in the variability of study outcomes.
Notably, over half of the elements essential for physiological functions in the human body are metals, including iron, zinc, selenium, and copper [16]. Both deficiencies and excesses of metals can result in adverse health outcomes. The toxicological impacts of metals are often observed in the core molecular processes associated with oxidative stress, notably through disturbances in the generation of reactive oxygen species. This disequilibrium can culminate in lipid peroxidation and cellular damage, including DNA impairment [17]. Prior research within the scientific community indicates a robust correlation between oxidative stress and the onset of hypertension. Oxidative stress induces vasoconstriction and impairs endothelial function, which is crucial for vasodilation, consequently leading to elevated blood pressure [18]. Mercury exposure, recognized as a toxic metal, has been demonstrated in animal studies to alter oxidative and antioxidant enzyme activities, thereby inducing oxidative stress and resulting in vascular dysfunction [19]. Similarly, research has indicated that arsenic and lead can also affect blood pressure. Extended exposure to toxic metals induces oxidative stress, thereby impairing the diastolic and constrictive functions of blood vessels, leading to endothelial dysfunction and the onset of hypertension. The role of zinc and copper in blood pressure regulation has gained widespread recognition, supported by evidence indicating reduced bioavailability of these elements in individuals with hypertension [20]. Metallic elements are integral to maintaining human health, with their equilibrium closely linked to the regulation of blood pressure and cardiovascular health. A comprehensive understanding of the complex interactions between metallic elements and hypertension is crucial for the development of effective preventive and therapeutic strategies.
Ganzhou City, located in the southern part of Jiangxi Province, is abundant in mineral resources and is renowned as the"Kingdom of Rare Earths". This study employs a cross-sectional survey methodology to examine the association between 19 urinary metals and indicators of blood pressure and oxidative stress within the specified region. Traditional regression analysis techniques face significant limitations when assessing the relationship between metal exposure and blood pressure in populations, primarily due to the presence of metal covariance and the variability in actual exposure levels. To overcome these challenges, we utilized a Bayesian kernel machine regression model, which provides a more precise evaluation of the association between mixed metal exposure and blood pressure.
Materials and methods
Participants and criteria for inclusion and exclusion
Participants in our study were selected from a cohort involved in a cross-sectional survey conducted by our research team in 2023 in Ganzhou City, Jiangxi Province, China. The survey aimed to examine the health implications of environmental exposures. In accordance with the study protocol, participants underwent extensive assessments, which included completing questionnaires, undergoing physical examinations, and providing fasting venous blood and morning urine samples. Blood pressure measurements were performed on-site for all participants. The classification of hypertension adhered to the criteria outlined in the World Health Organization's 2023 Global Report on Hypertension [2]. Hypertension was characterized by a systolic blood pressure (SBP) of 140 mmHg or greater, or a diastolic blood pressure (DBP) of 90 mmHg or greater, with a diagnosis confirmed if either threshold was reached. Participants were excluded based on the following criteria: (1) incomplete questionnaire data, (2) presence of severe cardiovascular disease or malignancy, (3) failure to provide blood or urine samples, and (4) abnormal urine metal levels. As a result, the study included a total of 659 participants.
The research protocol received approval from the Ethics Committee of Gannan Medical University (approval code: 2019218). We affirm that all aspects of the study adhered to relevant guidelines and regulations, ensuring compliance with the principles outlined in the Declaration of Helsinki. All participants were adults aged 18 years or older, and each provided written informed consent prior to participation in the study.
Measurement of urinary metals
The PerkinElmer NexION 350X, an inductively coupled plasma mass spectrometer produced by PerkinElmer (USA), was utilized to ascertain the concentrations of various urinary metals, including aluminum (Al), titanium (Ti), vanadium (V), chromium (Cr), iron (Fe), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), strontium (Sr), molybdenum (Mo), silver (Ag), cadmium (Cd), antimony (Sb), thallium (Tl), and lead (Pb). Urine specimens were collected from the participants and promptly transferred to 15 mL polypropylene centrifuge tubes. These samples were subsequently stored in a laboratory freezer maintained at a temperature of −80 °C. Before assaying the urine samples, remove the sample from the refrigerator and allow it to reach room temperature. A total of 500 µL of urine was diluted with 15 µL of 65% (v/v) HNO3 and left to acidify overnight in the refrigerator at a temperature of 4 ℃, the urine was then diluted to 5 mL with 1% HNO3 and centrifuged at 1000 rpm. The processed samples should be determined as soon as possible within 72 h. Due to the large number of elements determined, germanium, indium and bismuth were chosen as internal standards. In order to ensure the quality of the test, we used 1% HNO3 to clean the injection pipeline before injection and analysis, and when the background value was stable, we then carried out the determination of the standard curve, and ensured that the R2 of all the standard curves was > 0.999. Quality control was performed by spiking recovery experiments, which ranged from 89 to 105% for all metal elements. We corrected the final concentrations of urinary metals using urinary creatinine. Table S1 illustrated the basic profile of urinary metals in the whole population, including the limit of detection (LOD), detection rate and corrected final concentration for each metal element (μg/g). LOD/2 values are substituted for metal concentrations below the detection limit. As the proportion of participants with urinary barium concentrations below the limit of detection was 50%, further analysis of urinary barium was not pursued.
Oxidative stress biomarkers
The levels of superoxide dismutase (SOD), glutathione (GSH) and malondialdehyde (MDA) in plasma were quantitatively determined by enzyme-labeled instrument.
SOD activity was assessed via the hydroxylamine method (No. A001-1–2, Nanjing Jiancheng Bioengineering Institute, Nanjing, China), 0.05 mL of serum was taken according to the instructions, mixed with reagent with a vortex mixer, bathed in water at 37℃ for 40 min, and left at room temperature with chromogenic agent for 10 min, with measurements taken at a wavelength of 550 nm and expressed in U/mL. GSH concentrations were determined through a colorimetric assay (No. A006-1–1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China), took 0.05 mL of serum, mixed with reagent according to the instructions with a vortex mixer, centrifuge at 3500 rpm for 10 min, took the supernatant to be measured, with absorbance readings at 420 nm and results reported in mg/L. MDA levels were quantified utilizing the thiobarbituric acid (TBA) assay (No. A003-1–1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China), 0.05 mL of serum was taken, mixed with reagent according to the instructions with a vortex mixer, bathed in water at 95℃ for 40 min, cooled by running water, 4000 rpm centrifuged for 10 min, and the supernatant was taken, the absorbance wavelength was 532 nm, and the results were expressed in nmol/mL.
Covariates
Data collection was facilitated through structured questionnaires; prior to their administration, investigators underwent professional training to minimize potential bias, and respondents were duly informed. A thorough physical examination was performed, encompassing measurements of height, weight, heart rate, and blood pressure. The final analysis incorporated covariates such as age, gender, body mass index (BMI), and educational attainment. All statistical models employed in this study were adjusted for these covariates to isolate the effects of interest and enhance the validity of our conclusions.
Statistical analysis
The description of the data was conducted by selecting appropriate indicators based on the data type. Continuous data and data conforming to a normal distribution were characterized using the mean ± standard deviation, whereas data not conforming to a normal distribution were described using the median and quartiles. Categorical variables were summarized by frequency and percentage. Differences between datasets were evaluated using Student's t-test, the Mann–Whitney U-test, and the chi-square test. Due to the non-normal distribution of data on urinary metal concentrations, blood pressure, and indicators of oxidative stress, these variables were log-transformed for correlation analysis.
Polynomial logistic regression models were utilized to investigate the relationships between individual metals and blood pressure, individual metals and markers of oxidative stress, and the interplay between blood pressure and oxidative stress. These models were chosen due to their less stringent data requirements compared to traditional regression models. Urinary metal concentrations were stratified into creatinine-corrected quartiles, with the group exhibiting the lowest concentrations designated as the reference category.
In recent years, Bayesian kernel machine regression (BKMR) has gained prominence as a widely employed analytical technique in environmental health research. This method is especially adept at examining the combined effects of exposure to metal mixtures on blood pressure levels and at elucidating the interactions among various metals. As a non-parametric regression approach, BKMR offers significant advantages over traditional linear regression methods by accommodating both linear and non-linear relationships. Our model executes Markov chain Monte Carlo iterations to yield posterior inclusion probabilities (PIP). In the context of the metal mixing model, we opted to incorporate 19 metal elements to estimate the anticipated variations in all mixtures concerning blood pressure levels. For the single metal model, to assess the change in blood pressure levels from the 25 th to the 75 th percentile for an individual metal, all other metals were held constant at three predetermined values (25 th, 50 th, and 75 th percentiles).
Mediating effects models were utilized to evaluate the mediating role of oxidative stress levels in the association between exposure to single or mixed metals (all 19 metals included) and blood pressure levels. In this investigation, the independent variable (X) was classified as either single or mixed metals, while the mediator variable (M) represented oxidative stress levels. The dependent variable (Y) was defined as blood pressure levels. Within this analytical framework, several key effects can be derived: the direct effect (DE), which denotes the impact of single or mixed metals on blood pressure levels in the absence of oxidative stress mediation. The indirect effect (IE) represents the influence of single or mixed metals on blood pressure levels mediated through oxidative stress, while the total effect (TE) is defined as the aggregate of both indirect and direct effects. Furthermore, we will compute the mediating effect ratio.
All data were analyzed using R software (version 4.3.3, R Foundation for Statistical Computing). P-values less than 0.05 were considered to indicate statistical significance.
Results
Basic information description
The study population comprised 659 participants with a mean age of 49.85 years. The majority were female (82.40%), and most had attained an educational level of junior or senior high school (80.58% of the total population). The per capita monthly household income was predominantly in the lower range (1001–3000 RMB, 58.73%). Furthermore, 32.32% of the participants were diagnosed with hypertension, while 67.68% were not. The subgroup with hypertension was older on average (51.00 ± 11.77 years) compared to the non-hypertensive subgroup (49.30 ± 10.84 years) and exhibited higher rates of smoking and alcohol consumption. Significant differences were observed between the hypertensive and non-hypertensive groups in terms of education level, BMI, and occupation. The mean levels of oxidative stress markers measured among the participants were 127.28 U/mL for SOD, 4.03 mg/L for MDA, and 1.19 nmol/mL for GSH. As anticipated, levels of GSH and SOD were reduced, while MDA levels were elevated in hypertensive individuals compared to those without hypertension, with all differences reaching statistical significance (P < 0.05) (Table 1).
Table 1.
Basic characteristics and clinical parameters of the study population
| Overall (n = 659) |
With hypertension (n = 213) |
Without hypertension (n = 446) |
P Value | |
|---|---|---|---|---|
| Age, years | 49.85 ± 11.17 | 51.00 ± 11.77 | 49.30 ± 10.84 | 0.075 |
| Sex | < 0.001 | |||
| Male | 116 (17.60) | 52 (24.41) | 64 (14.35) | |
| Female | 543 (82.40) | 161 (75.59) | 382 (85.65) | |
| Education | 0.001 | |||
| Without formal education | 83 (12.59) | 41 (19.25) | 42 (9.42) | |
| Primary school | 278 (42.19) | 93 (43.66) | 185 (41.48) | |
| Junior high school | 253 (38.39) | 67 (31.46) | 186 (41.70) | |
| Senior high school and above | 40 (6.07) | 12 (5.63) | 28 (6.28) | |
| Average monthly household income per capita (yuan) | 0.948 | |||
| ≤ 1000 | 126 (19.12) | 40 (18.78) | 86 (19.28) | |
| 1001–3000 | 387 (58.73) | 127 (59.62) | 260 (58.30) | |
| ≥ 3000 | 146 (22.15) | 46 (21.60) | 100 (22.42) | |
| BMI (kg/m2) | < 0.01 | |||
| < 18.5 | 19 (2.88) | 3 (1.41) | 16 (3.59) | |
| 18.5–24.5 | 386 (58.57) | 101 (47.42) | 285 (63.90) | |
| 24.5–28 | 197 (29.89) | 81 (38.03) | 116 (26.01) | |
| ≥ 28 | 57 (8.65) | 28 (13.15) | 29 (6.50) | |
| Smok | 0.015 | |||
| Yes | 102 (15.48) | 44 (20.66) | 58 (13.00) | |
| No | 557 (84.52) | 169 (79.34) | 388 (87.00) | |
| Drink | 0.211 | |||
| Yes | 111 (16.84) | 42 (19.72) | 69 (15.47) | |
| No | 548 (83.16) | 171 (80.28) | 377 (84.53) | |
| Occupation | < 0.001 | |||
| Worker | 382 (57.97) | 105 (49.30) | 277 (62.11) | |
| Farmer | 228 (34.60) | 97 (45.54) | 131 (29.37) | |
| Other | 49 (7.44) | 11 (5.16) | 38 (8.52) | |
| SOD (U/mL) | 127.28 (118.76,134.05) | 65.41 (50.76,93.53) | 100.31 (62.69,93.53) | < 0.001 |
| MDA (nmol/mL) | 4.03 (2.70,6.72) | 4.65 (4.40,5.11) | 4.50 (4.15,4.73) | < 0.001 |
| GSH (mg/L) | 1.19 (0.64,2.35) | 1.31 (1.24,1.45) | 1.48 (1.30,1.82) | < 0.001 |
BMI body mass index, GSH Glutathione, SOD Superoxide dismutase, MDA Malondialdehyde. Normally and non-normally distributed variables were presented as mean ± SD and median (IQR), respectively. For categorical variables, values were presented as number (percentage). Continuous variables were compared with Student's t-test or Mann–Whitney U test, and the category variables were compared with Chi-square tests
Distribution of the urinary metals
Detailed information on the detection limits and detection rates for each metal is provided in Table S1, where Pb shows a detection rate of 73.6%, which is significantly below the 80% threshold. Table S2 provides a comprehensive analysis of the distribution of 19 urinary metals, expressed in micrograms per gram of urinary creatinine, across the entire study cohort, differentiating between hypertensive and non-hypertensive participants. Except for Cr, As, Se, and Mo, the concentrations of all other metals were significantly higher in the hypertensive group relative to their non-hypertensive counterparts. Nevertheless, these differences did not achieve statistical significance.
Spearman's correlation analysis of the metals revealed that all 19 urinary metals displayed positive correlations, with the majority being statistically significant. Interestingly, Sr exhibited negative correlations with several metals, most notably Sb and Tl, with a correlation coefficient (rs) of 0.91 and a significance level of P < 0.01 (Figure S1).
The single effect of metal exposure on blood pressure and oxidative stress levels
Upon analyzing urinary metals as continuous variables, it was determined that elevated levels of Cr, Fe, Mn, and Ni in urine were associated with increased diastolic blood pressure. Conversely, a significant correlation with early systolic blood pressure was observed exclusively for Fe and Mn (p < 0.05). Utilizing a multiple linear regression model that categorized the metals into distinct groups, it was found that the β coefficients (95% confidence intervals) for diastolic blood pressure in group Q2 were as follows: Al 0.032 (0.002, 0.063), Ti 0.043 (0.013, 0.074), Cr 0.037 (0.006, 0.067), Mn 0.033 (0.003, 0.064), and Mo 0.033 (0.003, 0.063). Fe was also identified as being associated with the Q4 group. In contrast, only Fe, Mn, and Mo in urine showed statistically significant correlations with systolic blood pressure at specified levels across all analyzed metals. Specifically, only Fe, Sb, and Pb demonstrated correlations with blood pressure that resulted in trend p-values below 0.05 (Tables 2 and 3).
Table 2.
Single-metal regression models for the associations of urinary metals and SBP
| Metals | βa (95%CI) | Quartiles of whole urine metals (μg/g creatinine) | ||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | ||
| Al | 0.002 (−0.007,0.011) | Ref | 0.031 (−0.002,0.063) | 0.011 (−0.021,0.044) | 0.014 (−0.018,0.047) | 0.217 |
| Ti | −0.005 (−0.015,0.005) | Ref | 0.028 (−0.004,0.060) | −0.000 (−0.033,0.032) | −0.001 (−0.034,0.031) | 0.441 |
| V | 0.009 (−0.005,0.022) | Ref | 0.000 (−0.032,0.033) | 0.024 (−0.009,0.056) | 0.012 (−0.020,0.045) | 0.224 |
| Cr | 0.007 (−0.005,0.018) | Ref | 0.025 (−0.007,0.058) | 0.019 (−0.013,0.052) | 0.011 (−0.022,0.043) | 0.568 |
| Fe | 0.006 (0.001,0.012)* | Ref | 0.036 (0.004,0.068)* | 0.024 (−0.009,0.056) | 0.037 (0.004,0.070)* | 0.008 |
| Mn | 0.006 (0.001,0.012)* | Ref | 0.029 (−0.003,0.061) | 0.038 (0.006,0.070)* | 0.011 (−0.021,0.043) | 0.515 |
| Co | 0.003 (−0.002,0.007) | Ref | 0.012 (−0.020,0.045) | 0.015 (−0.017,0.048) | 0.016 (−0.017,0.049) | 0.099 |
| Ni | 0.006 (−0.001,0.012) | Ref | 0.020 (−0.012,0.053) | 0.004 (−0.029,0.037) | 0.022 (−0.010,0.055) | 0.092 |
| Cu | 0.004 (−0.006,0.014) | Ref | −0.001 (−0.034,0.031) | 0.004 (−0.029,0.037) | 0.009 (−0.024,0.041) | 0.543 |
| As | −0.004 (−0.021,0.012) | Ref | −0.001 (−0.033,0.032) | 0.015 (−0.017,0.048) | −0.005 (−0.037,0.027) | 0.715 |
| Zn | −0.001 (−0.006,0.005) | Ref | 0.013 (−0.019,0.045) | −0.000 (−0.032,0.032) | −0.006 (−0.039,0.026) | 0.751 |
| Se | 0.008 (−0.008,0.023) | Ref | 0.015 (−0.017,0.048) | −0.001 (−0.034,0.031) | 0.005 (−0.028,0.038) | 0.676 |
| Sr | −0.001 (−0.004,0.003) | Ref | 0.014 (−0.018,0.046) | 0.004 (−0.028,0.037) | 0.009 (−0.023,0.041) | 0.754 |
| Mo | −0.001 (−0.013,0.011) | Ref | 0.037 (0.005,0.069)* | 0.008 (−0.024,0.041) | −0.006 (−0.038,0.027) | 0.171 |
| Ag | 0.001 (−0.002,0.003) | Ref | −0.017 (−0.049,0.015) | 0.030 (−0.002,0.062) | 0.008 (−0.024,0.040) | 0.072 |
| Cd | 0.005 (−0.004,0.015) | Ref | 0.026 (−0.006,0.058) | 0.013 (−0.019,0.046) | 0.021 (−0.012,0.053) | 0.196 |
| Sb | 0.003 (−0.001,0.006) | Ref | −0.003 (−0.036,0.029) | 0.026 (−0.006,0.059) | 0.015 (−0.018,0.047) | 0.054 |
| Tl | 0.005 (−0.003,0.013) | Ref | −0.009 (−0.041,0.024) | 0.017 (−0.015,0.050) | 0.007 (−0.026,0.039) | 0.075 |
| Pb | 0.001 (−0.002,0.004) | Ref | 0.003 (−0.029,0.036) | 0.011 (−0.022,0.044) | 0.015 (−0.018,0.047) | 0.048 |
Al aluminium, Ti titanium, V vanadium, Cr chromium, Mn manganese, Fe iron, Co cobalt, Ni nickel, Cu copper, Zn zinc, As arsenic, Se selenium, Sr strontium, Mo molybdenum, Ag silver, Cd cadmium, Sb antimony, Ba barium, Tl thallium, Pb lead. All models had adjustments for age, sex, BMI, smoke, drink, education, family income, occupation
amultivariate linear regression, urinary metal and systolic blood pressure were ln-transformed. *p < 0.05
Table 3.
Single-metal regression models for the associations of urinary metals and DBP
| Metals | βa (95%CI) | Quartiles of whole urine metals (μg/g creatinine) | ||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | ||
| Al | 0.004 (−0.004,0.012) | Ref | 0.032 (0.002,0.063)* | 0.008 (−0.023,0.039) | 0.008 (−0.023,0.040) | 0.733 |
| Ti | −0.001 (−0.011,0.009) | Ref | 0.043 (0.013,0.074)* | 0.005 (−0.026,0.035) | 0.016 (−0.014,0.047) | 0.922 |
| V | 0.005 (−0.008,0.018) | Ref | 0.008 (−0.022,0.039) | 0.030 (−0.001,0.061) | 0.003 (−0.028,0.033) | 0.481 |
| Cr | 0.012 (0.001,0.023)* | Ref | 0.037 (0.006,0.067)* | 0.015 (−0.016,0.046) | 0.017 (−0.014,0.047) | 0.710 |
| Fe | 0.006 (0.001,0.012)* | Ref | 0.025 (−0.005,0.056) | 0.022 (−0.009,0.052) | 0.033 (0.002,0.064)* | 0.017 |
| Mn | 0.007 (0.001,0.012)* | Ref | 0.033 (0.003,0.064)* | 0.031 (0.000,0.062)* | 0.010 (−0.020,0.041) | 0.814 |
| Co | 0.004 (−0.001,0.008) | Ref | 0.003 (−0.028,0.033) | 0.007 (−0.024,0.038) | 0.014 (−0.017,0.045) | 0.176 |
| Ni | 0.007 (0.001,0.013)* | Ref | −0.000 (−0.031,0.030) | −0.005 (−0.036,0.026) | 0.009 (−0.022,0.040) | 0.428 |
| Cu | 0.005 (−0.005,0.014) | Ref | 0.002 (−0.029,0.033) | −0.006 (−0.038,0.025) | −0.001 (−0.032,0.030) | 0.758 |
| As | −0.004 (−0.020,0.012) | Ref | 0.005 (−0.026,0.035) | 0.012 (−0.019,0.042) | −0.002 (−0.033,0.028) | 0.730 |
| Zn | 0.002 (−0.004,0.007) | Ref | 0.008 (−0.022,0.039) | 0.016 (−0.015,0.046) | 0.011 (−0.020,0.041) | 0.443 |
| Se | 0.014 (−0.001,0.029) | Ref | 0.018 (−0.012,0.049) | 0.018 (−0.013,0.048) | 0.011 (−0.020,0.042) | 0.724 |
| Sr | −0.001 (−0.004,0.003) | Ref | 0.011 (−0.020,0.041) | 0.009 (−0.022,0.039) | −0.003 (−0.033,0.028) | 0.810 |
| Mo | 0.001 (−0.011,0.012) | Ref | 0.033 (0.003,0.063)* | 0.014 (−0.016,0.045) | −0.009 (−0.040,0.021) | 0.173 |
| Ag | −0.001 (−0.003,0.002) | Ref | −0.015 (−0.046,0.016) | 0.014 (−0.016,0.045) | −0.011 (−0.042,0.019) | 0.772 |
| Cd | 0.006 (−0.003,0.015) | Ref | 0.010 (−0.021,0.040) | 0.008 (−0.023,0.039) | 0.017 (−0.014,0.048) | 0.245 |
| Sb | 0.003 (−0.001,0.006) | Ref | 0.014 (−0.016,0.045) | 0.028 (−0.003,0.058) | 0.024 (−0.007,0.054) | 0.043 |
| Tl | 0.002 (−0.005,0.010) | Ref | −0.006 (−0.036,0.025) | 0.011 (−0.020,0.042) | 0.004 (−0.027,0.035) | 0.255 |
| Pb | 0.002 (−0.001,0.005) | Ref | 0.012 (−0.018,0.043) | 0.015 (−0.016,0.046) | 0.023 (−0.008,0.054) | 0.037 |
Al aluminium, Ti titanium, V vanadium, Cr chromium, Mn manganese, Fe iron, Co cobalt, Ni nickel, Cu copper, Zn zinc, As arsenic, Se selenium, Sr strontium, Mo molybdenum, Ag silver, Cd cadmium, Sb antimony, Ba barium, Tl thallium, Pb lead. All models had adjustments for age, sex, BMI, smoke, drink, education, family income, occupation
amultivariate linear regression, urinary metal and diastolic blood pressure were ln-transformed. *p < 0.05
In the single-metal model, increases in the natural ln-transformed urine levels of Fe, Mn, Sb, and Tl were associated with significant decreases in GSH and superoxide SOD, whereas increases in urinary Ag and Sb levels were associated with significant increases in MDA (all P < 0.05) (Fig. 1). Upon converting urinary metal concentrations into categorical variables for analysis, it was observed that, except for manganese, urinary Fe, Sb, and Tl remained negatively correlated with GSH and SOD. In contrast, MDA exhibited a positive correlation with urinary Fe, Co, As, Sb, and Tl, with all trend p-values being less than 0.05 (Tables S3-S5). A significant association was identified between oxidative stress markers and blood pressure, characterized by positive correlations between GSH and SOD with both diastolic blood pressure and systolic blood pressure, and negative correlations between MDA and both diastolic blood pressure and systolic blood pressure, with all trend p-values being less than 0.05 (Table 4).
Fig. 1.
Single-metal regression model of metal concentrations and levels of oxidative stress indicators. Both metal concentrations and oxidative stress indicators were ln transformed. GSH, Glutathione; SOD, Superoxide Dismutase; MDA, Malondialdehyde. All models were based on age, sex, body mass index, smoking, alcohol consumption, education, household income, and occupation
Table 4.
The regression analysis of the relationship between of oxidative stress and blood pressure
| βa(95%CI) | Q1 | Q2 | Q3 | Q4 | P-trend | |
|---|---|---|---|---|---|---|
| GSH | ||||||
| DBP | −0,075 (−0110,−0.040)* | Ref | −0.003 (−0.034,0.027) | −0.036 (−0.066,−0.005) | −0.062 (−0.092,−0.031) | < 0.001 |
| SBP | −0.085 (−0.122,−0.048)* | Ref | −0.005 (−0.037,0.027) | −0.038 (−0.070,−0.006) | −0.069 (−0.102,−0.037) | < 0.001 |
| SOD | ||||||
| DBP | −0.033 (−0.047,−0.019)* | Ref | −0.003 (−0.034,0.027) | −0.036 (−0.066,−0.005) | −0.062 (−0.092,−0.031) | < 0.001 |
| SBP | −0.038 (−0.053,−0.024)* | Ref | −0.005 (−0.037,0.027) | −0.038 (−0.070,−0.006) | −0.069 (−0.102,−0.037) | < 0.001 |
| MDA | ||||||
| DBP | 0.150 (0.066,0.234)* | Ref | 0.041 (0.010,0.071) | 0.035 (0.004,0.065) | 0.043 (0.012,0.074) | < 0.001 |
| SBP | 0.185 (0.097,0.273)* | Ref | 0.033 (0.001,0.066) | 0.033 (0.001,0.065) | 0.047 (0.015,0.080) | < 0.001 |
DBP Diastolic blood pressure, SBP Systolic blood pressure, GSH Glutathione, SOD Superoxide Dismutase, MDA Malondialdehyde. All models had adjustments for age, sex, BMI, smoke, drink, education, family income, occupation. Oxidative stress and blood pressure were ln-transformed
amultivariate linear regression, urinary metal and DBP were ln-transformed. *p < 0.05
The combined effect of multi-metal co-exposure on blood pressure and oxidative stress levels
Figure S2 illustrates the synergistic effects of various metal combinations on blood pressure and oxidative stress. The Bayesian Kernel Machine Regression (BKMR) analysis identified a positive correlation between both systolic and diastolic blood pressure levels and the concentrations of the metal mixtures. Likewise, concentrations of SOD and GSH were positively correlated with these metal concentrations, whereas MDA demonstrated a negative correlation. Table S6 presents the Posterior Inclusion Probability (PIP) values for the metals in each model. BKMR conducted evaluations using single-metal models, wherein all other metals were set to different percentiles (25 th, 50 th, and 75 th) to observe how individual metrics for each metal varied as they increased from the 25 th to the 75 th percentile (Figure S3).
Subgroup analyses and interaction analyses
In age subgroup analysis, Fe exposure was positively correlated with both systolic and diastolic blood pressure in people < 60 years old, and Co exposure was positively correlated with systolic blood pressure in people ≥ 60 years old (p trend < 0.05) (Tables S7-S8). Tables S9-S10 describe the results of the gender subgroup analysis. Fe and Pb exposure were positively correlated with systolic blood pressure in male group (p trend < 0.05).
In the bivariate interaction analysis of BKMR model, there were potential interactions between As, Cr and various metals, which caused the increase of GSH. In SOD, there were potential interactions between Cu and Mn, and no metal interactions were found in other indicators (Supplementary documents 2–3).
Oxidative stress mediated the association between heavy metals and blood pressure
In the analyses presented above, we explored the effects of both individual and combined metal exposures on oxidative stress markers and blood pressure levels. Furthermore, the potential role of oxidative stress markers as mediators in the association between urinary metal concentrations and blood pressure was assessed through causal mediation analyses. In the single-metal model, GSH and SOD were identified as mediators in the relationship between urinary levels of Al, Fe, Mn, and Sb and diastolic blood pressure. Additionally, GSH and SOD mediated the relationship between urinary levels of Fe, Ni and Sb and systolic blood pressure. Conversely, MDA was found to mediate the association exclusively for urinary Ag and Sb with both diastolic blood pressure and systolic blood pressure, with all results achieving statistical significance at P < 0.05 (Figs. 2, 3 and 4). In the mixed-metal model, our results demonstrated that GSH and SOD significantly mediated the association between combined urinary metal levels and diastolic blood pressure. In particular, GSH contributed to 19.09% of the mediation effect. Importantly, oxidative stress markers did not demonstrate a mediating role in relation to systolic blood pressure (Figure S4-S5).
Fig. 2.
Mediating effects of GSH on the correlation between mixed metal levels and blood pressure. SBP, Systolic blood pressure; GSH, Glutathione; All models were adjusted for age, sex, body mass index, smoking, alcohol consumption, education, household income and occupation. Both metal concentrations and oxidative stress indicators were ln transformed
Fig. 3.
Mediating effects of SOD on the correlation between mixed metal levels and blood pressure. SBP, Systolic blood pressure; SOD, Superoxide dismutase; All models were adjusted for age, sex, body mass index, smoking, alcohol consumption, education, household income and occupation. Both metal concentrations and oxidative stress indicators were ln transformed
Fig. 4.
Mediating effects of MDA on the correlation between mixed metal levels and blood pressure. SBP, Systolic blood pressure; MDA, Malondialdehyde; All models were adjusted for age, sex, body mass index, smoking, alcohol consumption, education, household income and occupation. Both metal concentrations and oxidative stress indicators were ln transformed
Discussion
Our study employed a regression model to assess the influence of 19 metals on blood pressure. The findings revealed that urinary concentrations of Fe, Mn, Sb, and Pb were positively associated with blood pressure. Conversely, metals such as Ti, V, As, and Se showed no significant association with blood pressure levels. Moreover, our mixed metal–metal model revealed positive associations between urinary metal concentrations and both blood pressure and the antioxidant markers GSH and SOD. In contrast, negative correlations with MDA were observed. Additionally, urinary concentrations of Fe, Mn, and Sb were significantly correlated with oxidative stress levels, which were, in turn, significantly associated with blood pressure. Mediation analysis suggested that oxidative stress partially mediated the relationships between urinary levels of Al, Fe, Mn, and Sb, as well as mixed metals, and blood pressure. The study's findings indicate that oxidative stress may play a pivotal role in mediating the relationship between metal exposure and alterations in blood pressure responses.
Aluminium (Al), the most prevalent metal in the natural environment, is not required for human physiological functions [21]. In everyday life, individuals may encounter Al through multiple sources, including food additives, vaccines, and packaging materials [22]. Previous animal research has demonstrated that Al exposure can lead to hypertension by impairing erythrocyte membrane function through the induction of oxidative stress [23]. Wiggers et al. demonstrated that exposure to low doses of Al leads to endothelial dysfunction and reduced nitric oxide (NO) bioavailability, thereby diminishing vascular reactive conductance and resistance electrophoresis, ultimately resulting in vascular dysfunction and elevated blood pressure [24]. In our investigation, Al exhibited a positive, albeit non-significant, correlation with blood pressure, while showing a significant negative correlation with GSH and SOD. Mediation effect analysis revealed that GSH and SOD played an indirect role in the relationship between Al exposure and blood pressure. The current epidemiological literature investigating the potential link between Al exposure and hypertension risk is relatively sparse. An Australian study involving occupational miners reported an elevated risk of cardiovascular disease associated with exposure to Al dust [25]. Similarly, a Chinese study on Al plant workers demonstrated that individuals with higher plasma Al levels had a significantly increased likelihood of developing hypertension [26]. In contrast, cross-sectional studies conducted among older adults found no significant correlation between blood Al levels and the risk of hypertension [27]. Moreover, a study investigating mother-infant pairs in the United States identified no association between prenatal Al exposure and neonatal hypertension [28]. The inconsistency in these findings may be due to varying levels of Al exposure across different populations and the use of different measurement markers. Consequently, there is an urgent need for further research to clarify the relationship between Al exposure and blood pressure variations.
The toxicity of chromium (Cr), a transition metal, is affected by its valence state, although Cr has been demonstrated to offer several health benefits, such as lowering blood pressure and preventing cardiovascular disease [29, 30]. Nonetheless, existing research indicates that elevated Cr exposure is associated with an increased risk of hypertension [31]. Our study identified a positive correlation between Cr concentrations and diastolic blood pressure, although no significant association was observed with systolic blood pressure. Currently, the research findings on the relationship between Cr exposure and hypertension remain inconclusive. Several cross-sectional studies suggest a potential association between elevated blood Cr levels and an increased risk of developing hypertension [13]. Research in regions with Cr exposure has indicated a positive correlation between such exposure and an elevated risk of developing hypertension [32]. In contrast, other studies propose that Cr might contribute to a reduced risk of hypertension. A comprehensive analysis of the NHANES 2017–2018 data did not identify any significant association between urinary Cr levels and the prevalence of hypertension or changes in systolic blood pressure; however, it did reveal a negative association with diastolic blood pressure. Moreover, additional subgroup analyses indicated that the relationship between urinary Cr concentration and hypertension may vary across different populations [33]. Two further studies have corroborated these findings [34, 35]. Iron (Fe) is an essential element in the human body, predominantly present in the forms of hemoglobin and ferritin. This element is critical for the body's ability to effectively transport and store oxygen [36]. However, disruptions in iron metabolic homeostasis can lead to oxidative stress. Excessive accumulation of Fe induces oxidative stress, inflammation, and endothelial dysfunction, potentially resulting in endothelial damage and impaired vascular reactivity, thereby contributing to elevated blood pressure and hypertension. In our study, urinary Fe levels demonstrated a positive correlation with both diastolic blood pressure and systolic blood pressure, as well as with markers of oxidative stress. Subsequent mediation analysis suggested that oxidative stress partially mediates the relationship between Fe concentrations and blood pressure. Concurrently, a case–control study conducted in Iran identified an inverse association between dietary Fe intake and hypertension [37]. Conversely, researchers in China utilized a large-scale prospective cohort design to uncover a U-shaped association between dietary Fe intake and the incidence of new hypertension cases [38].
Manganese (Mn), an indispensable micronutrient, is a vital constituent of numerous proteins and enzymes, contributing significantly to essential physiological processes such as carbohydrate and lipid metabolism, growth and development, reproductive health, and tissue formation [39]. Nevertheless, excessive Mn accumulation can result in toxicity, manifesting as neurological damage, impaired myocardial contraction, and the onset of hypertension [40]. Conversely, certain studies suggest that Mn exhibits antioxidant properties and may play a potential role in blood pressure regulation [41]. Oxidative stress is implicated in the pathogenesis of hypertension, and Mn intake demonstrates a multifaceted interaction with both conditions. Although moderate Mn supplementation may offer protective benefits against hypertension, excessive intake could potentially intensify oxidative stress, thereby contributing to increased blood pressure [42]. This effect arises because Mn serves as a cofactor for antioxidant enzymes that participate in redox reactions, during which these enzymes can produce free radicals as part of the biochemical processes [43]. The effect of Mn exposure on blood pressure and the incidence of hypertension continues to be a significant focus of scientific research. Numerous cross-sectional studies conducted in diverse geographical locations have reported a positive correlation between Mn exposure and an increased risk of hypertension [32, 44]. Conversely, other studies have identified a negative correlation between Mn levels and blood pressure measurements [5]. Notably, a study involving hypertensive patients revealed that blood Mn concentrations were not associated with blood pressure but were correlated with age and seasonal variations [45]. This study did not identify any significant differences in urinary Mn excretion between individuals with hypertension and their normotensive counterparts. However, within the framework of the monometallic model used for analysis, urinary Mn demonstrated a positive correlation with diastolic blood pressure and systolic blood pressure levels in continuous variable assessments. Additionally, analysis of categorical variables indicated that the β coefficients (95% confidence interval) for diastolic blood pressure and systolic blood pressure levels in the second quartile (Q2) group were 0.031 (0.000–0.062) and 0.038 (0.006–0.070), respectively, when compared to those in the lowest quartile of urinary Mn concentration group (Q1).
The research findings indicated a significant trend, illustrating a positive correlation between lead (Pb) and antimony (Sb) concentrations and blood pressure measurements. Notably, Pb exhibited a particularly strong association with both systolic blood pressure and diastolic blood pressure, as evidenced by a P-trend value of less than 0.05. A substantial body of epidemiological studies has similarly demonstrated a positive correlation between urinary Pb levels and blood pressure measurements [1]. Furthermore, a related study on exposure to airborne metals identified a dose–response relationship, wherein increased Pb exposure was associated with a heightened risk of developing hypertension [46]. Animal studies provide additional evidence that low-level Pb exposure is a contributing factor in the development of hypertension. It is important to note that some studies have reported either no correlation or a negative correlation between Pb levels and blood pressure [47, 48]. Exposure to Pb, a toxic metal, induces oxidative stress and damages endothelial cells, thereby impairing vasodilation [49, 50]. Furthermore, Pb exposure adversely affects kidney function, which may contribute to the onset of hypertension associated with lead toxicity [51]. In contrast, research investigating the association between Sb and hypertension remains limited. Existing research primarily utilizes data from the NHANES database. Evidence from two scientific surveys indicates a positive association between urinary Sb concentrations and the prevalence of hypertension [52, 53]. Additionally, a cross-sectional study examining environmental exposures among children in China demonstrated a dose–response relationship between urinary Sb levels and elevated blood pressure related to hypertension [54]. Conversely, a study analyzing data from the 2005 U.S. National Air Database reported no significant correlation between Sb levels and hypertension [55], a finding that was supported by another similar study [56]. The discrepancies observed across these studies may be attributed to factors such as racial differences and the use of varying markers for measuring Sb.
This cross-sectional study offers several advantages. We evaluated 19 different metals, encompassing both essential and toxic elements, thereby enhancing the study's relevance by reflecting actual environmental exposures. The application of multiple statistical models strengthens the validity and reliability of our findings regarding the relationship between exposure to multiple metals and markers of blood pressure and oxidative stress. Given that studies have found an association between exposure to certain metals and increased blood pressure, it is advisable to strengthen monitoring and control of pollution from these metals, especially in densely populated areas. Health education programs can be developed for specific populations, such as children or the elderly, to raise their awareness of the risks of metal exposure and to provide practical advice on reducing exposure. Nevertheless, several limitations must be considered. The intrinsic characteristics of cross-sectional studies constrain the capacity to establish causality, in contrast to cohort studies. Potential selection bias and recall bias arising from self-reported covariates may influence the study outcomes. Furthermore, the exclusive reliance on urinary metal levels as biomarkers of exposure presents limitations, as these levels represent excreted metals and may be influenced by renal function. Future research should address these limitations by selecting suitable biological samples to evaluate metal exposure, taking into account the distinct metabolic properties of different metals. Prospective studies are crucial for establishing temporality and providing more definitive evidence concerning the relationship between metal exposure and lipid metabolism.
Conclusion
In conclusion, our analysis examined the cumulative effects of metal exposure and the influence of various metals on blood pressure and oxidative stress marker levels. Our study demonstrated that urinary concentrations of Fe, Mn, Sb, and their respective metal mixtures were positively correlated with blood pressure levels. Furthermore, significant associations were identified between these metals and markers of oxidative stress, indicating that oxidative stress may mediate the relationship between metal exposure and blood pressure. Further research is necessary to substantiate our findings.
Supplementary Information
Acknowledgments
Clinical trial number
Not applicable.
Authors’ contributions
F.Y: Conceptualization, Data curation, Methodology, Software, Writing – original draft, Project administration, Formal analysis, Investigation. L.H: Conceptualization, Data curation, Methodology, Software, Project administration Formal analysis, Investigation. Y.X.J and C.Y.J: Data curation, Methodology, Project administration, Investigation. Y.H: Software, Project administration, Investigation. J.H and J.W: Investigation, Project administration, Investigation. G.H.H, L.J.Z and Q.X: Project administration, Investigation. X.K.Z and Y.F.G: Writing – review & editing, Supervision, Project administration.
Funding
This project was supported by the National Natural Science Foundation of China (No.81903364); the National Natural Science Foundation of Jiangxi Province (20242BAB25551), the Key science and technology project of Ganzhou Science and Technology Bureau (GZ2024YL015).
Data availability
The datasets utilized in this study are not publicly available due to restrictions imposed by author permissions. However, they can be obtained from the corresponding author through a formal application process.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Xiaokang Zhang, Email: Zhangxiaokaju@163.com.
Yanfang Gao, Email: gaoyanfang1201@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets utilized in this study are not publicly available due to restrictions imposed by author permissions. However, they can be obtained from the corresponding author through a formal application process.