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Article

Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis

by
In-Gwon Kim
1,2,
Seonmi Hong
2,
Sojeong Yim
1,2,
Jang-Hun Jeong
2,
Kyunghi Choi
2,
Ju-Hee Lee
3,
Young-Seoub Hong
4,
Sang-Yong Eom
1,2,5,
Heon Kim
6 and
Yong-Dae Kim
1,2,7,*
1
Chungbuk Environmental Health Center, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
2
Department of Preventive Medicine, College of Medicine, Chungbuk National University, Cheongju 28644, Republic of Korea
3
Division of Cardiology, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju 28644, Republic of Korea
4
Department of Preventive Medicine, College of Medicine, Dong-A University, Busan 49315, Republic of Korea
5
Department of Office of Public Healthcare Service, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
6
Department of Occupational and Environmental Medicine, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
7
Chungbuk Regional Cancer Center, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(10), 1157; https://doi.org/10.3390/atmos15101157
Submission received: 21 August 2024 / Revised: 26 September 2024 / Accepted: 26 September 2024 / Published: 27 September 2024
(This article belongs to the Special Issue Research on Air Pollution and Human Exposures)
Browse Figures
Versions Notes

Abstract

:
High blood pressure (BP) is a significant risk factor for heart and brain diseases. Previous studies have suggested that heavy metals including lead (Pb), mercury (Hg), and cadmium (Cd) contribute to hypertension. This study examined the combined effects of heavy metals on blood pressure, considering sex differences. A health impact survey was conducted among 561 residents living near waste incineration facilities in Cheongju, Korea. Blood samples were analyzed for heavy metal concentrations and blood pressure was measured. Multiple linear regression and Bayesian kernel machine regression (BKMR) methods were used to evaluate the relationship between heavy metal exposure and BP. Men had higher blood Hg and Pb concentrations, whereas women had higher Cd levels. Multiple linear regression analysis revealed no significant association between heavy metals and systolic blood pressure. However, Cd was significantly associated with increased diastolic blood pressure for the total group and women, whereas Pb was associated with increased diastolic blood pressure in men. In the BKMR analysis, the joint effect of the three metals was significantly associated with diastolic blood pressure for individuals below the 45th percentile and above the 55th percentile in men. These findings underscore the importance of considering sex differences in environmental health studies and public health strategies.

Graphical Abstract

1. Introduction

Many studies on the impact of air pollution on health have reported various diseases including cardiovascular disease [1,2,3,4]. Among these, heavy metals have attracted considerable interest due to their common exposure through various pathways in everyday life. While various types of harmful heavy metals affect health, the ones most closely associated with cardiovascular diseases are lead (Pb), mercury (Hg), and cadmium (Cd) [5,6,7]. Heavy metals released from various sources primarily increase their concentrations in the atmosphere [8,9,10] and can also lead to soil and water pollution, contaminating crops. As a result, exposure to heavy metals through food is also common [11]. Heavy metals can enter the human body through the respiratory or digestive systems, accumulate in various organs, and are not easily excreted [12]. These characteristics suggest that heavy metal exposure can lead to various chronic diseases among residents living near environmentally polluted areas and serve as a good indicator of long-term exposure owing to their long latency periods. Among the sources of exposure to these harmful heavy metals, waste incineration facilities can simultaneously emit various types of heavy metals depending on the materials being incinerated [13]. Therefore, people living near incineration facilities are likely to be chronically exposed to relatively high concentrations of heavy metals.
High blood pressure is a significant risk factor for heart and brain diseases worldwide and is a leading cause of premature death, necessitating systematic management as a public health issue [14]. Hypertension is a complex metabolic disease caused by the intricate interplay between genetic and various environmental factors [15]. Epidemiological studies have reported a positive association between exposure to heavy metals, such as Pb, Hg, and Cd, and an increased risk of hypertension [16,17,18]. Significant differences in the body burden of heavy metals based on sex have been observed [19], along with the variations in the prevalence of hypertension between men and women [20]. Therefore, the effect of heavy metal exposure on blood pressure is likely to differ according to sex. Based on these facts, it is plausible to hypothesize that exposure to Pb, Hg, and Cd released from incineration facilities might influence blood pressure, with this effect potentially varying based on sex.
Typically, the health impacts of exposure to harmful pollutants have been assessed using multiple linear regression models after adjusting for known confounding variables [21,22,23]. However, these methods estimate the individual effects while adjusting for other pollutants, thereby limiting their ability to evaluate the combined effects of related pollutants. Additionally, multiple linear regression assumes a linear relationship between heavy metal exposure and blood pressure, restricting the analysis of nonlinear relationships. Therefore, there is a need for alternative analytical methods that can overcome the limitations of traditional methods in environmental epidemiology studies, particularly for residents living in vulnerable areas with a high potential for combined exposure to various heavy metals.
New methods aimed at assessing the combined health effects of heavy metal exposure were developed; these included quantile-based g-computation (QGC) and Bayesian kernel machine regression (BKMR) [24,25]. QGC, based on the weighted quantile sum (WQS), transforms the concentrations of all components into quantiles, although it can lead to information loss [24,26]. In BKMR models, the exposure–response relationships of chemical mixtures is iterated by the Gaussian kernel function, allowing for the flexible estimation of these relationships in a nonlinear and nonadditive manner [25]. Additionally, BKMR addresses multicollinearity through hierarchical variable selection, which groups highly correlated components. This method has increasingly been utilized in recent environmental epidemiology studies to evaluate the health impacts of combined exposure to various heavy metals [27,28,29]. Therefore, this study aimed to evaluate the effect of combined exposure to blood Pb, Hg, and Cd on blood pressure using the BKMR method based on health impact surveys of residents living near incinerators, a representative vulnerable area. The results were compared with those obtained from multiple linear regression analyses.

2. Materials and Methods

2.1. Study Subjects

This study is based on the findings from a health impact survey conducted on residents living in the Buki-myeon area of Cheongju, Republic of Korea, where three waste incineration facilities are located within a 3 km radius, and on residents based in a control area located 16–23 km away from the incineration facilities. This study was conducted following the acceptance of a petition from the residents by the Ministry of Environment, with all participants living in the area for over 10 years and voluntarily participating in the research. Additionally, the study area is a small rural village where more than 80% of the residents are engaged in agriculture or livestock farming. Among 1112 participants, 561 were included in the final analysis, including both biomaterial samples and health examinations, no work experience at the incinerator, and no history of hypertension medication.

2.2. Measurement of Blood Heavy Metal Concentrations

The blood levels of Hg, Pb, and Cd were analyzed in the collected samples. Blood Hg was analyzed using a direct mercury analyzer (DMA 80 Milestone) and the gold amalgamation method with 100 µL of well-mixed blood placed in the sample container of the analyzer. The blood Pb levels were measured using a polarized Zeeman atomic absorption spectrophotometer (Model Z-2700, Hitachi, Tokyo, Japan), and blood Cd levels were measured using a flameless atomic absorption spectrophotometer with a Zeeman graphite furnace (Z-8270, Hitachi, Tokyo, Japan). The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded with a digital blood pressure monitor (Omron HEM-7143, Omron healthcare, Kyoto, Japan). Participants’ BP was measured while seated, following a minimum 30 min rest upon arrival at the examination site to ensure stable physiological condition.

2.3. Statistical Analysis

The concentrations of heavy metals in the blood showed a right-skewed distribution and were log-transformed for inclusion in the regression model. To adjust for confounding variables, the regression model included age, sex (in analyses of the total group), alcohol consumption, smoking status, monthly household income, and diabetes status as covariates. Multiple linear regression models were employed to assess the relationship between heavy metal levels and SBP and DBP using the following equations:
Yi = β0 + β1Hgi + β2Pbi + β3Cdi + βTZi + ei,
Here, Y represents SBP or DBP, while Hg, Pb, and Cd denote the log-transformed concentrations of Hg, Pb, and Cd, respectively. Z represents covariates such as age, sex (in analyses of the total group), alcohol consumption, smoking status, monthly household income, and diabetes status.
BKMR was applied to estimate the combined and nonlinear effects of the three heavy metals on blood pressure. The equations for the BKMR model are as follows:
Yi = h (Hgi + Pbi + Cdi) + βTZi + ei,
where h is the modeling function for the nonlinear effects. A Markov chain Monte Carlo algorithm with 10,000 iterations was employed to analyze potential interactions between metal exposures and response curves, while keeping other metal exposures fixed at the 25th, 50th, and 75th percentiles. Statistical analyses were conducted using R software (version 4.2.3; R Foundation for Statistical Computing), with a p-value of <0.05 considered statistically significant.

2.4. Ethical Considerations

The study protocol was reviewed and approved by the Institutional Review Board of Chungbuk National University (CBNU202012-HRBR-0207), and all participants provided written informed consent.

3. Results

Of the 561 participants, 253 were men and 308 were women. The proportion of smokers and drinkers, including former smokers, significantly differed between men and women. However, there were no statistically significant differences in the average household income or average age between the sexes. The mean body mass index (BMI) was significantly higher in women compared to men. Nevertheless, the average SBP and DBP did not differ significantly between the sexes. The concentrations of heavy metals in the blood were expressed as geometric means and geometric standard deviations, considering their distribution characteristics. Men had significantly higher blood concentrations of Hg and Pb, whereas women had significantly higher Cd levels (Table 1).
Multiple linear regression analysis revealed no statistically significant associations between the SBP and any of the heavy metals in the total group. However, an increase in blood Cd concentration was statistically significantly associated with an increase in DBP (β = 1.903, 95% confidence intervals [CI]: 0.165, 3.641). Stratified analyses demonstrated that an increase in blood Pb concentration was positively associated with DBP (β = 3.298, 95% CI: 0.362, 6.235) in men, while an increase in blood Cd concentration was positively associated with DBP (β = 2.628, 95% CI: 0.143, 5.113) in women. No heavy metals were significantly associated with changes in SBP (Table 2).
The BKMR model estimates the combined exposure effects by calculating the posterior mean and 95% CIs for blood pressure changes associated with variations in the levels of the three heavy metals. The model predicted variations in blood pressure as the levels of three heavy metals changed concurrently, comparing these variations to the median levels of the metal mixture. Additionally, it assessed the blood pressure changes linked to the interquartile range (IQR) for each heavy metal, while keeping the other two metals constant at the 25th, 50th, and 75th percentiles.
In men, the joint effect estimates of the three metals on SBP were −1.07 at the 25th percentile and 0.64 at the 75th percentile, with no statistically significant associations observed at any point (Figure 1A). Blood concentrations of Cd, Hg, and Pb were positively associated with SBP, although these associations were not statistically significant. The effects of IQR increases in the three metals on SBP were not significantly different depending on the levels of the other two metals (Figure 1B). A parabolic nonlinear relationship was observed between blood Cd levels and SBP. The blood levels of Hg and Pb were found to have a greater impact on SBP when the Cd level was above the 50th percentile compared to when it was above the 25th percentile (Figure 1C,D).
The BKMR analysis of heavy metal exposure and DBP in the male participants revealed that the combined effect of the three heavy metals was significantly associated with DBP below the 45th percentile and above the 55th percentile. The combined effect within the 25th to 75th percentiles ranged from −1.97 to 1.56 (Figure 2A). When the concentrations of the other two heavy metals were held constant at the 25th, 50th, and 75th percentiles, a statistically significant relationship was found between the increase in the IQR of blood Pb concentration and DBP. The estimated changes in DBP for each IQR increase in blood Pb were 1.70 (95% CI: 0.10, 3.30), 1.71 (95% CI: 0.13, 3.29), and 1.71 (95% CI: 0.07, 3.35), respectively. The effect of the IQR increase in blood levels of Pb, Cd, and Hg on DBP did not significantly vary depending on the concentrations of the other two heavy metals (Figure 2B). When the other two heavy metals were fixed at their median values, there was a nonlinear relationship between blood Cd and Hg concentrations and DBP, whereas blood Pb concentration demonstrated a positive linear relationship with DBP. The levels of Pb, Hg, and Cd in the blood had additive effects on changes in DBP (Figure 2C,D).
In women, the joint effects of the three metals had a decreasing trend in SBP as the levels of the metals increased, particularly when the levels were above the median. The joint effects at the 25th and 75th percentiles ranged from 0.97 to −1.42, with statistically significant decreases (Figure 3A). No significant associations were observed between the effects of IQR changes of individual metals on SBP (Figure 3B). A nonlinear relationship was observed between blood Hg levels and SBP. As blood Pb levels increased, the effect of blood Hg and Cd on SBP decreased (Figure 3C,D).
For DBP in women, the joint effect estimates of the three metals were –0.83 at the 25th percentile and 0.52 at the 75th percentile, respectively, with no statistically significant associations at any point (Figure 4A). Blood Cd and Pb concentrations were positively associated with DBP, with Cd levels showing a statistically significant association. The effects of IQR increases in Cd on DBP were 1.30 (95% CI: −0.01, 2.62), 1.31 (95% CI: 0.02, 2.60), and 1.32 (95% CI: −0.01, 2.65) when the other two metals were fixed at the 25th, 50th, and 75th percentiles, respectively (Figure 4B). A nonlinear relationship was observed between blood Hg levels and DBP. The effect of blood Hg and Pb on DBP decreased as blood Cd levels increased (Figure 4C,D).
In the total group, the estimated joint effect of the three metals on SBP was −0.12 at the 25th percentile and −0.17 at the 75th percentile, with no statistically significant associations observed (Figure S1A). Blood Cd concentrations showed a positive correlation with SBP, while Hg and Pb concentrations exhibited negative correlations; however, these associations were not statistically significant. The estimated changes in SBP associated with IQR increases in blood Cd were 0.42 (95% CI: −1.07, 1.90), 0.39 (95% CI: −1.08, 1.86), and 0.36 (95% CI: −1.12, 1.85) when the concentrations of the other two metals were held constant at the 25th, 50th, and 75th percentiles, respectively (Figure S1B). A parabolic nonlinear relationship was identified between blood Cd levels and SBP (Figure S1C,D).
In the BKMR analysis for DBP, the joint effect of the blood Pb, Cd, and Hg was significantly associated with DBP below the 45th percentile and above the 55th percentile. The joint effect ranged from −1.25 to 0.91 between the 25th and 75th percentiles (Figure S2A). When the other two metals were fixed at specific percentiles, increases in blood Cd IQR were significantly associated with DBP. The effects of IQR increases in Pb, Cd, and Hg on DBP did not vary significantly depending on the levels of the other two metals (Figure S2B). A nonlinear relationship was identified between blood Cd and DBP, whereas Pb and Hg exhibited a positive linear association with DBP (Figure S2C,D).

4. Discussion

In this study, the combined effects of exposure to Cd, Hg, and Pb on SBP and DBP were evaluated according to sex. The impact of exposure to these three heavy metals on blood pressure was analyzed using multiple linear regression analysis and the BKMR method. The multiple linear regression analysis for all participants, as well as for men and women separately, revealed no statistically significant relationship between the blood concentrations of the three heavy metals and SBP across all analysis models. However, there was a statistically significant positive correlation between blood Cd levels in the total group and women in terms of DBP and between blood Pb levels in men. Additionally, these findings were corroborated by BKMR analysis. Although certain heavy metals demonstrated a nonlinear relationship with changes in blood pressure, the consistency of the results across both analytical methods demonstrated the robustness of this study’s findings. The Pb exposure was higher in men than in women, whereas the Cd exposure was higher in women. This differential exposure suggests that the heavy metals contributing to increased blood pressure differ between men and women.
One finding of this study is that the health impacts of heavy metal exposure vary by sex. This is generally known to differ based on sex [30,31]. Previous research has suggested that these differences occur due to differences in body fat, hormone types, and concentrations of competitive elements such as calcium and iron, as well as differences in dietary habits and cosmetic use [32]. Additionally, due to physiological differences between men and women, there is a possibility that the health effects of exposure to hazardous substances may differ between the sexes. Studies have shown that men are more vulnerable to cardiovascular diseases like hypertension compared to women, despite women generally being more affected by heavy metal exposure [33,34,35]. Although the health effects of heavy metals are greater in women than in men, several studies have reported that men are more vulnerable to cardiovascular diseases, including high blood pressure, than women [35]. The heavy metal most frequently associated with cardiovascular disease is Pb. Navas-Acien et al. [1,36] reported that Pb exposure is related to hypertension, arteriosclerosis, and coronary artery disease and that men have a greater increase in the incidence of high blood pressure due to Pb exposure than women. Additionally, a British study [2] reported an association between blood Pb concentration and DBP in men, but no association with SBP and no association in women, aligning with the conclusions of this study.
Cd is a heavy metal that is well known for its association with cardiovascular diseases. Liu et al. [3] reported that Cd exposure is related to endothelial dysfunction, a risk factor for cardiovascular disease, whereas Barregard et al. [4] reported that Cd exposure increased the occurrence of cardiovascular disease in men, but did not increase the risk of cardiovascular disease in women at the same concentration, indicating a difference in sensitivity between sexes.
In the case of Hg, another heavy metal associated with cardiovascular disease, the incidence of high blood pressure increased in men as blood Hg concentration increased, while women did not show a statistically significant increase in disease occurrence at the same Hg concentration [37,38]. These results are consistent with the findings of the current study, suggesting that men are relatively more vulnerable than women to developing cardiovascular diseases, such as high blood pressure, due to exposure to harmful heavy metals.
The precise mechanism underlying this sex difference has not yet been revealed. The most compelling hypothesis concerns the role of sex hormones. Estrogen, a female hormone, is known to have a protective effect on the occurrence of cardiovascular disease, whereas testosterone, the male hormone, has a negative effect on the cardiovascular system [30]. Smith et al. [33] suggest that men may be more vulnerable to cardiovascular disease caused by heavy metals due to the effects of testosterone.
In older adults, it is known that as vascular elasticity decreases, SBP increases more than DBP, which increases pulse pressure (SBP—DBP) and thereby increases the risk of cardiovascular disease [38]. However, the current study showed results that were contradictory to these existing findings. BKMR analysis using pulse pressure as the dependent variable showed that the joint effects of heavy metals were negatively related to pulse pressure in both men and women (Figure S3). The reasons for these differences could not be explained within the scope of this study. This limitation may be inherent to observational rather than experimental research.
When planning this study, we anticipated that the residents of this area would be highly exposed to various harmful heavy metals emitted from incinerators. However, the results showed that the concentration of Pb in the air in this area ranged from 0.002 to 0.025 µg/Sm3 depending on the season, which did not exceed the Ministry of Environment’s regulatory standard of 0.5 µg/Sm3. Similarly, Cd levels ranged from non-detectable to 0.001 µg/Sm3, which also did not exceed the WHO standard of 0.005 µg/Sm3. Notably, Hg was not detected in the air. The geometric mean concentration of Pb in the blood of the subjects in this study was 1.56 µg/dL. This was similar to the average value of the general adult population in Korea at 1.60 µg/dL (Korean National Environmental Health Survey (KoNEHS), 3rd stage, 2015–2017) [39], but lower than that of residents in areas near abandoned metal mines in Korea (2.27 µg/dL). The mean concentration of Hg in the blood was 1.59 µg/L, which was lower than the average for the general adult population in Korea, which is 2.75 µg/L. The mean blood Cd concentration was 0.98 µg/L, which was lower than that of residents in areas near abandoned metal mines in Korea (2.27 µg/dL), but higher than that of the general adult population in Korea (0.77 µg/L) [40]. The relatively low exposure to Pb, Hg, and Cd suggests that the impact of these heavy metals on blood pressure might have been underestimated.
Existing research results on the relationship between blood heavy metal concentrations and blood pressure are diverse. Although the exact cause of this diversity is unknown, most heavy metal exposures are often simultaneous exposures to multiple heavy metals rather than to a single heavy metal, and the interactions between heavy metals can affect health indicators. However, each study measured only a portion of the heavy metals to evaluate the relationship, or even if several heavy metals were measured, the individual relationship with a single heavy metal was identified at the analysis step [1,2,3]; therefore, there is a possibility that various results may be derived depending on the study. From this viewpoint, assessing the combined effects of different types of heavy metals using methods like BKMR can be a crucial tool in environmental epidemiology, as it helps to reduce bias.
Understanding the toxicity mechanism of a single heavy metal and its relationship with health effects is vital from a toxicological perspective. However, from a public health perspective, it is more meaningful to evaluate the combined effects of multiple exposures to various heavy metals rather than the individual effects of a single heavy metal. Evaluating the joint effects of multiple exposures may be more important in the process of finding evidence to determine whether the health effects observed in residents of environmentally vulnerable areas are caused by air pollution sources such as incinerators.
This study has several limitations. First, while various factors that could influence blood pressure were adjusted for as covariates, adjustment for other important factors, such as dietary habits, was still insufficient. Second, blood pressure may change throughout the day; however, in this study, blood pressure was measured only once. There is, therefore, a possibility of individual variation depending on the measurement time. Finally, because this study excluded all participants taking blood pressure medication, the number of participants was relatively small, which may have resulted in somewhat low statistical power. Further research with larger sample sizes is required.
Nevertheless, this study is significant, as it evaluated the effect of combined exposure to heavy metals, such as Pb, Hg, and Cd, on blood pressure by sex, among residents of incinerator areas. These results suggest that men and women respond differently to heavy metal exposure and that environmental epidemiological surveys should consider these differences to effectively mitigate the health impacts of heavy metal exposure. Future studies should continue to explore the underlying mechanisms driving these sex-specific differences to inform better public health strategies.

5. Conclusions

In this study, we evaluated the combined effects of exposure to Cd, Hg, and Pb on blood pressure based on sex. While no statistically significant relationship was found between these heavy metals and SBP, a positive correlation was observed between blood Cd levels and DBP in women and between blood Pb levels and DBP in men.
This study highlights that the health impacts of heavy metal exposure differ based on sex, with men exhibiting higher levels of Pb exposure and women showing higher levels of Cd exposure. The influence of sex hormones, such as the protective effect of estrogen and the adverse impact of testosterone on cardiovascular health, might explain these differences. However, the precise underlying mechanisms remain unclear and warrant further investigation.
Despite its limitations, including the exclusion of participants taking BP medication and potential individual variations in measurements, this study is significant in demonstrating sex-specific responses to heavy metal exposure. Future research should continue to explore these differences to inform better public health strategies and effectively mitigate the health effects of heavy metal exposure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15101157/s1. Figure S1 Joint effect of the heavy metals on SBP in the total population using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the SBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of SBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles (C) Univariate exposure-response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure-response functions: when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in SBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury; Figure S2 Joint effect of the heavy metals on DBP in the total population using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the DBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of DBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles (C) Univariate exposure-response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure-response functions: when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in DBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury; Figure S3 Joint effect of the heavy metals on pulse pressure in men (A) and women (B) using the BKMR regression model. The model was adjusted for age, drinking, smoking, diabetes mellitus status, economic status, and body mass index. The overall effect of combined metal exposure: 95% CI of the pulse pressure estimate at each quantile compared to when all heavy metals are at median concentration.

Author Contributions

Conceptualization, Y.-D.K. and H.K.; methodology, S.-Y.E. and H.K.; data curation, S.H.; formal analysis, I.-G.K.; investigation, Y.-S.H., Y.-D.K., and H.K.; writing—original draft preparation, I.-G.K.; writing—review and editing, S.Y., J.-H.J., K.C., J.-H.L., and Y.-D.K.; supervision, Y.-D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Institute of Environmental Research (NIER), and the Ministry of Environment (MOE) of the Republic of Korea, grant number NIER-2019-04-02-069.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Chungbuk National University (CBNU-202012-HRBR-0207).

Informed Consent Statement

Individuals were provided information about the purpose of this study, and those who wished to participate provided written consent.

Data Availability Statement

The data are unavailable due to the regulations of the Ministry of Environment, Republic of Korea.

Acknowledgments

We thank the relevant ministries, including the Ministry of Environment, the National Institute of Environmental Research, and Cheongju-si, Republic of Korea.

Conflicts of Interest

The authors wish to declare that there are no competing interests regarding the publication of this article.

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Figure 1. Joint effect of the heavy metals on SBP in the men’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the SBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of SBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in SBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
Figure 1. Joint effect of the heavy metals on SBP in the men’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the SBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of SBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in SBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
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Figure 2. Joint effect of the heavy metals on DBP in the men’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the DBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of DBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in DBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
Figure 2. Joint effect of the heavy metals on DBP in the men’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the DBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of DBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in DBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
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Atmosphere 15 01157 g003
Figure 3. Joint effect of the heavy metals on SBP in the women’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the SBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of SBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in SBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
Figure 3. Joint effect of the heavy metals on SBP in the women’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the SBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of SBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in SBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
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Figure 4. Joint effect of the heavy metals on DBP in the women’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the DBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of DBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in DBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
Figure 4. Joint effect of the heavy metals on DBP in the women’s group using the BKMR regression model. The model was adjusted for age, sex, drinking, smoking, diabetes mellitus status, economic status, and body mass index. (A) The overall effect of combined metal exposure: 95% CI of the DBP estimate at each quantile compared to when all heavy metals are at median concentration. (B) Single pollutant association: 95% CI of DBP estimates for the interquartile range change of each heavy metal concentration, with the other metals fixed at the 25th, 50th, and 75th percentiles. (C) Univariate exposure–response functions and 95% confidence bands for each heavy metal, with other pollutants fixed at the 50th percentile. (D) Bivariate exposure–response functions when the levels of other heavy metals are at the median, and the levels of the expos2 metal (row) are at the 25th, 50th, and 75th percentiles, respectively, illustrating the change in DBP level according to the variation in the expos1 (column) concentration. Cd = cadmium, Pb = lead, Hg = mercury.
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Table 1. Demographic characteristics of the study participants.
Table 1. Demographic characteristics of the study participants.
CharacteristicsTotal
(n = 561)		Men (n = 253)Women (n = 308)p-Value
Smoking status
Non-smoker367 (65.42)67 (26.48)300 (97.40)<0.001
Smoker194 (34.58)186 (73.52)8 (2.60)
Drinking status
Non-drinker249 (44.39)172 (67.98)77 (25.00)<0.001
Drinker312 (55.61)81 (32.02)231 (75.00)
Monthly income
<2 million won312 (55.61)140 (55.34)172 (55.84)0.372
2–6 million won 146 (26.02)72 (28.46)74 (24.03)
>6 million won7 (1.25)4 (1.58)3 (0.97)
Unknown96 (17.11)37 (14.62)59 (19.16)
Age (years)66.38 ± 12.1365.83 ± 11.2966.83 ± 12.780.295
BMI (kg/m2)24.19 ± 3.5923.73 ± 3.3324.57 ± 3.76<0.05
SBP (mmHg)129.31 ± 14.58130.02 ± 14.80128.73 ± 14.400.313
DBP (mmHg)76.47 ± 9.3976.91 ± 9.7376.11 ± 9.110.199
Blood Hg (µg/L)1.59 (1.91)2.04 (1.81)1.30 (1.86)<0.001
Blood Pb (µg/dL)1.56 (1.54)1.80 (1.48)1.38 (1.53)<0.001
Blood Cd (µg/L)0.98 (1.61)0.84 (1.61)1.10 (1.55)<0.001
Values are presented as arithmetic mean ± arithmetic standard deviation or geometric mean (geometric standard deviation) or number (%). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hg, mercury; Pb, lead; Cd, cadmium.
Table 2. Associations of blood Hg, Pb, and Cd levels with blood pressure.
Table 2. Associations of blood Hg, Pb, and Cd levels with blood pressure.
β (95% CI)
SBPDBP
Total
Blood Hg–0.250 (–2.388, 1.887)0.425 (–0.922, 1.772)
Blood Pb–0.752 (–3.737, 2.233)1.720 (–0.161, 3.602)
Blood Cd0.917 (–1.842, 3.675)1.903 (0.165, 3.641)
Men
Blood Hg0.899 (–2.329, 4.127)1.465 (–0.525, 3.456)
Blood Pb1.125 (–3.637, 5.887)3.298 (0.362, 6.235)
Blood Cd0.990 (–3.007, 4.988)1.457 (–1.008, 3.922)
Women
Blood Hg–1.087 (–3.925, 1.750)–0.309 (–2.129, 1.510)
Blood Pb–2.231 (–6.075, 1.614)0.612 (–1.853, 3.077)
Blood Cd1.805 (–2.070, 5.679)2.628 (0.143, 5.113)
SBP, systolic blood pressure; DBP, diastolic blood pressure; Hg, mercury; Pb, lead; Cd, cadmium. The model was adjusted for sex (only for the total population), age, smoking, drinking, diabetes mellitus status, economic status, and body mass index.
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Kim, I.-G.; Hong, S.; Yim, S.; Jeong, J.-H.; Choi, K.; Lee, J.-H.; Hong, Y.-S.; Eom, S.-Y.; Kim, H.; Kim, Y.-D. Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis. Atmosphere 2024, 15, 1157. https://doi.org/10.3390/atmos15101157

AMA Style

Kim I-G, Hong S, Yim S, Jeong J-H, Choi K, Lee J-H, Hong Y-S, Eom S-Y, Kim H, Kim Y-D. Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis. Atmosphere. 2024; 15(10):1157. https://doi.org/10.3390/atmos15101157

Chicago/Turabian Style

Kim, In-Gwon, Seonmi Hong, Sojeong Yim, Jang-Hun Jeong, Kyunghi Choi, Ju-Hee Lee, Young-Seoub Hong, Sang-Yong Eom, Heon Kim, and Yong-Dae Kim. 2024. "Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis" Atmosphere 15, no. 10: 1157. https://doi.org/10.3390/atmos15101157

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