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Article

The Impact of Blood Lead and Its Interaction with Occupational Factors and Air Pollution on Hypertension Prevalence

by
Yajun Gong
1,2,†,
Ying Wang
3,†,
Qiying Nong
1,
Peixia Hu
1,4,
Zhiqiang Li
1,3,
Xiangyuan Huang
1,4,
Meimei Zhong
1,4,
Xinyue Li
1,3,
Shaomin Wu
3,
Fangfang Zeng
3,
Na Zhao
1,2,3,4,
Yiru Qin
1,
Suhui Liu
1,3,
Jiaying Hong
1,
Ligang Hu
5,
Wangjian Zhang
3,* and
Yongshun Huang
1,2,3,4,*
1
Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou 510300, China
2
School of Public Health, Southern Medical University, Guangzhou 510515, China
3
Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-Sen Global Health Institute, Sun Yat-Sen University, Guangzhou 510080, China
4
School of Public Health, Shanxi Medical University, Taiyuan 030001, China
5
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Toxics 2024, 12(12), 861; https://doi.org/10.3390/toxics12120861
Submission received: 29 October 2024 / Revised: 22 November 2024 / Accepted: 26 November 2024 / Published: 27 November 2024
(This article belongs to the Section Human Toxicology and Epidemiology)
Browse Figure
Figure 1
<p>Flowchart of participant inclusion and exclusion.</p> ">
Versions Notes

Abstract

:
Large-scale epidemiological studies on the association of blood lead levels with blood pressure and hypertension prevalence are still limited, particularly among lead-exposed workers. The evidence is even more scarce on the interaction of blood lead levels with occupational variables and ambient air pollution levels. We developed mixed-effect models based on a large group of lead-exposed workers (N = 22,002). The results were also stratified by multiple groupings. Compared to participants with blood lead < 20 μg/L, those with levels > 20 μg/L had a 26–37% higher prevalence of hypertension, as well as a 0.65–13.7 mmHg higher systolic and diastolic blood pressure. Workers exposed to high PM10 levels had a 21–28% higher risk. Workers exposed to high temperatures had a 0.41–2.46 mmHg greater increase in blood pressure, and those not exposed to dust had a 1.29–1.65 mmHg greater blood pressure increase. Our findings suggested the negative impact of blood lead on blood pressure and the prevalence of hypertension, with workers exposed to high PM10 concentrations, those exposed to occupational high temperature, and those without dust exposure being more vulnerable.

1. Introduction

Due to its widespread use and environmental contamination, lead has multiple sources and pathways of exposure. Chronic lead exposure can lead to premature deaths, disease burden, and persistent negative effects on health [1,2]. According to estimates, in 2019, lead exposure resulted in a loss of 21.7 million disability-adjusted life years (DALYs) globally, including 4.6% of the burden of cardiovascular disease [3]. Among all subtypes of cardiovascular disease (CVD), hypertension and high blood pressure had the highest disease burden [4]. The World Health Organization estimated that a staggering 1.4 billion people worldwide are afflicted with hypertension [5]. An estimated 245 million adults have been diagnosed with hypertension in China alone [6]. This issue is particularly significant among workers who are exposure to higher concentrations of hazardous occupational factors. A large-scale epidemiological survey showed that, among Chinese workers, the prevalence of hypertension reached a staggering 25.7% [7], significantly surpassing the prevalence observed in the same age groups in the general population.
Previous epidemiological studies have identified lead exposure as a notable risk factor for hypertension in the general population [8,9], although the underlying biological mechanisms remain unclear. Some studies have investigated the association between lead exposure and hypertension among workers; however, the evidence is still limited and the conclusions are inconsistent. For instance, a study in Kenya reported a significant association between blood lead levels (BLLs) and changes in blood pressure [10] whereas a cohort study in the United States showed no association between hypertension and elevated blood lead from occupational sources [11]. Furthermore, existing studies may be challenged by small sample sizes and limited statistical power. Therefore, to clarify the hypertensive effect of BLLs among Chinese workers, an epidemiological study encompassing a large sample size is imperative.
In addition to lead, the workers may also be exposed to other occupational hazards, such as dust, noise, high temperature, and benzene, toluene, ethylbenzene, and xylenes (BTEX). The hypertensive impact of these hazards have been suggested in multiple previous studies [12,13,14,15]. Ambient air pollution may also play a significant hypertensive role among workers. The potential mechanisms include the notion that the indoor air quality largely depends on the ambient environment, particularly due to ventilation [16,17]. Additionally, workers engaged in outdoor activities may be directly exposed to ambient air pollution [18]. These occupational and environmental factors may interact with the blood lead to affect the risk of hypertension. However, existing studies primarily focus on the individual effects of these factors, while ignoring the interaction of blood lead with these factors on the prevalence of hypertension.
Therefore, we explored the association of BLLs with blood pressure and the prevalence of hypertension, and investigated the interaction effects of BLLs with occupational exposures and air pollutants in this study, based on the physical examination data of a large group of workers in South China.

2. Materials and Methods

2.1. Study Population

The present research was conducted based on the data collected from occupational health physical examination institutions across 21 cities of Guangdong in South China in 2020. We obtained data on age, sex, enterprise size, work address, and clinical and biochemical indicators of each participant. The dataset includes health examination information of 36,076 lead-exposed workers. We excluded participants carrying out re-examinations (n = 11,517) or pre-job examinations (n = 418) from this study. In addition, participants with missing data on work address (n = 126), blood lead concentration (n = 1879), or blood pressure (n = 134) were also excluded. Finally, 22,002 participants were included (Figure 1). The Ethics Committee of Guangdong Province Hospital for Occupational Disease Prevention and Treatment approved this study.

2.2. Outcomes

Systolic and diastolic blood pressure (DBP), and the prevalence of hypertension were the primary outcomes of this study. After a quiet rest of at least 5 min, blood pressure was measured twice, with measurements taken 2 min apart, using an electronic sphygmomanometer. The average of the two measurements was used as the final result. SPB ≥ 140 mmHg and/or DBP ≥ 90 mmHg was defined as hypertension according to 2018 Chinese Guidelines for the Management of Hypertension [19].

2.3. Measurement of Blood Lead

Blood lead is a commonly used biomarker that reflects an individual’s recent exposure to lead. All participants had venous blood samples collected after an overnight fasting. Technicians of the physical examination institutions used graphite furnace atomic absorption spectrometry to measure the BLLs. The detection limit was 20 μg/L. The World Health Organization suggested that effective measures should be taken to reduce or terminate lead exposure when BLLs exceed 50 μg/L [20]. Therefore, we chose 20 μg/L and 50 μg/L as cutoff points to divide the blood lead concentration of the participants into 3 levels to investigate the effect of different BLLs on workers’ blood pressure.

2.4. Covariates

The covariates involved in this study include basic characteristics of the participants, occupational exposures, air pollutant concentrations, and meteorological factors. Basic characteristics include age, gender, and enterprise size of the company where the participants were employed. Ages were classified into three groups: ≤30, 30–45, and >45 years old, respectively. According to “Statistically classified methods for large, medium, small and micro enterprises (2017)”, the size of enterprises was categorized as micro, small, medium, large, and unknown [21].
Considering that the lead-exposed workers in Guangdong are likely to be simultaneously exposed to productive dust, noise, high temperature, and BTEX, and the effects of these occupational hazards on blood pressure have been reported in previous studies [3,4,5,6], we collected data for these four additional occupational exposures. These variables were defined according to the Classification of Occupational Hazards in the Workplace of Occupational Diseases (GBZ/T229) [22] issued by the Ministry of Health of the People’s Republic of China, and was reported by the enterprise to the key occupational disease monitoring platform of Guangdong Province. Dust exposure was defined as the presence of productive dust in the workplace. Noise exposure referred to a noise in excess of 80 decibels (dB) during an 8 h workday. High temperature exposure meant that the average wet-bulb globe temperature (WBGT) index reaching or exceeding 25 °C in the workplace during production. BTEX exposure referred to exposure to benzene, toluene, ethylbenzene, and xylenes in the workplace.

2.5. Air Pollution and Meteorological Exposure

We obtained air pollution data from the ChinaHighAirPollutants (CHAP) dataset. Taking into account the spatiotemporal heterogeneity of air pollution, the dataset is generated from big data such as ground measurements, and model simulations using artificial intelligence. We obtained PM2.5, PM10, O3, SO2, and NO2 concentrations from this dataset at a spatial resolution of 1 km × 1 km. The cross-validation coefficients (CV-R2) for these pollutants were 0.92, 0.90, 0.87, 0.84, and 0.84, respectively. The root-mean-square-errors (RMSE) were 10.76 μg/m3, 21.12 μg/m3, 17.10 μg/m3, 10.07 μg/m3, and 7.99 μg/m3, respectively. Further details have been described in previous studies [23,24,25,26]. Furthermore, meteorological data were sourced from National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn). We obtained monthly temperature and humidity data with a spatial resolution of 1 km × 1 km from this dataset.
We employed the nearest distance matching method, using the geocoding of workplace address of each participant, to obtain monthly data on air pollutant concentrations, temperature, and humidity. Subsequently, we calculated the mean concentrations of pollutants, mean temperature, and humidity for the previous year based on the participants’ monthly average concentration during the 12 months prior to the date of their physical examination.

2.6. Statistical Analysis

We described continuous variables and categorical variables in terms of means (standard deviations, SD) and frequencies (percentages), respectively. One-way ANOVA and chi-square test were used to compare inter-group differences for continuous variables and categorical variables, respectively.
Taking into account the potential impact of regulatory policies, economic levels, etc. in different cities, mixed-effect models adjusted for city random intercept were used to evaluate the association of blood pressure and the prevalence of hypertension with BLLs. We developed the following models sequentially:
Model 1: the crude model adjusted for city random intercept;
Model 2: additionally adjusted for basic characteristics and meteorological factors including age, sex (male, female), enterprise size, temperature, and humidity, based on Model 1;
Model 3: additionally adjusted for occupational exposure including dust (yes, no), noise (yes, no), high temperature (yes, no), and BTEX (yes, no), based on Model 3;
Model 4: based on Model 3, additionally adjusted for air pollutants include PM2.5, PM10, O3, SO2. and NO2.
The multicollinearity issue was assessed using the variance inflation factor, and none of the values exceeded 5, indicating that there was no multicollinearity in our model. In addition, we modeled the BLLs as a continuous variable to test the trend of the effect across lead levels.
To evaluate the potential effect modification, we stratified the results by occupational exposure and the concentrations of pollutants based on the fully adjusted model. Specifically, we divided the continuous pollutants into low- and high-concentration groups using the median as a cutoff point. The cutoff values for PM2.5, PM10, O3, SO2, and NO2 were 24.52 μg/m3, 43.88 μg/m3, 103.36 μg/m3, 7.26 μg/m3, and 28.76 μg/m3, respectively.

2.7. Sensitivity Analyses

To confirm that the estimates were robust, we conducted several sensitivity analyses, which included excluding participants working in enterprises of unknown size and those with less than one year of service.
All statistical tests were 2-sided, and p < 0.05 was considered statistically significant. All analyses were performed using R version 4.2.2.

3. Results

3.1. Baseline Characteristic

As shown in Table 1, this study included 22,002 participants, of whom 13,278 (60.35%) were male and 8724 (39.65%) were female. The average age of participants was 34.83 years. Among these participants, 2805 (12.75%) had hypertension. The groups with BLLs ≤ 20 μg/L, between 20–50 μg/L, and >50 μg/L consisted of 9415 participants (42.79%), 9000 participants (40.91%), and 3587 participants (16.30%), respectively. The participants in the >50 μg/L group tended to be older, male, or employees of small businesses compared to those in the ≤20 μg/L group. We also found a significantly higher percentage of participants exposed to dust, noise, and high temperature in the >50 μg/L group compared to the ≤20 μg/L group.
In addition, the average concentrations of PM2.5, PM10, O3, SO2, and NO2 were measured at 24.44 μg/m3, 43.52 μg/m3, 102.78 μg/m3, 7.48 μg/m3, and 29.16 μg/m3, respectively. The correlation coefficient matrix indicated significant correlations (correlation coefficient > 0.75) between PM2.5 and PM10, as well as O3 (Table S1).

3.2. Association of Blood Lead Levels with Hypertension

Table 2 shows the association of blood lead with and SBP and DBP, as well as the prevalence of hypertension. The first two sections display the relationship between BLLs and SBP and DBP (i.e., β), while the third section presents the effect of BLLs on the prevalence of hypertension (i.e., OR). We observed a positive association between BLLs and the prevalence of hypertension (Table 2). In the final model, compared to participants with BLLs ≤ 20 μg/L, those with BLLs between 20–50 μg/L and those >50 μg/L had a higher prevalence of hypertension, with the adjusted odds ratio (OR) and 95% confidence interval (95% CI) being 1.26 (95% CI: 1.15, 1.40) and 1.37 (95% CI: 1.19, 1.57), respectively.
Moreover, we also identified a notable association between BLLs and SBP and DBP. Compared to participants with BLLs ≤ 20 μg/L, those with BLLs between 20–50 μg/L and BLLs > 50 μg/L were associated with a 1.24 (95% CI: 0.79, 1.70) mmHg and a 1.27 (95% CI: 0.60, 1.93) mmHg increase in SBP, and a 0.65 (95% CI: 0.32, 0.98) mmHg and a 1.02 (95% CI: 0.54, 1.50) mmHg increase in DBP, respectively.

3.3. Potential Modifiers on the Relationship of Blood Lead Levels and Hypertension

Table 3 presents the stratified results based on occupational exposures, revealing no significant interaction between BLLs and these exposures. When stratified by air pollutants (Table 4), we found that participants exposed to high PM10 concentrations tended to have a 28% higher OR for hypertension among the 20–50 μg/L group and a 21% higher OR for hypertension among the >50 μg/L group, relative to those exposed to low PM10 levels (Pinteraction = 0.04). Meanwhile, we observed significant modification effects of NO2 on the relationship between BLLs and the prevalence of hypertension (Pinteraction = 0.04). However, these effects were inconsistent across varying BLLs. A similar trend was also observed in SBP.
In addition, the analysis stratified by the occupational exposures on the association between BLLs and blood pressure showed that workers exposed to high temperatures were more susceptible (Table S2). Compared to workers not exposed to high temperatures, who experienced decreases in SBP of 0.41 mmHg and 2.46 mmHg due to variations in BLLs, those exposed to high temperatures experienced increases in SBP of 1.29 mmHg and 1.65 mmHg, respectively (Pinteraction = 0.02). This trend was also observed in DBP. Workers not exposed to dust exhibited more sensitivity to changes in both SBP and DBP due to variations in BLLs, with Pinteraction values of 0.01 for both SBP and DBP, respectively. Furthermore, workers exposed to high NO2 concentrations were more sensitive to changes in DBP due to variations in BLLs (Pinteraction = 0.02) (Table S3).

3.4. Sensitivity Analyses

Similar effect estimates were observed in the dataset excluding participants working in enterprises of unknown size (Table S4). Similarly, similar results were shown in a dataset excluding participants with less than one year of service (Table S5).

4. Discussion

We found that BLLs were significantly positively associated with blood pressure and the prevalence of hypertension in this study. Currently, the relationship between BLLs and hypertension remains a controversial issue, and the following studies are in agreement with our findings. A cross-sectional study in Haiti found that the group with BLLs exceeding 6.5 μg/dL was associated with a 2.42 mmHg (95% CI: 0.36, 4.49) and a 1.96 mmHg (95% CI: 0.56, 3.37) increase in SBP and DBP, respectively, relative to the group with below 3.4 μg/dL [27]. A population-based cross-sectional study in Sweden observed that participants with BLLs between 33 μg/L and 258 μg/L demonstrated an increase of 1–2 mmHg in SBP and DBP, and a 30% higher prevalence of hypertension (95% CI: 1.1, 1.5), relative to those with BLLs between 1.5–19 μg/L [28]. Several studies in adults over 40 years of age, menopausal women [29], the general population [30], and children [31] also support our findings. However, some previous studies have shown little or no association of BLLs with SBP and DBP and hypertension [11,32]. The variation in the results could potentially be attributed to the differences in the basic characteristics and lifestyle of the different study populations, such as age, diet, and exercise habits. Our study provides further substantiation that supports the association between BLLs and the prevalence of hypertension, suggesting that more effective protective measures are necessary to reduce lead exposure among lead-exposed workers.
Lead exposure may lead to hypertension through multiple pathways. One possible mechanism is that lead may induce oxidative stress by altering the activity of antioxidant enzymes, as well as enhance the generation of reactive oxygen species (ROS), reduce NO bioavailability, and elevate vascular resistance, thereby increasing blood pressure [33,34]. Oxidative stress can also activate transcription factors, promote cellular inflammation and apoptosis, and lead to the occurrence of hypertension [35]. In addition, another potential mechanism of hypertension may be the increased sympathetic tone and increased renin-angiotensin system (RAS) [36,37]. Lead-related hypertension may also be associated with genes such as the M allele in the AGT gene and G allele carriers in the EDN1 gene [38,39]. Lead levels in peripheral blood were also correlated with peripheral artery disease, which may also induce endothelial dysfunction through increased oxidative stress, leading to changes in blood pressure [40]. However, the mechanism needs to be further studied in more detail.
We also observed that participants exposed to higher concentrations of PM10 were subjected to stronger blood lead effects on hypertension. One possible reason is that certain components of air pollutants act synergistically with lead to cause hypertension. A study has shown that the combined exposure of lead and black carbon in PM significantly enhances oxidative stress, DNA damage, and the inflammatory response through different interactions and synergistic effects, which may further lead to the occurrence of hypertension [41]. Another possible reason is that air pollutants may react with lead, resulting in the easier absorption of lead into the body and increased lead toxicity [42,43]. However, due to the complexity of the interaction between air pollution and lead exposure, further research is needed to explore the underlying biological mechanisms. We observed the inconsistent modification effects of NO2 on the relationship between blood lead and hypertension at different BLLs. Therefore, more data are needed for further investigation.
We observed that participants without dust exposure were more susceptible to blood lead. Participants who were exposed to dust may be less sensitive to occupational harmful factors such as lead than those who are only exposed to lead due to their long-term exposure to more complex working environments. Participants who were occupationally exposed to high temperatures were more likely to be affected by blood lead to SBP. This may be due to the fact that higher temperatures increase lead bioavailability and mobility, leading to increased lead exposure and subsequent lead toxicity [44].
Our study has the following advantages. First, this study included 22,002 participants from 21 prefecture-level cities in Guangdong, South China. The sample size is large enough to be representative of lead-exposed workers and to provide enough statistical power. In addition, we evaluated the interaction effect of BLLs with occupational exposures and air pollutants. To our knowledge, these issues have not been addressed in previous studies. Our findings may provide new ideas for the prevention of hypertension in lead-exposed workers. Last but not least, our study indicated that no level of lead exposure, even below the WHO’s maximum acceptable level of <50 μg/L, can be considered completely free of the potential harm to blood pressure.
Despite these strengths, it is imperative that we acknowledge several limitations. Firstly, as this research is a cross-sectional study, there is an inherent limitation in identifying a clear causal relationship between BLLs and blood pressure. Second, we did not collect information on some covariates such as family history, antihypertensive medication history, lifestyle factors, and dietary factors, which may affect blood pressure. Future work should confirm these findings with more specific data. Third, we did not collect the indoor air pollution data in the workplace. Data on ambient air pollution were matched according to work addresses, potentially introducing misclassification bias and, consequently, underestimating the modification effect of air pollution on the effect of blood lead on hypertension. Finally, in this study, no specific exposure doses for dust, noise, high temperature, and other factors were collected, and more specific data should be used in future studies to confirm these findings.

5. Conclusions

In conclusion, our findings suggest the potential negative effect of blood lead on blood pressure and the prevalence of hypertension among workers, with participants exposed to high concentrations of PM10, those exposed to occupational high temperature, and those without dust exposure being more vulnerable. These findings suggest that companies and workers should take effective measures to reduce lead exposure to prevent hypertension in workers, taking into account the significance of reducing air pollution and occupational exposure.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics12120861/s1, Table S1. Pearson correlation coefficients for air pollutants; Table S2. Association of blood lead and blood pressure, stratified by occupational exposures; Table S3. Association of blood lead and blood pressure, stratified by air pollutants concentrations; Table S4. Association of blood lead levels with blood pressure and Hypertension in databases without indefinite enterprise size; Table S5. Association of blood lead levels with blood pressure and Hypertension in databases without participants less than one year of service.

Author Contributions

Y.G.: conceptualization, formal analysis, methodology, software, validation, visualization, writing—original draft, and writing—review and editing; Y.W.: methodology, validation, visualization, and writing—review and editing; Q.N.: conceptualization, formal analysis, funding acquisition, methodology, software, validation, visualization, and writing—review and editing; P.H., Z.L., X.H., M.Z., X.L., S.W., F.Z., N.Z., Y.Q., S.L., J.H. and L.H.: conceptualization, data curation, and investigation; W.Z.: project administration, resources, software, supervision, validation, and writing—review and editing; Y.H.: conceptualization, data curation, funding acquisition, investigation, methodology, project administration, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (grant number 22106022), the Guangdong Provincial Natural Science Foundation (grant number 2023A1515012756), the National Key Clinical Specialty Construction Project (2011-09), and the Biomedical Industry Innovation Subsidy of Guangzhou Science and Technology Bureau.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Guangdong Province Hospital for Occupational Disease Prevention and Treatment. The ethical review date was 26 July 2023 and the approval number was GDHOD MEC2023039. Informed consent was obtained from all patients included in the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data cannot be made publicly available upon publication because they contain sensitive personal information. The data that support the findings of this study are available upon reasonable request from the authors.

Acknowledgments

Acknowledgment is given for the data support from “Loess Plateau SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China. (http://loess.geodata.cn)”. Graphical Abstract was created with Figdraw.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Liao, L.M.; Friesen, M.C.; Xiang, Y.-B.; Cai, H.; Koh, D.-H.; Ji, B.-T.; Yang, G.; Li, H.-L.; Locke, S.J.; Rothman, N.; et al. Occupational Lead Exposure and Associations with Selected Cancers: The Shanghai Men’s and Women’s Health Study Cohorts. Environ. Health Perspect. 2016, 124, 97–103. [Google Scholar] [CrossRef]
  2. Walter, K. What Is Lead Poisoning? JAMA 2023, 329, 1040. [Google Scholar] [CrossRef] [PubMed]
  3. World Health Organization. The Public Health Impact of Chemicals: Knowns and Unknowns—Data Addendum for 2019; WHO: Geneva, Switzerland, 2021.
  4. Chen, L.; Xie, J.; Ma, T.; Chen, M.; Gao, D.; Li, Y.; Ma, Y.; Wen, B.; Jiang, J.; Wang, X.; et al. Greenness Alleviates the Effects of Ambient Particulate Matter on the Risks of High Blood Pressure in Children and Adolescents. Sci. Total Environ. 2022, 812, 152431. [Google Scholar] [CrossRef] [PubMed]
  5. World Health Organization. Guideline for the Pharmacological Treatment of Hypertension in Adults; WHO: Geneva, Switzerland, 2021.
  6. Hu, S.-S. Report on Cardiovascular Health and Diseases in China 2021: An Updated Summary. J. Geriatr. Cardiol. 2023, 20, 399–430. [Google Scholar] [CrossRef] [PubMed]
  7. Shen, Y.; Wang, X.; Wang, Z.; Zhang, L.; Chen, Z.; Zhu, M.; Chang, C.; Gao, R. Prevalence, Awareness, Treatment, and Control of Hypertension among Chinese Working Population: Results of a Workplace-Based Study. J. Am. Soc. Hypertens. 2018, 12, 311–322.e2. [Google Scholar] [CrossRef]
  8. He, P.; Yang, C.; He, D.; Zhao, S.; Xie, Y.; Wang, H.; Ma, J. Blood Lead, Systemic Inflammation, and Blood Pressure: Exploring Associations and Mediation Effects in Workers Exposed to Lead. Biol. Trace Elem. Res. 2021, 199, 2573–2581. [Google Scholar] [CrossRef]
  9. Bulka, C.M.; Scannell Bryan, M.; Persky, V.W.; Daviglus, M.L.; Durazo-Arvizu, R.A.; Parvez, F.; Slavkovich, V.; Graziano, J.H.; Islam, T.; Baron, J.A.; et al. Changes in Blood Pressure Associated with Lead, Manganese, and Selenium in a Bangladeshi Cohort. Environ. Pollut. 2019, 248, 28–35. [Google Scholar] [CrossRef]
  10. Were, F.H.; Moturi, M.C.; Gottesfeld, P.; Wafula, G.A.; Kamau, G.N.; Shiundu, P.M. Lead Exposure and Blood Pressure among Workers in Diverse Industrial Plants in Kenya. J. Occup. Environ. Hyg. 2014, 11, 706–715. [Google Scholar] [CrossRef]
  11. Yu, Y.-L.; Yang, W.-Y.; Thijs, L.; Melgarejo, J.D.; Yu, C.-G.; Wei, D.-M.; Wei, F.-F.; Nawrot, T.S.; Zhang, Z.-Y.; Staessen, J.A. Two-Year Responses of Office and Ambulatory Blood Pressure to First Occupational Lead Exposure. Hypertension 2020, 76, 1299–1307. [Google Scholar] [CrossRef]
  12. Li, X.; Wang, Y.; Liu, Q.; Sun, N.; Zhang, R.; Li, X.; Yuan, J. Association of occupational heat and noise exposure with hypertension. J. Prev. Med. 2019, 31, 1189–1192. [Google Scholar] [CrossRef]
  13. Wu, Q.; Han, L.; Xu, M.; Zhang, H.; Ding, B.; Zhu, B. Effects of Occupational Exposure to Dust on Chest Radiograph, Pulmonary Function, Blood Pressure and Electrocardiogram among Coal Miners in an Eastern Province, China. BMC Public Health 2019, 19, 1229. [Google Scholar] [CrossRef]
  14. Attarchi, M.; Golabadi, M.; Labbafinejad, Y.; Mohammadi, S. Combined Effects of Exposure to Occupational Noise and Mixed Organic Solvents on Blood Pressure in Car Manufacturing Company Workers. Am. J. Ind. Med. 2013, 56, 243–251. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, L.; Chen, S.; Chen, Z.; Yin, W.; Fu, W.; He, F.; Pan, Z.; Yi, G.; Tan, X. Relationship between Occupational Noise Exposure and Hypertension: Cross-Sectional Evidence from Real-World. Front. Public Health 2022, 10, 1037246. [Google Scholar] [CrossRef] [PubMed]
  16. Morawska, L.; Allen, J.; Bahnfleth, W.; Bennett, B.; Bluyssen, P.M.; Boerstra, A.; Buonanno, G.; Cao, J.; Dancer, S.J.; Floto, A.; et al. Mandating Indoor Air Quality for Public Buildings. Science 2024, 383, 1418–1420. [Google Scholar] [CrossRef] [PubMed]
  17. Murga, A.; Kuga, K.; Yoo, S.-J.; Ito, K. Can the Inhalation Exposure of a Specific Worker in a Cross-Ventilated Factory Be Evaluated by Time- and Spatial-Averaged Contaminant Concentration? Environ. Pollut. 2019, 252, 1388–1398. [Google Scholar] [CrossRef]
  18. Choudhary, H.; Tarlo, S.M. Airway Effects of Traffic-Related Air Pollution on Outdoor Workers. Curr. Opin. Allergy Clin. Immunol. 2014, 14, 106–112. [Google Scholar] [CrossRef] [PubMed]
  19. China Hypertension Prevention Guidelines Revision Committee 2018 Chinese guidelines for the management of hypertension. Chin. J. Cardiol. 2019, 24, 24–56.
  20. World Health Organization. WHO Guideline for the Clinical Management of Exposure to Lead; WHO: Geneva, Switzerland, 2021.
  21. National Bureau of Statistics of China. Statistically Classified Methods for Large, Medium, Small and Micro Enterprises (2017). Available online: https://www.stats.gov.cn/sj/tjbz/gjtjbz/202302/t20230213_1902763.html (accessed on 9 September 2024).
  22. GBZ/T 229.1~4-2010/2012; Classification of Occupational Disease Hazards in the Workplace. Ministry of Health of the People’s Republic of China Standards Press of China: Beijing, China, 2012.
  23. Wei, J.; Li, Z.; Lyapustin, A.; Sun, L.; Peng, Y.; Xue, W.; Su, T.; Cribb, M. Reconstructing 1-Km-Resolution High-Quality PM2.5 Data Records from 2000 to 2018 in China: Spatiotemporal Variations and Policy Implications. Remote Sens. Environ. 2021, 252, 112136. [Google Scholar] [CrossRef]
  24. Wei, J.; Li, Z.; Xue, W.; Sun, L.; Fan, T.; Liu, L.; Su, T.; Cribb, M. The ChinaHighPM10 Dataset: Generation, Validation, and Spatiotemporal Variations from 2015 to 2019 across China. Environ. Int. 2021, 146, 106290. [Google Scholar] [CrossRef] [PubMed]
  25. Wei, J.; Li, Z.; Li, K.; Dickerson, R.R.; Pinker, R.T.; Wang, J.; Liu, X.; Sun, L.; Xue, W.; Cribb, M. Full-Coverage Mapping and Spatiotemporal Variations of Ground-Level Ozone (O3) Pollution from 2013 to 2020 across China. Remote Sens. Environ. 2022, 270, 112775. [Google Scholar] [CrossRef]
  26. Wei, J.; Li, Z.; Wang, J.; Li, C.; Gupta, P.; Cribb, M. Ground-Level Gaseous Pollutants (NO2, SO2, and CO) in China: Daily Seamless Mapping and Spatiotemporal Variations. Atmos. Chem. Phys. 2023, 23, 1511–1532. [Google Scholar] [CrossRef]
  27. Yan, L.D.; Rouzier, V.; Pierre, J.L.; Lee, M.H.; Muntner, P.; Parsons, P.J.; Apollon, A.; St-Preux, S.; Malebranche, R.; Pierre, G.; et al. High Lead Exposure Associated with Higher Blood Pressure in Haiti: A Warning Sign for Low-Income Countries. Hypertension 2022, 79, 283–290. [Google Scholar] [CrossRef] [PubMed]
  28. Gambelunghe, A.; Sallsten, G.; Borné, Y.; Forsgard, N.; Hedblad, B.; Nilsson, P.; Fagerberg, B.; Engström, G.; Barregard, L. Low-Level Exposure to Lead, Blood Pressure, and Hypertension in a Population-Based Cohort. Environ. Res. 2016, 149, 157–163. [Google Scholar] [CrossRef] [PubMed]
  29. Nash, D.; Magder, L.; Lustberg, M.; Sherwin, R.W.; Rubin, R.J.; Kaufmann, R.B.; Silbergeld, E.K. Blood Lead, Blood Pressure, and Hypertension in Perimenopausal and Postmenopausal Women. JAMA 2003, 289, 1523. [Google Scholar] [CrossRef]
  30. Qu, Y.; Lv, Y.; Ji, S.; Ding, L.; Zhao, F.; Zhu, Y.; Zhang, W.; Hu, X.; Lu, Y.; Li, Y.; et al. Effect of Exposures to Mixtures of Lead and Various Metals on Hypertension, Pre-Hypertension, and Blood Pressure: A Cross-Sectional Study from the China National Human Biomonitoring. Environ. Pollut. 2022, 299, 118864. [Google Scholar] [CrossRef]
  31. Farzan, S.F.; Howe, C.G.; Chen, Y.; Gilbert-Diamond, D.; Cottingham, K.L.; Jackson, B.P.; Weinstein, A.R.; Karagas, M.R. Prenatal Lead Exposure and Elevated Blood Pressure in Children. Environ. Int. 2018, 121, 1289–1296. [Google Scholar] [CrossRef]
  32. Huang, Z. Association Between Blood Lead Level with High Blood Pressure in US (NHANES 1999–2018). Front. Public Health 2022, 10, 836357. [Google Scholar] [CrossRef]
  33. Vaziri, N.D. Mechanisms of Lead-Induced Hypertension and Cardiovascular Disease. Am. J. Physiol.-Heart Circ. Physiol. 2008, 295, H454–H465. [Google Scholar] [CrossRef] [PubMed]
  34. Farmand, F.; Ehdaie, A.; Roberts, C.K.; Sindhu, R.K. Lead-Induced Dysregulation of Superoxide Dismutases, Catalase, Glutathione Peroxidase, and Guanylate Cyclase. Environ. Res. 2005, 98, 33–39. [Google Scholar] [CrossRef]
  35. Vaziri, N.; Khan, M. Interplay of Reactive Oxygen Species and Nitric Oxide in the Pathogenesis of Experimental Lead-Induced Hypertension. Clin. Exp. Pharmacol. Physiol. 2007, 34, 920–925. [Google Scholar] [CrossRef]
  36. Toscano, C.M.; Simões, M.R.; Alonso, M.J.; Salaices, M.; Vassallo, D.V.; Fioresi, M. Sub-Chronic Lead Exposure Produces Β1-Adrenoceptor Downregulation Decreasing Arterial Pressure Reactivity in Rats. Life Sci. 2017, 180, 93–101. [Google Scholar] [CrossRef] [PubMed]
  37. Simões, M.R.; Ribeiro Júnior, R.F.; Vescovi, M.V.A.; De Jesus, H.C.; Padilha, A.S.; Stefanon, I.; Vassallo, D.V.; Salaices, M.; Fioresi, M. Acute Lead Exposure Increases Arterial Pressure: Role of the Renin-Angiotensin System. PLoS ONE 2011, 6, e18730. [Google Scholar] [CrossRef] [PubMed]
  38. Kim, H.-K.; Lee, H.; Kwon, J.-T.; Kim, H.-J. A Polymorphism in AGT and AGTR1 Gene Is Associated with Lead-Related High Blood Pressure. J. Renin-Angiotensin-Aldosterone Syst. 2015, 16, 712–719. [Google Scholar] [CrossRef] [PubMed]
  39. Lee, H.; Kim, H.-K.; Won, H.; Im, J.; Kwon, J.-T.; Kim, H.-J. Genetic Relationship between an Endothelin 1 Gene Polymorphism and Lead-Related High Blood Pressure. Mol. Cell. Toxicol. 2016, 12, 111–116. [Google Scholar] [CrossRef]
  40. Guallar, E.; Silbergeld, E.K.; Navas-Acien, A.; Malhotra, S.; Astor, B.C.; Sharrett, A.R.; Schwartz, B.S. Confounding of the Relation between Homocysteine and Peripheral Arterial Disease by Lead, Cadmium, and Renal Function. Am. J. Epidemiol. 2006, 163, 700–708. [Google Scholar] [CrossRef] [PubMed]
  41. Jiang, N.; Wen, H.; Zhou, M.; Lei, T.; Shen, J.; Zhang, D.; Wang, R.; Wu, H.; Jiang, S.; Li, W. Low-Dose Combined Exposure of Carboxylated Black Carbon and Heavy Metal Lead Induced Potentiation of Oxidative Stress, DNA Damage, Inflammation, and Apoptosis in BEAS-2B Cells. Ecotoxicol. Environ. Saf. 2020, 206, 111388. [Google Scholar] [CrossRef]
  42. Baltrusaitis, J.; Chen, H.; Rubasinghege, G.; Grassian, V.H. Heterogeneous Atmospheric Chemistry of Lead Oxide Particles with Nitrogen Dioxide Increases Lead Solubility: Environmental and Health Implications. Environ. Sci. Technol. 2012, 46, 12806–12813. [Google Scholar] [CrossRef]
  43. Edwards, R.D.; Lam, N.L.; Zhang, L.; Johnson, M.A.; Kleinman, M.T. Nitrogen Dioxide and Ozone as Factors in the Availability of Lead from Lead-Based Paints. Environ. Sci. Technol. 2009, 43, 8516–8521. [Google Scholar] [CrossRef]
  44. Levin, R.; Zilli Vieira, C.L.; Mordarski, D.C.; Rosenbaum, M.H. Lead Seasonality in Humans, Animals, and the Natural Environment. Environ. Res. 2020, 180, 108797. [Google Scholar] [CrossRef]
Toxics 12 00861 g001
Figure 1. Flowchart of participant inclusion and exclusion.
Figure 1. Flowchart of participant inclusion and exclusion.
Toxics 12 00861 g001
Table 1. Description of the study participants by blood lead levels.
Table 1. Description of the study participants by blood lead levels.
CharacteristicsOverall
(N = 22,002)		Blood Lead Levels
≤20 μg/L
(n = 9415)		20~50 μg/L
(n = 9000)		>50 μg/L
(n = 3587)		p-Value
Hypertension, n (%) <0.001
  Yes2805 (12.75)1029 (10.93)1163 (12.92)613 (17.09)
  No19,197 (87.25)8386 (89.07)7837 (87.08)2974 (82.91)
SBP, mmHg, mean (SD)122.85 (14.93)121.68 (14.57)123.38 (14.60)124.63 (16.37)<0.001
DBP, mmHg, mean (SD)78.11 (10.67)77.49 (10.47)78.07 (10.53)79.82 (11.34)<0.001
Age, year, mean (SD)34.83 (8.38)33.41 (7.95)34.66 (8.09)38.97 (8.83)<0.001
Sex, n (%) <0.001
  Male13,278 (60.35)5306 (56.36)5519 (61.32)2453 (68.39)
  Female8724 (39.65)4109 (43.64)3481 (38.68)1134 (31.61)
Enterprise size, n (%) <0.001
  Micro464 (2.11)249 (2.64)180 (2.00)35 (0.98)
  Small6952 (31.60)2416 (25.66)2730 (30.33)1806 (50.35)
  Medium5942 (27.01)2880 (30.59)2584 (28.71)478 (13.33)
  Large7434 (33.79)3818 (40.55)2604 (28.93)1012 (28.21)
  Unknown1210 (5.50)52 (0.55)902 (10.02)256 (7.14)
Dust exposure, n (%) <0.001
  Yes6987 (31.76)1625 (17.26)4136 (45.96)1226 (34.18)
  No15,015 (68.24)7790 (82.74)4864 (54.04)2361 (65.82)
Noise exposure, n (%) <0.001
  Yes2671 (12.14)968 (10.28)1104 (12.27)599 (16.70)
  No19,331 (87.86)8447 (89.72)7896 (87.73)2988 (83.30)
High temperature exposure, n (%) <0.001
  Yes1279 (5.81)346 (3.67)544 (6.04)389 (10.84)
  No20,723 (94.19)9069 (96.33)8456 (93.96)3198 (89.16)
BTEX exposure, n (%) <0.001
  Yes1030 (4.68)596 (6.33)380 (4.22)54 (1.51)
  No20,972 (95.32)8819 (93.67)8620 (95.78)3533 (98.49)
PM2.5, μg/m3, mean (SD)24.44 (2.33)24.27 (2.51)24.50 (2.16)24.77 (2.23)<0.001
PM10, μg/m3, mean (SD)43.52 (3.70)43.38 (3.93)43.79 (3.41)43.19 (3.73)<0.001
O3, μg/m3, mean (SD)102.78 (8.15)103.41 (8.08)102.35 (8.49)102.20 (7.27)<0.001
SO2, μg/m3, mean (SD)7.48 (1.40)7.61 (1.14)7.11 (1.29)8.07 (1.93)<0.001
NO2, μg/m3, mean (SD)29.16 (6.29)29.06 (6.53)29.60 (5.70)28.34 (6.91)<0.001
Temperature, °C, mean (SD)23.74 (0.43)23.82 (0.24)23.80 (0.32)23.40 (0.75)<0.001
Humidity, %, mean (SD)73.81 (2.81)73.96 (2.91)73.52 (2.80)74.15 (2.45)<0.001
Note: Values are mean (SD) or n (%). Abbreviations: SD: standard deviations; SBP: systolic blood pressure; DBP: diastolic blood pressure; PM2.5: particulate matter with an aerodynamic diameter ≤ 2.5 μm, PM10: particulate matter with an aerodynamic diameter ≤ 10 μm, O3: ozone, SO2: sulfur dioxide, NO2: nitrogen dioxide
Table 2. Association of blood lead levels with blood pressure and hypertension.
Table 2. Association of blood lead levels with blood pressure and hypertension.
OutcomesModel 1Model 2Model 3Model 4
SBP, β (95% CI)
  ≤20Ref. (0)Ref. (0)Ref. (0)Ref. (0)
  20–502.45 (2.01, 2.88)1.46 (1.02, 1.89)1.27 (0.82, 1.71)1.24 (0.79, 1.70)
  >504.21 (3.56, 4.86)1.18 (0.53, 1.83)1.12 (0.47, 1.78)1.27 (0.60, 1.93)
Ptrendp < 0.001p < 0.001p < 0.001p < 0.001
DBP, β (95% CI)
  ≤20Ref. (0)Ref. (0)Ref. (0)Ref. (0)
  20–501.21 (0.90, 1.52)0.67 (0.35, 0.98)0.47 (0.15, 0.79)0.65 (0.32, 0.98)
  >502.84 (2.37, 3.30)0.96 (0.49, 1.43)0.88 (0.40, 1.35)1.02 (0.54, 1.50)
Ptrendp < 0.001p < 0.001p < 0.001p < 0.001
Hypertension, OR (95% CI)
  ≤20Ref. (1)Ref. (1)Ref. (1)Ref. (1)
  20–501.36 (1.24, 1.49)1.21 (1.10, 1.33)1.22 (1.10, 1.34)1.26 (1.15, 1.40)
  >501.98 (1.75, 2.25)1.32 (1.16, 1.51)1.33 (1.17, 1.53)1.37 (1.19, 1.57)
Ptrendp < 0.001p < 0.001p < 0.001p < 0.001
Note: Model 1: the crude model adjusted for city random intercept; Model 2: additionally adjusted for basic characteristics and meteorological factors including age, sex, enterprise size, temperature, and humidity, based on Model 1; Model 3: additionally adjusted for occupational exposure including dust, noise, high temperature, and BTEX, based on Model 3; Model 4: additionally adjusted for air pollutants include PM2.5, PM10, O3, SO2, and NO2, based on Model 3. Abbreviations: SBP: systolic blood pressure; DBP: diastolic blood pressure.
Table 3. Association of blood lead and hypertension, stratified by occupational exposures.
Table 3. Association of blood lead and hypertension, stratified by occupational exposures.
OutcomeHypertension
OR (95% CI)Pinteraction
Blood Lead Levels, μg/L≤2020–50>50
Dust exposure
  NoRef. (1)1.27 (1.02, 1.59)1.39 (1.07, 1.81)0.70
  YesRef. (1)1.23 (1.10, 1.38)1.35 (1.13, 1.60)
Noise exposure
  NoRef. (1)1.14 (0.82, 1.57)1.01 (0.69, 1.49)0.77
  YesRef. (1)1.27 (1.15, 1.41)1.41 (1.22, 1.64)
High temperature exposure
  NoRef. (1)1.18 (0.70, 1.99)0.99 (0.57, 1.72)0.71
  YesRef. (1)1.26 (1.14, 1.39)1.38 (1.20, 1.59)
BTEX exposure
  NoRef. (1)1.04 (0.63, 1.72)2.57 (0.93, 7.02)0.31
  YesRef. (1)1.27 (1.15, 1.40)1.34 (1.17, 1.53)
Note: The factor used for stratification was excluded from the final model (i.e., Model 4). Abbreviations: OR: odds ratio, CI: confidence interval.
Table 4. Association of blood lead and hypertension, stratified by air pollutants concentrations.
Table 4. Association of blood lead and hypertension, stratified by air pollutants concentrations.
OutcomeHypertension
OR (95% CI)Pinteraction
Blood Lead Levels, μg/L≤2020–50>50
PM2.5
  LowRef. (1)1.13 (0.98, 1.31)1.27 (1.04, 1.55)0.22
  HighRef. (1)1.41 (1.23, 1.63)1.48 (1.22, 1.80)
PM10
  LowRef. (1)1.13 (0.97, 1.31)1.29 (1.07, 1.55)0.04
  HighRef. (1)1.41 (1.23, 1.62)1.39 (1.12, 1.72)
O3
  LowRef. (1)1.17 (1.01, 1.37)1.23 (0.99, 1.52)0.05
  HighRef. (1)1.34 (1.18, 1.53)1.47 (1.22, 1.76)
SO2
  LowRef. (1)1.14 (0.98, 1.33)1.31 (1.07, 1.60)0.28
  HighRef. (1)1.32 (1.16, 1.51)1.30 (1.05, 1.60)
NO2
  LowRef. (1)1.17 (1.01, 1.35)1.34 (1.11, 1.64)0.04
  HighRef. (1)1.38 (1.20, 1.59)1.30 (1.06, 1.60)
Note: The factor used for stratification was excluded from the final model (i.e., Model 4). Abbreviations: OR: odds ratio, CI: confidence interval, PM2.5: particulate matter with an aerodynamic diameter ≤ 2.5 μm, PM10: particulate matter with an aerodynamic diameter ≤ 10 μm, O3: ozone, SO2: sulfur dioxide, NO2: nitrogen dioxide.
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Gong, Y.; Wang, Y.; Nong, Q.; Hu, P.; Li, Z.; Huang, X.; Zhong, M.; Li, X.; Wu, S.; Zeng, F.; et al. The Impact of Blood Lead and Its Interaction with Occupational Factors and Air Pollution on Hypertension Prevalence. Toxics 2024, 12, 861. https://doi.org/10.3390/toxics12120861

AMA Style

Gong Y, Wang Y, Nong Q, Hu P, Li Z, Huang X, Zhong M, Li X, Wu S, Zeng F, et al. The Impact of Blood Lead and Its Interaction with Occupational Factors and Air Pollution on Hypertension Prevalence. Toxics. 2024; 12(12):861. https://doi.org/10.3390/toxics12120861

Chicago/Turabian Style

Gong, Yajun, Ying Wang, Qiying Nong, Peixia Hu, Zhiqiang Li, Xiangyuan Huang, Meimei Zhong, Xinyue Li, Shaomin Wu, Fangfang Zeng, and et al. 2024. "The Impact of Blood Lead and Its Interaction with Occupational Factors and Air Pollution on Hypertension Prevalence" Toxics 12, no. 12: 861. https://doi.org/10.3390/toxics12120861

APA Style

Gong, Y., Wang, Y., Nong, Q., Hu, P., Li, Z., Huang, X., Zhong, M., Li, X., Wu, S., Zeng, F., Zhao, N., Qin, Y., Liu, S., Hong, J., Hu, L., Zhang, W., & Huang, Y. (2024). The Impact of Blood Lead and Its Interaction with Occupational Factors and Air Pollution on Hypertension Prevalence. Toxics, 12(12), 861. https://doi.org/10.3390/toxics12120861

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