Introduction

The toxic metals are utilized widely in agricultural and industrial applications, such as pesticides, batteries, alloys and numerous other products.1 Prolonged and excessive exposure of toxic metals may result in damage to multiple organs.1 The kidney is a target organ for metal toxicity due to its properties of filtration, reabsorption and concentration of divalent ions.1 A number of environmental toxins such as cadmium, lead, mercury and arsenic have been linked to renal damage and the progression of chronic kidney disease.2,3 Although there is evidence to suggest a strong association between exposure to toxic metals and chronic kidney disease, the pathophysiological mechanisms are complex and not clearly understood.3

Cadmium is a toxic metal with considerable environmental and occupational concern.4 The toxicity of cadmium is associated with several clinical complications such as renal damage, bone disease and cancers.4,5 Notably, cadmium may cause clinical diseases even at very low exposure levels that are far below the limits set by the World Health Organization.4 A recent report of 13,958 US adults indicated a significant association between environmental exposure of cadmium and all-cause, cancer, cardiovascular disease, and coronary heart disease mortalities.6 A study of the Swedish population revealed that the exposure of occupational or relatively low environmental levels of cadmium may be a determinant for the development of end-stage renal disease (ESRD).7 In maintenance hemodialysis (HD) patients, the blood cadmium levels (BCLs) are higher than those of general population and are associated with increased mortality.8,9 There are studies demonstrated that BCLs are significantly associated with malnutrition, inflammation and even protein-energy wasting in patients with maintenance HD or peritoneal dialysis.10,11

In recent decades, air pollution has become a serious problem because of its toxicological effects on human health and the environment.12 A study of more than 8000 adults suggested that the fine-particulate air pollution contributes to the excess mortality in certain U.S cities.13 Of these fine particulates, the pollution by particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) is a crucial issue in the developing countries. The epidemiologic evidences indicated that PM2.5 is associated with the increased risks of stroke, cardiovascular events and all causes of mortality in general population.13–15 An environmental study reported that almost all lead, manganese and cadmium components are found in PM2.5 particle fraction.16 The industrial activities may substantially contribute to the content of cadmium in PM2.5 particles.17 A study showed that the cadmium levels in PM2.5 particles above the background level indicated moderate to high contamination of the environment.18

In 2015, we reported that BCL is an important determinant of mortality in maintenance HD patients.8 Reviewing the relevant literatures, studies on the relationship between environmental PM2.5 and BCL in patients with ESRD are limited, and this relationship remains uncertain. Based on our 2015 survey of HD patients,8 we conducted a cross-sectional study to explore the potential associations between environmental PM2.5 exposure, BCL and other clinical variables in patients undergoing maintenance HD.

Materials and MethodsEthics Statement

This study was conducted in accordance with the Declaration of Helsinki guidelines and was approved by the Medical Ethics Committee of Chang Gung Memorial Hospital, an academic medical center in Taiwan (IRB number: 98–1937B). Because this was a retrospective cross-sectional study, the informed consent was waived. All the data were analysed anonymously. All medical records during the study period, including medical history, laboratory data, and inclusion and exclusion criteria, were reviewed by nephrologists. In addition, all individual information was securely protected and was only available to the investigators. The study protocols were complied with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Patients

As stated above, this study was built on our report published in 2015, and all 937 patients in that study were initially included.8 In the previous work, all participants were recruited from the three HD centers of our hospital (Taipei, Linkou, and Taoyuan). We only enrolled patients who were older than 18 years old with dialysis vintage for more than 6 months, and had previous blood cadmium studies.8 Patients were excluded if they had histories of occupational exposure to cadmium, metal intoxication, or living in metal-contaminated areas. Patients also were excluded if they had the following criteria: cancers, active infection, hospitalization or surgery during the last three months before enrollment. Of the 937 patients in the previous report, 170 patients were smokers and 13 patients had no PM2.5 data available. Finally, this study included 754 maintenance HD patients (Figure 1).

Figure 1 Flow chart of patient selection and grouping.

Abbreviations: HD, hemodialysis; PM2.5, particulate matter with an aerodynamic diameter of 2.5 μm or less.

Most patients had been undergoing 4 h of HD three times per week. The patients utilized single-use dialyzers fitted with modified cellulose-based, polyamide or polysulfone membranes. The standard reverse osmosis system was equipped for water purification. In all patients, the dialysate had a standard ionic composition in a bicarbonate-based buffer.

We collected the following data from dialysis charts and/or electronic medical records, including demographics, co-morbidities, dialysis-related and biochemical data. Smoking behavior (defined as persons who had smoked in the past 30 days before study) was also recorded.

Measurements of Biochemical Parameters

We collected the blood samples during stable outpatient HD sessions to minimize the influence of acute events. The samples were drawn in syringes and needles which were batch-tested and shown not to be contaminated with heavy metals. All samples were taken from the arterial end of the vascular access immediately before the beginning of mid-week HD session, and then centrifuged and stored at −70 °C until used in assays.

We measured the high-sensitivity C-reactive protein (hsCRP) levels by immunonephelometry (Nanopia CRP; Daiichi Inc, Tokyo, Japan), with a detection limit of 0.15 mg/L. We determined the intact parathyroid hormone (iPTH) levels via a chemiluminometric immunoassay (ADVIA Centaur iPTH; Siemens Medical Solutions Diagnostics, New York, NY), with a reference range of 7 to 53 pg/mL. All other parameters were measured using an automated analyzer with a standard laboratory approach. We calculated the protein catabolism rate using validated equations and the data were normalized to the actual body weight.19 We evaluated the clearance of urea in study patients using the method described by Daugirdas20 and the data were expressed as Kt/Vurea. Serum calcium levels were corrected using serum albumin levels according to the formula: corrected calcium (mg/dL) = serum calcium (mg/dL) + 0.8 × (4.0 - serum albumin [g/dL]).

Measurements of Cadmium Levels

Based on the previous study, all 754 patients enrolled had BCL data.8 The main methods used to measure cadmium levels in the previous study were as follows. To ensure that patients were not exposed to contamination of cadmium during HD, we collected at least 2 samples of water and dialysate from the outlets of reverse osmosis systems and from the inlets of dialysate portion of the dialyzers at each HD center by using cadmium-free plastic bottles. Cadmium levels were measured as previously described.21,22 Briefly, 900 μL of modifier solution in deionized water and 100 μL of whole blood, or 100 μL of modifier solution and 900 μL dialysate were added to a 1.5-mL Eppendorf tube and immediately shaken. After overnight storage at 4°C, the tubes were warmed to room temperature, and then whirl-mixed for 5–10 s. The mixed sample was transferred to graphite furnace sampler cups and measured by electro-thermal atomic absorption spectrometry (SpectrAA-220Z; Varian, Palo Alto, CA) with Zeeman’s background correction and an L’vov platform. The coefficient of variation for cadmium measurements was 5.0% or less. External quality control was maintained via participation in the National Quality Control Program conducted by the Taiwan government.

Air Quality Status and Analysis

The Taiwan government monitored and recorded the concentrations of air pollution by a network of 27 monitoring stations. We analyzed the data from the Taiwan Air Quality Monitoring Network near or in the study patients’ living areas, including the database on the air quality status. The level of PM2.5 was checked every hour for 1 year. We calculated the average of approximately 8760 (24 × 365 = 8760) pieces of data from every monitoring station to determine the previous 1-year average level of PM2.5 in this study. PM2.5 data were generally obtained from monitoring stations in the same district. If a patient lived between 2 monitoring stations, we selected the data of the nearest station for analysis. If a patient lived in a district without a monitoring station, we referenced data from the nearest station (<15 km). Terrain was also considered; the data of the nearest monitoring station on the same side of a mountain that a patient lived on were analyzed. Because no previous study has focused on this issue, we evaluated the mean concentrations of PM2.5 in the previous 12 and 24 months for each patient. The normal limit of mean 24 hour PM2.5 is defined as level < 35 μg/m3.23

According to the tertiles of days with a daily mean PM2.5 concentration ≥ 35 μg/m3 in previous 24 months, we stratified patients into three subgroups for statistical analysis.

Statistical Analysis

We used Kolmogorov–Smirnov test to determine the distribution of continuous variables, and a P-value of > 0.05 indicated the normal distribution of data. Continuous variables with normal distribution were presented as mean ± standard deviation, and non-normal variables were expressed as median with interquartile range. Categorical variables are presented as number with percentage. We conducted logarithmic conversion for the non-normally distributed variables, including BCL, hsCRP, iPTH and ferritin. We used one-way analysis of variance (ANOVA) to compare the BCLs among the three groups. To identify factors associated with BCL, we used the linear regression models to determine the correlation between BCL and clinical variables, including baseline data, environmental PM2.5 concentrations and the days of PM2.5 exceeding the standard level over the past 12 and 24 months before study. All potential variables (P < 0.1) in univariate linear regression analysis were selected into multivariate linear regression model with forward stepwise methods. The standardized regression coefficient (β) and 95% confidence interval (CI) were obtained by these models.

We performed data analyses by using Statistical Package for Social Sciences (SPSS), Version 18.0 for Windows (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set at a value of P < 0.05.

ResultsCharacteristics of the Study Population

A total of 754 patients (304 men and 450 women) met the study criteria and were enrolled into this study. The mean age was 56.7 ± 13.3 years, and the mean HD vintage was 6.86 ± 5.37 years. The median BCL was 0.36 µg/L (range: 0.21, 0.79 µg/L). Table 1 lists the demographic and clinical characteristics, including age, gender, body mass index (weight/height2), co-morbidities, dialysis-related and biochemical data, and environmental PM2.5 information. Of all patients, 40.3% were men, 22.1% had diabetes mellitus, 38.3% had hypertension and 4.1% had previous cardiovascular diseases. The mean serum albumin level was 4.05 ± 0.34 g/dL and the median hsCRP level was 2.78 mg/L (range: 1.34, 6.13 mg/L). We analyzed the PM2.5 data in the previous 12 and 24 months, which were recorded from the 36 monitoring stations of Taiwan Air Quality Monitoring Network. The mean environmental PM2.5 concentration was 28.45 ± 3.57 μg/m3 during the 12 months, and 29.81 ± 3.47 μg/m3 during the 24 months before analysis.

Table 1 Demographics, Clinical Characteristics, Blood Cadmium Level and PM2.5 Data of Study Patients (n = 754)

Based on the tertiles of days with a daily mean PM2.5 concentration ≥ 35 μg/m3 in previous 24 months, we stratified patients into three groups and obtained the following results: the group A (exposure days < 201, n = 259) with mean BCL = 0.789 ± 0.090 µg/L, the group B (201 ≤ exposure days < 227, n = 299) with mean BCL = 0.750 ± 0.089 µg/L, and the group C (exposure days ≥ 227, n = 196) with mean BCL = 1.095 ± 0.136 µg/L. As shown in Figure 2, the patients in group C had a significantly higher BCL than those in groups A and B (P < 0.05).

Figure 2 Comparison of the BCLs of three patient groups based on the number of days with a daily mean PM2.5 concentration ≥ 35 μg/m3 in the previous 24 months. Patients in group C had a significantly higher BCL than those in groups A and B (P < 0.05).

Abbreviation: BCL, blood cadmium level.

Notes: Mean BCL in the previous 24 months: group A, 0.789 ± 0.090 µg/L; group B, 0.750 ± 0.089 µg/L; group C, 1.095 ± 0.136 µg/L. Days of exposure: group A, < 201 days; group B, 201 to 227 days; group C, ≥ 227 days.

Water and Dialysate Cadmium Levels

The cadmium of all the water and dialysate samples (n = 12) were less than 0.1 µg/L. This is far below the American Association for Advancement of Medical Instrumentation standards (cadmium, < 10 μg/L).

Associations Among BCLs and PM2.5 and Other Clinical Variables

In univariate linear regression analysis, log BCL was positively associated with age, log hsCRP, the mean PM2.5 concentrations of the previous 12 months and 24 months, and the days with a daily mean PM2.5 concentration exceeding the standard level in the previous 12 and 24 months. In contrast, log BCL was negatively associated with serum albumin and creatinine levels (Table 2).

Table 2 Associations Between Log BCL, Clinical Variables and PM2.5 Data in Study Patients (n = 754), Analyzed by Univariate Linear Regression Analysis

All potential variables with P < 0.1 in univariate linear regression analysis were selected into multivariate linear regression model with a stepwise method for further analysis. After adjusting for these potential variables, log BCL was positively associated with the mean PM2.5 concentration in previous 12 months (β: 0.076, 95% CI [0.001, 0.018], P = 0.037) (Table 3). After adjusting for these potential variables, log BCL was positively associated with the days of PM2.5 exceeding the standard level in previous 12 months (β: 0.072, 95% CI [0.0001, 0.0030], P = 0.047) (Table 4). Similarly, log BCL was positively associated the mean PM2.5 concentration in previous 24 months (β: 0.087, 95% CI [0.002, 0.020], P = 0.017) (Table 5), and positively associated with the days of PM2.5 exceeding the standard level in previous 24 months (β: 0.086, 95% CI [0.0001, 0.0010], P = 0.019) (Table 6).

Table 3 Associations Between Log BCL, Clinical Variables and PM2.5 Concentration in the Previous 12 Months, Analyzed by Multivariate Linear Regression Analysis with a Stepwise Method

Table 4 Associations Between Log BCL, Clinical Variables and Days with High PM2.5 in the Previous 12 Months, Analyzed by Multivariate Linear Regression Analysis with a Stepwise Method

Table 5 Associations Between Log BCL, Clinical Variables and PM2.5 Concentration in the Previous 24 Months, Analyzed by Multivariate Linear Regression Analysis with a Stepwise Method

Table 6 Associations Between Log BCL, Clinical Variables and Days with High PM2.5 in the Previous 24 Months, Analyzed by Multivariate Linear Regression Analysis with a Stepwise Method

Discussion

The analytical results of this study demonstrated a significant association between environmental PM2.5 exposure and BCLs in maintenance HD patients. Following the adjustment for potential variables, a positive correlation was observed between BCLs and the mean PM2.5 concentration, as well as the number of days during the previous 12 and 24 months in which PM2.5 levels exceeded the standard level in these patients. Based on a review of the published literature, this study may be the first to examine the association between PM2.5 exposure and BCLs in ESRD population.

Air pollution has become a major issue in recent decades and has a profound impact on human health.13,14 It is the single largest environmental contaminant causing a variety of diseases worldwide.24 There are several sources of the air pollutants, which are mainly emitted from the use of vehicles, fuel combustion and industrial activities in developing countries.12 As stated by the World Health Organization, the six primary air pollutants that have been identified as having a detrimental impact on human health and the environment are particulate air pollutants, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead.12 Of these pollutants, PM2.5 can enter the bloodstream and affect whole body of humans by deeply penetrating into the lung and damaging alveoli.15,25,26 The ambient PM2.5 may cause 4 million deaths annually from cardiopulmonary impairment such as heart diseases, respiratory infections, chronic pulmonary complications, cancers and others.24 In addition to cardiopulmonary diseases, PM2.5 may have contributed to other clinical complications, such as triggering diabetes mellitus and increasing the risk of adverse birth outcomes.27 Of note, PM2.5 can contribute to a range of harmful effects in humans at very low levels of environmental exposure, well below national standards.27 In basic studies, PM2.5 has detrimental effects on kidneys with normal glomerular filtration rate.28 The proposed mechanism of renal damage by PM2.5 is complicated, including PM2.5 accumulation in kidneys, endothelial dysfunction, renin-angiotensin system abnormalities, inflammation, oxidative stress, DNA damage, apoptosis, autophagy and others.28 In epidemiologic studies, PM2.5 is significantly correlated with lower glomerular filtration rate, higher prevalence of chronic kidney disease, and the increased risk of ESRD in general population.28–31 Although studies have demonstrated a correlation between PM2.5 and renal damage, the precise mechanisms remain unclear and warrant further investigation due to the complex composition of PM2.5 particles.12,13,16,17

Cadmium is a ubiquitous environmental pollutant in the natural and occupational environments.5,32 The higher levels of cadmium in the human body were associated with an increased risk of all-cause, cardiovascular and cancer mortality.5,6 Exposure to cadmium has been reported in several ways over the past decades, including working in a cadmium-using factory, eating contaminated food, living in cadmium-contaminated areas, smoking cigarettes, and others.5,8 It is noteworthy that cadmium could be carried by atmospheric PM2.5 particle because of its large specific surface area and the strong capacity to carry heavy metals.26 The cadmium incorporated with PM2.5 can enter the human body by inhalation and potentially damage several organs.13–15 In an experimental study in rats, low concentrations of cadmium in PM2.5 particles had been found to induce genetic toxicity by damaging DNA or chromosomes.33 A recent in vitro experiment demonstrated the pivotal role of cadmium accumulation by PM2.5 in renal injury.34 The findings revealed that PM2.5 extract significantly reduced the survival of renal tubular epithelial cells. In epidemiological studies, the presence of cadmium in PM2.5 particles above the background concentration has been linked to higher levels of environmental contamination and adverse effects on human health. A study conducted in China indicated that cadmium in PM2.5 particles is carcinogenic to humans, as the lifetime cancer risk posed by cadmium exceeds the threshold range for cancer risk.35 A recent study demonstrated that cadmium levels in children’s blood are associated with PM2.5 exposure, and that PM2.5-bound cadmium exposure contributes to the development of serious health risks in children.36 Nevertheless, further detailed and comprehensive studies are required to clarify the mechanisms underlying these processes.

In the healthy population, studies have shown an association between BCLs and exposure to PM2.5. A study of 300 children provided evidence that the levels of cadmium in both PM2.5 and blood are markedly elevated in an area engaged in the recycling of electronic waste compared to a reference area, with an increased prevalence of respiratory symptoms such as coughing.37 A study of 13,626 children found a significant correlation between ambient PM2.5 exposure and BCL, suggesting that environmental countermeasures are essential to reduce its impact on health.36 As blood cadmium has a very long half-life of about 7 to 16 years, it can serve as a useful marker for assessing body burden in the general population and is also likely to be a good index of body cadmium burden in ESRD patients.11 In the present study of patients undergoing maintenance HD, linear regression analysis showed that both mean PM2.5 concentrations and the number of days with PM2.5 above the standard level were positively associated with BCLs in the previous 12 and 24 months. Furthermore, according to the tertiles of days with a daily mean PM2.5 concentration above the normal limit in the previous 24 months, patients with the highest exposure days exhibited a significantly higher BCL than those in the other two patient groups. All these findings strongly suggest that inhaled PM2.5 particles may lead to chronic accumulation of cadmium in the bodies of ESRD patients. To our knowledge, this may be the first study to indicate an association between PM2.5 exposure and body burden of cadmium in chronic dialysis patients. Although the diversity and complexity of PM2.5 components make it difficult to determine the exact effects of cadmium accumulation, the loss of normal ability to excrete cadmium in urine and the difficulty of eliminating cadmium by dialysis may be the main reasons for the accumulation of cadmium in the blood of these patients.10 However, further research is needed to clarify this observation.

There were some limitations of this study. First, the daily intake of cadmium from food was not available. However, our dialysis centers routinely educate patients about dietary control, especially the importance of avoiding heavy metals. Second, the causal relationship between PM2.5 and BCLs could not be determined because of the cross-sectional design of this study. Hence, we used the mean PM2.5 levels in the previous 12 and 24 months for the analysis of BCLs, which is similar to a semi-cohort study. The findings suggest a potential correlation between exposure to PM2.5 and the body burden of cadmium in chronic dialysis patients. Third, as the study was conducted at a single center with a smaller sample size than other national surveys, the possibility of selection bias and a type 2 statistical error could not be discounted. A longitudinal prospective study with a larger sample size measuring PM2.5 exposure and BCLs at multiple time points is needed to assess the external validity of this study.

Conclusion

This study demonstrated that chronic exposure to PM2.5 is closely associated with BCLs in maintenance HD patients and that exposure to PM2.5-bound cadmium may contribute to adverse health effects in this population. The World Health Organization has classified cadmium as a human carcinogen and cancer is a common cause of death in ESRD patients.5,8,9 Our results strongly support for the strict enforcement of relevant legislations to protect the environment from the detrimental impacts on chronic dialysis patients. Further longitudinal studies are needed to confirm the analytical results presented here.

Abbreviations

BCL, blood cadmium level; β, standardized regression coefficient; CI, confidence interval; ESRD, end-stage renal disease; HD, hemodialysis; hsCRP, high-sensitivity C-reactive protein; iPTH, intact parathyroid hormone; PM2.5, particulate matter with an aerodynamic diameter of 2.5 μm or less.

Data Sharing Statement

The datasets for the current study were available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank all the parents who participated in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Disclosure

The authors declare that there are no conflicts of interest in this work.

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