Long-Term Exposure to Fine Particulate Matter (PM2.5) and Cardiovascular Disease Mortality among Renal Transplant Recipients
1. School of Public Health, Loma Linda University , 24951 Circle Drive, Loma Linda, CA 92354, USA
2. School of Nursing, Loma Linda University, 11262 Campus Street, Loma Linda, CA 92350, USA
3. School of Pharmacy, Loma Linda University, 24745 Stewart Street, Loma Linda, CA 92350, USA
4. Redlands Community Hospital, 305 Terracina Blvd, Redlands, CA 92350, USA
Academic Editor: Steven Potter
Received: August 11, 2019 | Accepted: December 10, 2019 | Published: December 17, 2019
OBM Transplantation 2019, Volume 3, Issue 4, doi:10.21926/obm.transplant.1904095
Recommended citation: Dehom S, Knutsen S, Shavlik D, Bahjri K, Ali H, Pompe L, Spencer-Hwang R. Long-Term Exposure to Fine Particulate Matter (PM2.5) and Cardiovascular Disease Mortality among Renal Transplant Recipients. OBM Transplantation 2019;3(4):21; doi:10.21926/obm.transplant.1904095.
© 2019 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.
Cardiovascular disease (CVD) is the leading cause of death in the United States, responsible for more deaths than cancer and chronic lower respiratory disease combined, according to 2015 statistics from the American Heart Association . While heart disease is a concern for the population as a whole, some groups are at greater risk for developing and dying from CVDs. One such vulnerable group is renal transplant recipients [2,3]. The incidence for CVDs is much higher among people with end-stage renal disease (ESRD) and continues even after renal transplantation, which can be partially explained by commonly shared risk factors between ESRD patients and renal transplant recipients, such as hypertension, diabetes mellitus, obesity, and dyslipidemia [4,5,6]. For most patients with ESRD, renal transplantation is the treatment of choice . Transplantation has been associated with better health outcomes and lower health care costs compared with dialysis. In 2015 alone, a total of 18,805 kidney transplant procedures were performed in the U.S., with 83,978 patients on the waiting list for kidney transplantation . Despite the gains in overall health, renal transplant recipients are still at much higher risk of morbidity and mortality from CVD compared to the general public, identifying a critical need for reducing CVD among this vulnerable population [2,3,8,9].
In addressing CVD risk among renal transplant recipients, it is important to consider risk factors and identify potential opportunities for intervention. In addition to the traditional risk factors, numerous epidemiological studies have linked ambient air pollutants, especially fine particulate matter, with CVD morbidity and mortality [10,11,12,13,14,15,16,17,18,19,20]. In 2015, ambient PM2.5 pollution alone was classified as the fifth-ranking mortality risk factor worldwide . Approximately 4.2 million deaths and 130.1 million disability-adjusted life-years have been attributed to ambient PM2.5 levels, causing a 20% increase in death compared with 1990 estimates . Moreover, higher risk rates associated with ambient air pollutant levels have been observed among subpopulations, such as renal transplant recipients , smokers , females [22,23], and people with diabetes [24,25,26]. In contrast, a reduction in PM2.5 levels and change in the composition of PM2.5 has been linked to a decline in CVD morbidity and mortality [27,28,29,30].
To date, very few studies have assessed the potential adverse effects of ambient air pollutants on renal transplant recipients, and even fewer studies have focused on particulate matter. In 2011, Spencer-Hwang, et al., reported a positive association between ambient O3 levels and risk of CHD mortality (RR=1.34; 95% CI, 1.03 – 1.76) among renal transplant recipients during the 7 year study period . However, the researchers did not find a significant association between CHD mortality and ambient PM10 levels (RR=0.95; 95% CI, 0.79 – 1.15). Unfortunately, this study lacked data on ambient PM2.5 levels, which are more strongly associated with CVD mortality than PM10 [11,23,31].
While PM10 has been significantly associated with adverse health outcomes [32,33], it is thought that PM2.5 can have a stronger effect, since its smaller particle size can penetrate deeper into the lungs, and is more easily transported into the blood stream, where it can cause greater damage .
In a 2016 study of mice, Nemmar, et al. found a significant association between prolonged exposure to diesel exhaust particles, consisting primarily of particulate matter, and the aggravation of chronic renal failure due to oxidative stress, inflammation and DNA damage .
There are few studies on the relationship between ambient air pollution and health outcomes among renal transplant recipients in general, and especially that of fine particulate matter. The purpose of this study was to assess the association between annual levels of ambient fine particulate matter (PM2.5), and risk of CVD-related mortality, including CHD, stroke, sudden cardiac arrest, and CHF after controlling for potential confounders among renal transplant recipients in the United States.
2. Materials and Methods
2.1 Study Population
Study participants were identified from the U.S. Renal Data System (USRDS), a national data repository containing extensive demographic (including updated residential ZIP codes), diagnostic, hospital site, and mortality data for persons living with ESRD .
The study population (N=106,354) includes renal transplant recipients, 18 years and older, who had their first transplant procedure between 2001 and 2015, with a minimum of one year of graft survival, and who have lived in the contiguous U.S. at the same ZIP code throughout follow-up. Subjects were followed until date of CVD mortality or censoring, which occurred at the time of death due to other causes or at the end of the follow-up period (12/31/2015). Subjects with prevalent CVD (n=9,269) at the time of transplant and, in addition, current smokers without CVD (n=3,228) were excluded (Table S1). Thus, the final analytic study population consists of 93,857 non-smoking renal transplant recipients.
The study was approved by the Loma Linda University Institutional Review Board (IRB) as required.
2.2 Outcome Assessment
Fatal cases of CHD were identified from the USRDS using the Centres for Medicare & Medicated Services (CMS) ESRD Death Notification codes (Table S2 in the Supplement) . Primary cause of death among renal transplant recipients was used to classify study participants into cases and non-cases based on the mortality categories in the ESRD Death Notification Form . The following are the fatal CVD outcomes assessed and their definitions:
Total CVD mortality: A subject classified with a total CVD mortality event if the underlying cause of death was CHD, CHF, stroke or sudden cardiac arrest as they are defined below.
CHD mortality: Underlying cause of death was myocardial infarction or atherosclerotic heart disease.
CHF mortality: Underlying cause of death was congestive heart disease.
Stroke mortality:Underlying cause of death was cerebrovascular accident, including intracranial haemorrhage or ischemic brain damage/anoxic encephalopathy.
Sudden cardiac arrest mortality:Underlying cause of death was cardiac arrhythmia or cardiac arrest, cause unknown.
2.3 Pollutant Exposure Assignment
To obtain robust estimates of air pollutants, integrated empirical geographic (IEG) regression models developed by Kim et al were used to calculate the annual-average concentrations of PM2.5, O3 , and NO2 after adjusting for several important geographical factors, including land use and population density [38,39,40]. In addition to satellite-derived estimates of air pollution levels, the daily measurements of air pollutants at all Air Quality System (AQS) monitoring sites from U.S. Environmental Protection Agency data repository were used to build the IEG regression models . The air pollutant estimates can be obtained from the Center for Air, Climate, and Energy Solutions (https://www.caces.us/data). Further details on the estimation method and model building are explained elsewhere . The annual mean concentrations of ambient PM2.5, O3 and NO2 from 2001 to 2015 were assigned to each individual based on residential ZIP codes using geographical information system software (GIS). Yearly levels of ambient air pollutants were assigned based on changing attained-age risk sets. These estimates were then merged with USRD data for each subject.
2.4 Potential Confounding Variables
The USRDS database encompasses a wealth of information on several important factors used to adjust for potential confounding effects, including demographics, lifestyle factors, medical history, and transplant-related factors. Covariates were added to the models with a-priori specification and included age; gender; race; primary cause of ESRD (diabetes, hypertension, primary glomerulonephritis, polycystic kidney disease, other factors); length in years from first ESRD services to first transplant (0-1, 2 -5, 6 - 10, 10+ years); donor type (deceased/living); ESRD network categories (low, medium, high transplant ratio); BMI categories (< 18.5, 18.5 - <25, 25 - <30 , 30+); types of anti-rejection medications (e.g., cyclosporine [yes/no] or tacrolimus [yes/no]); history of hypertension (yes/no), and history of diabetes (yes/no). Anti-rejection medications were evaluated on an intention-to-treat basis. ESRD regional networks were classified based on their standardized transplant ratio .
2.5 Statistical Analysis
Descriptive statistics for demographic and health characteristics in the overall study cohort were calculated according to quartiles of annual average PM2.5 levels (ug/m3) and given as mean ± standard deviation for continuous variables, and a number with valid percentages for categorical variables. Pearson Chi-square and one-way ANOVA were performed to evaluate the associations between these demographic and health characteristics and PM2.5 quartiles after assessing the assumptions of these statistical tests.
Time-dependent Cox-proportional hazard regression models with attained age as the time variable and left truncation by age at time of transplant were used to estimate the association between PM2.5 and risk of total CVD, CHD, stroke, sudden cardiac arrest and CHF mortality after adjusting for covariates. Ambient air pollutant levels were assigned within Cox regression models as a 1-year average incrementing yearly for each risk set.
The baseline Cox regression model was developed based on an a-priori specification that included PM2.5, gender, race and years since first transplant. Primary cause of ESRD (diabetes, hypertension, primary glomerulonephritis, polycystic kidney disease, other factors), length in years from first ESRD services and first transplant (0-1, 2 -5, 6 - 10, + 10 years), donor type, ESRD Network categories (low, medium, and high STR), BMI categories (< 18.5, 18.5 - <25, 25 - <30, 30 +), and immunosuppressive medications (cyclosporine and tacrolimus) were added to the final model.
Considering the change in the ambient levels of PM2.5 in the last few decades, PM2.5 categories were created by calculating the medians of 25th, 50th and 75th percentiles for the annual PM2.5 concentrations from 2001 through 2015. The linearity assumption for the main exposure variable with mortality outcomes was assessed graphically by plotting the estimated coefficients of PM2.5 quartiles; this was met for all outcomes except CHF.
Additionally, we assessed the relationship between PM2.5 quartiles and mortality outcomes. Time-dependent variables were included in the final model if the proportionality assumption was not met, and the effects were reported at an average age of 60 years as indicated in Table 1 and Table 2.
Sensitivity Analysis was also done where we included the 9,269 who were excluded at baseline because of prevalent CVD. We assessed each of the mortality outcomes using the same single-pollutant full model as was used in the analytic population (Table 1)
SAS (version 9.4; SAS Institute, Inc., Cary, NC, USA) was used to perform the main analyses of the study. ArcGIS Desktop (release 10.6; Esri, Redlands, CA, USA) was used to geocode air pollutant levels and create maps.
Table 1 Multivariable adjusted hazard ratios for CVD fatal events per 10 ug/m3 increment of PM2.5: Single pollutant model.
Table 2 Multivariable adjusted hazard ratios for CVD fatal events per 10 ug/m3 increment of PM2.5: Two pollutant models with Ozone and NO2.
3.1 Study Population
The study population included 93,857 non-smoking renal transplant recipients from across the entire continental U.S. (Figure 1) who had lived at the same ZIP code during the entire follow-up period. During a median follow-up of 14.91 years, 3,082 fatal CVD cases were reported, of which 624 were due to CHD; 245 to CHF; 1,810 were cardiac arrests, and 403 were due to stroke. The annual levels of ambient air pollutants are displayed in Table 3. When comparing demographics and health characteristics of the study cohort across differing categories of ambient PM2.5, significant differences in some of these factors were observed, especially among those in the highest quartile of PM2.5 (> 12 ug/m3). Transplant recipients in the highest quartile were more likely to be non-white; female; underweight (BMI<18.5); have received a kidney from a cadaveric donor; have ESRD attributed to diabetes or hypertension; have a longer period between first ESRD service and first transplant procedure, and to be registered within the ESRD network with a low or medium transplant ratio (Table 4). On the other hand, there was no significant difference in years since the first transplant between the PM2.5 quartiles (Table 4).
Figure 1 Distribution of renal transplant recipient cohort subjects (age >18 years and transplanted between Jan 2001 – Jan 2015) by county.
Table 3 Annual levels of PM2.5, O3, and NO2 by calendar year.
Table 4 Demographic and health characteristics of overall study cohort according to quartile of annual average of PM2.5 (ug/m3).
3.2 Total CVD Mortality Risk
3.2.1 Single-Pollutant Models
The basic model showed a strong and significant association between each 10 ug/m3 incremental increase in ambient PM2.5 and total CVD mortality (HR=2.48, 95% CI: 2.13 – 2.90). The estimate was slightly attenuated in the multivariable-adjusted full model with time-interaction adjusting for demographics and transplant-related factors (HR=1.85, 95% CI: 1.57 – 2.17) (Table 1).
3.2.2 Two-Pollutant Models
Compared with the single-pollutant full model, the hazard ratio for each 10 ug/m3 increase in PM2.5 for total CVD mortality, after adjusting for O3 and NO2 in two separate models, was somewhat strengthened, with HR=2.30 adjusting for NO2 HR=2.05 in the model with O3 (Table 2).
3.2.3 Models with PM2.5 Quartiles
In the single multivariable-adjusted full model with quartiles of PM2.5 as the exposure variable, the hazard ratio for the fourth quartile was 40% higher compared to the first quartile (HR=1.40, 95% CI: 1.24 – 1.59) (Table 5).
In the two-pollutant models, the hazard ratios for the fourth quartile were higher than the first quartile also after adjusting for O3 and NO2, 47% and 55%, respectively (Table 5).
3.3 CHD Mortality Risk
3.3.1 Single-Pollutant Models
The basic model showed that for each 10 ug/m3 increase in ambient PM2.5, CHD mortality increased three-fold (HR=3.17, 95% CI: 2.27 – 4.43), but was somewhat attenuated to 120% increase in the multivariable-adjusted full model (HR=2.20, 95% CI: 1.53 – 3.17) (Table 1).
3.3.2 Two-Pollutant Models
Compared with the single-pollutant full model, the strength of association between a 10 ug/m3 increase in ambient PM2.5 levels and risk of CHD mortality was further strengthened after adjusting for NO2 (HR=2.95) (Table 2), but remained virtually unchanged in the two-pollutant model with O3 (HR=2.19).
3.3.3 Models with PM2.5 Quartiles
In the single multivariable-adjusted full model with PM2.5 as a categorical variable, the hazard ratio for the fourth quartile was 86% higher than for the first quartile (Table 5).
In the two-pollutant models, comparing Q4 with Q1, the association with fatal CHD after adjusting for O3 was somewhat attenuated (HR=1.87) (Table 5) while the association was virtually the same after adjusting for NO2 (HR=2.22) (Table 5).
Table 5 Multivariable-adjusted hazard ratios for CVD fatal events per quartile of PM2.5: Single and two pollutant models.
3.4 Stroke Mortality Risk
3.4.1 Single-Pollutant Models
The basic model showed a strong association between each 10 ug/m3 increase in ambient PM2.5 levels and stroke mortality (HR=2.77, 95% CI: 1.81 – 4.25) and remained significant, but somewhat attenuated, in the multivariable-adjusted full model (HR=1.82, 95% CI: 1.15 – 2.89) (Table 1).
3.4.2 Two-Pollutant Models
Compared to the single-pollutant full model, the association between a 10 ug/m3 increase in ambient PM2.5 and the risk of stroke mortality increased to a HR of 3.20 (95%CI: 1.76 – 5.81) and 2.07 (95% CI: 1.23 – 3.47) when also controlling for NO2 and O3, respectively (Table 2).
3.4.3 Models with PM2.5 Quartiles
In the single multivariable-adjusted full model with PM2.5 as a categorical variable, the risk of stroke mortality for the fourth quartile was 32% higher than the hazard ratio for the first quartile (Table 5), but it was not statistically significant.
In the two-pollutant models, comparing Q4 with Q1, the hazard ratios were somewhat attenuated (Table 5).
3.5 Cardiac Arrest Mortality Risk
3.5.1 Single-Pollutant Models
Similar to the findings for CHD and stroke, the basic model showed a very strong association between each 10 ug/m3 increase in ambient PM2.5 and cardiac arrest mortality (HR=2.42, 95% CI: 1.98 – 2.96), which was somewhat attenuated in the multivariable-adjusted full model (HR=1.77, 95% CI: 1.42 – 2.19) (Table 1).
3.5.2 Two-Pollutant Models
As for the previous outcomes, the hazard ratios were strengthened to 2.02 and 1.95, respectively, when also controlling for NO2 and O3 (Table 2).
3.5.3 Models with PM2.5 Quartiles
In the single multivariable-adjusted full model with PM2.5 as a categorical variable, the risk was lower, but still statistically significant when comparing the fourth quartile with the first quartile (HR=1.30) (Table 5).
In the two-pollutant models, adjusting for O3 and NO2, respectively, the association remained more or less the same as in the continuous model (Table 5).
3.6 CHF Mortality Risk
Unlike the other mortality outcomes, there was no clear association found between ambient PM2.5 and CHF mortality in any of the basic, multivariable or two-pollutant models (Tables 3 and 4), nor in models when we used PM2.5 as a categorical variable (Table 5).
3.7 Sensitivity Analysis
The HR for all outcomes stayed virtually unchanged when including the 9,269 subjects with prevalent CVD who were excluded at baseline. The HR for CVD mortality was 1.90, for CHD 2.18, for stroke 1.95, for sudden death 1.83 and for CHF 1.02 with the 95% confidence intervals almost completely overlapping those for the single-pollutant full model in the analytic population (Table 1).
To the best of our knowledge, this is the first study to explore the relationship between ambient PM2.5 and the risk of premature death due to CVD and its subcategories among this highly sensitive population of renal transplant recipients. Because of the large number of subjects, we were able to study several subcategories of CVD mortality, namely CHD, stroke, cardiac arrest, and CHF mortality. We were also able to eliminate one of the major risk factors for CVD in that we excluded all current smokers. The results from this large retrospective cohort study support the hypothesis that ambient levels of PM2.5 is an independent risk factor for fatal CVD among the renal transplant population. We found detrimental effects on CVD mortality even for PM2.5 levels below the current EPA standard. In our previous study among renal transplant recipients, Spencer-Hwang et al. found significant positive associations between ambient O3 levels and the risk of CHD mortality, but not with ambient PM10 levels . Due to the limited number of PM2.5 air quality monitors in the nation at that time, the researchers were not able to assess the relationship between ambient PM2.5 and the risk of CHD mortality . In other populations, several studies have shown a stronger association between PM2.5 and CVD mortality than for PM10 [11,23]. The possible explanation is that the smaller particle size of PM2.5 can penetrate deeper into the lungs and then enter the blood stream, resulting in greater health effects .
Our current study was designed to reduce the gap in the literature on the CVD effects of increasing levels of ambient PM2.5 among renal transplant recipients. As we anticipated, the findings of this study provide strong evidence for adverse effects of ambient particulate air pollution on human health, especially among this vulnerable group. Positive associations were found between PM2.5 and all CVD mortality outcomes, except CHF mortality, in both single- and two-pollutant multivariable adjusted models. It is not clear why the association with CHF is lower, but the etiology of CHF is multifactorial and some of them may have no association with ambient air pollution. In addition, it is possible that for CHF, it is the immediate short-term particulate air pollution that is most important, not the average annual levels. This is supported by the meta-analyses of Shah, et al. , who found an increased risk of CHF associated with same-day ambient PM2.5 levels. In a recent ecologic cross-sectional study on CHF mortality, Bennett, et al. found a negative association with the particulate matter indicator . However, the PM indicator was PM less than 10 µg/m3 and they were contrasting rural versus urban areas. The levels of the PM indicator were slightly higher in the rural areas, where the CHF mortality was lower. It is hard to evaluate these findings, as they will be influenced by both the size and composition of the particles. The urban areas had higher levels of NOx, indicating higher traffic pollution, and thus, likely higher levels of fine PM. Also, as an ecologic study, there was no subject-specific information on lifestyle differences between those living in rural versus urban areas, although it is likely that the rural population had a better diet and more physical activity, which could have contributed to the lower rates in the rural areas. As a result of these findings, the team recommended that patients’ living condition (rural vs. urban) should be considered when assessing associations between particulate matter exposure and morbidity and mortality due to heart failure .
Our results are consistent, but stronger, than the findings of most previous cohort studies among the general population as well as among potentially sensitive subpopulations. However, similar to our findings, Chen et al., reporting from the earlier Adventist Health and Smog Study (AHSMOG 1) found a positive association of similar magnitude between PM2.5 and fatal CHD among non-smoking, mostly never smoking, non-Hispanic, white adult females during 22 years of follow up (RR=2.00, 95% CI: 1.51 – 2.64) .
Weaker associations have been reported by others from populations that were not limited to non- or never-smokers. In a study of 8,096 white subjects from the Harvard Six Cities study, Laden, et al. found that ambient PM2.5 levels were positively associated with an increase in CVD mortality (RR=1.28, 95% CI: 1.13 – 1.44) . Additionally, the reduction in ambient PM2.5 emissions in the second period of the study (between 1990 and 1998) was associated with lower CVD mortality risk . Moreover, Turner, et al. , studying 669,046 participants from the Cancer Prevention Study II, found positive associations between ambient PM2.5 and total CVD mortality (HR=1.07, 95% CI: 1.04 – 1.10); ischemic heart disease (HR=1.07; 95% CI, 1.04 – 1.10); dysrhythmias, heart failure, and cardiac arrest (HR=1.06, 95%CI: 1.00 – 1.13), and cerebrovascular disease (HR=1.13, 95% CI:1.06 – 1.21). These associations were much stronger when ambient air levels were restricted to near-source PM2.5 .
After adjusting for ecological covariates (median household income; percentage of people with < 125% of poverty-level income; percentage of unemployed individual aged ≥ 16 years; percentage of adults with < 12th grade education; and percentage of the population who were black or Hispanic), Pope III and colleagues  observed positive relations between ambient levels of PM2.5 and CVD mortality risk (HR=1.12; 95% CI, 1.10 – 1.15). In addition, pre-existing cardiometabolic risk factors were not significant effect modifiers of the association between PM2.5 and CVD mortality. Furthermore, PM2.5 was associated with increased mortality risks of type II diabetes (HR=1.25: 95% CI, 1.17 – 1.33) and hypertension (HR=1.26, 95% CI: 1.18 – 1.36) .
Among elderly white male veterans, Mehta, et al. found a significant association between one-year higher ambient levels of PM2.5 and a decline in renal function . A 2.1 μg/m3-higher 1-year ambient PM2.5 was linked with an additional annual reduction in the glomerular filtration rate (eGFR) of 0.60-mL/min/1.73 m2 per year (95% CI: –0.79, –0.40) .
Also, in a recent study, Malik et al. found a significant association between ambient PM2.5 and total mortality among myocardial infarction survivors (HR =1.13). However, no significant association was detected with ambient O3 (HR=1.01) .
Comparing our results with these previous investigations among various populations, we found stronger effects, which may be explained by the unique characteristics of this vulnerable population. Declining kidney function, prevalence of hypertension, diabetes, and use of immunosuppressive medications are factors that, compared to non-diseased subjects, may make non-smoking, renal transplant recipients more sensitive to ambient air pollutants and its effect on risk factors for cardiovascular disease.
4.1 Possible Biological Mechanism
Over the years, several biological mechanisms have been proposed to explain the relationship between ambient levels of air pollution and CVD morbidity and mortality. One of the possible explanations of the adverse effects of particulate matter on human health is that exposure to air pollutants have been linked to increased levels of pulmonary oxidative stress and inflammation, which leads to the release of inflammatory factors and free radicals into the bloodstream, causing cell injury [48,49,50]. Vascular inflammation has been associated with the development of atherosclerosis after transplantation, which leads to a higher number of CVD events [51,52]. The second common explanation is the translocation of small air pollution particles (PM2.5 and ultrafine) into blood circulation, and their ability to pass through the plasma membrane of different body cells and interact with them. These interactions may contribute to thrombosis and atherosclerotic plaque formations that eventually lead to changes in the cardiovascular system [10,49,50]. Additionally, sudden exposure to high levels of PM2.5 have been found to be associated with a significant change in the stability of atherosclerotic plaque and the thrombogenic process, which may trigger CVD events . Furthermore, researchers have found significant relationships between PM2.5 exposure and the occurrence of ventricular arrhythmias, especially among patients with coronary heart disease [54,55] and with diabetes or impaired glucose tolerance .
4.2 Strengths and Limitations
There are several strengths to this study. All health care professionals in the US are required by law to report all patient information for subjects who receive a diagnosis of chronic kidney disease as well as all who receive a renal transplant to USRD. The availability of this large database ensures that we are studying all US renal transplant recipients. In addition, this database includes a large number of important variables, allowing for the adjustment of several confounding effects. Another strength is our ability to adjust for ambient O3 and NO2 in two-pollutant models, allowing us to see how these modify the effect of PM2.5. Using this database assures generalizability from our study population of non-smoking renal transplant patients to the non-smoking renal transplant population at large. Furthermore, ZIP code-specific annual-average concentrations of air pollutants were assigned using previously available integrated empirical geographic (IEG) regression models, adjusting for several geographical factors that had high cross-validation statistics. Our findings are robust as demonstrated with the relatively narrow 95% CIs and the fact that the estimates stayed virtually unchanged even when the 9,269 subjects with prevalent CVD at baseline were included in the sensitivity analyses.
However, this study also has some limitations. Only annual ambient concentrations of PM2.5,Ozone and NO2 at the ZIP code level of the place of residence, rather than the subjects’ physical address, were available as exposure variables. In addition, place of work is not included in this database, preventing us from including such data in our exposure estimates. However, renal transplant recipients are likely live and work in close proximity, and thus with similar PM2.5 concentrations; these limitations are therefore unlikely to greatly influence their mean annual level of ambient air pollutants. Additionally, as a result of using the adjusted annual averages of ambient PM2.5 levels, we could not adjust for seasonal variations, which could explain the low levels of PM2.5. Yet, significant adverse health effects of PM2.5 at concentrations below EPA standards of 12 ug/m3 have been reported for both morbidity and mortality in other cohort studies [46,47,57] .
Another limitation is the lack of more specific classification on the death certificate, especially for stroke and cardiac arrest death. Moreover, no information on dietary factors and physical activity is available in the dataset. However, we were able to control for BMI, which partially accounts for such lifestyle factors.
In conclusion, this study is one of very few epidemiological studies to explore the association between ambient fine-particulate air pollution and risk of fatal CVD events among renal transplant recipients. Our findings demonstrate detrimental health effects even at PM2.5 levels below the EPA standard and thus begs the question of whether these standards need revision. More studies are needed to confirm our strong findings and to assess the potential association among other sensitive subgroups within this and other populations. Further research is also needed to explore the association between the various PM2.5 compositions and health outcomes. Ultimately, our findings may contribute to the development of preventive strategies to lessen the impact of air pollution on health outcomes in renal transplant recipients, and to reduce healthcare costs for patients with cardiovascular diseases. Our research findings may also help renal transplant recipients in making informed decisions to reduce personal exposure to ambient air pollution, particularly in highly polluted areas. Finally, managing modifiable CVD factors, including air pollution exposure, may eventually reduce the risk of graft loss and improve the quality of life for this vulnerable population.
The data reported here were supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. government.
The authors thank Dr. Julian Marshall and his research group at the University of Washington for sharing the air pollution data. In addition, we would like to thank Professor Keiji Oda for his assistance with the initial data management.
The authors declare that they have no relevant ﬁnancial interests.
The following additional materials are uploaded at the page of this paper.
1. Table S1: ESRD death notification codes used for CVD mortality outcomes.
2. Table S2: Smoking status and prevalent CVS cases according to quartiles of annual average of PM2.5 (ug/m3).
Salem Dehom: Conception, Study Design, Data Acquisition, Data Management, GIS, Data Analysis, Interpretation, Drafted Manuscript, Final Approval; Synnove Knutsen: Conception, Study Design, Data Analysis, Interpretation, Drafted Manuscript, Final Approval; David Shavlik: Conception, Study Design, Data Management, GIS, Data Analysis, Interpretation, Drafted Manuscript, Final Approval; Khaled Bahjri: Conception, Study Design, Data Acquisition, Data Analysis, Interpretation, Drafted Manuscript, Final Approval; Hatem Ali: Data Management, Drafted Manuscript, Final Approval; Lance Pompe: GIS, Final Approval; Rhonda Spencer-Hwang: Conception, Study Design, Data Acquisition, Data Management, Data Analysis, Interpretation, Drafted Manuscript, Final Approval.
The authors have not published or submitted any related papers from this study. The authors have no financial conflict of interest.
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