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Long-Term Exposure to Ambient Fine Particulate Matter and Chronic Kidney Disease: A Cohort Study Ta-Chien Chan,1,2 Zilong Zhang,3 Bo-Cheng Lin,1,4 Changqing Lin,5,6 Han-Bing Deng,3 Yuan Chieh Chuang,7 Jimmy W.M. Chan,8 Wun Kai Jiang,7 Tony Tam,9 Ly-yun Chang,7,10 Gerard Hoek,11 Alexis K.H. Lau,6,8 and Xiang Qian Lao3,12 1Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan 2Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan 3Jockey Club School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China 4Department of Real Estate and Built Environment, National Taipei University, New Taipei City, Taiwan 5Institute for the Environment, Hong Kong University of Science and Technology, Hong Kong, China 6Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China 7MJ Health Research Foundation, MJ Group, Taipei, Taiwan 8Division of Environment, Hong Kong University of Science and Technology, Hong Kong, China 9Department of Sociology, Chinese University of Hong Kong, Hong Kong, China 10Institute of Sociology, Academia Sinica, Taipei, Taiwan 11Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands 12Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, China

BACKGROUND: Chronic kidney disease (CKD) is a serious global public health challenge, but there is limited information on the connection between air pollution and risk of CKD.

OBJECTIVE: The aim of this study was to investigate the association between long-term exposure to particulate matter (PM) with an aerodynamic di- ameter of less than 2:5 lm (PM2:5) and the development of CKD in a large cohort. METHODS: A total of 100,629 nonCKD Taiwanese residents age 20 y or above were included in this study between 2001 and 2014. Ambient PM2:5 concentration was estimated at each participant’s address using a satellite-based spatiotemporal model. Incident CKD cases were identified by an esti- mated glomerular filtration rate (eGFR) of less than 60 mL=min=1:73 m2. We collected information on a wide range of potential confounders/modi- fiers during the medical examinations. Cox proportional hazard regression was applied to calculate hazard ratios (HRs). RESULTS: During the follow-up, 4,046 incident CKD cases were identified, and the incidence rate was 6.24 per 1,000 person-years. In contrast with participants with the first quintile exposure of PM2:5, participants with the fourth and fifth quintiles exposure of PM2:5 had increased risk of CKD de- velopment, adjusting for age, sex, educational level, smoking, drinking, body mass index, systolic blood pressure, fasting glucose, total cholesterol, and self-reported heart disease or stroke, with an HR [95% confidence interval (CI)] of 1.11 (1.02, 1.22) and 1.15 (1.05, 1.26), respectively. A signifi- cant concentration–response trend was observed (p < 0:001). Every 10 lg=m3 increment in the PM2:5 concentration was associated with a 6% higher risk of developing CKD (HR: 1.06, 95% CI: 1.02, 1.10). Sensitivity and stratified analyses yielded similar results.

CONCLUSIONS: Long-term exposure to ambient PM2:5 was associated with an increased risk of CKD development. Our findings reinforce the urgency to develop global strategies of air pollution reduction to prevent CKD. https://doi.org/10.1289/EHP3304

Introduction Chronic kidney disease (CKD) represents a serious global public health challenge and is increasingly prevalent in both developed and developing countries. The Global Burden of Disease Study 2015 estimated that deaths from CKD had increased by 31.7% from 0.9 million in 2005 to 1.2 million in 2015 and ranked as the 17th leading cause of death worldwide (GBD 2015; Mortality and Causes of Death Collaborators 2016). The most severe stage of CKD, end-stage renal disease, requires costly dialysis or trans- plant, seriously affects patients’ quality of life, and results in an enormous economic burden. Besides itself posing a direct threat, CKD is also closely associated with other forms of morbidity, especially cardiovascular disease, the leading global cause of death

(Gansevoort et al. 2013). The cardiovascular mortality rate is about two to three times higher in patients with stage 3 or 4 CKD than in those with normal kidney function [Kidney Disease: Improving Global Outcomes (KDIGO) Work Group 2013; Chronic Kidney Disease Prognosis Consortium 2010].

The traditional cardiovascular risk factors, such as obesity, hypertension and diabetes, are also CKD risk factors. Air pollu- tion has been regarded as a novel risk factor for cardiovascular diseases. Exposure to PM with an aerodynamic diameter of less than 2:5 lm (PM2:5) is causally associated with an increased risk of cardiovascular diseases (Brook et al. 2010). However, there is limited information about CKD and air pollution. To our knowl- edge, published research on the association between air pollution and incident CKD is limited to analyses of data from a cohort of U.S. veterans (Bowe et al. 2017, 2018). We therefore conducted a large cohort study to investigate the association between long- term exposure to PM2:5 and the development of CKD in 100,629 adults in Taiwan.

Methods

Study Participants The participants included in this study were drawn from a large cohort in Taiwan. The details of the cohort have been described in previous publications (Zhang et al. 2017; Wen et al. 2008; Zhang et al. 2018; Chang et al. 2016). Briefly, more than 0.5 million Taiwanese people participated in a standard medical examination program run by a pri- vate firm (MJ Health Management Institution, Taipei, Taiwan) from 1994 to 2014 (Chang et al. 2016). The participants received a series of

Address correspondence to Xiang Qian Lao, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, 4/F School of Public Health, Prince of Wales Hospital, Sha Tin, N.T., Hong Kong SAR, China. Telephone: +852 2252 8763. Email: [email protected] Supplemental Material is available online (https://doi.org/10.1289/EHP3304). The authors declare that they have no actual or potential competing financial

interests. Received 29 December 2017; Revised 3 August 2018; Accepted 24

September 2018; Published 15 October 2018. Note to readers with disabilities: EHP strives to ensure that all journal

content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.

Environmental Health Perspectives 107002-1 126(10) October 2018

A Section 508–conformant HTML version of this article is available at https://doi.org/10.1289/EHP3304.Research

medical examinations, including general physical examination, an- thropometric measurements, and functional tests of blood and urine. They also took part in a standard self-administered questionnaire survey during each visit and were encouraged to visit the medical center annually. All procedures of the program were approved in accordance with ISO 9001 standards (Chang et al. 2016). Each par- ticipant gave written consent prior to participation to authorize the use of data generated from the medical examination program. Personal identification was removed, and the data remained anon- ymous when released for research purposes. Ethical approval for this study has been obtained from the Joint Chinese University of Hong Kong—New Territories East Cluster Clinical Research Ethics Committee.

The details of participant selection for the present study are shown in Figure 1. The cohort database accumulated 590,976 partic- ipants between 1996 and 2014 (the questionnaire data have been computerized since 1996). We selected 432,433 participants who joined the program between 2001 and 2014, when the 2-y average PM2:5 exposure assessment was available. We computed their esti- mated glomerular filtration rate (eGFR) based on their serum creati- nine level using the equation from the Modification of Diet in Renal Disease (MDRD) Study (National Kidney Foundation 2002). We excluded 3,375 participants with an eGFR ≥200 mL=min=1:73 m2 or <2 mL=min=1:73 m2 because the values suggested that the measurements were probably incorrect due to occasional techni- cal errors (Levey et al. 2009). We further excluded participants

who had made only one medical visit. After these exclusions, 159,944 participants were selected. We then limited the cohort to participants without CKD at baseline by excluding those with an eGFR ≤60 mL=min=1:73 m2 [Kidney Disease: Improving Global Outcomes (KDIGO) Work Group 2013] or who reported physician-diagnosed kidney disease at their first visit. Because uri- nary protein is also an important syndrome for CKD, we also excluded those with urinary protein level ≥2:0 g=L at baseline, leaving 147,658 participants without prevalent CKD who were ≥20 years of age at baseline and had complete data for key varia- bles, including demographics, socioeconomic status, lifestyle, blood tests, and PM2:5 exposure. Finally, we excluded participants with <3 years of follow-up, resulting in a final cohort of 100,629 partici- pants enrolled between 2001 and 2011 for the present analysis. The follow-up duration of the 100,629 participants ranged from 3.0 to 13.0 y (mean, 6.5 y). The number of medical visits ranged from 2 to 18, with a mean of 4.2, totaling 424,455 medical observations. The mean ± SD visit interval was 2.0 ± 1.5 y. The selection pro- cess did not bring in substantial differences among the participants in terms of the distribution of age, sex, and cardiovascular risk fac- tors (Table S1).

Air Pollution Exposure Assessment The details for estimation of PM2:5 air pollution have been described elsewhere (Zhang et al. 2017). In brief, PM2:5 exposure was esti- mated at each participant`s address using a satellite-based spatio- temporal model with a high spatial resolution of 1 km × 1 km based on the aerosol optical depth data, which were derived from the two Moderate Resolution Imaging Spectroradiometer (MODIS) instru- ments aboard Terra and Aqua satellites from the U.S. National Aeronautics and Space Administration (Li et al. 2005; Lin et al. 2015). We have validated this model with ground-measured data from more than 70 monitoring stations in Taiwan, and the results are presented elsewhere (Zhang et al. 2017). The correlation coeffi- cients between the average satellite-retrieved and ground-level monitoring PM2:5 concentrations ranged from 0.79 to 0.83 in differ- ent years, and the mean percentage errors were around 20%.

The participants’ residential addresses were usually collected during each medical visit so the medical report could be mailed to them. Some participants provided a company address instead of a residential address. The address was geocoded into latitude and longitude data. and address-specific yearly average PM2:5 concen- trations were then calculated. For participants who provided their company address, PM2:5 was estimated at their company address. We estimated the annual average PM2:5 concentrations for the cal- endar year of each participant`s medical examination and the an- nual average for the previous year. The mean of these two averages (2-y average) was then calculated as an indicator of long-term ex- posure to ambient PM2:5 air pollution in this study. The baseline 2-y average PM2:5 concentrations (hereafter called “baseline PM2:5 exposure”) thus referred to average of the enrollment year and the previous year. The follow-up 2-y average PM2:5 concentrations (hereafter called “follow-up PM2:5 exposure”) referred to average of the follow-up medical examination year and the previous year.

Health Outcome and Covariates An overnight fasting blood sample was taken in the morning, and the serum creatinine was analyzed using a HITACHI 7150 (before 2005) or a TOSHIBA C8000 (after 2005) analyzer with the uncompensated Jaffe method involving an alkaline picrate ki- netic test (Myers et al. 2006). The eGFR level was calculated based on the following MDRD equation:Figure 1. Flowchart of participant selection.

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186:3 × ðserum creatinineÞ−1:154 × age−0:203 × ð0:742 for womenÞ

where serum creatinine is in mg/dL. Health outcome is incident CKD in the present study. After

the baseline assessment at the first visit, all participants were fol- lowed up, and the incident CKD was identified by medical assessment (defined as eGFR less than 60 mL=min=1:73 m2) in subsequent visits [Kidney Disease: Improving Global Outcomes (KDIGO) Work Group 2013]. The end point was the first occur- rence of CKD or the last visit if CKD did not occur.

In addition to serum creatinine measurement, the participants underwent a number of other medical examinations during their visits. The procedures of the medical examination program in this population have been described in previous publications (Zhang et al. 2017; Wen et al. 2008; Chang et al. 2016; Zhang et al. 2018). All examinations or tests were performed by trained medical professionals, and detailed information, including infor- mation on quality control, can be accessed in the technical reports released by the MJ Health Research Foundation (Chang et al. 2016).

A wide range of potential confounders or modifiers were con- sidered. Weight (to the nearest 0:1 kg) and barefoot height (to the nearest 0:1 cm) were measured with participants wearing light clothes using an auto-anthropometer (KN-5000A, Nakamura). Seated blood pressure was measured using a computerized auto- mercury sphygmomanometer (CH-5000, Citizen). An overnight fasting blood sample was also taken to measure total cholesterol using an auto-analyzer (7150, Hitachi). Urinary protein was an- alyzed using a ROCHE Miditron/ROCHE Cobas U411 semiau- tomated computer-assisted urinalysis system. Urinary protein results were reported at six levels: negative (<0:1 g=L), trace (0:1 ∼ 0:2 g=L), 1 plus (0:2 ∼ 1:0 g=L), 2 plus (1:0 ∼ 2:0 g=L), 3 plus (2:0 ∼ 4:0 g=L), and 4 plus (>4:0 g=L).

A self-administered questionnaire was used to collect infor- mation on demographic characteristics, occupational exposure, lifestyle, and medical history.

The following covariates were included in the data analysis: age (years), sex (male and female), education level [lower than high school (<10 y), high school (10–12 y), college or university (13–16 y) and postgraduate (>16 y)], smoking (never, ever, and cur- rent), and drinking (<once=week, 1–3 times/wk, and >3 times=wk), body mass index [BMI, calculated as weight (kg) divided by the square of height (m)], systolic blood pressure (mmHg), fasting glu- cose (mg/dL), total cholesterol (mg/dL), self-reported heart disease or stroke (yes or no), and urinary protein (at four levels: negative, trace, 1 plus, and 2 plus; participants with a level of 3 plus or above were excluded, as mentioned above).

Statistical Analysis We applied the Cox proportional hazard regression to investigate the association between PM2:5 and the incidence of CKD. The time scale used in the models was time-in-study (i.e., follow-up time). Five models were developed with the use of covariates from base- line visits: a) a crude model, with no adjustment; b) Model 1, adjusted for age, sex, education level, smoking, and drinking; c) Model 2, further adjusted for BMI, systolic blood pressure, fasting glucose, total cholesterol, and self-reported heart disease or stroke; d) Model 3, further adjusted for baseline eGFR level (a major de- terminant of kidney outcomes) (Tangri et al. 2011); and d) Model 4, further adjusted for the urinary protein level to observe its effects on the associations, because the urinary protein level is an impor- tant indicator for evaluation of renal function and the progress of CKD (Wen et al. 2008). We used the results from Model 2 as our main model, because both eGFR and urinary protein levels might

be precursors of CKD. The hazard ratio (HR) and 95% confidence interval (CI) were calculated to indicate the PM2:5 effects. The par- ticipants were categorized into five groups based on quintiles of baseline PM2:5 exposure and those with PM2:5 in the lowest quin- tile served as the reference group. A trend test was performed with the PM2:5 quintile treated as a numeric variable (an ordinal variable coded as 1–5) in the models. We also treated PM2:5 as a continuous variable and effect estimates were reported for each 10 lg=m3

increase in the PM2:5 concentration. We also conducted stratified analysis by baseline age (<65 y vs

≥65 y), baseline sex (male versus female), baseline smoking (never smoker versus ever smoker), baseline BMI (<25 kg=m2 versus ≥25 kg=m2 ), baseline hypertension (defined as systolic blood pres- sure ≥140 mmHg , diastolic blood pressure ≥90 mmHg or self- reported physician-diagnosed hypertension: yes versus no), baseline diabetes (defined as fasting glucose ≥126 mg=dL or self-reported physician-diagnosed diabetes: yes versus no), and baseline self- reported heart disease or stroke, because previous studies had sug- gested that these factors could amplify the adverse effects of PM air pollution (Brook et al. 2010). We introduced an interaction term [PM2:5 ðcontinuous variableÞ × each factor ðdichotomous variableÞ] in the Cox regression model to investigate the potential modifying effects. Each factor was examined separately. P values were calcu- lated for the product terms.

A series of sensitivity analyses were performed by a) including only participants who used a residential address (16,574 participants with 226 cases were excluded because they provided a company address); b) including only participants whose eGFR was measured with a HITACHI 7150 before 2005 (36,634 participants with 805 cases were excluded, because their eGFR were measured with a TOSHIBA C8000 since 2005); c) including all 147,658 nonCKD participants 20 years of age or older at baseline (i.e., the 47,029 par- ticipants with a follow-up duration less than 3 y plus the 100,629 participants with a follow-up duration greater than 3 y); and d) using a time-varying Cox model with PM2:5 and covariates treated as time-varying variables; e) conducting a sensitivity analysis (for comparison) using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula (Stevens et al. 2011) for eGRF calculation.

All statistical analyses were performed using R (version 3.2.3; R Core Team). A two-tailed p-value <0:05 was considered to indicate statistical significance.

Results Table 1 summarizes the participants’ general characteristics at baseline. The distribution of these characteristics was generally similar across the participants grouped by PM2:5 quintiles. Among the 100,629 participants, 4,046 incident CKD cases developed dur- ing the follow-up (the number of cases in the categories of eGFR of 45∼60, 30∼45, 15∼30, and ∼ 15mL=min=1:73 m2 were 3,944, 92, 8, and 2, respectively). The incidence rate was 6.24 per 1,000 per- son-years.

The locations of the participants are shown in Figure S1. The southwestern areas were generally the most heavily polluted and the middle-eastern areas were the least heavily polluted. The spa- tial pattern of exposure contrast throughout the island generally remained stable during the study period. Figure 2 shows the dis- tribution of baseline PM2:5 exposure. The baseline PM2:5 expo- sure increased slightly from 2001 to 2004 (the mean baseline PM2:5 exposure was 25.0, 26.7, 29.1, and 29:9 lg=m3 , respec- tively, for participants enrolled in year 2001, 2002, 2003, and 2004), and then declined and remained relatively stable from 2005 to 2011 (the mean baseline PM2:5 exposure was 27.5, 27.2, 27.4, 27.5, 28.1, 26.4, and 25:2 lg=m3, respectively for year

Environmental Health Perspectives 107002-3 126(10) October 2018

2005, 2006, 2007, 2008, 2009, 2010, and 2011). The overall mean ± SD was 27:1 ± 8:0 lg=m3 with an IQR of 10:4 lg=m3.

Table 2 shows the association between the PM2:5 and the inci- dence of CKD. A higher level of PM2:5 was associated with a higher risk of developing CKD. In the main model (Model 2), in contrast with the participants with the first quintile exposure, par- ticipants with the fourth or fifth quintiles were significantly asso- ciated, with an HR (95% CI) of 1.11 (1.01, 1.22) or 1.15 (1.05, 1.26) in CKD development, respectively. A significant concentra- tion–response trend was observed (p < 0:001). Every 10 lg=m3 increment in the PM2:5 level was associated with a 6% increased

risk of developing CKD (HR: 1.06, 95% CI: 1.02, 1.10). An addi- tional adjustment for baseline eGFR or urinary protein produced only marginal changes in the results.

Table 3 shows the results of the stratified analyses for potential modifiers. No significant effect modifications were observed when data analyses were stratified by age, sex, BMI, hypertension, diabetes, and cardiovascular disease history (all p values >0:05).

The results of sensitivity analyses 1 to 4 are presented in Table 4. Overall, the associations were consistent by excluding participants using company address, using TOSHIBA C8000

Table 1. Baseline characteristics of study participants stratified by PM2:5 quintiles between 2001 and 2011.

Characteristics All

(N = 100629) 1st quintile (N = 20119)

2nd quintile (N = 20130)

3rd quintile (N = 20122)

4th quintile (N = 20136)

5th quintile (N = 20122)

Mean 2-year PM2:5 lg=m3

(range) 27:1 ± 8:0 (5.8–49.6)

19:1 ± 1:8 (5.8–21.1)

22:2 ± 0:6 (>21:1–23:3)

24:2 ± 0:6 (>23:3–25:5)

29:0 ± 3:4 (>25:5–36:1)

41:1 ± 2:9 (>36:1–49:6)

Age mean ± SD, (years) 38:9 ± 11:3 39:9 ± 11:6 38:8 ± 11:2 38:1 ± 11:0 38:6 ± 11:3 39:0 ± 11:3 Male 52,837 (52.5) 10,280 (51.1) 10,694 (53.1) 10,669 (53.0) 10,774 (53.5) 10,420 (51.8) Education Lower than high school 13,354 (13.3) 3,074 (15.3) 2,472 (12.3) 2,271 (11.3) 2,722 (13.5) 2,815 (14.0) High school 20,351 (20.2) 4,320 (21.5) 3,901 (19.4) 3,631 (18.0) 4,081 (20.3) 4,418 (22.0) College 25,534 (25.4) 5,289 (26.3) 5,003 (24.9) 5,074 (25.2) 4,856 (24.1) 5,312 (26.4) University 29,777 (29.6) 5,389 (26.8) 6,046 (30.0) 6,470 (32.2) 6,029 (29.9) 5,843 (29.0) Postgraduate 11,613 (11.5) 2,047 (10.2) 2,708 (13.5) 2,676 (13.3) 2,448 (12.2) 1,734 (8.6) Cigarette smoking Never 74,172 (74.2) 14,855 (73.8) 14,791 (73.5) 14,923 (74.2) 14,903 (74.0) 15,240 (75.7) Former 5,441 (5.4) 1,136 (5.6) 1,098 (5.5) 1,049 (5.2) 1,123 (5.6) 1,035 (5.1) Current 20,476 (20.3) 4,128 (20.5) 4,241 (21.1) 4,150 (20.6) 4,110 (20.4) 3,847 (19.1) Alcohol drinking Never 84,024 (83.5) 16,758 (83.3) 16,904 (84.0) 16,849 (83.7) 16,787 (83.4) 16,726 (83.1) Former 2,292 (2.3) 470 (2.3) 430 (2.1) 459 (2.3) 433 (2.2) 500 (2.5) Current (≥1 time=week) 14,313 (14.2) 2,796 (14.4) 2,796 (13.9) 2,814 (14.0) 2,916 (14.5) 2,896 (14.4) Body mass index mean ± SD, (kg=m2) 22:9 ± 3:5 22:9 ± 3:4 22:9 ± 3:4 22:8 ± 3:5 23:0 ± 3:5 22:9 ± 3:5 Systolic blood pressure mean ± SD, (mmHg) 117:0 ± 16:4 116:9 ± 16:4 117:0 ± 16:3 116:9 ± 16:1 116:8 ± 16:6 117:2 ± 16:7 Fasting glucose mean ± SD, (mg/dL) 98:1 ± 17:7 98:2 ± 18:4 98:1 ± 17:3 98:2 ± 17:1 98:1 ± 18:0 97:8 ± 17:7 Total cholesterol mean ± SD, (mg/dL) 190:1 ± 34:6 189:9 ± 34:1 190:4 ± 34:7 190:5 ± 35:0 191:1 ± 34:9 188:7 ± 34:4 eGFR mean ± SD, (mL=min=1:73m2) 87:0 ± 14:9 86:5 ± 14:5 87:5 ± 15:0 87:9 ± 15:1 86:5 ± 14:9 86:7 ± 14:8 Self-reported heart disease or stroke 2,122 (2.1) 481 (2.4) 390 (1.9) 440 (2.2) 406 (2.0) 405 (2.0) Hypertension 12,603 (12.5) 2,684 (13.3) 2,534 (12.6) 2,393 (11.9) 2,432 (11.1) 2,560 (12.7) Diabetes 3,242 (3.2) 682 (3.4) 626 (3.1) 584 (2.9) 670 (3.3) 680 (3.4) Address Residential address 84,055 (83.5) 18,839 (93.6) 16,755 (83.2) 15,560 (77.3) 15,515 (77.1) 17,386 (86.4) Company address 16,574 (16.5) 1,280 (6.4) 3,375 (15.8) 4,562 (22.7) 4,621 (22.9) 2,736 (13.6)

Note: Data are presented as mean ± SD for continuous variables and number (percentage) for categorical variables. Data are complete for all variables.

Figure 2. Distribution of baseline PM2:5 exposure of the participants by year. Boxes cover the 25–75th percentile (interquartile range: IQR) with a center line for the median concentration. Whiskers extend to the highest observation within 3 IQRs of the box, with more extreme observations shown as circles.

Environmental Health Perspectives 107002-4 126(10) October 2018

analyzer since January, 2005, or including participants with a follow-up duration of less than 3 y. Using time-varying Cox regres- sion model also yielded similar results. The results of sensitivity analysis 5 using the CKD-EPI formula are presented in Table S2. We observed results very similar to those in Table 2.

Discussion Our study shows that long-term exposure to ambient PM2:5 was associated with an increased risk of incident CKD (based on eGFR <60 mL=min=1:73 m2 at a follow-up visit) among adult residents of Taiwan. Participants in this study with the fourth or the fifth quintiles of PM2:5 were significantly associated with increased risk of developing CKD, with an HR (95% CI) of 1.11 (1.01, 1.22) or 1.15 (1.05, 1.26), respectively, in comparison with the participants with the first quintile of PM2:5. Every 10 lg=m3

increase in the 2-y average of PM2:5 was associated with a 6% increase in the risk of CKD (95% CI: 2%, 10%).

The association between PM2:5 exposure and CKD development remained robust after adjustment for a wide range of potential

confounders and modifiers. It is well documented that demographic and lifestyle factors are associated with the risk of CKD. We used time-in-study as time scale in this study, adjusting for age at base- line, which is the typical approach in a Cox model. Although some alternative time scales (such as attained age and calendar time) were proposed to reduce bias, there is no option that can be assumed with certainty to be “the best” (Griffin et al. 2012). We observed slightly stronger associations after adjusting for these factors. In addition to demographic factors and lifestyles, cardiovascular risk factors are also closely associated with CKD development. Emerging literature has also suggested that air pollution is associated with cardiovascu- lar risk factors, such as hypertension and diabetes. However, adjust- ment for cardiovascular risk factors and diseases in this study did not significantly change the association, suggesting that they may not have significant intermediate effects in the association between PM exposure and CKD development. In addition, interaction tests did not show significant intermediate effects in subgroup analysis (Table 3). We also explored the potential effects of baseline eGFR and urinary protein. Our results showed that they had no significant influences on the association.

Current literature on PM2:5 and renal function is relatively sparse. A cross-sectional study was conducted in New Taipei City, Taiwan, with a sample size of 21,656 adults (Yang et al. 2017). Annual average PM10 and PMcourse concentrations at residential addresses were associated with lower eGFR and a higher prevalence of CKD, but PM2:5 was not significantly associated with either out- come. Another cross-sectional study was conducted in 1,103 patients with acute ischemic stroke in Boston, Massachusetts (Lue et al. 2013). This study found that living near a major roadway was associated with lower eGFR. Mehta et al. conducted a cohort study with 669 older men in the Boston metropolitan area (Mehta et al. 2016). Their results showed that an interquatrile range (IQR) (2:1 lg=m3 ) increment in 1-y average PM2:5 was associated with a decrease of 1.87 (95% CI: 0.76, 2.99) mL=min=1:73m2 in eGFR. Annual average exposures to PM2:5, PM10, nitrogen dioxide, and carbon monoxide were positively associated with incident CKD in a cohort of U.S. veterans (Bowe et al. 2017, 2018), which are in line with our findings. The HR of incident eGFR <60 mL=min=1:73 m2

in the study by Bowe et al. was 1.21 per 10 lg=m3 PM2:5 increase (95% CI: 1.14, 1.29) (Bowe et al. 2018), a result slightly higher than that in our study (1.06 per 10 lg=m3 PM2:5 increase). Our results may not be directly comparable with this study because it was con- ducted in the United States, where air pollution levels are relatively lower, and their participants were elderly (veterans). More studies from different regions and populations are required.

Potential mechanisms for associations between PM2:5 and CKD are not clear. However, many cardiovascular risk factors, such as smoking, obesity, hypertension, and diabetes are also risk factors for CKD. Individuals with CKD should be viewed as one of the groups

Table 2. Associations between incident CKD and long-term PM2:5 exposure in Taiwanese adults between 2001 and 2011(N = 100,629).

Exposure

Crude Modela Adjusted Model 1b Adjusted Model 2c Adjusted Model 3d Adjusted Model 4e

HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P

1st Quintile (5:8 − 21:1 lg=m3) Ref — Ref — Ref — Ref — Ref — 2nd Quintile (>21:1 ∼ 23:3 lg=m3) 0.97 (0.88,1.06) 0.50 1.06 (0.96,1.16) 0.24 1.05 (0.95,1.15) 0.36 1.09 (0.99,1.19) 0.09 1.09 (0.99,1.20) 0.07 3rd Quintile (>23:3–25:5 lg=m3) 0.91 (0.83,1.01) 0.06 1.05 (0.95,1.16) 0.31 1.04 (0.94,1.15) 0.46 1.11 (1.01,1.23) 0.03 1.11 (1.01,1.23) 0.03 4th Quintile (>25:5–36:1 lg=m3) 1.06 (0.96,1.17) 0.23 1.13 (1.03,1.25) 0.01 1.11 (1.01,1.22) 0.03 1.16 (1.05,1.28) 0.002 1.16 (1.05,1.28) 0.003 5th Quintile (>36:1–49:6 lg=m3) 1.06 (0.97,1.16) 0.21 1.16 (1.06,1.27) 0.002 1.15 (1.05,1.26) 0.003 1.17 (1.07,1.29) 0.001 1.16 (1.06,1.27) 0.002 Trend Test — 0.07 — 0.001 — 0.002 — <0:001 — 0.001 10 lg=m3 Increment 1.04 (1.00,1.08) 0.03 1.06 (1.02,1.10) 0.002 1.06 (1.02,1.10) 0.003 1.06 (1.02,1.10) 0.002 1.05 (1.01,1.09) 0.007

Note: CKD defined as eGFR <60 mL=min=1:73 m2. Covariates introduced in the Cox regression models were from baseline visits. Participants in the first quintile of PM2:5 exposure served as a reference (Ref) group. Data are complete for all variables. aCrude model: PM2:5. bModel 1: Crude model + age, sex, educational level, smoking, and drinking. cModel 2: Model 1 + BMI, systolic blood pressure, fasting glucose, total cholesterol, and self-reported heart disease or stroke. dModel 3: Model 2 + baseline eGFR. eModel 4: Model 3 + urine protein.

Table 3. Associations between incident CKD and long-term PM2:5 exposure in Taiwanese adults in stratified analyses between 2001 and 2011.

Subgroups Participants/Cases HR (95%CI)a Pinteraction Age <65 years 97698/3379 1.05 (1.00,1.09) 0.71 ≥65 years 2931/667 1.06 (0.96,1.16)

Sex Male 52837/1955 1.08 (1.03,1.14) 0.28 Female 47792/2091 1.04 (0.99,1.10) Smoking Never 74712/3105 1.05 (1.00,1.09) 0.21 Ever 25917/941 1.12 (1.03,1.21) BMI <25 kg=m2 75693/2623 1.06 (1.01,1.11) 0.83 ≥25 kg=m2 24936/1423 1.07 (1.00,1.14)

Hypertension No 88026/2701 1.07 (1.03,1.12) 0.38 Yes 12603/1345 1.03 (0.97,1.10) Diabetes No 97387/3621 1.07 (1.03,1.11) 0.28 Yes 3242/425 0.99 (0.88,1.11) Self-reported cardiovascular disease No 98507/3803 1.07 (1.03,1.11) 0.09 Yes 2122/243 0.90 (0.76,1.06)

Note: Results (HR) are reported for each 10 lg=m3 increase in PM2:5. Data are complete for all variables. aAdjusted for age (not in age-stratified analysis), sex (not in sex-stratified analysis), edu- cational level, smoking (not in smoking-stratified analysis), drinking, BMI (not in BMI- stratified analysis), systolic blood pressure (not in hypertension-stratified analysis), fast- ing glucose (not in diabetes-stratified analysis), total cholesterol, and self-reported heart disease or stroke (not in cardiovascular disease-stratified analysis). All covariates were from baseline visits.

Environmental Health Perspectives 107002-5 126(10) October 2018

with the highest risk for cardiovascular disease (Gansevoort et al. 2013). We therefore speculate that part of the mechanism of PM-related CKD may be similar to the pathway of PM-related car- diovascular diseases. Mounting evidence shows that exposure to PM2:5 may lead to systemic inflammation, oxidative stress, and atherosclerosis, which can induce endothelial damage, resulting in glomerulosclerosis, tubular atrophy, and interstitial fibrosis (Webster et al. 2017). In addition, air pollutants, including par- ticles, might traverse the alveolar space and penetrate the circula- tory system, where they can produce adverse effects on remote organs (Chin 2015). Yan et al. reported that exposure to PM may advance glomerulosclerosis and tubular damage in an animal model (Yan et al. 2014). Another potential mechanism is the toxic- ity of chemical compounds contained in PM, such as various heavy metals and polycyclic aromatic hydrocarbons. Studies have shown that environmental and occupational exposure to various chemi- cals may damage renal function (Lunyera et al. 2016; Kataria et al. 2015). Further studies to clarify the mechanism of PM-induced CKD are warranted.

This study has several important strengths. First, a longitudinal cohort study design was used to investigate long-term exposure to PM2:5 and incident CKD. Second, the large sample size increased our statistical power to better characterize the association between PM2:5 exposure and CKD development. The associations remain robust after adjusting for the confounders/modifiers. Third, we used the satellite-based spatiotemporal model with a high spatial resolution to estimate the long-term exposure level to PM2:5. This novel technology enables us to obtain the individual level of expo- sure to PM2:5 and overcome the spatial coverage and interpolation problems that occur when using only data from monitoring sta- tions. Furthermore, using the satellite data allowed us to trace the change of PM2�5 exposure over time and consider the impact of change on the development of CKD.

This study also has certain limitations. One limitation is that a single measurement of eGFR <60 mL=min=1:73 m2 was used to define CKD, as is common in large-scale epidemiological studies. In a clinical setting, diagnosis of CKD requires two measurements of eGFRs <60 mL=min=1:73 m2 separated by at least 90 d. A sin- gle measurement of eGFR <60 mL=min=1:73 m2 might be due to acute kidney disease or other diseases; thus, some participants may have been misclassified as having CKD based on this criterion. The study by Bowe et al. investigated the associations between PM2:5 and both incident eGFR <60 ml=min=1:73 m2 diagnosed by a sin- gle measurement and incident CKD diagnosed based on two meas- urements from medical records. A lower HR was found for the

incident eGFR <60 ml=min=1:73 m2 in comparison with the inci- dent CKD [HR per 10 lg=m3 PM2:5 increase: 1.21 (95% CI: 1.14, 1.29) versus 1.27 (95% CI: 1.17, 1.38)] (Bowe et al. 2018). Another limitation of our study is that information on exposure to indoor PM2:5 is not available, although we have accounted for smoking, which is one of the most important sources of indoor air pollution in a developed economy. In addition, the ambient PM2:5 was estimated at fixed addresses and the participants’ activity patterns were not considered. Meanwhile, a portion (16.5%) of the participants reported their company address rather than their residential address. However, our sensitivity analysis showed similar results when those participants who provided a company address were excluded. Third, the 100,629 participants included in this study were selected from a large cohort. Selection bias might be a concern. However, the excluded participants and included participants were comparable in terms of the distribution of age, sex, and cardiovascular risk factors. In addition, we investigated the association rather than prevalence estimate in this study. Thus, the exclusion should not affect our study conclusion. Moreover, a series of subgroup analyses and sen- sitivity analyses yielded similar results, demonstrating the robust- ness of the association. Fourth, we evaluated only the effects of particles because of the lack of information on gaseous air pollu- tants, such as nitrogen dioxide. This evaluation of only the effects particles should not affect our conclusion, however, because these of pollutants are generally correlated. The collinear issue between pollutants remains to be solved. Finally, the main results of this study were based on the 2-y average PM2:5 exposure at baseline. The baseline PM2:5 exposure changed slightly over time during the study period (increased from 25:0 lg=m3 in 2001 to 29:9 lg=m3 in 2004, and declined from 27:5 lg=m3 in 2005 to 25:2 lg=m3 in 2011). However, the spatial pattern of exposure contrast throughout the island generally remained stable during the study period, and the Sensitivity Analysis 4, which took into account temporal variations of PM2:5 and other covariates, yielded similar results.

Conclusions We found in this large-cohort study that long-term exposure to PM2:5 was associated with an increased risk of CKD. Although the estimated increase in risk was small at the individual level, the relevant public significance could be tremendous, given that exposure to air pollution is ubiquitous. CKD not only contributes to total mortality, but also seriously affects the patients’ quality of life. Our findings support the global strategies of air pollution reduction to prevent CKD development.

Table 4. Associations between incident CKD and long-term PM2:5 exposure in Taiwanese adults in sensitivity analyses.

Exposure

Sensitivity Analysis 1a Sensitivity Analysis 2b Sensitivity Analysis 3c Sensitivity Analysis 4d

HRe (95% CI) P HRe (95% CI) P HRe (95% CI) P HRe (95% CI) P

1st Quintile Ref — Ref — Ref — — — 2nd Quintile 1.05 (0.95,1.16) 0.34 0.98 (0.88,1.10) 0.76 0.98 (0.90,1.06) 0.55 1.06 (0.90,1.17) 0.25 3rd Quintile 1.04 (0.94,1.15) 0.49 1.02 (0.92,1.14) 0.69 0.94 (0.86,1.02) 0.12 0.93 (0.84,1.03) 0.14 4th Quintile 1.15 (1.04,1.27) 0.005 1.16 (1.04,1.29) 0.006 1.05 (0.97,1.13) 0.24 1.03 (0.93,1.14) 0.58 5th Quintile 1.15 (1.05,1.27) 0.004 1.17 (1.05,1.30) 0.005 1.08 (1.01,1.17) 0.04 1.15 (1.04,1.27) 0.006 Trend Test — 0.001 — <0:001 — 0.01 0.03 10 lg=m3 Increment 1.06 (1.02,1.10) 0.003 1.08 (1.03,1.12) <0:001 1.05 (1.02,1.09) 0.002 1.07 (1.02,1.11) 0.007

Note: CKD defined as eGFR <60 mL=min=1:73 m2. Participants in the first quintile of PM2:5 exposure served as a reference (Ref) group. Data are complete for all variables. The cut- off points of the quintiles were based on the distribution of participants’ baseline PM2:5 exposure unless otherwise indicated. aLimited to 84,055 participants who provided a residential address (3,820 cases), 16,574 participants who provided company address only were excluded. Mean baseline PM2:5 exposure = 27:0 ± 8:1 lg=m3. bLimited to 63,995 participants whose serum creatinine was measured by HITACHI 7150 before 2005 (3,241 cases). Mean baseline PM2:5 exposure = 26:9 ± 8:3 lg=m3. c147,685 participants, including 100,629 with ≥3 years of follow-up (included in main analysis) and 47,029 with <3 years of follow-up. Mean baseline PM2:5 exposure = 27:0 ± 7:8 lg=m3. dTime-varying Cox regression model with PM2:5 and covariates treated as time-varying variables (100,629 participants, 4,046 cases). Quintile cutoff points were based on the distribu- tion over all observations, including the baseline PM2:5 exposure and follow-up PM2:5 exposure. Mean 2-year PM2:5 of all observations = 27:2 ± 7:7 lg=m3. eHR adjusted for age, sex, educational level, smoking, drinking, body mass index, systolic blood pressure, fasting glucose, total cholesterol, and self-reported heart disease or stroke. Covariates introduced in the Cox regression models were from baseline visits (except Sensitivity Analysis 4).

Environmental Health Perspectives 107002-6 126(10) October 2018

Acknowledgments This study is in part supported by the Environmental Health

Research Fund of the Chinese University of Hong Kong (7104946). Z.Z. is in part supported by the PhD Studentship of the Chinese University of Hong Kong. H.B.D. is in part supported by the Faculty Postdoctoral Fellowship Scheme of Faculty of Medicine of the Chinese University of Hong Kong. We would like to thank the MJ Health Research Foundation for authorizing us to use the MJ health data (Authorization code MJHR2015002A). Our interpretations and conclusions do not necessarily represent the views of the MJ Health Research Foundation. We are grateful to the anonymous reviewers and the editors for their valuable comments.

References Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. 2017. Associations of ambient coarse

particulate matter, nitrogen dioxide, and carbon monoxide with the risk of kid- ney disease: a cohort study. Lancet Planet Health 1(7):e267–e276, PMID: 29851625, https://doi.org/10.1016/S2542-5196(17)30117-1.

Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. 2018. Particulate matter air pollution and the risk of incident CKD and progression to ESRD. J Am Soc Nephrol 29(1):218–230, PMID: 28935655, https://doi.org/10.1681/ASN.2017030253.

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, et al. 2010. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 121(21):2331–2378, PMID: 20458016, https://doi.org/10.1161/CIR.0b013e3181dbece1.

Chang L, Tsai SP, Wang ML, et al. 2016. MJ Health Database, MJ Health Research Foundation Technical Report, MJHRF-TR-01. www.mjhrf.org/file/en/report/ MJHRF-TR-01MJ%20Health%20Database.pdf [assessed 31 April 2017].

Chin MT. 2015. Basic mechanisms for adverse cardiovascular events associated with air pollution. Heart 101(4):253–256, PMID: 25552258, https://doi.org/10.1136/ heartjnl-2014-306379.

Chronic Kidney Disease Prognosis Consortium. 2010. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 375(9731):2073–2081, PMID: 20483451, https://doi.org/10.1016/S0140- 6736(10)60674-5.

Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, Jafar TH, Heerspink HJL, Mann JF, et al. 2013. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 382(9889):339–352, PMID: 23727170, https://doi.org/10.1016/S0140-6736(13)60595-4.

GBD (Global Burden of Disease). 2015. Mortality and Causes of Death Collaborators. 2016. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analy- sis for the Global Burden of Disease Study 2015. Lancet 388(10053):1459–1544, PMID: 27733281, https://doi.org/10.1016/S0140-6736(16)31012-1.

Kataria A, Trasande L, Trachtman H. 2015. The effects of environmental chemicals on renal function. Nat Rev Nephrol 11(10):610–625, PMID: 26100504, https://doi.org/10. 1038/nrneph.2015.94.

Kidney Disease: Improving Global Outcomes (KDIGO) Work Group. 2013. KDIGO clinical practice guideline for evaluation and management of chronic kidney disease. Kidney Int Suppl 3(1):1–163.

Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. 2009. A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604– 612, PMID: 19414839, https://doi.org/10.7326/0003-4819-150-9-200905050-00006.

Li CC, Lau AKH, Mao JT, Chu DA. 2005. Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong. IEEE T Geosci Remote 43(11):2650–2658, https://doi.org/10.1109/TGRS.2005.856627.

Lin CQ, Li Y, Yuan ZB, Lau AKH, Li CC, Fung JCH. 2015. Using satellite remote sens- ing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sens Environ 156:117–128, https://doi.org/10.1016/j.rse.2014.09.015.

Lue SH, Wellenius GA, Wilker EH, Mostofsky E, Mittleman MA. 2013. Residential proximity to major roadways and renal function. J Epidemiol Community Health 67(8):629–634, PMID: 23669275, https://doi.org/10.1136/jech-2012-202307.

Lunyera J, Mohottige D, Von Isenburg M, Jeuland M, Patel UD, Stanifer JW. 2016. CKD of uncertain etiology: a systematic review. Clin J Am Soc Nephrol 11(3):379–385, PMID: 26712810, https://doi.org/10.2215/CJN.07500715.

Mehta AJ, Zanobetti A, Bind MA, Kloog I, Koutrakis P, Sparrow D, et al. 2016. Long- Term exposure to ambient fine particulate matter and renal function in older men: The Veterans Administration Normative Aging Study. Environ Health Perspect 124(9):1353–1360, PMID: 26955062, https://doi.org/10.1289/ehp.1510269.

Griffin BA, Anderson GL, Shih RA, Whitsel EA. 2012. Use of alternative time scales in Cox proportional hazard models: implications for time-varying environmental exposures. Stat Med 31(27):3320–3327, PMID: 22531976, https://doi.org/10.1002/ sim.5347.

Myers GL, Miller WG, Coresh J, Fleming J, Greenberg N, Greene T, et al. 2006. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem 52(1):5–18, PMID: 16332993, https://doi.org/10.1373/clinchem. 2005.0525144.

National Kidney Foundation. 2002. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 39(2 Suppl1):S1–S266, PMID: 11904577.

Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, et al. 2011. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int 79(5):555–562, PMID: 21107446, https://doi.org/10.1038/ki.2010.462.

Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, et al. 2011. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 305(15):1553–1559, PMID: 21482743, https://doi.org/10.1001/jama.2011.451.

Webster AC, Nagler EV, Morton RL, Masson P. 2017. Chronic kidney disease. Lancet 389(10075):1238–1252, PMID: 27887750, https://doi.org/10.1016/S0140- 6736(16)32064-5.

Wen CP, Cheng TY, Tsai MK, Chang YC, Chan HT, Tsai SP, et al. 2008. All-cause mortal- ity attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet 371(9631):2173–2182, PMID: 18586172, https://doi.org/ 10.1016/S0140-6736(08)60952-6.

Yan Y-H, C-K Chou C, Wang J-S, Tung C-L, Li Y-R, Lo K, et al. 2014. Subchronic effects of inhaled ambient particulate matter on glucose homeostasis and tar- get organ damage in a type 1 diabetic rat model. Toxicol Appl Pharmacol 281(2):211–220, PMID: 25454026, https://doi.org/10.1016/j.taap.2014.10.005.

Yang YR, Chen YM, Chen SY, Chan CC. 2017. Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis. Environ Health Perspect 125(4):602–607, PMID: 27713105, https://doi.org/10. 1289/EHP302.

Zhang Z, Chang L-Y, Lau AKH, Chan TC, Chieh Chuang Y, Chan J, et al. 2017. Satellite-based estimates of long-term exposure to fine particulate matter are associated with C-reactive protein in 30 034 Taiwanese adults. Int J Epidemiol 46(4):1126–1236, PMID: 28541501, https://doi.org/10.1093/ije/dyx069.

Zhang Z, Guo C, Lau AKH, Chan T-C, Chuang YC, Lin C, et al. 2018. Long-term expo- sure to fine particulate matter, blood pressure, and incident hypertension in Taiwanese adults. Environ Health Perspect 126(1):017008, PMID: 29351544, https://doi.org/10.1289/EHP2466.

Environmental Health Perspectives 107002-7 126(10) October 2018

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