Association of PM2.5 Changes with Advanced Kidney Disease Progression and Mortality in Patients with Early CKD: A Nationwide Retrospective Cohort Study in Taiwan

Association of PM2.5 Changes with Advanced Kidney Disease Progression and Mortality in Patients with Early CKD: A Nationwide Retrospective Cohort Study in Taiwan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of PM2.5 Changes with Advanced Kidney Disease Progression and Mortality in Patients with Early CKD: A Nationwide Retrospective Cohort Study in Taiwan Shih-Feng Chen, Yu-Ching Lai, Yu-Huei Chien, Kuo-Chin Hung, I-Wen Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7989965/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Fine particulate matter (PM 2.5 ) is an increasingly recognized risk factor for kidney disease. However, evidence on the kidney-protective effects of improvements in PM 2.5 exposure is limited. Early intervention in chronic kidney disease (CKD) is essential. We aimed to explore the association of changes in long-term PM 2.5 exposure with advanced CKD progression and mortality among early-stage CKD patients. Methods This retrospective cohort study enrolled 423,855 early CKD patients (stages 1–3a) between 2012 and 2021, with follow-up through 2022. PM 2.5 change (ΔPM 2.5 ) was defined as the difference between the 365-day mean concentration before enrollment and the 365-day mean before follow-up end. Multivariate Cox proportional hazards models assessed associations of ΔPM 2.5 with advanced CKD progression, dialysis initiation, and mortality, both per 1 µg/m³ change and across tertiles of ΔPM 2.5 . Restricted cubic spline analyses characterized concentration-response relationships. Results Each 1 µg/m³ reduction in PM 2.5 was associated with 8%, 5%, and 7% lower risks of advanced CKD progression, dialysis initiation, and mortality, respectively. Compared with the reference tertile, participants in the most improved tertile exhibited 19%, 9%, and 28% lower risks, whereas those in the most deteriorated tertile had 2.25-, 1.59-, and 1.94-fold higher risks. Spline analyses indicated near-linear relationships, with protective effects in CKD progression and mortality plateauing beyond a 15 µg/m³ reduction. Conclusions Improvements in PM 2.5 exposure are associated with reduced risks of advanced CKD progression, dialysis initiation, and mortality among early CKD patients. Public health strategies promoting air quality may represent effective early interventions to protect kidney health. PM2.5 change early CKD patients advanced CKD Dialysis Mortality Figures Figure 1 Figure 2 Figure 3 Background Fine particulate matter (PM 2.5 ) is a well-established risk factor for adverse health outcomes. Over the past two decades, evidence has linked PM 2.5 exposure to stroke, coronary artery disease, chronic obstructive pulmonary disease, hypertension, rheumatoid arthritis, and diabetes mellitus [ 1 – 6 ]. Recent studies in last decade also showed its association with both the development and progression of kidney disease [ 7 – 11 ]. Despite these risks, over 99% of the global population lives in regions where long-term PM 2.5 concentrations exceed World Health Organization standards [ 12 ]. Many countries have therefore prioritized air quality improvement as a public health policy. Chronic kidney disease (CKD) affects more than 10% of the global population and imposes a major healthcare and socioeconomic burden [ 13 ]. Progression to advanced CKD and subsequent end-stage kidney disease (ESKD) magnifies this impact [ 14 – 16 ]: although only 0.038% of the global population has ESKD, they account for over 2–4% of healthcare expenditure in some countries [ 17 ]. In Taiwan, treated ESKD patients comprise 0.36% of the population but consume over 9% of National Health Insurance reimbursements [ 18 ]. To address this, the Taiwan government launched a national Pre-ESKD Care Program for advanced CKD patients (stages 3b–5, eGFR < 45 ml/min/1.73m²) in 2007, later extending coverage to earlier stages (stages 1–3a, eGFR ≥ 45 ml/min/1.73m²) through the Early CKD Care Program in 2011 [ 19 ]. However, most preventive efforts have targeted traditional risk factors such as diabetes and hypertension, with limited focus on environmental hazards including air pollution [ 20 , 21 ]. Emerging evidence has revealed the plausible pathophysiology of how PM 2.5 induces kidney injury, including oxidative stress, immune response, inflammation, endothelial injury, apoptosis and genotoxic effect [ 22 , 23 ]. Taiwan has the world’s highest incidence and prevalence of treated ESKD and ranks fourth among OECD countries in PM 2.5 exposure [ 24 , 25 ]. Although ambient PM 2.5 has declined since 2005 due to regulatory efforts, evidence on whether such reductions mitigate CKD progression remains scarce. Recent findings by Chen et al. (2025) suggest that lowering long-term PM 2.5 exposure reduces the risks of dialysis progression and mortality in advanced CKD [ 26 ], however, it remains uncertain whether these results can be extrapolated to patients with early-stage CKD. Early intervention for CKD is widely recognized as a key component of the global public health agenda [ 27 ]. This study examines the association of changes in long-term ambient PM 2.5 (ΔPM 2.5 ) level and the risks of progression to advanced CKD and mortality among patients with early CKD (stages 1–3a, eGFR ≥ 45 ml/min/1.73m²). Methods This retrospective, nationwide, population-based cohort study integrated data from the Taiwan Air Quality Monitoring Database (TAQMD) and the National Health Insurance Research Database (NHIRD) to investigate the association between changes in ambient fine particulate matter (ΔPM 2.5 ) and the risk of progression to advanced CKD (including initiation of maintenance dialysis), initiation of maintenance dialysis, and all-cause mortality among patients with early CKD. The TAQMD, established by the Ministry of Environment (MOENV), provides daily measurements of six air pollutants (PM 2.5 , PM 10 , O 3 , CO, SO 2 , and NO 2 ) from 84 monitoring stations across Taiwan. Data accuracy is ensured through annual calibration against reference standards, adjustments for environmental factors, inter-station comparisons, internal and external audits, and independent verification using mobile monitoring systems, all conducted in accordance with international standards, including ISO 9001 [ 28 ]. The NHIRD encompasses de-identified administrative and healthcare data covering over 99.8% of Taiwan’s population enrolled in the compulsory National Health Insurance (NHI) program. The database, maintained by the Health and Welfare Data Science Center (HWDC), contains comprehensive information on patient demographics, diagnoses, procedures, and medical expenditures, enabling full-population epidemiological research [ 29 , 30 ]. Since 2011, the NHI has implemented the Early CKD Care Program, which provides multidisciplinary care to patients with CKD stages 1–3a [ 19 ]. Diseases were coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) before December 2015 and both ICD-9-CM and Tenth Revision (ICD-10-CM) thereafter. This study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 114002-N). Eligible participants included adults aged ≥ 18 years with early CKD (stages 1–3a, eGFR ≥ 45 ml/min/1.73m²) who resided in areas with continuous PM 2.5 monitoring for at least 12 consecutive months between January 1, 2012, and December 31, 2021. Residential location was determined based on the medical institution visited for outpatient care of acute upper respiratory tract infection (ICD-9-CM: 460; ICD-10-CM: J00) or allergic rhinitis/sinusitis (ICD-9-CM: 472, 473, 477; ICD-10-CM: J30–J32) before enrollment. For individuals without such records, residential information was derived from employment data in the Registry for Beneficiaries database [ 26 ]. The enrollment date in the Early CKD Care Program was designated as the index date. Among 1,001,131 patients enrolled during the study period, those with missing demographic information (n = 4,108), age < 18 or ≥ 100 years (n = 2,115), prior enrollment in the Pre-ESKD Care Program (n = 4,924), follow-up duration < 365 days (n = 25,524), or progression to end-stage kidney disease (ESKD) requiring maintenance dialysis within 365 days (n = 217) were excluded. Patients residing in postal areas lacking air-quality monitoring stations (n = 532,868) and those in regions without available PM 2.5 data (n = 7,520) were also excluded. The final analytic cohort comprised 423,855 adults with early CKD ( Fig. 1 ) . The change in PM 2.5 concentration (ΔPM 2.5 ) for each participant was calculated in three steps. First, the mean daily PM 2.5 concentration during the 365 days preceding the index date (concentration A) was determined. Second, the mean daily concentration during the 365 days before the end of follow-up (concentration B) was calculated. Third, ΔPM 2.5 was defined as B – A (µg/m³), where negative values indicated improved air quality and positive values represented deterioration [ 26 ]. For categorical analyses, ΔPM 2.5 was stratified into tertiles in ascending order, with tertile 1 representing the largest reduction in PM 2.5 , tertile 2 denoting minimal change, and tertile 3 indicating the greatest increase in PM 2.5 exposure. The primary outcome of this study was progression to advanced CKD, including the initiation of maintenance dialysis. This was defined as the date on which patients with early CKD participated in the pre-ESKD Care Program (for CKD stages 3b-5, eGFR < 45 ml/min/1.73m²) or applied for the catastrophic illness card for maintenance dialysis. Participation in both the Early CKD Care Program and the Pre-ESKD Care Program was identified using Taiwan NHI reimbursement codes. The other outcome was all-cause mortality occurring beyond 365 days after the index date. Mortality data—including date and causes of death—were obtained by linking patient records to the Taiwan Death Registry within the HWDC. For patients who experienced an outcome, follow-up ended at the time of the event; for those who did not, follow-up continued until December 31, 2022. Baseline covariates included demographic characteristics (age, sex, and monthly insurance salary), comorbid conditions (diabetes mellitus, hypertension, chronic obstructive pulmonary disease, coronary artery disease, liver cirrhosis, heart failure, stroke, and malignancy), and the Charlson Comorbidity Index (CCI) score. Comorbidities were identified from inpatient and outpatient claims recorded during the 12 months preceding the index date. Zip code area-level covariates included the degree of urbanization, categorized into four levels (lowest, low, high, and highest) according to the classification proposed by Liu et al. (2006) [ 31 ], corresponding to city, town, suburban, and rural areas [ 32 ], as well as annual changes in concentrations of NO₂, CO, and SO₂. Statistical Analysis Baseline characteristics of patients across the ΔPM 2.5 tertiles were assessed using linear contrasts from the general linear model for continuous variables and the Cochran–Armitage trend test for categorical variables. Cox proportional hazards models were applied to evaluate the association between ΔPM 2.5 and the risks of outcomes, with all baseline characteristics listed in Table 1 included as covariates. Three models were specified: (a) Model 1, unadjusted, incorporating only ΔPM 2.5 ; (b) Model 2, adjusted for individual-level covariates, including age, sex, monthly income, all comorbidities as well as the CCI score, and the index year of enrollment; and (c) Model 3, further adjusted for urbanization level of the residence and the annual change of NO 2 and SO 2 . CO was not included in the multivariable Cox model owing to its strong collinearity with NO 2 . Table 1 Baseline characteristics of patients with early CKD according to the tertile groups of annual PM 2.5 change Variable Total ( n = 423 855) Tertile 1 ( n = 139 850) Tertile 2 ( n = 139 908) Tertile 3 ( n = 144 097) P trend Range of PM 2.5 change, µg/m 3 -28.0 to 21.2 -28.0 to -10.2 -10.2 to -4.9 -4.9 to 21.2 - Mean change of air-pollutants PM 2.5 , µg/m 3 -8.4 ± 5.7 -15.2 ± 3.7 -7.4 ± 1.5 -2.8 ± 1.4 < 0.001 NO 2 , ppb -3.7 ± 2.2 -5.3 ± 1.9 -3.7 ± 1.7 -2.0 ± 1.7 < 0.001 CO, ppm -1.2 ± 0.8 -1.7 ± 0.8 -1.1 ± 0.7 -0.8 ± 0.7 < 0.001 SO 2 , ppb -1.6 ± 1.0 -2.2 ± 1.1 -1.6 ± 0.8 -1.1 ± 0.6 < 0.001 Age, year 64.2 ± 13.3 63.4 ± 12.8 64.3 ± 13.4 64.8 ± 13.7 < 0.001 Age ≥ 65 years 210 940 (49.8) 65 093 (46.5) 70 233 (50.2) 75 614 (52.5) < 0.001 Male sex 221 583 (52.3) 71 604 (51.2) 73 381 (52.5) 76 598 (53.2) < 0.001 Urbanization level of the residence < 0.001 The lowest level 12 552 (3.0) 6 010 (4.3) 3 496 (2.5) 3 046 (2.1) Low level 107 292 (25.3) 37 498 (26.8) 38 738 (27.7) 31 056 (21.6) High level 212 495 (50.1) 69 829 (49.9) 73 443 (52.5) 69 223 (48.0) The highest level 91 516 (21.6) 26 513 (19.0) 24 231 (17.3) 40 772 (28.3) Monthly income, NTD 0.735 ≤ 19,047 113 843 (26.9) 36 704 (26.3) 37 503 (26.8) 39 636 (27.5) 19,047 − 26,400 164 891 (38.9) 56 196 (40.2) 54 570 (39.0) 54 125 (37.6) ≥ 26,400 145 121 (34.2) 46 950 (33.6) 47 835 (34.2) 50 336 (34.9) Comorbidity Diabetes mellitus 256 745 (60.6) 79 589 (56.9) 85 193 (60.9) 91 963 (63.8) < 0.001 Hypertension 286 329 (67.6) 93 955 (67.2) 94 776 (67.7) 97 598 (67.7) 0.002 Coronary artery disease 73 450 (17.3) 23 505 (16.8) 24 718 (17.7) 25 227 (17.5) < 0.001 COPD 23 625 (5.6) 6 685 (4.8) 8 131 (5.8) 8 809 (6.1) < 0.001 Liver cirrhosis 6 225 (1.5) 1 697 (1.2) 2 026 (1.5) 2 502 (1.7) < 0.001 Heart failure 5 941 (1.4) 1 153 (0.8) 2 097 (1.5) 2 691 (1.9) < 0.001 Stroke 6 165 (1.5) 1 789 (1.3) 2 140 (1.5) 2 236 (1.6) < 0.001 Malignancy 33 299 (7.9) 9 053 (6.5) 10 985 (7.9) 13 261 (9.2) < 0.001 Charlson Comorbidity Index 3.0 ± 2.1 2.7 ± 1.9 3.1 ± 2.1 3.3 ± 2.3 < 0.001 Abbreviation: CKD, chronic kidney disease; ESRD, end stage renal disease; PM, particulate matter; NTD, New Taiwan Dollar; COPD, chronic obstructive pulmonary disease; Data are presented as frequency (%) or mean ± standard deviation. To further delineate the exposure–response relationship, the continuous ΔPM 2.5 was modeled as a flexible restricted cubic spline (RCS) variables, with adjustments for the same covariates specified in Model 3. The knots were positioned at the 10th, 50th, and 90th percentiles of the distribution. Effect modification by baseline characteristics was assessed through stratified analyses according to age (19,047 NTD [levels 2–3]). RCS analyses were conducted in R version 4.3.2 (R Foundation for Statistical Computing) using the rms package, while all other statistical analyses were performed in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided P value < 0.05 was considered statistically significant. All analyses were carried out on-site at the HWDC facility. Results This study included 423,855 adults with early CKD residing in regions with available PM 2.5 measurements between 2012 and 2021. The mean age of the cohort was 64.2 years, and 49.8% were older than 65 years. Males represented 52.3% of the participants. With respect to monthly insured salary, 38.9% of patients were classified in the NT $ 19,047–26,400 category, 34.2% in the ≥ NT $ 26,400 category, and 26.9% in the ≤ NT $ 19,047 category. The distribution of ΔPM 2.5 ranged from − 28.0 to 21.2 µg/m³. Based on tertile classification, 139,850 patients were assigned to tertile 1 (–28.0 to − 10.2 µg/m³), 139,908 to tertile 2 (–10.2 to − 4.9 µg/m³), and 144,097 to tertile 3 (–4.9 to 21.1 µg/m³). Significant differences across tertiles were noted for nearly all baseline characteristics, including age, selected comorbidities, CCI score, changes in mean concentrations of gaseous pollutants (NO₂, CO, SO₂), and residential urbanization level, whereas monthly income did not differ significantly ( Table 1 ) . According to the multivariable Cox proportional hazards model, each 1-µg/m³ reduction in ΔPM 2.5 (indicating improved ambient PM 2.5 ) was associated with an approximate 8% lower risk of progression to advanced CKD (hazard ratio [HR], 0.923; 95% confidence interval [CI], 0.920–0.926), an equivalently 5% lower risk of dialysis initiation (HR, 0.953; 95% CI, 0.947–0.960), and a nearly 7% lower risk of mortality (HR, 0.925; 95% CI, 0.923–0.927) (Model 3 in Table 2 ). When tertile 2 (ΔPM 2.5 − 10.2 to − 4.9 µg/m³, reflecting the smallest change in PM 2.5 ) was used as the reference, patients in tertile 1 (ΔPM 2.5 − 28.0 to − 10.2 µg/m³) experienced a 19% lower risk of progression to advanced CKD, a 9% lower risk of dialysis initiation, and a 28% lower risk of mortality. Conversely, participants in tertile 3 (ΔPM 2.5 − 4.9 to 21.2 µg/m³) had a 2.25-fold higher risk of progression to advanced CKD, a 1.59-fold higher risk of dialysis initiation, and a 1.94-fold higher risk of mortality ( Model 3 in Table 3 ) . Table 2 The association between the annual PM 2.5 change (as continuous variable, per µg/m 3 decrease) and the risk of outcomes in various adjustment models Outcome / Model Hazard ratio (95% CI) P value Advance CKD Model 1 0.787 (0.784–0.789) < 0.001 Model 2 0.791 (0.788–0.794) < 0.001 Model 3 0.923 (0.920–0.926) < 0.001 Chronic dialysis Model 1 0.835 (0.830–0.841) < 0.001 Model 2 0.835 (0.830–0.841) < 0.001 Model 3 0.953 (0.947–0.960) < 0.001 All-cause death Model 1 0.809 (0.807–0.811) < 0.001 Model 2 0.821 (0.819–0.823) < 0.001 Model 3 0.925 (0.923–0.927) < 0.001 Abbreviation: PM, particulate matter; CI, confidence interval; Model 1: Crude model without adjustment; Model 2: Adjusted for age, sex, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, the index year of enrollment; Model 3: Further adjusted for urbanization level of the residence and the annual change of NO 2 and SO 2 . Table 3 The association between the tertile of annual PM 2.5 change (as categorical variable) and the risk of outcomes in various adjustment models Outcome / Model / PM 2.5 change No. of patients Incidence rate (95% CI)† Hazard ratio (95% CI) P value Advance CKD Model 1 Tertile 1 139 893 6.8 (6.6–6.9) 0.37 (0.35–0.38) < 0.001 Tertile 2 139 828 11.3 (11.1–11.5) Reference Tertile 3 144 134 30.8 (30.2–31.4) 4.16 (4.03–4.29) < 0.001 Model 2 Tertile 1 139 893 6.8 (6.6–6.9) 0.38 (0.36–0.39) < 0.001 Tertile 2 139 828 11.3 (11.1–11.5) Reference - Tertile 3 144 134 30.8 (30.2–31.4) 4.04 (3.92–4.16) < 0.001 Model 3 Tertile 1 139 893 6.8 (6.6–6.9) 0.81 (0.78–0.84) < 0.001 Tertile 2 139 828 11.3 (11.1–11.5) Reference Tertile 3 144 134 30.8 (30.2–31.4) 2.25 (2.17–2.32) < 0.001 Chronic dialysis Model 1 Tertile 1 139 893 2.0 (1.9–2.1) 0.36 (0.34–0.39) < 0.001 Tertile 2 139 871 2.5 (2.4–2.6) Reference - Tertile 3 144 091 3.1 (2.9–3.2) 2.78 (2.57–3.00) < 0.001 Model 2 Tertile 1 139 893 2.0 (1.9–2.1) 0.37 (0.34–0.39) < 0.001 Tertile 2 139 871 2.5 (2.4–2.6) Reference - Tertile 3 144 091 3.1 (2.9–3.2) 2.75 (2.55–2.97) < 0.001 Model 3 Tertile 1 139 893 2.0 (1.9–2.1) 0.91 (0.85–0.98) 0.009 Tertile 2 139 871 2.5 (2.4–2.6) Reference - Tertile 3 144 091 3.1 (2.9–3.2) 1.59 (1.47–1.73) < 0.001 All-cause death Model 1 Tertile 1 139 850 16.6 (16.4–16.8) 0.33 (0.32–0.34) < 0.001 Tertile 2 139 908 28.6 (28.3–29.0) Reference - Tertile 3 144 097 53.2 (52.5–54.0) 3.25 (3.19–3.32) < 0.001 Model 2 Tertile 1 139 850 16.6 (16.4–16.8) 0.36 (0.35–0.37) < 0.001 Tertile 2 139 908 28.6 (28.3–29.0) Reference - Tertile 3 144 097 53.2 (52.5–54.0) 3.05 (2.99–3.11) < 0.001 Model 3 Tertile 1 139 850 16.6 (16.4–16.8) 0.72 (0.70–0.73) < 0.001 Tertile 2 139 908 28.6 (28.3–29.0) Reference - Tertile 3 144 097 53.2 (52.5–54.0) 1.94 (1.90–1.99) < 0.001 Abbreviation: PM, particulate matter; CI, confidence interval; † Number of events per 1,000 person-years; Model 1: Crude model without adjustment; Model 2: Adjusted for age, sex, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, the index year of enrollment; Model 3: Further adjusted for urbanization level of the residence and the annual change of NO 2 and SO 2 . The RCS analyses which used ΔPM 2.5 = 0 µg/m³ as the reference point, revealed that increases in PM 2.5 levels (ΔPM 2.5 >0 µg/m³) were associated with a progressively higher risk of advanced CKD, whereas reductions in PM 2.5 (ΔPM 2.5 < 0 µg/m³) corresponded to a lower risk. However, the risk reduction reached a plateau in participants experiencing substantial improvements in air quality (ΔPM 2.5 < − 15 µg/m³) ( Fig. 2 A ) . For maintenance dialysis, the RCS model demonstrated an approximately linear positive association with ΔPM 2.5 (although the P for linearity and non-linearity < 0.001), although the slope was slightly attenuated in regions with ΔPM 2.5 0 µg/m³) was linked to an increased risk ( P for linearity and non-linearity < 0.001), whereas improvements in PM 2.5 (ΔPM 2.5 < 0 µg/m³) were protective. Nevertheless, the survival benefit plateaued once reductions in PM 2.5 exceeded approximately 15 µg/m³ ( Fig. 2 C ) . Stratified analyses, in alignment with the Cox regression results, demonstrated that improvements in ambient PM 2.5 were associated with lower risks of outcomes. A 1-µg/m³ reduction in ΔPM 2.5 yielded a greater decrease in the risk of progression to advanced CKD (including transition to maintenance dialysis) among patients aged ≥ 65 years (HR, 0.918 vs. 0.928; P for interaction < 0.001), females (HR, 0.914 vs. 0.927; P for interaction < 0.001), and individuals with a monthly income ≤ 19,047 NTD (HR, 0.908 vs. 0.927; P for interaction < 0.001), compared with their respective counterparts ( Fig. 3 A ) . Regarding mortality, stratified analyses revealed a greater risk reduction with a 1-µg/m³ decrease in ΔPM 2.5 among patients aged < 65 years (HR, 0.919 vs. 0.928; P for interaction < 0.001), females (HR, 0.921 vs. 0.927; P for interaction < 0.001), and those with diabetes (HR, 0.923 vs. 0.927; P for interaction = 0.004), compared with their respective counterparts ( Fig. 3 B ) . Discussion This study demonstrated that improvements in long-term PM 2.5 air quality were associated with a reduced risk of progression to advanced CKD and maintenance dialysis among patients with early CKD, whereas worsening ambient PM 2.5 was linked to increased disease progression. These findings are consistent with prior studies reporting the health benefits of reduced PM 2.5 exposure, such as lower hypertension incidence, improved cardiovascular outcomes, and decreased COPD prevalence [ 33 – 35 ]. Importantly, our results align with limited research directly demonstrating the beneficial effects of PM 2.5 reduction on CKD development and progression to maintenance dialysis [ 26 , 36 ]. Bo et al. (2021b) reported a 25% lower risk of CKD development per 5 µg/m³ reduction in ambient PM 2.5 among Taiwanese adults, with an approximately linear exposure–response relationship [ 36 ], while Chen et al. (2025) found that a 1 µg/m³ reduction in PM 2.5 was associated with an 11% lower risk of dialysis progression, also showing a near-linear relationship between ΔPM 2.5 and dialysis incidence in patients with advanced CKD [ 26 ]. In the current study, although the ΔPM 2.5 –dialysis relationship was nearly linear, the protective effect on progression to advanced CKD plateaued among early CKD patients once PM 2.5 improvements exceeding 15 µg/m³. We further observed that long-term reductions in PM 2.5 exposure were associated with decreased mortality in early CKD patients, although the protective effect plateaued beyond a 15 µg/m³ improvement. This finding aligns with limited prior studies reporting mortality reductions following air pollution improvement in the general population [ 37 , 38 ] and complements earlier work linking higher PM 2.5 exposure to greater mortality risk in CKD and ESKD populations [ 39 – 41 ]. Notably, our results are consistent with Chen et al. (2025), who reported that PM 2.5 improvement reduced mortality in advanced CKD, with a plateaued protective effect beyond a 5 µg/m³ improvement [ 26 ]. This plateau phenomenon, together with the plateaued effect on progression to advanced CKD described above, may reflect the intrinsic vulnerability of CKD patients with complex comorbidities to disease progression and premature mortality [ 42 , 43 ], wherein residual confounding and comorbidity burden exert stronger influences than environmental PM 2.5 reduction. Consequently, even substantial improvements in air quality may not fully mitigate mortality risk in the most vulnerable early CKD population. Several biological mechanisms may underlie these observed associations. Miller (2017) demonstrated that inhaled particulate matter can translocate systemically and damage extrapulmonary organs, including the kidneys [ 44 ]. Additionally, PM 2.5 -induced pulmonary inflammation may trigger systemic inflammation, endothelial injury, oxidative stress, metabolic disturbances, autophagy & pyroptosis, and genotoxicity, ultimately contributing to renal damage [ 22 , 23 , 45 , 46 ].These mechanistic insights provide biological plausibility for the observed reductions in disease progression and mortality associated with long-term improvements in PM₂.₅ exposure among CKD patients, as shown in our study. To our knowledge, this is the first population-based study to evaluate ΔPM₂.₅ in relation to advanced CKD progression, dialysis initiation, and mortality in early-stage CKD, offering valuable evidence to inform public health strategies for kidney health promotion and air quality improvement. After adjustment for baseline characteristics such as age, sex, diabetes, and monthly insured salary, the significant association between lowering PM 2.5 exposure and reduced risks of progression to advanced CKD (including transition to maintenance dialysis) and mortality remained. Subgroup analyses showed that early CKD patients aged ≥ 65 experienced a greater reduction in the risk of progression to advanced CKD per 1-µg/m³ decrease in PM 2.5 , whereas those aged < 65 exhibited larger reductions in mortality risk compared with their older counterparts. Hallan et al. (2024) observed that among patients with early CKD, the mean ESKD rate per 1,000 person-years was lower in those aged ≥ 65 than in those aged < 65, whereas the mean mortality rate per 1,000 person-years was higher in the older group [ 47 ]. Ito and Mori (2025) reported that early CKD in younger individuals—especially those with early-onset hypertension—tends to progress more rapidly to ESKD, while in older adults, advancement to severe stages occurs at a slower pace [ 48 ]. These findings support the age-specific subgroup analyses in our study. Regarding the effect of sex, our study revealed that each 1-µg/m³ reduction in PM 2.5 conferred a greater decrease in the risks of progression to advanced CKD and mortality in females compared with males. These results echo previous studies demonstrating that males have a higher risk of CKD development and that male CKD patients exhibit a greater mortality risk under comparable increases in long-term PM 2.5 exposure compared with females [ 8 , 49 ]. The higher risk of PM 2.5 -associated CKD development and mortality in men compared with women may result from interactions between PM 2.5 and male-specific factors, including hormonal profiles, occupational exposures, and lifestyle behaviors [ 50 ]. Our study found that among early CKD patients, those with diabetes—the most common underlying cause of CKD—experienced a greater reduction in the risk of mortality per 1-µg/m³ decrease in long-term PM 2.5 exposure compared with non-diabetic patients. This observation is consistent with prior evidence demonstrating that, given comparable increases in long-term PM 2.5 exposure, CKD patients with diabetes exhibit a lower relative increase in the hazards of eGFR decline or mortality compared with non-diabetic CKD patients [ 39 , 51 , 52 ]. Persistent hyperglycemia accelerates the formation of advanced glycation end products, promoting reactive oxygen species generation, chronic inflammation, and endothelial dysfunction, ultimately contributing to diabetic complications [ 53 ]. Given the intrinsically heightened inflammatory state in diabetic CKD and the baseline elevated risks of mortality, reductions in long-term PM 2.5 exposure are likely to confer greater benefits in mitigating CKD-related mortality. Our study demonstrated that among early CKD patients, individuals with lower monthly income experienced a greater decrease in the risk of progression to advanced CKD per 1-µg/m³ reduction in long-term PM 2.5 exposure compared with those with higher income. These results diverge from prior research examining the impact of socioeconomic status (SES) on CKD-related outcomes, which demonstrates that lower SES is associated with increased disease severity, accelerated progression, and a higher risk of ESKD [ 54 , 55 ]. This difference suggests that, although income is a key indicator of socioeconomic status, relying on it alone in research may introduce potential bias. This study has several strengths. First, it employs a large, nationwide cohort to investigate the association between ΔPM 2.5 and progression to advanced CKD or mortality in early CKD, allowing for comprehensive adjustment for baseline confounders and providing sufficient statistical power for subgroup analyses, thereby enhancing the reliability and generalizability of the findings. Second, ΔPM 2.5 was evaluated not only in single-pollutant models but also with adjustment for co-pollutants, further strengthening the robustness of the results. Third, to our knowledge, this is the first study to specifically assess the impact of ΔPM 2.5 on progression to advanced CKD, dialysis initiation, and mortality among patients with early CKD, thereby laying the groundwork for future research on air pollution mitigation and early kidney health interventions. However, several limitations warrant consideration. First, ambient PM 2.5 was estimated from fixed-site monitoring stations, which may not fully capture individual indoor exposures, potentially resulting in exposure misclassification. Nonetheless, evidence indicates that indoor PM 2.5 derived from outdoor sources can substantially contribute to adverse health outcomes [ 56 ]. Second, seasonal variations in PM 2.5 concentrations in Taiwan may affect ΔPM 2.5 estimation [ 57 ], though this impact was mitigated by employing a 12-month measurement window. Third, the use of 3-digit zip code–level data may not precisely reflect proximity to air-quality monitoring stations [ 58 ]; however, previous studies indicate consistent associations with mortality risk even within a 10 km radius. Furthermore, non-differential exposure misclassification typically biases results toward the null [ 59 ], yet significant associations were still observed, underscoring the robustness of our findings. Fourth, the NHIRD does not include information on lifestyle behaviors, anthropometric measures, or biochemical parameters related to kidney function, which may also introduce bias. Fifth, as this was an observational study, it is not capable to infer a causal relationship between ΔPM 2.5 and the health outcomes. In conclusion, reductions in ambient PM 2.5 were associated with lower risks of progression to advanced CKD, initiation of dialysis, and mortality, whereas deteriorating air quality was linked to increased risks among patients with early CKD. These findings underscore the importance of air pollution mitigation as an early intervention to preserve kidney health and reduce disease progression and mortality in this population. Abbreviations CKD chronic kidney disease ESKD end-stage kidney disease TAQMD the Taiwan Air Quality Monitoring Database NHIRD the National Health Insurance Research Database MOENV the Ministry of Environment NHI National Health Insurance HWDC the Health and Welfare Data Science Center ICD-9-CM the International Classification of Diseases, Ninth Revision, Clinical Modification ICD-10-CM the International Classification of Diseases, Tenth Revision, Clinical Modification CCI the Charlson Comorbidity Index RCS restricted cubic spline HR hazard ratio CI confidence interval SES socioeconomic status Declarations Ethics approval This study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 114002-N) on June 5, 2025. The organization and operation of this Institutional Review Board (IRB) complied with the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines. Competing interests The authors disclose the following potential competing interests: Shih-Feng Chen has received financial support from New Taipei City Hospital, Sanchung Branch, and is employed by the same institution. The remaining authors report no known financial interests or personal relationships that could have influenced the work presented in this study. Conflict of interest None Clinical trial number Not applicable Consent to participate Not applicable (This retrospective cohort study utilized de-identified data from the NHIRD, which is protected under regulations governing beneficiary privacy established by the Taiwanese government.) Supplementary material Strobe checklist ICMJE disclosure form Funding This study was funded by New Taipei City Hospital. The funding institution had no role in the study design, data collection, data analysis, decision to publish, or manuscript preparation. Author Contribution S.F.C. conceived and designed the study, developed the methodology, performed data analysis, drafted the original manuscript, and manuscript revision. Y.C.L. contributed to project administration, data analysis, and investigation. Y.H.C. was responsible for data curation, formal analysis, and investigation. K.C.H. assisted with methodological development, investigation, data interpretation, and manuscript revision. I.W.W. supervised the study, validated the findings, and contributed to study conceptualization, methodological design, and critical manuscript review. All authors read and approved the final version of the manuscript. Acknowledgement The authors express their gratitude to Mr. Yen-Chang Chen (B.A. Physics/Accounting, UCLA, USA) for assistance with English language editing and acknowledge financial support from New Taipei City Hospital. Data Availability The first dataset utilized in this study was obtained from the National Health Insurance Research Database (NHIRD), which is maintained by the Health and Welfare Data Science Center (HWDC). As the NHIRD is not publicly accessible, researchers must submit a formal application to the HWDC, Department of Statistics, Ministry of Health and Welfare, Taiwan (https://dep.mohw.gov.tw/DOS/cp-5119-59201-113.html) to obtain access. The second dataset was derived from the Taiwan Air Quality Monitoring Database (TAQMD), managed by the Ministry of Environment, Taiwan. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers invited by journal 09 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7989965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545099724,"identity":"a2a0f01b-ba45-42a7-8511-d49947c4d0a4","order_by":0,"name":"Shih-Feng Chen","email":"","orcid":"","institution":"New Taipei City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shih-Feng","middleName":"","lastName":"Chen","suffix":""},{"id":545099725,"identity":"c50b4e3a-a985-4058-8e05-9bb8062dacb5","order_by":1,"name":"Yu-Ching Lai","email":"","orcid":"","institution":"New Taipei City 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1","display":"","copyAsset":false,"role":"figure","size":57216,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the inclusion and exclusion process of study participants.\u003c/p\u003e\n\u003cp\u003eCKD, chronic kidney disease; ESKD, end-stage kidney disease; PM, particulate matter.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7989965/v1/3ae9702e9d9dca176a67bc64.png"},{"id":96330011,"identity":"b04aeea5-eff4-4494-8c6a-30c105588639","added_by":"auto","created_at":"2025-11-20 00:45:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104595,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between changes in PM\u003csub\u003e2.5\u003c/sub\u003e levels and the risk of progression to advanced CKD (A), ESKD requiring maintenance dialysis (B), and all-cause mortality (C) among patients with early CKD. Changes in PM\u003csub\u003e2.5\u003c/sub\u003e were modeled using restricted cubic splines. All covariates listed in Table 1 were adjusted in the multivariable Cox proportional hazards model.\u003c/p\u003e\n\u003cp\u003eCKD, chronic kidney disease; ESKD, end-stage kidney disease; PM, particulate matter; CI, confidence interval; CKD, chronic kidney disease; PM, particulate matter.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7989965/v1/aeaba9d48cf8b6bcfd498fb1.png"},{"id":96330013,"identity":"e57fa0f2-948c-457b-970f-9a55dee217d4","added_by":"auto","created_at":"2025-11-20 00:45:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283761,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between changes in PM\u003csub\u003e2.5\u003c/sub\u003e levels and the risk of progression to advanced CKD (including initiation of maintenance dialysis) (A) and all-cause mortality (B) among patients with early CKD.\u003c/p\u003e\n\u003cp\u003eCCI, Charlson’s Comorbidity Index; CI, confidence interval; CKD, chronic kidney disease; HR, hazard ratio; NTD, New Taiwan Dollar; PM, particulate matter.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7989965/v1/a4c06d8821a611f9acdbb766.png"},{"id":96369426,"identity":"9865d3a4-18ce-48c2-a322-7033a4cd9b76","added_by":"auto","created_at":"2025-11-20 10:20:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1818823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7989965/v1/a73c3aed-41da-4dc7-be00-febb46fc2773.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of PM2.5 Changes with Advanced Kidney Disease Progression and Mortality in Patients with Early CKD: A Nationwide Retrospective Cohort Study in Taiwan","fulltext":[{"header":"Background","content":"\u003cp\u003eFine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) is a well-established risk factor for adverse health outcomes. Over the past two decades, evidence has linked PM\u003csub\u003e2.5\u003c/sub\u003e exposure to stroke, coronary artery disease, chronic obstructive pulmonary disease, hypertension, rheumatoid arthritis, and diabetes mellitus [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies in last decade also showed its association with both the development and progression of kidney disease [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite these risks, over 99% of the global population lives in regions where long-term PM\u003csub\u003e2.5\u003c/sub\u003e concentrations exceed World Health Organization standards [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Many countries have therefore prioritized air quality improvement as a public health policy.\u003c/p\u003e\u003cp\u003eChronic kidney disease (CKD) affects more than 10% of the global population and imposes a major healthcare and socioeconomic burden [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Progression to advanced CKD and subsequent end-stage kidney disease (ESKD) magnifies this impact [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: although only 0.038% of the global population has ESKD, they account for over 2\u0026ndash;4% of healthcare expenditure in some countries [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In Taiwan, treated ESKD patients comprise 0.36% of the population but consume over 9% of National Health Insurance reimbursements [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To address this, the Taiwan government launched a national Pre-ESKD Care Program for advanced CKD patients (stages 3b\u0026ndash;5, eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73m\u0026sup2;) in 2007, later extending coverage to earlier stages (stages 1\u0026ndash;3a, eGFR\u0026thinsp;\u0026ge;\u0026thinsp;45 ml/min/1.73m\u0026sup2;) through the Early CKD Care Program in 2011 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, most preventive efforts have targeted traditional risk factors such as diabetes and hypertension, with limited focus on environmental hazards including air pollution [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Emerging evidence has revealed the plausible pathophysiology of how PM\u003csub\u003e2.5\u003c/sub\u003e induces kidney injury, including oxidative stress, immune response, inflammation, endothelial injury, apoptosis and genotoxic effect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTaiwan has the world\u0026rsquo;s highest incidence and prevalence of treated ESKD and ranks fourth among OECD countries in PM\u003csub\u003e2.5\u003c/sub\u003e exposure [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although ambient PM\u003csub\u003e2.5\u003c/sub\u003e has declined since 2005 due to regulatory efforts, evidence on whether such reductions mitigate CKD progression remains scarce. Recent findings by Chen et al. (2025) suggest that lowering long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure reduces the risks of dialysis progression and mortality in advanced CKD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], however, it remains uncertain whether these results can be extrapolated to patients with early-stage CKD. Early intervention for CKD is widely recognized as a key component of the global public health agenda [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This study examines the association of changes in long-term ambient PM\u003csub\u003e2.5\u003c/sub\u003e (ΔPM\u003csub\u003e2.5\u003c/sub\u003e) level and the risks of progression to advanced CKD and mortality among patients with early CKD (stages 1\u0026ndash;3a, eGFR\u0026thinsp;\u0026ge;\u0026thinsp;45 ml/min/1.73m\u0026sup2;).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective, nationwide, population-based cohort study integrated data from the Taiwan Air Quality Monitoring Database (TAQMD) and the National Health Insurance Research Database (NHIRD) to investigate the association between changes in ambient fine particulate matter (ΔPM\u003csub\u003e2.5\u003c/sub\u003e) and the risk of progression to advanced CKD (including initiation of maintenance dialysis), initiation of maintenance dialysis, and all-cause mortality among patients with early CKD. The TAQMD, established by the Ministry of Environment (MOENV), provides daily measurements of six air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, CO, SO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e) from 84 monitoring stations across Taiwan. Data accuracy is ensured through annual calibration against reference standards, adjustments for environmental factors, inter-station comparisons, internal and external audits, and independent verification using mobile monitoring systems, all conducted in accordance with international standards, including ISO 9001 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The NHIRD encompasses de-identified administrative and healthcare data covering over 99.8% of Taiwan\u0026rsquo;s population enrolled in the compulsory National Health Insurance (NHI) program. The database, maintained by the Health and Welfare Data Science Center (HWDC), contains comprehensive information on patient demographics, diagnoses, procedures, and medical expenditures, enabling full-population epidemiological research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Since 2011, the NHI has implemented the Early CKD Care Program, which provides multidisciplinary care to patients with CKD stages 1\u0026ndash;3a [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Diseases were coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) before December 2015 and both ICD-9-CM and Tenth Revision (ICD-10-CM) thereafter. This study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 114002-N).\u003c/p\u003e\u003cp\u003eEligible participants included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with early CKD (stages 1\u0026ndash;3a, eGFR\u0026thinsp;\u0026ge;\u0026thinsp;45 ml/min/1.73m\u0026sup2;) who resided in areas with continuous PM\u003csub\u003e2.5\u003c/sub\u003e monitoring for at least 12 consecutive months between January 1, 2012, and December 31, 2021. Residential location was determined based on the medical institution visited for outpatient care of acute upper respiratory tract infection (ICD-9-CM: 460; ICD-10-CM: J00) or allergic rhinitis/sinusitis (ICD-9-CM: 472, 473, 477; ICD-10-CM: J30\u0026ndash;J32) before enrollment. For individuals without such records, residential information was derived from employment data in the Registry for Beneficiaries database [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The enrollment date in the Early CKD Care Program was designated as the index date. Among 1,001,131 patients enrolled during the study period, those with missing demographic information (n\u0026thinsp;=\u0026thinsp;4,108), age\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026ge;\u0026thinsp;100 years (n\u0026thinsp;=\u0026thinsp;2,115), prior enrollment in the Pre-ESKD Care Program (n\u0026thinsp;=\u0026thinsp;4,924), follow-up duration\u0026thinsp;\u0026lt;\u0026thinsp;365 days (n\u0026thinsp;=\u0026thinsp;25,524), or progression to end-stage kidney disease (ESKD) requiring maintenance dialysis within 365 days (n\u0026thinsp;=\u0026thinsp;217) were excluded. Patients residing in postal areas lacking air-quality monitoring stations (n\u0026thinsp;=\u0026thinsp;532,868) and those in regions without available PM\u003csub\u003e2.5\u003c/sub\u003e data (n\u0026thinsp;=\u0026thinsp;7,520) were also excluded. The final analytic cohort comprised 423,855 adults with early CKD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe change in PM\u003csub\u003e2.5\u003c/sub\u003e concentration (ΔPM\u003csub\u003e2.5\u003c/sub\u003e) for each participant was calculated in three steps. First, the mean daily PM\u003csub\u003e2.5\u003c/sub\u003e concentration during the 365 days preceding the index date (concentration A) was determined. Second, the mean daily concentration during the 365 days before the end of follow-up (concentration B) was calculated. Third, ΔPM\u003csub\u003e2.5\u003c/sub\u003e was defined as B \u0026ndash; A (\u0026micro;g/m\u0026sup3;), where negative values indicated improved air quality and positive values represented deterioration [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For categorical analyses, ΔPM\u003csub\u003e2.5\u003c/sub\u003e was stratified into tertiles in ascending order, with tertile 1 representing the largest reduction in PM\u003csub\u003e2.5\u003c/sub\u003e, tertile 2 denoting minimal change, and tertile 3 indicating the greatest increase in PM\u003csub\u003e2.5\u003c/sub\u003e exposure.\u003c/p\u003e\u003cp\u003eThe primary outcome of this study was progression to advanced CKD, including the initiation of maintenance dialysis. This was defined as the date on which patients with early CKD participated in the pre-ESKD Care Program (for CKD stages 3b-5, eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73m\u0026sup2;) or applied for the catastrophic illness card for maintenance dialysis. Participation in both the Early CKD Care Program and the Pre-ESKD Care Program was identified using Taiwan NHI reimbursement codes. The other outcome was all-cause mortality occurring beyond 365 days after the index date. Mortality data\u0026mdash;including date and causes of death\u0026mdash;were obtained by linking patient records to the Taiwan Death Registry within the HWDC. For patients who experienced an outcome, follow-up ended at the time of the event; for those who did not, follow-up continued until December 31, 2022.\u003c/p\u003e\u003cp\u003eBaseline covariates included demographic characteristics (age, sex, and monthly insurance salary), comorbid conditions (diabetes mellitus, hypertension, chronic obstructive pulmonary disease, coronary artery disease, liver cirrhosis, heart failure, stroke, and malignancy), and the Charlson Comorbidity Index (CCI) score. Comorbidities were identified from inpatient and outpatient claims recorded during the 12 months preceding the index date. Zip code area-level covariates included the degree of urbanization, categorized into four levels (lowest, low, high, and highest) according to the classification proposed by Liu et al. (2006) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], corresponding to city, town, suburban, and rural areas [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], as well as annual changes in concentrations of NO₂, CO, and SO₂.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics of patients across the ΔPM\u003csub\u003e2.5\u003c/sub\u003e tertiles were assessed using linear contrasts from the general linear model for continuous variables and the Cochran\u0026ndash;Armitage trend test for categorical variables. Cox proportional hazards models were applied to evaluate the association between ΔPM\u003csub\u003e2.5\u003c/sub\u003e and the risks of outcomes, with all baseline characteristics listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e included as covariates. Three models were specified: (a) Model 1, unadjusted, incorporating only ΔPM\u003csub\u003e2.5\u003c/sub\u003e; (b) Model 2, adjusted for individual-level covariates, including age, sex, monthly income, all comorbidities as well as the CCI score, and the index year of enrollment; and (c) Model 3, further adjusted for urbanization level of the residence and the annual change of NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e. CO was not included in the multivariable Cox model owing to its strong collinearity with NO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of patients with early CKD according to the tertile groups of annual PM\u003csub\u003e2.5\u003c/sub\u003e change\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;423 855)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;139 850)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;139 908)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;144 097)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange of PM\u003csub\u003e2.5\u003c/sub\u003e change, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-28.0 to 21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-28.0 to -10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.2 to -4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.9 to 21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean change of air-pollutants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e, ppb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO, ppm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e, ppb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210 940 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 093 (46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 233 (50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 614 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 583 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 604 (51.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 381 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 598 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization level of the residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe lowest level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 552 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 010 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 496 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 046 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 292 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 498 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 738 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 056 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212 495 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 829 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 443 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69 223 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe highest level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 516 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 513 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 231 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 772 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly income, NTD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;19,047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 843 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 704 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 503 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 636 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19,047\u0026thinsp;\u0026minus;\u0026thinsp;26,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 891 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 196 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 570 (39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 125 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;26,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 121 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 950 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 835 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 336 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256 745 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 589 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 193 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91 963 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 329 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 955 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 776 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97 598 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary artery disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 450 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 505 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 718 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 227 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 625 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 685 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 131 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 809 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver cirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 225 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 697 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 026 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 502 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 941 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 153 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 097 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 691 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 165 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 789 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 140 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 236 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 299 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 053 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 985 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 261 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviation: CKD, chronic kidney disease; ESRD, end stage renal disease; PM, particulate matter; NTD, New Taiwan Dollar; COPD, chronic obstructive pulmonary disease;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as frequency (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo further delineate the exposure\u0026ndash;response relationship, the continuous ΔPM\u003csub\u003e2.5\u003c/sub\u003e was modeled as a flexible restricted cubic spline (RCS) variables, with adjustments for the same covariates specified in Model 3. The knots were positioned at the 10th, 50th, and 90th percentiles of the distribution. Effect modification by baseline characteristics was assessed through stratified analyses according to age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), sex, absence or presence of diabetes, and monthly salary (\u0026le;\u0026thinsp;19,047 NTD [level 1] vs. \u0026gt;19,047 NTD [levels 2\u0026ndash;3]). RCS analyses were conducted in R version 4.3.2 (R Foundation for Statistical Computing) using the rms package, while all other statistical analyses were performed in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were carried out on-site at the HWDC facility.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 423,855 adults with early CKD residing in regions with available PM\u003csub\u003e2.5\u003c/sub\u003e measurements between 2012 and 2021. The mean age of the cohort was 64.2 years, and 49.8% were older than 65 years. Males represented 52.3% of the participants. With respect to monthly insured salary, 38.9% of patients were classified in the NT\u003cspan\u003e$\u003c/span\u003e19,047\u0026ndash;26,400 category, 34.2% in the \u0026ge;\u0026thinsp;NT\u003cspan\u003e$\u003c/span\u003e26,400 category, and 26.9% in the \u0026le;\u0026thinsp;NT\u003cspan\u003e$\u003c/span\u003e19,047 category.\u003c/p\u003e\u003cp\u003eThe distribution of ΔPM\u003csub\u003e2.5\u003c/sub\u003e ranged from \u0026minus;\u0026thinsp;28.0 to 21.2 \u0026micro;g/m\u0026sup3;. Based on tertile classification, 139,850 patients were assigned to tertile 1 (\u0026ndash;28.0 to \u0026minus;\u0026thinsp;10.2 \u0026micro;g/m\u0026sup3;), 139,908 to tertile 2 (\u0026ndash;10.2 to \u0026minus;\u0026thinsp;4.9 \u0026micro;g/m\u0026sup3;), and 144,097 to tertile 3 (\u0026ndash;4.9 to 21.1 \u0026micro;g/m\u0026sup3;). Significant differences across tertiles were noted for nearly all baseline characteristics, including age, selected comorbidities, CCI score, changes in mean concentrations of gaseous pollutants (NO₂, CO, SO₂), and residential urbanization level, whereas monthly income did not differ significantly \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eAccording to the multivariable Cox proportional hazards model, each 1-\u0026micro;g/m\u0026sup3; reduction in ΔPM\u003csub\u003e2.5\u003c/sub\u003e (indicating improved ambient PM\u003csub\u003e2.5\u003c/sub\u003e) was associated with an approximate 8% lower risk of progression to advanced CKD (hazard ratio [HR], 0.923; 95% confidence interval [CI], 0.920\u0026ndash;0.926), an equivalently 5% lower risk of dialysis initiation (HR, 0.953; 95% CI, 0.947\u0026ndash;0.960), and a nearly 7% lower risk of mortality (HR, 0.925; 95% CI, 0.923\u0026ndash;0.927) (Model 3 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When tertile 2 (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026minus;\u0026thinsp;10.2 to \u0026minus;\u0026thinsp;4.9 \u0026micro;g/m\u0026sup3;, reflecting the smallest change in PM\u003csub\u003e2.5\u003c/sub\u003e) was used as the reference, patients in tertile 1 (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026minus;\u0026thinsp;28.0 to \u0026minus;\u0026thinsp;10.2 \u0026micro;g/m\u0026sup3;) experienced a 19% lower risk of progression to advanced CKD, a 9% lower risk of dialysis initiation, and a 28% lower risk of mortality. Conversely, participants in tertile 3 (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026minus;\u0026thinsp;4.9 to 21.2 \u0026micro;g/m\u0026sup3;) had a 2.25-fold higher risk of progression to advanced CKD, a 1.59-fold higher risk of dialysis initiation, and a 1.94-fold higher risk of mortality \u003cb\u003e(\u003c/b\u003eModel 3 in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association between the annual PM\u003csub\u003e2.5\u003c/sub\u003e change (as continuous variable, per \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e decrease) and the risk of outcomes in various adjustment models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome / Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvance CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.787 (0.784\u0026ndash;0.789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.791 (0.788\u0026ndash;0.794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.923 (0.920\u0026ndash;0.926)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic dialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.835 (0.830\u0026ndash;0.841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.835 (0.830\u0026ndash;0.841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.953 (0.947\u0026ndash;0.960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.809 (0.807\u0026ndash;0.811)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.821 (0.819\u0026ndash;0.823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.925 (0.923\u0026ndash;0.927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviation: PM, particulate matter; CI, confidence interval;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eModel 1: Crude model without adjustment;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eModel 2: Adjusted for age, sex, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, the index year of enrollment;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eModel 3: Further adjusted for urbanization level of the residence and the annual change of NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association between the tertile of annual PM\u003csub\u003e2.5\u003c/sub\u003e change (as categorical variable) and the risk of outcomes in various adjustment models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome / Model / PM\u003csub\u003e2.5\u003c/sub\u003e change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncidence rate\u003c/p\u003e\u003cp\u003e(95% CI)\u0026dagger;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvance CKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8 (6.6\u0026ndash;6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37 (0.35\u0026ndash;0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3 (11.1\u0026ndash;11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.8 (30.2\u0026ndash;31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.16 (4.03\u0026ndash;4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8 (6.6\u0026ndash;6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38 (0.36\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3 (11.1\u0026ndash;11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.8 (30.2\u0026ndash;31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.04 (3.92\u0026ndash;4.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8 (6.6\u0026ndash;6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81 (0.78\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3 (11.1\u0026ndash;11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.8 (30.2\u0026ndash;31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25 (2.17\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic dialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.9\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.34\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.5 (2.4\u0026ndash;2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1 (2.9\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78 (2.57\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.9\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37 (0.34\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.5 (2.4\u0026ndash;2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1 (2.9\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75 (2.55\u0026ndash;2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.9\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.85\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.5 (2.4\u0026ndash;2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1 (2.9\u0026ndash;3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.47\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.6 (16.4\u0026ndash;16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33 (0.32\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.6 (28.3\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.2 (52.5\u0026ndash;54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.25 (3.19\u0026ndash;3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.6 (16.4\u0026ndash;16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.35\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.6 (28.3\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.2 (52.5\u0026ndash;54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.05 (2.99\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.6 (16.4\u0026ndash;16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72 (0.70\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.6 (28.3\u0026ndash;29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.2 (52.5\u0026ndash;54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94 (1.90\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: PM, particulate matter; CI, confidence interval;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; Number of events per 1,000 person-years;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: Crude model without adjustment;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: Adjusted for age, sex, monthly income, all comorbidities as well as the Charlson Comorbidity Index score, the index year of enrollment;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3: Further adjusted for urbanization level of the residence and the annual change of NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe RCS analyses which used ΔPM\u003csub\u003e2.5\u003c/sub\u003e = 0 \u0026micro;g/m\u0026sup3; as the reference point, revealed that increases in PM\u003csub\u003e2.5\u003c/sub\u003e levels (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026gt;0 \u0026micro;g/m\u0026sup3;) were associated with a progressively higher risk of advanced CKD, whereas reductions in PM\u003csub\u003e2.5\u003c/sub\u003e (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026lt; 0 \u0026micro;g/m\u0026sup3;) corresponded to a lower risk. However, the risk reduction reached a plateau in participants experiencing substantial improvements in air quality (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026lt; \u0026minus;\u0026thinsp;15 \u0026micro;g/m\u0026sup3;) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. For maintenance dialysis, the RCS model demonstrated an approximately linear positive association with ΔPM\u003csub\u003e2.5\u003c/sub\u003e (although the \u003cem\u003eP\u003c/em\u003e for linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although the slope was slightly attenuated in regions with ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026lt; \u0026minus;\u0026thinsp;7 \u0026micro;g/m\u0026sup3; \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. With respect to mortality, the RCS analysis likewise indicated that worsening air quality (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026gt;0 \u0026micro;g/m\u0026sup3;) was linked to an increased risk (\u003cem\u003eP\u003c/em\u003e for linearity and non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas improvements in PM\u003csub\u003e2.5\u003c/sub\u003e (ΔPM\u003csub\u003e2.5\u003c/sub\u003e \u0026lt; 0 \u0026micro;g/m\u0026sup3;) were protective. Nevertheless, the survival benefit plateaued once reductions in PM\u003csub\u003e2.5\u003c/sub\u003e exceeded approximately 15 \u0026micro;g/m\u0026sup3; \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eStratified analyses, in alignment with the Cox regression results, demonstrated that improvements in ambient PM\u003csub\u003e2.5\u003c/sub\u003e were associated with lower risks of outcomes. A 1-\u0026micro;g/m\u0026sup3; reduction in ΔPM\u003csub\u003e2.5\u003c/sub\u003e yielded a greater decrease in the risk of progression to advanced CKD (including transition to maintenance dialysis) among patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (HR, 0.918 vs. 0.928; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), females (HR, 0.914 vs. 0.927; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and individuals with a monthly income\u0026thinsp;\u0026le;\u0026thinsp;19,047 NTD (HR, 0.908 vs. 0.927; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared with their respective counterparts \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Regarding mortality, stratified analyses revealed a greater risk reduction with a 1-\u0026micro;g/m\u0026sup3; decrease in ΔPM\u003csub\u003e2.5\u003c/sub\u003e among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (HR, 0.919 vs. 0.928; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), females (HR, 0.921 vs. 0.927; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those with diabetes (HR, 0.923 vs. 0.927; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.004), compared with their respective counterparts \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that improvements in long-term PM\u003csub\u003e2.5\u003c/sub\u003e air quality were associated with a reduced risk of progression to advanced CKD and maintenance dialysis among patients with early CKD, whereas worsening ambient PM\u003csub\u003e2.5\u003c/sub\u003e was linked to increased disease progression. These findings are consistent with prior studies reporting the health benefits of reduced PM\u003csub\u003e2.5\u003c/sub\u003e exposure, such as lower hypertension incidence, improved cardiovascular outcomes, and decreased COPD prevalence [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Importantly, our results align with limited research directly demonstrating the beneficial effects of PM\u003csub\u003e2.5\u003c/sub\u003e reduction on CKD development and progression to maintenance dialysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Bo et al. (2021b) reported a 25% lower risk of CKD development per 5 \u0026micro;g/m\u0026sup3; reduction in ambient PM\u003csub\u003e2.5\u003c/sub\u003e among Taiwanese adults, with an approximately linear exposure\u0026ndash;response relationship [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], while Chen et al. (2025) found that a 1 \u0026micro;g/m\u0026sup3; reduction in PM\u003csub\u003e2.5\u003c/sub\u003e was associated with an 11% lower risk of dialysis progression, also showing a near-linear relationship between ΔPM\u003csub\u003e2.5\u003c/sub\u003e and dialysis incidence in patients with advanced CKD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the current study, although the ΔPM\u003csub\u003e2.5\u003c/sub\u003e\u0026ndash;dialysis relationship was nearly linear, the protective effect on progression to advanced CKD plateaued among early CKD patients once PM\u003csub\u003e2.5\u003c/sub\u003e improvements exceeding 15 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e\u003cp\u003eWe further observed that long-term reductions in PM\u003csub\u003e2.5\u003c/sub\u003e exposure were associated with decreased mortality in early CKD patients, although the protective effect plateaued beyond a 15 \u0026micro;g/m\u0026sup3; improvement. This finding aligns with limited prior studies reporting mortality reductions following air pollution improvement in the general population [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and complements earlier work linking higher PM\u003csub\u003e2.5\u003c/sub\u003e exposure to greater mortality risk in CKD and ESKD populations [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Notably, our results are consistent with Chen et al. (2025), who reported that PM\u003csub\u003e2.5\u003c/sub\u003e improvement reduced mortality in advanced CKD, with a plateaued protective effect beyond a 5 \u0026micro;g/m\u0026sup3; improvement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This plateau phenomenon, together with the plateaued effect on progression to advanced CKD described above, may reflect the intrinsic vulnerability of CKD patients with complex comorbidities to disease progression and premature mortality [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], wherein residual confounding and comorbidity burden exert stronger influences than environmental PM\u003csub\u003e2.5\u003c/sub\u003e reduction. Consequently, even substantial improvements in air quality may not fully mitigate mortality risk in the most vulnerable early CKD population.\u003c/p\u003e\u003cp\u003eSeveral biological mechanisms may underlie these observed associations. Miller (2017) demonstrated that inhaled particulate matter can translocate systemically and damage extrapulmonary organs, including the kidneys [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, PM\u003csub\u003e2.5\u003c/sub\u003e-induced pulmonary inflammation may trigger systemic inflammation, endothelial injury, oxidative stress, metabolic disturbances, autophagy \u0026amp; pyroptosis, and genotoxicity, ultimately contributing to renal damage [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].These mechanistic insights provide biological plausibility for the observed reductions in disease progression and mortality associated with long-term improvements in PM₂.₅ exposure among CKD patients, as shown in our study. To our knowledge, this is the first population-based study to evaluate ΔPM₂.₅ in relation to advanced CKD progression, dialysis initiation, and mortality in early-stage CKD, offering valuable evidence to inform public health strategies for kidney health promotion and air quality improvement.\u003c/p\u003e\u003cp\u003eAfter adjustment for baseline characteristics such as age, sex, diabetes, and monthly insured salary, the significant association between lowering PM\u003csub\u003e2.5\u003c/sub\u003e exposure and reduced risks of progression to advanced CKD (including transition to maintenance dialysis) and mortality remained. Subgroup analyses showed that early CKD patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 experienced a greater reduction in the risk of progression to advanced CKD per 1-\u0026micro;g/m\u0026sup3; decrease in PM\u003csub\u003e2.5\u003c/sub\u003e, whereas those aged\u0026thinsp;\u0026lt;\u0026thinsp;65 exhibited larger reductions in mortality risk compared with their older counterparts. Hallan et al. (2024) observed that among patients with early CKD, the mean ESKD rate per 1,000 person-years was lower in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 than in those aged\u0026thinsp;\u0026lt;\u0026thinsp;65, whereas the mean mortality rate per 1,000 person-years was higher in the older group [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Ito and Mori (2025) reported that early CKD in younger individuals\u0026mdash;especially those with early-onset hypertension\u0026mdash;tends to progress more rapidly to ESKD, while in older adults, advancement to severe stages occurs at a slower pace [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings support the age-specific subgroup analyses in our study.\u003c/p\u003e\u003cp\u003eRegarding the effect of sex, our study revealed that each 1-\u0026micro;g/m\u0026sup3; reduction in PM\u003csub\u003e2.5\u003c/sub\u003e conferred a greater decrease in the risks of progression to advanced CKD and mortality in females compared with males. These results echo previous studies demonstrating that males have a higher risk of CKD development and that male CKD patients exhibit a greater mortality risk under comparable increases in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure compared with females [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The higher risk of PM\u003csub\u003e2.5\u003c/sub\u003e-associated CKD development and mortality in men compared with women may result from interactions between PM\u003csub\u003e2.5\u003c/sub\u003e and male-specific factors, including hormonal profiles, occupational exposures, and lifestyle behaviors [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study found that among early CKD patients, those with diabetes\u0026mdash;the most common underlying cause of CKD\u0026mdash;experienced a greater reduction in the risk of mortality per 1-\u0026micro;g/m\u0026sup3; decrease in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure compared with non-diabetic patients. This observation is consistent with prior evidence demonstrating that, given comparable increases in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure, CKD patients with diabetes exhibit a lower relative increase in the hazards of eGFR decline or mortality compared with non-diabetic CKD patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Persistent hyperglycemia accelerates the formation of advanced glycation end products, promoting reactive oxygen species generation, chronic inflammation, and endothelial dysfunction, ultimately contributing to diabetic complications [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Given the intrinsically heightened inflammatory state in diabetic CKD and the baseline elevated risks of mortality, reductions in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure are likely to confer greater benefits in mitigating CKD-related mortality.\u003c/p\u003e\u003cp\u003eOur study demonstrated that among early CKD patients, individuals with lower monthly income experienced a greater decrease in the risk of progression to advanced CKD per 1-\u0026micro;g/m\u0026sup3; reduction in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure compared with those with higher income. These results diverge from prior research examining the impact of socioeconomic status (SES) on CKD-related outcomes, which demonstrates that lower SES is associated with increased disease severity, accelerated progression, and a higher risk of ESKD [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This difference suggests that, although income is a key indicator of socioeconomic status, relying on it alone in research may introduce potential bias.\u003c/p\u003e\u003cp\u003eThis study has several strengths. First, it employs a large, nationwide cohort to investigate the association between ΔPM\u003csub\u003e2.5\u003c/sub\u003e and progression to advanced CKD or mortality in early CKD, allowing for comprehensive adjustment for baseline confounders and providing sufficient statistical power for subgroup analyses, thereby enhancing the reliability and generalizability of the findings. Second, ΔPM\u003csub\u003e2.5\u003c/sub\u003e was evaluated not only in single-pollutant models but also with adjustment for co-pollutants, further strengthening the robustness of the results. Third, to our knowledge, this is the first study to specifically assess the impact of ΔPM\u003csub\u003e2.5\u003c/sub\u003e on progression to advanced CKD, dialysis initiation, and mortality among patients with early CKD, thereby laying the groundwork for future research on air pollution mitigation and early kidney health interventions.\u003c/p\u003e\u003cp\u003eHowever, several limitations warrant consideration. First, ambient PM\u003csub\u003e2.5\u003c/sub\u003e was estimated from fixed-site monitoring stations, which may not fully capture individual indoor exposures, potentially resulting in exposure misclassification. Nonetheless, evidence indicates that indoor PM\u003csub\u003e2.5\u003c/sub\u003e derived from outdoor sources can substantially contribute to adverse health outcomes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Second, seasonal variations in PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in Taiwan may affect ΔPM\u003csub\u003e2.5\u003c/sub\u003e estimation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], though this impact was mitigated by employing a 12-month measurement window. Third, the use of 3-digit zip code\u0026ndash;level data may not precisely reflect proximity to air-quality monitoring stations [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; however, previous studies indicate consistent associations with mortality risk even within a 10 km radius. Furthermore, non-differential exposure misclassification typically biases results toward the null [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], yet significant associations were still observed, underscoring the robustness of our findings. Fourth, the NHIRD does not include information on lifestyle behaviors, anthropometric measures, or biochemical parameters related to kidney function, which may also introduce bias. Fifth, as this was an observational study, it is not capable to infer a causal relationship between ΔPM\u003csub\u003e2.5\u003c/sub\u003e and the health outcomes.\u003c/p\u003e\u003cp\u003eIn conclusion, reductions in ambient PM\u003csub\u003e2.5\u003c/sub\u003e were associated with lower risks of progression to advanced CKD, initiation of dialysis, and mortality, whereas deteriorating air quality was linked to increased risks among patients with early CKD. These findings underscore the importance of air pollution mitigation as an early intervention to preserve kidney health and reduce disease progression and mortality in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eESKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eend-stage kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAQMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Taiwan Air Quality Monitoring Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHIRD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe National Health Insurance Research Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMOENV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Ministry of Environment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health Insurance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHWDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Health and Welfare Data Science Center\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD-9-CM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe International Classification of Diseases, Ninth Revision, Clinical Modification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD-10-CM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe International Classification of Diseases, Tenth Revision, Clinical Modification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Charlson Comorbidity Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esocioeconomic status\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003eThis study was approved by the Research Ethics Review Committee of New Taipei City Hospital (protocol No. NTPC 114002-N) on June 5, 2025. The organization and operation of this Institutional Review Board (IRB) complied with the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors disclose the following potential competing interests: Shih-Feng Chen has received financial support from New Taipei City Hospital, Sanchung Branch, and is employed by the same institution. The remaining authors report no known financial interests or personal relationships that could have influenced the work presented in this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to participate\u003c/h2\u003e\u003cp\u003eNot applicable (This retrospective cohort study utilized de-identified data from the NHIRD, which is protected under regulations governing beneficiary privacy established by the Taiwanese government.)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eSupplementary material\u003c/h2\u003e\u003cp\u003eStrobe checklist\u003c/p\u003e\u003cp\u003eICMJE disclosure form\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by New Taipei City Hospital. The funding institution had no role in the study design, data collection, data analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.F.C. conceived and designed the study, developed the methodology, performed data analysis, drafted the original manuscript, and manuscript revision. Y.C.L. contributed to project administration, data analysis, and investigation. Y.H.C. was responsible for data curation, formal analysis, and investigation. K.C.H. assisted with methodological development, investigation, data interpretation, and manuscript revision. I.W.W. supervised the study, validated the findings, and contributed to study conceptualization, methodological design, and critical manuscript review. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their gratitude to Mr. Yen-Chang Chen (B.A. Physics/Accounting, UCLA, USA) for assistance with English language editing and acknowledge financial support from New Taipei City Hospital.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe first dataset utilized in this study was obtained from the National Health Insurance Research Database (NHIRD), which is maintained by the Health and Welfare Data Science Center (HWDC). As the NHIRD is not publicly accessible, researchers must submit a formal application to the HWDC, Department of Statistics, Ministry of Health and Welfare, Taiwan (https://dep.mohw.gov.tw/DOS/cp-5119-59201-113.html) to obtain access. The second dataset was derived from the Taiwan Air Quality Monitoring Database (TAQMD), managed by the Ministry of Environment, Taiwan. Unlike the NHIRD, the TAQMD is publicly available, and historical ambient temperature and air quality data can be downloaded from the Air Quality Index (AQI) platform: Air Quality Index (AQI) | 環境部環境資料開放平臺. [https://data.moenv.gov.tw/](https:/data.moenv.gov.tw)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCesaroni G, Forastiere F, Stafoggia M, Andersen ZJ, Badaloni C, Beelen R, et al. Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. BMJ. 2014;348:f7412.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe YS, Xu YQ, Cao F, Gao ZX, Ge M, He T, et al. Association of long-term exposure to PM2. 5 constituents and green space with arthritis and rheumatoid arthritis. 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Heart. 2004;90(8):956\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PM2.5 change, early CKD patients, advanced CKD, Dialysis, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-7989965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7989965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) is an increasingly recognized risk factor for kidney disease. However, evidence on the kidney-protective effects of improvements in PM\u003csub\u003e2.5\u003c/sub\u003e exposure is limited. Early intervention in chronic kidney disease (CKD) is essential. We aimed to explore the association of changes in long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure with advanced CKD progression and mortality among early-stage CKD patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study enrolled 423,855 early CKD patients (stages 1\u0026ndash;3a) between 2012 and 2021, with follow-up through 2022. PM\u003csub\u003e2.5\u003c/sub\u003e change (ΔPM\u003csub\u003e2.5\u003c/sub\u003e) was defined as the difference between the 365-day mean concentration before enrollment and the 365-day mean before follow-up end. Multivariate Cox proportional hazards models assessed associations of ΔPM\u003csub\u003e2.5\u003c/sub\u003e with advanced CKD progression, dialysis initiation, and mortality, both per 1 \u0026micro;g/m\u0026sup3; change and across tertiles of ΔPM\u003csub\u003e2.5\u003c/sub\u003e. Restricted cubic spline analyses characterized concentration-response relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEach 1 \u0026micro;g/m\u0026sup3; reduction in PM\u003csub\u003e2.5\u003c/sub\u003e was associated with 8%, 5%, and 7% lower risks of advanced CKD progression, dialysis initiation, and mortality, respectively. Compared with the reference tertile, participants in the most improved tertile exhibited 19%, 9%, and 28% lower risks, whereas those in the most deteriorated tertile had 2.25-, 1.59-, and 1.94-fold higher risks. Spline analyses indicated near-linear relationships, with protective effects in CKD progression and mortality plateauing beyond a 15 \u0026micro;g/m\u0026sup3; reduction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eImprovements in PM\u003csub\u003e2.5\u003c/sub\u003e exposure are associated with reduced risks of advanced CKD progression, dialysis initiation, and mortality among early CKD patients. Public health strategies promoting air quality may represent effective early interventions to protect kidney health.\u003c/p\u003e","manuscriptTitle":"Association of PM2.5 Changes with Advanced Kidney Disease Progression and Mortality in Patients with Early CKD: A Nationwide Retrospective Cohort Study in Taiwan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 00:45:37","doi":"10.21203/rs.3.rs-7989965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T13:29:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T07:36:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197988315423496496505441421920167528683","date":"2026-01-28T00:12:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T15:29:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300761734988775121005136735483651225336","date":"2025-11-24T15:12:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-09T12:15:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T10:31:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T10:29:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2025-10-30T13:28:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4efe89ea-4af6-4608-9503-62c3dfb91dc5","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T13:40:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 00:45:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7989965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7989965","identity":"rs-7989965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}