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American Journal of Kidney Diseases

Association of Long-term Ambient Fine Particulate Matter (PM2.5) and Incident CKD: A Prospective Cohort Study in China

  • Author Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Jing-wen Duan
    Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Author Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Ya-lan Li
    Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Author Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Shen-xin Li
    Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
    Affiliations
    Department of Surveying and Remote Sensing Science, School of Geosciences and Info-physics, Central South University, Changsha, China
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  • Yi-ping Yang
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Fei Li
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Yan Li
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Jie Wang
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Pei-zhi Deng
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Jing-jing Wu
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Wei Wang
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Chang-jiang Meng
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Ru-jia Miao
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Zhi-heng Chen
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Bin Zou
    Affiliations
    Department of Surveying and Remote Sensing Science, School of Geosciences and Info-physics, Central South University, Changsha, China
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  • Hong Yuan
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Jing-jing Cai
    Correspondence
    Address for Correspondence: Jing-jing Cai, PhD, Department of Cardiology, Third Xiangya Hospital, Central South University Changsha, China
    Affiliations
    Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
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  • Yao Lu
    Correspondence
    Yao Lu, PhD, Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
    Affiliations
    Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China

    Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China

    School of Life Course Sciences, King’s College London, London, United Kingdom
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  • Author Footnotes
    ∗ J.D., Y.L., and S.L. contributed equally to this work
Open AccessPublished:April 22, 2022DOI:https://doi.org/10.1053/j.ajkd.2022.03.009

      Rationale & Objective

      Increasing evidence has linked ambient fine particulate matter (ie, particulate matter no larger than 2.5 μm [PM2.5]) to chronic kidney disease (CKD), but their association has not been fully elucidated, especially in regions with high levels of PM2.5 pollution. This study aimed to investigate the long-term association of high PM2.5 exposure with incident CKD in mainland China.

      Study Design

      Prospective cohort study.

      Setting & Participants

      72,425 participants (age ≥18 years) without CKD were recruited from 121 counties in Hunan Province, China.

      Exposure

      Annual mean PM2.5 concentration at the residence of each participant derived from a long-term, full-coverage, high-resolution (1 × 1 km2), high-quality dataset of ground-level air pollutants in China.

      Outcomes

      Incident CKD during the interval between the baseline examination of each participant (2005-2017) and the end of follow-up through 2018.

      Analytical Approach

      Cox proportional hazards models were used to estimate the independent association of PM2.5 with incident CKD and the joint association of PM2.5 with temperature or humidity on the development of PM2.5-related CKD. Restricted cubic splines were used to model exposure-response relationships.

      Results

      Over a median follow-up of 3.79 (IQR, 2.03-5.48) years, a total of 2,188 participants with incident CKD were identified. PM2.5 exposure was associated with incident CKD with an adjusted hazard ratio of 1.71 (95% CI, 1.58-1.85) per 10-μg/m3 greater long-term exposure. Multiplicative interactions between PM2.5 and humidity or temperature on incident CKD were detected (all P < 0.001 for interaction), whereas an additive interaction was detected only for humidity (relative risk due to interaction, 3.59 [95% CI, 0.97-6.21]).

      Limitations

      Lack of information on participants’ activity patterns such as time spent outdoors.

      Conclusions

      Greater long-term ambient PM2.5 pollution is associated with incident CKD in environments with high PM2.5 exposure. Ambient humidity has a potentially synergetic effect on the association of PM2.5 with the development of CKD.

      Plain-Language Summary

      Exposure to a form of air pollution known as fine particulate matter (ie, particulate matter ≤2.5 μm [PM2.5]) has been linked to an increased risk of chronic kidney disease (CKD), but little is known about how PM2.5 affects CKD in regions with extremely high levels of PM2.5 pollution. This longitudinal cohort study in China investigates the effect of PM2.5 on the incidence of CKD and whether temperature or humidity interact with PM2.5. Our findings suggest that long-term exposure to high levels of ambient PM2.5 significantly increased the risk of CKD in mainland China, especially in terms of cumulative average PM2.5. The associations of PM2.5 and incident CKD were greater in high-humidity environments. These findings support the recommendation that reducing PM2.5 pollution should be a priority to decrease the burden of associated health risks, including CKD.

      Graphical abstract

      Index Words

      Fine particulate matter (ie, particulate matter no larger than 2.5 μm [PM2.5]) air pollution, a toxic mixture of solid and liquid particles in the ambient atmosphere, has become a globally recognized public health concern.
      Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016.
      Ambient PM2.5 can be inhaled into the lungs and cause a range of adverse health effects, including higher risks of respiratory disease, cardiovascular disease (CVD), lung cancer, and mortality.
      • Mukherjee A.
      • Agrawal M.
      A global perspective of fine particulate matter pollution and its health effects.
      ,
      • Liu C.
      • Chen R.
      • Sera F.
      • et al.
      Ambient particulate air pollution and daily mortality in 652 cities.
      Recent research studies have implicated long-term exposure to PM2.5 as a contributor to kidney dysfunction through hemodynamic changes, oxidative stress, systemic inflammation, and other potential pathways.
      • Liu B.
      • Fan D.
      • Huang F.
      Relationship of chronic kidney disease with major air pollutants - a systematic review and meta-analysis of observational studies.
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Particulate matter air pollution and the risk of incident CKD and progression to ESRD.
      • Blum M.F.
      • Surapaneni A.
      • Stewart J.D.
      • et al.
      Particulate matter and albuminuria, glomerular filtration rate, and incident CKD.
      • Chan T.C.
      • Zhang Z.
      • Lin B.C.
      • et al.
      Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort Study.
      • Mehta A.J.
      • Zanobetti A.
      • Bind M.A.
      • et al.
      Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
      • Nemmar A.
      • Al-Salam S.
      • Zia S.
      • Yasin J.
      • Al Husseni I.
      • Ali B.H.
      Diesel exhaust particles in the lung aggravate experimental acute renal failure.
      • Nemmar A.
      • Karaca T.
      • Beegam S.
      • et al.
      Prolonged pulmonary exposure to diesel exhaust particles exacerbates renal oxidative stress, inflammation and DNA damage in mice with adenine-induced chronic renal failure.
      Chronic kidney disease (CKD) is the 11th leading cause of death worldwide.
      Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.
      China has the highest number of patients with CKD in the world, with 132.3 million in 2017. Globally, approximately 17%-20% of the CKD burden has been attributed to PM2.5 pollution, which may potentially be modified at the individual and population levels.
      Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.
      ,
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Estimates of the 2016 global burden of kidney disease attributable to ambient fine particulate matter air pollution.
      Although epidemiologic and clinical studies have found an association between ambient PM2.5 and increased risk of CKD,
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Particulate matter air pollution and the risk of incident CKD and progression to ESRD.
      • Blum M.F.
      • Surapaneni A.
      • Stewart J.D.
      • et al.
      Particulate matter and albuminuria, glomerular filtration rate, and incident CKD.
      • Chan T.C.
      • Zhang Z.
      • Lin B.C.
      • et al.
      Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort Study.
      • Mehta A.J.
      • Zanobetti A.
      • Bind M.A.
      • et al.
      Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
      ,
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Estimates of the 2016 global burden of kidney disease attributable to ambient fine particulate matter air pollution.
      there are still some uncertainties because little evidence comes from regions with severe air pollution, such as China, where the PM2.5 pollution is 5- to 10-fold higher than the limit of 10 μg/m3 set by the World Health Organization.
      To our knowledge, only one cross-sectional study, which included 47,204 participants, has explored the association between PM2.5 and CKD prevalence in mainland China.
      • Li G.
      • Huang J.
      • Wang J.
      • et al.
      Long-term exposure to ambient PM(2.5) and increased risk of CKD prevalence in China.
      This study was limited to a coarse spatial resolution of 10 × 10 km2, which may reduce accuracy and increase misclassification of assessments of PM2.5 exposure. Moreover, whether climate change, specifically in temperature and humidity, exacerbates the development of PM2.5-related CKD remains unknown and is important because climate change has previously been reported to alter the association between PM2.5 and other health outcomes.
      • Hsu W.H.
      • Hwang S.A.
      • Kinney P.L.
      • Lin S.
      Seasonal and temperature modifications of the association between fine particulate air pollution and cardiovascular hospitalization in New York State.
      ,
      • Liu M.
      • Xue X.
      • Zhou B.
      • et al.
      Population susceptibility differences and effects of air pollution on cardiovascular mortality: epidemiological evidence from a time-series study.
      To address these outstanding questions, we conducted a large cohort study to estimate the risk of incident CKD associated with long-term exposure to ambient PM2.5 in mainland China and evaluated the joint effect of humidity or temperature with ambient PM2.5 on the incidence of CKD.

      Methods

      Study Population

      We used the existing data from an ongoing longitudinal study in Hunan, China, of which a detailed description has been published.
      • Lu Y.
      • Pechlaner R.
      • Cai J.
      • et al.
      Trajectories of age-related arterial stiffness in Chinese men and women.
      Briefly, this study cohort is dynamic, and we included the participants who underwent an annual physical examination at the Health Management Center in The Third Xiangya Hospital of Central South University (Changsha, China), the largest medical institution in central China. The original database was generated from electronic records of these physical examinations, which occurred at approximately 7.3 million medical visits. There were 99,960 individuals identified as having had at least 2 estimated glomerular filtration rate (eGFR) assessments between 2005 and 2018. Each eligible participant had a geocoded residential address at the baseline examination (ie, first visit) between 2005 and 2017 and had at least 2 eGFR assessments until their last follow-up for CKD through 2018.
      Of these participants, 27,535 were excluded because they were diagnosed with CKD before the baseline examination (n = 2,439) or because PM2.5 information was unavailable (n = 25,096). Thus, a total of 72,425 participants were included in the final analysis (Fig S1). Ethical approval of our study was obtained from the institutional review board of The Third Xiangya Hospital of Central South University (no. R18030). Each study participant was required to give written informed consent.

      Exposure Assessment

      Annual mean PM2.5 concentrations from 2000 to 2018, measured with a spatial resolution of 1 × 1 km2, were derived from the China High Air Pollutants dataset (https://weijing-rs.github.io/product.html). The China High Air Pollutants dataset was generated from Moderate Resolution Imaging Spectroradiometer/Terra + Aqua Multiangle Implementation of Atmospheric Correction aerosol optical depth products together with other auxiliary data (eg, ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.
      • Wei J.
      • Li Z.Q.
      • Lyapustin A.
      • et al.
      Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications.
      ,
      • Wei J.
      • Li Z.Q.
      • Cribb M.
      • et al.
      Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees.
      The estimated annual PM2.5 concentrations from the dataset are highly correlated with ground-based measurements (R2 = 0.94).
      • Wei J.
      • Li Z.Q.
      • Lyapustin A.
      • et al.
      Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications.
      These annual PM2.5 concentrations were assigned to each participant based on the longitude and latitude of the address collected from the electronic medical records of The Third Xiangya Hospital obtained at the participant’s first visit though the Baidu Web service application programming interface of “Geocoder” with Python version 3.8.3.
      • Wang L.
      • Chen G.
      • Pan Y.
      • et al.
      Association of long-term exposure to ambient air pollutants with blood lipids in Chinese adults: the China Multi-Ethnic Cohort study.
      Because we did not know the optimum exposure time window for analysis, we calculated 2 main indicators of PM2.5 for each participant. The primary exposure of interest is the cumulative average PM2.5, assessed as the average of annual mean PM2.5 concentrations from the year of the baseline date to that of the end point date. In the main and subsequent analysis, cumulative average PM2.5 was considered as the long-term exposure because this time window had the strongest connection with CKD (Fig S2). The secondary exposure is the previous 0-10–year average PM2.5, assessed as the annual mean PM2.5 concentration for the year of the baseline date or the 1-10–year average of annual mean PM2.5 concentrations before the year of the baseline date (Fig S3).
      For climate change parameters and other pollutants, we obtained the values for each participant based on the longitude and latitude of their address and calculated their cumulative average particulate matter smaller than 1 μm (PM1), particulate matter smaller than 10 μm (PM10), and baseline annual average temperature, humidity, black carbon, ozone (O3), and nitrogen dioxide (NO2) as a long-term exposure. Detailed information on the assessment of temperature, humidity, and other air pollutants is provided in Item S1.

      Outcome Definition

      The primary outcome in the present analysis was incident CKD (defined as eGFR <60 mL/min/1.73 m2 or self-reported physician-diagnosed CKD).
      • Stevens P.E.
      • Levin A.
      Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.
      After overnight fasting (≥8 hours), venous blood samples of participants were drawn in the morning, and serum creatinine was measured with an automated biochemical analyzer (model 7600; Hitachi) at the Health Management Center of The Third Xiangya Hospital. The eGFR was calculated based on the MDRD Study equation
      • Ma Y.C.
      • Zuo L.
      • Chen J.H.
      • et al.
      Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease.
      : eGFR = 175 × (serum creatinine)−1.234 × age−0.179 (×0.79 if female), where serum creatinine is measured in milligrams per deciliter and age is measured in years. Each of the 72,425 participants who did not have CKD based on the baseline examination was encouraged to annually return for follow-up (with a determination of eGFR) until the outcome event or censoring.

      Clinical Characteristics

      A detailed self-administered questionnaire and standard clinical examinations conducted by trained physicians or nurses were completed at the baseline and each follow-up examination. Covariates included age, sex, residence address, education, body mass index, smoking, alcohol use, exercise, systolic blood pressure, diastolic blood pressure, fasting blood glucose, uric acid, total cholesterol, diabetes, hypertension, and history of CVD. Details of covariate definitions are provided in Item S2.

      Statistical Analysis

      Baseline characteristics of participants classified by quartiles of cumulative average PM2.5 concentration are presented as medians ± IQR for continuous variables (because all had nonnormal distribution) and as frequencies (percentage) for categorical variables; these PM2.5-status subgroups were compared by Kruskal-Wallis tests or χ2 test, respectively. We used Cox proportional hazards models to estimate the independent effect of long-term exposure to PM2.5 and risk of incident CKD, including cumulative average PM2.5 concentration and previous 0-10–year average PM2.5 concentration. Follow-up time, as a time scale, was calculated as the interval from the date of the baseline assessment to the first occurrence of CKD or the last physical examination (for censoring events such as loss to follow-up, end of the study, or death) if incident CKD had not been identified.
      The hazard ratio (HR) and 95% CI for incident CKD were calculated with exposure as a continuous variable (per 10-μg/m3 greater ambient PM2.5 concentration) and as a categorical variable (in quartiles). We examined the association by 4 steps: a crude (unadjusted) model; model 1, adjusted for baseline age and sex; model 2, adjusted for baseline age, sex, body mass index, smoking, alcohol consumption, physical activity, and education; and model 3, adjusted for all covariates included in model 2 plus baseline systolic and diastolic blood pressure, fasting blood glucose, total cholesterol, uric acid, eGFR, hypertension, diabetes, history of CVD, temperature, and humidity.
      We modeled the exposure dose-response curves of long-term exposure to PM2.5 and risk of incident CKD using a restricted cubic spline with 5 knots at the 5th, 25th, 50th, 75th, and 95th percentiles of PM2.5 concentration. In addition, we performed analyses stratified by age (<65 or ≥65 years), sex (male or female), smoking (ever a smoker or never a smoker), alcohol use (ever a drinker or never a drinker), exercise (no or yes), body mass index (<24, 24-28, or ≥28 kg/m2), diabetes (no or yes), hypertension (no or yes), and history of CVD (no or yes) to characterize potential effect modifications. Adjusted HRs for the cross-tabulation by categorical PM2.5 concentration (quartiles) and categorical humidity/temperature (low vs high) were also calculated.
      To assess the joint associations of long-term exposure to PM2.5 and humidity or temperature on the risk of developing CKD, the multiplicative interaction and the additive interaction were assessed by incorporating a cross-product term for PM2.5 and humidity/temperature using Cox models. Specifically, we estimated P values for the multiplicative interaction by adding the cross-product term with the main effect terms for each in separate models. For additive interaction, we modeled the relative risk due to interaction (RERI), attributable proportion, and synergy index using a previously described method, with RERI being our focus.
      • Knol M.J.
      • van der Tweel I.
      • Grobbee D.E.
      • Numans M.E.
      • Geerlings M.I.
      Estimating interaction on an additive scale between continuous determinants in a logistic regression model.
      ,
      • Andersson T.
      • Alfredsson L.
      • Källberg H.
      • Zdravkovic S.
      • Ahlbom A.
      Calculating measures of biological interaction.
      A RERI of 0 indicates no additive interaction, a positive RERI indicates a positive interaction, and a negative RERI indicates a negative interaction.
      In addition, we conducted several sensitivity analyses to confirm the robustness of our estimates by (i) excluding the participants with existing comorbidities affecting kidney function, including diabetes, hypertension, and history of CVD; (ii) plotting the exposure-response curves of PM1 and PM10 for incident CKD risk; (iii) recalculating eGFR by the Xiangya equation derived from the Chinese population
      • Li D.Y.
      • Yin W.J.
      • Yi Y.H.
      • et al.
      Development and validation of a more accurate estimating equation for glomerular filtration rate in a Chinese population.
      ; (iv) using the previous 1-year average PM2.5 concentration as an alternative indicator for long-term exposure because it was strongest within the prebaseline exposure (Table S1); and (v) further adjusting for other common air pollutants, including O3, black carbon, and NO2. All analyses and plotted graphs were performed in Stata software version 16.0 or R version 3.5.3 in addition to the software described above. All P values were 2-sided, and a value lower than 0.05 was considered statistically significant.

      Results

      Baseline Characteristics of the Study Population

      There were 72,425 participants whose baseline visits were between 2005 and 2018; the median age was 38 (IQR, 30-48) years, and 58% were male. The distribution of all participant characteristics at baseline showed statistically significant differences across PM2.5 concentration quartiles; those in the highest quartile of PM2.5 exposure tended to have lower eGFR values (P < 0.05 for all; Table 1). Study participants’ locations and the associated long-term mean concentrations of PM2.5 exposure (ie, the average of annual mean PM2.5 concentrations during the 13 years between 2005 and 2018) are shown in Figure 1. The median cumulative average PM2.5 exposure concentration for all participants was 70.27 (IQR, 61.21-74.88) μg/m3 over the entire follow-up period, with a range from 31.58 to 109.33 μg/m3 (Table 1; Table S2).
      Table 1Baseline Characteristics by Quartile of Long-term Exposure to PM2.5
      VariableOverallCumulative average PM2.5, μg/m3P
      Q1: ≤61.21Q2: >61.21 to ≤70.27Q3: >70.27 to ≤74.88Q4: >74.88
      No. of participants72,42518,10818,10918,12918,079
      Age, y38 (30-48)39.55 (29-47)40.36 (30-48)39.38 (29-47)41.44 (30-49)<0.001
      Male sex42,249 (58.33%)10,446 (57.69%)10,342 (57.11%)10,272 (56.66%)11,189 (61.89%)<0.001
      BMI, kg/m223.37 (21.03-25.73)23.64 (21.10-25.86)23.59 (21.08-25.80)23.33 (20.83-25.58)23.53 (21.10-25.71)<0.001
      Educational level<0.001
       High school or lower9,394 (16.12%)2,840 (17.55%)2,741 (17.57%)1,941 (13.00%)9,394 (16.19%)
       University/college or above48,883 (83.88%)13,340 (82.45%)12,863 (82.43%)12,988 (87.00%)9,692 (83.88%)
      Smoking status<0.001
       Never a smoker37,393 (66.77%)10,641 (67.00%)9,884 (66.92%)10,099 (68.54%)6,769 (63.77%)
       Ever a smoker18,607 (33.23%)5,240 (33.00%)4,886 (33.08%)4,636 (31.46%)3,845 (36.23%)
      Alcohol use<0.001
       Never a drinker34,031 (62.14%)10,271 (64.77%)9,407 (64.49%)9,194 (63.42%)5,159 (52.50%)
       Ever a drinker20,736 (37.86%)5,587 (35.23%)5,179 (35.51%)5,303 (36.58%)4,667 (47.50%)
      Exercise<0.001
       Yes33,837 (60.62%)9,956 (62.76%)9,099 (61.68%)9,028 (61.94%)5,754 (54.15%)
       No21,979 (39.38%)5,907 (37.24%)5,652 (38.32%)5,548 (38.06%)4,872 (45.85%)
      Hypertension20,207 (27.90%)5,952 (32.87%)5,610 (30.98%)4,050 (22.34%)4,595 (25.42%)<0.001
      Diabetes4,897 (6.76%)1,401 (7.74%)1,324 (7.31%)1,013 (5.59%)1,159 (6.41%)<0.001
      History of CVD8,123 (11.22%)3,107 (17.16%)2,169 (11.98%)1,297 (7.15%)1,550 (8.57%)<0.001
      TC, mmol/L4.74 (4.18-5.39)4.90 (4.28-5.49)4.84 (4.18-5.40)4.75 (4.10-5.30)4.78 (4.12-5.32)<0.001
      UA, μmol/L295 (230-361)315.5 (247.0-378.0)294.2 (223.0-355.0)290.0 (221.0-351.0)298.8 (232.0-358.0)<0.001
      FBG, mmol/L5.00 (4.67-5.40)5.26 (4.75-5.45)5.22 (4.70-5.44)5.08 (4.57-5.34)5.16 (4.63-5.40)<0.001
      SBP, mm Hg120 (110-130)121 (110-130)121 (110-130)120 (108-130)122 (110-132)<0.001
      DBP, mm Hg74 (68-82)75 (67-82)75 (66-82)75 (67-82)75 (68-82)0.006
      eGFR, mL/min/1.73 m2114.6 (92.9-146.5)125.9 (95.0-149.4)125.1 (93.9-148.8)122.5 (91.6-145.3)120.5 (91.4-141.8)<0.001
      Previous 1-year PM2.5, μg/m373.56 (68.25-80.68)68.46 (62.6-75.34)77.62 (71.18-85.61)73.87 (68.6-78.81)75.37 (71.33-79.11)<0.001
      Ambient temperature, °C18.07 (17.74-18.39)18.13 (17.95-18.33)18.07 (17.77-18.42)18.09 (17.73-18.44)18.01 (17.68-18.35)<0.001
      Humidity, %79.21 (76.90-80.42)80.97 (80.09-82.53)78.64 (76.01-79.88)78.04 (76.23-79.75)78.20 (76.42-79.75)<0.001
      Data presented as median (IQR) or number (percentage). Abbreviations: BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FBG, fasting plasma glucose; PM2.5, particulate matter ≤2.5 μm; Q, quartile; SBP, systolic blood pressure; TC, total cholesterol; UA, uric acid.
      Figure thumbnail gr1
      Figure 1Spatial distribution of participants and associated long-term mean concentrations of particulate matter no larger than 2.5 μm (PM2.5). (A) Location of Hunan province in China. (B) The numbers of participants in each county of Hunan province. (C) Location of participants in relation to long-term mean PM2.5 concentration.

      Long-term Exposure to PM2.5 and Risk for Incident CKD

      During the median follow-up period of 3.79 (IQR, 2.03-5.48) years, incident CKD developed in a total of 2,188 (3.02%) participants, an incidence rate of 732.45 per 100,000 person-years (Table 2). Compared with participants exposed to the lowest quartile of PM2.5, the adjusted HRs for incident CKD among those exposed to the highest quartile of PM2.5 were 1.84 (95% CI, 1.57-2.15) based on the previous 1-year PM2.5 level and 2.55 (95% CI, 2.16-3.00) based on cumulative average PM2.5 level (model 3; Table 2). Per 10-μg/m3 greater PM2.5 concentration, the adjusted HRs for CKD were 1.28 (95% CI, 1.21-1.36) and 1.71 (95% CI, 1.58-1.85) based on previous 1-year average PM2.5 and cumulative average PM2.5 concentrations, respectively. When comparing distinct indicators of long-term PM2.5 exposure, including cumulative average PM2.5 concentration and previous 0-10–year average PM2.5, a moderately strong association was found for the previous 1-year average PM2.5 level (adjusted HR, 1.28 [95% CI, 1.21-1.36]), but cumulative average PM2.5 appeared to have the highest association (adjusted HR, 1.71 [95% CI, 1.58-1.85]; Fig S2; Table S1).
      Table 2Association of Long-term Exposure to PM2.5 With the Risk of Incident CKD
      Categorical analysisP for trendContinuous analysis, per 10 μg/m3 greater
      Q1Q2Q3Q4
      Previous 1-year PM2.5
       PM2.5 range, μg/m3≤68.25>68.25-≤73.56>73.56-≤80.68>80.68
       Person-years77,43685,77977,50758,003298,725
       No. of cases4855596684762,188
       Incidence rate
      Incidence rate per 100,000 person-years.
      623.33651.67861.86820.65732.45
       HR (95% CI)
      Crude model
      Crude model: unadjusted; model 1: adjusted for baseline age and sex; model 2: further adjusted for baseline body mass index, smoking, alcohol use, exercise, and education; model 3: further adjusted for baseline systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, uric acid, estimated glomerular filtration rate, hypertension, diabetes, history of cardiovascular disease, temperature, and humidity.
      1.00 (ref)1.01 (0.90-1.14)1.43 (1.28-1.61)
      P < 0.001.
      1.61 (1.42-1.84)
      P < 0.001.
      <0.0011.20 (1.15-1.26)
      P < 0.001.
      Model 11.00 (ref)0.95 (0.84-1.08)1.20 (1.07-1.35)
      P < 0.01.
      1.72 (1.51-1.96)
      P < 0.001.
      0.0011.22 (1.16-1.28)
      P < 0.001.
      Model 21.00 (ref)1.03 (0.90-1.18)1.30 (1.14-1.49)
      P < 0.001.
      1.66 (1.44-1.92)
      P < 0.001.
      <0.0011.21 (1.15-1.28)
      P < 0.001.
      Model 31.00 (ref)0.97 (0.84-1.13)1.44 (1.25-1.66)
      P < 0.001.
      1.84 (1.57-2.15)
      P < 0.001.
      <0.0011.28 (1.21-1.36)
      P < 0.001.
      Cumulative average PM2.5
       PM2.5 range, μg/m3≤61.21>61.21-≤70.27>70.27-≤74.88>74.88
       Person-years51,35376,702110,41460,256298,725
       No. of cases3514965547872,188
       Incidence rate683.51646.66501.751,306.09732.45
       HR (95% CI)
      Crude model1.00 (ref)0.79 (0.69-0.91)
      P < 0.01.
      0.51 (0.44-0.58)
      P < 0.001.
      1.74 (1.54-1.98)
      P < 0.001.
      <0.0011.38 (1.29-1.47)
      P < 0.001.
      Model 11.00 (ref)0.72 (0.63-0.83)
      P < 0.001.
      0.51 (0.44-0.58)
      P < 0.001.
      1.35 (1.19-1.53)
      P < 0.001.
      0.011.24 (1.16-1.32)
      P < 0.001.
      Model 21.00 (ref)0.70 (0.60-0.82)
      P < 0.001.
      0.52 (0.44-0.60)
      P < 0.001.
      1.91 (1.66-2.20)
      P < 0.001.
      <0.0011.44 (1.34-1.56)
      P < 0.001.
      Model 31.00 (ref)0.97 (0.82-1.14)0.75 (0.63-0.90)
      P < 0.01.
      2.55 (2.16-3.00)
      P < 0.001.
      <0.0011.71 (1.58-1.85)
      P < 0.001.
      Abbreviations: CKD, chronic kidney disease; CI, confidence interval; HR, hazard ratio; PM2.5, particulate matter ≤2.5 μm; Q, quartile; ref, reference.
      a Incidence rate per 100,000 person-years.
      b Crude model: unadjusted; model 1: adjusted for baseline age and sex; model 2: further adjusted for baseline body mass index, smoking, alcohol use, exercise, and education; model 3: further adjusted for baseline systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, uric acid, estimated glomerular filtration rate, hypertension, diabetes, history of cardiovascular disease, temperature, and humidity.
      c P < 0.001.
      d P < 0.01.

      Stratified Analyses and Exposure Dose-Response Curves

      The association between long-term exposure to PM2.5 and incident CKD was nonlinear across the entire range, with the exposure dose-response curves becoming upward-sloping at higher PM2.5 exposure levels (>70 μg/m3; Fig 2). Additionally, stratified analyses revealed that the association of incident CKD with long-term exposure to PM2.5 was affected by baseline sex, age, exercise, body mass index, smoking, alcohol use, diabetes, hypertension, CVD, temperature, and humidity (Fig 3; Figs S3 and S4). Across the full PM2.5 concentration range, the risk of incident CKD was significantly higher among adults aged more than 65 years, patients with diabetes and CVD, and those living in environments with high humidity (>79.21%); when PM2.5 concentration was more than 74.88 μg/m3, the risk of incident CKD was significantly greater among those living in environments with low temperature (≤18.07°C; Fig 3; Table 3; Fig S4; Table S3).
      Figure thumbnail gr2
      Figure 2Exposure dose-response curves for the association between long-term exposure to particulate matter no larger than 2.5 μm (PM2.5) and incident chronic kidney disease. The model was adjusted for baseline age, sex, body mass index, smoking, alcohol use, exercise, education, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, uric acid, estimated glomerular filtration rate, hypertension, diabetes, history of cardiovascular disease, temperature, and humidity. Curves were fitted as a smooth term using a restricted cubic spline with 5 knots (at the 5th, 25th, 50th, 75th, and 95th percentiles). The reference value of PM2.5 concentration was 31.58 μg/m³. The shading indicates 95% CIs. The histogram represents the frequency distribution of PM2.5 exposure among study participants. HR, hazard ratio.
      Figure thumbnail gr3
      Figure 3Adjusted HRs and 95% CIs for per 10-μg/m3 greater long-term exposure to particulate matter no larger than 2.5 μm and risk of incident chronic kidney disease by specific baseline characteristics. Model adjusted for baseline age, sex, body mass index (BMI), smoking, alcohol use, exercise, education, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, uric acid, estimated glomerular filtration rate, hypertension, diabetes, cardiovascular disease (CVD), temperature, and humidity. CI, confidence interval; HR, hazard ratio.
      Table 3Association Between Long-term Exposure to PM2.5 and the Risk of Incident CKD Stratified by Ambient Humidity and Temperature
      Cumulative average PM2.5, μg/m3P for interaction
      Q1: ≤61.21Q2: >61.21 to ≤70.27Q3: >70.27 to ≤74.88Q4: >74.88
      Humidity<0.001
       Low: ≤79.21%1.00 (ref)1.21 (0.85-1.73)1.04 (0.73-1.48)2.85 (2.02-4.03)
      P < 0.001.
       High: >79.21%3.35 (2.36-4.76)
      P < 0.001.
      2.53 (1.77-3.60)
      P < 0.001.
      1.61 (1.12-2.31)
      P < 0.05.
      7.16 (5.05-10.14)
      P < 0.001.
      Temperature<0.001
       Low: ≤18.07°C1.00 (ref)0.85 (0.67-1.07)0.60 (0.47-0.75)
      P < 0.001.
      2.33 (1.88-2.88)
      P < 0.001.
       High: >18.07°C1.17 (0.92-1.49)0.64 (0.50-0.82)
      P < 0.001.
      0.52 (0.41-0.66)
      P < 0.001.
      1.34 (1.06-1.71)
      P < 0.05.
      Data presented as HR (95% CI); model adjusted for baseline age, sex, body mass index, smoking, alcohol use, exercise, and education, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, uric acid, estimated glomerular filtration rate, hypertension, diabetes, history of cardiovascular disease, temperature, and humidity. Abbreviations: CKD, chronic kidney disease; CI, confidence interval; HR, hazard ratio; PM2.5, particulate matter ≤2.5 μm; Q, quartile; ref, reference.
      a P < 0.001.
      b P < 0.05.

      Joint Associations of PM2.5 With Humidity or Temperature on the Risk of CKD

      We observed a multiplicative interaction between long-term exposure to PM2.5 and humidity or temperature on incident CKD (all P < 0.001 for interaction), but evidence of overall additive interaction was found for humidity (RERI, 3.59 [95% CI, 0.97 to 6.21]) but not temperature (RERI, 113.34 [95% CI, −53.25 to 279.93]; Table S4).

      Sensitivity Analyses

      The association of long-term exposure to PM2.5 with incident CKD was shown to be robust in sensitivity analyses. For the association of CKD with cumulative average PM2.5 concentration, the adjusted HRs were consistent with the results from the main analyses after sequentially excluding the participants who had been diagnosed with diabetes, hypertension, or CVD before baseline examination (Tables S5 and S6) and became higher when other common air pollutants (ie, black carbon, O3, and NO2) were included (Table S7). However, for the association of CKD with previous 1-year average PM2.5 concentration, the HR became insignificant after adjusting for these common air pollutants (Table S7), suggesting that the cumulative average PM2.5 concentration is the more critical exposure window for long-term exposure. The shapes of the associations of long-term exposure to PM1 and PM10 with incident CKD showed the same trend, but long-term exposure to PM2.5 exhibited the greatest HR value (Fig 2; Fig S5). We also observed similar results when using the Xiangya equation to calculate eGFR (Table S8).

      Discussion

      In this large cohort study of participants in 121 counties of Hunan, China, we confirmed that greater ambient PM2.5 exposure was associated with an increased risk of developing CKD. This effect was robust after adjustment for major covariates, and there was a strong positive association between cumulative average PM2.5 concentration and CKD risk, especially when adjusted for other air pollutants such as black carbon, O3, and NO2. Furthermore, our study found that there is a potential synergistic effect of humidity and PM2.5 on the risk of incident CKD. These findings suggest that reducing PM2.5 air pollution may yield benefits related to kidney health.
      Multiple epidemiologic studies have examined the effects of long-term exposure to PM2.5, mostly including 1-year or 2-year average PM2.5 exposure, on the risk of CKD in countries or regions with relatively low PM2.5 concentrations such as the United States, Taiwan, and Poland.
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Particulate matter air pollution and the risk of incident CKD and progression to ESRD.
      • Blum M.F.
      • Surapaneni A.
      • Stewart J.D.
      • et al.
      Particulate matter and albuminuria, glomerular filtration rate, and incident CKD.
      • Chan T.C.
      • Zhang Z.
      • Lin B.C.
      • et al.
      Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort Study.
      • Mehta A.J.
      • Zanobetti A.
      • Bind M.A.
      • et al.
      Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
      ,
      • Yang Y.R.
      • Chen Y.M.
      • Chen S.Y.
      • Chan C.C.
      Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis.
      ,
      • Kuźma Ł.
      • Małyszko J.
      • Bachórzewska-Gajewska H.
      • Kralisz P.
      • Dobrzycki S.
      Exposure to air pollution and renal function.
      The findings reveal some uncertainties about the association between long-term exposure to PM2.5 and the risk of CKD. Four cohort studies conducted in the United States found a significant association between PM2.5 exposure and risk of incident CKD.
      • Bowe B.
      • Xie Y.
      • Li T.
      • Yan Y.
      • Xian H.
      • Al-Aly Z.
      Particulate matter air pollution and the risk of incident CKD and progression to ESRD.
      • Blum M.F.
      • Surapaneni A.
      • Stewart J.D.
      • et al.
      Particulate matter and albuminuria, glomerular filtration rate, and incident CKD.
      • Chan T.C.
      • Zhang Z.
      • Lin B.C.
      • et al.
      Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort Study.
      • Mehta A.J.
      • Zanobetti A.
      • Bind M.A.
      • et al.
      Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
      A cohort study in Taiwan found that increased PM2.5 concentrations were associated with greater risk of CKD (HR, 1.74 [95% CI, 1.53-1.98]).
      • Lin S.Y.
      • Ju S.W.
      • Lin C.L.
      • et al.
      Air pollutants and subsequent risk of chronic kidney disease and end-stage renal disease: a population-based cohort study.
      Another study in Taiwan reported that, for every 5-μg/m3 lower PM2.5 concentration, the risk of CKD was 25% lower (HR, 0.75 [95% CI, 0.73-0.78]).
      • Bo Y.
      • Brook J.R.
      • Lin C.
      • et al.
      Reduced ambient PM(2.5) was associated with a decreased risk of chronic kidney disease: a longitudinal cohort study.
      However, in a cross-sectional study of 21,656 Taiwanese adults, no relationship was found between PM2.5 exposure and CKD prevalence, with an odds ratio of 1.03 (0.97-1.09).
      • Yang Y.R.
      • Chen Y.M.
      • Chen S.Y.
      • Chan C.C.
      Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis.
      By contrast, a cross-sectional study in Poland demonstrated that exposure to increased PM2.5 levels was associated with the development of CKD.
      • Kuźma Ł.
      • Małyszko J.
      • Bachórzewska-Gajewska H.
      • Kralisz P.
      • Dobrzycki S.
      Exposure to air pollution and renal function.
      These inconsistent results may be due to geographical differences in the link between PM2.5 and CKD risk. Therefore, it is critical to investigate the health hazards in regions with high levels of PM2.5 pollution such as mainland China.
      In our study, long-term PM2.5 concentrations (median, 72.27 [IQR, 61.21-74.88] μg/m3) were nearly 3-fold higher than in Taiwan (IQR, 21.1-26.6 μg/m3), 6-fold higher than in the United States (IQR, 11.4-12.2 μg/m3), and 7-fold higher than in Poland (median, 10.9 μg/m3).
      • Mehta A.J.
      • Zanobetti A.
      • Bind M.A.
      • et al.
      Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
      ,
      • Yang Y.R.
      • Chen Y.M.
      • Chen S.Y.
      • Chan C.C.
      Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis.
      ,
      • Kuźma Ł.
      • Małyszko J.
      • Bachórzewska-Gajewska H.
      • Kralisz P.
      • Dobrzycki S.
      Exposure to air pollution and renal function.
      Recently, Li et al
      • Li G.
      • Huang J.
      • Wang J.
      • et al.
      Long-term exposure to ambient PM(2.5) and increased risk of CKD prevalence in China.
      did a cross-sectional study in China and found a positive association between 2-year mean PM2.5 exposure and CKD prevalence; for every 10-μg/m3 greater PM2.5 concentration, the odds ratio for prevalent CKD was estimated to be 1.28 (95% CI, 1.22-1.35). Consistent with results of this cross-sectional study, our cohort study showed that every 10-μg/m3 greater previous 1-year PM2.5 and cumulative average PM2.5 concentration was associated with increased risk of incident CKD (HRs of 1.28 [95% CI, 1.21-1.36] and 1.71 [95% CI, 1.58-1.85], respectively).
      The potential mechanisms by which PM2.5 pollution may contribute to CKD risk remain to be elucidated. It is commonly thought that inflammation and oxidative stress facilitate deterioration of kidney function.
      • He M.
      • Ichinose T.
      • Ito T.
      • et al.
      Investigation of inflammation inducing substances in PM2.5 particles by an elimination method using thermal decomposition.
      ,
      • Xu M.X.
      • Ge C.X.
      • Qin Y.T.
      • et al.
      Prolonged PM2.5 exposure elevates risk of oxidative stress-driven nonalcoholic fatty liver disease by triggering increase of dyslipidemia.
      In addition, the kidney is a highly vascular organ and is prone to the development of vascular dysfunction. A previous study suggested that endothelial/vascular dysfunction may be the underlying mechanism for CKD caused by PM2.5.
      • Lue S.H.
      • Wellenius G.A.
      • Wilker E.H.
      • Mostofsky E.
      • Mittleman M.A.
      Residential proximity to major roadways and renal function.
      Some studies in animal models provide further evidence for a link between PM2.5 exposure and CKD risk.
      • Aztatzi-Aguilar O.G.
      • Uribe-Ramírez M.
      • Narváez-Morales J.
      • De Vizcaya-Ruiz A.
      • Barbier O.
      Early kidney damage induced by subchronic exposure to PM(2.5) in rats.
      ,
      • Tavera Busso I.
      • Mateos A.C.
      • Juncos L.I.
      • Canals N.
      • Carreras H.A.
      Kidney damage induced by sub-chronic fine particulate matter exposure.
      Researchers exposed rats to PM2.5 and found that PM2.5 exposure may accelerate reductions in glomerular filtration rate and induce early kidney damage.
      • Aztatzi-Aguilar O.G.
      • Uribe-Ramírez M.
      • Narváez-Morales J.
      • De Vizcaya-Ruiz A.
      • Barbier O.
      Early kidney damage induced by subchronic exposure to PM(2.5) in rats.
      In another rat study, Tavera Busso et al found that exposure to PM2.5 may cause changes in kidney tissue that promote a decline in kidney function.
      • Tavera Busso I.
      • Mateos A.C.
      • Juncos L.I.
      • Canals N.
      • Carreras H.A.
      Kidney damage induced by sub-chronic fine particulate matter exposure.
      Our study has some limitations that should be noted. First, this study did not account for details of socioeconomic status such as access to health care and household income, which may be associated with the development of CKD. Second, data on population density (rural vs urban) are also not available in our study. The lack of data on indoor air pollution exposure, the used of a fixed address to assess PM2.5 exposure, and the lack of information on participants’ activity patterns are other limitations.
      Despite these limitations, our study has several noteworthy advantages. This cohort study conducted in mainland China had a large sample size that is representative of the general Chinese population and adds to evidence of the association between high levels of PM2.5 exposure and risk of CKD. In addition, given the nonnegligible influence of humidity and temperature on PM2.5, we evaluated the effect of the joint interaction of temperature or humidity on the development of CKD associated with PM2.5. Furthermore, we calculated multiple long-term exposure windows, including prebaseline exposure with distinct time periods and cumulative average PM2.5 concentration over the follow-up period, to estimate the optimum exposure time windows to define exposure risk and the longitudinal effects on CKD.
      In conclusion, we identified a positive association of long-term ambient PM2.5 exposure with risk of incident CKD in the context of high concentrations of PM2.5 in mainland China and a potential synergistic effect of humidity with PM2.5 on the development of CKD. These findings support the conclusion that long-term exposure to PM2.5 poses a significant health risk to the general population, and additional measures and strategies are therefore needed to reduce the burden of disease caused by PM2.5 air pollution.

      Article Information

      Authors’ Full Names and Academic Degrees

      Jing-wen Duan, MD, Ya-lan Li, MD, Shen-xin Li, PhD, Yi-ping Yang, BD, Fei Li, MD, Yan Li, MD, Jie Wang, MD, Pei-zhi Deng, MD, Jing-jing Wu, MD, Wei Wang, MD, Chang-jiang Meng, MD, Ru-jia Miao, PhD, Zhi-heng Chen, PhD, Bin Zou, PhD, Hong Yuan, PhD, Jing-jing Cai, PhD, and Yao Lu, PhD.

      Authors’ Contributions

      Research idea and study design: J-wD; data acquisition: all authors; data analysis: J-wD, S-xL, Y-lL, Y-pY; data interpretation: J-wD, S-xL, Y-lL, Y-pY, J-jC, YL; study supervision or mentorship: HY, J-jC, LY. J-wD, Y-lL, and S-xL contributed equally to this work. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate.

      Support

      This study was supported by grants from the National Natural Science Foundation of China (81800393, 82170437), the Outstanding Young Investigator of Hunan province (2020JJ2056), the Hunan Youth Talent Project (2019RS2014), Key Research and Development Project of Hunan (2020WK2010), and the National Key Research and Development Program (2018YFC1311300, 2019YFF0216304, 2019YFF0216305). The funders had no role in study design, data collection, analysis, reporting, or the decision to submit for publication.

      Financial Disclosure

      The authors declare that they have no relevant financial interests.

      Peer Review

      Received October 9, 2021. Evaluated by 3 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and the Editor-in-Chief. Accepted in revised form March 5, 2022.

      Supplementary Material

      References

      1. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016.
        Lancet. 2017; 390: 1151-1210
        • Mukherjee A.
        • Agrawal M.
        A global perspective of fine particulate matter pollution and its health effects.
        Rev Environ Contam Toxicol. 2018; 244: 5-51
        • Liu C.
        • Chen R.
        • Sera F.
        • et al.
        Ambient particulate air pollution and daily mortality in 652 cities.
        N Engl J Med. 2019; 381: 705-715
        • Liu B.
        • Fan D.
        • Huang F.
        Relationship of chronic kidney disease with major air pollutants - a systematic review and meta-analysis of observational studies.
        Environ Toxicol Pharmacol. 2020; 76103355
        • Bowe B.
        • Xie Y.
        • Li T.
        • Yan Y.
        • Xian H.
        • Al-Aly Z.
        Particulate matter air pollution and the risk of incident CKD and progression to ESRD.
        J Am Soc Nephrol. 2018; 29: 218-230
        • Blum M.F.
        • Surapaneni A.
        • Stewart J.D.
        • et al.
        Particulate matter and albuminuria, glomerular filtration rate, and incident CKD.
        Clin J Am Soc Nephrol. 2020; 15: 311-319
        • Chan T.C.
        • Zhang Z.
        • Lin B.C.
        • et al.
        Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort Study.
        Environ Health Perspect. 2018; 126107002
        • Mehta A.J.
        • Zanobetti A.
        • Bind M.A.
        • et al.
        Long-term exposure to ambient fine particulate matter and renal function in older men: the Veterans Administration Normative Aging Study.
        Environ Health Perspect. 2016; 124: 1353-1360
        • Nemmar A.
        • Al-Salam S.
        • Zia S.
        • Yasin J.
        • Al Husseni I.
        • Ali B.H.
        Diesel exhaust particles in the lung aggravate experimental acute renal failure.
        Toxicol Sci. 2010; 113: 267-277
        • Nemmar A.
        • Karaca T.
        • Beegam S.
        • et al.
        Prolonged pulmonary exposure to diesel exhaust particles exacerbates renal oxidative stress, inflammation and DNA damage in mice with adenine-induced chronic renal failure.
        Cell Physiol Biochem. 2016; 38: 1703-1713
      2. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.
        Lancet. 2020; 396: 1204-1222
      3. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.
        Lancet. 2020; 395: 709-733
        • Bowe B.
        • Xie Y.
        • Li T.
        • Yan Y.
        • Xian H.
        • Al-Aly Z.
        Estimates of the 2016 global burden of kidney disease attributable to ambient fine particulate matter air pollution.
        BMJ Open. 2019; 9e022450
        • Li G.
        • Huang J.
        • Wang J.
        • et al.
        Long-term exposure to ambient PM(2.5) and increased risk of CKD prevalence in China.
        J Am Soc Nephrol. 2021; 32: 448-458
        • Hsu W.H.
        • Hwang S.A.
        • Kinney P.L.
        • Lin S.
        Seasonal and temperature modifications of the association between fine particulate air pollution and cardiovascular hospitalization in New York State.
        Sci Total Environ. 2017; 578: 626-632
        • Liu M.
        • Xue X.
        • Zhou B.
        • et al.
        Population susceptibility differences and effects of air pollution on cardiovascular mortality: epidemiological evidence from a time-series study.
        Environ Sci Pollut Res Int. 2019; 26: 15943-15952
        • Lu Y.
        • Pechlaner R.
        • Cai J.
        • et al.
        Trajectories of age-related arterial stiffness in Chinese men and women.
        J Am Coll Cardiol. 2020; 75: 870-880
        • Wei J.
        • Li Z.Q.
        • Lyapustin A.
        • et al.
        Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications.
        Remote Sens Environ. 2021; 252112136
        • Wei J.
        • Li Z.Q.
        • Cribb M.
        • et al.
        Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees.
        Atmos Chem Phys. 2020; 20: 3273-3289
        • Wang L.
        • Chen G.
        • Pan Y.
        • et al.
        Association of long-term exposure to ambient air pollutants with blood lipids in Chinese adults: the China Multi-Ethnic Cohort study.
        Environ Res. 2021; 197111174
        • Stevens P.E.
        • Levin A.
        Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.
        Ann Intern Med. 2013; 158: 825-830
        • Ma Y.C.
        • Zuo L.
        • Chen J.H.
        • et al.
        Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease.
        J Am Soc Nephrol. 2006; 17: 2937-2944
        • Knol M.J.
        • van der Tweel I.
        • Grobbee D.E.
        • Numans M.E.
        • Geerlings M.I.
        Estimating interaction on an additive scale between continuous determinants in a logistic regression model.
        Int J Epidemiol. 2007; 36: 1111-1118
        • Andersson T.
        • Alfredsson L.
        • Källberg H.
        • Zdravkovic S.
        • Ahlbom A.
        Calculating measures of biological interaction.
        Eur J Epidemiol. 2005; 20: 575-579
        • Li D.Y.
        • Yin W.J.
        • Yi Y.H.
        • et al.
        Development and validation of a more accurate estimating equation for glomerular filtration rate in a Chinese population.
        Kidney Int. 2019; 95: 636-646
        • Yang Y.R.
        • Chen Y.M.
        • Chen S.Y.
        • Chan C.C.
        Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis.
        Environ Health Perspect. 2017; 125: 602-607
        • Kuźma Ł.
        • Małyszko J.
        • Bachórzewska-Gajewska H.
        • Kralisz P.
        • Dobrzycki S.
        Exposure to air pollution and renal function.
        Sci Rep. 2021; 1111419
        • Lin S.Y.
        • Ju S.W.
        • Lin C.L.
        • et al.
        Air pollutants and subsequent risk of chronic kidney disease and end-stage renal disease: a population-based cohort study.
        Environ Pollut. 2020; 261114154
        • Bo Y.
        • Brook J.R.
        • Lin C.
        • et al.
        Reduced ambient PM(2.5) was associated with a decreased risk of chronic kidney disease: a longitudinal cohort study.
        Environ Sci Technol. 2021; 55: 6876-6883
        • He M.
        • Ichinose T.
        • Ito T.
        • et al.
        Investigation of inflammation inducing substances in PM2.5 particles by an elimination method using thermal decomposition.
        Environ Toxicol. 2019; 34: 1137-1148
        • Xu M.X.
        • Ge C.X.
        • Qin Y.T.
        • et al.
        Prolonged PM2.5 exposure elevates risk of oxidative stress-driven nonalcoholic fatty liver disease by triggering increase of dyslipidemia.
        Free Radic Biol Med. 2019; 130: 542-556
        • Lue S.H.
        • Wellenius G.A.
        • Wilker E.H.
        • Mostofsky E.
        • Mittleman M.A.
        Residential proximity to major roadways and renal function.
        J Epidemiol Community Health. 2013; 67: 629-634
        • Aztatzi-Aguilar O.G.
        • Uribe-Ramírez M.
        • Narváez-Morales J.
        • De Vizcaya-Ruiz A.
        • Barbier O.
        Early kidney damage induced by subchronic exposure to PM(2.5) in rats.
        Part Fibre Toxicol. 2016; 13: 68
        • Tavera Busso I.
        • Mateos A.C.
        • Juncos L.I.
        • Canals N.
        • Carreras H.A.
        Kidney damage induced by sub-chronic fine particulate matter exposure.
        Environ Int. 2018; 121: 635-642