Particulate matter exposure and end-stage renal disease risk in IgA nephropathy

Abstract

Long-term exposure to particulate matter has been increasingly implicated in the progression of chronic kidney disease (CKD). However, its impact on IgA nephropathy (IgAN), a leading cause of end-stage renal disease (ESRD), remains unclear. A total of 1768 IgAN patients, confirmed by renal biopsy were included in this cohort study. Long-term exposure to PM_2.5 and PM₁₀ was assessed using high-resolution satellite-based data from the China High Air Pollutants (CHAP) dataset. Cox proportional hazards models were used to estimate the associations between PM_2.5 or PM₁₀ and ESRD risk, adjusting for demographic, clinical, and biochemical covariates. Over a median follow-up of 3.63 years, 209 participants progressed to ESRD. Higher exposure to both PM_2.5 and PM₁₀ was significantly associated with an increased risk, with hazard ratios of 1.62 and 1.36 per 10 μg/m³ increase, respectively. A nonlinear dose-response relationship was observed, with risk increasing markedly beyond threshold levels. Trajectory modeling of prebaseline exposure identified a subgroup with persistently high and fluctuating particulate matter exposure that showed the highest risk. This study provides strong evidence that prolonged exposure to ambient particulate matter contributes to renal disease progression in individuals with IgAN.

Keywords

IgA nephropathy / end-stage renal disease / PM_2.5 / PM₁₀ / air pollution

1 Introduction

As the most prevalent primary glomerulonephritis worldwide, IgA nephropathy (IgAN) is a significant cause of end-stage renal disease (ESRD) [1–3]. It is marked by the accumulation of IgA-dominant immune complexes within the mesangium, leading to progressive glomerular injury. While proteinuria, estimated glomerular filtration rate (eGFR), and histopathological features guide risk stratification, substantial variability in disease progression suggests the involvement of additional factors beyond genetic susceptibility and immune dysregulation [4,5].

Emerging evidence links air pollution to chronic kidney disease (CKD) progression. Cohort studies have linked prolonged exposure to fine particulate matter and elevated albuminuria, decreased eGFR, and increased CKD risk [6,7]. In China, increasing membranous nephropathy (MN) prevalence has been correlated with increasing particulate matter exposure, underscoring potential nephrotoxic effects [8].

Given that IgAN is often triggered by mucosal infections and immune activation [9,10], external environmental factors, including air pollution, may contribute to renal injury and accelerate disease progression. Recent immunological studies have highlighted the role of mucosal immune dysregulation in the pathogenesis of IgAN [11]. Key cytokines such as BAFF, APRIL, and TGF-β are involved in mucosal B cell activation and IgA class switching. Notably, sustained activation of Toll-like receptors (TLRs), particularly in mucosal tissues, can induce overproduction of galactose-deficient IgA1 (Gd-IgA1), a pivotal pathogenic factor in IgAN development [9]. An experimental study in murine models has demonstrated that chronic particulate matter exposure can elicit immune dysregulation, notably through IL-17 pathway activation and TGF-β upregulation, leading to inflammatory damage and fibrotic remodeling [12]. Although direct evidence linking PM exposure to Gd-IgA1 production is currently lacking, these data support a biologically plausible connection between particulate matter exposure and maladaptive immune activation relevant to IgAN.

PM_2.5 (0.1 µm < particles ≤2.5 µm in diameter) and PM₁₀ (2.5 µm < particles ≤10 µm in diameter), respectively, are major air pollutants linked to systemic oxidative stress, inflammation, endothelial dysfunction, and immune dysregulation [13,14]. Experimental studies suggest that inhaled particulate matter can reach the renal microvasculature, induce glomerular and tubular injury, and contribute to renal fibrosis [15,16]. While air pollution has been recognized as a risk factor for CKD, its specific impact on IgAN remains poorly understood.

To address these gaps, we conducted a cohort study to assess the associations between long-term exposure to PM_2.5 or PM₁₀ and ESRD risk in patients with IgAN. Using high-resolution satellite-derived air pollution data, we assessed cumulative exposures after enrollment and uniquely employed group-based trajectory modeling (GBTM) to characterize exposure patterns before baseline. Our study aimed to quantify the impact of PM_2.5 and PM₁₀ on kidney disease progression in a cohort of IgAN patients in Sichuan Province, China.

2 Materials and methods

2.1 Study participants

This study included patients diagnosed with IgAN through renal biopsy at West China Hospital of Sichuan University between 2010 and 2021. Of the 2503 patients screened, 1768 met the inclusion criteria. Eligible participants had a confirmed diagnosis of primary IgAN, a clearly defined baseline date, an eGFR ≥ 15 mL/min/1.73 m² at baseline, and permanent residency in Sichuan Province with a stable address and no relocation during the study period. Patients with secondary IgAN, active infections, or a follow-up duration of less than three months were excluded. The Ethics Committee of West China Hospital, Sichuan University, granted approval for this study, and all participants provided written informed consent before enrollment.

2.2 Exposure assessment

Ambient PM_2.5 and PM₁₀ concentrations were estimated via high-resolution satellite-based data from the China High Air Pollutants (CHAP) data set, which integrates multiple sources, such as ground station measurements, satellite retrievals, atmospheric reanalysis, and machine learning models [17,18]. This data set provides nationwide, high-resolution (1 km × 1 km) exposure estimates, improving spatial accuracy and reflecting pollution variability more effectively than traditional methods do. Each participant’s exposure was determined by geocoding their residence and matching it to the nearest 1 km × 1 km grid point. Exposure was defined in two ways: prebaseline exposure trajectories over the five years preceding renal biopsy, modeled using the GBTM, and cumulative exposure after enrollment, calculated as the mean PM_2.5 and PM₁₀ concentrations from baseline to ESRD or censoring. The trajectories of the PM_2.5 and PM₁₀ concentrations over the five years preceding baseline were assessed using GBTM, a specialized latent class growth analysis technique that identifies distinct exposure patterns over time [17, 18]. GBTM allows for the classification of participants into groups on the basis of both the level and trend of air pollution exposure before study entry, accounting for interindividual variations in exposure patterns. To determine the optimal number of trajectory groups, models with two to five classes were fitted, and the best-fitting model was selected based on Bayesian information criterion (BIC), Akaike information criterion (AIC), entropy, and posterior probability assignment [19]. The detailed methodological steps for the GBTM, including model diagnostics and posterior probability distributions, are provided in the Supplementary Method 1.

2.3 Outcome assessment

The primary outcome was incident ESRD, defined as an eGFR < 15 mL/min/1.73 m², initiation of chronic dialysis, or kidney transplantation.

2.4 Covariates

At the time of renal biopsy and follow-up visits, demographic, clinical, and laboratory data were gathered. Baseline assessments included age, sex, smoking status, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), eGFR, 24-h urine protein excretion (UP), uric acid (UA), hemoglobin (Hb), total cholesterol (TC), and triglycerides (TG). Histopathological severity was evaluated using the Oxford-MESTC classification. Treatment history, including steroids and renin‒angiotensin system blocker (RASB) therapy, was recorded. These treatment variables were modeled as fixed baseline covariates based on prescription records at or shortly after renal biopsy and were not treated as time-dependent variables due to data limitations. Hyperuricemia was defined as a serum uric acid level exceeding 420 mmol/L in men and 360 mmol/L in women.

2.5 Statistical analysis

The baseline characteristics of the sample were summarized as percentages for categorical categories and as the means with standard deviations for continuous variables. Person-time was computed from baseline to the date of ESRD or the last follow-up, whichever occurred first. Differences between groups were compared with the Kruskal‒Wallis test for continuous variables and the chi-square test for categorical variables.

Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between PM_2.5 or PM₁₀ exposure and ESRD risk. Exposure was analyzed using quartiles of the mean concentrations of PM_2.5 and PM₁₀ after enrollment, increments per 10 μg and the prebaseline concentration trajectory. The models were adjusted sequentially: Model 1 included age and sex; Model 2 further adjusted for eGFR, UP, UA, MAP, Oxford-MESTC, use of steroids and RASB after diagnosis; and Model 3 included all variables from Model 2 plus smoking, total cholesterol, serum triglycerides and hypertension. The exposure‒response relationships were examined using restricted cubic splines with five knots at the 5th, 25th, 50th, 75th, and 95th percentiles of the PM_2.5/PM₁₀ concentration.

Stratified analyses were conducted based on age (< 30 years vs. ≥ 30 years), sex (male vs. female), smoking status (yes vs. no), mean arterial pressure (< 105 mmHg vs. ≥105 mmHg), UP (< 2 g/24 h vs. ≥2 g/24 h), baseline eGFR (< 60 mL/min/1.73 m² vs. ≥ 60 mL/min/1.73 m²), hyperuricemia (yes vs. no), triglycerides (≤ 1.7 mmol/L vs. > 1.7 mmol/L), total cholesterol (≤5.17 mmol/L vs. > 5.17 mmol/L), Oxford classification, steroid use (yes vs. no), and RASB use (yes vs. no). Multiplicative interactions between PM_2.5/PM₁₀ exposure and covariates were tested by incorporating cross-product terms into the Cox models. To assess the robustness of the findings, several sensitivity analyses were performed. First, different exposure time windows were evaluated, examining PM_2.5/PM₁₀ exposure at 1, 2, 3, 4, and 5 years before biopsy. Second, we excluded participants who developed ESRD within six months postbaseline to reduce the risk of reverse causation.

3 Results

3.1 Baseline characteristics of IgAN patients

According to the inclusion criteria, 1768 IgAN patients were included in this study, of whom 209 (11.8%) developed ESRD during follow-up. The characteristics of the study population across quartiles of cumulative PM_2.5 and PM₁₀ exposure after enrollment are presented in Table 1. The annual average PM_2.5 concentrations after enrollment ranged from 9.2 to 87.1 μg/m³, with a median of 45.92 μg/m³, whereas the PM₁₀ concentrations ranged from 19 to 145.2 μg/m³, with a median of 74.24 μg/m³. Patients in the highest quartile of PM_2.5 exposure had higher SBP, CHOL, and UP compared to those in the lowest quartile. A similar trend was observed for PM₁₀, where individuals in the highest exposure group (> 95.01 g/m³) had a significantly greater ESRD incidence (22% vs. 5.9%, P < 0.001) and increased proteinuria. Participants with higher PM_2.5 exposure after enrollment were more likely to present with C1 and C2 Oxford classifications, while those in lower exposure groups were more likely to have S1 classifications. Similar patterns were observed for PM₁₀ exposure. Details of the excluded data set are provided in Table S1.

3.2 Spatial distribution of PM_2.5/PM₁₀ exposure and ESRD incidence in Sichuan

The regional distributions of PM_2.5 and PM₁₀ exposure and the five-year ESRD incidence rates across cities in Sichuan Province are presented in Fig. 1. Considerable variability in long-term PM_2.5 and PM₁₀ concentrations was observed across the 21 prefecture-level cities in Sichuan Province, with Chengdu showing the highest levels of both pollutants. The five-year incidence of ESRD also varied substantially across cities, with higher rates generally seen in areas with elevated pollution levels.

3.3 Associations between PM_2.5/PM₁₀ exposure and the risk of ESRD

During the follow-up period, 209 cases of ESRD were recorded. The incidence rate was highest among participants in the highest quartile of cumulative PM_2.5 (> 58.24 μg/m³) and PM₁₀ (> 95.01 μg/m³) exposure, at 64.3 and 69.0 per 1000 person-years, respectively (Table 2). Because of the extremely high collinearity between PM_2.5 and PM₁₀, we did not perform multipollutant models, as including both pollutants in the same model could lead to unstable estimates and inflated standard errors. In the fully adjusted model (Model 3), participants in the highest quartile of PM_2.5 exposure had a 249% greater risk of ESRD (HR 3.49, 95% CI 2.16–5.62, P < 0.001) compared to those in the lowest quartile. Compared with participants in the lowest quartile of PM₁₀ exposure, the adjusted HR for ESRD among those in the highest quartile was 3.75 (95% CI 2.33–6.06, P < 0.001). For continuous exposure analysis, each 10 μg/m³ increase in PM_2.5 and PM₁₀ concentrations was associated with an increased risk of ESRD, with adjusted HRs of 1.62 (95% CI 1.43–1.82, P < 0.001) and 1.36 (95% CI 1.26–1.46, P < 0.001), respectively. The associations remained robust across all models with sequential adjustments for demographic, clinical, and biochemical factors.

3.4 Stratified analyses and exposure dose‒response relationship

The relationship between long-term PM_2.5/PM₁₀ exposure and ESRD risk was largely consistent across subgroups (Fig. 2). Stratified analyses indicated that the relationship between long-term PM_2.5/PM₁₀ exposure and incident ESRD was influenced by baseline eGFR and UP levels. The associations between long-term exposure to PM_2.5 or PM₁₀ and ESRD risk were nonlinear, as shown in the exposure‒response curves (Fig. 3). The risk of ESRD remained relatively stable at lower exposure levels but increased sharply beyond a threshold concentration. Specifically, the HRs began to rise continuously when the PM_2.5 concentration exceeded 50.16 μg/m³ and the PM₁₀ concentration exceeded 76.7 μg/m³.

3.5 Associations between prebaseline PM_2.5/PM₁₀ exposure trajectories and ESRD risk

GBTM identified three distinct PM_2.5 exposure patterns during the five years preceding baseline (Fig. S1A). Group 1 (n = 305, 17.25%) was characterized by light-to-moderate pollution levels with a decreasing trend, with a mean concentration of 43.4 µg/m³ (range: 14.3–52.4). Group 2 (n = 529, 29.92%) showed moderate-to-high pollution with a decreasing trend (mean = 62.2 µg/m³; range: 52–72.3). Group 3 (n = 934, 52.83%) represented high and fluctuating pollution levels (mean = 76.6 µg/m³; range: 62–83.4). Compared with Group 1 (Fig. S1B), participants in Group 3 had a significantly increased risk of ESRD (HR 1.78, 95% CI 1.01–3.15).

Similarly, three trajectory groups were identified for PM₁₀ (Fig. S1C). Group 1 (n = 306, 17.31%) exhibited light-to-moderate and decreasing pollution levels (mean = 71.4 µg/m³; range: 27.2–85.7), Group 2 (n = 535, 30.26%) had moderate and decreasing exposure (mean = 100 µg/m³; range: 84.5–117), and Group 3 (n = 927, 52.43%) showed moderate but fluctuating pollution levels (mean = 122 µg/m³; range: 102–133). However, no statistically significant differences in ESRD risk were observed across the PM₁₀ trajectory groups (Fig. S1D).

3.6 Sensitivity analyses

Similar associations between PM_2.5/PM₁₀ exposure and ESRD risk were observed across different prebaseline exposure time windows, with the strongest effects noted at a 4-year lag, suggesting a potential delayed impact of air pollution on kidney disease progression (Table S2). The results remained consistent after excluding participants who developed ESRD within six months postbaseline, reducing concerns about reverse causation (Table S3). Third, to examine the joint effect of PM_2.5 and PM₁₀, we applied a quantile g-computation (QGComp) model, treating both pollutants as a mixture. The model estimated that each one-unit increase in the weighted mixture index (reflecting increasing exposure to both PM_2.5 and PM₁₀) was associated with a 75% increase in ESRD risk (HR 1.75; 95% CI 1.48–2.08), supporting the robustness of our primary results (Supplementary Method 2). These consistent results across various analytic strategies reinforce the validity of our main findings.

4 Discussion

Growing evidence suggests that air pollution plays a significant role in the progression of kidney disease [20–22]. In this study, we systematically examined the associations between long-term PM_2.5 and PM₁₀ exposure and the risk of ESRD in a cohort of 1768 biopsy-confirmed IgAN patients from Sichuan, China. Our findings revealed that higher exposure levels were associated with an increased risk of ESRD, independent of demographic, clinical, and biochemical factors. Notably, the dose‒response relationship was nonlinear, with ESRD risk remaining stable at lower exposure levels but rising sharply beyond a critical threshold.

These results align with previous studies, including a multicenter study from China, which demonstrated that for every 10 μg/m³ increase in PM_2.5 exposure, the risk of ESRD in IgAN patients increased by 14%, even after adjusting for cardiovascular risk factors and urbanization levels [23]. Similarly, a Korean cohort study on primary glomerulonephritis (GN) confirmed the detrimental effects of PM_2.5 and PM₁₀ exposure on renal function, showing significant associations with both renal function decline and increased risk of kidney failure [24]. Various studies have elucidated the mechanisms through which particulate matter induces kidney injury [25–27]. Inhaled fine particulate matter can enter the bloodstream, accumulate in renal tissue, and disrupt redox homeostasis, leading to excessive ROS production and mitochondrial dysfunction, which in turn activate the NLRP3 inflammasome, triggering pyroptosis and inflammatory cytokine release that exacerbates tubular epithelial cell damage, while also inducing autophagy dysregulation through increased proautophagy signaling and the suppression of negative regulators (mTOR, P62), ultimately contributing to maladaptive cellular responses and kidney dysfunction [15,28]. Owing to their inherent immune abnormalities—such as aberrant IgA1 glycosylation, immune complex deposition, and chronic renal inflammation—PM_2.5-induced immune dysregulation may further accelerate disease progression in IgAN patients [29,30]. Extensive studies have shown that particulate matter can stimulate macrophages to produce proinflammatory cytokines (TNF-α, IL-1β), which may enhance mesangial cell inflammatory responses and exacerbate glomerular injury in IgAN patients [31]. Additionally, PM_2.5 may suppress the Th1 response, leading to decreased IFN-γ levels, thereby impairing the clearance of IgA immune complexes, while an enhanced Th2 response may promote IgE production and eosinophil infiltration, further amplifying inflammation [32]. More importantly, PM_2.5 may stimulate Th17 cell differentiation, resulting in increased release of IL-17A, a pathway implicated in chronic inflammation and fibrosis during IgAN progression [33]. Therefore, PM_2.5 may amplify the autoimmune response in IgAN patients through multiple immune-modulating mechanisms, accelerating renal injury and increasing the risk of progression to ESRD.

Our stratified analysis revealed that the association between PM_2.5/PM₁₀ exposure and ESRD risk was more pronounced in IgAN patients with higher baseline eGFRs and lower proteinuria levels. These findings suggest that air pollution may accelerate renal function decline even in patients with relatively preserved kidney function. This observation aligns with previous studies investigating the role of air pollution in kidney disease progression. A large Taiwan residents cohort study revealed that PM_2.5 exposure was more strongly associated with CKD risk among individuals with a lower BMI, without hypertension, and without diabetes [34]. Similar patterns have been observed in previous studies, where the impact of PM_2.5 on CKD progression was stronger in individuals with better baseline renal function but attenuated in those with advanced CKD, likely due to dominant intrinsic disease mechanisms. This finding highlights the importance of early environmental pollution mitigation strategies for IgAN patients with preserved renal function and mild disease [35]. Importantly, existing evidence on this topic remains limited and somewhat inconsistent. For example, in a Chinese multicenter IgAN cohort, no significant effect of baseline renal function or proteinuria from PM_2.5 or PM₁₀ on ESRD risk was found [23]. The heterogeneity in findings may be attributed to differences in study populations, exposure assessment methods, and disease progression models.

Unlike previous studies that primarily used static or cumulative exposure measures, we applied the GBTM to classify distinct patterns of PM_2.5 and PM₁₀ exposure prior to baseline. This approach allows for a more nuanced assessment of how long-term air pollution exposure trends influence ESRD risk, capturing both exposure intensity and variability over time. The observed associations between air pollution and ESRD risk across different trajectory groups provide additional evidence that chronic exposure, rather than short-term fluctuations, contributes to renal disease progression. Previous research has demonstrated the utility of the GBTM in air pollution studies, including the assessment of short-term pollution variability and health outcomes such as sleep disturbances [36].

Our study has several significant strengths. First, it focuses on a specific glomerular disease—IgAN—using pathological diagnosis as the gold standard, addressing a gap in research on this particular population. Second, the substantial sample size and extended follow-up duration offer strong evidence linking elevated PM_2.5 and PM₁₀ exposure to an increased risk of ESRD in IgAN patients. Additionally, we assessed two long-term exposure windows, including prebaseline exposure trajectories and cumulative mean PM_2.5 concentrations during follow-up, allowing for a comprehensive evaluation of the longitudinal impact of air pollution on ESRD progression. However, our study also has certain limitations. First, as it was conducted within a single province in China, the findings may not be fully generalizable to the entire country, although our conclusions are consistent with those from a multicenter cohort study in China. Second, our data set did not include other common pollutants, such as O₃ and NO₂, nor did it account for potential indoor air pollution exposure. Third, given that PM_2.5 constitutes part of PM₁₀, their strong collinearity prevented simultaneous inclusion in the model to avoid bias. As a result, their independent effects could not be fully separated. Nevertheless, we conducted a quantitative mixture analysis using the QGComp method in the sensitivity analysis to capture their joint contribution to ESRD risk. Furthermore, owing to data limitations, we were unable to incorporate socioeconomic factors, such as urbanization and household income, which could influence both air pollution exposure and disease progression. Additionally, corticosteroid and RASB therapy were modeled as fixed covariates at baseline rather than time-dependent variables, which may not fully capture dynamic treatment effects during follow-up. Future studies with detailed longitudinal treatment records are warranted to address this. Finally, while the application of GBTM enabled a novel perspective on exposure dynamics, we acknowledge that GBTM itself is not a methodological innovation. Its use in our study represents an application of an established statistical technique to a new clinical context rather than a development of a new analytical method.

In conclusion, we identified a significant association between long-term ambient PM_2.5 and PM₁₀ exposure and an increased risk of ESRD in IgAN patients, highlighting the potential impact of air pollution on kidney disease progression. These findings underscore the need for strengthened environmental policies and targeted interventions to mitigate air pollution-related health risks and reduce the burden of ESRD among vulnerable individuals.

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