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. 2025 Mar 14;98(3):321–329. doi: 10.1007/s00420-025-02127-w

Association of long-term exposure to air pollutants with benign prostatic hyperplasia among middle-aged and older men in China

Wenming Shi 1, Jie V Zhao 1,✉
PMCID: PMC11972197  PMID: 40085202

Abstract

Purpose

Air pollution has been an important risk factor for human health. However, little is known about the impacts of air pollutants on benign prostatic hyperplasia (BPH) in men. We aimed to explore the association of long-term exposure to air pollutants with BPH among men.

Methods

We leveraged the nationally representative data from the China Health and Retirement Longitudinal Study, a total of 8,826 participants aged 45 years and above from 125 Chinese cities were enrolled in 2015. Annual fine particulate matter (PM2.5), coarse particles (PM2.5−10), nitrogen dioxide (NO2), sulfur dioxide, carbon monoxide, and ozone were estimated using satellite-based models. Multivariate logistic regression models were used to assess the risk of BPH associated with air pollutants. The restricted cubic spline model was performed to explore the exposure-response relationships with BPH.

Results

Of the 8,826 participants (mean age: 60.3 years), the prevalence of BPH was 14.5%. Each 10 µg/m3 rise in PM2.5 (odds ratio 1.04, 95% confidence intervals: 1.01–1.07) and PM2.5−10 (1.06, 1.02–1.10) were associated with prevalent BPH. Compared with the lowest quartile levels, higher PM2.5 and PM2.5−10 exposure were related to an increased risk of BPH. There were non-linear relationship between PM2.5−10 and NO2 exposure with prevalent BPH. The association with BPH was more pronounced in participants who were overweight/obesity.

Conclusion

This study suggests that long-term air pollutants exposure, especially for PM2.5 and PM2.5−10, is associated with BPH among middle-aged and older men. Our findings provide epidemiological evidence for policymakers and researchers to improve prostate health by reducing air pollution.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00420-025-02127-w.

Keywords: Air pollution, Benign prostatic hyperplasia, Coarse particle, Middle-aged and older adults, PM2.5

Introduction

Benign prostatic hyperplasia (BPH), characterized as the unregulated enlargement of the prostate gland, is a common urological disease usually occurring in aging men (Awedew et al. 2022; Zeng et al. 2022). As a main cause of lower urinary tract symptoms (LUTS), BPH followed by substantial LUTS affects 50–75% of men over 50 years (Egan 2016). Previous studies have indicated that BPH is associated with severe morbidity, and complications including urinary tract infection, acute urinary retention, and acute kidney failure (Speakman et al. 2015; Stroup et al. 2012), which brings a heavy burden for patients (Speakman et al. 2015). Consequently, identifying the potential risk factors associated with BPH assumes paramount merits within the dominance of public health.

Air pollution, especially particulate matter (PM), is suggested to be a vital environmental risk of death globally (Tian et al. 2019). Inhalable PM can be divided into fine PM (PM2.5) and coarse PM with a fraction size between 2.5 and 10 μm (PM2.5−10). Different from PM2.5, PM2.5−10 is generally formed by mechanical grinding and resuspension of solid substance (Daellenbach et al. 2020), and it typically deposits in the upper respiratory tract or larger airways, and the chemical components with more crustal materials (i.e., silicon, calcium) in PM2.5−10 (Peng et al. 2008). The differences in deposition locations and composition suggest that PM2.5 and PM2.5−10 may have varied effects on people’s health. The potential mechanisms for PM include oxidative stress, DNA damage, systematic inflammation, and endocrine dysfunction (Zhang et al. 2022a). During the past years, a growing number of epidemiological studies have shown that air pollutants are associated with multiple adverse health outcomes including cardiovascular diseases (Kaufman et al. 2016), respiratory diseases (Shi et al. 2021), cancer (Kayyal-Tarabeia et al. 2024), and urinary incontinence symptoms (Liu et al. 2024), and carries a heavy burden. Currently, the etiology of BPH is not well understood, but factors such as hormone disruption, and inflammation are believed to play a role in the progress of this disease (Rastrelli et al. 2019). Existing studies suggest that PM exposure is related to higher testosterone levels in men, and specific PM components, like polycyclic aromatic hydrocarbons (PAHS), may possess hormone-modulating properties (Radwan et al. 2016; Wei et al. 2021a, b). However, the potential impact of air pollution on the risk of BPH has been scarcely investigated. To our knowledge, only one study reported that air pollutants exposure, i.e., nitrogen oxides (NOX) and PM10, was associated with BPH in Korea (Shim et al. 2016), whereas it is based on an ecological study and hence has limited ability of causal inference. There is a notable absence of studies that have assessed the impacts of other air pollutants like PM2.5 and PM2.5−10 on BPH to date. In addition, whether the demographic and behavior factors modify the associations of air pollutants with BPH is still unknown.

China has a rapidly aging population and is facing several air pollution problems (Liu et al. 2022; PayneXu 2022). To address the research gap, we leveraged the nationally representative data to investigate the association of various air pollutants (PM2.5, PM2.5−10, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) with BPH in China. We also performed an investigation into the potential modification effects of demographic and behaviour factors that may exhibit susceptibility to the association of air pollution with BPH.

Methods

Study population

The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative project among middle-aged and elderly adults in mainland China. The CHARLS aims to collect high-quality microdata among residents aged 45 years or above from 450 villages/communities to analyze the aging in China and improve interdisciplinary studies on aging. Detailed design and sampling approaches have been described previously (Zhao et al. 2014). Initially, 17,708 respondents were recruited from 28 provinces in 2011 using a multistage probability sampling strategy (see eMethods), and then were followed up every two to three years. During each survey, the demographic information, lifestyle and behavior characteristics, and health conditions were collected.

In the current study, a total of 21,095 respondents were recruited in the CHARLS 2015. We excluded women, individuals younger than 45 years or without age data, and those with the missing outcome, 8,826 individuals from 125 county-cities (eMethods, Figure S1) across 28 provinces were screened for the final analyses. Figure S2 shows the flowchart of the participants’ selection. The CHARLS was ethnically approved by the Peking University institutional review board (IRB00001052–11015). Informed consent was signed by each participant. Our study was in accordance with the ethical principles of the Declaration of Helsinki.

Exposure estimation

Data on annual average concentrations of PM2.5 and PM10 at 1 km resolution (Wei et al. 2021a, b, 2023a, b), NO2, SO2, CO, and O3 at 10 km resolution (Wei et al. 2022, 2023a, b) across China from 2011 to 2015 were retrieved from the ChinaHighAirPollutants (CHAP) dataset (available at https://weijing-rs.github.io/ product.html). The CHAP is a high-quality product for air pollution, which used a conglomeration of multiple sources including ground-based measurements, satellite models, atmospheric reanalysis, and was generated using artificial intelligence to account for the spatiotemporal variations of air pollution. The cross-validation coefficient of determination (R2) ranged from 0.80 to 0.92 for the predictions of these six air pollutants (Wei et al. 2021a, b, 2022, 2023a, b). Outdoor PM2.5−10 was calculated by subtracting the annual PM2.5 from PM10. Given the privacy considerations, the specific residential addresses of participants were geocoded at the county-city level in the CHARLS, similar to previous studies (Zhao et al. 2025; Hu et al. 2023; Shi et al. 2023; Han et al. 2022). Exposure to air pollutants was thus evaluated according to the gridded estimates within the 125 Chinese county-cities. We utilized two-year average concentrations of air pollutants before 2015 survey as long-term exposure for each participant in the main analyses.

Assessment of BPH

In accordance with previous studies from the CHARLS (Xiong et al. 2022; Zhang et al., 2022b), individuals were asked during the face-to-face interview, “Have you ever been diagnosed with prostate hyperplasia (excluding prostatic cancer)? Moreover, researchers also explained the main symptoms of BPH to the participants (Xiong et al. 2022). The diagnosis of BPH was based on a positive response to this question, after understanding the symptoms of BPH.

Covariates

Demographics included age, body mass index (BMI), education attainment, marital status, and place of residence (urban or rural). lifestyle and Behavior factors including tobacco smoke (never, current, or previous), alcohol use (yes or no, specifying if they had ever consumed alcohol) (Shi et al. 2024), physical activity levels (low, moderate, or high), and the self-rated health status (good, fair, or poor) were recorded. Additionally, annual average temperature and relative humidity (RH) was obtained from the China Meteorological Data Service Center.

Statistical analyses

The distributions of the baseline characteristics were described, and the χ2 test and t-test were adopted for comparison of categorical variables and continuous variables, respectively. The distributions of the exposure levels of air pollutants were described. Spearman’s correlation analyses was conducted to explore the correlation between any two air pollutants.

Multivariable logistic regression was applied to explore the association of the two-year average of air pollutants with prevalent BPH. The estimated odds ratio (OR) was reported for per 10 µg/m3 increase in air pollutants except for CO (per 1 µg/m3). Both crude and adjusted models were performed to analyze the association. According to the priori knowledge and existing literature (Xiong et al. 2020; Morita et al. 2013; Parsons 2007; Burke et al. 2006), we developed a directed acyclic graph (DAG) to determine which candidate covariate should be adjusted in the multivariate analyses (Figure S3), using the online DAGitty tool (www.dagitty.net). The model finally included age, BMI, educational attainment, marital status, tobacco smoke, alcohol use, self-rated health status, residence, annual temperature, and RH. Moreover, we assessed the relationships by dividing air pollutant levels into four quartiles and calculated the estimates using the lowest quartile as the reference. Trend analyses were conducted by modeling each quartile level of pollutants as an ordinal variable. Furthermore, to examine the exposure-response relationships between air pollutants with BPH, a restricted cubic spline (RCS) model with three knots was conducted.

We performed several stratification analyses by following variables: age (≥ 65 years or < 65 years), overweight/obesity (yes or no; specifying by BMI ≥ 24.0 kg/m2), smoke exposure (yes or no), alcohol use (yes or no), and place of residence (urban or rural). Sensitivity analyses by conducting a two-pollutant model to evaluate the association. We examined the robustness of the association by using five-year exposure before the survey as a sensitivity analysis. Considering that only a subset of the respondents were collected data on physical activity in the CHARLS (Li et al. 2020), we adjusted for physical activity in the sub-sample (n = 4,398).

All analyses were conducted using STATA software (version 16.0), and two-tailed P < 0.05 were considered statistically significant.

Results

Of the 8,826 male adults (mean age 60.3 [SD 9.8] years), 1283(14.5%) had BPH in 2015. Table 1 shows that individuals with BPH are more likely to be older, unmarried, have a higher BMI, smoke, and consume alcohol, compared with their counterparts.

Table 1.

The characteristics of the study participants

Characteristics Total
(N = 8826)		 BPH P-value
Yes (N = 1283) No (N = 7543)
Age (years) 60.3 ± 9.8 64.0 ± 10.0 59.7 ± 9.6 < 0.001
BMI (kg/m2 ) 23.41 ± 3.48 23.97 ± 3.57 23.31 ± 3.45 0.001
Education attainment #
≤ Primary school 4568 (51.8) 623 (48.6) 3945 (52.3) < 0.001
Middle school 1899 (21.5) 293 (22.8) 1806 (23.9)
≥ High school 1276 (14.5) 265 (20.7) 1011 (13.4)
Marital status
Married 8004 (90.7) 1141 (88.9) 6863 (91.0) 0.019
Unmarried/ widowed 822 (9.3) 142 (11.1) 680 (9.0)
Residence
Urban 3440 (39.0) 618 (48.2) 2822 (37.4) < 0.001
Rural 5386 (61.0) 665 (51.8) 4721 (62.6)
Smoke status
Never 2379 (27.0) 334 (26.0) 2045 (27.1) < 0.001
Current 4481 (50.8) 534 (41.6) 3947 (52.3)
Previous 1966 (22.3) 415 (32.3) 1551 (20.6)
Alcohol drink
Have 5668 (64.2) 861 (67.1) 4807 (63.7) 0.020
Have not 3158 (35.8) 422 (32.9) 2736 (36.3)
Physical activity a
Low 859 (9.7) 143 (11.1) 716 (9.5) 0.063
Moderate 2362 (26.8) 364 (28.4) 1998 (26.5)
High 1177 (13.3) 154 (12.0) 1023 (13.6)
Self-rated health status
Good 2377 (26.9) 193 (15.0) 2184 (29.0) < 0.001
Fair 4509 (51.2) 641 (50.0) 3868 (51.3)
Poor 1940 (22.0) 449 (35.0) 1491 (19.7)
Meteorological factor
Ambient temperature (℃) 14.77 ± 4.73 14.58 ± 4.93 14.80 ± 4.69 0.118
Ambient RH (%) 71.30 ± 9.04 70.60 ± 9.24 71.41 ± 9.00 0.004
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a indicated physical activity is in the sub-sample. # the sum of the ratio is not 100% due to missing data

The mean exposure concentrations of PM2.5, PM2.5−10, NO2, SO2, CO, and O3 during the cross-sectional period were 66.13 (SD:22.68) µg/m3, 47.21 (20.97) µg/m3, 35.44 (10.84) µg/m3, 34.46 (17.02) µg/m3, 1.13 (0.39) mg/m3, 82.04 (13.63) µg/m3, respectively. Table 2 also shows the correlation between any two of the pollutants.

Table 2.

Distribution and correlation of the exposure concentration of air pollutants

Air pollutants
(µg/m3)		 Mean SD Percentiles Correlation
P 5 P 25 P 50 P 75 P 95 PM2.5 PM2.5−10 NO2 SO2 CO O3
PM2.5 66.13 22.68 31.75 48.70 66.85 79.10 104.80 1.00
PM2.5−10 47.21 20.97 23.15 31.60 41.15 58.70 81.55 0.782** 1.00
NO2 35.44 10.84 18.99 27.47 34.79 41.74 54.76 0.715** 0.570** 1.00
SO2 34.46 17.02 13.48 23.43 28.82 46.24 66.31 0.697** 0.759** 0.522** 1.00
CO 1.13 0.39 0.66 0.89 1.01 1.37 1.88 0.544** 0.555** 0.417** 0.574** 1.00
O3 82.04 13.63 58.40 72.44 81.05 93.38 103.83 0.271** 0.354** 0.254** 0.244** 0.058** 1.00
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Note: **P < 0.01

Table 3 displays the cross-sectional associations of exposure to air pollutants with prevalent BPH. After controlling for confounders, each 10 µg/m3 increase in PM2.5 (OR 1.04, 95% CI: 1.01–1.07) and PM2.5−10 (1.06, 1.02–1.10) were positively associated with prevalent BPH. In the quartile analysis, the highest quartile levels of PM2.5 and PM2.5−10 were related to a higher prevalence of BPH, compared to the lowest quartile (Ptrend <0.05). However, the quartile analysis showed no significance for NO2, SO2, CO, and O3 in the adjusted model (Table 3). We observed significant nonlinear relationships between PM2.5−10 and NO2 exposure with prevalent BPH (Pnonlinear <0.05, Fig. 1). Notably, the RCS curve for the association of PM2.5 and prevalent BPH is approximately linear overall (Fig. 1).

Table 3.

Cross-sectional associations of long-term exposure to air pollutants with prevalent BPH

Air pollutants
µg/m3 		Model 1 Model 2
OR (95% CI) P-value OR (95% CI) P-value
PM2.5
Per 10 µg/m3 1.05 (1.03–1.08) 0.001 1.04 (1.01–1.07) 0.018
Q1 1.00 — 1.00 —
Q2 1.22 (1.02–1.45) 0.030 1.14 (0.92–1.41) 0.231
Q3 1.43 (1.21–1.70) 0.000 1.30 (1.05–1.60) 0.017
Q4 1.37 (1.15–1.63) 0.000 1.26 (1.02–1.56) 0.034
P for trend — 0.000 — 0.018
PM2.5−10
Per 10 µg/m3 1.07 (1.04–1.10) 0.000 1.06 (1.02–1.10) 0.002
Q1 1.00 — 1.00 —
Q2 1.64 (1.37–1.96) 0.000 1.63 (1.31–2.04) 0.001
Q3 1.49 (1.25–1.78) 0.000 1.31 (1.05–1.63) 0.019
Q4 1.68 (1.41–2.01) 0.000 1.59 (1.27–1.99) 0.001
P for trend — 0.000 — 0.001
NO2
Per 10 µg/m3 1.08 (1.02–1.14) 0.009 1.05 (0.98–1.12) 0.160
Q1 1.00 — 1.00 —
Q2 1.17 (0.99–1.40) 0.071 1.09 (0.88–1.34) 0.449
Q3 1.29 (1.09–1.53) 0.003 1.16 (0.94–1.42) 0.171
Q4 1.24 (1.05–1.47) 0.014 1.17 (0.95–1.44) 0.146
P for trend — 0.007 — 0.121
SO2
Per 10 µg/m3 1.03 (0.99–1.06) 0.145 1.01 (0.96–1.05) 0.821
Q1 1.00 — 1.00 —
Q2 1.17 (0.98–1.38) 0.078 1.12 (0.91–1.38) 0.269
Q3 1.22 (1.02–1.45) 0.026 1.07 (0.86–1.32) 0.547
Q4 1.22 (1.02–1.44) 0.025 1.07 (0.87–1.33) 0.512
P for trend — 0.026 — 0.635
CO
Per 1 mg/m3 1.05 (0.90–1.22) 0.529 0.99 (0.82–1.19) 0.877
Q1 1.00 — 1.00 —
Q2 1.01 (0.85–1.20) 0.905 0.95 (0.77–1.18) 0.649
Q3 1.09 (0.92–1.29) 0.312 1.02 (0.83–1.26) 0.823
Q4 1.21 (1.02–1.43) 0.027 1.10 (0.89–1.35) 0.378
P for trend — 0.015 — 0.283
O3
Per 10 µg/m3 1.01 (0.97–1.05) 0.711 0.98 (0.93–1.04) 0.556
Q1 1.00 — 1.00 —
Q2 1.37 (1.15–1.62) 0.001 1.17 (0.95–1.45) 0.136
Q3 1.24 (1.04–1.47) 0.014 1.16 (0.93–1.43) 0.183
Q4 1.16 (0.97–1.39) 0.108 1.07 (0.86–1.33) 0.560
P for trend — 0.291 — 0.668
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Model 1: crude model

Model 2: adjusted for age, BMI, education attainment, marital status, smoke, alcohol drink, self-rated health status, residence, ambient temperature, and relative humidity

Fig. 1.

Fig. 1

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Exposure-response relationship between long-term air pollutants exposure with prevalent BPH. Note: model adjusted for age, BMI, education attainment, marital status, smoke, alcohol drink, self-rated health status, residence, ambient temperature, and relative humidity

Figure 2 shows the subgroup analysis of the association with BPH by age, BMI, place of residence, and behavior characteristics separately. We observed a higher risk of BPH in individuals who were overweight/obesity after exposure to PM2.5−10 (p interaction = 0.003). However, the association with BPH was not modified by age, smoke, alcohol drinking, and residence (Fig. 2).

Fig. 2.

Fig. 2

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Association of air pollutants exposure with BPH stratified by age, BMI, residence, and behaviour characteristics. Note: the estimates were obtained from each 10 µg/m3 increment in air pollutants except for CO (1 mg/m3). Model adjusted for age, BMI, education attainment, marital status, tobacco smoke, alcohol use, self-rated health status, residence, ambient temperature, and relative humidity except for the stratified variable

After further adjusting for other air pollutants, the two-pollutant models showed similar results for PM2.5 and PM2.5−10 (Table S1). Sensitivity analysis by using five-year air pollutant exposure showed a robust association of PM2.5 and PM2.5−10 with BPH (Table S2). By considering physical activity in the sub-sample, a significant association was observed for PM2.5−10 (Table S3).

Discussion

Our study provided additional evidence to investigate the associations of multiple air pollutants with BPH risk. Long-term exposure to PM2.5 and PM2.5−10 were associated with a higher prevalence of BPH among middle-aged and older Chinese men. There were non-linear relationship between PM2.5−10 and NO2 with BPH. The association was more evident in individuals who were overweight/obesity after exposure to PM2.5−10. Sensitivity analyses proved the stability of our findings.

The prevalence of BPH was 14.5% among the participants, close to that in a previous study (13.1%) in China (Zhang et al., 2022b). Currently, only one study by Shim et al. examined the association of air pollutants with BPH in Korea, and they found PM exposure was in relation to BPH (Shim et al. 2016), in line with our findings. However, Korea’s study (Shim et al. 2016) assessed the associations using an ecological study, with a relatively small sample size (n = 1734), and might have more confounding. By contrast, our national study added the knowledge by analyzing the associations of air pollutant exposure with BPH in China. The findings of our study are the first to reveal an association between PM2.5−10 with BPH in China. The sources of PM2.5−10 are from windblown dust, traffic vehicles, and agriculture activities, whereas PM2.5 is primarily from the combustion processes (Peng et al. 2008). An in-vivo study has shown that PM2.5−10 exposure is more likely to induce extensive interstitial inflammations compared to PM2.5 (Happo et al. 2010), which is related to the development of BPH. Nonetheless, future investigations are warranted to verify our findings. In addition, the RCS curve demonstrated a significant exposure-response relationship between NO2 with prevalent BPH, echoing the positive association found in Korea’s study (Shim et al. 2016).

To date, the mechanisms underline the associations of air pollution with BPH remain unclear, while some possible explanations have been favored. One explanation is that PM can directly impact the prostate by disrupting cell membranes and causing apoptosis via mitochondrial dysfunction (Youogo et al. 2022). Furthermore, it is suggested that the major source of PM2.5−10 was industrial and traffic emissions in China (Chen et al. 2019). The chemical components of PM, i.e., polycyclic aromatic hydrocarbons (PAHS), benzene, and metal may act as endocrine disruptors and adversely affect prostate health, leading to the progress of BPH (Radwan et al. 2016; Shim et al. 2016). Compared to PM2.5, PM2.5−10 is more likely to deposit in the upper and larger airways, thereby increasing the risk of asthma (Peng et al. 2008). It is suggested that asthma is related to the risk of BPH due to chronic inflammation (Peng et al. 2020). In addition, epidemiological evidence has linked higher PM exposure to elevated testosterone levels in men (Wei et al. 2021a; Zhou et al. 2018). Given that the progression of BPH is believed to depend on high androgen levels (Rastrelli et al. 2019), this may partially explain the mechanisms.

Our stratification analysis suggested that individuals who were overweight/obesity had a higher risk of BPH after PM2.5−10 exposure. A previous Mendelian randomization (MR) study showed an independent role of high BMI with an increased risk of BPH (Wang et al. 2022). Moreover, evidence indicated obesity could drive a state of chronic inflammation and oxidative stress, resulting in prostate tissue immune cell infiltration, tissue remodeling, and clinical BPH (Furukawa et al. 2004; Parsons et al. 2013).

Our study has several notable strengths. First, it was the first national study to explore the associations of various air pollutants with BPH risk among middle-aged and older men in China. The findings contributed to adding the limited evidence on outdoor PM2.5 and PM2.5−10 with BPH in men. Second, the nationally representative sample coupled with the high-resolution satellite models helps improve the quality of findings. Furthermore, the robust association of PM2.5−10 with prevalent BPH, which, for the first time, highlights the development of public health regulations of coarse PM to prevent BPH in China.

Some limitations should be acknowledged. First, due to data limitations in the CHARLS, we could only estimate air pollutant concentrations at the county-city level, However, the measurement error generally biases toward the null (Hutcheon et al. 2010). Second, the CHARLS questionnaire did not allow us to assess BPH and its severity using established clinical tests. While prior studies have suggested that the CHARLS question for diagnosing BPH was reliable (Xiong et al. 2021; Zhang et al. 2019), the potential misclassification might exist. Hence, caution should be paid to these findings. Moreover, the LUTS could not be evaluated due to the absence of a definite diagnosis in the CHARLS. Last, the unobserved and residual confounding such as diets and hormone status could not be considered due to unavailable data.

Conclusions

This study firstly demonstrated that long-term PM2.5 and PM2.5−10 exposure were associated with prevalent BPH among middle-aged and older men in China. The association was more pronounced in individuals who were overweight/obesity. The findings highlight that environmental interventions should be tailored to improve prostate health in men by reducing air pollution.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (610.3KB, doc)

Acknowledgements

Thanks to the National School of Development in Peking University for providing the CHARLS datasets.

Author contributions

Wenming Shi: Conceptualisation, formal analysis, methodology, software, writing– original draft. Jie V Zhao: Supervision, methodology, writing– review & editing.

Funding

None.

Data availability

This study used the open-access dataset which can be applied from the website http://charls.pku.edu.cn/en/.

Declarations

Ethical approval

The CHARLS was ethnically approved by the Peking University institutional review board (IRB00001052–11015). Informed consent was signed by each participant.

Competing interests

None declared.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Awedew AF, Han H, Abbasi B (2022) The global, regional, and national burden of benign prostatic. Lancet Health Longev 3(11):e754–e776. 10.1016/S2666-7568(22)00213-6. hyperplasia in 204 countries and territories from 2000 to 2019: a systematic analysis for the Global Burden of Disease Study 2019 [DOI] [PMC free article] [PubMed]
  2. Burke JP, Rhodes T, Jacobson DJ (2006) Association of anthropometric measures with the presence and progression of benign prostatic hyperplasia. Am J Epidemiol 164(1):41–46. 10.1093/aje/kwj151 [DOI] [PubMed] [Google Scholar]
  3. Chen R, Yin P, Meng X (2019) Associations between Coarse Particulate Matter Air Pollution and cause-specific mortality: a nationwide analysis in 272 Chinese cities. Environ Health Perspect 127(1):17008. 10.1289/EHP2711 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Daellenbach KR, Uzu G, Jiang J (2020) Sources of particulate-matter air pollution and its oxidative potential in Europe. Nature 587(7834):414–419. 10.1038/s41586-020-2902-8 [DOI] [PubMed] [Google Scholar]
  5. Egan KB (2016) The Epidemiology of Benign Prostatic Hyperplasia Associated with lower urinary tract symptoms: Prevalence and Incident Rates. Urol Clin N Am 43(3):289–297. 10.1016/j.ucl.2016.04.001 [DOI] [PubMed] [Google Scholar]
  6. Furukawa S, Fujita T, Shimabukuro M (2004) Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest 114(12):1752–1761. 10.1172/JCI21625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Han S, Zhang F, Yu H (2022) Systemic inflammation accelerates the adverse effects of air pollution on metabolic syndrome: findings from the China health and Retirement Longitudinal Study (CHARLS). Environ Res 215(Pt 1):114340. 10.1016/j.envres.2022.114340 [DOI] [PubMed] [Google Scholar]
  8. Happo MS, Salonen RO, Halinen AI (2010) Inflammation and tissue damage in mouse lung by single and repeated dosing of urban air coarse and fine particles collected from six European cities. Inhal Toxicol 22(5):402–416. 10.3109/08958370903527908 [DOI] [PubMed] [Google Scholar]
  9. Hu K, Hale JM, Kulu H (2023) A Longitudinal Analysis of the Association between Long-Term exposure to Air Pollution and cognitive function among adults aged 45 and older in China. J Gerontol Ser B-Psychol Sci Soc Sci 78(3):556–569. 10.1093/geronb/gbac162 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Hutcheon JA, Chiolero A, Hanley JA (2010) Random measurement error and regression dilution bias. BMJ-British Med J 340c2289. 10.1136/bmj.c2289 [DOI] [PubMed]
  11. Kaufman JD, Adar SD, Barr RG (2016) Association between air pollution and coronary artery calcification within six metropolitan areas in the USA (the multi-ethnic study of atherosclerosis and Air Pollution): a longitudinal cohort study. Lancet 388(10045):696–704. 10.1016/S0140-6736(16)00378-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kayyal-Tarabeia I, Zick A, Kloog I (2024) Beyond lung cancer: air pollution and bladder, breast and prostate cancer incidence. Int J Epidemiol 53(4). 10.1093/ije/dyae093 [DOI] [PubMed]
  13. Li X, Zhang W, Zhang W (2020) Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study. BMC Public Health 20(1):1682. 10.1186/s12889-020-09671-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Liu C, Chen R, Meng X (2022) Criteria air pollutants and hospitalizations of a wide spectrum of cardiovascular diseases: a nationwide case-crossover study in China. Eco Environ Health 1(4):204–211. 10.1016/j.eehl.2022.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Liu X, Ma K, Liu S (2024) Household air pollution and urinary incontinence symptoms among older adults in LASI: a large-scale population-based study. BMC Public Health 24(1):1462. 10.1186/s12889-024-18834-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Morita T, Kubo KK, Fujisaki A (2013) Involvement of magnitude of ambient temperature change in nonspecific effect in perceived placebo effect on lower urinary tract symptoms: study on switching of naftopidil in patients with benign prostatic hyperplasia. RES. REP. UROL. 583– 9010.2147/RRU.S42583 [DOI] [PMC free article] [PubMed]
  17. Parsons JK (2007) Modifiable risk factors for benign prostatic hyperplasia and lower urinary tract symptoms: new approaches to old problems. J Urol 178(2):395–401. 10.1016/j.juro.2007.03.103 [DOI] [PubMed] [Google Scholar]
  18. Parsons JK, Sarma AV, Mcvary K (2013) Obesity and benign prostatic hyperplasia: clinical connections, emerging etiological paradigms and future directions. J Urol 189(1 Suppl):S102–S106. 10.1016/j.juro.2012.11.029 [DOI] [PubMed] [Google Scholar]
  19. Payne CF, Xu KQ (2022) Life Course Socioeconomic Status and healthy longevity in China. Demography 59(2):629–652. 10.1215/00703370–9830687. [DOI] [PubMed] [Google Scholar]
  20. Peng RD, Chang HH, Bell ML (2008) Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. JAMA-J Am Med Assoc 299(18):2172–2179. 10.1001/jama.299.18.2172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Peng YH, Huang CW, Chou CY (2020) Association between Asthma and risk of benign prostatic hyperplasia: a retrospective population-based study. Aging Male 23(5):599–606. 10.1080/13685538.2018.1552253 [DOI] [PubMed] [Google Scholar]
  22. Radwan M, Jurewicz J, Polanska K (2016) Exposure to ambient air pollution–does it affect semen quality and the level of reproductive hormones? Ann. Hum Biol 43(1):50–56. 10.3109/03014460.2015.1013986 [DOI] [PubMed] [Google Scholar]
  23. Rastrelli G, Vignozzi L, Corona G (2019) Testosterone and Benign Prostatic Hyperplasia. Sex Med Rev 7(2):259–271. 10.1016/j.sxmr.2018.10.006 [DOI] [PubMed] [Google Scholar]
  24. Shi W, Liu C, Annesi-Maesano I, Ambient (2021) PM(2.5) and its chemical constituents on lifetime-ever pneumonia in Chinese children: a multi-center study. Environ Int 146106176. 10.1016/j.envint.2020.106176 [DOI] [PubMed]
  25. Shi W, Zhang T, Yu Y (2023) Association of indoor solid fuel use and long-term exposure to ambient PM(2.5) with sarcopenia in China: a nationwide cohort study. Chemosphere 344140356. 10.1016/j.chemosphere.2023.140356 [DOI] [PubMed]
  26. Shi W, Li Y, Huang Y (2024) Association of indoor solid fuel pollution with risk factors for cardiovascular disease among Chinese adults: a nationally multi-center study. Build Environ 256111513. 10.1016/j.buildenv.2024.111513
  27. Shim SR, Kim JH, Song YS (2016) Association between air pollution and benign prostatic hyperplasia: an ecological study. Arch Environ Occup Health 71(5):289–292. 10.1080/19338244.2015.1093458 [DOI] [PubMed] [Google Scholar]
  28. Speakman M, Kirby R, Doyle S (2015) Burden of male lower urinary tract symptoms (LUTS) suggestive of benign prostatic hyperplasia (BPH) - focus on the UK. BJU Int 115(4):508–519. 10.1111/bju.12745 [DOI] [PubMed] [Google Scholar]
  29. Stroup SP, Palazzi-Churas K, Kopp RP (2012) Trends in adverse events of benign prostatic hyperplasia (BPH) in the USA, 1998 to 2008. BJU Int 109(1):84–87. 10.1111/j.1464-410X.2011.10250.x [DOI] [PubMed] [Google Scholar]
  30. Tian Y, Liu H, Wu Y (2019) Association between ambient fine particulate pollution and hospital admissions for cause specific cardiovascular disease: time series study in 184 major Chinese cities. BMJ-British Med J 367l6572. 10.1136/bmj.l6572 [DOI] [PMC free article] [PubMed]
  31. Wang YB, Yang L, Deng YQ (2022) Causal relationship between obesity, lifestyle factors and risk of benign prostatic hyperplasia: a univariable and multivariable mendelian randomization study. J Transl Med 20(1):495. 10.1186/s12967-022-03722-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wei D, Li S, Liu X (2021a) Long-term exposure to particulate matter and residential greenness in relation to androgen and progesterone levels among rural Chinese adults. Environ Int 153106483. 10.1016/j.envint.2021.106483 [DOI] [PubMed]
  33. Wei J, Li Z, Xue W (2021b) The ChinaHighPM(10) dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environ Int 146106290. 10.1016/j.envint.2020.106290 [DOI] [PubMed]
  34. Wei J, Li Z, Li K (2022) Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sens Environ 270112775. 10.1016/j.rse.2021.112775
  35. Wei J, Li Z, Chen X (2023a) Separating Daily 1 km PM(2.5) Inorganic Chemical composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data. Environ Sci Technol 57(46):18282–18295. 10.1021/acs.est.3c00272 [DOI] [PubMed] [Google Scholar]
  36. Wei J, Li Z, Wang J (2023b) Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmos Chem Phys 23(2):1511–1532. 10.5194/acp-23-1511-2023 [Google Scholar]
  37. Xiong Y, Zhang Y, Li X (2020) The prevalence and associated factors of lower urinary tract symptoms suggestive of benign prostatic hyperplasia in aging males. Aging Male 23(5):1432–1439. 10.1080/13685538.2020.1781806 [DOI] [PubMed] [Google Scholar]
  38. Xiong Y, Zhang YC, Jin T (2021) Depressive males have higher odds of lower urinary tract symptoms suggestive of benign prostatic hyperplasia: a retrospective cohort study based on propensity score matching. Asian J Androl 23(6):633–639. 10.4103/aja.aja_12_21 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Xiong Y, Zhang Y, Zhang F (2022) Reduced sleep duration increases the risk of lower urinary tract symptoms suggestive of benign prostatic hyperplasia in middle-aged and elderly males: a national cross-sectional study. Aging Male 25(1):159–166. 10.1080/13685538.2022.2079627 [DOI] [PubMed] [Google Scholar]
  40. Youogo L, Parent ME, Hystad P (2022) Ambient air pollution and prostate cancer risk in a population-based Canadian case-control study. Environ Epidemiol 6(4):e219. 10.1097/EE9.0000000000000219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zeng XT, Jin YH, Liu TZ (2022) Clinical practice guideline for transurethral plasmakinetic resection of prostate for benign prostatic hyperplasia (2021 Edition). MILITARY MED RES 9(1):14. 10.1186/s40779-022-00371-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Zhang W, Zhang X, Li H (2019) Prevalence of lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH) in China: results from the China Health and Retirement Longitudinal Study. BMJ Open 9(6). 10.1136/bmjopen-2018-022792. e22792 [DOI] [PMC free article] [PubMed]
  43. Zhang Q, Meng X, Shi S (2022a) Overview of particulate air pollution and human health in China: evidence, challenges, and opportunities. Innov (Camb) 3(6):100312. 10.1016/j.xinn.2022.100312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Zhang W, Cao G, Sun Y (2022b) Depressive symptoms in individuals diagnosed with lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH) in middle-aged and older Chinese individuals: results from the China Health and Retirement Longitudinal Study. J Affect Disord 296660–296666. 10.1016/j.jad.2021.09.045 [DOI] [PubMed]
  45. Zhao Y, Hu Y, Smith JP (2014) Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 43(1):61–68. 10.1093/ije/dys203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhao Q, Feng Q, Seow WJ (2025) Impact of air pollution on depressive symptoms and the modifying role of physical activity: evidence from the CHARLS study. J Hazard Mater 482136507. 10.1016/j.jhazmat.2024.136507 [DOI] [PubMed]
  47. Zhou N, Jiang C, Chen Q (2018) Exposures to Atmospheric PM(10) and PM(10– 2.5) affect male Semen Quality: results of MARHCS Study. Environ Sci Technol 52(3):1571–1581. 10.1021/acs.est.7b05206 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (610.3KB, doc)

Data Availability Statement

This study used the open-access dataset which can be applied from the website http://charls.pku.edu.cn/en/.

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