Environmental hazards and the risk of development of idiopathic urethral strictures

Introduction

Anterior urethral stricture disease (aUSD) is a relatively common urologic condition that may arise from pelvic trauma, sexually transmitted infections, urologic procedures, and hypospadias. Many cases in North American case series, however, are idiopathic, without evidence of specific predisposing factors (1). Prior investigations postulate that subacute and repeated mild perineal trauma may be the primary exposures driving the development of idiopathic aUSD (2). This theory is primarily supported by similarities in most common location and appearance between the idiopathic and traumatic strictures. However, recent studies in idiopathic stricture cohorts incorporating extensive patient history and pathologic analysis have failed to demonstrate consistent evidence of disproportionate rates of subacute trauma history (3). Moreover, the pathologic analyses of idiopathic strictures were more comparable to strictures of an inflammatory origin than of known traumatic strictures. For all stricture types, serum inflammatory analysis demonstrated higher levels of systemic inflammatory cytokines, similar to other systemic inflammatory conditions. Additional work has also identified a higher risk of idiopathic aUSD amongst first and second degree relatives, suggesting either a potential hereditary component in the development of idiopathic aUSD or a common exposure history (4,5). All together, these recent studies suggest a more complex interaction of genetic risk and systemic inflammatory predispositions to stricture disease rather than subacute trauma as the primary risk factor.

Environmental exposures, especially air pollution, have been linked to numerous detrimental health conditions including heart disease, stroke, and chronic pulmonary disease (6). Long term exposure to air pollution has been linked to higher circulating levels of inflammatory cytokines, lower levels of anti-inflammatory serum biomarkers (7) and a shift within regulatory cells of the immune system toward a proinflammatory phenotype (8). In addition to analysis of direct exposure to toxins in the pulmonary and circulatory system, the complex role of genetic predisposition and environmental risk on several inflammatory conditions, both systemic and in other visceral organs, is being investigated. For instance, multiple studies have identified possible associations between air pollution risk and the development of ulcerative colitis (9,10). Research in rheumatoid arthritis (RA) also suggests there may be an interplay between genetic risk factors and environmental pollutants in the overall risk for development of RA (11). Similarly, environmental exposures may also play roles in the development of other systemic inflammatory conditions including psoriasis (12) and systemic lupus erythematous (13). We sought to identify if similar to these other conditions if there could be a link between urethral stricture disease and environmental exposures.

This study leverages a unique population-level data resource, the Utah Population Database (UPDB), to identify associations between industrial air pollution and idiopathic aUSD. Environmental data from Utah is unique for several reasons. First, Utah’s watershed is within a terminal basin and primary population centers are in mountain valleys, both of which trap and concentrate water pollutants and urban smog and wild fire smoke. Secondarily, Utah has a significant history of industrial pollution, including 26 designated Superfund sites within Utah. These sites are primarily former mining and industrial areas contaminated with hazardous waste like heavy metals and chemicals. These sites range from rural smelting sites to peripherally urban mining sites to fully urban water contamination from PCE (tetrachloroethylene) and other industrial chemicals. The cleanup efforts and recognition of the ecological toll of these sites has also led to heightened monitoring and data available for these exposures with highly specific geographic localization of exact chemical exposures to Utahns (14). Although often treated as a singular exposure, air pollution is a complex mixture of particulate and gaseous pollutants, and emissions from industrial facilities can contribute a variety of chemical groups to local air pollution composition. Describing associations between low-level chronic exposure to industrial air pollution and exposure during developmentally sensitive periods is an important step to finding meaningful drivers of idiopathic aUSD risk. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0338/rc).

Methods

Cohort creation

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All study procedures and materials were approved by the University of Utah Institutional Review Board (IRB #00091839). The cohort was derived using a previously described UPDB cohort of adult male patients with idiopathic aUSD (4). UPDB is a unique, statewide database that links longitudinal demographic, medical, and genealogical data from over 11 million individuals who have lived in Utah from the 1970s to the present. Briefly, all men with a diagnosis of idiopathic urethral stricture were identified in the UPDB using ICD-9 and 10 codes for unspecified etiology urethral strictures (Table S1). Cases with diagnosis codes for hypospadias, traumatic, infectious, or iatrogenic strictures were excluded. Each patient was matched 1:1 with a non-affected control (Figure S1).

Environmental exposure to industrial air pollution

Exposure to air pollution from industrial sources was estimated using the Risk-Screening Environmental Indicators Geographic Microdata (RSEI-GM) model, published by the Environmental Protection Agency (EPA). RSEI-GM is a screening-level tool to compare chemical levels with estimates of air concentrations for over 700 chemicals. These models incorporate information on chemical levels from mandatory reporting in Toxics Release Inventory (TRI), facility-specific information, and chemical fate and transport (15). This captures industrial sectors including manufacturing, metal mining, power generation, and hazardous waste treatment (16). All chemicals with non-zero levels were abstracted from RSEI-GM for Utah from 1995–2020 (N=310) and converted to annual daily average concentrations for the Uber H3 level 7 hexagonal grid cells for linkage with residential histories (17). Chemicals were excluded from the analysis if they were not reported to TRI prior to 2000 or if there was no variation in exposure across the study period, meaning they could not be divided into quantiles for analysis.

Neighborhood socioeconomic status (SES)

The Area Deprivation Index (ADI) was used to control for socioeconomic disadvantage. ADI is allows for ranking of neighborhoods by socioeconomic disadvantage and includes factors across income, education, employment and housing quality, defining neighborhood as a census block group (18,19). Utah-specific deciles of ADI were collapsed into five categories and converted to hexagonal grid and cells prior to being spatially linked with residential location for cases and controls.

Statistical analysis

Descriptive statistics on subject demographics and pollutant exposure levels were calculated along with t-tests, analysis of variance (ANOVA), and Chi-squared tests, as appropriate, to compare these measures across groups. Quantile g-computation (qgcomp) was used to estimate the joint effect of all components in the industrial air pollution mixture on risk for idiopathic aUSD. A mixture approach was selected as the individual chemicals in the mixture may act independently, interact synergistically or antagonistically with one another, or exert no effect. As the chemicals originate from similar and overlapping sources, focusing on the mixture as a whole allows for simplicity of inference and effect estimates that integrate over multiple exposures.

Cases and controls were linked with the chemical mixture data based on residential location and a time-weighted average was calculated for each of three exposure windows: (I) prenatal: 1 year prior to birth; (II) first year of life; (III) 5 years prior to stricture diagnosis, or 5 years prior to age at which their matched case was diagnosed for controls. Cases and controls were excluded if they could not be linked with exposures in at least one window of interest, such as if they were not living in Utah or did not have recorded residential locations during the exposure window. Exposures were divided into quartiles for each window and results are presented per quartile increase of the mixture (i.e., if each component was simultaneously increased by one quartile). A marginal risk ratio (RR) was estimated for each exposure window using diagnosis with aUSD as a binary outcome. Marginal parameters estimate RRs for the population average exposure effect using 500 bootstrap repetitions to estimate 95% confidence intervals. Mixture weights for each chemical mixture component were also estimated to describe the proportion of the overall mixture effect contributed by each chemical. All models controlled for race/ethnicity and ADI. The model for exposure 5 years prior to diagnosis additionally controlled for age at the start of the exposure window. A significance threshold of 0.05 was used for all tests. All statistical analyses were performed using R (version 4.4.1) and Stata (version 18.0) and geospatial analyses and linkage were carried out in Python (version 3.8).

Results

Study population

A total of 4,777 eligible men were identified for inclusion in the analysis: 2,352 men with a history of idiopathic urethral stricture diagnosed from 1995 to 2021 and 2,425 matched controls. Of the eligible cases and controls, 790 were excluded for either not having a listed residential location or residing in a location without exposure data. Of the total remaining 3,987 unique men, there were 2,048 cases of idiopathic urethral stricture and 1,939 controls (Figure S1). Men diagnosed with idiopathic strictures were more likely to be non-Hispanic White than controls and were less likely to have a body mass index (BMI) 25–29.9 kg/m² (Table 1). Data for exposure to industrial chemical air pollution was available for 1,753 in the prenatal period, 1,761 individuals in the first year of life, and 3,398 individuals within the 5 years prior to diagnosis.

Chemical exposures in case and controls

Total chemical concentrations for each time window are detailed in Table S2. Similar exposure patterns were seen for the three exposure windows. Men diagnosed with idiopathic urethral stricture disease tended to live in areas with higher Z-score standardized concentrations for nearly all included chemicals (Table S2, Figure 1).

Figure 1 Chemical distributions for exposure in first year of life. The distribution of level of exposure in each cohort within the first year of life by all chemicals in the analysis by standardized z-score. The figure is organized by exposure weight within the first year of life from most to least correlated to stricture development. Exposure weight is based on a modeled joint mixture effect not exclusively on marginal effect side for each individual chemical. Outliers greater than 95th percentile are excluded.

Risk for diagnosis with idiopathic urethral stricture disease with exposure to industrial air pollution

Exposure to the chemical mixture within the first year of life (Figure 1) was significantly associated with increased risk of aUSD across each quartile of exposure in a dose dependent fashion (P=0.002) (Table 2, Figure 2). No significant increases in risk of idiopathic aUSD were observed for the prenatal period or 5 years prior to diagnosis (Table 2, Figure 2). Within the first year of life (the only exposure window with detected effect), no single chemical dominated the mixture, with all calculated weights <20% (Table S3). Though no specific chemicals had dominant contributions to the mixture model, ethylbenzene (13%), tetrachloroethylene (10%), and Molybdenum trioxide (9%) had the highest weights in mixture (Figure S2).

Figure 2 Cumulative stricture risk by environmental exposure. Relative risk of stricture development by quartile of chemical exposure level for each time window. *, P<0.05. CI, confidence interval; Dx, diagnosis; RR, relative risk.

Discussion

Using a population level analysis of environmental exposures in an idiopathic aUSD cohort, this study identifies a possible dose-dependent association between exposure to industrial air pollution mixtures in the first year of life and the development of idiopathic aUSD later in life. However, it did not demonstrate an association between risk from exposures in the prenatal period or in the 5 years prior to diagnosis. This analysis is unique because of the in depth exposure data from across Utah linked to UPDB medical and administrative records, allowing for detailed analysis of the link between disease incidence and local exposures over a large population and long time-frame.

Evidence suggesting that idiopathic aUSD is driven by inflammatory or fibrotic processes, rather than simply or solely subacute trauma, supports the idea that both genetic and environmental triggers may play a role in disease development (3-5). Other conditions that are driven by inflammatory processes, like pediatric-onset inflammatory bowel disease (20) and endometriosis (21), have similarly shown associations with exposure to air pollution in childhood. These are part of growing body of evidence that infancy poses a specific window of susceptibility to environmental exposures compared to adult exposures (22). The high rate of growth and development during infancy has been postulated to be important in the relatively more significant downstream impact of exposures compared to other timepoints in development (23). Moreover, environmental exposures beyond air pollution can be more significant in infants from increased contact with the ground where pollutants can settle and normal hand and mouthing behaviors as part of learning and environmental exploration (24).

In urologic cancers, the role of direct toxin concentration in urine from aromatic amines and benzenes in industrial dye or tobacco exposure is a well understood risk factor for bladder malignancy risk (25). Some of the chemicals in our mixture model have chemical properties similar to teratogenic chemicals or some evidence themselves for links to urinary cancer risk. For example, Tetrachloroethylene has been linked to increased risk of bladder cancer in professional dry cleaners, suggestive of metabolite accumulation in urine which could also potentially increase the urethral exposure as a possible mechanism of stricture risk (26).

The potential impact of environmental exposures in infancy on aUSD risk is unlikely to be a sole explanation for disease development. Though our study does not have the data to support any specific mechanism or relationship, as is seen with most complex disease processes, the pathways involved in stricture development are likely multifactorial. Previous research, utilizing the UPDB, has demonstrated a heritable risk of aUSD, suggesting a genetic component to the disease (4). It is possible that genetic vulnerability, potentially in immune-related genes, could create a primed state whereby exposure to environmental triggers of inflammation (i.e., chemical exposure, air pollutants), amplify immune dysregulation and disease development. This has been more clearly delineated in the role of pro-inflammatory cytokines influencing genetic risk in the development of gastric cancer (27). Though our data does not come close to elucidating the mechanism of stricture development, the findings could be consistent with a “multi-hit” hypothesis that during critical developmental periods toxin accumulation in the urinary tract could potentiate genetic risk factors which combined with the insult of subacute trauma later in life could lead to stricture development that would not have otherwise developed with subacute trauma alone. The next step in exploring this possible association in urethral stricture disease would be genetic analysis and the development of cytokine or other inflammatory biomarker profiles.

While urethral stricture disease is treated very successfully with surgery, urethroplasty still has long-term morbidity and a substantial number of patients with recurrent strictures after urethroplasty (28). There are also patients with other comorbidities or frailty that are not good candidates for urethroplasty. Innovations such as the paclitaxel drug-coated urethral dilating balloons have demonstrated substantial improvement in outcomes relative to prior minimally invasive conventional treatments, such as direct vision internal urethrotomy (29). This is important for many patients with recurrent strictures or those not candidates for a urethroplasty. For these patients as well as for patients with new diagnoses who are good candidates for upfront endoscopic treatment, an improved understanding of the mechanisms driving idiopathic stricture development could aid in the development of molecular pathway-specific anti-inflammatory drug delivery as part of minimally invasive treatment options.

This study has several notable limitations. The use of the UPDB has substantial improvements in administrative, environmental, and health record data over any other US based database but still has limitations in diagnosis coding accuracy, missing data, and limitations in overall granularity of data. This includes the possibility of selection bias from the exclusion of the subset of patients with missing location data, especially given the higher amount of missing data in the prenatal and first year of life. In addition, environmental data is based on residential location and cannot account for individual variation which could represent inaccuracies of measurements of individual exposures. Of note, this population is also based on a prior retrospective cohort used for the analysis of familial urethral stricture disease risk. While our model does not account for familial risk in of itself, we acknowledge that residential location is inherently a potentially confounding risk with hereditability. However, given the much stronger association of exposure within the first year of life relative to prenatal risk which likely carried very similar location to family member, this would argue against a substantial confounding presence. In addition, the risk of urban locations having higher air pollution burdens but improved access to a urologist could have driven the results observed in this study. However, the lack of dose dependent response for the 5 years prior to diagnosis but with the first year of life, which was generally 10 to 20 years prior to diagnosis, would argue against differential access to urologists in areas with higher air pollution burden as a substantial confounding variable. In addition, our study does not elucidate the specific mechanisms of how pollutants could increase the risk of aUSD. Given the substantial number of other inflammatory conditions that may have some environmental relationship, there could be non-specific, systemic pro-inflammatory pathways that pollutant exposures potentiate, increasing the risk of development of urethral stricture disease as one of many inflammatory conditions. Alternatively, it could be possible that urethral stricture disease has unique exposure risk with direct exposure to specific pollutants in the urine more similar to genitourinary malignancy risk. We were also limited in analysing environmental exposures outside of the EPA dataset which could have a confounding impact.

Conclusions

Through a population level analysis of environmental exposures, there is a potential increase in the relative risk of idiopathic aUSD with exposure to air pollutants in the first year of life. Further research into genetic and inflammatory pathways in the development of urethral stricture disease is warranted to better understand this possible environmental link.

Acknowledgments

We thank the Utah Population Database (UPDB) staff of Huntsman Cancer Institute, University of Utah (funded in part by the Huntsman Cancer Foundation) for their role in the ongoing collection, maintenance and support of the UPDB. We also acknowledge partial support for the UPDB through grant P30 CA2014 from the National Cancer Institute, University of Utah and from the University of Utah’s program in Personalized Health and Utah Clinical and Translational Science Institute.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0338/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0338/prf

Funding: The study was supported by the University of Utah Division of Urology, and was partially supported for the UPDB through grant P30 CA2014 from the National Cancer Institute, University of Utah and from the University of Utah’s program in Personalized Health and Utah Clinical and Translational Science Institute.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0338/coif). J.B.M. serves as the unpaid editorial board member of Translational Andrology and Urology from August 2024 to July 2026. J. M. H. serves as the unpaid editorial board member of Translational Andrology and Urology from August 2025 to June 2027. C.H.S. was supported, in part, by TL1 DK147552 which is unrelated to this project. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study had approval from the University of Utah Institutional Review Board (IRB #00091839). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Cotter KJ, Hahn AE, Voelzke BB, et al. Trends in Urethral Stricture Disease Etiology and Urethroplasty Technique From a Multi-institutional Surgical Outcomes Research Group. Urology 2019;130:167-74. [Crossref] [PubMed]
2. Viers BR, Pagliara TJ, Rew CA, et al. Characteristics of Idiopathic Urethral Strictures: A Link to Remote Perineal Trauma? Urology 2017;110:228-33. [Crossref] [PubMed]
3. Gutierrez WR, Luo Y, Dahmoush L, et al. Deep Phenotyping the Anterior Urethral Stricture: Characterizing the Relationship Between Inflammation, Fibrosis, Patient History, and Disease Pathophysiology. J Urol 2024;212:153-64. [Crossref] [PubMed]
4. Hebert KJ, Horns JJ, Paudel N, et al. Familial Associated Idiopathic Urethral Stricture Disease: An Assessment Using Pedigree Data From the Utah Population Database. Urology 2025;201:125-31. [Crossref] [PubMed]
5. Isali I, Wong TR, Wu CW, et al. Genomic Risk Factors for Urethral Stricture: A Systematic Review and Gene Network Analysis. Urology 2024;184:251-8. [Crossref] [PubMed]
6. Bhatnagar A. Cardiovascular Effects of Particulate Air Pollution. Annu Rev Med 2022;73:393-406. [Crossref] [PubMed]
7. Vogli M, Peters A, Wolf K, et al. Long-term exposure to ambient air pollution and inflammatory response in the KORA study. Sci Total Environ 2024;912:169416. [Crossref] [PubMed]
8. Tripathy S, Marsland AL, Kinnee EJ, et al. Long-Term Ambient Air Pollution Exposures and Circulating and Stimulated Inflammatory Mediators in a Cohort of Midlife Adults. Environ Health Perspect 2021;129:57007. [Crossref] [PubMed]
9. Li FR, Wu KY, Fan WD, et al. Long-term exposure to air pollution and risk of incident inflammatory bowel disease among middle and old aged adults. Ecotoxicol Environ Saf 2022;242:113835. [Crossref] [PubMed]
10. Suarez RG, Osornio-Vargas AR, Wine E. Ambient Air Pollution and Pediatric Inflammatory Bowel Diseases: An Updated Scoping Review. Dig Dis Sci 2022;67:4342-54. [Crossref] [PubMed]
11. Zhang J, Fang XY, Wu J, et al. Association of Combined Exposure to Ambient Air Pollutants, Genetic Risk, and Incident Rheumatoid Arthritis: A Prospective Cohort Study in the UK Biobank. Environ Health Perspect 2023;131:37008. [Crossref] [PubMed]
12. Wu J, Ma Y, Yang J, et al. Exposure to Air Pollution, Genetic Susceptibility, and Psoriasis Risk in the UK. JAMA Netw Open 2024;7:e2421665. [Crossref] [PubMed]
13. Xing M, Ma Y, Cui F, et al. Air Pollution, Genetic Susceptibility, and Risk of Incident Systemic Lupus Erythematosus: A Prospective Cohort Study. Arthritis Rheumatol 2024;76:1530-7. [Crossref] [PubMed]
14. US EPA. Superfund Sites in Reuse in Utah [Overviews and Factsheets] [Internet]. 2016 [cited 2025 Dec 11]. Available online: https://www.epa.gov/superfund-redevelopment/superfund-sites-reuse-utah
15. US EPA. RSEI Geographic Microdata (RSEI-GM): 1988-2022 TRI Data [Data and Tools] [Internet]. 2024 [cited 2025 Dec 30]. Available online: https://www.epa.gov/rsei/rsei-geographic-microdata-rsei-gm
16. US EPA. Toxics Release Inventory (TRI) Program [Collections and Lists] [Internet]. 2013 [cited 2025 Dec 30]. Available online: https://www.epa.gov/toxics-release-inventory-tri-program
17. H3: Hexagonal Hierarchical Spatial Index [Internet]. Uber Technologies, Inc.; 2025 [cited 2025 Dec 30]. Available online: https://h3geo.org/
18. Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. N Engl J Med 2018;378:2456-8. [Crossref] [PubMed]
19. University of Wisconsin School of Medicine and Public Health. Area Deprivation Index [Internet]. 2015. Available online: https://www.neighborhoodatlas.medicine.wisc.edu/
20. Elten M, Benchimol EI, Fell DB, et al. Ambient air pollution and the risk of pediatric-onset inflammatory bowel disease: A population-based cohort study. Environ Int 2020;138:105676. [Crossref] [PubMed]
21. Amazouz H, Gouesbet S, Bourhis L, et al. Early-life environmental exposures and the risk of endometriosis/adenomyosis in the NutriNet-Santé cohort. Sci Total Environ 2025;968:178790. [Crossref] [PubMed]
22. Wright RO. Environment, susceptibility windows, development, and child health. Curr Opin Pediatr 2017;29:211-7. [Crossref] [PubMed]
23. Grandjean P, Landrigan PJ. Neurobehavioural effects of developmental toxicity. Lancet Neurol 2014;13:330-8. [Crossref] [PubMed]
24. Hauptman M, Woolf AD. Childhood Ingestions of Environmental Toxins: What Are the Risks?. Pediatr Ann 2017;46:e466-e471. [Crossref] [PubMed]
25. Letašiová S, Medve'ová A, Šovčíková A, et al. Bladder cancer, a review of the environmental risk factors. Environ Health 2012;11:S11. [Crossref] [PubMed]
26. Vlaanderen J, Straif K, Ruder A, et al. Tetrachloroethylene exposure and bladder cancer risk: a meta-analysis of dry-cleaning-worker studies. Environ Health Perspect 2014;122:661-6. [Crossref] [PubMed]
27. McLean MH, El-Omar EM. Genetic aspects of inflammation. Curr Opin Pharmacol 2009;9:370-4. [Crossref] [PubMed]
28. Erickson BA, Tuong MN, Zorn AN, et al. Development and Validation of the Length, Segment, and Etiology Anterior Urethral Stricture Disease Staging System Using Longitudinal Urethroplasty Outcomes Data From the Trauma and Urologic Reconstructive Network of Surgeons. J Urol 2025;213:512-23. [Crossref] [PubMed]
29. DeLong J, Virasoro R, Pichardo M, et al. Long-Term Outcomes of Recurrent Bulbar Urethral Stricture Treatment With the Optilume Drug-Coated Balloon: Five-Year Results From the ROBUST I Study. J Urol 2025;213:90-8. [Crossref] [PubMed]