Abstract
Background/Aim: Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder associated with systemic comorbidities. Its potential link with urolithiasis, a common urological disease, remains unclear.
Patients and Methods: We conducted a multicenter, retrospective cohort study using the TriNetX US Collaborative Network. Adults ≥18 years with HS diagnoses and more than two visits between 2005 and 2018 were identified and matched 1:1 with non-HS controls using propensity scores. Patients with prior urolithiasis or malignancy were excluded. The final analytic cohort included 79,567 patients with HS and 79,567 controls. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. Sensitivity analyses applied alternative definitions, wash-out periods, and follow-up durations. Comparator groups included psoriasis, rosacea, and androgenic alopecia. Stratified analyses were performed by age and sex.
Results: HS was associated with an increased risk of urolithiasis in 15-year follow-up (HR=1.334, 95% CI=1.260-1.413). This association persisted across sensitivity analyses. Comparisons with psoriasis, rosacea, and androgenic alopecia also demonstrated significantly elevated risks (HRs ranging from 1.247 to 1.296). Stratified analyses showed consistent results in both sexes (males: HR=1.340, 95% CI=1.202-1.493; females: HR=1.326, 95% CI=1.214-1.417) and age groups (18-64 years: HR=1.317, 95% CI=1.237-1.402; ≥65 years: HR=1.308, 95% CI=1.160-1.475). Site-specific analyses demonstrated increased risks for renal, ureteric, and lower urinary tract calculi.
Conclusion: HS is associated with elevated risk of urolithiasis, consistent across analytic models and subgroups. These findings highlight the existence of overlapping inflammatory pathways and underscore the importance of renal monitoring in HS management.
Introduction
Hidradenitis suppurativa (HS) is a chronic inflammatory disorder that is associated with many systemic manifestations. Recurring painful nodules, abscesses, and scarring are common characteristics affecting areas such as skin folds of armpits, breasts, groin, perineum, gluteal and perianal areas of the body (1). These disfiguring and irritating condition distress patients and significantly reduce their quality of life (2). However, the etiology of HS is multifactorial, including genetics, environmental factors, lifestyle, hormone status and pathogenic microorganisms (1, 2). These factors trigger perifollicular lymphocytic infiltration around terminal hair follicles with subsequent sebaceous gland loss. Moreover, pro-inflammatory cytokines secreted by the adaptive immune system, such as tumor necrosis factor (TNF), interleukin-1β (IL1β) and IL17, further aggravate immune cell infiltration and inflammation (1, 3). As HS progresses, hyperkeratinized, dilated, ruptured follicles ultimately release follicular content into the surrounding tissues, resulting in the inflammatory process spreading. The substantial comorbidity burden and high outpatient healthcare costs underscore the growing recognition of the unmet therapeutic needs in HS and its associated conditions (4, 5).
Urolithiasis is a universal urological disease diagnosed by the presence of one or more stones in the urinary tract (6). Because of its high prevalence, ranging from 4 to 20%, it exerts a notable economic burden on medical systems (7, 8). To implement better treatment, non-dietary risk factors such as family history, systemic disorders, environmental factors, urinary risk factors, and some dietary risk factors must be considered thoroughly (9).
Recent study indicates that patients with psoriasis are prone to urolithiasis (10). Both HS and psoriasis are chronic inflammatory skin disorders whose co-existence is increasingly being described in many studies, possibly depending on common pathogenetic pathways (11). However, to the best of our understanding, there is insufficient evidence in the existing literature that describes the real-world relationship between HS and urolithiasis. Therefore, we conducted a retrospective cohort study to address the knowledge gap between the risk of developing urolithiasis in patients with HS.
Patients and Methods
Data source and study design. We performed a multi-center, retrospective cohort study using the TriNetX research network (TriNetX LLC, Cambridge, MA, USA). TriNetX is a global-federated electronic health-record analytics platform that aggregates de-identified patient-level data contributed by healthcare organizations worldwide. The platform has been widely used for outcome studies in clinical epidemiology and pharmacoepidemiology (12-14). For this investigation, analyses were restricted to the United States collaborative network within TriNetX, which contained electronic health records from >60 US healthcare organizations and approximately 120 million unique patients.
This study was approved by the Institutional Review Board of Chung Shan Medical University Hospital (CS1-25002). Due to the deidentification process of TriNetX research network, the need for informed consent was waived by the Institutional Review Board of Chung Shan Medical University Hospital.
Cohort assembly. We identified individuals with at least two documented healthcare encounters and a diagnosis of HS to form the exposed cohort. The comparator cohort comprised patients without any prior HS diagnosis who underwent a general medical examination. The observation window spanned 2005 to 2018. To enhance cohort homogeneity and reduce immortal-time and survivor biases, we applied the following exclusions prior to matching: age <18 years at index; death recorded before or on the index date; and any record of urolithiasis preceding the index date (Figure 1).
Patient selection process.
Definitions of exposure, outcomes, and covariates. Exposure (HS diagnosis), study endpoints, and all covariates were defined using administrative coding systems available within TriNetX. Specifically, diagnoses were ascertained with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes (15) (Table I). Covariates selected a priori included age at index, sex, race, body mass index, tobacco use, alcohol use, substance use, comorbidity burden, indicators of healthcare utilization, and proxies of socioeconomic status.
Proxy codes utilized in this study.
Propensity score matching. To control for measured confounding, we conducted 1:1 propensity-score matching between HS cases and non-HS controls. Propensity scores were estimated using the specified baseline covariates, and pairs were formed via greedy nearest-neighbor matching with a caliper of 0.1 on the propensity-score scale. Covariate balance post-matching was evaluated using standardized mean differences (SMDs); an SMD >0.10 signaled a meaningful residual imbalance.
Primary analysis and effect estimation. The matched analytic sample comprised 79,567 patients with HS and 79,567 controls. For each endpoint, we estimated hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). All computations were executed within the TriNetX analytics environment, which implements time-to-event analyses on de-identified data.
Sensitivity and stratification analyses. To evaluate the reliability of our findings, we conducted several complementary analyses. Firstly, we repeated the propensity-score matching procedure using alternative algorithms to determine whether the association between HS and urolithiasis remained consistent across different matching strategies, thereby reducing concerns of overmatching. We also applied varying post-index exclusion or “wash-out” periods, which allowed us to assess whether the observed associations persisted after removing events that occurred shortly after cohort entry, thus minimizing the possibility of reverse causation or early detection bias. In addition, we examined the effect of altering the duration of follow-up. This approach enabled us to capture delayed or long-term outcomes and to confirm that the association was not dependent on the specific length of surveillance. To address potential misclassification of HS, we redefined exposure using alternative sets of administrative coding definitions and reassessed the results. We also accounted for possible medical surveillance bias by selecting comparator groups with other inflammatory skin diseases known to be associated with systemic comorbidities. This strategy helped distinguish true associations from artifacts that might arise if the control population had lower healthcare utilization compared with individuals with HS. To explore effect heterogeneity, we conducted stratified analyses by age group and sex, repeating the matching and outcome estimation within each stratum.
Results
Before matching, baseline characteristics such as age, sex, and some of the races (White, Black or African American, and Asian), socioeconomic status, lifestyle, one of the comorbidities (diabetes mellitus), medical utilization status, and laboratory data were significantly different (SMD>0.1) between the HS and healthy control groups. After propensity-score matching, two groups with the same 79,567 patients with HS were balanced for all of the baseline characteristics shown in Table II.
Baseline characteristics of study participants before and after propensity score matching.
The risk of a patient with HS developing urolithiasis was significantly higher than that in non-HS controls (Table III). In the analysis of various models, patients with HS had an increased risk of urolithiasis with a hazard ratio of 1.335 (95% CI=1.285-1.386) in Model 1a, which was a crude model without propensity-score matching. The risk was further elevated in Model 2b, which included age, sex, race, comorbidities, and body mass index status as covariates, with an HR of 1.384 (95% CI=1.308-1.464). Taking various wash-out periods into consideration, the HR for patients with HS developing urolithiasis was 1.331 (95% CI=1.251-1.416) for 12 months of wash-out period in Model 1d, 1.330 (95% CI=1.251-1.415) for 24 months in Model 2e, 1.327 (95% CI=1.240-1.420) for 36 months in Model 3f.
Hazard ratios (HR) with 95% confidence interval (CI) for urolithiasis in patients with hidradenitis suppurativa (HS) under various models. Except in the analyses of matching covariates, propensity-score matching (PSM) was used in all analyses using age at index, sex, race, body mass index (BMI), comorbidities (diabetes mellitus, hypertension, hyperlipidemia, chronic kidney disease, sexually transmitted disease), status of smoking, alcoholism and substance use, medical utilization status, socioeconomic status (problems related to housing/economic circumstances, potential health hazards related to socioeconomic/psychosocial circumstances).
With different follow-up periods, the HR for patients with HS was 1.369 (95% CI=1.261-1.485) for a 5-year follow-up in Model 1g, 1.338 (95% CI=1.256-1.425) for a 10-year follow-up in Model 2h, 1.334 (95% CI=1.260-1.413) for a 15-year follow-up in Model 3i. Model 1j, Model 2k, and Model 3l were different claim-based algorithms with various patients included as the HS group. Only patients diagnosed HS with more than two visits and never diagnosed with cutaneous abscess by specialists at the same time were included as the HS group in Model 1j, which showed an HR of 1.229 (95% CI=1.145-1.320). In Model 2k, only patients diagnosed with HS with more than two visits and limited to those with at least one inpatient visit with HS diagnosis were included as the HS group, and this led to an HR of 1.565 (95% CI=1.446-1.695). In Model 3l, only patients diagnosed with HS with more than two visits and with the record of underwent drainage and incision were included as the HS group, producing an HR of 1.613 (95% CI=1.416-1.837). With various comparators, the comparative arm was set as patients with psoriasis in Model 1m, leading to an HR of 1.296 (95% CI=1.219-1.378). Model 2n, whose comparative arm was set as patients with rosacea, produced an HR of 1.293 (95% CI=1.208-1.383). The other model was Model 3o, whose comparative arm was set as patients with androgenic alopecia, giving an HR of 1.247 (95% CI=1.108-1.403).
We conducted stratified analysis by sex and age at index date. A higher risk of new-onset urolithiasis occurring in the HS cohort compared with the control cohort across males and females was observed. In males, the HR of developing urolithiases was 1.340 (95% CI=1.202-1.493) and in the female population, it was 1.326 (95% CI=1.214-1.417). When stratified by age, the observed association between HS and urolithiasis remained, for patients with HS aged 18-64 years old having an HR of 1.317(95% CI=1.237-1.402) and those over 65 years old an HR of 1.308 (95% CI=1.160-1.475) (Table IV).
Stratification analysis of urolithiasis risk in patients with hidradenitis suppurativa (HS) in 5-year follow-up.
To further elucidate the relationship between HS and urolithiasis, we analyzed site-specific risks of urinary calculus. For renal calculus, patients with HS exhibited a 1.312-fold increased risk compared with individuals without HS (95% CI=1.234-1.395). The HR for ureteric calculus was 1.489 (95% CI=1.316-1.685), while for calculus in the lower urinary tract, the HR was 1.554 (95% CI=1.157-2.086) in patients with HS relative to their non-HS counterparts (Figure 2).
Risk of calculus in different sites in patients with hidradenitis suppurativa (HS) comparing with non-HS patients. CI: Confidence interval.
Discussion
We report that HS is associated with urolithiasis in real-world settings. In stratified analyses, both males and females demonstrated an approximately 1.3-fold higher HR compared with non-HS groups, with statistical significance observed. In addition, groups aged 18-64 and over 65 years old also had HRs of 1.317 (95% CI=1.237-1.402) and 1.308 (95% CI=1.160-1.475), which consolidated our findings with age-stratification analysis.
Several potential mechanisms may explain the observed association of urolithiasis with HS. Cytokines in the pathophysiologic process of HS might play a critical role. TNFα as a T-helper (Th)1/Th17-associated cytokine, triggers the inflammatory cascade in patients with HS (16). Increased serum IL6 levels have also found in patients with HS, contributing to the development of HS (17). In mechanisms regarding HS, fibroblasts produce C-X-C motif chemokine ligand 1 (CXCL1) as well as CXCL6, which recruit neutrophilic granulocytes (1, 18). By investigating the shift in thiol/disulfide homeostasis and the increase in ischemia-modified albumin levels, oxidative stress was thought of as an important HS etiopathogenesis (19). In addition, recent study indicated that IL17A and IL-17F mediate inflammatory pathways in HS pathogenesis (20). With regard to urolithiasis, one study showed that patients with urolithiasis had a high serum TNFα and IL6 levels (21). Oxidative stress along with toll-like receptor 4, nuclear factor-kB and NOD-like receptor family, pyrin domain containing 3 inflammasome pathways were related to calcium oxalate crystal-induced renal responses in HK-2 cells and rat kidneys (22). Moreover, recent research demonstrated that C-C motif chemokine ligand 2, CXCL1 and CXCL2 released by renal tubular epithelial cells upon stimulation by calcium oxalate accompanied by activating NF-kB, IL17 and TNF signaling pathways (23, 24). Another research pointed that urolithiasis were associated with various cytokines, such as CXCL1, macrophage inflammatory protein-1a, IL5, IL7, IL8 and monokine induced by interferon-γ (25). Since overlapping cytokine profiles and shared pathogenic mechanisms are implicated in both HS and urolithiasis, this might theoretically account for the observed association. However, further laboratory-based study is needed to investigate the actual immunological interaction between these two diseases.
Higher risk of acute kidney injury and chronic kidney disease was found in patients with HS (26). A higher estimated glomerular filtration rate associated with renal dysfunction was found in the hospital group with HS (27), which demonstrates the possible association between HS and renal dysfunction. Recent systematic review also showed increased risk of renal diseases in patients with HS, with low heterogeneity through analysis of observational studies (28). Patients diagnosed with urolithiasis had a higher risk of incident chronic kidney disease compared with those without urolithiasis (29). With regard to possible factors, higher risk of chronic kidney disease was also associated with stones containing a higher proportion of uric acid and with a higher neutrophil-to-lymphocyte ratio (30). Renal function evaluated by estimated glomerular filtration rate varied depending on stone composition, including calcium-containing stones (calcium oxalate and calcium phosphate) and non-calcium-containing stones (struvite and uric acid) (31), which emphasizes the potential impact on renal function caused by renal stones. Although both HS and urolithiasis share an association with renal dysfunction and renal disease, the precise interaction among proinflammatory cytokines, HS-related systemic comorbidities, and other residual factors remains uncertain. Nevertheless, the observed relationship between HS and urolithiasis may represent a useful perspective for understanding the complex interplay linking HS to renal dysfunction.
Previous study indicated that patients with psoriasis are prone to presenting urolithiasis (10). Using computed tomography analysis, psoriasis was found to be a risk factor for urolithiasis, with an adjusted odds ratio of 9.12 (32). Mendelian randomization study also showed a causal relationship between psoriasis vulgaris and urolithiasis (33). Therefore, we conducted an analysis using Model 1m, substituting the control cohort with patients with psoriasis, and patients with HS still presented an increased risk of urolithiasis, with an HR of 1.296 (95% CI=1.219-1.378), which proves further association between HS and urolithiasis.
Our study has several strengths, including a cohort design with a large sample size and the use of a reliable dataset. We also employed multiple analytic models, including various comparator groups, to mitigate potential medical surveillance bias. Nevertheless, several limitations should be acknowledged. Firstly, as this was an observational study, causality between HS and urolithiasis cannot be established. Secondly, despite the application of propensity-score matching, the study population was predominantly composed of White and African American individuals, with lower representation of Asian and American Indian groups, which may limit the generalizability of our findings. Thirdly, due to dataset constraints, we were unable to stratify patients with HS by disease severity, preventing a more complete assessment of outcomes across different clinical stages, and potentially introducing information bias. Fourthly, although we attempted to reduce confounding through matching key variables, residual confounding from unmeasured factors may still have influenced the results. Finally, misclassification bias may exist due to possible diagnostic inaccuracies in identifying HS.
In conclusion, our findings demonstrate an increased risk of urolithiasis among individuals with HS across different follow-up periods. This real-world association may provide clinicians with additional insights into the complex interplay between HS and renal dysfunction.
Footnotes
Authors’ Contributions
All the Authors were involved in drafting or revising the article and approved of the submitted version. Study conception and design: Chang HC, Hsu YH, Wu MC, Gau SY. Data acquisition: Chang HC and Gau SY. Data analysis and demonstration: Chang HC and Gau SY. Original draft preparation: Chang HC, Hsu YH, Wu MC and Gau SY.
Data Availability
Data in this study were retrieved from TriNetX Research Network. All data available in the database were administrated by the TriNetX platform. Detailed information can be retrieved at the official website of the research network (https://trinetx.com).
Conflicts of Interest
The Authors have no conflicts of interest to declare.
Funding
This study was partially funded by Chung Shan Medical University Hospital (CSH-2025-C-007).
Artificial Intelligence (AI) Disclosure
During the preparation of this manuscript, a large language model (ChatGPT 4o, OpenAI) was used solely for language editing and stylistic improvements in select paragraphs. No sections involving the generation, analysis, or interpretation of research data were produced by generative AI. All scientific content was created and verified by the Authors. Furthermore, no figures or visual data were generated or modified using generative AI or machine learning-based image enhancement tools.
- Received September 24, 2025.
- Revision received November 4, 2025.
- Accepted November 7, 2025.
- Copyright © 2026 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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