Association Between Psoriasis and Prostate Cancer Risk in Men: A Retrospective Cohort Study

Abstract

Objective

Psoriasis is a chronic immune-mediated inflammatory disease, but its association with prostate cancer remains unclear. This study evaluated the relationship between psoriasis and the risk of prostate cancer in men.

Methods

We conducted a retrospective cohort study using data from 2006 to 2023 within the TriNetX global federated research network. Male patients aged 50–84 years with psoriasis were identified and propensity score-matched 1:1 to controls without psoriasis based on age and relevant clinical characteristics. A 2-year lag period was applied to reduce reverse causation. Hazard ratios (HRs), 95% confidence intervals (CIs), and cumulative incidence were estimated using the TriNetX “Compare Outcomes” module.

Results

After matching and lag exclusion, 127,630 patients with psoriasis and 128,309 controls were included. Over a maximum follow-up of 6 years, 1,243 cases of prostate cancer occurred in the psoriasis group and 521 in controls, corresponding to cumulative incidences of 0.974% and 0.406%, respectively (absolute risk difference, 0.568%). Psoriasis was associated with a significantly increased risk of prostate cancer compared with controls (HR 2.09; 95% CI, 1.89–2.32).

Conclusion

Psoriasis was associated with an increased relative risk of prostate cancer; however, the absolute risk difference was small. These findings suggest a potential link between systemic inflammation and prostate cancer but may have limited immediate clinical implications and are unlikely to warrant changes in current clinical practice. Given the observational design, causal inference cannot be established, and further studies are warranted to clarify underlying mechanisms and clinical relevance.

Key points

What is Known

• Psoriasis is a chronic immune-mediated inflammatory disease.

• Psoriasis has been associated with a modestly increased overall cancer risk.

What is New Here

• Psoriasis is associated with an increased risk of prostate cancer in men aged 50–84 years.

Translational Impact

• These findings extend evidence that psoriasis is a systemic inflammatory condition with potential oncologic associations.

• Further studies are warranted to elucidate the underlying mechanisms and clinical implications of the observed association.

Introduction

Psoriasis is a chronic immune-mediated inflammatory disease characterized by dysregulated Th1/Th17 pathways and systemic inflammation, affecting approximately 2–3% of the global population and associated with substantial morbidity beyond skin involvement [1–5]. Psoriasis is increasingly recognized as a systemic inflammatory disorder rather than a skin-limited disease [5–7], with evidence linking it to multiple comorbidities, including psoriatic arthritis, cardiometabolic diseases, gastrointestinal diseases, and malignancies [7, 8].

Epidemiologic studies and meta-analyses suggest a modestly increased overall cancer risk in patients with psoriasis, particularly for keratinocyte cancers, lymphoma, and smoking-related malignancies; however, associations with site-specific cancers remain heterogeneous and inconsistent [9–12]. Prostate cancer is the most frequently diagnosed non-cutaneous cancer among men worldwide, with incidence strongly influenced by age, androgen signaling, genetic susceptibility, and prostate-specific antigen (PSA) screening practices that substantially affect detection [13–16].

Although psoriasis is associated with a modestly increased overall cancer risk, two meta-analyses have reported no association between psoriasis and prostate cancer [11, 17]. This discrepancy may reflect a methodological gap, as prostate cancer has rarely been evaluated as a primary site-specific endpoint in psoriasis-related malignancy research with adequate control for confounding variables.

Given these methodological uncertainties, large-scale real-world data analyses with rigorous adjustment for confounding and detection bias are needed to clarify whether psoriasis is associated with the risk of prostate cancer. Therefore, we conducted a retrospective cohort study using a large multicenter electronic health record database to evaluate the association between psoriasis and incident prostate cancer among men aged 50–84 years, applying propensity score matching, lag-time analysis, and adjustment for prostate cancer screening proxies to minimize confounding and detection bias.

Methods

Study Design and Data Source

This retrospective cohort analysis was performed using data derived from the TriNetX research network, a global federated platform aggregating de-identified electronic health records from multiple healthcare institutions. The database included standardized information on patient characteristics, diagnoses, procedures, medication use, and laboratory measurements [18, 19]. For the present study, longitudinal health records were obtained from 67 participating healthcare organizations, the majority of which were based in the United States, covering the period from 2006 to 2023. TriNetX allows investigators to conduct analyses within its secure environment and provides only summary-level results, without access to patient-level identifiers. As all data were fully anonymized prior to analysis, institutional review board approval was not required. All statistical analyses were conducted directly within the TriNetX platform, with data access finalized on April 10, 2026.

Eligibility Criteria and Participant Selection

To enhance the robustness of follow-up and minimize potential bias from insufficient observation time, inclusion was limited to male patients with at least two years of follow-up data and at least five outpatient visits after cohort entry. Patients with any documented diagnosis of prostate cancer (ICD-10 code C61) prior to the index date were excluded to ensure the assessment of newly diagnosed cases only.

The exposure group comprised patients aged 50 to 84 years who had a recorded diagnosis of psoriasis (ICD-10 code L40). For these patients, the index date was defined as the first occurrence of a psoriasis diagnosis within the database. The control group included patients within the same age range who had no recorded history of psoriasis. For the control group, the index date was defined as the first qualifying healthcare encounter meeting the inclusion criteria, as automatically determined by the platform.

Covariate Assessment

Baseline variables incorporated demographic factors and clinically relevant comorbidities. Specifically, these variables included age and a range of pre-existing comorbidities: alcohol-related disorders, benign prostatic hyperplasia, cerebrovascular disease, chronic kidney disease, diabetes mellitus, hyperlipidemia, hypertension, prostate inflammatory disease, ischemic heart disease, overweight/obesity, and tobacco use. All comorbidities were identified based on ICD-10 codes recorded in the electronic health records. PSA values were not directly analyzed due to substantial missing data; instead, the proportion of patients undergoing PSA testing was used as a proxy for healthcare utilization. Comorbidities were assessed during a predefined baseline window spanning from 12 months before cohort entry to the day immediately preceding the index date. These variables were used both to describe baseline characteristics of the study population and to account for differences in underlying health status between the two groups.

Outcome Assessment and Lag Period

The study outcome of interest was newly diagnosed prostate cancer, identified based on the first recorded diagnosis of prostate cancer (ICD-10 code C61) occurring after cohort entry. To minimize bias related to reverse causation—whereby undetected or preclinical malignancy could affect healthcare utilization prior to formal diagnosis—a fixed latency period of two years following the index date was applied. Prostate cancer diagnoses arising during this initial two-year window were therefore excluded from outcome assessment. This two-year window was defined as a lag period. Subsequently, patients were followed until the earliest of the following events: diagnosis of prostate cancer, death, loss to follow-up, or administrative censoring at six years after the index date. Considering the long latent and often asymptomatic course of prostate cancer, an additional sensitivity analysis was performed incorporating a longer, three-year lag period. This alternative specification applied identical inclusion criteria and analytic methods, allowing evaluation of the stability of the results under more conservative assumptions regarding disease latency.

Statistical Analysis

To address potential baseline imbalances between patients with psoriasis and the control group, propensity scores were estimated using demographic characteristics and pre-existing comorbidities, followed by 1:1 matching using a nearest-neighbor algorithm without replacement. Descriptive statistics were generated for the matched cohorts, with continuous variables reported as mean ± standard deviation and categorical variables summarized as frequencies and proportions. Balance between groups was evaluated using standardized mean differences (SMDs), with thresholds below 0.1 considered indicative of acceptable covariate balance. Because patients meeting lag-period exclusion criteria were removed after matching, the final analytic cohorts no longer retained an exact 1:1 matched structure. Time-to-event analyses were conducted using Cox proportional hazards models implemented through the TriNetX “Compare Outcomes” module, producing hazard ratios (HRs) with 95% confidence intervals (CIs) and estimates of cumulative incidence of prostate cancer. The proportional hazards assumption was examined and found to be met. Event-free survival distributions were visualized using the Kaplan–Meier method (1 minus KM estimate), and group differences were evaluated with the log-rank test. All statistical tests were two-sided, and a P value less than 0.05 was considered statistically significant.

Results

Baseline Characteristics of Study Participants

Table 1 presents the baseline characteristics of study participants. Before matching, the psoriasis group included 129,002 patients, while the control group included 6,768,845 patients. After 1:1 propensity score matching (N = 129,002 per group), a 2-year lag period was applied. During this lag period, 1,372 patients in the psoriasis group and 693 patients in the control group were excluded due to a diagnosis of prostate cancer, resulting in final cohorts of 127,630 and 128,309 patients, respectively (Table 2). Figure 1 presents the selection process of study participants.

Table 1.

Baseline characteristics of psoriasis and control groups before and after propensity score matching

Characteristic	Before matching			After matching
	Psoriasis (n = 129,002)	Control (n = 6,768,845)	SMD	Psoriasis (n = 129,002)	Control (n = 129,002)	SMD
Age at index, mean ± SD (years)	63.20 ± 8.20	63.10 ± 8.46	0.0045	63.20 ± 8.20	63.10 ± 8.56	0.0092
Age group (years)
50–64, n (%)	75,107 (58.22%)	3,980,669 (58.81%)	0.0119	75,107 (58.22%)	75,108 (58.22%)	< 0.0001
65–84, n (%)	53,895 (41.78%)	2,788,176 (41.19%)	0.0119	53,895 (41.78%)	53,894 (41.78%)	< 0.0001
Comorbidities
Hypertension, n (%)	50,761 (39.35%)	1,424,328 (21.04%)	0.4069	50,761 (39.35%)	50,766 (39.35%)	< 0.0001
Hyperlipidemia, n (%)	45,668 (35.40%)	1,227,451 (18.13%)	0.3976	45,668 (35.40%)	45,668 (35.40%)	< 0.0001
Diabetes mellitus, n (%)	23,432 (18.16%)	650,222 (9.61%)	0.2494	23,432 (18.16%)	23,439 (18.16%)	0.0001
Ischemic heart disease, n (%)	16,790 (13.02%)	474,419 (7.01%)	0.2011	16,790 (13.02%)	16,791 (13.02%)	< 0.0001
Overweight/obesity, n (%)	14,053 (10.89%)	320,013 (4.73%)	0.2313	14,053 (10.89%)	14,051 (10.89%)	< 0.0001
Benign prostatic hyperplasia, n (%)	12,117 (9.39%)	300,826 (4.44%)	0.1959	12,117 (9.39%)	12,115 (9.39%)	< 0.0001
Chronic kidney disease, n (%)	8,091 (6.27%)	234,271 (3.46%)	0.1309	8,091 (6.27%)	8,083 (6.27%)	0.0003
Cerebrovascular disease, n (%)	5,000 (3.88%)	159,041 (2.35%)	0.0880	5,000 (3.88%)	4,994 (3.87%)	0.0002
Alcohol-related disorders, n (%)	4,194 (3.25%)	97,952 (1.45%)	0.1193	4,194 (3.25%)	4,191 (3.25%)	0.0001
Tobacco use, n (%)	3,050 (2.36%)	77,778 (1.15%)	0.0926	3,050 (2.36%)	3,049 (2.36%)	< 0.0001
Prostate inflammatory disease, n (%)	597 (0.46%)	16,578 (0.25%)	0.0367	597 (0.46%)	585 (0.45%)	0.0014
Laboratory
PSA test performed, n (%)	19,128 (14.83%)	506,914 (7.49%)	0.239	19,128 (14.83%)	19,130 (14.83%)	< 0.0001

PSA prostate-specific antigen, SD standard deviation, SMD standardized mean difference

Values of SMD < 0.1 after propensity score matching indicate good balance between groups

Table 2.

Risk of prostate cancer associated with psoriasis

Analysis window	Excluded (Psoriasis/ Control)	Effective N (Psoriasis/ Control)	Prostate cancer events (Psoriasis/ Control)	Cumulative incidence (%)	Hazard ratio (95% CI)
Primary analysis
2-year lag	1,372/ 693	127,630/ 128,309	1,243/ 521	0.974%/ 0.406%	2.09 (1.89–2.32)
Sensitivity analysis
3-year lag	1,779/ 831	127,223/ 128,171	836/ 383	0.657%/ 0.299%	1.94 (1.72–2.19)

95% CI, 95% confidence interval

After 1:1 propensity score matching, each cohort initially included 129,002 patients. Patients who were diagnosed with prostate cancer prior to the time window were excluded, resulting in slightly different cohort sizes for each analysis. Cumulative incidence was calculated as (events / effective N) × 100 and rounded to three decimals

Fig. 1.

Selection of the study population. Male patients aged 50–84 years with ≥ 2 years of follow-up data and ≥ 5 outpatient visits were identified from the TriNetX network and stratified by psoriasis status. After 1:1 propensity score matching, patients who developed prostate cancer within the 2-year lag period were excluded, yielding the final analytic cohorts

After matching, the two groups were well balanced across all measured variables, with SMDs < 0.1. The mean age at the index date was approximately 63 years. Approximately 58% of patients in both groups were aged 50–64 years. The mean follow-up duration was 3.97 years (standard deviation [SD], 2.03) in the psoriasis group and 3.49 years (SD, 2.27) in the control group.

Association Between Psoriasis and Prostate Cancer (primary analysis: 2-year lag)

Over a maximum follow-up of 6 years, 1,243 cases of prostate cancer occurred in the psoriasis group and 521 cases occurred in the control group, corresponding to cumulative incidences of 0.974% and 0.406%, respectively. The Cox proportional hazards model showed that psoriasis was associated with an increased risk of prostate cancer compared with controls (HR 2.09; 95% CI, 1.89–2.32) (Table 2). Testing of the proportional hazards assumption revealed no evidence of violation in the primary 2-year lag analysis (χ² = 1.967, P = 0.161).

In age-stratified analyses, psoriasis was associated with an increased risk of prostate cancer in both age groups. Among patients aged 50–64 years, psoriasis was associated with a higher risk compared with controls (HR 1.38; 95% CI, 1.22–1.56). Similarly, among those aged 65–84 years, psoriasis was also associated with an elevated risk (HR 1.87; 95% CI, 1.63–2.13). Differences between overall and stratified HR estimates might reflect variations in baseline risk across age groups, as well as differences in cohort construction and weighting, given that stratified analyses were conducted in separately defined cohorts.

Kaplan–Meier curves showed a higher cumulative incidence of prostate cancer in the psoriasis group than in controls, with a significant difference by the log-rank test (P < 0.0001; Fig. 2).

Fig. 2.

Kaplan–Meier curves for the cumulative incidence of prostate cancer in the psoriasis and control groups over a maximum follow-up of 6 years

Sensitivity Analysis

To assess the robustness and temporal consistency of the observed association, a sensitivity analysis was performed using a 3-year lag period. The cumulative incidence of prostate cancer was 0.657% in the psoriasis group and 0.299% in the control group, corresponding to a HR of 1.94 (95% CI, 1.72–2.19) (Table 2). Testing of the proportional hazards assumption showed no evidence of violation (χ² = 0.009, P = 0.925).

Discussion

In this cohort study, psoriasis was associated with a significantly increased risk of prostate cancer compared with controls. Over a maximum follow-up of 6 years and after applying a 2-year lag period to minimize reverse causation, this association was robust and further supported by Kaplan–Meier analyses demonstrating a consistently higher cumulative incidence of prostate cancer in the psoriasis group. Sensitivity analyses using a longer, 3-year lag period yielded comparable estimates, indicating temporal consistency and reinforcing the robustness of the observed association. The elevated risk persisted across age strata, with statistically significant associations observed in both middle-aged (50–64 years) and older (65–84 years) men. However, the magnitude of the association appeared to be greater in older men, suggesting that the relationship may vary by age and should not be interpreted as uniform across age strata.

Though the mechanisms underlying the observed association between psoriasis and prostate cancer were not directly investigated in this study, several biological mechanisms may explain this association. First, chronic systemic inflammation, a hallmark of psoriasis, promotes carcinogenesis through persistent activation of proinflammatory cytokines, leading to enhanced cellular proliferation, angiogenesis, and inhibition of apoptosis, thereby creating a tumor-promoting microenvironment and facilitating malignant transformation [1, 2, 7, 20–22]. Second, immune dysregulation, characterized by sustained Th1 and Th17 activation, may impair antitumor immunity and reduce immune surveillance, allowing early neoplastic cells to evade detection [22–24]. Third, metabolic comorbidities and lifestyle factors commonly associated with psoriasis, such as obesity and smoking, may further increase the risk of prostate cancer and act synergistically with systemic inflammation [25–28]. However, further studies are warranted to elucidate the underlying mechanisms and determine the causal nature of this association.

Two meta-analyses have reported no significant association between psoriasis and prostate cancer [11, 17], possibly due to differences in study design, population characteristics, and adjustment for confounding factors. In contrast, the strength of our study lies in its focus on prostate cancer as a predefined site-specific outcome, with propensity score matching and time-to-event modeling adjusting for relevant confounders. Clinically, these findings suggest that chronic systemic inflammation in psoriasis patients extends beyond cutaneous manifestations, highlighting the need to incorporate psoriasis status into long-term cancer risk assessment models.

Several limitations should be acknowledged. First, this was an observational study; therefore, causal inference could not be established. The observed association between psoriasis and prostate cancer should be interpreted as a statistical association rather than evidence of a causal relationship. Second, although propensity score matching was applied to balance measured variables, residual confounding due to unmeasured variables could not be excluded. Important lifestyle factors such as alcohol consumption, smoking, and body mass index were not directly available in the database. Instead, alcohol-related disorders, tobacco use, and overweight/obesity were included as proxy variables based on ICD-10 codes; however, these proxies might not fully capture exposure intensity or duration. Third, patients with psoriasis might have had more frequent healthcare encounters than controls, increasing the likelihood of prostate cancer detection and introducing potential surveillance bias. To mitigate this concern, we restricted the cohort to patients with at least two years of available follow-up data and at least five outpatient visits after cohort entry. In addition, PSA testing status was incorporated into the propensity score matching procedure as a proxy for healthcare utilization and prostate cancer screening opportunity, resulting in good balance between groups after matching. Nonetheless, residual detection bias might persist because only information on whether PSA testing was performed was available, without data on testing frequency, timing, longitudinal screening patterns, or PSA values. This limitation might have restricted our ability to fully account for differences in prostate cancer screening intensity. Therefore, residual surveillance bias cannot be completely excluded. Fourth, misclassification of psoriasis or prostate cancer was possible based on ICD-10 codes. However, this approach is standard in studies using TriNetX and other claims-based databases, and the validity of ICD-10–based case definitions has been supported by prior published studies using TriNetX [29, 30]. Fifth, the control group had a shorter mean follow-up duration than the psoriasis group, which might have contributed to the lower number of observed prostate cancer events. Although Cox proportional hazards models were used to account for differences in follow-up time, this approach might not fully eliminate bias related to unequal observation periods between groups. In particular, a longer follow-up in the psoriasis group might have provided greater opportunity for event detection. Therefore, residual bias due to differential follow-up duration could not be entirely excluded. Sixth, detailed information on psoriasis severity, disease subtypes, and treatment modalities was not available in the database. These factors might be associated with differential systemic inflammatory burden and could potentially influence prostate cancer risk. As a result, we were unable to evaluate whether the observed association varied according to disease severity or treatment exposure, which might limit the clinical interpretability of our findings. Therefore, the results should be interpreted with caution. Seventh, formal statistical testing for interaction between age and psoriasis was not performed due to limitations of the TriNetX platform. Therefore, it remains unclear whether the observed differences in effect size between age groups reflect true effect modification or are due to random variation. Eighth, although formal statistical testing did not indicate violation of the proportional hazards assumption, more detailed evaluation of time-varying effects was not feasible due to platform constraints. In particular, the TriNetX platform did not allow generation of Schoenfeld residual plots or flexible modeling of time-varying coefficients. Therefore, potential temporal heterogeneity in the association could not be fully assessed, and future studies using databases with patient-level access are warranted to further evaluate this issue. Finally, the generalizability of our findings may be limited by the characteristics of the study population. The TriNetX network consists of data from participating healthcare organizations, which may not fully represent the broader population. Differences in demographic composition, healthcare access, and clinical practice patterns may influence the observed associations. Therefore, the applicability of these findings to other populations should be interpreted with caution, and further studies in diverse settings are needed to confirm these results.

Conclusions

In this cohort study, psoriasis was associated with an increased relative risk of prostate cancer compared with controls in men aged 50–84 years. However, the absolute risk difference between groups was small. This association was consistent across age groups and robust in sensitivity analyses, suggesting a temporally stable relationship. Although causal inference is limited by the observational design and the potential for residual confounding and detection bias, these findings should be interpreted as indicating a statistical association with limited immediate clinical implications. Further studies incorporating detailed clinical data, including disease severity, treatment exposure, and cancer screening information, are warranted to clarify the underlying mechanisms and clinical relevance.

Author Contributions

Shih-Wei Lai: Formal analysis, Writing – original draft, and Writing – review & editing. Kuan-Fu Liao: Conceptualization.

Funding

No funding.

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Ethics Approval and Consent to Participate

This study was performed in accordance with the Declaration of Helsinki. This study is exempt from informed consent. The data reviewed represent a secondary analysis of existing data, do not involve intervention or interaction with human subjects, and are de-identified in accordance with the de-identification standard defined in Section § 164.514(a) of the HIPAA Privacy Rule. The process by which the data are de-identified is attested to through a formal determination by a qualified expert as defined in Section § 164.514(b)(1) of the HIPAA Privacy Rule. This formal determination by a qualified expert was refreshed on December 2020.

Consent for Publication

Not applicable.

Competing interests

The authors declare no competing interests.