Abstract
Background
Prostatitis-like symptoms (PLS) significantly affect quality of life in males. While numerous studies have explored the prevalence of PLS in the general population, research on kidney transplantation (KT) recipients remains relatively scarce. Therefore, this study aimed to evaluate the prevalence of PLS among KT recipients and explore its potential risk factors.
Methods
Male KT recipients were consecutively recruited from our outpatient clinic from February 15, 2023, to December 31, 2023. Demographic data, lifestyle data, dietary habits, and data from the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) were collected through an optimized questionnaire. Univariate and multivariate logistic regression analyses were used to identify potential risk factors associated with PLS in KT recipients.
Results
A total of 503 KT recipients were collected, 283 eligible recipients were enrolled, and 37 (7.3%) reported PLS (NIH-CPSI pain score ≥ 4). Univariate and multivariate logistic regression analyses revealed that older age (odds ratio [OR]: 1.17, 95% confidence interval [CI]: 1.05–1.31, P = 0.005), higher postoperative stable creatinine level (OR: 1.03, 95% CI: 1.01–1.05, P = 0.041), a sedentary lifestyle (OR: 31.97, 95% CI: 3.88–263.61, P = 0.001), the habit of urination after ejaculation (OR: 5.39, 95% CI: 1.18–24.67, P = 0.030), and the presence of severe negative emotions (OR: 2.48, 95% CI: 1.65–3.72, P < 0.001) may be risk factors for PLS. In contrast, a higher education level (OR: 0.43, 95% CI: 0.26–0.69, P < 0.001) and high daily water intake (≥ 2000 mL; OR: 0.08, 95% CI: 0.01–0.57, P = 0.012) may be protective factors.
Conclusion
This study represents the first evaluation of PLS prevalence in the KT population and has identified several potential risk factors for PLS in this group. These findings provide potential targets for clinicians in the prevention and treatment of PLS following KT.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12894-025-02013-0.
Keywords: Kidney transplantation, Prostatitis-like symptoms, Risk factors
Introduction
Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is one of the most common urogenital disorders [1, 2] and is characterized by a complex array of clinical manifestations, including lower urinary tract symptoms, chronic perineal and pelvic pain, and even sexual dysfunction [3]. These factors significantly impair the quality of life of individuals with CP/CPPS, with an extent of impairment comparable to that associated with myocardial infarction, angina, and Crohn's disease [4–6]. The etiology of this disease remains unclear, with potential mechanisms involving damage from urinary reflux, dietary factors, pelvic floor dysfunction, hormonal imbalances, and autoimmune dysregulation [7, 8]. Although the epidemiological characteristics of Prostatitis-like symptoms (PLS) have been studied in the general male population, with reported prevalence ranging from 2 to 15% [9–11], the prevalence and characteristics of PLS in this particular population of kidney transplantation (KT) recipients are still relatively limited.
KT, as an effective treatment for end-stage renal disease, can significantly improve patients' quality of life [12]. However, long-term immunosuppressive therapy increases the risk of infection and may alter recipients' susceptibility to various diseases, including PLS. The state of immunosuppression may not only increase the risk of certain infections but also impact the regulation of inflammatory responses, both of which are key factors in the pathogenesis of PLS. KT recipients may exhibit a different prevalence and progression of PLS due to factors such as physical and psychological stress, chronic pain, and changes in quality of life after surgery. Since PLS may cause a series of uncomfortable symptoms that may cause these KT recipients to feel more tired, anxious or depressed in their daily lives, severely affecting their quality of life. Studies [13, 14] suggest that reduced quality of life may be associated with an increase in postoperative complications, such as infection, acute rejection after transplantation, and graft loss, affecting postoperative recovery and long-term health outcomes of KT recipients. These studies highlight the importance of taking PLS seriously in KT patients.
In view of the above background, this study aimed to explore the epidemiological characteristics of PLS in KT recipients, including its prevalence and related risk factors. By identifying modifiable risk factors, our findings seek to inform the development of preventive strategies to reduce the incidence of PLS and improve the quality of life in this population.
Materials and methods
Participants recruitment
We consecutively recruited male KT patients at the KT follow-up clinic of the First Affiliated Hospital of Anhui Medical University between February 15, 2023 and December 31, 2023 and the questionnaire completion was performed in the outpatient clinic. This was a single-center, cross-sectional observational study. A predesigned questionnaire composed of 31 variables related to PLS was adapted to collect potential risk factors [15], while the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) was utilized to identify PLSs and evaluate the severity of symptoms [16]. Two independent trained researchers were responsible on-site for distributing, collecting, and verifying the questionnaires. Standardized face-to-face interviews were conducted, exposure variables and outcome measures were collected at the same time point, and this study was approved by the Institutional Review Board of the First Affiliated Hospital of Anhui Medical University (Ethics Approval: PJ-2022–04–46). Informed consent was obtained from the participants at the beginning of the investigation.
Inclusion and exclusion criteria
All enrolled recipients received triple immunosuppression (tacrolimus, prednisone, and mycophenolate). The primary inclusion criterion was that blood concentrations of tacrolimus (ng/mL) remained within the normal range(4–15 ng/ml) following KT. As a potent inhibitor of calcineurin, tacrolimus is typically prescribed post kidney transplantation to markedly decrease the likelihood of organ rejection [17]. The corresponding tacrolimus concentration was adjusted according to the different time after KT, and the other drugs were basically the same among different patients. All participants were required to be older than 18 years. Furthermore, individuals with an acute genitourinary infection or those who had undergone pelvic surgery within the previous 3 months were excluded from the study.
Variable identification
As previously described [15], the questionnaire collected information on demographics, dietary habits, and medical history, the detailed contents of the questionnaire are provided in Table S1. The categorical variables included educational level (illiterate or semi- illiterate, primary school, junior high school, high school, associate degree and above), economic pressure (no, yes), hold urine (no, yes), occupational hazards (no, yes), sedentary lifestyle (> 8 h/d with < 20 d/m, > 8 h/d with ≥ 20 d/m), anxiety (no, yes), dysphoria (no, yes), alopecia (no, yes), water intake (< 2000 mL/d, ≥ 2000 mL/d), gastrointestinal distress susceptibility to spicy food intake (no, yes), frequency of spicy food intake/week (0 d, 1–2 d, 3–5 d, 6–7 d), degree of spicy food intake (0 SHU, 1–1499 SHU, 1500–2999 SHU, ≥ 3000 SHU), dietary preferences (normal diet, bland diet, salty and spicy diet), frequency of ejaculation (≤ 1 time/w, ≥ 2 times/w), holding urine before ejaculation (no, yes), and urination after ejaculation (no, yes). Accordingly, age (years), BMI (kg/m2), metabolic equivalent of task (calculated as the product of exercise intensity and the duration of exercise per day), self-rating of bad emotion, daily alcohol intake and daily tar intake were recorded as continuous variables, the detailed methodology for data collection is described in Table S2.
We also collected baseline information related to KT recipients, which included deceased donor (no, yes), peritoneal dialysis before KT (no, yes), dialysis duration before KT (years), time post KT dialysis (months), tacrolimus concentration(ng/ml) and postoperative stable creatinine level (mg/dL), which was defined as the baseline value with fluctuations within ± 15% during the preceding two weeks [18].
Outcome measurement
The primary outcome of this study was the prevalence of PLS among KT recipients, and the secondary outcome was the identification of risk factors for PLS. The independent variable is dichotomic outcome referring to the diagnose of PLS, based on the score of pain/discomfort domain of the NIH-CPSI. The NIH-CPSI consists of three sections, with a total score of 43 points, which contains scores in three domains, pain/discomfort domain (Q1–4 total score: 21 points), urinary symptoms domain (Q5–6 total score: 10 points), and quality of life impact domain (Q7–9 total score: 12 points). This pain/discomfort domain includes pain in the perineum, tip of the penis (not related to urination), pubic or bladder area, and pain or burning during urination and ejaculation, along with its frequency and severity [19]. Participants scoring ≥ 4 points in the pain/discomfort domain over the past week, regardless of the total NIH-CPSI score, are classified as suffering from PLS and assigned to the PLS group [16, 20–22]. Conversely, participants who scored zero in the total NIH-CPSI were assigned to the control group, participants not included in the PLS group but with any scores for NIH-CPSI were excluded from the study.
Statistical analysis
The Kolmogorov‒Smirnov test was initially performed to evaluate the normality of continuous variables [23]. Continuous variables that follow a normal distribution were presented as mean ± standard deviation (SD), discrete variables and continuous variables that do not follow a normal distribution were presented as median and interquartile (IQR). Non-dichotomous qualitative data within nominal variables were transformed into dummy variables, with the category of 'no' or the lowest value as the reference, which allow nominal variables to enter the regression model for analysis and ensure interpretability and consistency of the results, they were presented in frequency and percentage (n, %), similar to ordinal variables. The Mann‒Whitney U test or the chi-square test/Fisher's exact test were utilized for baseline comparative analysis to identify confounders. Univariate logistic regression analysis was employed to screen for predisposing factors for PLS, followed by an adjustment using bidirectional stepwise regression analysis to identify independent predictors for PLS. This adjustment included variables with a P value less than 0.1 in the univariate analysis and identified confounders. The Cochran-Armitage trend test was used to detect the linear trend of the ordered categorical variables. Multicollinearity analysis was conducted to assess collinearity among independent variables, employing a variance inflation factor (VIF) threshold greater than 5 [24]. We also conducted a post-hoc power analysis using G*Power 3.1. Data processing and analysis were carried out using R version 4.3.0, released on February 12, 2024, alongside the Storm Statistical Platform (available at www.medsta.cn/software), and the results were validated using SPSS Statistics 25.0 (IBM Corp., Armonk, N.Y., USA).
Results
Summary of participants’ baseline characteristics
The study initially included 503 participants, 10 of whom were excluded due to incomplete data or acute genitourinary infection within the previous 3 months. Of the remaining 493 subjects, 210 reported symptoms of urination or mild pain, leaving 283 subjects for the final analysis. It was found that 7.3% (37 out of 503) of the participants had experienced PLS in the last week, while 48.8% (246 out of 503) of the participants had a total NIH-CPSI score of 0 and showed no symptoms of acute genitourinary infection (Fig. 1). Baseline analysis showed that post-KT recipients with PLS were significantly older, with a median age of 50 years compared with 39 years for those without PLS (P < 0.001). They also had lower levels of education (P < 0.001) and a lower percentage of deceased donors (37.84% vs. 59.35%, P = 0.014). Additionally, the postoperative stable creatinine level was marginally higher in the PLS group (P = 0.039). However, no significant differences were observed between the two groups in terms of BMI, economic pressure, history of peritoneal dialysis before KT, duration of dialysis before KT, time post-KT, and tacrolimus levels (Table 1). This suggests that there might be a confounding effect complicating the identification of predisposing factors for PLS in post-KT recipients.
Fig. 1.
Flow chart
Table 1.
Baseline Characteristics of Post-Kidney Transplant Patients with & without PLS
| Variables | Total (n = 283) |
Control (n = 246) |
PLS (n = 37) |
Statistic | P-value |
|---|---|---|---|---|---|
| Demographics | |||||
| Age, median [IQR] | 40.00 (35.00—45.00) | 39.00 (35.00—43.00) | 50.00 (46.00—52.00) | Z = 6.972 | < 0.001 |
| BMI, median [IQR] | 23.42 (21.11—25.71) | 23.46 (21.27—25.65) | 22.39 (19.03—25.74) | Z = 1.432 | 0.152 |
| Educational level, n (%) | χ2 = 50.447F | < 0.001 | |||
| Illiterate or semi- illiterate | 19 (6.71) | 9 (3.66) | 10 (27.03) | ||
| Primary school | 36 (12.72) | 24 (9.76) | 12 (32.43) | ||
| Junior high school | 42 (14.84) | 36 (14.63) | 6 (16.22) | ||
| High school | 79 (27.92) | 76 (30.89) | 3 (8.11) | ||
| Associate degree and above | 107 (37.81) | 101 (41.06) | 6 (16.22) | ||
| Economic pressure, n (%) | χ2 = 0.000F | 1.000 | |||
| No | 23 (8.13) | 20 (8.13) | 3 (8.11) | ||
| Yes | 260 (91.87) | 226 (91.87) | 34 (91.89) | ||
| Prostatitis-like Symptoms | |||||
| Pain score, mean median [IQR] | 0.00 (0.00—0.00) | 0.00 (0.00—0.00) | 6.00 (5.00—8.00) | Z = 28.574 | < 0.001 |
| NIH-CPSI score, median [IQR] | 0.00 (0.00—0.00) | 0.00 (0.00—0.00) | 14.00 (10.00—17.00) | Z = 28.571 | < 0.001 |
| Basic information of kidney transplantation | |||||
| Deceased donors, n (%) | χ2 = 6.057 | 0.014 | |||
| No | 123 (43.46) | 100 (40.65) | 23 (62.16) | ||
| Yes | 160 (56.54) | 146 (59.35) | 14 (37.84) | ||
| Peritoneal dialysis before KT, n (%) | χ2 = 0.606 | 0.436 | |||
| No | 199 (70.32) | 175 (71.14) | 24 (64.86) | ||
| Yes | 84 (29.68) | 71 (28.86) | 13 (35.14) | ||
| Dialysis duration before KT (years), median [IQR] | 3.00 (2.00—4.00) | 3.00 (2.00—4.00) | 3.00 (2.00—5.00) | Z = 0.021 | 0.983 |
| Time post-KT (months), median [IQR] | 35.00 (23.00—50.00) | 35.50 (24.00—49.00) | 30.00 (20.00—57.00) | Z = 0.583 | 0.560 |
| Postoperative stable creatinine level (mg/dL), median [IQR] | 120.00 (110.00—130.00) | 120.00 (110.00—130.00) | 124.00 (110.00—140.00) | Z = 2.085 | 0.039 |
| Tacrolimus concentration (ng/mL), median [IQR] | 6.90 (5.60—7.60) | 6.90 (5.60—7.60) | 7.00 (6.00—7.00) | Z = 0.224 | 0.824 |
Statistic methods: Z, Mann–Whitney U test; χ2, Chi-Squared Test/Fisher's Exact Test (F)
Abbreviation: BMI Body mass index, PLS Prostatitis-like symptoms, KT kidney transplantation, n Number, IQR Interquartile range, SD Standard deviation, mg Milligram, ng Nanogram, dL Deciliter, mL Milliliter
Crude associations with PLS in post-KT recipients: a univariable screen
Recently, it has been considered that abnormal immune system function is closely related to the occurrence of PLS, making the identification of potential susceptibility factors in post-KT recipients crucial for improved prevention, especially given the state of immunosuppression in this population. Through univariate regression analysis, we initially found that variables significantly correlated with PLS (2), including a sedentary lifestyle (> 8 h/day along with > 20 days/month, OR: 7.97, 95% CI: 2.61–24.28), anxiety (OR: 8.26, 95% CI: 3.61–18.92), dysphoria (OR: 5.98, 95% CI: 2.88–12.42), high self-rating of bad emotion (OR: 1.98, 95% CI: 1.60–2.46), alopecia (OR: 2.46, 95% CI: 1.21–4.97), and high susceptibility to gastrointestinal distress from spicy food intake (OR: 8.97, 95% CI: 4.23–19.03), were significantly correlated with PLS. Additionally, higher water intake (≥ 2000 mL/day) (OR: 0.22, 95% CI: 0.10–0.48) was associated with a reduced risk of PLS.
Table 2.
Crude Associations with PLS in Post-KT Recipients: A Univariable Screen
| Variables | Control (n = 246) | PLS (n = 37) | OR (95% CI) | P-value |
|---|---|---|---|---|
| Hold urine | ||||
| No | 210 (85.37) | 31 (83.78) | 1.00 (Reference) | |
| Yes | 36 (14.63) | 6 (16.22) | 1.13 (0.44 - 2.90) | 0.801 |
| Occupational hazards | ||||
| No | 231 (93.90) | 32 (86.49) | 1.00 (Reference) | |
| Yes | 15 (6.10) | 5 (13.51) | 2.41 (0.82 - 7.07) | 0.110 |
| Sedentary lifestyle (> 8 h/d) | ||||
| ≤ 20 d/m | 239 (97.15) | 30 (81.08) | 1.00 (Reference) | |
| > 20 d/m | 7 (2.85) | 7 (18.92) | 7.97 (2.61 - 24.28) | < 0.001 |
| MET, median [IQR] | 1.25 (0.75, 3.00) | 1.50 (0.75, 3.00) | 1.07 (0.95 - 1.22) | 0.264 |
| Anxiety | ||||
| No | 171 (69.51) | 8 (21.62) | 1.00 (Reference) | |
| Yes | 75 (30.49) | 29 (78.38) | 8.26 (3.61 - 18.92) | < 0.001 |
| Dysphoria | ||||
| No | 193 (78.46) | 14 (37.84) | 1.00 (Reference) | |
| Yes | 53 (21.54) | 23 (62.16) | 5.98 (2.88 - 12.42) | < 0.001 |
| Self-rating of bad emotion, median [IQR] | 3.00 (2.00 - 5.00) | 7.00 (6.00 - 8.00) | 1.98 (1.60 - 2.46) | < 0.001 |
| Alopecia | ||||
| No | 154 (62.60) | 15 (40.54) | 1.00 (Reference) | |
| Yes | 92 (37.40) | 22 (59.46) | 2.46 (1.21 - 4.97) | 0.013 |
| Alcohol intake (g/d), median [IQR] | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.94 (0.77–1.14.77.14) | 0.520 |
| Tar intake (mg/d), median [IQR] | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.89 (0.75–1.07.75.07) | 0.211 |
| Water intake | ||||
| < 2000 mL/d | 26 (10.57) | 13 (35.14) | 1.00 (Reference) | |
| ≥ 2000 mL/d | 220 (89.43) | 24 (64.86) | 0.22 (0.10 - 0.48) | < 0.001 |
| Gastrointestinal distress susceptibility to spicy food intake | ||||
| No | 204 (82.93) | 13 (35.14) | 1.00 (Reference) | |
| Yes | 42 (17.07) | 24 (64.86) | 8.97 (4.23 - 19.03) | < 0.001 |
| Frequency of spicy food intake/w | ||||
| 0 d | 32 (13.01) | 4 (10.81) | 1.00 (Reference) | |
| 1–2 d | 161 (65.45) | 24 (64.86) | 1.19 (0.39 - 3.67) | 0.759 |
| 3–5 d | 44 (17.89) | 9 (24.32) | 1.64 (0.46 - 5.78) | 0.445 |
| 6–7 d | 9 (3.66) | 0 (0.00) | 0.00 (0.00 - Inf) | 0.986 |
| Degree of spicy food intake (SHU) | ||||
| 0 | 25 (10.16) | 2 (5.41) | 1.00 (Reference) | |
| 1–1499 | 70 (28.46) | 6 (16.22) | 1.07 (0.20 - 5.66) | 0.935 |
| 1500-2999 | 124 (50.41) | 23 (62.16) | 2.32 (0.51 - 10.47) | 0.274 |
| ≥ 3000 | 27 (10.98) | 6 (16.22) | 2.78 (0.51 - 15.06) | 0.236 |
| Dietary Preferences | ||||
| Normal diet | 181 (73.58) | 25 (67.57) | 1.00 (Reference) | |
| Bland diet | 39 (15.85) | 7 (18.92) | 1.30 (0.52 - 3.22) | 0.571 |
| Salty and spicy diet | 26 (10.57) | 5 (13.51) | 1.39 (0.49 - 3.96) | 0.535 |
| Frequency of ejaculation | ||||
| ≤ 1 time/w | 109 (44.31) | 13 (35.14) | 1.00 (Reference) | |
| ≥ 2 times/w | 137 (55.69) | 24 (64.86) | 1.47 (0.71 - 3.02) | 0.295 |
| Hold urine before ejaculation | ||||
| No | 215 (87.40) | 30 (81.08) | 1.00 (Reference) | |
| Yes | 31 (12.60) | 7 (18.92) | 1.62 (0.65 - 4.00) | 0.297 |
| Urination after ejaculation | ||||
| No | 86 (34.96) | 7 (18.92) | 1.00 (Reference) | |
| Yes | 160 (65.04) | 30 (81.08) | 2.30 (0.97 - 5.46) | 0.058 |
Statistic methods: Univariate logistic regression analysis
Abbreviation: PLS Prostatitis-like symptoms, n Number, OR Odds ratio, IQR Interquartile range, CI Confidence interval, h Hours, d, Day (s), m Month, MET Metabolic equivalent of task, g Gram, mg Milligram, mL Milliliter, SHU Scoville Heat Units, w week
Multivariable logistic analysis of independent predictors for PLS in post-KT recipients
In the multivariable model, none of the included variables showed multicollinearity (all VIFs < 2.5). Through bidirectional stepwise regression analysis (Table 3), age was identified as a significant predictor, with each additional year increasing the likelihood of experiencing PLS by 17% (adjusted OR = 1.17, P = 0.005), while for every 1 mg/dl increase in postoperative creatinine, the risk of PLS increased by 3% (adjusted OR = 1.03, P = 0.041). Education level was also found to significantly impact the risk, with higher education levels being associated with a decreased risk of PLS (adjusted OR = 0.43, P < 0.001). Specifically, individuals who had achieved an associate degree or higher were 95% less likely to develop PLS than those who were illiterate or semi- illiterate (adjusted P < 0.05). Notably, leading a sedentary lifestyle for more than 8 h per day for more than 20 days a month significantly increased the risk (adjusted OR = 31.97, P = 0.001), as did urination after ejaculation (adjusted OR = 5.39, P = 0.030). As expected, higher water intake (≥ 2000 mL/day) significantly reduced the risk of PLS (adjusted OR = 0.08, P = 0.012). Additionally, people who experienced PLS reported more severe negative emotions (adjusted OR = 2.48, P < 0.001). The post-hoc statistical power for the significant predictors in the multivariable model were list in Table S3, the results show high statistical power for the associations of education level, sedentary lifestyle (> 8 h/d, > 20 d/m), water intake (≥ 2000 mL/day) and urination after ejaculation with PLS. In contrast, the power was inadequate for age, postoperative stable creatinine level, and negative emotion. This inadequate power for the latter variables is likely attributable to the limited sample size. In conclusion, these findings underscore the importance of considering a range of demographic, lifestyle, and psychological factors as independent predictors of PLS in recipients after KT.
Table 3.
Multivariable Logistic Analysis of Independent Predictors for PLS in Post-KT Recipients
| Variables | aBeta | aS.E | aZ | aOR (95% CI) | aP-value |
|---|---|---|---|---|---|
| Age | 0.16 | 0.06 | 2.83 | 1.17 (1.05—1.31) | 0.005 |
| Educational level | −0.85 | 0.25 | −3.44 | 0.43 (0.26—0.69) | < 0.001* |
| Illiterate or semi- illiterate | 1.00 (Reference) | ||||
| Primary school | −0.76 | 1.03 | −0.74 | 0.47 (0.06—3.54) | 0.462 |
| Junior high school | −2.12 | 1.02 | −2.07 | 0.12 (0.02—0.89) | 0.038 |
| High school | −4.41 | 1.35 | −3.27 | 0.01 (0.00—0.17) | 0.001 |
| Associate degree and above | −3 | 1.28 | −2.34 | 0.05 (0.00—0.61) | 0.003 |
| Sedentary lifestyle (> 8 h/d) | |||||
| ≤ 20 d/m | 1.00 (Reference) | ||||
| > 20 d/m | 3.46 | 1.08 | 3.22 | 31.97 (3.88—263.61) | 0.001 |
| Postoperative Stable Creatinine Level (mg/dL) | 0.03 | 0.01 | 2.04 | 1.03 (1.01—1.05) | 0.041 |
| Self-rating of bad emotion | 0.91 | 0.21 | 4.39 | 2.48 (1.65—3.72) | < 0.001 |
| Water intake | |||||
| < 2000 mL/d | 1.00 (Reference) | ||||
| ≥ 2000 mL/d | −2.58 | 1.03 | −2.51 | 0.08 (0.01—0.57) | 0.012 |
| Urination after ejaculation | |||||
| No | 1.00 (Reference) | ||||
| Yes | 1.69 | 0.78 | 2.17 | 5.39 (1.18—24.67) | 0.030 |
Statistic methods: aAdjustment was made using bidirectional stepwise regression analysis by including variables with a P-value of less than 0.1 in the univariable analysis (Hold urine, Sedentary lifestyle, Anxiety, Dysphoria, Self-rating of bad emotion, Alopecia, Water intake, Gastrointestinal distress susceptibility to spicy food intake, Urination after ejaculation) and those unevenly distributed at baseline (Age, Educational level, Posthumous kidney donation, Postoperative Stable Creatinine Level). *, P for tend was analyzed using Cochran-Armitage trend test under the above-mentioned adjustment
Abbreviation: PLS Prostatitis-like symptoms, OR Odds ratio, CI Confidence interval, mg Milligram, dL Deciliter, h Hours, d Day (s), m Month, mL Milliliter
Discussion
Prostatitis is a common urological outpatient disorder, and a previous study reported that nearly 2 million men visit clinics each year in the United States seeking medical care for prostatitis [25]. In previous studies, the global prevalence of PLS has been reported to be 2%−10% [26]. In a cross-sectional survey based on a Chinese population, 1071 (8.4%) individuals reported PLS [20]. In our study, the prevalence of PLS in this particular group of KT recipients was 7.3%, which is consistent with the previously reported prevalence. Our findings suggest that PLS is common among adult males, regardless of KT status.
Age is a potential risk factor for many diseases. Previous studies have shown that the prevalence of PLS increases with age [27, 28]. In a survey conducted in Penang, Malaysia, the prevalence increased with age: it was 6.3% in 20- to 30-year-old men, 8.9% in 31- to 40-year-old men, and 12.6% in 41- to 50-year-old men (P < 0.001) [29]. However, some studies have reported a higher incidence of PLS in younger men than in older men [30]. In our study, we found that age is an independent risk factor for PLS in the KT population. Although the underlying mechanisms are unclear, the intractability and recurrence of PLS may be reasons why age is associated with PLS. In this study, it was found that KT recipients with higher levels of education had a lower risk of developing PLS, which was consistent with previous studies [20, 31]. Ku et al. [22] conducted a survey of 16,321 males aged 20 years from the community and reported that the likelihood of developing PLS was 1.8 times greater for junior high graduates and 1.4 times greater for high school graduates than for university graduates. However, some studies argue that the level of education has no significant relationship with PLS [32]. The higher risk of PLS associated with lower educational levels may be due to individuals in this group typically paying less attention to healthcare and engaging in unhealthy lifestyles, such as smoking and drinking.
Physiological factors may contribute to the development of PLS. Some studies consider a sedentary lifestyle to be a risk factor for PLS [15], while others find no association between a sedentary lifestyle and PLS [27]. Our study revealed that a sedentary lifestyle (> 8 h/day along with > 20 days/month) was an independent risk factor for PLS. The amount of drinking water has also been reported to have an impact on the PLS, and a few studies have shown that increased water consumption is a protective factor against PLS [15, 27]. In the present study, this protective effect was obvious, but the underlying mechanisms remain unclear. This study also revealed that urination after ejaculation was able to increase the risk of PLS, possibly through dilatation of the prostatic ducts after ejaculation, and that urine may reflux along these dilated ducts into the prostatic lumen, which in turn increases the risk of prostatitis. In addition, we also found that higher creatinine levels after KT were associated with PLS, possibly because higher creatinine levels indicate a diminished ability of the kidneys to clear waste and excess fluid, leading to the accumulation of metabolic waste in the body that increases the risk of PLS. Previous study has shown that systolic blood pressure is positively correlated with storage symptoms and negatively correlated with voiding symptoms in benign prostate enlargement patients with lower urinary tract symptoms, which may be related to storage symptoms inducing sympathetic overactivity and thereby increasing systolic blood pressure [33]. However, in the present study, blood pressure was not included as an observed variable at the design and data collection stages; therefore, we were unable to evaluate the relationship between systolic blood pressure and PLS in the KT population, future studies may need to incorporate this variable into the analytical model to further assess this association and to test this clinically important hypothesis.
Psychological factors have also been reported to contribute to the development of PLS. With multiple studies indicating that stress could increase the risk of PLS [27, 34, 35]. Wang et al. [36] reported that severe negative emotions (such as sadness, anxiety, irritability, and depression) are independent risk factors for PLS. Lien et al. [37] conducted a case‒control study on prostatitis among patients with depression (13,019 individuals) and without depression (53,026 individuals), revealing that individuals with depression had a 1.76 times greater risk of developing PLS than those without depression. Depression may be involved in the occurrence and clinical course of PLS [38, 39]; in fact, depression and PLS may share some common pathophysiological mechanisms [40], the prostate responds to emotional stimuli through the autonomic nervous system, and experimental evidence also supports the theory that psychological stress may lead to prostate dysfunction [41]. In our study, we found that severe negative emotions lead to an increased risk of PLS in KT recipients, consistent with previous findings. However, in this study, it was difficult to determine whether severe negative emotions were the cause or the result of PLS.
Previous studies have revealed several potential risk factors for PLS, but its exact etiology remains unclear [15, 42]. Previous studies have shown that abnormalities of the immune system may play an important role in PLS [43]. KT recipients have a special immune environment due to long-term immunosuppressants, which provides us with a unique perspective for exploring the etiology of PLS. Piovesan et al. [44] investigated 216 individuals with BPH and reported that the prevalence of type IV prostatitis in the general population who did not receive immunosuppressants was significantly higher than that in the immunosuppressed KT population (78% vs 9%, P < 0.001), suggesting that immunosuppressants have a protective effect against prostatitis. The prevalence of PLS in the KT population in our study was similar to that in the unselected population, the possible reason for this difference is that chronic immunosuppression may, on one hand, attenuate immune-mediated inflammatory responses (exerting a potential “protective effect”), while on the other hand, it may increase the susceptibility to infection, and interact with factors such as age, lifestyle, and psychological stress. These opposite effects may produce a nearly mutually canceling "net effect", thereby explaining why the overall prevalence has not increased. Consistent with this, the independent factors identified by our multivariate model are mainly adjustable non-immune factors (such as sedentary behavior, water intake, negative emotions), suggesting that the occurrence of PLS in KT recipients is more influenced by physiological and psychological factors rather than solely determined by immunosuppression. Moreover, in our study, no microbial culture was conducted to identify which prostatitis type, which might also be the cause of this difference. Factors related to KT, such as type of dialysis, dialysis duration before KT, and time post-KT, showed no difference between recipients with PLS and those without PLS. The results showed that other related factors, such as lifestyle, living environment and psychological factors, played important roles in the occurrence of PLS.
This study represents the first report on PLS within the KT population, identifying the incidence and associated risk factors for PLS among this specific group for the first time. However, there are several limitations to this study. Firstly, symptoms may be related to preexisting lower urinary tract diseases other than prostatitis, and all KT recipients underwent lower abdominal surgery, which may have caused the study to overestimate the prevalence of PLS. Secondly, owing to the lack of standardized microbiological evaluation at enrollment (including urine culture, expressed prostatic secretion/post-massage urine, and semen cultures), consequently, we could not distinguish prostatitis phenotypes (e.g., NIH category II, category III, and granulomatous prostatitis [45]) and infection-related etiologies could not be adjudicated at the individual level, causal inferences about immunosuppression as a risk factor for PLS should be interpreted with caution. Finally, given the limited sample size and single-center setting which constrain external validity, this research should be positioned as an exploratory investigation. Therefore, the current results are merely preliminary correlational evidence and do not allow causal inference. Multicenter, larger sample-size and adequately prospective cohorts are warranted to accurately determine the prevalence of PLS and definitively validate its risk factors in KT recipients.
Conclusion
This is the first study to investigate PLS within the KT population, revealing that the prevalence of PLS among KT recipients is similar to that in the general adult male population and that age, educational level, sedentary lifestyle, postoperative stable creatinine level, water intake (< 2000 ml/day), severe negative emotions and urination after ejaculation were identified as potential risk factors for PLS in the KT population. For KT recipients with a high risk of PLS, interventions focusing on lifestyle and psychological aspects should be considered.
Supplementary Information
Abbreviations
- PLS
Prostatitis-like symptoms
- KT
Kidney transplantation
- CP/CPPS
Chronic prostatitis/chronic pelvic pain syndrome
- NIH-CPSI
National Institutes of Health Chronic Prostatitis Symptom Index
Authors’ contributions
All authors played a substantial role in this study, from conceptualization, study formulation, implementation, and data gathering to analysis and interpretation, or in all these areas; took part in the initial drafting, subsequent revisions, or in-depth critique of the manuscript; endorsed the final version set for publication; agreed on the choice of the journal for submission; and collectively assumed responsibility for every element of the work.
Funding
This study is supported by the National Natural Science Foundation of China (Grant No. 82470783).
Data availability
The data of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
All procedures involving human participants were conducted in accordance with the ethical standards of The Committee on Medical Ethics of The First Affiliated Hospital of Anhui Medical University (Ethics Approval. No. PJ-2022–04-46) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all the subjects when they were enrolled.
Consent for publication
All authors have read and approved the manuscript for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fei Zhang, Chen Jin and Pengfei Li contributed equally to the study.
Contributor Information
Xianguo Chen, Email: cxg7866186@126.com.
Chaozhao Liang, Email: liang_chaozhao@ahmu.edu.cn.
Guiyi Liao, Email: liaoguiyi@ahmu.edu.cn.
References
- 1.DeWitt-Foy ME, Nickel JC, Shoskes DA. Management of chronic prostatitis/chronic pelvic pain syndrome. Eur Urol Focus. 2019;5(1):2–4. [DOI] [PubMed] [Google Scholar]
- 2.Schwartz ES, Xie A, La JH, Gebhart GF. Nociceptive and inflammatory mediator upregulation in a mouse model of chronic prostatitis. Pain. 2015;156(8):1537–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Okamoto K, Kurita M, Yamaguchi H, Numakura Y, Oka M. Effect of tadalafil on chronic pelvic pain and prostatic inflammation in a rat model of experimental autoimmune prostatitis. Prostate. 2018;78(10):707–13. [DOI] [PubMed] [Google Scholar]
- 4.Motrich RD, Maccioni M, Molina R, Tissera A, Olmedo J, Riera CM, et al. Reduced semen quality in chronic prostatitis patients that have cellular autoimmune response to prostate antigens. Hum Reprod. 2005;20(9):2567–72. [DOI] [PubMed] [Google Scholar]
- 5.Gao J, Xu C, Liang C, Su P, Peng Z, Shi K, et al. Relationships between intravaginal ejaculatory latency time and National Institutes of Health-chronic prostatitis symptom index in the four types of premature ejaculation syndromes: a large observational study in China. J Sex Med. 2014;11(12):3093–101. [DOI] [PubMed] [Google Scholar]
- 6.Wenninger K, Heiman JR, Rothman I, Berghuis JP, Berger RE. Sickness impact of chronic nonbacterial prostatitis and its correlates. J Urol. 1996;155(3):965–8. [PubMed] [Google Scholar]
- 7.De Marzo AM, Platz EA, Sutcliffe S, Xu J, Grönberg H, Drake CG, et al. Inflammation in prostate carcinogenesis. Nat Rev Cancer. 2007;7(4):256–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Penna G, Mondaini N, Amuchastegui S, Degli Innocenti S, Carini M, Giubilei G, Fibbi B, Colli E, Maggi M, Adorini L: Seminal plasma cytokines and chemokines in prostate inflammation: interleukin 8 as a predictive biomarker in chronic prostatitis/chronic pelvic pain syndrome and benign prostatic hyperplasia. Eur Urol. 2007;51(2):524–533; discussion 533. [DOI] [PubMed]
- 9.Krieger JN, Lee SW, Jeon J, Cheah PY, Liong ML, Riley DE: Epidemiology of prostatitis. Int J Antimicrob Agents. 2008;31 Suppl 1(Suppl 1):S85–90. [DOI] [PMC free article] [PubMed]
- 10.Tan JK, Png DJ, Liew LC, Li MK, Wong ML. Prevalence of prostatitis-like symptoms in Singapore: a population-based study. Singapore Med J. 2002;43(4):189–93. [PubMed] [Google Scholar]
- 11.Marszalek M, Wehrberger C, Hochreiter W, Temml C, Madersbacher S. Symptoms suggestive of chronic pelvic pain syndrome in an urban population: prevalence and associations with lower urinary tract symptoms and erectile function. J Urol. 2007;177(5):1815–9. [DOI] [PubMed] [Google Scholar]
- 12.Hernández D, Muriel A, Abraira V. Current state of clinical end-points assessment in transplant: key points. Transplant Rev (Orlando). 2016;30(2):92–9. [DOI] [PubMed] [Google Scholar]
- 13.Habwe VQ. Posttransplantation quality of life: more than graft function. Am J Kidney Dis. 2006;47(4 Suppl 2):S98-110. [DOI] [PubMed] [Google Scholar]
- 14.Chen ZL, Ding X, Zhuo L, Li RH, Zhang H. Health-promoting lifestyle and its predictors in renal transplant recipients in Hunan, China: a cross-sectional study. Patient Prefer Adherence. 2024;18:1205–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Zhang M, Jin C, Kong X, Meng J, Fan S, Ding Y, et al. Identification of novel susceptibility factors related to CP/CPPS-like symptoms: evidence from a multicenter case-control study. Prostate. 2022;82(7):772–82. [DOI] [PubMed] [Google Scholar]
- 16.Nickel JC, Downey J, Hunter D, Clark J. Prevalence of prostatitis-like symptoms in a population based study using the National Institutes of Health chronic prostatitis symptom index. J Urol. 2001;165(3):842–5. [PubMed] [Google Scholar]
- 17.Yu M, Liu M, Zhang W, Ming Y. Pharmacokinetics, pharmacodynamics and pharmacogenetics of tacrolimus in kidney transplantation. Curr Drug Metab. 2018;19(6):513–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Pöge U, Gerhardt T, Palmedo H, Klehr HU, Sauerbruch T, Woitas RP. MDRD equations for estimation of GFR in renal transplant recipients. Am J Transplant. 2005;5(6):1306–11. [DOI] [PubMed] [Google Scholar]
- 19.Litwin MS, McNaughton-Collins M, Fowler FJ, Jr., Nickel JC, Calhoun EA, Pontari MA, Alexander RB, Farrar JT, O'Leary MP: The National Institutes of Health chronic prostatitis symptom index: development and validation of a new outcome measure. Chronic Prostatitis Collaborative Research Network. J Urol. 1999;162(2):369–375. [DOI] [PubMed]
- 20.Liang CZ, Li HJ, Wang ZP, Xing JP, Hu WL, Zhang TF, et al. The prevalence of prostatitis-like symptoms in China. J Urol. 2009;182(2):558–63. [DOI] [PubMed] [Google Scholar]
- 21.Mändar R, Korrovits P, Rahu K, Rahu M, Sibul EL, Mehik A, et al. Dramatically deteriorated quality of life in men with prostatitis-like symptoms. Andrology. 2020;8(1):101–9. [DOI] [PubMed] [Google Scholar]
- 22.Ku JH, Kim ME, Lee NK, Park YH. Influence of environmental factors on chronic prostatitis-like symptoms in young men: results of a community-based survey. Urology. 2001;58(6):853–8. [DOI] [PubMed] [Google Scholar]
- 23.Habibzadeh F. Data distribution: normal or abnormal? J Korean Med Sci. 2024;39(3):e35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology (Sunnyvale). 2016;6(2):227. 10.4172/2161-1165.1000227. [DOI] [PMC free article] [PubMed]
- 25.Collins MM, Stafford RS, O’Leary MP, Barry MJ. How common is prostatitis? A national survey of physician visits. J Urol. 1998;159(4):1224–8. [PubMed] [Google Scholar]
- 26.Collins MM, Meigs JB, Barry MJ, Walker Corkery E, Giovannucci E, Kawachi I. Prevalence and correlates of prostatitis in the health professionals follow-up study cohort. J Urol. 2002;167(3):1363–6. [PubMed] [Google Scholar]
- 27.Chen X, Hu C, Peng Y, Lu J, Yang NQ, Chen L, et al. Association of diet and lifestyle with chronic prostatitis/chronic pelvic pain syndrome and pain severity: a case-control study. Prostate Cancer Prostatic Dis. 2016;19(1):92–9. [DOI] [PubMed] [Google Scholar]
- 28.Wang Y, He L, Zhou Z, Jin L, Meng J, Sun G, et al. The association between metabolic syndrome and the National Institutes of Health Chronic Prostatitis Symptom Index: results from 1673 men in China. Urology. 2013;82(5):1103–7. [DOI] [PubMed] [Google Scholar]
- 29.Cheah PY, Liong ML, Yuen KH, Teh CL, Khor T, Yang JR, et al. Chronic prostatitis: symptom survey with follow-up clinical evaluation. Urology. 2003;61(1):60–4. [DOI] [PubMed] [Google Scholar]
- 30.Schaeffer AJ, Landis JR, Knauss JS, Propert KJ, Alexander RB, Litwin MS, et al. Demographic and clinical characteristics of men with chronic prostatitis: the National Institutes of Health chronic prostatitis cohort study. J Urol. 2002;168(2):593–8. [PubMed] [Google Scholar]
- 31.Clemens JQ, Brown SO, Kozloff L, Calhoun EA: Predictors of symptom severity in patients with chronic prostatitis and interstitial cystitis. J Urol. 2006;175(3 Pt 1):963–966; discussion 967. [DOI] [PubMed]
- 32.Mehik A, Hellström P, Lukkarinen O, Sarpola A, Järvelin M. Epidemiology of prostatitis in Finnish men: a population-based cross-sectional study. BJU Int. 2000;86(4):443–8. [DOI] [PubMed] [Google Scholar]
- 33.Güven EO, Selvi I, Karaismailoğlu E. Association between benign prostate enlargement-related storage and voiding symptoms and systolic blood pressure: a single-center cross-sectional study. Sao Paulo Med J. 2019;137(5):446–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Pavone C, Caldarera E, Liberti P, Miceli V, Di Trapani D, Serretta V, et al. Correlation between chronic prostatitis syndrome and pelvic venous disease: a survey of 2,554 urologic outpatients. Eur Urol. 2000;37(4):400–3. [DOI] [PubMed] [Google Scholar]
- 35.Barone B, De Luca L, Napolitano L, Reccia P, Crocetto F, Creta M, et al. Lower urinary tract symptoms and mental health during COVID-19 pandemic. Arch Ital Urol Androl. 2022;94(1):46–50. [DOI] [PubMed] [Google Scholar]
- 36.Wang Y, Chen C, Zhu C, Chen L, Han Q, Ye H. Social determinants of chronic prostatitis/chronic pelvic pain syndrome related lifestyle and behaviors among urban men in China: a case-control study. BioMed Res Int. 2016;2016:1687623. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Lien CS, Chung CJ, Lin CL, Chang CH. Increased risk of prostatitis in male patients with depression. World J Biol Psychiatry. 2020;21(2):111–8. [DOI] [PubMed] [Google Scholar]
- 38.Chung SD, Huang CC, Lin HC. Chronic prostatitis and depressive disorder: a three year population-based study. J Affect Disord. 2011;134(1–3):404–9. [DOI] [PubMed] [Google Scholar]
- 39.Ahn SG, Kim SH, Chung KI, Park KS, Cho SY, Kim HW. Depression, anxiety, stress perception, and coping strategies in Korean military patients with chronic prostatitis/chronic pelvic pain syndrome. Korean J Urol. 2012;53(9):643–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Nickel JC. Understanding chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). World J Urol. 2013;31(4):709–10. [DOI] [PubMed] [Google Scholar]
- 41.Ku JH, Kim SW, Paick JS. Quality of life and psychological factors in chronic prostatitis/chronic pelvic pain syndrome. Urology. 2005;66(4):693–701. [DOI] [PubMed] [Google Scholar]
- 42.Chen Y, Li J, Hu Y, Zhang H, Yang X, Jiang Y, et al. Multi-factors including inflammatory/immune, hormones, tumor-related proteins and nutrition associated with chronic prostatitis NIH IIIa+b and IV based on FAMHES project. Sci Rep. 2017;7(1):9143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Chen L, Zhang M, Liang C. Chronic prostatitis and pelvic pain syndrome: another autoimmune disease? Arch Immunol Ther Exp. 2021;69(1):24. [DOI] [PubMed] [Google Scholar]
- 44.Piovesan AC, Torricelli FC, Antonopoulos IM, Falci R Jr, Kanashiro H, Nahas WC. Histologic inflammatory changes on the prostatic gland due to immunosuppression for kidney transplantation. Urology. 2012;79(3):662–4. [DOI] [PubMed] [Google Scholar]
- 45.Crocetto F, Barone B, De Luca L, Creta M. Granulomatous prostatitis: a challenging differential diagnosis to take into consideration. Future Oncol. 2020;16(13):805–6. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data of this study are available from the corresponding author upon reasonable request.