The Clinical Value Of The Prostatic Exosomal Protein Expression In The Diagnosis Of Chronic Prostatitis And Development And Validation Of Predictive Model For Chronic Prostatitis

Part 1: The Clinical Value of the Prostatic Exosomal Protein Expression in the Diagnosis of Chronic ProstatitisResearch Background and Objective:Chronic prostatitis(CP)is one of the most common diseases in the genitourinary systems of young and middle-aged men.At present,the etiology and pathogenesis of CP are complicated,the clinical manifestations lack specificity due to individual differences,and there is no objective and accurate diagnostic method.The clinical diagnosis and treatment effects have been unsatisfactory.In this study,levels of urinary prostatic exosomal protein(PSEP)were detected to evaluate the clinical potential of PSEP as a diagnostic marker of chronic prostatitis(CP).Materials and Methods:The level of urinary PSEP was measured in 412 cases by an enzyme-linked immunosorbent assay kit,including 202 controls and 210 CP cases.Of the CP patients,116 cases met the definition of the USA National Institutes of Health category III(NIH-III),with 60 cases of NIH-IIIA and 56 cases of NIH-IIIB.The ages,body mass indexes(BMI),white blood cell(WBC)levels in expressed prostatic secretions(EPS),lecithin body counts in EPS,urine PSEP levels both before and after prostate massageobtained from the CP patients and NIH-CPSI scores were analyzed.In addition,we conducted a follow-up study on 36 patients with chronic prostatitis,and analyzed the urine PSEP levels and NIH-CPSI scores of patients with chronic prostatitis before and after treatment.Results:In the diagnosis of CP,the PSEP contents in the urine samples before and after prostate massage manifested a sensitivity of 86.93% vs.61.06%,a specificity of 85.15% vs.85.15%,and a total coincidence rate of 85.24% vs.61.06%,respectively.The ROC identified a cutoff value of 1.26 ng/m L.The area under the ROC curve was 0.926 vs.0.709 for the before and after massage PSEP contents,respectively.Besides,the PSEP levels were significantly higher in the pre-therapy(average 4.41 ng/m L)and post-treatment groups(average 7.81 ng/m L)compared with the average value of 0.29ng/ m L in the health control group.During the follow-up of patients with CP,the improvement in symptoms was not correlated with the level changes of PSEP.The NIH-CPSI score and sub-score of NIH-IIIA group were not correlated with PSEP levels,while the NIH-CPSI score in NIH-IIIB group was significantly negatively correlated with PSEP(P=0.014).The pain symptoms(P=0.006)and symptom severity(P = 0.021)were significantly negatively correlated with PSEP level.Conclusion:Measurement of PSEP levels for the clinical diagnosis of CP is objective and painless.It could be a novel,simple,and noninvasive molecular marker for the diagnosis of CP.Part 2: Preliminary Construction and Validation of Prediction Model Related to Chronic ProstatitisResearch Background and Objective:Chronic prostatitis / chronic pelvic pain syndrome(CP / CPPS)is one of the most common types of chronic prostatitis.CP / CPPS patients can have a variety of clinical symptoms,of which pain symptoms are the most common,such as pelvic pain or perineum pain.The pain symptoms of chronic prostatitis seriously affect the quality of life of patients,and also bring heavy economic and medical burden to the society.The purpose of this study is to establish a model to predict the severity of pain in patients with chronic prostatitis,and to provide new ideas for the diagnosis and treatment of patients and prognosis of disease.Materials and Methods:By analyzing the data of 204 CP / CPPS patients in the training queue,a prediction model was built.In this study,15 clinical variables of the subjects were selected and multiple logistic regression analysis was used to screen out the prediction candidate factors.The discrimination,accuracy and clinical practicability of the prediction model were evaluated by making receiver operating characteristic curve(ROC),calibration chart and decision curve analysis,and the performance of the prediction model was verified by the validation queue.Results:Multiple logistic regression analysis was used to establish a nomogram model,which included age,lecithin body grade in prostatic fluid,keeping urine,emotional anxiety or irritability,contraception and smoking behavior.The model shows good discrimination.The AUC of the training queue is 0.737,while the AUC of the validation queue is0.716,which is consistent with the results suggested by the calibration chart and the decision curve.Conclusions:1.Age,the level of lecithin corpuscles in prostatic fluid,keeping urine,anxiety and agitation,sex without using contraception and smoking were all independent risk factors of pain severity in patients with chronic prostatitis.2.The clinical prediction model based on age,lecithin body grade in prostatic fluid,keeping urine,anxiety and agitation,contraception and smoking has excellent stability,accuracy and clinical application value.It is beneficial to predict the severity of pain in CP / CPPS patients.