慢性骨盆疼痛综合征前列腺液16S rRNA基因检测及临床意义

Detection of Bacterial 16S rRAN Gene in EPS of Men with Chronic Pelvic Pain Syndrome and Its Clinical Significance

目的 :通过前列腺液 16SrRNA基因的检测 ,探讨慢性骨盆疼痛综合征 (CPPS)的细菌感染性病因及其对抗生素疗效的预测价值。 方法 :用通用引物PCR法检测 5 9例CPPS病人前列腺液 (EPS)和初段尿 (VB1)细菌16SrRNA基因。若只在EPS检测到条带 ,或EPS信号比VB1强 10倍以上 ,则细菌信号判断为阳性。所有病人口服左氧氟沙星及阿奇霉素治疗 4周。与治疗前相比 ,治疗后症状严重性积分指数 (SSI)、症状频度问卷积分 (SFQ)、前列腺炎症状积分指数 (NIH CPSI)中疼痛问题的总积分 (quasi CPSI)减少 5 0 %以上 ,或病人主观总体评价改善5 0 %以上作为治疗有效的标准。 结果 :5 9例CPPS病人中细菌信号阳性 46例、阴性 13例。细菌信号阳性与阴性两组治疗有效率分别为 6 5 %～ 74%、0 ,疗效差异有极显著性 (P <0 .0 1)。 结论 :部分CPPS与细菌感染有关 ,前列腺 16SrRNA基因检测的结果对选择抗生素治疗有指导意义。

Objectives: To investigate the cause of chronic pelvic pain syndrome (CPPS) and the correlation between presence of bacterial signal and the response to antibiotics by detecting bacterial 16S rRNA gene signal using polymerase chain reaction (PCR). Methods: EPS and first void urine (VB 1) from 59 men with CPPS were analyzed by PCR for bacterial signal using universal primers specific for bacterial 16S rRNA gene. If the bacterial signal was found only in EPS or the EPS bacterial signal was at least 10 times stronger than the VB 1 one, positive result could be decided for bacterial signal detection. All patients were treated with levofloxin and azitromycin for 4 weeks. The treatment could be considered effective if more than 50% improvement, compared to the state before the treatment, was achieved in the symptom severity index(SSI), symptom frequency questionnaire(SFQ), national institutes of health chronic prostatitis symptom index of pain(quasi-CPSI) or the patients' general subjective assessment. Results: Positive bacterial signal was detected in 46 of the 59 CPPS patients; The diffierence of improvement rates between positive bacterial signal group and negative bacterial signal group, which were 65% ～74% and 0 respectively,was significant. Conclusions: The basis of detecting 16S rRNA gene signal by PCR in clinical practice was built. The study shows that bacterial infection is related to CPPS. Bacterial signal detection results can help predict the effect of antimicrobial therapy.