The Urinary Microbiome Differs Significantly Between Patients With Chronic Prostatitis/Chronic Pelvic Pain Syndrome and Controls as Well as Between Patients With Different Clinical Phenotypes.

Objective To study the urinary microbiome of patients with Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) compared with controls. Methods We identified 25 patients with CP/CPPS and 25 men who were either asymptomatic or only had urinary symptoms. Midstream urine was collected. Symptom severity was measured with the National Institutes of Health Chronic Prostatitis Symptom Index and clinical phenotype with UPOINT. Total DNA was extracted from the urine pellet and bacterial-specific 16Sr-DNA–capture identified by MiSeq sequencing. Taxonomic and functional bioinformatic analyses used principal coordinate analysis (PCoA)/MacQIIME, LEfSe, and PiCRUSt algorithms. Results Patients and controls were similar ages (52.3 vs 57.0 years, P  = .27). For patients, median duration was 48 months, mean Chronic Prostatitis Symptom Index was 26.0, and mean UPOINT domains was 3.6. Weighted 3D UniFrac PCoA revealed tighter clustering of controls distinct from the wider clustering of cases ( P  = .001; α-diversity P  = .005). Seventeen clades were overrepresented in patients, for example, Clostridia, and 5 were underrepresented, eg, Bacilli, resulting in predicted perturbations in functional pathways. PiCRUSt inferred differentially regulated pathways between cases and controls that may be of relevance including sporulation, chemotaxis, and pyruvate metabolism. PCoA-derived microbiomic differences were noted for neurologic/systemic domains ( P  = .06), whereas LEfSe identified differences associated with each of the 6 clinical features. Conclusion Urinary microbiomes from patients with CP/CPPS have significantly higher alpha(phylogenetic) diversity which cluster differently from controls, and higher counts of Clostridia compared with controls, resulting in predicted perturbations of functional pathways which could suggest metabolite-specific targeted treatment. Several measures of severity and clinical phenotype have significant microbiome differences.