Use of disease-specific biomarkers in identifying interstitial cystitis and guiding effective treatment for its different subtypes.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a heterogeneous condition with unidentifiable causes and unknown pathophysiology. Exploring potential biomarkers with high reliability might predict IC/BPS subtypes and guide specific treatment to achieve successful outcomes. The currently used clinical biomarkers to identify IC/BPS include pain symptoms, low bladder capacity, bladder wall thickness, and serum or urinary parameters. The wide application of serum biomarkers for identifying IC/BPS from patients with similar bladder symptoms is not established yet due to their low specificity. Patients with IC/BPS exhibit distinct urinary inflammatory cytokine profiles and oxidative stress biomarkers compared with controls, and the urine levels of biomarkers were correlated with pathologic conditions of the bladder. Identifying IC/BPS subtypes, including Hunner's IC (HIC) and non-Hunner's IC (NHIC) with different cystoscopic characteristics, can facilitate specific treatments with satisfactory outcomes. Using a cluster of biomarkers has been reported to aid in the detection of IC/BPS patients, and patients with HIC can be recognized from patients with IC/BPS. Machine learning using the decision tree model can further provide high accuracy for predicting treatment outcomes. Currently, a satisfactory disease biomarker that can be used to identify IC/BPS subtypes and guide appropriate bladder therapy is lacking. In the future, the treatment goal may be achieved by combining noninvasive clinical parameters with cluster of urinary biomarkers and constructing nomograms for identifying IC/BPS in patients with lower urinary tract symptoms and discriminating patients with HIC from those with IC/BPS.