Interstitial cystitis/bladder pain syndrome (IC/BPS) is mysterious and difficult to diagnose without cystoscopic hydrodistention. Diagnosis of IC/BPS is difficult without invasive cystosvopic hydrodistention under anesthesia. This study aimed to identify patients with IC/BPS by integrating clinical characteristics such as bladder capacity and bladder pain symptom severity and a cluster of urine biomarkers that had been previously reported sensitive to identify IC/BPS.

A total of 385 women with and without clinically diagnosed IC/BPS (n = 344 and 41, respectively) were retrospectively enrolled. Patients were diagnosed with HIC (n=39) or NHIC (n=344) by cystoscopic hydrodistention under anesthesia. The functional bladder capacity (FBC) recorded by 3-day voiding diary and visual analog score (VAS) of bladder pain were also recorded. Thirteen urine inflammatory cytokines, chemokines, and oxidative stress biomarkers based on previously reported predictors of IC/BPS were assayed using commercial microsphere kits. The dataset was randomly divided into training (70%) and test (30%) sets for model construction and validation using logistic regression and stepwise variable selection techniques. To construct the predictive models, univariate analysis was performed to evaluate the discriminative power of each urinary biomarker, measured by the area under the curve (AUC). Biomarkers with AUC values <0.6 were excluded from further modeling. Predictor variables were selected through a stepwise forward selection process based on AIC in logistic regression. Multivariate logistic regression was then employed, with variables selected through stepwise forward selection based on log-likelihood criteria. The discriminative performance of each model was evaluated on the test set using AUC, accuracy, sensitivity, and specificity in both training and test sets.

The distribution of controls and IC/BPS in patient characteristics in the training set (n=269) and test set (n=116) are equal. The urinary biomarkers levels are also not significantly different between subgroups. By setting the screening criterion to AUC ≥0.60, the potential urinary biomarkers for identifying IC/BPS cases were 8-hydroxy-2′-deoxyguanosine (8-OHdG), tumor necrosis factor-alpha (TNF-α), eotaxin, monocyte chemoattractant protein-1 (MCP-1), 8-isoprostane, and eotaxin. (Fig.1) Because MCP-1 was unevenly distributed in NHIC, it was deleted from the candidate biomarker list. (Fig.2) After model performance of various variable combinations in training set and test set, 8-OHdG plus TNF-α, or 8-OHdG alone can yield a satisfactory accuracy, sensitivity, and specificity in discriminating IC/BPS from non-IC patients. Adding FBC or VAS can increase the accuracy, sensitivity, and specificity. (Fig.3)

Because IC/BPS is a bladder disorder of chronic inflammation, in addition to urine cytokines, the levels of urine oxidative stress biomarkers and inflammatory cytokine profiles are elevated in patients with IC/BPS, showing to be distinct from those of the controls and in each NHIC subgroup. Both 8-OHdG and 8-isoprostane showed a high diagnostic ability to distinguish ESSIC type 2 IC/BPS (NHIC) from the controls. In addition, they both showed positive and negative correlations with the glomerulation grade and MBC, respectively. Using urinary biomarkers 8-OHdG plus TNF-α, or 8-OHdG alone can yield a satisfactory accuracy, sensitivity, and specificity in discriminating IC/BPS from non-IC patients. Small FBC and increased VAS of bladder pain in patients with suspicious IC/BPS can increase the diagnostic accuracy of IC/BPS.

Using a cluster of urinary biomarkers, namely, TNF-α or 8-OHdG and 8-isoprostane, can identify patients with IC/BPS from a study cohort, and adding the FBC and VAS of bladder pain can increase the diagnostic accuracy of IC/BPS from a patient cohort. Integrating FBC, bladder pain VAS, and cluster of urinary biomarkers can be used to discriminating IC/BPS.

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