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| EDITORIAL COMMENT |
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| Year : 2021 | Volume
: 32
| Issue : 1 | Page : 8 |
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A step toward precision diagnosis of interstitial cystitis
Hann-Chorng Kuo
Department of Urology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation; School of Medicine, Tzu Chi University, Hualien, Taiwan
| Date of Submission | 18-Jan-2021 |
| Date of Acceptance | 27-Jan-2021 |
| Date of Web Publication | 27-Mar-2021 |
Correspondence Address:
Hann-Chorng Kuo
Department of Urology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Section 3, Chung-Yang Road, Hualien
Taiwan
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/UROS.UROS_15_21
How to cite this article:
Kuo HC. A step toward precision diagnosis of interstitial cystitis. Urol Sci 2021;32:8 |
Interstitial cystitis/bladder pain syndrome (IC/BPS) is a mystery of functional urology. For 100 years, urologists still cannot find the underlying pathophysiology of IC/BPS and also cannot establish an algorithm for the diagnosis of this bladder disorder. IC/BPS is a syndrome of constellation of symptoms. These symptoms might be bladder centered or not.[1] How to make an accurate diagnosis of IC/BPS without using invasive cystoscopic hydrodistention and bladder biopsy is a big challenge.[2] The ESSIC type 2 IC/BPS patients also express multiple urine cytokines with high sensitivity and high specificity. The urine cytokines MCP-1, RANTES, CXCL10, IL-7, and eotaxin-1 are statistically significant in differentiating IC/BPS and controls. MCP-1, CXCL10, eotaxin-1, and RANTES were positively correlated with glomerulation grade and negatively correlated with maximal bladder capacity.[3] These cytokines might serve as biomarkers in the diagnosis and mapping the clinical characteristics of IC/BPS. This review article reports a study calling the IC-risk score currently undergoing in the US to develop a simple diagnostic test based on several urine proteins, through machine learning technology.[4] However, based on their study protocol, only IC/BPS patients and controls are involved. I believe that this will be the weakest point of this study because differential diagnosis between IC/BPS and controls is not difficult, based on the characteristic symptoms of IC/BPS. The difficult diagnostic test for IC/BPS is to find those with true bladder-centered IC/BPS patients from a large cohort of patients with bladder hypersensitivity, overactive bladder (OAB), and not bladder-centered BPS. In a recent study, the urinary cytokines with high diagnostic values (Area under curve >0.7) to distinguish IC/BPS and OAB included IL-10, RANTES, eotaxin, CXCL10, IL-12, p70, NGF, IL-6, IL-17A, MCP-1, and IL-1RA. However, the accuracy cannot reach a satisfactory high rate, except the cytokine MIP-1β, which has the highest sensitivity (92.2%) to discriminate diseased study patients from controls.[5] Although there is still no accurate diagnostic test to identify IC/BPS from patients with bladder hypersensitive symptoms, using urinary biomarkers is a correct road in the precision diagnosis of specific bladder disorders.
| References | |  |
| 1. | Jhang JF, Kuo HC. Pathomechanism of interstitial cystitis/bladder pain syndrome and mapping the heterogeneity of disease. Int Neurourol J 2016;20:S95-104.  |
| 2. | Yu WR, Jhang JF, Ho HC, Jiang YH, Lee CL, Hsu YH, et al. Cystoscopic hydrodistention characteristics provide clinical and long-term prognostic features of interstitial cystitis after treatment. Sci Rep 2021;11:455. |
| 3. | Jiang YH, Jhang JF, Hsu YH, Ho HC, Wu YH, Kuo HC. Urine cytokines as biomarkers for diagnosing interstitial cystitis/bladder pain syndrome and mapping its clinical characteristics. Am J Physiol Renal Physiol 2020;318:F1391-9. |
| 4. | Chancellor MB, Lamb LE. Toward a validated diagnostic test with machine learning algorithm for interstitial cystitis. Urol Sci 2021;32:2-7. [Full text] |
| 5. | Jiang YH, Jhang JF, Hsu YH, Ho HC, Wu YH, Kuo HC. Urine biomarkers in ESSIC type 2 interstitial cystitis/bladder pain syndrome and overactive bladder with developing a novel diagnostic algorithm. Sci Rep 2021;11:914. |
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