ASN's Mission

ASN leads the fight to prevent, treat, and cure kidney diseases throughout the world by educating health professionals and scientists, advancing research and innovation, communicating new knowledge, and advocating for the highest quality care for patients.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005


The Latest on Twitter

Kidney Week

Abstract: FR-OR103

Identifying MiRNA Biomarkers for Diagnosis of Encapsulating Peritoneal Sclerosis

Session Information

Category: Dialysis

  • 703 Dialysis: Peritoneal Dialysis


  • Huang, Chiu-Ching, China Medical University and Hospitals, Taichung, Taiwan
  • Ma, Nianhan, National Central University, Zhongli District, Taiwan
  • Li, An-Lun, National Central University, Zhongli District, Taiwan
  • Chen, J. B., Chang Gung Memorial Hospital-Kaohsiung, Kaohsiung Hsien, TAIWAN, Taiwan
  • Tseng, Chin Chung, National Cheng Kung University Hospital, Tainan, Taiwan

Group or Team Name

  • Taiwan EPS Consortium

Encapsulating peritoneal sclerosis(EPS) is a serious complication of chronic peritoneal dialysis (PD). Late diagnosis is associated with high mortality. With the advance of new diagnostic technology, such as microRNA (miRNA), we attempted to develop a non-invasive test to assist the diagnosis of EPS.


We examined miRNAs expression profiles of PD fluids from patients with or without EPS by high-throughput and quantification real-time PCR array. We used the high-throughput miRNA array cards as primary screen tool for analysis . The analysis of miRNA was conducted using the Running TaqMan® Low Density Arrays on Vii7 RealTime PCR Systems. Candidate miRNAs were selected to verify in another group of patients by single qRT-PCR assay. The model for EPS prediction was developed by multiple logistic regression.


We collected overnight PD fluids from 72 non-EPS (controls) and 25 EPS patients. The screening set included PD fluids from 28 patients (20 of non-EPS vs. 8 of EPS-ongoing cases). We compared the ratio values of two miRNA expression levels between EPS and non-EPS samples. Eight candidate miRNAs were selected. The training set was conducted using 69 samples (52 of non-EPS vs 17 of EPS-ongoing) to produce the good area under curve (AUC) value of diagnostic miRNA classifier. The miRNA combination ratios with the top five ROC values were selected to calculate the combined AUC by logistic regression. The value of AUC to distinguish EPS from non-EPS with five miRNAs in PD fluid was 0.9723 (Figure 1). From results of the training set, six different miRNA expressions and two ratios of two miRNA expressions in the PD fluid showed significant difference between EPS and non-EPS patients .


We identify a miRNA classifier with the combination of top five miRNAs' expression in PD fluids to assist the diagnosis of EPS.

Figure 1. The ROC analysis of the EPS miRNA classifier with the combination of top five miRNAs expressions and the box plots showed that the score of non-EPS and EPS miRNA classifier distribution.


  • Government Support - Non-U.S.