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E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
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Abstract titles should be brief and reflect the content of the abstract.
Interstitial fibrosis (IF) is strongly associated with disease outcomes in patients with nephrotic syndrome. However, kidney biopsies are invasive and often performed after disease initiation, which may result in missed opportunities for early prognosis and timely intervention. There is a critical need to develop non-invasive biomarkers that reflect underlying disease mechanisms and can serve as surrogates for IF.
Urine proteomics data (SomaScan assay v4.1), kidney transcriptomic profiles, and IF—from 64 patients with nephrotic syndrome (NS) in the NEPTUNE cohort were integrated to identify urinary protein markers. Pearson correlation and linear regression models were applied to select significant markers, with significance set at p ≤ 0.05. Pathway analysis using Ingenuity software was conducted to identify enriched canonical pathways. Candidate markers were validated in the KPMP (Kidney Precision Medicine Project), which includes 111 patients with hypertensive CKD, DKD, and acute kidney injury (AKI).
A total of 274 urinary proteins and 2,031 tubulointerstitial genes were significantly correlated with IF (adjusted p ≤ 0.05) in the NEPTUNE cohort. Pathway enrichment analysis of IF-associated genes revealed 480 significantly enriched canonical pathways. Among the most significant ones were interleukin signaling, axonal guidance, and the STAT3 pathway, known to be linked to kidney disease progression. By integrating urinary biomarkers with genes representing key pathways, we identified a four-marker panel (CST6, CD209, IL18BP and TWSG1) capable of predicting IF using a linear regression model (Adj R2: 0.538, p= 2.98e-08). These findings were further evaluated in the KPMP cohort. Apart from the IL18BP, the three remaining markers, representing IF-associated pathways, demonstrated the same direction of correlation and similar magnitudes with IF. We constructed a linear model using these three markers predicting IF using KPMP participants and the same covariates used for NEPTUNE (N=65, Adj. R2: 0.102, p = 0.077) as well as in adjudicated subgroups of patients, with the highest model fit in the DKD subset (N=24, Adj. R2: 0.3, p = 0.089). In both NEPTUNE and KPMP-DKD patients, adding the biomarker panel improved the predicted IF compared to the predictions using clinical variables only (Table 1). The predicted IF is significantly and highly correlated with patients’ actual IF, with R=0.78 and 0.74, for NEPTUNE and KPMP-DKD patients, respectively.
Table1: Regression model in NEPTUNE and KPMP cohorts showing additive value in prediction of IF with the urine protein markers
Cohort
M0(Adj. R2,pvalue)
M0+Protiens (Adj. R2,pvalue)
LRtest pvalue
Neptune
0.291, 1.18e-04
0.538,2.98E-08
1.968e-6
KPMP _all patients
0.125, 0.0238
0.102, 0.0773
0.6338
KPMP - DKD
0.21, 0.101
0.3, 0.089
0.06384
We identified urinary biomarkers representing intra-kidney enriched canonical pathways that are associated with IF that can predict interstitial fibrosis in patients with NS. These biomarkers have potential for clinical implementation, allowing frequent monitoring, facilitating earlier prognosis, and more targeted therapies. The biomarker panel identified in patients with NS, and validated in those with adjudicated DKD, suggest potential shared molecular mechanisms underlying interstitial injury.
