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Abstract: FR-PO913

Differentiating Primary and Secondary Focal Segmental Glomerulosclerosis Using Non-Invasive Urine Biomarkers

Session Information

Category: CKD (Non-Dialysis)

  • 2201 CKD (Non-Dialysis): Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Hendry, Bruce M., Travere Therapeutics Inc, San Diego, California, United States
  • Siwy, Justyna, Mosaiques-Diagnostics GmbH, Hannover, Germany
  • Mischak, Harald, Mosaiques-Diagnostics GmbH, Hannover, Germany
  • Wendt, Ralph, St. Georg Hospital Leipzig, Leipzig, Germany
  • Beige, Joachim H., St. Georg Hospital Leipzig, Leipzig, Germany
  • Catanese, Lorenzo, Klinikum Bayreuth GmbH and Kuratorium for Dialysis and Transplantation (KfH) Bayreuth, Bayreuth, Germany
  • Wolf, Michael, Travere Therapeutics Inc, San Diego, California, United States
  • Rupprecht, Harald D., Klinikum Bayreuth GmbH and Kuratorium for Dialysis and Transplantation (KfH) Bayreuth, Bayreuth, Germany
Background

Focal segmental glomerulosclerosis (FSGS) includes primary (p) and secondary (s) forms. These subclasses differ in management and prognosis, but differentiation is challenging. We aimed to identify specific urine protein/peptides discriminating between pFSGS and sFSGS, and to combine these into a classifier using machine learning.

Methods

56 urine samples were collected at two different centers (17 pFSGS and 39 sFSGS) prior to biopsy. Samples were analyzed using capillary electrophoresis coupled mass spectrometry (CE-MS). Additional CE-MS datasets were extracted from a urinary proteome database to increase specificity. For biomarker definition, data from additional age/sex matched healthy controls (HC, n=98) and patients with other chronic kidney disease (CKD, n=100) were used. Independent specificity assessment was performed in additional data of HC (n=110) and CKD (n=170).

Results

Proteomics data from patients with pFSGS were first compared to HC (n=98). This resulted in 1054 biomarker candidates. Then, the pFSGS group was compared to sFSGS. In the third step, to define biomarkers independent of other forms of CKD, data of pFSGS patients were compared to data from different CKD etiologies (n=100). Only biomarker candidates also significant in the second and third statistical comparison were accepted as specific biomarkers for pFSGS. The 95 biomarkers defined were combined in a classifier, FSGS-95. Total cross validation of this classifier resulted in an area under the receiving operating curve (AUC) of 0.94. The specificity investigated in an additional independent set of HC and CKD of other etiologies resulted in 100% for HC and 92.5% for CKD, respectively. The defined biomarkers are mostly fragments of different collagens (54%) decreased in pFSGS. We also observed reduced abundance of polymeric immunoglobulin receptor fragments and increased alpha-1-antitrypsin, transthyretin, and uromodulin peptides.

Conclusion

Based on a preliminary review of CE-MS analysis and leveraging machine learning, development of a urine peptide-based classifier that selectively detects pFSGS is feasible; however, analysis of specificity and sensitivity in an independent sample should be completed to support this approach.

Funding

  • Commercial Support