ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

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

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

Please note that you are viewing an archived section from 2023 and some content may be unavailable. To unlock all content for 2023, please visit the archives.

Abstract: SA-OR89

Plasma Proteome Profiling for Remission Diagnosis in ANCA Vasculitis

Session Information

Category: Glomerular Diseases

  • 1401 Glomerular Diseases: From Inflammation to Fibrosis

Authors

  • Kettritz, Ralph, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Jerke, Uwe, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Kirchner, Marieluise, Core Unit Proteomics, Berlin Institute of Health (BIH) at Charité and MDC, Berlin, Germany
  • Bartolomaeus, Theda Ulrike Particia, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Ebert, Maximilian, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Kling, Lovis, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Forslund, Sofia Kirke, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
  • Mertins, Philipp, Core Unit Proteomics, Berlin Institute of Health (BIH) at Charité and MDC, Berlin, Germany
  • Eckardt, Kai-Uwe, Nephrology and Medical Intensive Care, Charité, Berlin, Germany
  • Schreiber, Adrian, Experimental and Clinical Research Center (ECRC) and Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association and Charité, Berlin, Germany
Background

Systemic anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) requires intensive immunosuppressive treatment that is deescalated once patients achieved remission. Thus, reliably diagnosing remission has therapeutic implications but remains challenging. We hypothesized that the plasma proteome harbors objective information that may assist clinicians in diagnosing AAV remission.

Methods

Plasma proteomes from 50 healthy controls (HC), 59 active, and 55 remission AAV patients were analyzed with LC-MS/MS based proteomics. For data analysis, a machine learning pipeline was established, containing confounder analysis, LASSO regression and Likelihood ratio test. After “leave-one-out” validation the final biomarker combination for ANCA disease status assignment was tested on the 20/80 data split.

Results

From 970 identified proteins, 605 passed the quality check for quantification and 325 were differently expressed. The principal component analysis showed excellent separation of active and remission AAV patients. Using machine learning, we identified a 5-protein biomarker combination with the potential to separate active AAV from remission patients, namely leucine-rich alpha-2-glycoprotein 1, beta-2-microglobolin, insulin-like growth factor-binding protein 3, tenascin X, and alpha-2-HS-glycoprotein. Assessing all AAV patients, the 5-protein panel showed an AUC of 0.94 with a negative predictive value (NPV) of 87.5% and performed better than ANCA titer (AUC 0.75, NPV 63.6%) or c-reactive protein (CRP) (AUC 0.91, NPV 73.3%) using a binary logistic regression model of remission diagnosis. In challenging remission patients with positive ANCA, the panel was the better classifier compared to CRP (AUC 0.96, NPV 85.7% versus AUC 0.92, NPV 75.0%), and better than ANCA in challenging remission patients with increased CRP (AUC 0.82, NPV 83.3% versus AUC 0.68 without any value in diagnosing remission).

Conclusion

Using proteomics combined with machine learning, we identified a protein signature that may assist clinicians in diagnosing AAV remission and guiding immunosuppressive treatment.