Abstract: FR-OR07
Noninvasive Identification of Acute Tubular Injury Using Plasma Proteomics
Session Information
- AKI Prediction and Assessment: Traditional and Novel Tools
November 03, 2023 | Location: Room 118, Pennsylvania Convention Center
Abstract Time: 05:24 PM - 05:33 PM
Category: Acute Kidney Injury
- 101 AKI: Epidemiology, Risk Factors, and Prevention
Authors
- Schmidt, Insa Marie, Boston University, Boston, Massachusetts, United States
- Surapaneni, Aditya L., NYU Langone Health, New York, New York, United States
- Upadhyay, Dhairya Anil, NYU Langone Health, New York, New York, United States
- Zhao, Runqi, Boston University, Boston, Massachusetts, United States
- Yeo, Wan-Jin, NYU Langone Health, New York, New York, United States
- Schlosser, Pascal, Johns Hopkins University Center for Health Security, Baltimore, Maryland, United States
- Srivastava, Anand, University of Illinois Chicago, Chicago, Illinois, United States
- Stillman, Isaac Ely, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States
- Grams, Morgan, NYU Langone Health, New York, New York, United States
- Waikar, Sushrut S., Boston University, Boston, Massachusetts, United States
Background
Biomarkers for the non-invasive assessment of acute tubular injury (ATI) are needed in patients with kidney disease.
Methods
Using the SomaScan proteomics platform, we measured 6592 circulating plasma proteins in 434 individuals with biopsy-confirmed kidney diseases and pathologist-adjudicated semi-quantitative assessments of histopathologic ATI. We identified proteomic correlates of ATI severity. For the proteins with the strongest associations with ATI, we evaluated cell-specific gene expression in patients with AKI in the Kidney Precision Medicine Project (KPMP).
Results
Fifty-three % of individuals had no ATI, 30% had mild ATI, 13% had moderate, and 3% had severe ATI. After multivariable adjustment and correction for multiple testing, 170 proteins were associated with ATI. The proteins with the strongest associations with greater ATI severity were osteopontin (p=9.8E-18), macrophage mannose receptor 1 (p=2.2E-16), and tenascin (p=1.4E-14) (Figure 1). Previously identified proteins such as kidney-injury molecule-1 and tumor necrosis factor receptor superfamily member 1 were also associated with ATI (p=9.9E-10 and 5.7E-06, respectively). The top proteins with inverse associations with ATI were plasma serine protease inhibitor (p= 6.1E-11), cholinesterase (p= 1.3E-10), and neuropeptide S (p=1.4E-10). In KPMP snRNA sequencing data, SPP1 (the gene encoding osteopontin) was primarily expressed in thick ascending limb (TAL) and proximal tubular (PT) cell clusters (p=4.2E-141 and 5.2E-108, comparing the expression in TAL and PT with all other cell clusters, respectively).
Conclusion
Plasma proteomic approaches may identify novel biomarkers to non-invasively identify biopsy-proven ATI.
Funding
- NIDDK Support