Full text

Turn on search term navigation

© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms.

Methods

Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261).

Results

We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury.

Conclusions

Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Plain language summary

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Details

Title
Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection
Author
Paranjpe, Ishan 1 ; Jayaraman, Pushkala 2   VIAFID ORCID Logo  ; Su, Chen-Yang 3   VIAFID ORCID Logo  ; Zhou, Sirui 4   VIAFID ORCID Logo  ; Chen, Steven 5 ; Thompson, Ryan 6   VIAFID ORCID Logo  ; Del Valle, Diane Marie 7 ; Kenigsberg, Ephraim 8   VIAFID ORCID Logo  ; Zhao, Shan 9   VIAFID ORCID Logo  ; Jaladanki, Suraj 10   VIAFID ORCID Logo  ; Chaudhary, Kumardeep 11 ; Ascolillo, Steven 7   VIAFID ORCID Logo  ; Vaid, Akhil 7   VIAFID ORCID Logo  ; Gonzalez-Kozlova, Edgar 7   VIAFID ORCID Logo  ; Kauffman, Justin 7 ; Kumar, Arvind 7   VIAFID ORCID Logo  ; Paranjpe, Manish 12 ; Hagan, Ross O. 10 ; Kamat, Samir 7 ; Gulamali, Faris F. 10 ; Xie, Hui 13 ; Harris, Joceyln 13 ; Patel, Manishkumar 13   VIAFID ORCID Logo  ; Argueta, Kimberly 13 ; Batchelor, Craig 13 ; Nie, Kai 13 ; Dellepiane, Sergio 14 ; Scott, Leisha 13 ; Levin, Matthew A. 15   VIAFID ORCID Logo  ; He, John Cijiang 14   VIAFID ORCID Logo  ; Suarez-Farinas, Mayte 16   VIAFID ORCID Logo  ; Coca, Steven G. 14 ; Chan, Lili 14 ; Azeloglu, Evren U. 14   VIAFID ORCID Logo  ; Schadt, Eric 17   VIAFID ORCID Logo  ; Beckmann, Noam 18   VIAFID ORCID Logo  ; Gnjatic, Sacha 19   VIAFID ORCID Logo  ; Merad, Miram 5   VIAFID ORCID Logo  ; Kim-Schulze, Seunghee 20 ; Richards, Brent 21   VIAFID ORCID Logo  ; Glicksberg, Benjamin S. 22   VIAFID ORCID Logo  ; Charney, Alexander W. 23   VIAFID ORCID Logo  ; Nadkarni, Girish N. 24   VIAFID ORCID Logo 

 Stanford University, Department of Medicine, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956) 
 Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 McGill University, Lady Davis Institute, Jewish General Hospital, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); McGill University, Department of Computer Science, Quantitative Life Sciences, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649) 
 McGill University, Lady Davis Institute, Jewish General Hospital, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); McGill University, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649) 
 Icahn School of Medicine at Mount Sinai, The Precision Immunology Institute, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 Icahn School of Medicine at Mount Sinai, The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 Icahn School of Medicine at Mount Sinai, The Precision Immunology Institute, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Icahn Institute for Genomics and Multiscale Biology, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
 Icahn School of Medicine at Mount Sinai, Department of Anesthesiology, Perioperative and Pain Medicine, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
10  Icahn School of Medicine at Mount Sinai, The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
11  CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Clinical Informatics, New Delhi, India (GRID:grid.417639.e) 
12  Harvard Medical School, Division of Health Sciences and Technology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
13  Icahn School of Medicine at Mount Sinai, Human Immune Monitoring Center, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
14  Icahn School of Medicine at Mount Sinai, Department of Medicine, Division of Nephrology, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
15  Icahn School of Medicine at Mount Sinai, The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Department of Anesthesiology, Perioperative and Pain Medicine, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
16  Icahn School of Medicine at Mount Sinai, Department of Biostatistics, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
17  Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
18  Icahn School of Medicine at Mount Sinai, The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
19  Icahn School of Medicine at Mount Sinai, Department of Oncological Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
20  Icahn School of Medicine at Mount Sinai, The Precision Immunology Institute, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Human Immune Monitoring Center, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
21  McGill University, Lady Davis Institute, Jewish General Hospital, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); McGill University, Department of Computer Science, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); McGill University, Department of Human Genetics, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); Department of Twin Research, King’s College London, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
22  Data Science and Machine Learning, Character Biosciences, New York, USA (GRID:grid.13097.3c) 
23  McGill University, Lady Davis Institute, Jewish General Hospital, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine (CBIPM), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, The Pamela Sklar Division of Psychiatric Genomics, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
24  Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); McGill University, Department of Epidemiology, Biostatistics and Occupational Health, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); Icahn School of Medicine at Mount Sinai, The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Icahn School of Medicine at Mount Sinai, Department of Medicine, Division of Nephrology, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351) 
Pages
81
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
e-ISSN
2730664X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2825584736
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.