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Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.
Paranjpe, Ishan; Jayaraman, Pushkala; Su, Chen-Yang; Zhou, Sirui; Chen, Steven; Thompson, Ryan; Del Valle, Diane Marie; Kenigsberg, Ephraim; Zhao, Shan; Jaladanki, Suraj; Chaudhary, Kumardeep; Ascolillo, Steven; Vaid, Akhil; Gonzalez-Kozlova, Edgar; Kauffman, Justin; Kumar, Arvind; Paranjpe, Manish; Hagan, Ross O; Kamat, Samir; Gulamali, Faris F; Xie, Hui; Harris, Joceyln; Patel, Manishkumar; Argueta, Kimberly; Batchelor, Craig; Nie, Kai; Dellepiane, Sergio; Scott, Leisha; Levin, Matthew A; He, John Cijiang; Suarez-Farinas, Mayte; Coca, Steven G; Chan, Lili; Azeloglu, Evren U; Schadt, Eric; Beckmann, Noam; Gnjatic, Sacha; Merad, Miram; Kim-Schulze, Seunghee; Richards, Brent; Glicksberg, Benjamin S; Charney, Alexander W; Nadkarni, Girish N.
Affiliation
  • Paranjpe I; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Jayaraman P; The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Su CY; Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.
  • Zhou S; Department of Computer Science, Quantitative Life Sciences, McGill University, Montreal, QC, Canada.
  • Chen S; Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.
  • Thompson R; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
  • Del Valle DM; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kenigsberg E; The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Zhao S; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Jaladanki S; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Chaudhary K; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Ascolillo S; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Vaid A; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Gonzalez-Kozlova E; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kauffman J; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kumar A; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Paranjpe M; Clinical Informatics, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.
  • Hagan RO; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kamat S; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Gulamali FF; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Xie H; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Harris J; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Patel M; Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA.
  • Argueta K; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Batchelor C; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nie K; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Dellepiane S; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Scott L; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Levin MA; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • He JC; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Suarez-Farinas M; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Coca SG; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Chan L; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Azeloglu EU; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Schadt E; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Beckmann N; Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Gnjatic S; The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Merad M; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kim-Schulze S; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Richards B; Department of Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Glicksberg BS; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Charney AW; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Nadkarni GN; Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Commun Med (Lond) ; 3(1): 81, 2023 Jun 12.
Article in En | MEDLINE | ID: mdl-37308534
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.
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.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Commun Med (Lond) Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Commun Med (Lond) Year: 2023 Document type: Article Affiliation country: