Evidence map›Paper›PMID 41141506›Full record

ArticleKidney international reports2025

Liquid Biopsy-Multiomics Link Adhesion Pathway Dysregulation to Kidney Injury Severity.

Nanditha Anandakrishnan, Zhengzi Yi, Zeguo Sun, Tong Liu, Jonathan Haydak, Sean Eddy, Pushkala Jayaraman, Stefanie DeFronzo, Aparna Saha, Qian Sun and 28 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Kidney international reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

38 authors.

Nanditha AnandakrishnanDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Zhengzi YiDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Zeguo SunDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Tong LiuCenter for Advanced Proteomics Research, Rutgers University, Newark, New Jersey, USA.
Jonathan HaydakDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Sean EddyDepartment of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
Pushkala JayaramanDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Stefanie DeFronzoDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Aparna SahaDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Qian SunCenter for Advanced Proteomics Research, Rutgers University, Newark, New Jersey, USA.
Dai YangThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Anthony MendozaDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Gohar MosoyanDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Huei Hsun WenDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Jia FuDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Thomas KehrerDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Rajasree MenonDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Edgar A OttoDepartment of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
Bradley GodfreyDepartment of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
Joanna YangDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Mayte Suarez-FarinasDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Sean LefftersDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Akosua TwumasiDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Kristin MeliambroDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Alexander W CharneyThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Adolfo García-SastreDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Kirk N CampbellDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
G Luca GusellaDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
John Cijiang HeDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Lisa MiorinDepartment of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Girish N NadkarniDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Juan WisniveskyDepartment of Medicine, Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Hong LiCenter for Advanced Proteomics Research, Rutgers University, Newark, New Jersey, USA.
Matthias KretzlerDepartment of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
Steve G CocaDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Lili ChanDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Weijia ZhangDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Evren U AzelogluDepartment of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Funding

Hippo-YAP in podocyte health and diseaseR01DK122807 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Kirk N Campbell · 2019 to 2026
$4.8M
Prediction of Major Adverse Kidney Events and Recovery (Pred-MAKER) in COVID-19 PatientsR01DK118222 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Evren U. AZELOGLU · 2018 to 2026
$4.3M
Biomechanical drivers of cystogenesisR01DK131047 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AZELOGLU, EVREN U., GUSELLA, GABRIELE LUCA · 2021 to 2024
$2.7M
NIDDK NIH HHS R01 DK118222NIDDK NIH HHS R01 DK122807NIDDK NIH HHS R01 DK131047
6 · The paper itself

Abstract

Introduction: Severe acute kidney injury (AKI) is strongly associated with the risk of developing chronic kidney disease; however, little is known about the cell type-specific mechanisms driving kidney injury severity. Methods: In this multicenter observational study, we used clinically obtained liquid biopsy proteomics and machine learning (ML) to predict severe outcomes in patients with COVID-associated and non-COVID AKI. Further, we orthogonally combined 169 urine proteomics with 437 plasma proteomics samples and 40 urine sediment single-cell transcriptomics samples to identify complementary dysregulated mechanisms. Results: Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrate predictive power for both discovery and validation set with AUC of 87% and 76%, respectively. These predictive proteomics features obtained demonstrate that cell adhesion and autophagy-associated pathways are uniquely impacted in severe AKI. Differentially abundant proteins (DAPSs) associated with these pathways are highly expressed in cells of the juxtamedullary nephron, endothelial cells (ECs), and podocytes, indicating that these kidney cell types could be potential targets. Single-cell transcriptomic analysis in the Conclusion: Collectively, these data suggest that ECM degradation and adhesion-associated mechanisms could be the main driver of severe kidney injury.

Indexed as

AKICOVID-19kidney organoidsmachine learningmultiomicsurine proteomics

Identifiers

PMID41141506
PMCPMC12545944

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.