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Published on: April 12, 2021
Development of a MALDI MS-based platform for early detection of acute kidney injury
Emma Carrick1, Jill Vanmassenhove2, Griet Glorieux2
1Institute of Cardiovascular and Medical Sciences, Glasgow, UK.
Purpose:
Septic acute kidney injury (AKI) is associated with poor outcome. This can partly be attributed to delayed diagnosis and incomplete understanding of the underlying pathophysiology. Our aim was to develop an early predictive test for AKI based on the analysis of urinary peptide biomarkers by MALDI-MS.
Experimental Design:
Urine samples from 95 patients with sepsis were analyzed by MALDI-MS. Marker search and multimarker model establishment were performed using the peptide profiles from 17 patients with existing or within the next 5 days developing AKI and 17 with no change in renal function. Replicates of urine sample pools from the AKI and non-AKI patient groups and normal controls were also included to select the analytically most robust AKI markers.
Results:
Thirty-nine urinary peptides were selected by cross-validated variable selection to generate a support vector machine multidimensional AKI classifier. Prognostic performance of the AKI classifier on an independent validation set including the remaining 61 patients of the study population (17 controls and 44 cases) was good with an area under the receiver operating characteristics curve of 0.82 and a sensitivity and specificity of 86% and 76%, respectively.
Conclusion And Clinical Relevance:
A urinary peptide marker model detects onset of AKI with acceptable accuracy in septic patients. Such a platform can eventually be transferred to the clinic as fast MALDI-MS test format.
Insights
A new urinary peptide test can predict acute kidney injury (AKI) in sepsis patients early. This MALDI-MS based approach offers a promising tool for timely diagnosis and improved patient outcomes.
Area of Science:
- Biomarker discovery
- Proteomics
- Clinical diagnostics
Background:
- Septic acute kidney injury (AKI) leads to poor patient outcomes.
- Delayed diagnosis and poor understanding of pathophysiology contribute to this challenge.
Purpose of the Study:
- Develop an early predictive test for AKI in sepsis patients.
- Utilize urinary peptide biomarkers analyzed by MALDI-MS.
Main Methods:
- Analyzed urine samples from 95 sepsis patients using MALDI-MS.
- Selected 39 urinary peptides to create a support vector machine AKI classifier.
- Validated the classifier on an independent patient cohort.
Main Results:
- The AKI classifier demonstrated good prognostic performance.
- Achieved an AUC of 0.82 with 86% sensitivity and 76% specificity.
- Identified a robust set of urinary peptide biomarkers.
Conclusions:
- A urinary peptide marker model accurately detects AKI onset in septic patients.
- This platform can be developed into a rapid clinical MALDI-MS test.
- Enables earlier diagnosis and potential intervention for septic AKI.
Related Concept Videos
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention
01:22Acute Kidney Injury I: Introduction
01:20Acute Kidney Injury V: Interprofessional Care
01:29Acute Kidney Injury III: Clinical Manifestations