Issue 9, 2024

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

Abstract

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.

Graphical abstract: Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

Supplementary files

Article information

Article type
Paper
Submitted
19 জানু 2024
Accepted
18 মার্চ 2024
First published
19 মার্চ 2024

Analyst, 2024,149, 2600-2608

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

I. Pereira, J. N. S. Sboto, J. L. Robinson and C. G. Gill, Analyst, 2024, 149, 2600 DOI: 10.1039/D4AN00099D

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