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Alzheimer's Research & Therapy
Published in: Alzheimer's Research & Therapy 1/2023

Open Access 01-12-2023 | Alzheimer's Disease | Research

Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning

Authors: Yuye Wang, Yu Sun, Yu Wang, Shuhong Jia, Yanan Qiao, Zhi Zhou, Wen Shao, Xiangfei Zhang, Jing Guo, Bin Zhang, Xiaoqian Niu, Yi Wang, Dantao Peng

Published in: Alzheimer's Research & Therapy | Issue 1/2023

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Abstract

Background

Alzheimer’s disease is a prevalent disease with a heavy global burden. Proteomics is the systematic study of proteins and peptides to provide comprehensive descriptions. Aiming to obtain a more accurate and convenient clinical diagnosis, researchers are working for better biomarkers. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.

Methods

We firstly enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples, and conducted an LC–MS/MS analysis. In parallel, clinical data were collected, and clinical examinations were performed. After statistical and bioinformatics analyses, significant risk factors and differential urinary proteins were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.

Results

Fifty-seven AD patients, 43 MCI patients, and 62 CN subjects were enrolled. A total of 3366 proteins were identified, and 608 urine proteins were finally included in the analysis. There were 33 significantly differential proteins between the AD and CN groups and 15 significantly differential proteins between the MCI and CN groups. AD diagnostic panel included DDC, CTSC, EHD4, GSTA3, SLC44A4, GNS, GSTA1, ANXA4, PLD3, CTSH, HP, RPS3, CPVL, age, and APOE ε4 with an AUC of 0.9989 in the training test and 0.8824 in the test set while MCI diagnostic panel included TUBB, SUCLG2, PROCR, TCP1, ACE, FLOT2, EHD4, PROZ, C9, SERPINA3, age, and APOE ε4 with an AUC of 0.9985 in the training test and 0.8143 in the test set. Besides, diagnostic proteins were weakly correlated with cognitive functions.

Conclusions

In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN.
Appendix
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Metadata
Title
Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
Authors
Yuye Wang
Yu Sun
Yu Wang
Shuhong Jia
Yanan Qiao
Zhi Zhou
Wen Shao
Xiangfei Zhang
Jing Guo
Bin Zhang
Xiaoqian Niu
Yi Wang
Dantao Peng
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Alzheimer's Research & Therapy / Issue 1/2023
Electronic ISSN: 1758-9193
DOI
https://doi.org/10.1186/s13195-023-01324-4

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