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

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. Uri...

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Autores principales: Wang, Yuye, Sun, Yu, Wang, Yu, Jia, Shuhong, Qiao, Yanan, Zhou, Zhi, Shao, Wen, Zhang, Xiangfei, Guo, Jing, Zhang, Bin, Niu, Xiaoqian, Wang, Yi, Peng, Dantao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625308/
https://www.ncbi.nlm.nih.gov/pubmed/37925455
http://dx.doi.org/10.1186/s13195-023-01324-4
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author Wang, Yuye
Sun, Yu
Wang, Yu
Jia, Shuhong
Qiao, Yanan
Zhou, Zhi
Shao, Wen
Zhang, Xiangfei
Guo, Jing
Zhang, Bin
Niu, Xiaoqian
Wang, Yi
Peng, Dantao
author_facet Wang, Yuye
Sun, Yu
Wang, Yu
Jia, Shuhong
Qiao, Yanan
Zhou, Zhi
Shao, Wen
Zhang, Xiangfei
Guo, Jing
Zhang, Bin
Niu, Xiaoqian
Wang, Yi
Peng, Dantao
author_sort Wang, Yuye
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01324-4.
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spelling pubmed-106253082023-11-05 Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning Wang, Yuye Sun, Yu Wang, Yu Jia, Shuhong Qiao, Yanan Zhou, Zhi Shao, Wen Zhang, Xiangfei Guo, Jing Zhang, Bin Niu, Xiaoqian Wang, Yi Peng, Dantao Alzheimers Res Ther Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-023-01324-4. BioMed Central 2023-11-04 /pmc/articles/PMC10625308/ /pubmed/37925455 http://dx.doi.org/10.1186/s13195-023-01324-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Yuye
Sun, Yu
Wang, Yu
Jia, Shuhong
Qiao, Yanan
Zhou, Zhi
Shao, Wen
Zhang, Xiangfei
Guo, Jing
Zhang, Bin
Niu, Xiaoqian
Wang, Yi
Peng, Dantao
Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title_full Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title_fullStr Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title_full_unstemmed Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title_short Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer’s disease: findings based on urine proteomics and machine learning
title_sort identification of novel diagnostic panel for mild cognitive impairment and alzheimer’s disease: findings based on urine proteomics and machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625308/
https://www.ncbi.nlm.nih.gov/pubmed/37925455
http://dx.doi.org/10.1186/s13195-023-01324-4
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