ArticleAlzheimer's research & therapy2023
Identification of novel diagnostic panel for mild cognitive impairment and Alzheimer's disease: findings based on urine proteomics and machine learning.
Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- AI-Enabled Modeling for Alzheimer's Disease Risk Prediction and Validation.Revista de neurologia · 2026Article
- Chaperonin in health and disease.Molecular biomedicine · 2026Review
- Exosomal gene-based predictive model and therapeutic target identification for Alzheimer's disease: A bioinformatics analysis.PloS one · 2026Article
- Multimodal neuroimaging discrimination of Alzheimer's disease, mild cognitive impairment, and late-life depression using electroencephalography and functional near-infrared spectroscopy: integrating electrophysiological and hemodynamic biomarkers.Frontiers in aging neuroscience · 2026Article
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Macrophage Phospholipase D3 promotes atherosclerosis via exacerbating foam cell formation and inducing inflammatory responses.International journal of cardiology. Heart & vasculature · 2025Article
- Article
- Article
- Insights into Biomarkers of Alzheimer's Disease: From Core Markers to Emerging Directions.Aging and disease · 2025Review
- Proteomic landscape of Alzheimer's disease: emerging technologies, advances and insights (2021 - 2025).Molecular neurodegeneration · 2025Review
- Negative Effect of Gst-35 on the Health Span of Caenorhabditis elegans Through Lysosomal Dysfunction via the Pmk-1 and Skr Genes.Aging cell · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- LOU/c/jall rat as a model of resilience in the context of streptozotocin-induced cognitive impairment.Frontiers in aging neuroscience · 2025Article
- Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies.Signal transduction and targeted therapy · 2024Review
- Association between gynecologic cancer and Alzheimer's disease: a bidirectional mendelian randomization study.BMC cancer · 2024Article
- Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts.Journal of Alzheimer's disease : JAD · 2024Article
- Targeted plasma metabolomics reveals potential biomarkers of the elderly with mild cognitive impairment in Qingdao rural area.Frontiers in aging neuroscience · 2024Article
- Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
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13 authors.
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Abstract
backgroundAlzheimer'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.
methodsWe 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.
resultsFifty-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.
conclusionsIn conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN.
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