ArticleScience advances2019
A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer's disease.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Loss of REST associated with Alzheimer's disease pathology is ameliorated by NAD.Brain : a journal of neurology · 2026Article
- Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.International journal of molecular sciences · 2025Article
- A longitudinal cohort study uncovers plasma protein biomarkers predating clinical onset and treatment response of rheumatoid arthritis.Nature communications · 2025Article
- Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.Alzheimer's research & therapy · 2025Article
- Biomarker discovery in Alzheimer's and neurodegenerative diseases using Nucleic Acid Linked Immuno-Sandwich Assay.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning.Frontiers in aging neuroscience · 2025Article
- LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study.Clinical and translational medicine · 2025Article
- Plasma biomarkers in patients with age-related sarcopenia: a proteomic exploration and experimental validation.Aging clinical and experimental research · 2024Article
- The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics.CNS neuroscience & therapeutics · 2024Review
- Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine.Chemical reviews · 2024Review
- Comparative Proteomic Profiling of Blood Plasma Revealed Marker Proteins Involved in Temporal Lobe Epilepsy.International journal of molecular sciences · 2024Article
- Evaluation of cerebrospinal fluid levels of synaptic vesicle protein, VAMP-2, across the sporadic Alzheimer's disease continuum.Alzheimer's research & therapy · 2023Article
- Ketone bodies mediate alterations in brain energy metabolism and biomarkers of Alzheimer's disease.Frontiers in neuroscience · 2023Review
- Omics-based biomarkers discovery for Alzheimer's disease.Cellular and molecular life sciences : CMLS · 2022Review
- Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning.International journal of molecular sciences · 2022Article
- Cerebrospinal fluid levels of fatty acid-binding protein 3 are associated with likelihood of amyloidopathy in cognitively healthy individuals.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
- Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks.Frontiers in aging neuroscience · 2022Article
- Connected speech markers of amyloid burden in primary progressive aphasia.Cortex; a journal devoted to the study of the nervous system and behavior · 2021Article
- Biological age in healthy elderly predicts aging-related diseases including dementia.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer's disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.