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Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology
BACKGROUND: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer’s disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With thi...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665586/ https://www.ncbi.nlm.nih.gov/pubmed/34895338 http://dx.doi.org/10.1186/s13578-021-00712-3 |
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author | Povala, Guilherme Bellaver, Bruna De Bastiani, Marco Antônio Brum, Wagner S. Ferreira, Pamela C. L. Bieger, Andrei Pascoal, Tharick A. Benedet, Andrea L. Souza, Diogo O. Araujo, Ricardo M. Zatt, Bruno Rosa-Neto, Pedro Zimmer, Eduardo R. |
author_facet | Povala, Guilherme Bellaver, Bruna De Bastiani, Marco Antônio Brum, Wagner S. Ferreira, Pamela C. L. Bieger, Andrei Pascoal, Tharick A. Benedet, Andrea L. Souza, Diogo O. Araujo, Ricardo M. Zatt, Bruno Rosa-Neto, Pedro Zimmer, Eduardo R. |
author_sort | Povala, Guilherme |
collection | PubMed |
description | BACKGROUND: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer’s disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. METHODS: We used CSF measurements of three soluble Aβ peptides (Aβ(1–38), Aβ(1–40) and Aβ(1–42)) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. RESULTS: Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. CONCLUSIONS: Our results demonstrate that, by applying a refined ML analysis, a combination of Aβ isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-021-00712-3. |
format | Online Article Text |
id | pubmed-8665586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86655862021-12-13 Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology Povala, Guilherme Bellaver, Bruna De Bastiani, Marco Antônio Brum, Wagner S. Ferreira, Pamela C. L. Bieger, Andrei Pascoal, Tharick A. Benedet, Andrea L. Souza, Diogo O. Araujo, Ricardo M. Zatt, Bruno Rosa-Neto, Pedro Zimmer, Eduardo R. Cell Biosci Research BACKGROUND: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer’s disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. METHODS: We used CSF measurements of three soluble Aβ peptides (Aβ(1–38), Aβ(1–40) and Aβ(1–42)) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. RESULTS: Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. CONCLUSIONS: Our results demonstrate that, by applying a refined ML analysis, a combination of Aβ isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13578-021-00712-3. BioMed Central 2021-12-11 /pmc/articles/PMC8665586/ /pubmed/34895338 http://dx.doi.org/10.1186/s13578-021-00712-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Povala, Guilherme Bellaver, Bruna De Bastiani, Marco Antônio Brum, Wagner S. Ferreira, Pamela C. L. Bieger, Andrei Pascoal, Tharick A. Benedet, Andrea L. Souza, Diogo O. Araujo, Ricardo M. Zatt, Bruno Rosa-Neto, Pedro Zimmer, Eduardo R. Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title | Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title_full | Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title_fullStr | Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title_full_unstemmed | Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title_short | Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology |
title_sort | soluble amyloid-beta isoforms predict downstream alzheimer’s disease pathology |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665586/ https://www.ncbi.nlm.nih.gov/pubmed/34895338 http://dx.doi.org/10.1186/s13578-021-00712-3 |
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