Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology

• Autor(a) principal: Povala, Guilherme
• Data de Publicação: 2021
• Outros Autores: Bellaver, Bruna, De Bastiani, Marco Antônio, Brum, Wagner Scheeren, Ferreira, Pâmela Cristina Lukasewicz, Bieger, Andrei, Pascoal, Tharick Ali, Benedet, Andréa L., Souza, Diogo Onofre Gomes de, Araújo, Ricardo Matsumura de, Zatt, Bruno, Rosa Neto, Pedro, Zimmer, Eduardo Rigon
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/237160
• topic: Peptídeos beta-amilóides; Líquido cefalorraquidiano; Isoformas de proteínas; Tauopatias; Doença de Alzheimer; Biomarcadores; Alzheimer’s disease; Amyloid-beta; Tau pathology; Neurodegeneration; Machine learning; Proteomics

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 (Aβ1–38, Aβ1–40 and Aβ1–42) 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.