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A metabolite‐basedmachine learningapproach to diagnoseAlzheimer‐typedementia in blood:Results from theEuropean MedicalInformation Frameworkfor… Daniel StamateMin KimPetroula Proitsi2019 год

A metabolite‐based machine learning approach to diagnose Alzheimer‐type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
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Аннотация: Abstract Introduction Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. Methods This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD‐type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). Results On the test data, DL produced the AUC of 0.85 (0.80–0.89), XGBoost produced 0.88 (0.86–0.89) and RF produced 0.85 (0.83–0.87). By comparison, CSF measures of amyloid, p‐tau and t‐tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. Discussion This study showed that plasma metabolites have the potential to match the AUC of well‐established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.
Год издания: 2019
Авторы: Daniel Stamate, Min Kim, Petroula Proitsi, Sarah Westwood, Alison L. Baird, Alejo Nevado‐Holgado, Abdul Hye, Isabelle Bos, Stephanie J. B. Vos, Rik Vandenberghe, Charlotte E. Teunissen, Mara ten Kate, Philip Scheltens, Silvy Gabel, Karen Meersmans, Olivier Blin, Jill Richardson, Ellen De Roeck, Sebastiaan Engelborghs, Kristel Sleegers, Régis Bordet, Lorena Ramit, Petronella Kettunen, Magda Tsolaki, Frans R.J. Verhey, Daniel Alcolea, Alberto Lleó, Gwendoline Peyratout, Mikel Tainta, Peter Johannsen, Yvonne Freund‐Levi, Lutz Frölich, Valerija Dobričić, Giovanni B. Frisoni, José Luís Molinuevo, Anders Wallin, Julius Popp, Pablo Martínez‐Lage, Lars Bertram, Kaj Blennow, Henrik Zetterberg, Johannes Streffer, Pieter Jelle Visser, Simon Lovestone, Cristina Legido‐Quigley
Издательство: Elsevier BV
Источник: Alzheimer s & Dementia Translational Research & Clinical Interventions
Ключевые слова: Metabolomics and Mass Spectrometry Studies, Bioinformatics and Genomic Networks, Diet and metabolism studies
Другие ссылки: Alzheimer s & Dementia Translational Research & Clinical Interventions (HTML)
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