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
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 cognitive...
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| author | Daniel Stamate Min Kim Petroula Proitsi Sarah Westwood Alison 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 Verhey Daniel Alcolea Alberto Lléo Gwendoline Peyratout Mikel Tainta Peter Johannsen Yvonne Freund‐Levi Lutz Frölich Valerija Dobricic Giovanni B. Frisoni José L. Molinuevo Anders Wallin Julius Popp Pablo Martinez‐Lage Lars Bertram Kaj Blennow Henrik Zetterberg Johannes Streffer Pieter J. Visser Simon Lovestone Cristina Legido‐Quigley |
| author_facet | Daniel Stamate Min Kim Petroula Proitsi Sarah Westwood Alison 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 Verhey Daniel Alcolea Alberto Lléo Gwendoline Peyratout Mikel Tainta Peter Johannsen Yvonne Freund‐Levi Lutz Frölich Valerija Dobricic Giovanni B. Frisoni José L. Molinuevo Anders Wallin Julius Popp Pablo Martinez‐Lage Lars Bertram Kaj Blennow Henrik Zetterberg Johannes Streffer Pieter J. Visser Simon Lovestone Cristina Legido‐Quigley |
| author_sort | Daniel Stamate |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-677a8b0e0beb49259e23bfd0e83c6fcc |
| institution | DOAJ |
| issn | 2352-8737 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
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| series | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
| spelling | doaj-art-677a8b0e0beb49259e23bfd0e83c6fcc2025-08-20T03:22:00ZengWileyAlzheimer’s & Dementia: Translational Research & Clinical Interventions2352-87372019-01-015193393810.1016/j.trci.2019.11.001A metabolite‐based machine learning approach to diagnose Alzheimer‐type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohortDaniel Stamate0Min Kim1Petroula Proitsi2Sarah Westwood3Alison Baird4Alejo Nevado‐Holgado5Abdul Hye6Isabelle Bos7Stephanie J.B. Vos8Rik Vandenberghe9Charlotte E. Teunissen10Mara Ten Kate11Philip Scheltens12Silvy Gabel13Karen Meersmans14Olivier Blin15Jill Richardson16Ellen De Roeck17Sebastiaan Engelborghs18Kristel Sleegers19Régis Bordet20Lorena Ramit21Petronella Kettunen22Magda Tsolaki23Frans Verhey24Daniel Alcolea25Alberto Lléo26Gwendoline Peyratout27Mikel Tainta28Peter Johannsen29Yvonne Freund‐Levi30Lutz Frölich31Valerija Dobricic32Giovanni B. Frisoni33José L. Molinuevo34Anders Wallin35Julius Popp36Pablo Martinez‐Lage37Lars Bertram38Kaj Blennow39Henrik Zetterberg40Johannes Streffer41Pieter J. Visser42Simon Lovestone43Cristina Legido‐Quigley44Division of Population Health, Health Services Research and Primary CareUniversity of ManchesterManchesterUKSteno Diabetes Center CopenhagenGentofteDenmarkInstitute of Psychiatry, Psychology and NeuroscienceMaurice Wohl Clinical Neuroscience Institute, King's College LondonLondonUKDepartment of PsychiatryUniversity of OxfordOxfordUKDepartment of PsychiatryUniversity of OxfordOxfordUKDepartment of PsychiatryUniversity of OxfordOxfordUKInstitute of Psychiatry, Psychology and NeuroscienceMaurice Wohl Clinical Neuroscience Institute, King's College LondonLondonUKDepartment of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceAlzheimer Centrum LimburgMaastricht UniversityMaastrichtthe NetherlandsDepartment of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceAlzheimer Centrum LimburgMaastricht UniversityMaastrichtthe NetherlandsDepartment of NeurologyAlzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMCAmsterdamthe NetherlandsDepartment of Radiology and Nuclear MedicineVU University Medical CenterAmsterdamthe NetherlandsDepartment of NeurologyAlzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMCAmsterdamthe NetherlandsDepartment of NeurologyAlzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMCAmsterdamthe NetherlandsDepartment of Clinical ChemistryNeurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical CentersVrije Universiteitthe NetherlandsUniversity Hospital LeuvenLeuvenBelgiumAIX Marseille University, INS, Ap‐hmMarseilleFranceNeurosciences Therapeutic Area GlaxoSmithKline R&DStevenageUKFaculty of Psychology & Educational Sciences VrijeUniversiteit Brussel (VUB)BrusselsBelgiumReference Center for Biological Markers of Dementia (BIODEM), University of AntwerpAntwerpBelgiumInstitute Born‐Bunge, University of AntwerpAntwerpBelgiumUniversity of Lille, Inserm, CHU LilleLilleFranceAlzheimer's Disease & Other Cognitive Disorders UnitHospital Clínic‐IDIBAPSBarcelonaSpainInstitute of Neuroscience and Physiology, Sahlgrenska Academy at University of GothenburgGothenburgSweden1st Department of NeurologyAHEPA University HospitalMakedoniaThessalonikiGreeceDepartment of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceAlzheimer Centrum LimburgMaastricht UniversityMaastrichtthe NetherlandsMemory Unit Neurology DepartmentHospital de la Santa Creu i Sant PauBarcelonaSpainMemory Unit Neurology DepartmentHospital de la Santa Creu i Sant PauBarcelonaSpainUniversity Hospital of LausanneLausanneSwitzerlandCenter for Research and Advanced Therapies, Fundacion CITA‐alzheimer FundazioaDonostia/San SebastianSpainDanish Dementia Research CentreRigshospitalet, Copenhagen University HospitalCopenhagenDenmarkInstitute of Psychiatry, Psychology and NeuroscienceMaurice Wohl Clinical Neuroscience Institute, King's College LondonLondonUKDepartment of Geriatric PsychiatryZentralinstitut für Seelische Gesundheit, University of HeidelbergMannheimGermanyLübeck Interdisciplinary Platform for Genome AnalyticsInstitutes of Neurogenetics and Cardiogenetics, University of LübeckLübeckGermanyUniversity of GenevaGenevaSwitzerlandUniversity of Lille, Inserm, CHU LilleLilleFranceInstitute of Neuroscience and Physiology, Sahlgrenska Academy at the University of GothenburgGothenburgSwedenUniversity Hospital of LausanneLausanneSwitzerlandCenter for Research and Advanced Therapies, Fundacion CITA‐alzheimer FundazioaDonostia/San SebastianSpainLübeck Interdisciplinary Platform for Genome AnalyticsInstitutes of Neurogenetics and Cardiogenetics, University of LübeckLübeckGermanyDepartment of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, University of GothenburgMölndalSwedenDepartment of Psychiatry and NeurochemistryInstitute of Neuroscience and Physiology, University of GothenburgMölndalSwedenReference Center for Biological Markers of Dementia (BIODEM), Institute Born‐Bunge, University of AntwerpAntwerpBelgiumDepartment of Psychiatry and NeuropsychologySchool for Mental Health and NeuroscienceAlzheimer Centrum LimburgMaastricht UniversityMaastrichtthe NetherlandsDepartment of PsychiatryUniversity of OxfordOxfordUKSteno Diabetes Center CopenhagenGentofteDenmarkAbstract 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.https://doi.org/10.1016/j.trci.2019.11.001EMIF‐ADAlzheimer's diseaseMetabolomicsBiomarkersMachine‐Learning |
| spellingShingle | Daniel Stamate Min Kim Petroula Proitsi Sarah Westwood Alison 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 Verhey Daniel Alcolea Alberto Lléo Gwendoline Peyratout Mikel Tainta Peter Johannsen Yvonne Freund‐Levi Lutz Frölich Valerija Dobricic Giovanni B. Frisoni José L. Molinuevo Anders Wallin Julius Popp Pablo Martinez‐Lage Lars Bertram Kaj Blennow Henrik Zetterberg Johannes Streffer Pieter J. Visser Simon Lovestone Cristina Legido‐Quigley 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 Alzheimer’s & Dementia: Translational Research & Clinical Interventions EMIF‐AD Alzheimer's disease Metabolomics Biomarkers Machine‐Learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | EMIF‐AD Alzheimer's disease Metabolomics Biomarkers Machine‐Learning |
| url | https://doi.org/10.1016/j.trci.2019.11.001 |
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