Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the...

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Autores: Gómez-Pascual, A.|||0000-0003-2348-9083, Xu, J.|||0000-0002-6341-5938, Wretlind, A.|||0000-0001-6083-5227, Shi, L., Buckley, Noel J.|||0000-0003-1152-0653, Tijms, Betty M.|||0000-0002-2612-1797, Vos, Stephanie J.B.|||0000-0002-2045-9818, Engelborghs, Sebastiaan|||0000-0003-0304-9785, Sleegers, Kristel|||0000-0002-0283-2332, Wallin, Anders|||0000-0001-9506-0513, Lleó, Alberto|||0000-0002-2568-5478, Popp, Julius|||0000-0002-0068-0312, Martinez-Lage, P., Streffer, J., Barkhof, Frederik|||0000-0003-3543-3706, Zetterberg, Henrik|||0000-0003-3930-4354, Visser, Pieter Jelle|||0000-0001-8008-9727, Lovestone, Simon|||0000-0003-0473-4565, Bertram, Lars|||0000-0002-0108-124X, Nevado-Holgado, Alejo|||0000-0001-9276-2720, Proitsi, Petroula|||0000-0002-2553-6974, Botía, Juan A.|||0000-0002-6992-598X, Legido-Quigley, Cristina|||0000-0002-4018-214X, Naccache, Talel, Hooshmand, Kourosh, Gabrielli, Martina, Lombardo, Marta Tiffany, ten Kate, Mara|||0000-0002-8290-8543, Frisoni, Giovanni B.|||0000-0002-6419-1753, Gualerzi, Alice, Picciolini, Silvia, Verderio, Claudia
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:323202
Acesso em linha:https://ddd.uab.cat/record/323202
https://dx.doi.org/urn:doi:10.1016/j.compbiomed.2024.108588
Access Level:acceso abierto
Palavra-chave:Alzheimer's disease
Integrative omics
Machine learning
Metabolomics
Mild cognitive impairment
Proteomics
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spelling Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's diseaseGómez-Pascual, A.|||0000-0003-2348-9083Xu, J.|||0000-0002-6341-5938Wretlind, A.|||0000-0001-6083-5227Shi, L.Buckley, Noel J.|||0000-0003-1152-0653Tijms, Betty M.|||0000-0002-2612-1797Vos, Stephanie J.B.|||0000-0002-2045-9818Engelborghs, Sebastiaan|||0000-0003-0304-9785Sleegers, Kristel|||0000-0002-0283-2332Wallin, Anders|||0000-0001-9506-0513Lleó, Alberto|||0000-0002-2568-5478Popp, Julius|||0000-0002-0068-0312Martinez-Lage, P.Streffer, J.Barkhof, Frederik|||0000-0003-3543-3706Zetterberg, Henrik|||0000-0003-3930-4354Visser, Pieter Jelle|||0000-0001-8008-9727Lovestone, Simon|||0000-0003-0473-4565Bertram, Lars|||0000-0002-0108-124XNevado-Holgado, Alejo|||0000-0001-9276-2720Proitsi, Petroula|||0000-0002-2553-6974Botía, Juan A.|||0000-0002-6992-598XLegido-Quigley, Cristina|||0000-0002-4018-214XNaccache, TalelHooshmand, KouroshGabrielli, MartinaLombardo, Marta Tiffanyten Kate, Mara|||0000-0002-8290-8543Frisoni, Giovanni B.|||0000-0002-6419-1753Gualerzi, AlicePicciolini, SilviaVerderio, ClaudiaAlzheimer's diseaseIntegrative omicsMachine learningMetabolomicsMild cognitive impairmentProteomicsBackground: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.Universitat Autònoma de Barcelona. Departament de Medicina 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/323202https://dx.doi.org/urn:doi:10.1016/j.compbiomed.2024.108588reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3232022026-06-06T12:50:31Z
dc.title.none.fl_str_mv Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
title Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
spellingShingle Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
Gómez-Pascual, A.|||0000-0003-2348-9083
Alzheimer's disease
Integrative omics
Machine learning
Metabolomics
Mild cognitive impairment
Proteomics
title_short Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
title_full Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
title_fullStr Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
title_full_unstemmed Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
title_sort Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
dc.creator.none.fl_str_mv Gómez-Pascual, A.|||0000-0003-2348-9083
Xu, J.|||0000-0002-6341-5938
Wretlind, A.|||0000-0001-6083-5227
Shi, L.
Buckley, Noel J.|||0000-0003-1152-0653
Tijms, Betty M.|||0000-0002-2612-1797
Vos, Stephanie J.B.|||0000-0002-2045-9818
Engelborghs, Sebastiaan|||0000-0003-0304-9785
Sleegers, Kristel|||0000-0002-0283-2332
Wallin, Anders|||0000-0001-9506-0513
Lleó, Alberto|||0000-0002-2568-5478
Popp, Julius|||0000-0002-0068-0312
Martinez-Lage, P.
Streffer, J.
Barkhof, Frederik|||0000-0003-3543-3706
Zetterberg, Henrik|||0000-0003-3930-4354
Visser, Pieter Jelle|||0000-0001-8008-9727
Lovestone, Simon|||0000-0003-0473-4565
Bertram, Lars|||0000-0002-0108-124X
Nevado-Holgado, Alejo|||0000-0001-9276-2720
Proitsi, Petroula|||0000-0002-2553-6974
Botía, Juan A.|||0000-0002-6992-598X
Legido-Quigley, Cristina|||0000-0002-4018-214X
Naccache, Talel
Hooshmand, Kourosh
Gabrielli, Martina
Lombardo, Marta Tiffany
ten Kate, Mara|||0000-0002-8290-8543
Frisoni, Giovanni B.|||0000-0002-6419-1753
Gualerzi, Alice
Picciolini, Silvia
Verderio, Claudia
author Gómez-Pascual, A.|||0000-0003-2348-9083
author_facet Gómez-Pascual, A.|||0000-0003-2348-9083
Xu, J.|||0000-0002-6341-5938
Wretlind, A.|||0000-0001-6083-5227
Shi, L.
Buckley, Noel J.|||0000-0003-1152-0653
Tijms, Betty M.|||0000-0002-2612-1797
Vos, Stephanie J.B.|||0000-0002-2045-9818
Engelborghs, Sebastiaan|||0000-0003-0304-9785
Sleegers, Kristel|||0000-0002-0283-2332
Wallin, Anders|||0000-0001-9506-0513
Lleó, Alberto|||0000-0002-2568-5478
Popp, Julius|||0000-0002-0068-0312
Martinez-Lage, P.
Streffer, J.
Barkhof, Frederik|||0000-0003-3543-3706
Zetterberg, Henrik|||0000-0003-3930-4354
Visser, Pieter Jelle|||0000-0001-8008-9727
Lovestone, Simon|||0000-0003-0473-4565
Bertram, Lars|||0000-0002-0108-124X
Nevado-Holgado, Alejo|||0000-0001-9276-2720
Proitsi, Petroula|||0000-0002-2553-6974
Botía, Juan A.|||0000-0002-6992-598X
Legido-Quigley, Cristina|||0000-0002-4018-214X
Naccache, Talel
Hooshmand, Kourosh
Gabrielli, Martina
Lombardo, Marta Tiffany
ten Kate, Mara|||0000-0002-8290-8543
Frisoni, Giovanni B.|||0000-0002-6419-1753
Gualerzi, Alice
Picciolini, Silvia
Verderio, Claudia
author_role author
author2 Xu, J.|||0000-0002-6341-5938
Wretlind, A.|||0000-0001-6083-5227
Shi, L.
Buckley, Noel J.|||0000-0003-1152-0653
Tijms, Betty M.|||0000-0002-2612-1797
Vos, Stephanie J.B.|||0000-0002-2045-9818
Engelborghs, Sebastiaan|||0000-0003-0304-9785
Sleegers, Kristel|||0000-0002-0283-2332
Wallin, Anders|||0000-0001-9506-0513
Lleó, Alberto|||0000-0002-2568-5478
Popp, Julius|||0000-0002-0068-0312
Martinez-Lage, P.
Streffer, J.
Barkhof, Frederik|||0000-0003-3543-3706
Zetterberg, Henrik|||0000-0003-3930-4354
Visser, Pieter Jelle|||0000-0001-8008-9727
Lovestone, Simon|||0000-0003-0473-4565
Bertram, Lars|||0000-0002-0108-124X
Nevado-Holgado, Alejo|||0000-0001-9276-2720
Proitsi, Petroula|||0000-0002-2553-6974
Botía, Juan A.|||0000-0002-6992-598X
Legido-Quigley, Cristina|||0000-0002-4018-214X
Naccache, Talel
Hooshmand, Kourosh
Gabrielli, Martina
Lombardo, Marta Tiffany
ten Kate, Mara|||0000-0002-8290-8543
Frisoni, Giovanni B.|||0000-0002-6419-1753
Gualerzi, Alice
Picciolini, Silvia
Verderio, Claudia
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
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author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat Autònoma de Barcelona. Departament de Medicina
dc.subject.none.fl_str_mv Alzheimer's disease
Integrative omics
Machine learning
Metabolomics
Mild cognitive impairment
Proteomics
topic Alzheimer's disease
Integrative omics
Machine learning
Metabolomics
Mild cognitive impairment
Proteomics
description Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/323202
https://dx.doi.org/urn:doi:10.1016/j.compbiomed.2024.108588
url https://ddd.uab.cat/record/323202
https://dx.doi.org/urn:doi:10.1016/j.compbiomed.2024.108588
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
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https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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instname:Universitat Autònoma de Barcelona
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