학술논문
Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease.
Document Type
Academic Journal
Author
Gómez-Pascual A; Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain; Steno Diabetes Center Copenhagen, Herlev, Denmark.; Naccache T; Department of Data Science, City University of London, United Kingdom.; Xu J; Institute of Pharmaceutical Science, King's College London, London, United Kingdom.; Hooshmand K; Steno Diabetes Center Copenhagen, Herlev, Denmark.; Wretlind A; Steno Diabetes Center Copenhagen, Herlev, Denmark.; Gabrielli M; CNR Institute of Neuroscience, 20854, Vedano al Lambro, Italy.; Lombardo MT; CNR Institute of Neuroscience, 20854, Vedano al Lambro, Italy; School of Medicine and Surgery, University of Milano-Bicocca, 20126, Italy.; Shi L; Novo Nordisk Research Centre Oxford (NNRCO), Oxford, United Kingdom.; Buckley NJ; Department of Psychiatry, University of Oxford, United Kingdom; Kavli Institute for Nanoscience Discovery, Denmark.; Tijms BM; Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands.; Vos SJB; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.; Ten Kate M; Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands.; Engelborghs S; Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Department of Neurology and Bru-BRAIN, UZ Brussel and Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussels, Belgium.; Sleegers K; Complex Genetics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Institute Born-Bunge, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.; Frisoni GB; University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.; Wallin A; Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.; Lleó A; Neurology Department, Hospital Sant Pau, Barcelona, Spain, Centro de Investigación en Red en enfermedades neurodegenerativas (CIBERNED).; Popp J; Old age psychiatry, University Hospital of Lausanne, University of Lausanne, Switzerland; Department of Geriatric Psychiatry, University Hospital of Psychiatry Zürich, University of Zürich, Switzerland.; Martinez-Lage P; CITA-Alzheimer Foundation, San Sebastian, Spain.; Streffer J; AC Immune SA, Lausanne, Switzerland, formerly Janssen R&D, LLC. Beerse, Belgium at the time of study conduct.; Barkhof F; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, United Kingdom.; Zetterberg H; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, United Kingdom; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom.; Visser PJ; Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.; Lovestone S; Department of Psychiatry, University of Oxford, United Kingdom; Janssen Medical (UK), High Wycombe, United Kingdom.; Bertram L; Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany; Department of Psychology, University of Oslo, Oslo, Norway.; Nevado-Holgado AJ; Department of Psychiatry, University of Oxford, United Kingdom.; Gualerzi A; IRCCS Fondazione Don Carlo Gnocchi ONLUS in Milan, Italy.; Picciolini S; IRCCS Fondazione Don Carlo Gnocchi ONLUS in Milan, Italy.; Proitsi P; Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.; Verderio C; CNR Institute of Neuroscience, 20854, Vedano al Lambro, Italy.; Botía JA; Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain.; Legido-Quigley C; Steno Diabetes Center Copenhagen, Herlev, Denmark; Institute of Pharmaceutical Science, King's College London, London, United Kingdom. Electronic address: cristina.legido_quigley@kcl.ac.uk.
Source
Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Subject
Language
English
Abstract
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.
Competing Interests: Declaration of competing interest SL is named as an inventor on biomarker intellectual property protected by Proteome Sciences and Kings College London unrelated to the current study and within the past five years has advised for Optum labs, Merck, SomaLogic and been the recipient of funding from AstraZeneca and other companies via the IMI funding scheme. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, reMYND, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). AL has served at scientific advisory boards of Fujirebio Europe, Eli Lilly, Novartis, Nutricia and Otsuka and is the inventor of a patent on synaptic markers in CSF (all unrelated to this study). JP has served at scientific advisory boards of Fujirebio Europe, Eli Lilly and Nestle Institute of Health Sciences, all unrelated to this study. SE has received unrestricted research grants from Janssen Pharmaceutica and ADx Neurosciences and has served at scientific advisory boards of Biogen, Eisai, Novartis, Nutricia/Danone, all unrelated to this study. FB is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI and Prothena. Consultant for Roche, Biogen, Merck, IXICO, Jansen, Combinostics. Research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD, all unrelated to this study. CLQ has received funding related to this study from Pfizer and via the IMI funding scheme, in the past five years has been the recipient of funding from Novo Nordisk, unrelated to this study.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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.
Competing Interests: Declaration of competing interest SL is named as an inventor on biomarker intellectual property protected by Proteome Sciences and Kings College London unrelated to the current study and within the past five years has advised for Optum labs, Merck, SomaLogic and been the recipient of funding from AstraZeneca and other companies via the IMI funding scheme. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, reMYND, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). AL has served at scientific advisory boards of Fujirebio Europe, Eli Lilly, Novartis, Nutricia and Otsuka and is the inventor of a patent on synaptic markers in CSF (all unrelated to this study). JP has served at scientific advisory boards of Fujirebio Europe, Eli Lilly and Nestle Institute of Health Sciences, all unrelated to this study. SE has received unrestricted research grants from Janssen Pharmaceutica and ADx Neurosciences and has served at scientific advisory boards of Biogen, Eisai, Novartis, Nutricia/Danone, all unrelated to this study. FB is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI and Prothena. Consultant for Roche, Biogen, Merck, IXICO, Jansen, Combinostics. Research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD, all unrelated to this study. CLQ has received funding related to this study from Pfizer and via the IMI funding scheme, in the past five years has been the recipient of funding from Novo Nordisk, unrelated to this study.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)