TY - JOUR
T1 - Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease
AU - Gómez-Pascual, Alicia
AU - Naccache, Talel
AU - Xu, Jin
AU - Hooshmand, Kourosh
AU - Wretlind, Asger
AU - Gabrielli, Martina
AU - Lombardo, Marta Tiffany
AU - Shi, Liu
AU - Buckley, Noel J.
AU - Tijms, Betty M.
AU - Vos, Stephanie J.B.
AU - ten Kate, Mara
AU - Engelborghs, Sebastiaan
AU - Sleegers, Kristel
AU - Frisoni, Giovanni B.
AU - Wallin, Anders
AU - Lleó, Alberto
AU - Popp, Julius
AU - Martinez-Lage, Pablo
AU - Streffer, Johannes
AU - Barkhof, Frederik
AU - Zetterberg, Henrik
AU - Visser, Pieter Jelle
AU - Lovestone, Simon
AU - Bertram, Lars
AU - Nevado-Holgado, Alejo J.
AU - Gualerzi, Alice
AU - Picciolini, Silvia
AU - Proitsi, Petroula
AU - Verderio, Claudia
AU - Botía, Juan A.
AU - Legido-Quigley, Cristina
N1 - Funding Information:
This research was conducted as part of the EMIF-AD MBD project which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement [no 115372], resources of which are composed of financial contribution from the European Union\u2032s Seventh Framework Programme [FP7/2007-2013] and EFPIA companies\u2032 in-kind contribution. The DESCRIPA study was funded by the European Commission within the 5th framework program [QLRT-2001-2455]. The EDAR study was funded by the European Commission within the 5th framework program [contract # 37670]. The Leuven cohort was funded by the Stichting voor Alzheimer Onderzoek [grant numbers #11020, #13007 and #15005]. RV is a senior clinical investigator of the Flemish Research Foundation (FWO). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government [allocation 17.0.1.08.12.0000.2.454.01.41142.001.H]. We acknowledge the contribution of the personnel of the Genomic Service Facility at the VIB-U Antwerp Center for Molecular Neurology. The research at VIB-CMN is funded in part by the University of Antwerp Research Fund. HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council [#2018-02532], the European Research Council [#681712], Swedish State Support for Clinical Research [#ALFGBG-720931], the Alzheimer Drug Discovery Foundation (ADDF), USA [#201809-2016862], and the UK Dementia Research Institute at UCL. FB is supported by the NIHR biomedical research centre at UCLH. LS is funded by the Virtual Brain Cloud from European commission [grant no. H2020-SC1-DTH-2018-1]. R.G. was supported by the National Institute for Health Research [NIHR] Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u2032s College London. This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u2032s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. JX, AW and CLQ thank Lundbeck Fonden for the support. Finally, this publication has also been made possible by the support of the Fundaci\u00F3n S\u00E9neca-Agencia de Ciencia y Tecnolog\u00EDa de la Regi\u00F3n de Murcia (Spain), which finances the PhD of Alicia G\u00F3mez-Pascual [21259/FPI/19].
Funding Information:
This research was conducted as part of the EMIF-AD MBD project which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement [no 115372], resources of which are composed of financial contribution from the European Union's Seventh Framework Programme [FP7/2007\u20132013] and EFPIA companies\u2032 in-kind contribution. The DESCRIPA study was funded by the European Commission within the 5th framework program [QLRT-2001-2455]. The EDAR study was funded by the European Commission within the 5th framework program [contract # 37670]. The Leuven cohort was funded by the Stichting voor Alzheimer Onderzoek [grant numbers #11020, #13007 and #15005]. RV is a senior clinical investigator of the Flemish Research Foundation (FWO). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government [allocation 17.0.1.December 08, 0000.2.454.01.41142.001.H]. We acknowledge the contribution of the personnel of the Genomic Service Facility at the VIB-U Antwerp Center for Molecular Neurology. The research at VIB-CMN is funded in part by the University of Antwerp Research Fund. HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council [#2018\u201302532], the European Research Council [#681712], Swedish State Support for Clinical Research [#ALFGBG-720931], the Alzheimer Drug DiscoveryFoundation (ADDF), USA [#201809\u20132016862], and the UK Dementia Research Institute at UCL. FB is supported by the NIHR biomedical research centre at UCLH. LS is funded by the Virtual Brain Cloud from European commission [grant no. H2020-SC1-DTH-2018-1]. R.G. was supported by the National Institute for Health Research [NIHR] Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. JX, AW and CLQ were supported by Lundbeck Foundation, Denmark [grant R344-2020-989]. Finally, this publication has also been made possible by the support of the Fundaci\u00F3n S\u00E9neca-Agencia de Ciencia y Tecnolog\u00EDa de la Regi\u00F3n de Murcia (Spain), which finances the PhD of Alicia G\u00F3mez-Pascual [21259/FPI/19]. The results published here are in whole or in part based on data obtained from Agora, a platform initially developed by the NIA-funded AMP-AD consortium that shares evidence in support of AD target discovery. Agora is available at: https://doi.org/10.57718/agora-adknowledgeportal.
Publisher Copyright:
© 2024 The Authors
PY - 2024/6/1
Y1 - 2024/6/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Integrative omics
KW - Machine learning
KW - Metabolomics
KW - Mild cognitive impairment
KW - Proteomics
U2 - 10.1016/j.compbiomed.2024.108588
DO - 10.1016/j.compbiomed.2024.108588
M3 - Article
SN - 0010-4825
VL - 176
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 108588
ER -