%0 Journal Article
%T Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease.
%A Gómez-Pascual A
%A Naccache T
%A Xu J
%A Hooshmand K
%A Wretlind A
%A Gabrielli M
%A Lombardo MT
%A Shi L
%A Buckley NJ
%A Tijms BM
%A Vos SJB
%A Ten Kate M
%A Engelborghs S
%A Sleegers K
%A Frisoni GB
%A Wallin A
%A Lleó A
%A Popp J
%A Martinez-Lage P
%A Streffer J
%A Barkhof F
%A Zetterberg H
%A Visser PJ
%A Lovestone S
%A Bertram L
%A Nevado-Holgado AJ
%A Gualerzi A
%A Picciolini S
%A Proitsi P
%A Verderio C
%A Botía JA
%A Legido-Quigley C
%J Comput Biol Med
%V 176
%N 0
%D 2024 Jun 13
%M 38761503
%F 6.698
%R 10.1016/j.compbiomed.2024.108588
%X <B>BACKGROUND: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.