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. 2024 Jun:176:108588.
doi: 10.1016/j.compbiomed.2024.108588. Epub 2024 May 13.

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

Alicia Gómez-Pascual  1 , Talel Naccache  2 , Jin Xu  3 , Kourosh Hooshmand  4 , Asger Wretlind  4 , Martina Gabrielli  5 , Marta Tiffany Lombardo  6 , Liu Shi  7 , Noel J Buckley  8 , Betty M Tijms  9 , Stephanie J B Vos  10 , Mara Ten Kate  9 , Sebastiaan Engelborghs  11 , Kristel Sleegers  12 , Giovanni B Frisoni  13 , Anders Wallin  14 , Alberto Lleó  15 , Julius Popp  16 , Pablo Martinez-Lage  17 , Johannes Streffer  18 , Frederik Barkhof  19 , Henrik Zetterberg  20 , Pieter Jelle Visser  21 , Simon Lovestone  22 , Lars Bertram  23 , Alejo J Nevado-Holgado  24 , Alice Gualerzi  25 , Silvia Picciolini  25 , Petroula Proitsi  26 , Claudia Verderio  5 , Juan A Botía  27 , Cristina Legido-Quigley  28
Affiliations
Free article

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

Alicia Gómez-Pascual et al. Comput Biol Med. 2024 Jun.
Free article

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.

Keywords: Alzheimer's disease; Integrative omics; Machine learning; Metabolomics; Mild cognitive impairment; Proteomics.

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Conflict of interest statement

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.

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