Integrative Multimodal Metabolomics to Early Predict Cognitive Decline Among Amyloid Positive Community-Dwelling Older Adults

Author:

Tremblay-Franco Marie12,Canlet Cécile12,Carriere Audrey3,Nakhle Jean3,Galinier Anne34,Portais Jean-Charles35,Yart Armelle3,Dray Cédric3,Lu Wan-Hsuan67,Bertrand Michel Justine89,Guyonnet Sophie67,Rolland Yves67,Vellas Bruno67,Delrieu Julien67,Barreto Philippe de Souto67,Pénicaud Luc3,Casteilla Louis3,Ader Isabelle3ORCID,

Affiliation:

1. Toxalim (Research Center in Food Toxicology), Toulouse University, INRAE, ENVT, INP-Purpan, UPS , Toulouse , France

2. Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE , Toulouse , France

3. Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier , Toulouse , France

4. Institut Fédératif de Biologie, CHU Purpan , Toulouse , France

5. MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse Biotechnology Institute , INSA de Toulouse INSA/CNRS 5504 - UMR INSA/INRA 792,Toulouse , France

6. Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse) , Toulouse , France

7. CERPOP UMR 1295, University of Toulouse III, INSERM, UPS , Toulouse , France

8. Lipidomic, MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics , Toulouse , France

9. I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS) , Toulouse , France (Biological Sciences Section)

Abstract

Abstract Alzheimer’s disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.

Funder

INSERM

Région Occitanie Pyrénées-Méditerranée

European Regional Development Fund

Gérontopôle of Toulouse

French Ministry of Health

European Lead Factory

ExonHit Therapeutics SA

Avid Radiopharmaceuticals Inc

Centre Hospitalier Universitaire de Toulouse

Association Monegasque pour la Recherche sur la maladie d’Alzheimer

the INSERM-University of Toulouse III UMR 1295 Research Unit

Publisher

Oxford University Press (OUP)

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