| Title: |
Integrative Multimodal Metabolomics to Early Predict Cognitive Decline Among Amyloid Positive Community-Dwelling Older Adults |
| Authors: |
Tremblay-Franco, Marie; Canlet, Cécile; Carriere, Audrey; Nakhle, Jean; Galinier, Anne; Portais, Jean-Charles; Yart, Armelle; Dray, Cédric; Lu, Wan-Hsuan; Bertrand Michel, Justine; Guyonnet, Sophie; Rolland, Yves; Vellas, Bruno; Delrieu, Julien; Barreto, Philippe de Souto; Pénicaud, Luc; Casteilla, Louis; Ader, Isabelle |
| Contributors: |
Duque, Gustavo; 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 |
| Source: |
The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences ; volume 79, issue 5 ; ISSN 1079-5006 1758-535X |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2024 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/gerona/glae077 |
| DOI: |
10.1093/gerona/glae077/56902185/glae077.pdf |
| Availability: |
https://doi.org/10.1093/gerona/glae077; https://academic.oup.com/biomedgerontology/advance-article-pdf/doi/10.1093/gerona/glae077/56902185/glae077.pdf; https://academic.oup.com/biomedgerontology/article-pdf/79/5/glae077/57180825/glae077.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.84AC9AF2 |
| Database: |
BASE |