Format

Send to

Choose Destination
EBioMedicine. 2019 Sep 3. pii: S2352-3964(19)30573-0. doi: 10.1016/j.ebiom.2019.08.051. [Epub ahead of print]

Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study.

Author information

1
ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France.
2
ICANalytcis Platforms, Institute of Cardiometabolism and Nutrition ICAN, Paris, France.
3
Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Université Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France.
4
Laboratoire des Signaux et Systèmes, CentraleSupélec, Université Paris-Saclay, Gif sur Yvette, France.
5
Centre Acquisition et Traitement des Images, Paris, France.
6
Laboratoire d'Imagerie Biomédicale, Nuclear Medicine Department, Sorbonne Université, Hôpital de la Salpêtrière, Paris, France.
7
ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France; Centre des Maladies Cognitives et Comportementales, Sorbonne Université, Hôpital de la Salpêtrière, Paris, France; Inria, Aramis-Project Team, Paris, France.
8
Centre des Maladies Cognitives et Comportementales, Sorbonne Université, Hôpital de la Salpêtrière, Paris, France.
9
ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France. Electronic address: fanny.mochel@upmc.fr.
10
ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, Paris, France. Electronic address: marie-claude.potier@upmc.fr.

Abstract

BACKGROUND:

One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.

METHODS:

We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects.

FINDINGS:

Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity.

INTERPRETATION:

This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid.

KEYWORDS:

Alzheimer; Amyloid PET; Asymptomatic; Biomarkers; Multi-omics; Prediction

PMID:
31492558
DOI:
10.1016/j.ebiom.2019.08.051
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center