Evidence map›Paper›PMID 31492558›Full record

ArticleEBioMedicine2019

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

Laura Xicota, Farid Ichou, François-Xavier Lejeune, Benoit Colsch, Arthur Tenenhaus, Inka Leroy, Gaëlle Fontaine, Marie Lhomme, Hugo Bertin, Marie-Odile Habert and 5 more

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  6. Review
  7. Article
  8. Tackling neurodegenerationFrontiers in molecular neuroscience · 2024
    Review
  9. mFrontiers in cell and developmental biology · 2024
    Review
  10. Review
  11. Review
  12. A guide to multi-omics data collection and integration for translational medicine.Computational and structural biotechnology journal · 2023
    Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 6 institutions in 1 country.

Laura XicotaICM 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.
Farid IchouICANalytcis Platforms, Institute of Cardiometabolism and Nutrition ICAN, Paris, France.
François-Xavier LejeuneICM 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.
Benoit ColschService de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Université Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France.
Arthur TenenhausLaboratoire des Signaux et Systèmes, CentraleSupélec, Université Paris-Saclay, Gif sur Yvette, France.
Inka LeroyICM 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.
Gaëlle FontaineICM 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.
Marie LhommeICANalytcis Platforms, Institute of Cardiometabolism and Nutrition ICAN, Paris, France.
Hugo BertinCentre Acquisition et Traitement des Images, Paris, France.
Marie-Odile HabertLaboratoire d'Imagerie Biomédicale, Nuclear Medicine Department, Sorbonne Université, Hôpital de la Salpêtrière, Paris, France.
Stéphane EpelbaumICM 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.
Bruno DuboisCentre des Maladies Cognitives et Comportementales, Sorbonne Université, Hôpital de la Salpêtrière, Paris, France.
Fanny MochelICM 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.
Marie-Claude PotierICM 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.
INSIGHT study group
Centre National de la Recherche Scientifique · FRSorbonne Université · FRUnité de recherche sur les maladies cardiovasculaires et métaboliques · FRCognitive Neuroimaging Lab · FRCommissariat à l'Énergie Atomique et aux Énergies Alternatives · FRUniversité Paris-Saclay · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne 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.

methodsWe 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.

findingsUnivariate 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.

interpretationThis 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.

Indexed as

GenomicsMetabolomicsProteomicsAgedAged, 80 and overAlzheimer DiseaseAmyloid beta-PeptidesArea Under CurveBrainCase-Control StudiesFemaleHumansMaleProtein Aggregation, PathologicalAmyloid beta-PeptidesAlzheimerAmyloid PETAsymptomaticBiomarkersMulti-omicsPrediction

Identifiers

PMID31492558
PMCPMC6796577
OpenAlexW2971576937

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.