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Cited by 51Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMetabolomics and Mass Spectrometry Studies

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

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Xicota, Laura, Ichou, Farid, Lejeune, François-Xavier, Colsch, Benoit, Tenenhaus, Arthur, Leroy, Inka, Fontaine, Gaëlle, Lhomme, Marie +6 more·2019·EBioMedicine
DOI 10.1016/j.ebiom.2019.08.051PMC PMC6796577
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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.

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  • Peripheral blood transcriptome analyses can classify patients into immune and metabolic subgroups with diagnostic performance comparable to established biomarker frameworks like Amyloid, Tau, and Neurodegeneration (ATN).

    “Complementary analyses of peripheral blood transcriptomes have enabled patient classification into immune and metabolic subgroups, achieving diagnostic performance comparable to established biomarker frameworks like Amyloid, Tau, and Neurodegeneration (ATN)71,349,351,398.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [351]

    The Results (Univariate analyses of omics data; page not provided) identify peripheral-blood genes associated with inflammation and fatty acid metabolism, and report that a transcriptomic classifier distinguished amyloid-positive from amyloid-negative asymptomatic individuals (AUC 0.881). They do not classify patients into immune and metabolic subgroups or compare diagnostic performance with the Amyloid, Tau, and Neurodegeneration (ATN) framework. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • In the INSIGHT-preAD cohort, unsupervised learning revealed blood-based multi-omic signatures capable of predicting early amyloid deposition with over 99% accuracy.

    “In the INSIGHT-preAD cohort, unsupervised learning revealed blood-based multi-omic signatures capable of predicting early amyloid deposition with over 99% accuracy351.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [351]

    The Results report a blood-based metabolomics-plus-transcriptomics signature with an AUC of 0.994 for distinguishing amyloid-positive from amyloid-negative participants in a leave-one-out analysis. But the Methods describe the integration as supervised, not unsupervised, and AUC is not classification accuracy. The Discussion also says the signature requires confirmation in an independent cohort. The indexed passages have no page numbers. (LLM, conf=0.92)

    Experimental — accuracy not yet measured

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Authors
Xicota, L., Ichou, F., Lejeune, F.-X., Colsch, B., Tenenhaus, A., Leroy, I., Fontaine, G., Lhomme, M., Bertin, H., Habert, M.-O., Epelbaum, S., Dubois, B., Mochel, F., & Potier, M.-C.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Unité de recherche sur les maladies cardiovasculaires et métaboliques (FR)
  • Université Paris-Saclay (FR)
  • Assistance Publique – Hôpitaux de Paris (FR)
  • Centre d'Acquisition et de Traitement des Images (FR)
  • Sorbonne Université (FR)
Corresponding author
Potier, Marie-Claude
ORCID
Venue
EBioMedicine
Publisher
Elsevier BV
Published
2019-09
Volume
47
Pages
518-528
ISSN
2352-3964 (ISSN-L)
DOI
10.1016/j.ebiom.2019.08.051
PMC
PMC6796577
OpenAlex
W2971576937
Cited by
51
References
4
Citations by year
2026: 2 · 2025: 4 · 2024: 10 · 2023: 5 · 2022: 19 · 2021: 8 · 2020: 3
FWCI
5.98
Type
journal article
Language
en
Open access
Licence
Locations
  • EBioMedicine (publishedVersion, cc-by-nc-nd)
  • SSRN Electronic Journal (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by-nc-nd)
  • HAL (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Institut National de la Santé et de la Recherche Médicale
  • Centre Hospitalier Universitaire de Bordeaux (CIC EC7)
  • Institut de Cardiologie de Montréal
  • Pfizer (ANR-10-AIHU-06)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Keywords
Alzheimer's disease, brain amyloid deposition, preclinical Alzheimer's disease, multi-omics signatures, blood biomarkers, medium-chain fatty acids, p-nitrophenol, inflammation, fatty acid metabolism, plasma metabolomics, plasma lipidomics, blood transcriptome
MeSH terms
Aged, Aged, 80 and over, Alzheimer Disease, Brain, Female, Humans, Male, Case-Control Studies, Amyloid beta-Peptides, Area Under Curve, Genomics, Proteomics, Metabolomics, Protein Aggregation, Pathological

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