Neurotheranostics: The Next Frontier for Health Span.


Journal

Journal of nuclear medicine technology
ISSN: 1535-5675
Titre abrégé: J Nucl Med Technol
Pays: United States
ID NLM: 0430303

Informations de publication

Date de publication:
05 Dec 2023
Historique:
received: 23 01 2023
revised: 14 04 2023
medline: 7 12 2023
pubmed: 17 8 2023
entrez: 16 8 2023
Statut: epublish

Résumé

With an aging U.S. population, advancements in the treatment of Alzheimer disease (AD) and other neurodegenerative diseases are key to the maximization of health span. The recent approval of 2 antiamyloid antibodies, which decrease brain amyloid load, places us on the cusp of breakthrough therapies that target the mechanism of the disease rather than just treating the symptoms. Although the trials that led to these approvals studied patients with mild early symptoms, multiple ongoing trials have enrolled cognitively normal patients screened for AD biomarkers including risk factors for amyloid positivity, family history, and genetic markers. Thus, amyloid PET can help identify an at-risk population that can be enrolled for antiamyloid therapy to prevent AD symptoms from ever developing. In this review, we examine the paradigm of neurotheranostics and how PET biomarkers of amyloid, tau, inflammation, and neurodegeneration could characterize the pathologic stage of AD and therefore allow for personalized therapy.

Identifiants

pubmed: 37586855
pii: jnmt.123.265502
doi: 10.2967/jnmt.123.265502
doi:

Substances chimiques

Amyloid 0
Biomarkers 0
Amyloid beta-Peptides 0

Types de publication

Review Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

266-270

Informations de copyright

© 2023 by the Society of Nuclear Medicine and Molecular Imaging.

Auteurs

Meryam A Losee (MA)

George Washington School of Medicine, Washington, DC.

John P Seibyl (JP)

Institute for Neurodegenerative Disorders, New Haven, Connecticut; and.

Phillip H Kuo (PH)

Departments of Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, Arizona pkuo@email.arizona.edu.

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Classifications MeSH