PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol.
Alzheimer’s disease
Biomarkers
Electroencephalography
Event-related potential
Neuropsychology
Subjective cognitive decline
Journal
BMC neurology
ISSN: 1471-2377
Titre abrégé: BMC Neurol
Pays: England
ID NLM: 100968555
Informations de publication
Date de publication:
12 Aug 2023
12 Aug 2023
Historique:
received:
22
06
2023
accepted:
28
07
2023
medline:
14
8
2023
pubmed:
13
8
2023
entrez:
12
8
2023
Statut:
epublish
Résumé
As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia. We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD. NCT05569083.
Sections du résumé
BACKGROUND
BACKGROUND
As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.
METHODS
METHODS
We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ
DISCUSSION
CONCLUSIONS
This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.
TRIAL REGISTRATION NUMBER (TRN)
UNASSIGNED
NCT05569083.
Identifiants
pubmed: 37573339
doi: 10.1186/s12883-023-03347-8
pii: 10.1186/s12883-023-03347-8
pmc: PMC10422810
doi:
Substances chimiques
Biomarkers
0
Amyloid beta-Peptides
0
Banques de données
ClinicalTrials.gov
['NCT05569083']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
300Subventions
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Organisme : Regione Toscana
ID : D18D20001300002
Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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