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2018 ; 12
(ä): 275
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An Algorithm for Preclinical Diagnosis of Alzheimer s Disease
#MMPMID29760644
Khan TK
Front Neurosci
2018[]; 12
(ä): 275
PMID29760644
show ga
Almost all Alzheimer's disease (AD) therapeutic trials have failed in recent
years. One of the main reasons for failure is due to designing the
disease-modifying clinical trials at the advanced stage of the disease when
irreversible brain damage has already occurred. Diagnosis of the preclinical
stage of AD and therapeutic intervention at this phase, with a perfect target,
are key points to slowing the progression of the disease. Various AD biomarkers
hold enormous promise for identifying individuals with preclinical AD and
predicting the development of AD dementia in the future, but no single AD
biomarker has the capability to distinguish the AD preclinical stage. A
combination of complimentary AD biomarkers in cerebrospinal fluid (A?(42), tau,
and phosphor-tau), non-invasive neuroimaging, and genetic evidence of AD can
detect preclinical AD in the in-vivo ante mortem brain. Neuroimaging studies have
examined region-specific cerebral blood flow (CBF) and microstructural changes in
the preclinical AD brain. Functional MRI (fMRI), diffusion tensor imaging (DTI)
MRI, arterial spin labeling (ASL) MRI, and advanced PET have potential
application in preclinical AD diagnosis. A well-validated simple framework for
diagnosis of preclinical AD is urgently needed. This article proposes a
comprehensive preclinical AD diagnostic algorithm based on neuroimaging, CSF
biomarkers, and genetic markers.