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Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics... BACKGROUNDAlzheimer's disease (AD) is an age‐dependent neurodegenerative disease hallmarked by the accumulation of extracellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tau tangles that emerge decades before symptom onset.1 This preclinical period is followed by a prodromal stage during which a diagnosis of amnestic mild cognitive impairment (MCI) indicates high probability of conversion to dementia within several years.2 Because AD has a complex, multifactorial etiology with genetic and modifiable risk factors, diagnostic accuracy in preclinical periods is limited, which poses a particular challenge for clinical trial enrollment in which pre‐screening precision is critical to minimize cost and subject burden. Thus, there is an outstanding need to develop tools that can identify individuals with a high probability of converting to AD for timely diagnosis and streamlined clinical trial screening.Cerebrospinal fluid (CSF) or positron emission tomography (PET) measures of amyloid and tau pose obstacles to routine clinical use due to their cost, invasiveness, and radiation exposure. Inexpensive, non‐invasive, and widely available approaches to quantify personalized AD risk will improve clinicians’ ability to select patients with the greatest potential for therapeutic benefit, and to guide clinical trial enrichment to minimize trial cost and patient burden. To this end, accurate longitudinal prediction of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Alzheimer s & Dementia Wiley

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References (40)

Publisher
Wiley
Copyright
© 2023 the Alzheimer's Association.
ISSN
1552-5260
eISSN
1552-5279
DOI
10.1002/alz.13112
Publisher site
See Article on Publisher Site

Abstract

BACKGROUNDAlzheimer's disease (AD) is an age‐dependent neurodegenerative disease hallmarked by the accumulation of extracellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tau tangles that emerge decades before symptom onset.1 This preclinical period is followed by a prodromal stage during which a diagnosis of amnestic mild cognitive impairment (MCI) indicates high probability of conversion to dementia within several years.2 Because AD has a complex, multifactorial etiology with genetic and modifiable risk factors, diagnostic accuracy in preclinical periods is limited, which poses a particular challenge for clinical trial enrollment in which pre‐screening precision is critical to minimize cost and subject burden. Thus, there is an outstanding need to develop tools that can identify individuals with a high probability of converting to AD for timely diagnosis and streamlined clinical trial screening.Cerebrospinal fluid (CSF) or positron emission tomography (PET) measures of amyloid and tau pose obstacles to routine clinical use due to their cost, invasiveness, and radiation exposure. Inexpensive, non‐invasive, and widely available approaches to quantify personalized AD risk will improve clinicians’ ability to select patients with the greatest potential for therapeutic benefit, and to guide clinical trial enrichment to minimize trial cost and patient burden. To this end, accurate longitudinal prediction of

Journal

Alzheimer s & DementiaWiley

Published: Nov 1, 2023

Keywords: Alzheimer's disease; amyloid; genetics; magnetic resonance imaging; memory; mild cognitive impairment; multimodal prediction; tau

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