Multipredictor risk models for predicting individual risk of Alzheimer's disease.

ABSTRACT:

Background

Early prevention of Alzheimer's disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.

Methods

A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident pr

SUBMITTER: Hou XH 

PROVIDER: S-EPMC10614397 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature