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

ABSTRACT:

Introduction

There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD).

Methods

Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS.

Results

The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MH

SUBMITTER: Reas ET 

PROVIDER: S-EPMC10620101 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature