Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

ABSTRACT:

Introduction

Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.

Methods

We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated.

Results

A model combining all biomarker modalities and established in ADAD

SUBMITTER: Franzmeier N 

PROVIDER: S-EPMC7222030 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature