Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.

Methods

Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected

SUBMITTER: Wang X 

PROVIDER: S-EPMC11348433 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature