Alzheimer's Disease-Like Brain Pattern Biomarker: Capturing Risks and Predicting Disease Onset

Area of Science:

• Neuroscience
• Biomarkers
• Neuroimaging

Background:

• Early detection of Alzheimer's disease (AD) is crucial for timely intervention.
• Existing biomarkers often lack sensitivity to subtle, early pathological changes.
• Identifying individuals at high risk for AD progression is a significant clinical challenge.

Purpose of the Study:

• To develop and validate a novel biomarker, the Regional Vulnerability Index (RVI-AD), for early Alzheimer's disease detection.
• To assess the association of RVI-AD with known AD risk factors, including APOE-e4 genotype and cardiovascular health.
• To evaluate the predictive power of RVI-AD for cognitive decline and conversion to dementia.

Main Methods:

• Calculated regional effect sizes to define AD brain deficit patterns in amyloid-positive cases versus controls.
• Developed RVI-AD as a linear index of individual brain similarity to these AD patterns.
• Validated RVI-AD in three cohorts: Amish Connectome Project (N=335), UK Biobank (N=26,010), and ADNI (N=1,932), assessing genetic (APOE-e4) and cardiovascular (FCVRS) risks.

Main Results:

• RVI-AD was significantly elevated in individuals with the APOE-e4 allele across all cohorts (p<0.05).
• Framingham Cardiovascular Risk Scores (FCVRS) contributed to higher RVI-AD in an APOE-e4-specific manner (p<0.01).
• In the ADNI cohort, RVI-AD predicted conversion from mild cognitive impairment (MCI) to dementia within 10 years (AUC=74%), particularly within the first 3 years.

Conclusions:

• RVI-AD effectively detects the impact of APOE-e4 and cardiovascular risks in healthy aging individuals.
• Elevated RVI-AD predicts future dementia conversion, especially in high-risk populations.
• RVI-AD shows promise as a noninvasive, clinically accessible biomarker for early Alzheimer's disease risk assessment.