Abstract
INTRODUCTION: Preclinical Alzheimer's disease (AD) can be described relative to biomarker positivity onset time. METHODS: We estimated time from amyloid positivity (A+) using sampled iterative local approximation (SILA) in a longitudinal autosomal dominant AD (ADAD) sample (N = 379) with amyloid positron emission tomography. We compared (1) predicted age at A+ to imputed age, (2) estimated age at A+ to estimated age at symptom onset, and (3) variance in cognitive performance explained. RESULTS: Mean error between imputed and SILA-estimated age at A+ (N = 26) was 1.15 years. Age at A+ explained 39% of estimated years to symptom onset (EYO) variance. Time from A+ explained 19% of cognitive composite variance and 14% of Clinical Dementia Rating Sum of Boxes CDR-SB variance; EYO explained 43% and 57%, respectively. DISCUSSION: SILA estimates A+ age in ADAD with reasonably good accuracy. SILA-estimated time from A+ describes the start of pathology, but the time from A+ onset to symptoms is variable in ADAD and better described by EYO. Highlights: Amyloid chronicity predicts a 14-year preclinical AD phase in ADAD. SILA accurately estimates age at A+ (MAE < 2 years). EYO outperforms chronicity in predicting symptom onset. APP mutation carriers show atypical amyloid accumulation. Chronicity models help reveal AD heterogeneity in preclinical stages.
| Original language | English |
|---|---|
| Article number | e70812 |
| Journal | Alzheimer's and Dementia |
| Volume | 21 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2025 Oct |
Bibliographical note
Publisher Copyright:© 2025 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Alzheimer's disease
- biomarkers
- genetic causes of Alzheimer's disease
- numeric methods
ASJC Scopus subject areas
- Epidemiology
- Health Policy
- Developmental Neuroscience
- Clinical Neurology
- Geriatrics and Gerontology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health
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