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Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease
Julie K Wisch1, Ziqiao Jiao2, Peter R Millar1
1Department of Neurology, Washington University School of Medicine, St. Louis (MO), 63110 USA.
Objective:
Disease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice.
Methods:
We conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings.
Results:
Inter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers.
Interpretation:
DPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers.
Funding:
National Institute on Aging, Alzheimer's Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.
Insights
Disease progression modeling (DPM) for Alzheimer disease (AD) is limited by biomarker variability. Inter-individual differences significantly impact temporal estimates, while intra-individual variability affects stability. Amyloid PET shows superior performance over fluid biomarkers.
Area of Science:
- Neuroscience
- Biomarker Development
- Computational Biology
Background:
- Disease Progression Modeling (DPM) is crucial for staging Alzheimer's disease (AD).
- DPM accuracy relies on biomarker accumulation rates and reliability.
- Systematic assessment of variability's impact on DPM is needed for clinical applications.
Purpose of the Study:
- To assess the contributions of inter- and intra-individual variability to DPM performance.
- To evaluate DPM robustness across different amyloid biomarkers (PET, CSF, plasma).
- To inform the use of DPM for individual-level predictions in AD research and trials.
Main Methods:
- Simulation studies using empirical amyloid biomarker noise properties.
- Analysis of autosomal dominant and sporadic AD cohorts with PET, CSF, and plasma biomarkers.
- Group-level DPM performance assessed by Mean Average Error (MAE) and Root Mean Squared Error (RMSE).
Main Results:
- Inter-individual variability was the primary driver of temporal estimation errors.
- Intra-individual variability decreased the stability of DPM estimates.
- Amyloid PET demonstrated superior performance compared to CSF and plasma biomarkers in research data.
Conclusions:
- DPM performance is fundamentally limited by biomarker dynamic range and reliability.
- Current DPM methods are more robust at the group level.
- Biomarkers with higher variability (e.g., fluid biomarkers >10-15%) are more suitable for group-level DPM.
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