bioRxivBioinformaticsPreprint
Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease
Julie K. WischiD, Ziqiao Jiao, Peter R MillariD, Nicole Sarah McKayiD, Aleksandra BericiD, Wenjing Lin, Bryce Baker, Jennifer Stauber, Sam Rose Preminger, Mathias JuckeriD, Nicolas R. BarthélemyiD, Jasmeer P. ChhatwaliD, Natalie Sarah RyaniD, Suzanne E. SchindleriD, John C. MorrisiD, Carlos CruchagaiD, Tammie L.S. BenzingeriD, Celeste M M. KarchiD, Randall John BatemaniD, Eric McDadeiD, Jorge J. Llibre‐GuerraiD, the Frontotemporal Lobar Degeneration Neuroimaging Initiative, the Frontotemporal Lobar Degeneration Neuroimaging Initiative, Brian Andrew GordoniD, Beau M. AncesiD, Laura IbáñeziD
Abstract
ObjectiveDisease 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. MethodsWe 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. ResultsInter-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. InterpretationDPM 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. FundingNational Institute on Aging, Alzheimers 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.
- organism: human
- assay: imaging
- assay: proteomics
- assay: metabolomics
- research focus: benchmarking
- approach: simulation
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Data-driven models of dominantly-inherited Alzheimer’s disease progression
Neil P. Oxtoby, Alexandra L. Young, … Daniel C. Alexander·BrainPeer-reviewed·Feb 8, 2018
- A mathematical model of the Alzheimer's Disease biomarker cascade demonstrates statistical pitfalls in identifying neurobiological surrogates of cognitive reserve
Florian Udo Fischer, Susanne Gerber, … the Alzheimer’s Disease Neuroimaging Initiative·bioRxiv·Oct 26, 2023
- Continuous amyloid staging complements global amyloid burden in early Alzheimer's disease
Haoyuan Liu, Mingzhao Tong, … For the Alzheimers Disease Neuroimaging Initiative·bioRxiv·Oct 11, 2026
- Predicting time to dementia using a quantitative template of disease progression
Murat Bilgel, Bruno Jedynak, Alzheimer’s Disease Neuroimaging Initiative·Alzheimer s & Dementia Diagnosis Assessment & Disease MonitoringPeer-reviewed·Feb 27, 2019
- Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease
Jeffrey R. Petrella, Wenrui Hao, … for the Alzheimer’s Disease Computational Modeling Initiative·Computational and Mathematical Methods in MedicinePeer-reviewed·Feb 3, 2019
- Clinical and biomarker changes in sporadic Alzheimer's disease
Junjie Zhuo, Yuanchao Zhang, … the Alzheimer’s Disease Neuroimaging Initiative·bioRxiv·May 8, 2020