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Data-driven models of dominantly-inherited Alzheimer's disease progression
Oxtoby NP, Young AL, Cash DM, Benzinger TLS, Fagan AM, Morris JC, Bateman RJ, Fox NC et al.. Brain : a journal of neurology, 2018-05-01
Topic: Amyloid, tau and blood-test diagnosis
See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article.Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, se
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APA 7th
Oxtoby, N. P., Young, A. L., Cash, D. M., Benzinger, T. L. S., Fagan, A. M., Morris, J. C., et al. (2018). Data-driven models of dominantly-inherited Alzheimer's disease progression. Brain : a journal of neurology. https://doi.org/10.1093/brain/awy050
Vancouver
Oxtoby NP, Young AL, Cash DM, Benzinger TLS, Fagan AM, Morris JC, et al. Data-driven models of dominantly-inherited Alzheimer's disease progression. Brain : a journal of neurology. 2018. doi:10.1093/brain/awy050. PMID: 29579160.
BibTeX
@article{oxtoby2018-29579160,
author = {Oxtoby, N. P. and Young, A. L. and Cash, D. M. and Benzinger, T. L. S. and Fagan, A. M. and Morris, J. C. and Bateman, R. J. and Fox, N. C. and others},
title = {{Data-driven models of dominantly-inherited Alzheimer's disease progression}},
journal = {Brain : a journal of neurology},
year = {2018},
doi = {10.1093/brain/awy050},
pmid = {29579160},
url = {https://doi.org/10.1093/brain/awy050}
}
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Key findings
- We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset.
- We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate).
- Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset).
- We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined).
Why it matters
This modelling work describes the order in which biomarkers change in dominantly-inherited Alzheimer's disease, using data from an observational study. It does not test any treatment, so it offers no direct evidence for treating, curing or preventing the disease.
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Study details
- Study designlongitudinal study
- Participants338
- Evidence strengthModerate
How this study scored 67/100
Published in a leading specialty journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Brain : a journal of neurology) |
| Journal's citation record | 4 / 4 | Brain: journal h-index 503 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 338 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 146 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.6 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 4 / 6 | Cited 10.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 39 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Daniel C. Alexander: h-index 93 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multicenter collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29579160) |
| Free to read | 2 / 2 | Free to read |
| Total | 67 / 100 | — |
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