A data-driven model of biomarker changes in sporadic Alzheimer’s disease
Open accessAbstract
We demonstrate the use of a probabilistic generative model to explore the biomarker changes occurring as Alzheimer's disease develops and progresses. We enhanced the recently introduced event-based model for use with a multi-modal sporadic disease data set. This allows us to determine the sequence in which Alzheimer's disease biomarkers become abnormal without reliance on a priori clinical diagnostic information or explicit biomarker cut points. The model also characterizes the uncertainty in the ordering and provides a natural patient staging system. Two hundred and eighty-five subjects (92 cognitively normal, 129 mild cognitive impairment, 64 Alzheimer's disease) were selected from the Alzheimer's Disease Neuroimaging Initiative with measurements of 14 Alzheimer's disease-related biomarkers including cerebrospinal fluid proteins, regional magnetic resonance imaging brain volume and rates of atrophy measures, and cognitive test scores. We used the event-based model to determine the sequence of biomarker abnormality and its uncertainty in various population subgroups. We used patient stages assigned by the event-based model to discriminate cognitively normal subjects from those with Alzheimer's disease, and predict conversion from mild cognitive impairment to Alzheimer's disease and cognitively normal to mild cognitive impairment. The model predicts that cerebrospinal fluid levels become abnormal first, followed by rates of atrophy, then cognitive test scores, and finally regional brain volumes. In amyloid-positive (cerebrospinal fluid amyloid-β1-42 < 192 pg/ml) or APOE-positive (one or more APOE4 alleles) subjects, the model predicts with high confidence that the cerebrospinal fluid biomarkers become abnormal in a distinct sequence: amyloid-β1-42, phosphorylated tau, total tau. However, in the broader population total tau and phosphorylated tau are found to be earlier cerebrospinal fluid markers than amyloid-β1-42, albeit with more uncertainty. The model's staging system strongly separates cognitively normal and Alzheimer's disease subjects (maximum classification accuracy of 99%), and predicts conversion from mild cognitive impairment to Alzheimer's disease (maximum balanced accuracy of 77% over 3 years), and from cognitively normal to mild cognitive impairment (maximum balanced accuracy of 76% over 5 years). By fitting Cox proportional hazards models, we find that baseline model stage is a significant risk factor for conversion from both mild cognitive impairment to Alzheimer's disease (P = 2.06 × 10(-7)) and cognitively normal to mild cognitive impairment (P = 0.033). The data-driven model we describe supports hypothetical models of biomarker ordering in amyloid-positive and APOE-positive subjects, but suggests that biomarker ordering in the wider population may diverge from this sequence. The model provides useful disease staging information across the full spectrum of disease progression, from cognitively normal to mild cognitive impairment to Alzheimer's disease. This approach has broad application across neurodegenerative disease, providing insights into disease biology, as well as staging and prognostication.
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Neuroimaging and biomarker studies show consistencies in the sequential onset of amyloid deposition, tau, FDG-PET hypometabolism, grey matter atrophy, and clinical presentation between late-onset and autosomal-dominant Alzheimer's disease.
“Nevertheless, neuroimaging and biomarker studies show consistencies in the sequential onset of amyloid deposition, tau, FDG-PET hypometabolism, grey matter atrophy, and clinical presentation between late-onset and autosomal-dominant Alzheimer’s disease (Bateman et al., 2012; Benzinger et al., 2013; Young et al., 2014; Gordon et al., 2018; Oxtoby et al., 2018), suggesting substantial commonalities”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Young et al., 2014
The Results (Event sequences; page unavailable) report an ordering of CSF biomarkers, atrophy measures and cognitive changes in the sporadic-disease cohort. The Introduction describes FDG-PET hypometabolism as part of a hypothetical sequence, not a sequence established by this study. It also says whether findings from familial Alzheimer’s disease apply to sporadic disease “remains to be determined,” so the claimed consistency between the two is not established by this paper. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA widely held view is that amyloid biomarkers represent the earliest evidence of AD neuropathologic change currently detectable in living persons.
“Consequently, a widely held view is that amyloid biomarkers represent the earliest evidence of AD neuropathologic change currently detectable in living persons [9,11,72,103,104].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [103]
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NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
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“Nevertheless, neuroimaging and biomarker studies show consistencies in the sequential onset of amyloid deposition, tau, FDG-PET hypometabolism, grey matter atrophy, and clinical presentation between late-onset and autosomal-dominant Alzheimer’s disease (Bateman et al., 2012; Benzinger et al., 2013; Young et al., 2014;…”
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- Authors
- Young, A. L., Oxtoby, N. P., Daga, P., Cash, D. M., Fox, N. C., Ourselin, S., Schott, J. M., & Alexander, D. C.
- Affiliations
- University College London (GB)
- UCL Queen Square Institute of Neurology (GB)
- Corresponding author
- Young, Alexandra L.
- Cash, David M.
- Alexander, Daniel C.
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07-09
- Volume
- 137
- Issue
- 9
- Pages
- 2564-2577
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4132648
- OpenAlex
- W2136195493
- Cited by
- 308
- References
- 52
- Citations by year
- 2026: 20 · 2025: 24 · 2024: 33 · 2023: 43 · 2022: 29 · 2021: 29 · 2020: 36 · 2019: 24 · 2018: 26 · 2017: 11 · 2016: 14 · 2015: 16 · 2014: 3
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Brain (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, AG024904)
- Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University of Southern California (W81XWH-12-2-0012, U01 AG024904)
- Medpace (U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Synarc (U01 AG024904)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-)
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
- National Institute for Health and Care Research (NF-SI-0513-10134, U01 AG024904)
- Brain Research Trust
- University College London
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University College London Hospitals NHS Foundation Trust
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (5u01ag024904-10, W81XWH, W81XWH-12-2-0012)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- University of California, San Diego
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- Engineering and Physical Sciences Research Council (EP/J020990/1, EP/J020990/01, 1219295, U01 AG024904, FP7-ICT-2011-9-601055, J020990)
- National Institute on Aging (U01-AG-024904, U24AG021886)
- FP7 Information and Communication Technologies (FP7‐ICT‐2011‐9‐601055)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- sporadic Alzheimer's disease, Alzheimer's disease biomarkers, probabilistic generative models, event-based model, disease staging, mild cognitive impairment, conversion to Alzheimer's disease, cerebrospinal fluid biomarkers, amyloid-beta 42, phosphorylated tau, total tau, brain atrophy, APOE ε4, amyloid-positive
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Cross-Sectional Studies, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Models, Neurological, Neuropsychological Tests, Peptide Fragments, Biomarkers, Databases, Factual, Amyloid beta-Peptides, tau Proteins, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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