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Record W7117251636 · doi:10.1002/alz70856_100574

Heterogeneity and progression of amyloid and vascular injury in Alzheimer's and Mixed dementia cohorts

2025· article· en· W7117251636 on OpenAlexaff
Julie Ottoy, Andrew Clappison, Eric Yin, Min Su Kang, Erin Gibson, Katrina Carver, Joel Ramirez, Miracle Ozzoude, Katherine Zukotynski, Stephanie Berberian, Ginelle Feliciano, Christopher J.M. Scott, Walter Swardfager, Fuqiang Gao, Lauren Abby Woods, Eric E. Smith, Nesrine Rahmouni, Ging‐Yuek Robin Hsiung, Robert Laforce, Frank S. Prato, Phillip H. Kuo, Jean‐Paul Soucy, Jean‐Claude Tardif, Pedro Rosa‐Neto, Sandra E. Black, Maged Goubran

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMontreal Heart InstituteUniversité LavalHotchkiss Brain InstituteMcGill UniversityUniversity of CalgaryMontreal Neurological Institute and HospitalUniversity of TorontoWestern UniversitySunnybrook Health Science CentrePositive Living Society of British ColumbiaOntario Brain Institute
Fundersnot available
KeywordsAmyloid (mycology)Vascular dementiaDementiaβ amyloidAmyloid βVascular disease

Abstract

fetched live from OpenAlex

BACKGROUND: Alzheimer's disease (AD) is a heterogeneous disorder that is often comorbid with cerebral small vessel disease (SVD). Previous studies used highly characterized AD cohorts to identify imaging-derived subtypes to explain patient heterogeneity. Here, we studied two distinct dementia cohorts (one with a low SVD burden and one heterogeneous cohort with a Fazekas score >2) to define imaging-derived subtypes. We then determined, within each subtype, if amyloid or free-water could predict vascular lesions at baseline and over time. METHOD: We studied 262 individuals across two cohorts. The longitudinal TRIAD ("low-SVD") cohort included cognitively normal, MCI, and AD dementia (baseline, year2, year3: N = 202, 100, 70). The MITNEC-C6 ("high-SVD") cohort included real-world patients with mixed dementia and moderate-to-severe periventricular white matter hyperintensity (WMH) burden (N = 60). We quantified WMH and enlarged perivascular space (PVS) volumes based on FLAIR-MRI and T1w-MRI, respectively, in the white matter using our novel deep learning segmentation tool segCSVD (Gibson et al. 2024 HBM). Disease subtypes were identified through the Subtype and Stage Inference (SuStaIn) algorithm (Figure 1A) using the following markers: 18F-AZD4694 or 18F-AV45 amyloid-SUVR in the AD-signature regions, total WMH, total PVS, basal ganglia PVS, and DTI-derived free-water, fractional anisotropy and mean diffusivity in the normal-appearing white matter. RESULT: Both cohorts showed a 'vascular-first' subtype (green) and a 'mixed' subtype (purple) (Figure 1B-C). The low-SVD cohort additionally showed an 'amyloid-first' subtype (red). In both cohorts, greater amyloid was significantly associated with greater WMH volume in the 'mixed' subtypes (Figure 2A). Both cohorts also showed a positive association of free-water with WMH and PVS volume in each of the subtypes (Figure 2B). Finally, greater baseline amyloid predicted faster WMH growth in the 'vascular-first' subtype (Figure 3A). Whereas, in the 'mixed' subtype, greater baseline free-water but not amyloid predicted faster WMH growth (Figure 3B). Neither amyloid nor free-water predicted WMH growth in the 'amyloid-first' subtype nor did they predict PVS growth. CONCLUSION: In the mixed subtype, which is likely the most common subtype in memory clinics and community-based samples, amyloid was associated with WMH volume at baseline, but greater free-water levels predicted WMH growth over time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.323
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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