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Record W4417240373 · doi:10.1007/s00401-025-02970-8

Distinct cerebrovascular pathways underlying Alzheimer’s disease-related neurodegeneration

2025· article· en· W4417240373 on OpenAlexfundno aff
Rosaleena Mohanty, Sophia Wheatley, Konstantinos Chiotis, Anna Marseglia

Bibliographic record

VenueActa Neuropathologica · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierKarolinska InstitutetEisaiNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's Association
KeywordsArteriolosclerosisCerebral amyloid angiopathyWhite matterNeuropathologyAtrophyNeuroimagingNeurodegenerationFractional anisotropyHippocampal sclerosisAlzheimer's disease

Abstract

fetched live from OpenAlex

The etiology of cerebrovascular pathology is heterogeneous. Independent or synergistic role of this pathology relative to Alzheimer's disease (AD) pathology is necessary to clarify distinct neurodegenerative pathways. We evaluated the interplay of various cerebrovascular markers postmortem and their in vivo neuroimaging, clinical and neuropathologic correlates using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In 109 individuals, postmortem cerebrovascular pathology (atherosclerosis of the circle of Willis, cerebral amyloid angiopathy [CAA], arteriolosclerosis, white matter rarefaction, old infarcts, microinfarcts, hemorrhages, other ischemic/vascular changes) was characterized. Additionally, we assessed in vivo neuroimaging (cortical thickness, subcortical volume, white matter lesion burden, glucose standardized uptake value ratio, fractional anisotropy of white matter tracts, cerebral blood flow), cognitive, and neuropathologic measures (atrophy, AD pathology and copathologies including Lewy body, TDP-43, hippocampal sclerosis). The study sample had mean (standard deviation) age of 82.9 (7.2) years and included 29 women (27%) and 84 (77%) with intermediate/high AD neuropathologic change. Arteriolosclerosis and CAA emerged as dominant cerebrovascular markers using multiple correspondence analysis. More severe arteriolosclerosis was explained by higher white matter lesion burden and greater postmortem hippocampal atrophy (β = 143.2, 95% CI 63.9 to 230.1, p = 0.0003), but not AD pathology. More severe CAA was explained by fractional anisotropy (β = - 20, 95% CI - 41.5 to -3.1, p = 0.02) adjusted for AD pathology and reduced integrity of superior cerebellar peduncle, posterior thalamic radiation, and sagittal stratum tracts (rho < - 0.6, false discovery rate corrected p < 0.05). More severe CAA was also explained by cortical atrophy and AD pathology (β = 0.6, 95% CI 0.2 to 1.2, p = 0.007), and associated with poorer memory (β = - 0.2, 95% CI - 0.3 to -0.09, p = 0.0009). Results demonstrate two dominant cerebrovascular pathways. An arteriolosclerosis-driven pathway is unspecific to AD pathology, whereas a CAA-driven pathway is specific to AD pathology. Cerebrovascular pathology is associated with AD pathology in an etiology-dependent manner which may influence eligibility for treatment or treatment-emergent adverse events in disease-modifying therapies for AD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.384
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.319
Teacher spread0.263 · 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 teacher head, 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

Citations4
Published2025
Admission routes1
Has abstractyes

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