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Record W4404283621 · doi:10.1093/brain/awae372

Patterns of tau, amyloid and synuclein pathology in ageing, Alzheimer’s disease and synucleinopathies

2024· article· en· W4404283621 on OpenAlexfundno aff
Sean J. Colloby, Kirsty E. McAleese, Lauren Walker, Daniel Erskine, Jon B. Toledo, Paul C. Donaghy, Ian G. McKeith, Alan Thomas, Johannes Attems, John‐Paul Taylor

Bibliographic record

VenueBrain · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersMedical Research CouncilAlzheimer’s SocietyNIHR Newcastle Biomedical Research CentreNewcastle upon Tyne Hospitals NHS Foundation TrustAlzheimer’s Research UKNational Institute for Health and Care ResearchAlzheimer SocietyNewcastle UniversityAlzheimer's Society
KeywordsDementia with Lewy bodiesPathologyDementiaSynucleinopathiesNeuroscienceAmyloid (mycology)Alzheimer's diseaseLewy bodyAlpha-synucleinPsychologyMedicineParkinson's diseaseDisease

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is neuropathologically defined by deposits of misfolded hyperphosphorylated tau (HP-tau) and amyloid-β. Lewy body (LB) dementia, which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is characterized pathologically by α-synuclein aggregates. HP-tau and amyloid-β can also occur as co-pathologies in LB dementia, and a diagnosis of mixedAD/DLB can be made if present in sufficient quantities. We hypothesized that the spread of these abnormal proteins selectively affects vulnerable areas, resulting in pathologic regional covariance that differentially associates with pre-mortem clinical characteristics. Our aims were to map regional quantitative pathology (HP-tau, amyloid-β, α-synuclein) and investigate the spatial distributions from tissue microarray post-mortem samples across healthy aging, AD and LB dementia. The study involved 159 clinico-pathologically diagnosed human post-mortem brains (48 controls, 47 AD, 25 DLB, 20 mixedAD/DLB, 19 PDD). The burden of HP-tau, amyloid-β and α-synuclein was quantitatively assessed in cortical and subcortical areas. Principal components (PC) analysis was applied across all cases to determine the pattern nature of HP-tau, amyloid-β and α-synuclein. Further analyses explored the relationships of these pathological patterns with cognitive and symptom variables. Cortical (tauPC1) and temporo-limbic (tauPC2) patterns were observed for HP-tau. For amyloid-β, a cortical-subcortical pattern (amylPC1) was identified. For α-synuclein, four patterns emerged: 'posterior temporal-occipital' (synPC1), 'anterior temporal-frontal' (synPC2), 'parieto-cingulate-insula' (synPC3), and 'frontostriatal-amygdala' (synPC4). Distinct synPC scores were apparent among DLB, mixedAD/DLB and PDD, and may relate to different spreading patterns of α-synuclein pathology. In dementia, cognitive measures correlated with tauPC1,tauPC2 and amylPC1 pattern scores (P ≤ 0.02), whereas such variables did not relate to α-synuclein parameters in these or combined LB dementia cases. Mediation analysis then revealed that in the presence of amylPC1, tauPC1 had a direct effect on global cognition in dementia (n = 65, P = 0.04), while tauPC1 mediated the relationship between amylPC1 and cognition through the indirect pathway (amylPC1 → tauPC1 → global cognition) (P < 0.05). Last, in synucleinopathies, synPC1 and synPC4 pattern scores were associated with visual hallucinations and motor impairment, respectively (P = 0.02). In conclusion, distinct patterns of α-synuclein pathology were apparent in LB dementia, which could explain some of the disease heterogeneity and differing spreading patterns among these conditions. Visual hallucinations and motor severity were associated with specific α-synuclein topographies in LB dementia that may be important to the clinical phenotype and could, after necessary testing/validation, be integrated into semiquantitative routine pathological assessment.

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 score0.308
Threshold uncertainty score0.496

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 head0.017
GPT teacher head0.273
Teacher spread0.255 · 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

Citations24
Published2024
Admission routes1
Has abstractyes

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