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Record W4206696241 · doi:10.1002/alz.056326

Amyloid‐independent vascular contributions to cortical atrophy and cognition in a multi‐center mixed cohort with low to severe small vessel disease

2021· article· en· W4206696241 on OpenAlexaff
Julie Ottoy, Miracle Ozzoude, Katherine Zukotynski, Sabrina Adamo, Christopher J.M. Scott, Vincent Gaudet, Joel Ramirez, Walter Swardfager, Benjamin Lam, Aparna Bhan, Alex Kiss, Stephen C. Strother, Christian Bocti, Michael Borrie, Howard Chertkow, Richard Frayne, Ging‐Yuek Robin Hsiung, Robert Laforce, Michael D. Noseworthy, Frank S. Prato, Demetrios J. Sahlas, Eric E. Smith, Vesna Sossi, Alexander Thiel, Jean‐Paul Soucy, Jean‐Claude Tardif, Maged Goubran, Sandra E. Black

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityMontreal Heart InstituteUniversity of British ColumbiaHotchkiss Brain InstituteUniversité LavalUniversity of CalgaryUniversity of TorontoUniversité de SherbrookeWestern UniversityHôpital de l'Enfant-JésusSunnybrook Health Science CentreJewish General HospitalBaycrest HospitalLawson Health Research InstituteMontreal Neurological Institute and HospitalUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsDementiaAtrophyCohortCardiologyMedicineInternal medicinePsychologyAmyloid (mycology)Stroke (engine)PathologyHyperintensityCognitive declinePathologicalDiseaseMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Background Small vessel disease (SVD) often co‐exists with Alzheimer’s disease (AD) pathology (up to 60%) and may facilitate AD progression. However, SVD is currently not integrated as a pathological factor within the ATN research criteria. Up to now, the majority of studies that investigated effects of SVD on brain atrophy and cognition were limited to either AD cohorts with low SVD burden (e.g., ADNI) or cognitively normal elderly with high SVD burden. Thus, there is a need to investigate the effects of SVD in a cohort spanning low to severe SVD and amyloid‐beta pathology. Method Our study included 118 subjects in total. Fifty‐nine subjects were recruited in a multi‐site study (MITNEC) from dementia and SVD‐stroke clinics (64% amyloid‐beta+) who had severe SVD burden as quantified by white matter hyperintensity volumes [WMH; median(IQR): 30.2(22.2)cm3] and Fazekas score 2.5‐3. In addition, we included 59 cognitively normal/early‐MCI subjects from ADNI (44% amyloid‐beta+) with low‐to‐moderate WMH [median(IQR): 5.8(9.1)cm3]. We performed vertex‐wise regressions, investigating associations of cortical thickness with amyloid‐beta (18F‐florbetapir‐SUVRpons) or vascular burden (WMH), corrected for WMH or amyloid‐beta, respectively, and age, sex, education. Further, mediation analyses investigated whether the effects of amyloid‐beta or vascular burden on cognition (MMSE, MoCA, Trails‐B, semantic fluency, ANART, and Boston‐naming) were mediated by cortical thickness. Result We observed a significant effect of vascular burden on cortical thickness independent of amyloid‐beta. This effect was stronger than the vascular‐independent effect of amyloid‐beta on thickness (Fig. 1). Furthermore, we observed additive effects of vascular burden and amyloid‐beta on cognition (semantic fluency: ß=‐0.29 [p=0.002] and ß=‐0.27 [p=0.001], respectively; Trails‐B: ß=+0.18 [p=0.03] and ß=+0.22 [p=0.02], respectively). Here, the effects on semantic fluency were significantly mediated by cortical thickness (vascular→thickness→semantic: 35% mediation, 95%CI[‐0.19,‐0.02]; amyloid‐beta→thickness→semantic: 19% mediation, 95%CI[‐0.11,‐0.003]) (Fig. 1). Conclusion In our study of mixed AD/SVD and control subjects, the effect of SVD burden (WMH) exceeded the effect of amyloid‐beta on neurodegeneration alone. Furthermore, vascular burden contributed to semantic loss both directly and through its impact on neurodegeneration. As such, the presence of cerebrovascular comorbidities supports the idea of combinational therapeutic approaches where SVD factors may be targeted alongside amyloid‐beta to halt neurodegeneration and cognitive decline.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.290
Teacher spread0.274 · 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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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