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Record W4390199603 · doi:10.1002/alz.079189

Association of plasma biomarkers with imaging markers of brain atrophy and white matter disease across three neurodegenerative diseases and cerebrovascular disease

2023· article· en· W4390199603 on OpenAlexaffabout
Erlan Sanchez, Gillian Coughlan, Tim Wilkinson, Andrée‐Ann Baril, Malcolm A. Binns, Robert Bartha, Sean Symons, Robert A. Hegele, Sandra E. Black, Anthony E. Lang, Maria Carmela Tartaglia, Elizabeth Finger, Morris Freedman, Richard H. Swartz, Hlin Kvartsberg, Henrik Zetterberg, Douglas P. Munoz, Mario Masellis

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOntario Brain InstituteToronto Western HospitalSunnybrook Health Science CentreRobarts Clinical TrialsUniversity of TorontoWestern UniversityBaycrest HospitalMcGill UniversityDouglas Mental Health University InstituteUniversity Health NetworkHealth Sciences CentreQueen's UniversitySunnybrook Hospital
Fundersnot available
KeywordsGrey matterPathologyAtrophyMedicineWhite matterHyperintensityDiseaseDementiaCerebrospinal fluidCognitive declineMagnetic resonance imagingInternal medicineOncologyRadiology

Abstract

fetched live from OpenAlex

Abstract Background It is necessary to better understand the value of plasma biomarkers in reflecting ongoing neurodegenerative processes before widespread use as diagnostic and prognostic tools in specialized clinics and as markers of disease progression in trials. Herein, we investigate their association with imaging markers of brain atrophy and white matter disease across three common neurodegenerative diseases and cerebrovascular disease. Method Patients from the curated multi‐site Ontario Neurodegenerative Disease Research Initiative (ONDRI) were included in this study, classified by diagnostic group: Alzheimer’s disease/Mild cognitive impairment (AD/MCI, n = 126, age = 71.0±8.2, 55%M), frontotemporal dementia (FTD, n = 53, age = 67.8±7.1, 64%M), Parkinson’s disease (PD, n = 140, age = 67.9±6.3, 78%M) and cerebrovascular disease (CVD, n = 161, age = 69.2±7.4, 68%M). Plasma concentrations of Aβ40 and Aβ42 (Aβ42/40 ratio), glial fibrillary acidic protein (GFAP), neurofilament light (NfL) and phosphorylated‐tau181 (p‐tau181) were measured using high‐sensitivity Simoa assays. Volumes of regional grey matter, ventricular cerebrospinal fluid, white matter hyperintensities, perivascular spaces, lacunes and strokes were extracted using the semi‐automated SABRE pipeline on 3T structural MRI sequences harmonized across sites. Fractions of supratentorial total intracranial volume were used when appropriate. Linear regression models controlling for age and sex were used to test the association between plasma biomarkers and MRI variables in each group separately. Result In AD/MCI, higher levels of GFAP, NfL and p‐tau181 were all associated with extensive grey matter atrophy, ventricular expansion and, excluding p‐tau181, with increased white matter hyperintensities. In FTD, increased Aβ42/40 ratio was associated with frontal grey matter atrophy, and higher levels of NfL were associated with ventricular expansion. In PD, higher levels of NfL were associated with frontal, temporal and hippocampal grey matter atrophy. In CVD, higher levels of GFAP and NfL were associated with temporal and subcortical grey matter atrophy and ventricular expansion. Higher levels of GFAP were also associated with enlarged perivascular spaces, while higher levels of NfL were associated with increased lacunes. Finally, still in CVD, higher levels of GFAP and p‐tau181 were associated with increased stroke volumes. Conclusion Excluding Aβ42/40, plasma biomarkers appear to reflect various levels of brain atrophy in all neurodegenerative diseases studied, but reflect markers of white matter disease only in AD/MCI and CVD.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.266
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 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
Published2023
Admission routes2
Has abstractyes

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