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Record W7119507419 · doi:10.1002/alz70856_106640

White matter hyperintensities are associated with glymphatic impairment and antioxidant pathway in healthy aging

2025· article· en· W7119507419 on OpenAlexaff
Flavie E. Detcheverry, Fanta Dabo, Manpreet Singh, Alexandra T. Star, Sneha Senthil, Soraya Lahlou, Ali Filali‐Mouhim, Rozie Arnaoutelis, Dumitru Fetco, Jamie Near, Arsalan S. Haqqani, Sridar Narayanan, AmanPreet Badhwar

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsSunnybrook HospitalMcGill UniversityUniversity of TorontoMontreal Neurological Institute and HospitalUniversité de MontréalNational Research Council CanadaInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsHyperintensityChoroid plexusGlymphatic systemPosterior cingulateHealthy agingAging brainBrain agingWhite matterCerebrospinal fluid

Abstract

fetched live from OpenAlex

Abstract Background White matter hyperintensities (WMHs), a marker of vascular‐brain injury in older adults, constitute an Alzheimer's disease (AD) risk‐factor (Debette et al. , 2019). Recent studies in AD associate WMHs to (a) enlarged choroid plexus (ChP) (Hong et al. , 2024), a structure involved in glymphatic clearance of AD‐related proteins and waste from the brain (Hauglund et al. , 2020), and (b) lower brain levels of glutathione, an antioxidant metabolite (Detcheverry et al. , 2024). However, the interplay between WMHs, ChP, and brain metabolites in healthy aging remains poorly understood, which is essential for unraveling their role in dementia progression. We investigated the relationship between WMH volume, ChP volume, brain metabolites, and plasma proteins. Method 7T‐MRI/MRS and plasma samples were acquired from 83 healthy adults (42W/41M) aged 20‐79. ChP and WMHs were manually segmented on T1‐weighted MP2RAGE and 3D FLAIR images, respectively, and normalized for head size. Metabolites in the posterior cingulate cortex were measured with single‐voxel STEAM MRS. Mass spectrometry‐based proteomics was performed on plasma. GLMs were performed for WMH volume and ChP volume (controlling for age); and MRS‐detected brain metabolites. Exploratory analyses were performed on plasma proteins and (a) WMH volume (controlling for age), followed by functional enrichment analysis on the significant proteins using STRING; and (b) MRS‐detected brain metabolites (Spearman). Result We found significant associations (Figure 1) between WMH volume and (a) ChP volume (all: p <0.001; women: p <0.001), (b) five brain metabolites, including glutathione ( p adjusted < 0.05), and (c) age (all: p <0.001; women: p <0.05; men: p <0.05). WMH volume was also significantly associated with 21/315 plasma proteins ( p unadjusted <0.05: all, N = 9; women, N = 15; men, N = 4), with the majority of biological processes linked to antioxidant activity ( p adjusted < 0.05). Additionally, several of the 21 proteins (e.g., flavin reductase [NADPH], catalase) were significantly associated ( p unadjusted <0.05) with brain levels of the antioxidant glutathione. Conclusion We demonstrated a link between greater WMH and ChP volumes, indicating an interplay between vascular‐brain injury and glymphatic function, with the antioxidant system playing a key role.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.248
Teacher spread0.230 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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