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Record W4221091248 · doi:10.1093/brain/awac107

Progression of cerebral white matter hyperintensities is related to leucocyte gene expression

2022· article· en· W4221091248 on OpenAlexafffund
Glen C. Jickling, Bradley P. Ander, Xinhua Zhan, Boryana Stamova, Heather Hull, Charles DeCarli, Frank R. Sharp

Bibliographic record

VenueBrain · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsUniversity of Alberta
FundersNational Institute on AgingCanadian Institutes of Health ResearchNational Institutes of HealthUniversity Hospital FoundationCanada Research ChairsHeart and Stroke Foundation of Canada
KeywordsHyperintensityWhite matterMedicineInterquartile rangePathologyQuartileInternal medicineCardiologyMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Cerebral white matter hyperintensities are an important contributor to ageing brain pathology. Progression in white matter hyperintensity volume is associated with cognitive decline and gait impairment. Understanding the factors associated with white matter hyperintensity progression provides insight into pathogenesis and may identify novel treatment targets to improve cognitive health. We postulated that the immune system interaction with cerebral vessels and tissue may be associated with disease progression, and thus evaluated the relationship of blood leucocyte gene expression to progression of cerebral white matter hyperintensities. A brain MRI was obtained at baseline in 166 patients assessed for a cognitive complaint, and then repeated at regular intervals over a median of 5.9 years (interquartile range 3.5-8.2 years). White matter hyperintensity volumes were measured by semi-automated segmentation and percentage change in white matter hyperintensity per year calculated. A venous blood sample obtained at baseline was used to measure whole-genome expression by RNA sequencing. The relationship between change in white matter hyperintensity volumes over time and baseline leucocyte gene expression was analysed. The mean age was 77.8 (SD 7.5) years and 60.2% of participants were female. The median white matter hyperintensity volume was 13.4 ml (SD 17.4 ml). The mean change in white matter hyperintensity volume was 12% per year. Patients were divided in quartiles by percentage change in white matter hyperintensity volume, which was: -3.5% per year in quartile 1, 7.4% per year in quartile 2, 11.7% in quartile 3 and 33.6% per year in quartile 4. There were 148 genes associated with changing white matter hyperintensity volumes over time (P < 0.05 r > |0.2|). Genes and pathways identified have roles in endothelial dysfunction, extracellular matrix remodelling, altered remyelination, inflammation and response to ischaemia. ADAM8, CFD, EPHB4, FPR2, Wnt-B-catenin, focal adhesion kinase and SIGLEC1 were among the identified genes. The progression of white matter hyperintensity volumes over time is associated with genes involved in endothelial dysfunction, extracellular matrix remodelling, altered remyelination, inflammation and response to ischaemia. Further studies are needed to evaluate the role of peripheral inflammation in relation to rate of white matter hyperintensity progression and the contribution to 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.998

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)0.0030.000

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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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.

Study designBench or experimental
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

Citations14
Published2022
Admission routes2
Has abstractyes

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