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Record W7133075780

Biomarkers of Agitation in Patients with Alzheimer's disease

2019· dissertation· W7133075780 on OpenAlexfundno aff
Myuri Ruthirakuhan

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchAlzheimer SocietyConsortium canadien en neurodégénérescence associée au vieillissementAlzheimer's Drug Discovery Foundation
KeywordsBiomarkerDiseaseSeverity of illnessPsychomotor agitationClinical trialLongitudinal study
DOInot available

Abstract

fetched live from OpenAlex

Agitation is challenging to treat in Alzheimer’s disease (AD) and has significant implications for patients and caregivers. A major source of difficulty in identifying safe and effective treatments for agitation is the lack of validated biomarkers. The goal of this work was to investigate biomarkers of agitation in patients with AD. Study 1: This study systematically reviewed 57 papers investigating biomarkers of agitation in AD. Studies 2-5: Blood samples were collected from 38 participants enrolled in a randomized, placebo-controlled trial investigating nabilone, a synthetic cannabinoid, for the treatment of agitation in AD. The cross-sectional and longitudinal associations between agitation and 24-S-hydroxycholesterol (cerebrocholesterol (Cchol) (study 2), lipid peroxidation markers of oxidative stress (OS) (study 3), and inflammatory cytokines (study 4) and a biosignature of response to nabilone (study 5) were investigated. These processes are altered in AD and may be associated with agitation and/or endocannabinoid signalling. Study 1: Of six classes of biomarkers identified, most were diagnostic in nature, substantiating the need for studies investigating the longitudinal associations between biomarkers and agitation in AD. Study 2: Cchol was associated with agitation severity cross-sectionally, and longitudinally. However, Cchol did not predict response to nabilone, and did not change over time with nabilone. Study 3: Baseline levels of the late-stage lipid-peroxidation marker, 4-hydroxynonenal (4-HNE) were associated with agitation severity cross-sectionally, and longitudinally. However changes in 4-HNE were not associated with changes in agitation severity in either phase. Study 4: The pro-inflammatory cytokine, tumor necrosis factor-alpha (TNF-α) was associated with agitation severity cross-sectionally. Lower baseline and decreases in TNF-α were associated with decreases in agitation severity in the nabilone phase only. Study 5: Inflammatory cytokines, and markers of OS predicted response to nabilone based on symptoms of verbal and physical agitation, respectively. Findings from the nabilone trial suggest that Cchol, 4-HNE and TNF-α may be useful markers of agitation severity. Additionally, response to nabilone may be influenced by the degree of OS and neuroinflammation a patient may have. As there are no validated biomarkers of agitation, identifying markers of agitation and response would assist in identifying patients who may benefit from treatment with nabilone.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.367
Teacher spread0.344 · 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
Published2019
Admission routes1
Has abstractyes

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