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Record W4410949387 · doi:10.1093/brain/awaf194

Mild behavioural impairment-apathy and core Alzheimer's disease cerebrospinal fluid biomarkers

2025· article· en· W4410949387 on OpenAlexafffund
Daniella Vellone, Rebeca Leon, Zahra Goodarzi, Nils D. Forkert, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICODoD Alzheimer's Disease Neuroimaging InitiativeH. Lundbeck A/SServierEisaiNational Institute for Health and Care ResearchNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's Association
KeywordsApathyCerebrospinal fluidMedicineDiseaseAlzheimer's diseaseNeurosciencePsychologyPathology

Abstract

fetched live from OpenAlex

Apathy is a common neuropsychiatric symptom (NPS) in Alzheimer's disease (AD) but can emerge earlier in prodromal and even preclinical stages as part of mild behavioural impairment (MBI-apathy), a syndrome defined by emergent and persistent NPS. In dementia, apathy is associated with higher morbidity, mortality and caregiver distress. However, the significance of MBI-apathy in dementia-free persons, including its associations with AD biomarkers, remains unclear. This study aimed to determine whether MBI-apathy is associated with biomarker evidence of amyloid-beta (Aβ) and tau [phosphorylated (p-tau) and total (t-tau)] in CSF. Because MBI predicts incident dementia better than NPS without MBI, we aimed to determine the association between apathy and AD biomarkers when it occurred as part of the MBI syndrome and when it did not. Dementia-free participants with mild cognitive impairment or normal cognition from the Alzheimer's Disease Neuroimaging Initiative were stratified by NPS status (MBI-apathy, non-apathy MBI, non-MBI NPS and no-NPS) based on the Neuropsychiatric Inventory (NPI) or NPI-Questionnaire (NPI-Q). Linear regressions modelled cross-sectional associations between NPS status (predictor) and CSF biomarker ratios (Aβ42/Aβ40, p-tau181/Aβ42 and t-tau/Aβ42; primary outcomes) and levels (Aβ40, Aβ42, p-tau181 and t-tau; exploratory outcomes), adjusting for age, sex, apolipoprotein E4, education, Mini-Mental State Examination and NPI version. Hierarchical linear mixed-effects (LME) models assessed longitudinal associations over 2 years, incorporating random intercepts and slopes to account for repeated measures. Fixed effects included NPS status, all covariates from the linear regression model, and an interaction term between NPS status and time. Among 477 participants (176 cognitively normal), 52 had MBI-apathy. Primary cross-sectional analyses showed that, compared with the no-NPS group, MBI-apathy was associated with higher CSF p-tau181/Aβ42 [11.25% (2.56%-20.68%); adjusted P = 0.018] and t-tau181/Aβ42 [10.26% (2.42%-18.70%); adjusted P = 0.018]. Exploratory analyses revealed that MBI-apathy was associated with higher CSF p-tau181 [5.98% (0.50%-11.77%); P = 0.032]. Primary LME models showed that MBI-apathy was associated with higher CSF p-tau181/Aβ42 [11.34% (2.55%-20.88%); adjusted P = 0.022] and t-tau181/Aβ42 [10.34% (2.41-18.88%); adjusted P = 0.022] over 2 years. Exploratory LME models revealed that MBI-apathy was associated with higher CSF p-tau181 [6.03% (0.56%-11.81%); P = 0.032] and t-tau [4.96% (0.07%-10.09%); P = 0.049] over 2 years. MBI-apathy was significantly associated with core AD biomarkers cross-sectionally and longitudinally, over 2 years, underscoring its relevance as a marker of AD pathological burden. An overall MBI composite score might reflect a broader spectrum of pathology and warrants further investigation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

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)0.0010.000

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 head0.025
GPT teacher head0.280
Teacher spread0.256 · 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 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

Citations10
Published2025
Admission routes2
Has abstractyes

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