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Record W4312088463 · doi:10.1002/alz.060983

Mild behavioral impairment is associated with plasma p‐tau181 in a dementia‐free population

2022· article· en· W4312088463 on OpenAlexaff
Zahinoor Ismail, Maryam Ghahremani, Hung‐Yu Chen, Meng Wang, Henrik Zetterberg, Eric E. Smith

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsDementiaAlzheimer's Disease Neuroimaging InitiativeBiomarkerInternal medicinePopulationCognitive declineMedicineNeuroimagingProportional hazards modelCognitive impairmentPsychologyCognitionAlzheimer's diseaseDiseaseClinical psychologyOncologyPsychiatryChemistry

Abstract

fetched live from OpenAlex

Abstract Background Mild behavioral impairment (MBI) is a validated neurobehavioral syndrome describing emergent and persistent neuropsychiatric symptoms (NPS) as an at‐risk state for incident cognitive decline and dementia. In cognitively normal older adults MBI is associated with positron emission tomography and cerebrospinal fluid measured amyloid‐β and tau, the characteristic hallmarks of Alzheimer’s disease (AD). Plasma p‐tau181 is a blood‐based biomarker recently identified as an accessible alternative for in‐vivo detection of AD pathologies. So far, no study has explored plasma p‐tau181 in MBI. Here, we investigated the cross‐sectional and longitudinal associations of MBI with plasma p‐tau181 in dementia‐free older adults, and the associations of MBI with risk of AD. Method The sample included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who were cognitively unimpaired (CU) or had mild cognitive impairment (MCI). MBI status was determined using NPS total score at baseline and year‐one. NPS scores were derived from the Neuropsychiatric Inventory and categorized as persistent NPS (i.e., MBI, score>0 at both visits); transient/impersistent NPS (score>0 at only one visit); and no NPS (score = 0 at both visits). Cross‐sectionally, linear regression models were fitted with p‐tau181 as dependent variable and NPS profile as independent variable. Longitudinally, Cox proportional hazards models examined associations between NPS profile and incident dementia. Multilevel linear mixed effect (MLME) models assessed the associations between NPS status, categorized as within‐person NPS variability (impersistent NPS, not consistent with MBI) and between‐person NPS burden (persistent NPS, consistent with MBI), and p‐tau181 across four years. Result The final sample consisted of 571 dementia‐free participants (age 72.2, 46.8% females). Compared to no NPS, participants with persistent NPS but not transient NPS had higher baseline plasma ptau‐181 levels. Longitudinally, persistent NPS were associated with 3.42 times greater risk for dementia compared to no NPS. MLME analyses on annual measures of NPS and p‐tau181 over four years demonstrated that only the between‐person NPS measure was associated with higher p‐tau181 levels. Conclusion These findings extend the evidence base that MBI is a clinically relevant syndrome associated with the AD pathophysiological process. Incorporating MBI into clinical screening may help to identify those with preclinical or prodromal AD.

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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.313
Teacher spread0.278 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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