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Record W4390195151 · doi:10.1002/alz.072704

A longitudinal multimodal biomarker study of memory clinic MCI patients with MBI – are there links to Alzheimer disease?

2023· article· en· W4390195151 on OpenAlexaff
Zahinoor Ismail, Rebeca Leon, H. Chen, Philippe Robert, Eric E. Smith

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsDementiaInternal medicineProportional hazards modelMemory clinicHazard ratioPsychologyLogistic regressionMedicineAtrophyAlzheimer's diseaseAlzheimer's Disease Neuroimaging InitiativeOncologyDiseaseConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Mild cognitive impairment (MCI) and mild behavioral impairment (MBI) are at‐risk states for incident dementia. MBI represents behavioral risk, and MCI represents cognitive risk. In memory clinic MCI patients, we interrogated the relationship between MBI status (+/‐), cerebral atrophy, cerebrospinal fluid (CSF) biomarkers, and incident all‐cause dementia and incident Alzheimer disease (AD) dementia. Method MBI was operationalized as persistent neuropsychiatric symptoms (NPS) in MEMENTO study participants. MRI regions were selected a priori for Braak stages I‐V. CSF markers included amyloid‐β (Aβ), hyperphosphorylated‐tau (p‐tau), and tau. Linear regression models were fitted with regional thickness/volume or CSF markers as dependent variables, and MBI status as independent variable, adjusted for age, sex, and education. Logistic regression was conducted to investigate if MBI status was associated with amyloid‐β positivity (defined as <813 pg/mL), with MBI status as predictor, adjusting for age, sex, education and MMSE. Kaplan‐Meier curves were generated for dementia‐free survival out to 6 years. Cox proportional hazard regressions, adjusted for age, sex, education, MMSE score, and MCI subtype were utilized to investigate the rates of incident all‐cause dementia, as well as incident AD dementia, in MBI+ vs MBI‐ patients with MCI. Result Of the 820 participants (mean age(±SD) 72.54(8.21), 57.80% female), 29.5% had MBI. Compared to MBI‐, MBI+ status was associated with lower entorhinal thickness (b = –0.066, p = 0.04) and smaller hippocampal volume (b = –0.069, p< 0.01). In the CSF subsample (n = 154, mean age = 69.6, 48.1% female), MBI predicted amyloid‐β positivity (odds ratio = 2.19, p = 0.05), and was associated with 7.4% lower Aβ42 level (p = 0.01), 17.8% higher CSF p‐tau/Aβ42 (p<0.01) and 18.9% higher t‐tau/Aβ42 ratio (p = 0.02). Longitudinally, MBI+ had lower dementia‐free survival (log rank test; p<0.0001), with adjusted hazard ratios (HRs) of 2.76 (95%CI 1.99‐3.8; p<0.001) for all‐cause dementia and 2.73 (95%CI 1.83‐4.07; p<0.001) for AD dementia. Conclusion MBI served as a clinical marker to identify a subgroup of MCI patients with Alzheimer disease‐related morphological changes in the entorhinal cortex and hippocampus, and CSF biomarkers consistent with AD. MCI with MBI had a greater incidence rate of AD dementia than MCI without MBI. MBI case status may be used to improve detection of prodromal AD in MCI.

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.344
Teacher spread0.291 · 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
Published2023
Admission routes1
Has abstractyes

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