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Record W7117242447 · doi:10.1002/alz70857_100017

Characterizing the contribution of alcohol use towards neuropsychological profiles in mild cognitive impairment

2025· article· en· W7117242447 on OpenAlexaffabout
Ari B. Cuperfain, Sara Pishdadian, Malcolm A. Binns, Sandra E. Black, Howard Chertkow, M. FREEDMAN, Janine Louis, Clement Ma, Mario Masellis, Paula McLaughlin, Joel Ramirez, David F. Tang‐Wai, Carmela M. Tartaglia, Sanjeev Kumar

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsHealth Sciences CentreBaycrest HospitalSunnybrook Health Science CentreNova Scotia Health AuthorityCentre for Addiction and Mental HealthUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCognitive impairmentNeuropsychologyCognitionAlcoholMemory impairmentAmnesiaCognitive disorder

Abstract

fetched live from OpenAlex

BACKGROUND: Excessive alcohol use is known to increase the risk of dementia, however, how alcohol specifically effects cognition in patients with neurocognitive disorders is unclear. In this study, we characterized the effects of excessive alcohol use on cognitive domains in those with mild cognitive impairment (MCI). METHOD: Two multicenter cohorts, the Comprehensive Assessment of Neurodegeneration and Dementia and the Ontario Neurodegenerative Disease Research Initiative, provided data for this study. Participants were diagnosed with MCI due to Alzheimer's disease (AD-MCI) or cerebro-vascular disease (V-MCI), and were categorized based on their current and past alcohol use into 'zero', 'low-medium' (less than 1 to 7 standard drinks/week), or 'high' (>7 standard drinks/week) alcohol use groups. We generated age-corrected composite cognitive domain scores for Processing Speed, Memory Encoding, Recall and Recognition, Executive Control, Executive Function, Visuoperceptual, and Language domains, and compared them among the three groups while controlling for sex, pre-morbid intelligence, and diagnosis. RESULT: The zero alcohol group included 157 participants (females = 48.4%; V-MCI = 52.2%) with mean (SD) age 71.26 (6.93) years, the low-medium alcohol group included 213 participants (females = 42.2%; V-MCI = 44.1%) with mean (SD) age 71.64 (7.77) years, and the high alcohol group included 73 participants (females = 20.5%; V-MCI = 50.1%) with mean (SD) age 72.23 (7.51) years. The groups differed only in Processing Speed (F(2,407) = 3.298, p = 0.038), Memory Encoding (F(2,394) = 4.689, p = 0.010) and Recall (F(2,403) = 3.997, p = 0.019). Pairwise comparisons revealed a significant difference between the low-medium and high alcohol groups in both Memory Encoding (p = 0.019) and Recall (p = 0.018), with participants in the high alcohol group demonstrating lower performance. There were no differences between the zero alcohol group and other groups. There were no pairwise differences for Processing Speed. CONCLUSION: In participants with AD-MCI or V-MCI, excessive alcohol use is associated with worse memory encoding and retrieval, but not retention. This is consistent with a more dysexecutive memory profile than a typical amnestic cognitive profile. Future studies should investigate underlying mechanisms for these deficits, and their impact on prognosis of the illness.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.407

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)0.0000.000

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 head0.048
GPT teacher head0.332
Teacher spread0.284 · 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.

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
Published2025
Admission routes2
Has abstractyes

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