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Record W4406050651 · doi:10.1002/alz.091470

Vascular risk factors, subjective cognitive decline, and mild behavioral impairment: A CAN‐PROTECT study

2024· article· en· W4406050651 on OpenAlexaffabout
Dylan X. Guan, Aditya Aundhakar, Sarah Tomaszewski Farias, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsDementiaPsychologyCognitionOverweightGerontologyVascular dementiaRisk factorMedicineMarital statusBody mass indexClinical psychologyDiseaseInternal medicinePsychiatryPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Subjective cognitive decline (SCD) and mild behavioral impairment (MBI) identify older persons that are more likely to be at preclinical stages of Alzheimer's disease (AD) than those without SCD and MBI. However, vascular co-pathologies may also contribute to new onset and persistent cognitive and behavioral symptoms. We investigated vascular risk factor associations with SCD and MBI in older persons without mild cognitive impairment or dementia. METHOD: Data for 1285 participants from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN-PROTECT) study were analyzed [Figure 1]. Vascular risk factors were body mass index (normal, overweight, obese), hypertension, high cholesterol, diabetes, and smoking (never, past, active). Outcomes were measured using the Everyday Cognition (ECog-II) scale and MBI Checklist (MBI-C). SCD was operationalized based on a score of ≥2 (i.e., consistently a little or much worse) on any ECog-II item. MBI+ status was defined by MBI-C total scores ≥8. Propensity scores were used to balance age, sex, years of education, marital status, and ethnocultural origin across exposure groups using inverse probability of treatment weighting. Weighted negative binomial and logistic regressions were used to model vascular risk factor (exposure) associations with ECog-II and MBI-C total scores, and SCD and MBI+ statuses, respectively. RESULT: Participant characteristics are summarized in Table 1. As shown in Table 2, all vascular risk factors assessed in the study were associated with higher ECog-II total score, with the exception of past (i.e., not active), smoking behavior. Diabetes and active smoking showed the largest magnitudes of effect in relation to ECog-II total scores. Active smoking and obesity showed the largest magnitudes of effect in relation to MBI-C total scores. When assessing outcomes as categorical variables (i.e., SCD+ and MBI+), the same associations were found. CONCLUSION: Vascular risk factors were associated with poorer everyday cognition, more severe MBI symptoms, and greater odds for classification as SCD+ and MBI+ in a sample of older persons without objective cognitive impairment. These findings suggest potential vascular contributions to cognitive and behavioral markers traditionally linked to 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)0.005
metaresearch head score (Gemma)0.005
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 score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.340
Teacher spread0.307 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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