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Record W4409885694 · doi:10.1093/braincomms/fcaf163

Vascular risk factor associations with subjective cognitive decline and mild behavioural impairment

2025· article· en· W4409885694 on OpenAlexafffundabout
Dylan X. Guan, Aditya Aundhakar, Sarah Tomaszewski Farias, Clive Ballard, Byron Creese, Anne Corbett, Ellie Pickering, Pamela Roach, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchKillam TrustsNational Institute for Health and Care ResearchAlzheimer SocietyHotchkiss Brain Institute
KeywordsCognitive impairmentRisk factorCognitive declineCognitionPsychologyClinical psychologyMedicineDementiaPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

Subjective cognitive decline and mild behavioural impairment identify older persons more likely to have early Alzheimer's disease. Vascular co-pathologies may also contribute to new onset and persistent cognitive and behavioural symptoms later in life. We investigated vascular risk factor associations with subjective cognitive decline and mild behavioural impairment. Cross-sectional data for 1285 (81.0% female) participants without mild cognitive impairment or dementia enrolled in the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging were analyzed. Vascular risk factors included body mass index class, self-reported clinician diagnoses of hypertension, high cholesterol, diabetes, self-reported smoking, and the cumulative number of vascular risk factors. Outcomes were the Everyday Cognition scale and Mild Behavioural Impairment Checklist. Logistic and negative binomial regressions were used to model odds and severity of subjective cognitive decline and mild behavioural impairment as a function of individual or cumulative vascular risk factors. Having three or more vascular risk factors (odds ratio = 1.23, 95% confidence interval [1.04-1.47]), actively smoking (odds ratio = 1.54, 95% confidence interval [1.29-1.82]), being overweight (odds ratio = 1.46, 95% confidence interval [1.22-1.74]), and having diabetes (odds ratio = 1.29, 95% confidence interval [1.09-1.53]) were associated with higher odds of subjective cognitive decline. Having any number of vascular risk factors was dose-dependently associated with higher odds of mild behavioural impairment, as were all five vascular risk factors individually; active smokers (odds ratio = 2.67, 95% confidence interval [2.25-3.18]) and obese persons (odds ratio = 2.29, 95% confidence interval [1.91-2.75]) had over twice the odds of mild behavioural impairment. Vascular risk factors associations with subjective cognitive decline were stronger in participants with mild behavioural impairment. All vascular risk factors were linked to higher Everyday Cognition and Mild Behavioural Impairment Checklist total scores, indicating greater subjective cognitive decline and mild behavioural impairment symptom severity. Overweight body mass index, hypertension, and high cholesterol associations with subjective cognitive decline and mild behavioural impairment were stronger in middle-aged adults than older adults, but diabetes and active smoking had greater effects in older adults. Vascular risk factors are strongly related to experiences of cognitive and behavioural changes in later life, even in the absence of objective cognitive impairment. Furthermore, vascular associations with subjective cognitive decline symptoms may be more pronounced in persons with concomitant behavioural decline. Vascular pathologies may contribute to both cognitive and behavioural markers traditionally linked to Alzheimer's disease in older persons, prior to mild cognitive impairment and dementia.

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)0.001
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 score0.020
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.358
Teacher spread0.324 · 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

Citations6
Published2025
Admission routes3
Has abstractyes

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