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Record W3112939916 · doi:10.1002/alz.040893

Variation in retinal microvascular parameters is associated with mild cognitive impairment and Alzheimer’s disease

2020· article· en· W3112939916 on OpenAlexaboutno aff
R. A. O’Neill, Alexander P. Maxwell, Ruth Hogg, Anthony Peter Passmore, Nicola Quinn, Frank Kee, Ian Young, Gareth J. McKay, Bernadette McGuinness

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsDementiaConfoundingCognitionMedicineRetinalAudiologyLogistic regressionMontreal Cognitive AssessmentCognitive declineInternal medicineCardiologyGerontologyDiseasePsychologyOphthalmologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Alzheimer's disease (AD) is a neurodegenerative condition characterised by cognitive decline. Mild Cognitive Impairment (MCI) represents a transitional state between ageing normally and all forms of dementia, although those with MCI will not automatically convert to dementia. The retinal and cerebral microvasculature share similar embryological origins and physiological characteristics. Improved imaging technologies enable non‐invasive measurement of retinal microvascular parameters (RMPs). We investigated associations between RMPs and cognitive function in participants with AD and MCI. Method RMPs (arteriolar/venular diameter, fractal dimension, vessel tortuosity) were measured from optic disc centred fundus images and analysed using semi‐automated software. Thirty‐four MCI and thirty‐two AD participants were recruited for the study from the Belfast Health and Social Care Trust memory clinics. Nine MCI and thirteen AD participants were excluded as their retinal images were of insufficient quality for image analysis. Associations were assessed by logistic regression with comparison to an age and sex‐matched control group from the NICOLA study as the reference category. Models were adjusted for potential confounders including smoking, diabetes, hypertension, total cholesterol and Mini Mental State Exam (MMSE) score. P<0.05 was considered significant. Result Data were included for 126 participants with cognitive function measures and sufficient retinal images. Twenty‐five participants had a clinical diagnosis of MCI and nineteen AD. Eighty‐two age and gender‐matched controls with no previous history of cognitive impairment, and MMSE and Montreal Cognitive Assessment scores >26/30 were included. Decreased arteriolar fractal dimension (OR: 0.30; 95%CI: 0.10, 0.91; P=0.03) was significantly associated with AD in all models. Decreased venular fractal dimension and both arteriole and venular diameter were significantly associated with MCI in all models (P <0.05). No further associations were detected (P >0.05). Conclusion Our findings identified variation in RMPs in association with MCI and AD in an older population. Additionally, previous studies have reported associations between reduced fractal dimension with MCI and AD. We were able to replicate these associations in unadjusted analyses, and following adjustment for confounders. These non‐invasive retinal measures may help identify mechanistic pathways of microvascular complications early in the disease process in individuals at increased risk of MCI and 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 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 score0.042
Threshold uncertainty score0.743

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 head0.027
GPT teacher head0.267
Teacher spread0.241 · 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
Published2020
Admission routes1
Has abstractyes

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