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Record W2912111215 · doi:10.1161/circ.135.suppl_1.29

Abstract 29: Olfactory Function and Neurocognitive Outcomes in Old Age: the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS)

2017· article· en· W2912111215 on OpenAlexaboutno aff
Priya Palta, Honglei Chen, Jennifer A. Deal, David S. Knopman, Michael Griswold, Gerardo Heiss, Thomas H. Mosley

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeurocognitiveNeuropsychologyMontreal Cognitive AssessmentBoston Naming TestCognitionDementiaLogistic regressionAudiologyOdds ratioNeuropsychological testInternal medicinePsychiatryDisease

Abstract

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Introduction: Impairment in the sense of smell is associated with plaques and tangles in the olfactory region of the brain, which connects to the hippocampus where neuropathologic changes related to mild cognitive impairment (MCI) and dementia due to Alzheimer’s disease are first sited. Olfactory impairments may thus be a marker for poor cognitive function and MCI. We assessed olfaction and cognitive function in 6055 White and Black men and women aged 60-99 years. Methods: Sense of smell was measured in ARIC-NCS participants (2011-2013) by the 12-item Sniffin’ Sticks screening test (score range: 0-12, median: 10). A clinically validated threshold (smell score <6) defined olfactory impairment (OI). A multidimensional neuropsychological assessment (10 tests) ascertained performance in domains of memory, language, executive function/processing speed, and global cognition. For relative comparisons across the tests, raw cognitive test scores were standardized to z scores and averaged to yield domain scores. Following review of neuropsychological assessments, medical history, cerebral magnetic resonance imaging, and physical examinations, MCI was classified by a neurologist and neuropsychologist, and adjudicated by a third reviewer. Multivariable linear regression estimated the mean difference in domain-specific z scores among participants with and without OI. Logistic regression was used to quantify the prevalence odds of MCI in participants with vs. those without OI. Models were adjusted for age, sex, race, education, ARIC study center, hypertension, diabetes, smoking, and ApoE4. Race and sex were explored as effect modifiers. Results: The participants’ mean age was 76 years; 41% were male and 23% Black. The prevalence of olfactory impairment was 14%. Compared to participants with no OI, those with OI had a statistically significantly lower mean z score across all cognitive domains [memory: Beta= -0.37 (95% confidence interval [CI]: -0.45, -0.30); language: Beta= -0.39 (95% CI: -0.46, -0.33); executive function/processing speed: Beta= -0.24 (95% CI: -0.32, -0.17); global cognition: Beta= -0.34 (95% CI: -0.41, -0.26). Effect modification by race was observed in the domain of language. Blacks with OI had a greater mean difference in language z score compared to Blacks without OI (Beta= -0.57 (95% CI: -0.70, -0.44)). OI was associated with MCI in Whites, but not Blacks: white participants with OI had greater odds of MCI (Odds Ratio [OR] =1.76, 95% CI: 1.40, 2.21). Sex did not modify these associations. Conclusions: Compared to average cognitive aging (annual rate of decline of 0.04-0.05 standard deviation units/year) relatively large differences in standardized cognitive domain scores are observed between those with and without olfactory impairment among older adults. An impaired sense of smell may serve as a readily accessible early marker of neurodegeneration.

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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.219
GPT teacher head0.301
Teacher spread0.081 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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