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Record W2616705824 · doi:10.1016/j.jalz.2017.03.003

Associations of lower vitamin D concentrations with cognitive decline and long‐term risk of dementia and Alzheimer's disease in older adults

2017· article· en· W2616705824 on OpenAlexaff
Catherine Féart, Catherine Helmer, B. Merle, François R. Herrmann, Cédric Annweiler, Jean‐François Dartigues, Cécile Delcourt, Cécilia Samieri

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsWestern University
FundersFondation de FranceDanoneFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheSanofi
KeywordsDementiaCognitive declineHazard ratioMedicineProspective cohort studyAlzheimer's diseaseVitamin D and neurologyCohortGerontologyCohort studyDiseasePopulationConfidence intervalInternal medicinePediatricsEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypovitaminosis D has been associated with several chronic conditions; yet, its association with cognitive decline and the risk of dementia and Alzheimer's disease (AD) has been inconsistent. METHODS: The study population consisted of 916 participants from the Three-City Bordeaux cohort aged 65+, nondemented at baseline, with assessment of vitamin D status and who were followed for up to 12 years. RESULTS: In multivariate analysis, compared with individuals with 25(OH)D sufficiency (n = 151), participants with 25(OH)D deficiency (n = 218) exhibited a faster cognitive decline. A total of 177 dementia cases (124 AD) occurred: 25(OH)D deficiency was associated with a nearly three-fold increased risk of AD (hazard ratio = 2.85, 95% confidence interval 1.37-5.97). DISCUSSION: This large prospective study of French older adults suggests that maintaining adequate vitamin D status in older age could contribute to slow down cognitive decline and to delay or prevent the onset of dementia, especially of AD etiology.

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 score0.054
Threshold uncertainty score0.666

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 studies0.0000.001
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 head0.022
GPT teacher head0.316
Teacher spread0.295 · 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

Citations148
Published2017
Admission routes1
Has abstractyes

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