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Record W3090811311 · doi:10.1111/jgs.16853

Low Vitamin D Levels and Risk of Incident Delirium in 351,000 Older <scp>UK</scp> Biobank Participants

2020· article· en· W3090811311 on OpenAlexfundno aff
Luke C. Pilling, Lindsay Jones, Jane Masoli, João Delgado, Janice L. Atkins, Jack Bowden, Richard H. Fortinsky, George A. Kuchel, David Melzer

Bibliographic record

VenueJournal of the American Geriatrics Society · 2020
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
FundersNational Institute on AgingMedical Research Council CanadaMedical Research CouncilNational Institute for Health and Care ResearchAlzheimer's Society
KeywordsMedicineDeliriumHazard ratioProspective cohort studyVitamin D and neurologyIncidence (geometry)CohortProportional hazards modelCohort studyInternal medicineConfidence intervalvitamin D deficiencyPediatricsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES Delirium is common in older adults, especially following hospitalization. Because low vitamin D levels may be associated with increased delirium risk, we aimed to determine the prognostic value of blood vitamin D levels, extending our previous genetic analyses of this relationship. DESIGN Prospective cohort analysis. SETTING Community‐based cohort study of adults from 22 cities across the United Kingdom (the UK Biobank). PARTICIPANTS Adults aged 60 and older by the end of follow‐up in the linked hospital inpatient admissions data, up to 14 years after baseline (n = 351,320). MEASUREMENTS At baseline, serum vitamin D (25‐OH‐D) levels were measured. We used time‐to‐event models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between vitamin D deficiency and incident hospital‐diagnosed delirium, adjusted for age, sex, assessment month, assessment center, and ethnicity. We performed Mendelian randomization genetic analysis in European participants to further investigate vitamin D and delirium risk. RESULTS A total of 3,634 (1.03%) participants had at least one incident hospital‐diagnosed delirium episode. Vitamin D deficiency (<25 nmol/L) predicted a large incidence in delirium (HR = 2.49; 95% CI = 2.24–2.76; P = 3*10 −68 , compared with >50 nmol/L). Increased risk was not limited to the deficient group: insufficient levels (25–50 nmol/L) were also at increased risk (HR = 1.38; 95% CI = 1.28–1.49; P = 4*10 −18 ). The association was independent of calcium levels, hospital‐diagnosed fractures, dementia, and other relevant cofactors. In genetic analysis, participants carrying more vitamin D–increasing variants had a reduced likelihood of incident delirium diagnosis (HR = .80 per standard deviation increase in genetically instrumented vitamin D: .73–.87; P = 2*10 −7 ). CONCLUSION Progressively lower vitamin D levels predicted increased risks of incident hospital‐diagnosed delirium, and genetic evidence supports a shared causal pathway. Because low vitamin D levels are simple to detect and inexpensive and safe to correct, an intervention trial to confirm these results is urgently needed.

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)0.004
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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

Citations36
Published2020
Admission routes1
Has abstractyes

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