MétaCan
Menu
Back to cohort
Record W4404229310 · doi:10.1001/jamaneurol.2024.3774

Frailty Trajectories Preceding Dementia in the US and UK

2024· article· en· W4404229310 on OpenAlexaff
David Ward, Jonny P Flint, Thomas J. Littlejohns, Isabelle F. Foote, Marco Canevelli, Lindsay Wallace, Emily H Gordon, David J. Llewellyn, Janice M. Ranson, Ruth E. Hubbard, Kenneth Rockwood, Erwin Stolz

Bibliographic record

VenueJAMA Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersNational Institute on Aging
KeywordsDementiaGerontologyPsychologyMedicinePhysical medicine and rehabilitationPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

Importance: An accessible marker of both biological age and dementia risk is crucial to advancing dementia prevention and treatment strategies. Although frailty is a candidate for that role, the nature of the relationship between frailty and dementia is not well understood. Objective: To clarify the temporal relationship between frailty and incident dementia by investigating frailty trajectories in the years preceding dementia onset. Design, Setting, and Participants: Participant data came from 4 prospective cohort studies: the English Longitudinal Study of Ageing, the Health and Retirement Study, the Rush Memory and Aging Project, and the National Alzheimer Coordinating Center. Data were collected between 1997 and 2024 and were analyzed from July 2023 to August 2024. The settings were retirement communities, national-level surveys, and a multiclinic-based cohort. Included individuals were 60 years or older and without cognitive impairment at baseline. Included individuals also had data on age, sex, education level, and ethnicity and a frailty index score calculated at baseline. Exposure: Frailty was the main exposure, with participants' degrees of frailty quantified using retrospectively calculated frailty index scores. Main Outcomes and Measures: Incident all-cause dementia ascertained through physician-derived diagnoses, self- and informant-report, and estimated classifications based on combinations of cognitive tests. Results: The participant number before exclusions was 87 737. After exclusions, data from 29 849 participants (mean [SD] age, 71.6 [7.7] years; 18 369 female [62%]; 257 963 person-years of follow-up; 3154 cases of incident dementia) were analyzed. Bayesian generalized linear mixed regression models revealed accelerations in frailty trajectories 4 to 9 years before incident dementia. Overall, frailty was positively associated with dementia risk (adjusted hazard ratios [aHRs] ranged from 1.18; 95% CI, 1.13-1.24 to 1.73; 95% CI, 1.57-1.92). This association held among participants whose time between frailty measurement and incident dementia exceeded the identified acceleration period (aHRs ranged from 1.18; 95% CI, 1.12-1.23 to 1.43; 95% CI, 1.14-1.80). Conclusions and Relevance: These findings suggest that frailty measurements may be used to identify high-risk population groups for preferential enrolment into clinical trials for dementia prevention and treatment. Frailty itself may represent a useful upstream target for behavioral and societal approaches to dementia prevention.

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)0.005
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 score0.288
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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

Citations48
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJAMA NeurologySame topicFrailty in Older AdultsFrench-language works237,207