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Record W4406201331 · doi:10.1002/alz.083897

The relationship between physiological biomarkers, physical function, and fall‐risk among people living with dementia

2024· article· en· W4406201331 on OpenAlexaboutno aff
Stephen Hamill, Yanbin Dong, Haidong Zhu, Ying Huang, André Soares, Jennifer L. Waller, Lufei Young, Dawnchelle Robinson‐Johnson, Richard Sams, Mark W. Hamrick, Deborah A. Jehu

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaGerontologyFunction (biology)MedicinePsychologyEnvironmental healthBiologyInternal medicineDiseaseEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract Background People living with dementia (PWD) have upregulated inflammatory pathways, exaggerated metabolic aging, and cellular aging. They also have declines in physical function and heightened fall‐risk. Understanding the physiologic factors that influence physical decline and fall‐risk in PWD is vital to assess and prevent adverse health outcomes, such as future falls. The purpose of this study was to explore the association between physiological biomarkers, physical decline, and fall‐risk in PWD. Method In this cross‐sectional study, we used the baseline data of n=42 PWD in residential care facilities from our pilot randomized controlled trial [NCT05488951]. We assessed fall‐risk with the Morse Fall Scale and pulled fall history in the last 6 months from incident reports in medical charts. Participants completed two 4‐meter usual pace walking trials. We assessed two trials of maximum quadriceps strength on each leg with a portable dynamometer. We drew fasted blood and measured inflammatory biomarkers (Interleukin(IL)‐1b, IL‐6, IL‐8, IL‐10, IL‐12p70, IL‐17A, IL‐18, IL23, IL‐33, chemokine ligand 2, tumor necrosis factor‐a, human interferon (INF)‐a2, INFg), metabolic aging (kynurenine), and cellular aging (telomere length). Separate multiple linear regressions were performed for each biomarker, with gait speed, leg strength, fall history, and the Morse Fall Scale as variables of interest. We controlled for age, sex, and the Montreal Cognitive Assessment in each model. Result Fall history (β=5.61, p=0.03) and older age (β=0.49, p=0.005) were associated with greater INF‐a2 (R2=0.49, p=0.07). Fall history (β=4.93, p=0.07) showed a trend for a relationship with greater IL‐10 (R2=0.50, p=0.04). Older age (β=0.28, p=0.009) and lower MOCA scores (β=‐0.40, p=0.04) were related to greater IL‐12p70 (R2=0.64, p=0.003). Older age (β=9.26, p=0.01), fall history (β=74.42, p=0.04), and poorer leg strength (β=‐7.40, p=0.06) were related to greater kynurenine (R2=0.49, p=0.02). Conclusion Our exploratory findings suggest that there may be a relationship between certain physiological biomarkers (INF‐a2, IL‐10, kynurenine), physical function, and fall history. These inflammatory and metabolic aging biomarkers may play an important role for physical function and fall‐risk in PWD. This preliminary research may have implications for screening and monitoring of physical decline and fall‐risk among PWD.

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)0.003
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.323
Teacher spread0.285 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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