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Record W4406209385 · doi:10.1002/alz.088007

Exercising Physiological Pathways: The Impact of Exercise on Metabolic Aging, Cellular Aging, and Inflammatory Biomarkers in All‐Cause Dementia

2024· article· en· W4406209385 on OpenAlexaboutno aff
Mitchell Hanson, Yanbin Dong, Haidong Zhu, André Soares, Colleen Hergott, Jennifer L. Waller, Lufei Young, Dawnchelle Robinson‐Johnson, Richard Sams, Mark W. Hamrick, Deborah A. Jehu

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMedicineGerontologyCellular AgingBioinformaticsNeurosciencePsychologyInternal medicineBiologyDiseaseBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Physiological changes, including metabolic and cellular aging, as well as increased inflammation, occur in people living with dementia (PWD). While there is existing evidence in other populations suggesting that exercise may improve physiological outcomes, their impact in PWD remains unclear. This randomized controlled trial (RCT) aimed to assess the effects of exercise on serum levels of metabolic aging, cellular aging, and inflammatory blood biomarkers relative to usual care alone in PWD. Method This pilot RCT involved n=42 PWD (exercise n=21; usual care n=21) in one nursing home and one assisted living facility (NCT05488951). The adapted Otago Exercise Program involved 30 minutes of tailored lower body strength and balance exercises and 30 minutes of walking 3x/week for 6 months. We drew fasted blood at baseline and 6 months. Blood samples were stored at ‐80°F and analyzed for metabolic aging (kynurenine), cellular aging (telomere length), and inflammatory biomarkers (interleukin‐6, IL‐1b, interferon alpha2, IFNg, tumor necrosis factor a, chemokine ligand 2, IL‐8, IL‐10, IL‐12p70, IL‐17A, IL‐18, IL‐23, IL‐33). Generalized mixed models were used for intent‐to‐treat (n=42) and per protocol analyses (usual care: n=21; n=9 exercisers with ≥2/3 adherence), controlling for age, race, sex, and the Montreal Cognitive Assessment. Result The intent‐to‐treat analysis revealed no differences in physiological biomarkers between groups. The per protocol analysis revealed a trend for reduced inflammation in IL‐1b (exercise: 9.73 to 6.16 pg/mL; usual care:10.51 to 15.46 pg/mL; p=0.09) and IL‐8 (exercise: 39.34 to 25.92 pg/mL; usual care:78.99 to 93.04 pg/mL; p=0.09). Additionally, the control group increased telomere length compared to the exercise group (exercise:8.0 to 7.9 kb; usual care:7.9 to 8.7 kb; p=0.01). Conclusions The trend for exercise reducing inflammation in the per protocol analysis suggests that a greater amount of exercise (i.e., ≥2/3 exercise adherence) may be necessary to reduce inflammation in PWD. The nuanced relationship between exercise reducing cellular aging in the usual care group requires further exploration, as we had a small sample size in our per protocol analysis. Our pilot findings may inform a larger RCT to determine a potential interplay between exercise and physiological biomarkers in 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.309
Teacher spread0.259 · 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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