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Record W4312086910 · doi:10.1002/alz.068108

Handgrip Strength is Related to Regional Brain Volumes in a Cohort of Cognitively Impaired Older Adults with Confirmed Amyloid Burden

2022· article· en· W4312086910 on OpenAlexaboutno aff
Somayeh Meysami, Cyrus A. Raji, Emily S. Popa, Aarthi S. Ganapathi, Tess Bookheimer, Colby B. Slyapich, Kyron P. Pierce, Casey J. Richards, Jaya M. Gill, Melanie G. Lampa, Molly K. Rapozo, John F. Hodes, Ryan M. Glatt, Ynez M. Tongson, Claudia L. Wong, Mihae Kim, Verna R. Porter, Scott Kaiser, Stella E. Panos, Richelin V. Dye, Karen J. Miller, Susan Y. Bookheimer, Neil A. Martin, Santosh Kesari, Daniel F. Kelly, Prabha Siddarth, Jennifer E. Bramen, David A. Merrill

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileMedicineDementiaCohortPhysical medicine and rehabilitationBody mass indexPhysical therapyLateralityPsychologyInternal medicineCardiologyGerontologyAudiology

Abstract

fetched live from OpenAlex

Abstract Background Handgrip strength is important for performing activities of daily living[1]. In older adults, weaker handgrip strength and asymmetry are associated with poorer cognition. o better understand mechanisms, we sought to quantify the relationship between handgrip strength and regional volumes quantified on brain MR imaging. Method We selected 32 participants (mean age=70.8±7.3 [range 57‐89] years, 53.1% female, 90.6% right‐handed, mean body mass index BMI=23.9±4.1) from the Pacific Brain Health Center at Providence St. John’s Health Center, with Alzheimer dementia biomarker evidence of amyloidosis[2]. Mean Montreal Cognitive Assessment score for all participants was 21.3±3.8 points. Handgrip strength on dominant and non‐dominant hands was measured using the NIH Motor Toolbox[3] as part of a cognitive fitness assessment using a hydraulic hand dynamometer. The resulting scores included handgrip strength and percentile comparisons to normative data. Asymmetry scores were calculated. Regional brain volumes, including lobar structures and the hippocampus, were measured from T1‐weighted MR images using Neuroreader[4]. Partial correlations (rp), adjusting for age, sex, BMI and total intracranial volume modeled handgrip strength, asymmetry, and brain volumes with a significance threshold of p<0.05. Result In the dominant hand, higher handgrip strength scores and percentiles were associated with larger volumes in the left frontal lobe (rp=+0.51, p=0.007; rp =+0.47, p=0.01) and right parietal lobe (rp=+0.40, p=0.03; rp=+0.39, p=0.04). In the non‐dominant hand, higher handgrip strength score and percentiles were associated with smaller total cerebral spinal fluid (CSF) volume (rp =‐0.55, p=0.004; rp =‐0.52, p =0.006) and larger volumes within the left hippocampus (rp=+0.45, p=0.01; rp=+.43, p=0.02), right hippocampus (rp=+0.47, p=0.01; rp=+0.43, p=0.02), and right parietal lobe (rp=+0.39, p=0.04; rp=+0.44, p=0.02). Handgrip strength asymmetry was inversely related to right hippocampal volume (rp=‐0.58, p=0.002) and positively correlated to CSF volume (rp=+0.39, p=0.04). Conclusion Greater handgrip strength was related to larger regional brain volumes. A higher number of brain regions were related to the non‐dominant hand. Asymmetry was associated with lower right hippocampal volume and higher CSF volume. Interventions focused on improving handgrip strength may seek to include quantified brain volumes on MR imaging as endpoints.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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
Published2022
Admission routes1
Has abstractyes

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