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Record W4417448285 · doi:10.1093/jbmr/zjaf188

Associations of muscle strength and functional power with longitudinal change in HR-pQCT bone parameters: the Osteoporotic Fractures in Men (MrOS) Study

2025· article· en· W4417448285 on OpenAlexaff
Nina Z. Heilmann, Kerri Freeland, Tong Yu, Paolo Caserotti, Andrew J. Burghardt, Mary Winger Knueven, Bradley C. Nindl, Kristine Ruppert, Marcia L. Stefanick, Mary Bouxsein, N.E. Lane, Jane A. Cauley, Elsa S. Strotmeyer

Bibliographic record

VenueJournal of Bone and Mineral Research · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingNational Institutes of HealthSyddansk UniversitetAmgen
KeywordsGrip strengthBone mineralQuantitative computed tomographyJumpOsteoporosisBone densityMuscle strengthHand strengthTibia

Abstract

fetched live from OpenAlex

Both bone and muscle function decline with age and are anatomically and functionally related. However, whether and to what extent muscle function (ie, strength and power) may predict longitudinal changes in bone microarchitecture and strength is unclear. The Osteoporotic Fractures in Men (MrOS) Study included assessments of peak jump power (W) from a force plate and maximum grip strength (kg) from a dynamometer, both normalized to body weight at visit 4 (2014-2016). We investigated the associations of jump power and grip strength with annual percent change in volumetric BMD, microarchitecture, and strength at the distal tibia (DT) and radius (DR) from HR-pQCT between visit 4 and visit 5 (2020-2022; 6.2 ± 0.6 yr follow-up; N = 225; age 82.8 ± 3.0 yr; 89% White). Mean jump power was 22.9 ± 5.6 W/kg and grip strength was 0.49 ± 0.1 kg/kg. During follow-up (median [IQR]), failure load (-0.88 [-1.71, -0.31]%), total BMD (-0.57 [-1.12, -0.18]%), cortical BMD (-1.24 [-2.03, -0.67]%), trabecular BMD (-0.05 [-0.47, 0.20]%), and trabecular thickness (-0.37 [-0.64, -0.12]%) declined at the DT, while at the DR, failure load (-1.02 [-2.19, -0.04]%), total BMD (-0.64 [-1.20, -0.18]%), and cortical BMD (-1.38 [-2.15, -0.71]%) declined (all p ≤ .05). Significant increases were observed for total area at both skeletal sites (DT: 0.04 [0.01, 0.08]%; DR: 0.07[-0.06, 0.16]%; both p ≤ .05). Multivariable linear regression models were adjusted for age, White race, clinic site, respective HR-pQCT initial values, percentage weight change, alcohol consumption, medication count, chronic disease history, falls, and hip pain. Higher grip strength was significantly associated with a smaller percent/year increase in total area at the DT (p ≤ .05) but not at the DR. Neither jump power nor grip strength was associated with change in failure load, BMD, or trabecular thickness at either skeletal site. Associations between grip strength and changes in tibial bone geometry provide insight into potential mechanisms for bone loss and targets for musculoskeletal interventions to reduce fracture risk.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.095
GPT teacher head0.415
Teacher spread0.320 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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