Evidence map›Paper›PMID 19419307›Full record

ArticleJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research2009

Bone, muscle, and physical activity: structural equation modeling of relationships and genetic influence with age.

Dean H Lang, David E Conroy, Arimantas Lionikas, Holly A Mack, Lars Larsson, George P Vogler, David J Vandenbergh, David A Blizard, Gerald E McClearn, Neil A Sharkey

Abstract read
In one paragraph

Article in Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Bone and skeletal muscle: neighbors with close ties.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2013
    Review
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Dean H LangDepartment of Kinesiology, College of Health and Human Development, The Pennsylvania State University, University Park,Pennsylvania 16802, USA. tcl133@psu.edu
David E Conroy
Arimantas Lionikas
Holly A Mack
Lars Larsson
George P Vogler
David J Vandenbergh
David A Blizard
Gerald E McClearn
Neil A Sharkey

Funding

QUANTITATIVE TRAIT LOCI AND LONGEVITYP01AG014731 · NIA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI MCCLEARN, GERALD E · 1999 to 2003
$6.1M
TRAINING IN GENETICS OF COMPLEX BEHAVIORS IN AGINGT32AG000276 · NIA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI VOGLER, GEORGE P · 2000 to 2009
$1.2M
BONE QUALITY: A COMPOSITE OF COMPLEX PHENOTYPIC TRAITSR01AG021559 · NIA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI SHARKEY, NEIL A · 2002 to 2007
$812k
NIA NIH HHS AG00276NIA NIH HHS P01 AG014731NIA NIH HHS P01 AG14731NIA NIH HHS R01 AG021559NIA NIH HHS R01 AG21559NIA NIH HHS T32 AG000276
6 · The paper itself

Abstract

Correlations among bone strength, muscle mass, and physical activity suggest that these traits may be modulated by each other and/or by common genetic and/or environmental mechanisms. This study used structural equation modeling (SEM) to explore the extent to which select genetic loci manifest their pleiotropic effects through functional adaptations commonly referred to as Wolff's law. Quantitative trait locus (QTL) analysis was used to identify regions of chromosomes that simultaneously influenced skeletal mechanics, muscle mass, and/or activity-related behaviors in young and aged B6xD2 second-generation (F(2)) mice of both sexes. SEM was used to further study relationships among select QTLs, bone mechanics, muscle mass, and measures of activity. The SEM approach provided the means to numerically decouple the musculoskeletal effects of mechanical loading from the effects of other physiological processes involved in locomotion and physical activity. It was found that muscle mass was a better predictor of bone mechanics in young females, whereas mechanical loading was a better predictor of bone mechanics in older females. An activity-induced loading factor positively predicted the mechanical behavior of hindlimb bones in older males; contrarily, load-free locomotion (i.e., the remaining effects after removing the effects of loading) negatively predicted bone performance. QTLs on chromosomes 4, 7, and 9 seem to exert some of their influence on bone through actions consistent with Wolff's Law. Further exploration of these and other mechanisms through which genes function will aid in development of individualized interventions able to exploit the numerous complex pathways contributing to skeletal health.

Indexed as

Age FactorsModels, BiologicalPhysical Conditioning, AnimalAnimalsBiomechanical PhenomenaBone and BonesFemaleMaleMiceMice, Inbred C57BLMice, Inbred DBAMusclesOrgan SizeQuantitative Trait Loci

Identifiers

PMID19419307
PMCPMC2730930

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.