Evidence map›Paper›PMID 20467582›Full record

ArticleInternational journal of body composition research2009

Validation of quantitative magnetic resonance for the determination of body composition of mice.

A S Jones, M S Johnson, T R Nagy

Abstract read
In one paragraph

Article in International journal of body composition research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
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  7. Article
  8. Genetic Deletion ofNutrients · 2019
    Article
  9. Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2.Mammalian genome : official journal of the International Mammalian Genome Society · 2018
    Article
  10. Article
  11. Article
  12. Article
  13. Validation of Body Condition Indices and Quantitative Magnetic Resonance in Estimating Body Composition in a Small Lizard.Journal of experimental zoology. Part A, Ecological genetics and physiology · 2016
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Leptin resistance is a secondary consequence of the obesity in ciliopathy mutant mice.Proceedings of the National Academy of Sciences of the United States of America · 2013
    Article
  20. Article
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

3 authors.

A S JonesDepartment of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
M S Johnson
T R Nagy

Funding

Why is the prevalence of obesity so high in U.S. Southern States? Regional predictors of BMI and obesity treatment response.P30DK056336 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Gareth R Dutton · 2000 to 2026
$31.9M
Alabama Neuroscience Blueprint Core CenterP30NS057098 · NINDS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI WILSON, SCOTT MICHAEL · 2006 to 2010
$9.5M
UAB Diabetes Research and Training CenterP60DK079626 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI GARVEY, W TIMOTHY · 2008 to 2012
$8.4M
NIDDK NIH HHS P30 DK056336NIDDK NIH HHS P60 DK079626NINDS NIH HHS P30 NS057098
6 · The paper itself

Abstract

objectiveThe aim of this study was to assess the precision and accuracy of a quantitative magnetic resonance (QMR) instrument for measuring body composition in live, non-anesthetized mice.

methodsForty-eight mice of varying strains, ages and body weights (15.3 to 50.2g) were scanned three times each in the QMR instrument. Animals were killed and chemical carcass analysis performed for comparison. Precision was assessed as the coefficient of variation (CV) for the triplicate scans and accuracy was determined by comparing the first QMR data with the chemical analysis. Prediction equations were generated by linear regression analysis and used in a cross-validation study in which 26 mice were scanned once each, killed, and chemical carcass analysis performed.

resultsThe mean CV was 1.58% for fat mass (FM) and 0.78% for lean-tissue mass (LTM). QMR significantly (P<0.01) overestimated FM (7.76±5.93 vs. 6.03±5.17g) and underestimated LTM (20.73±6.19 vs. 22.48±6.75g) when compared with chemical carcass analysis. A strong relationship between QMR and chemical data (r(2)=0.99 and r(2)=0.97 for fat and LTM respectively; P<0.0001) allowed for the generation of correction equations that were applied to QMR data in the cross-validation study. There was no significant difference between data predicted from QMR and chemical carcass data for FM and LTM (P=0.15 and 0.10 respectively).

conclusionThe QMR instrument showed excellent precision and data was highly correlated with chemical carcass analysis. This combined with QMR's speed for whole animal analysis (95 seconds) make it a highly feasible and useful method for the determination of body composition in live, non-anesthetized mice.

Identifiers

PMID20467582
PMCPMC2868277

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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.