Evidence map›Paper›PMID 26063330›Full record

ArticleInternational journal of obesity (2005)2015

Fully-automated, high-throughput micro-computed tomography analysis of body composition enables therapeutic efficacy monitoring in preclinical models.

S K Wyatt, K H Barck, L Kates, J Zavala-Solorio, J Ross, G Kolumam, J Sonoda, R A D Carano

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in International journal of obesity (2005), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Tissue engineering. Part C, Methods · 2017
    Article
  7. Article
  8. QuantitativeBone reports · 2016
    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

8 authors.

S K WyattDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
K H BarckDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
L KatesDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
J Zavala-SolorioDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
J RossDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
G KolumamDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.
J SonodaDepartment of Molecular Biology, Genentech, Inc., South San Francisco, CA, USA.
R A D CaranoDepartment of Biomedical Imaging, Genentech, Inc., South San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe ability to non-invasively measure body composition in mouse models of obesity and obesity-related disorders is essential for elucidating mechanisms of metabolic regulation and monitoring the effects of novel treatments. These studies aimed to develop a fully automated, high-throughput micro-computed tomography (micro-CT)-based image analysis technique for longitudinal quantitation of adipose, non-adipose and lean tissue as well as bone and demonstrate utility for assessing the effects of two distinct treatments.

methodsAn initial validation study was performed in diet-induced obesity (DIO) and control mice on a vivaCT 75 micro-CT system. Subsequently, four groups of DIO mice were imaged pre- and post-treatment with an experimental agonistic antibody specific for anti-fibroblast growth factor receptor 1 (anti-FGFR1, R1MAb1), control immunoglobulin G antibody, a known anorectic antiobesity drug (rimonabant, SR141716), or solvent control. The body composition analysis technique was then ported to a faster micro-CT system (CT120) to markedly increase throughput as well as to evaluate the use of micro-CT image intensity for hepatic lipid content in DIO and control mice. Ex vivo chemical analysis and colorimetric analysis of the liver triglycerides were performed as the standard metrics for correlation with body composition and hepatic lipid status, respectively.

resultsMicro-CT-based body composition measures correlate with ex vivo chemical analysis metrics and enable distinction between DIO and control mice. R1MAb1 and rimonabant have differing effects on body composition as assessed by micro-CT. High-throughput body composition imaging is possible using a modified CT120 system. Micro-CT also provides a non-invasive assessment of hepatic lipid content.

conclusionsThis work describes, validates and demonstrates utility of a fully automated image analysis technique to quantify in vivo micro-CT-derived measures of adipose, non-adipose and lean tissue, as well as bone. These body composition metrics highly correlate with standard ex vivo chemical analysis and enable longitudinal evaluation of body composition and therapeutic efficacy monitoring.

Indexed as

X-Ray MicrotomographyAdipose TissueAnimalsBody CompositionDisease Models, AnimalImage Interpretation, Computer-AssistedMaleMiceMice, ObeseObesityReproducibility of ResultsSensitivity and Specificity

Identifiers

PMID26063330
OpenAlexW1496151260

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.