Evidence mapPaperPMID 26768490Full record

ArticleNMR in biomedicine2015

Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies.

Magnus Borga, E Louise Thomas, Thobias Romu, Johannes Rosander, Julie Fitzpatrick, Olof Dahlqvist Leinhard, Jimmy D Bell

3 registry-linked trialsAbstract readComparative StudyEvaluation StudyValidation Study
PubMed Publisher
In one paragraph

Article in NMR in biomedicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
3.6field-weighted citation impact, top 7% 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.

NCT03038620 phase4completedstarted 2017, after this paper: background citation

Impact of Liraglutide 3.0 on Body Fat Distribution, Visceral Adiposity, and Cardiometabolic Risk Markers In Overweight and Obese Adults at High Risk for Cardiovascular Disease

Ran2017Enrolled235Registered outcomes37Posted comparisons0ConditionsCardiovascular Diseases, Fat Disorder, Obesity, VisceralArmsliraglutide, Placebo
Open the trial in the graph
NCT05184283 terminatednot on this mapstarted 2022, after this paper: background citation

Longitudinal Assessment of Muscle Health in Patients Undergoing Liver Transplantation (LT) for Hepatocellular Carcinoma (HCC)

TypeobservationalSponsorColumbia UniversityRan2022 to 2023Enrolled11ConditionsNAFLD, Hepatocellular Carcinoma, Liver Diseases, Liver Cancer
NCT05477277 narecruitingnot on this mapstarted 2022, after this paper: background citation

Adverse Outcomes and Mortality in Liver Transplant

TypeinterventionalSponsorMayo ClinicRan2022 to 2027Enrolled100ConditionsEnd Stage Liver DIsease, Sarcopenia, Sarcopenic Obesity, CirrhosisArmsMAsS
3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 75 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Sleep chart of biological aging clocks across organs and omics.medRxiv : the preprint server for health sciences · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Multi-organ imaging-derived polygenic indexes for brain and body health.medRxiv : the preprint server for health sciences · 2024
    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

7 authors at 2 institutions in 2 countries.

Magnus BorgaDepartment of Biomedical Engineering, Linköping University, Sweden.
E Louise ThomasDepartment of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
Thobias RomuDepartment of Biomedical Engineering, Linköping University, Sweden.
Johannes RosanderAdvanced MR Analytics AB, Linköping, Sweden.
Julie FitzpatrickDepartment of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
Olof Dahlqvist LeinhardAdvanced MR Analytics AB, Linköping, Sweden.
Jimmy D BellDepartment of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
Linköping University · SEUniversity of Westminster · GB

Funding

Medical Research Council MC_U120061305
6 · The paper itself

Abstract

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as MRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat-muscle quantitation system (AMRA(TM) Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRA(TM) Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRA(TM) Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRA(TM) Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variation was 1.4% for IAAT and 1.2% for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRA(TM) Profiler, opening up the possibility of large-scale human phenotypic studies.

Indexed as

AdiposityAbdominal FatAdultAgedAlgorithmsFemaleHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalMaleMiddle AgedReproducibility of ResultsSensitivity and SpecificitySubcutaneous Fat, Abdominalabdominal fatadipose tissueDixonfat quantitationMRIobesity

Identifiers

PMID26768490
OpenAlexW1912016580

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

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 Socratic graph.