Evidence map›Paper›PMID 35388643›Full record

ReviewEndocrinology, diabetes & metabolism2022

Adipose tissue measurement in clinical research for obesity, type 2 diabetes and NAFLD/NASH.

Adrian Vilalta, Julio A Gutiérrez, SuZanne Chaves, Moisés Hernández, Silvia Urbina, Marcus Hompesch

Abstract readReview
In one paragraph

Review in Endocrinology, diabetes & metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Anthropometric Measures of Adiposity as Markers of Kidney Dysfunction: A Cross-Sectional Study.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2023
    Article
  12. Article
  13. Review
  14. From an Apple to a Pear: Moving Fat around for Reversing Insulin Resistance.International journal of environmental research and public health · 2022
    Review
  15. Review
  16. 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

6 authors.

Adrian VilaltaProSciento, San Diego, California, USA.ORCID 0000-0003-0379-8276
Julio A GutiérrezProSciento, San Diego, California, USA.
SuZanne ChavesProSciento, San Diego, California, USA.
Moisés HernándezProSciento, San Diego, California, USA.
Silvia UrbinaProSciento, San Diego, California, USA.
Marcus HompeschProSciento, San Diego, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionExcess body fat is linked to higher risks for metabolic syndrome, type 2 diabetes mellitus (T2DM), and cardiovascular disease (CV), among other health conditions. However, it is not only the level but also the distribution of body fat that contributes to increased disease risks. For example, an increased level of abdominal fat, or visceral adipose tissue (VAT), is associated with a higher risk of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH).

methodsA review of the most relevant primary and secondary sources on body composition from the last 25 years was conducted. Relevant articles were identified using PUBMED and Google Scholar. Narrative synthesis was performed as statistical pooling was not possible due to the heterogeneous nature of the studies.

resultsThe body mass index (BMI) is commonly used as a proxy measure of body fatness. However, BMI does not reflect the level and distribution of body fat. Other anthropometric methods such as waist circumference measurement and waist-hip ratio, as well as methodologies like hydro densitometry, bioelectrical impedance, and isotope dilution are also limited in their ability to determine body fat distribution. Imaging techniques to define body composition have greatly improved performance over traditional approaches. Ultrasound (US), computed tomography (CT), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), are now commonly used in clinical research. Of these, MRI can provide the most accurate and high-resolution measure of body composition. In addition, MRI techniques are considered the best for the determination of fat at the organ level. On the other hand, imaging modalities require specialized, often expensive equipment and expert operation.

conclusionsAnthropometric methods are suitable for rapid, high-volume screening of subjects but do not provide information on body fat distribution. Imaging techniques are more accurate but are expensive and do not lend themselves for high throughput. Therefore, successful trial strategies require a tiered approach in which subjects are first screened using anthropometric methods followed by more sophisticated modalities during the execution of the trial. This article provides a brief description of the most clinically relevant adipose tissue measurement techniques and discusses their value in obesity, diabetes, and NAFLD/NASH clinical research.

Indexed as

Diabetes Mellitus, Type 2Non-alcoholic Fatty Liver DiseaseAdipose TissueHumansObesityWaist Circumferenceadipose tissue distributiondiabetesNAFLD/NASHobesity

Identifiers

PMID35388643
PMCPMC9094496

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.