Evidence mapPaperPMID 37710156Full record

SynthesisBMC medical imaging2023

Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies.

Sofia Mouchti, Josefina Orliacq, Gillian Reeves, Zhengming Chen

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
5.3field-weighted citation impact, top 3% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Association between pain intensity and body composition in adults with chronic non-specific low back pain: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025
    Pooled it
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  14. Where Muscle Matters: How Regional Differences, Pain, and Gender Define Gamer Health.International journal of environmental research and public health · 2025
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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

4 authors at 1 institution in 1 country.

Sofia MouchtiCancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Josefina OrliacqCancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Gillian ReevesCancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK. Gill.Reeves@ndph.ox.ac.uk.
Zhengming ChenMRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK. Zhengming.Chen@ndph.ox.ac.uk.
University of Oxford · GB

Funding

Cancer Research UK 29186Cancer Research UK A29186Medical Research Council MC_U137686851Medical Research Council MC_UU_00017/1Wellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

backgroundIn studies of the association of adiposity with disease risk, widely used anthropometric measures of adiposity (e.g. body-mass-index [BMI], waist circumference [WC], waist-hip ratio [WHR]) are simple and inexpensive to implement at scale. In contrast, imaging-based techniques (e.g. magnetic resonance imaging [MRI] and dual x-ray absorptiometry [DXA]) are expensive and labour intensive, but can provide more accurate quantification of body fat composition. There is, however, limited evidence about the relationship between conventional and imaging-derived measures of adiposity.

methodsWe searched Scopus and Web of Science for published reports in English of conventional versus imaging-derived measurements of adiposity. We identified 42 articles (MRI = 22; DXA = 20) that met selection criteria, involving 42,556 (MRI = 15,130; DXA = 27,426) individuals recruited from community or hospital settings. Study-specific correlation coefficients (r) were transformed using Fisher's Z transformation, and meta-analysed to yield weighted average correlations, both overall and by ancestry, sex and age, where feasible. Publication bias was investigated using funnel plots and Egger's test.

resultsOverall, 98% of participants were 18 + years old, 85% male and 95% White. BMI and WC were most strongly correlated with imaging-derived total abdominal (MRI-derived: r = 0.88-; DXA-derived: 0.50-0.86) and subcutaneous abdominal fat (MRI-derived: 0.83-0.85), but were less strongly correlated with visceral abdominal fat (MRI-derived: 0.76-0.79; DXA-derived: 0.80) and with DXA-derived %body fat (0.76). WHR was, at best, strongly correlated with imaging-derived total abdominal (MRI-derived: 0.60; DXA-derived: 0.13), and visceral abdominal fat (MRI-derived: 0.67; DXA-derived: 0.65), and moderately with subcutaneous abdominal (MRI-derived: 0.54), and with DXA-derived %body fat (0.58). All conventional adiposity measures were at best moderately correlated with hepatic fat (MRI-derived: 0.36-0.43). In general, correlations were stronger in women than in men, in Whites than in non-Whites, and in those aged 18 + years.

conclusionsIn this meta-analysis, BMI and WC, but not WHR, were very strongly correlated with imaging-derived total and subcutaneous abdominal fat. By comparison, all three measures were moderately or strongly correlated with imaging-based visceral abdominal fat, with WC showing the greatest correlation. No anthropometric measure was substantially correlated with hepatic fat. Further larger studies are needed to compare these measures within the same study population, and to assess their relevance for disease risks in diverse populations.

Indexed as

Adipose TissueBody CompositionAdolescentAnthropometryBody Mass IndexDiagnostic ImagingFemaleHumansMaleObesityAdiposityAnthropometricCorrelationDXAImagingmeta-analysisMRI

Identifiers

PMID37710156
PMCPMC10503139
OpenAlexW4386741493

What Socratic holds

Textmetadata
LicenceTDM
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.