Evidence map›Paper›PMID 35974044›Full record

ArticleScientific reports2022

Cross-calibration of two dual-energy X-ray absorptiometry devices for the measurement of body composition in young children.

Jaz Lyons-Reid, Timothy Kenealy, Benjamin B Albert, Kate A Ward, Nicholas Harvey, Keith M Godfrey, Shiao-Yng Chan, Wayne S Cutfield

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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 at 4 institutions in 3 countries.

Jaz Lyons-ReidLiggins Institute, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
Timothy KenealyLiggins Institute, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
Benjamin B AlbertLiggins Institute, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
Kate A WardMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
Nicholas HarveyMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
Keith M GodfreyMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
Shiao-Yng ChanSingapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Wayne S CutfieldLiggins Institute, The University of Auckland, Private Bag 92019, Auckland, New Zealand. w.cutfield@auckland.ac.nz.
University of Auckland · NZUniversity Hospital Southampton NHS Foundation Trust · GBSingapore Institute for Clinical Sciences · SGUniversity of Southampton · GB

Funding

British Heart Foundation RG/15/17/3174Department of Health 17/63/154Department of Health IS-BRC-12-15-20004Department of Health NF-SI-0515-10042Medical Research Council G0400491Medical Research Council MC_PC_21001Medical Research Council MC_PC_21003Medical Research Council MC_PC_21022Medical Research Council MC_U147585819Medical Research Council MC_U147585824Medical Research Council MC_U147585827Medical Research Council MC_UP_A620_1014Medical Research Council MC_UU_12011/1Medical Research Council MC_UU_12011/4
6 · The paper itself

Abstract

This study aimed to cross-calibrate body composition measures from the GE Lunar Prodigy and GE Lunar iDXA in a cohort of young children. 28 children (mean age 3.4 years) were measured on the iDXA followed by the Prodigy. Prodigy scans were subsequently reanalysed using enCORE v17 enhanced analysis ("Prodigy enhanced"). Body composition parameters were compared across three evaluation methods (Prodigy, Prodigy enhanced, iDXA), and adjustment equations were developed. There were differences in the three evaluation methods for all body composition parameters. Body fat percentage (%BF) from the iDXA was approximately 1.5-fold greater than the Prodigy, whereas bone mineral density (BMD) was approximately 20% lower. Reanalysis of Prodigy scans with enhanced software attenuated these differences (%BF: - 5.2% [95% CI - 3.5, - 6.8]; and BMD: 1.0% [95% CI 0.0, 1.9]), although significant differences remained for all parameters except total body less head (TBLH) total mass and TBLH BMD, and some regional estimates. There were large differences between the Prodigy and iDXA, with these differences related both to scan resolution and software. Reanalysis of Prodigy scans with enhanced analysis resulted in body composition values much closer to those obtained on the iDXA, although differences remained. As manufacturers update models and software, researchers and clinicians need to be aware of the impact this may have on the longitudinal assessment of body composition, as results may not be comparable across devices and software versions.

Indexed as

Body CompositionBone DensityAbsorptiometry, PhotonCalibrationChildChild, PreschoolHumansReproducibility of ResultsSoftware

Identifiers

PMID35974044
PMCPMC9381538
OpenAlexW4292178426

What OpenQuestion 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 OpenQuestion graph.