Evidence map›Paper›PMID 39562582›Full record

ArticleScientific reports2024

Development and validation of a new anthropometric equation to predict skeletal muscle mass in a heterogeneous caucasian population.

Daniel Rojano-Ortega, Heliodoro Moya-Amaya, Antonio Molina-López, Antonio Jesús Berral-Aguilar, Francisco José Berral-de la Rosa

Abstract readValidation Study
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Daniel Rojano-OrtegaCTS-595 Research Group, Department of Informatics and Sports, Universidad Pablo de Olavide, Carretera de Utrera km 1, 41013, Sevilla, Spain. drojort@upo.es.ORCID 0000-0002-4486-0040
Heliodoro Moya-AmayaCTS-595 Research Group, Department of Informatics and Sports, Universidad Pablo de Olavide, Carretera de Utrera km 1, 41013, Sevilla, Spain.
Antonio Molina-LópezCTS-595 Research Group, Department of Informatics and Sports, Universidad Pablo de Olavide, Carretera de Utrera km 1, 41013, Sevilla, Spain.
Antonio Jesús Berral-AguilarCTS-595 Research Group, Department of Informatics and Sports, Universidad Pablo de Olavide, Carretera de Utrera km 1, 41013, Sevilla, Spain.
Francisco José Berral-de la RosaCTS-595 Research Group, Department of Informatics and Sports, Universidad Pablo de Olavide, Carretera de Utrera km 1, 41013, Sevilla, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessment of skeletal muscle mass (SMM) is essential to monitor physical performance and health status. The most widely used anthropometric equations have repeatedly demonstrated to overestimate or underestimate SMM in different populations. Herein, we developed and cross-validated a new anthropometric regression equation for estimating SMM, using dual-energy X-ray absorptiometry (DXA) as the reference method. A group of 206 healthy Caucasian participants aged 18-65 years were included in the final analysis. Participants underwent a DXA scan, and body mass, stature, four skinfolds (biceps, triceps, subscapular, and supracrestal) and four breadths (femoral, humeral, ankle, and wrist) were assessed by an accredited anthropometrist. Accuracy was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland-Altman plots. The proposed equation explained 91.3% of the variance in the DXA-derived SMM percentage, with a low random error (SEE = 1.95%), and a very strong agreement (CCC = 0.94). In addition, it demonstrated no fixed or proportional bias and a relatively low individual variability (3.84%). The new anthropometric equation can accurately predict SMM percentage in a Caucasian population with a wide age range (18-65 years).

Indexed as

Absorptiometry, PhotonAnthropometryMuscle, SkeletalWhite PeopleAdolescentAdultAgedBody CompositionFemaleHumansMaleMiddle AgedYoung AdultBody compositionDXAHealthKinanthropometrySkeletal muscle mass

Identifiers

PMID39562582
PMCPMC11576900

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Registered trials

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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.