Evidence map›Paper›PMID 38050325›Full record

ArticleJournal of cachexia, sarcopenia and muscle2024

Using magnetic resonance imaging to measure head muscles: An innovative method to opportunistically determine muscle mass and detect sarcopenia.

Miguel German Borda, Gustavo Duque, Mario Ulises Pérez-Zepeda, Jonathan Patricio Baldera, Eric Westman, Anna Zettergren, Jessica Samuelsson, Silke Kern, Lina Rydén, Ingmar Skoog and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 5 citations in OpenAlex.

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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

11 authors at 8 institutions in 7 countries.

Miguel German BordaCentre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
Gustavo DuqueResearch Institute of the McGill University Health Centre, Montreal, Québec, Canada.
Mario Ulises Pérez-ZepedaInstituto Nacional de Geriatría, Dirección de Investigación, Ciudad de México, México.ORCID 0000-0003-4374-976X
Jonathan Patricio BalderaCentre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
Eric WestmanDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Anna ZettergrenInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Jessica SamuelssonInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Silke KernInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Lina RydénInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Ingmar SkoogInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Dag AarslandCentre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
University of Gothenburg · SESahlgrenska University Hospital · SEKarolinska Institutet · SEKing's College London · GBMcGill University Health Centre · CAPontificia Universidad Javeriana · COUniversidad Anáhuac · MXUniversidad Autónoma de Santo Domingo · DO

Funding

ALF from Swedish State ALFGBG-771071ALF from Swedish State ALFGBG-81392ALF from Swedish State ALFGBG-965923Alzheimerfonden AF-737641Alzheimerfonden AF-842471Alzheimerfonden AF-929959Alzheimerfonden AF-939825Helse Vest (Western Norway Regional Health Authority)King's College LondonNational Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation TrustStiftelsen DemensfondenStiftelsen Psykiatriska ForskningsfondenSwedish Research Council 2019-02075
6 · The paper itself

Abstract

backgroundSarcopenia is associated with multiple adverse outcomes. Traditional methods to determine low muscle mass for the diagnosis of sarcopenia are mainly based on dual-energy X-ray absorptiometry (DXA), whole-body magnetic resonance imaging (MRI) and bioelectrical impedance analysis. These tests are not always available and are rather time consuming and expensive. However, many brain and head diseases require a head MRI. In this study, we aim to provide a more accessible way to detect sarcopenia by comparing the traditional method of DXA lean mass estimation versus the tongue and masseter muscle mass assessed in a standard brain MRI.

methodsThe H70 study is a longitudinal study of older people living in Gothenburg, Sweden. In this cross-sectional analysis, from 1203 participants aged 70 years at baseline, we included 495 with clinical data and MRI images available. We used the appendicular lean soft tissue index (ALSTI) in DXA images as our reference measure of lean mass. Images from the masseter and tongue were analysed and segmented using 3D Slicer. For the statistical analysis, the Spearman correlation coefficient was used, and concordance was estimated with the Kappa coefficient.

resultsThe final sample consisted of 495 participants, of which 52.3% were females. We found a significant correlation coefficient between both tongue (0.26) and masseter (0.33) with ALSTI (P < 0.001). The sarcopenia prevalence confirmed using the alternative muscle measure in MRI was calculated using the ALSTI (tongue = 2.0%, masseter = 2.2%, ALSTI = 2.4%). Concordance between sarcopenia with masseter and tongue versus sarcopenia with ALSTI as reference has a Kappa of 0.989 (P < 0.001) for masseter and a Kappa of 1 for the tongue muscle (P < 0.001). Comorbidities evaluated with the Cumulative Illness Rating Scale were significantly associated with all the muscle measurements: ALSTI (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.07-1.26, P < 0.001), masseter (OR 1.16, 95% CI 1.07-1.26, P < 0.001) and tongue (OR 1.13, 95% CI 1.04-1.22, P = 0.002); the higher the comorbidities, the higher the probability of having abnormal muscle mass.

conclusionsALSTI was significantly correlated with tongue and masseter muscle mass. When performing the sarcopenia diagnostic algorithm, the prevalence of sarcopenia calculated with head muscles did not differ from sarcopenia calculated using DXA, and almost all participants were correctly classified using both methods.

Indexed as

SarcopeniaAgedCross-Sectional StudiesFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMuscle, SkeletalWhole Body ImagingdementiadiagnosisgeriatricsH70neurodegenerative disorderssarcopenia

Identifiers

PMID38050325
PMCPMC10834349
OpenAlexW4389343192

What OpenQuestion holds

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LicenceCC BY-NC
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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.