Validity of A-mode ultrasound for muscle assessments: Comparisons with magnetic resonance imaging

Validity of A-mode ultrasound for muscle assessments: Comparisons with magnetic resonance imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Validity of A-mode ultrasound for muscle assessments: Comparisons with magnetic resonance imaging Ana Paula Fayh, Rodrigo Albert Rüegg, Jarson P Costa-Pereira, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398687/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background & aims Skeletal muscle assessments are essential in clinical and research contexts. Amplitude mode ultrasound (A-mode ultrasound) offers a practical alternative, but comparisons with the gold standard (i.e., magnetic resonance imaging, MRI) remain underexplored. This study aimed to evaluate the validity of skeletal muscle assessment from A-mode ultrasound in comparison to MRI among healthy young adults. Methods Ninety-five physically active individuals (60% female, 29 ± 6 years) had biceps and quadriceps muscle thicknesses measured using the BodyMetrix® device (i.e., A-mode ultrasound). MRI scans at the third lumbar vertebrae level were used to estimate skeletal muscle area (SMA in cm2). Pearson’s correlations and linear regressions were used to assess correlations and associations, respectively. Results Biceps muscle thickness (BMT) showed a strong correlation with SMA from MRI (r = 0.82; R2 = 0.68, all p-values < 0.001), outperforming rectus femoris (r = 0.51, R2 0.26, p-value < 0.001) and vastus intermedius (r = 0.42, R2 0.18, p-value < 0.001). Conclusion Our results demonstrate the potential use of A-mode ultrasound for skeletal muscle assessments, particularly for the biceps area (i.e., measuring BMT), although with modest value for the thigh region. Our study supports the use of A-mode ultrasound due to its lower cost, portability and bed-side feasibility and potential performance against MRI. Health sciences/Risk factors Health sciences/Health care/Medical imaging body composition magnetic resonance imaging skeletal muscle ultrasound INTRODUCTION Body composition assessment is pivotal in both research and clinical settings, due to its relevance to overall health status, disease prevention, and treatment decision-making [ 1 ]. Among the various components of body composition, skeletal muscle has gained increasing attention due to its association with functional capacity, metabolic health, and prognosis in various conditions [ 2 , 3 ]. Reductions in muscle mass, for instance, are linked to worse adverse outcomes [ 4 – 7 ]. Thus, accurate measures of skeletal muscle tissue may support preventive and treatment decisions [ 8 – 11 ]. Ultrasound scans has been increasingly used for such purpose due to its portability, non-radiation exposure, and lower operational cost [ 12 , 13 ]. Different modes can be used for ultrasound measures, including B-mode (brightness mode), and A-mode (amplitude mode) [ 14 ]. B-mode scans provide two-dimensional images, commonly used for measuring muscle thickness and cross-sectional area [ 14 ], while A-mode devices offer one-dimensional signal based on the amplitude of echoes from tissue interfaces [ 14 ]. A-mode devices, such as the BodyMetrix™ [ 15 ], were initially “validated” to estimate body fat percentage [ 16 – 18 ]. However, despite its practicality and easy use, studies demonstrating BodyMetrix’s validity to assess skeletal muscle remain scarce. Our previous study using computed tomography (CT) scans as the reference method, in a sample of patients with cancer, we have demonstrated that muscle thickness measures from A-mode ultrasound had weak-to-moderate correlations with skeletal muscle area (SMA in cm 2 ) at the third lumbar vertebra (L3) [ 19 ]. These results are partially explained by the interval between CT scans and A-mode ultrasound measures (± 3 mo), reinforcing the importance of additional studies using more gold standard methods, such as magnetic resonance imaging (MRI). Our hypothesis is that muscle thickness measures from A-mode ultrasound can be a good surrogate for MRI. Thus, this study aimed to explore the potential validity of muscle thickness measures from A-mode ultrasound against MRI among subject individuals. METHODS Study design This was a single-center observational study with cross-sectional data collection. Adult volunteers without diagnosed diseases were eligible. Data collection occurred between July 2023 and November 2024. Participants were recruited through announcements on social media platforms and printed flyers distributed across the university campus at the Federal University of Rio Grande do Norte, Brazil. The study adhered to the ethical principles outlined in the Declaration of Helsinki [ 20 ] and national protocols (Resolution 466/2012 of the Ministry of Health) [ 21 ]. Local Research Ethics Committee approved the study (CAAE: 68852423.5.0000.5292). All individuals signed the informed consent form. Participants We included individuals aged between 20 and 45 years, without physical limitations preventing anthropometric or functional assessments, and with ability to comprehend and adhere to the study protocols. Individuals were excluded if they presented with chronic wasting conditions, or exhibited abnormal findings on MRI, including ascites, subcutaneous edema, or anatomical anomalies. Anthropometric assessment Body weight (kg) and height (m) were measured using a calibrated Filizola® scale equipped with an integrated stadiometer, and then used to calculate body mass index (BMI, expressed in kg/m 2 ). BMI categories followed the classification standards set by the World Health Organization (WHO): underweight (< 18.5 kg/m 2 ), normal range (18.5–24.9 kg/m 2 ), overweight (25.0-29.9 kg/m 2 ), and obesity (≥ 30.0 kg/m 2 ) [ 22 ]. Ultrasound evaluation Muscle thickness measurements for the biceps (BMT, mm), rectus femoris (RFMT, mm), and vastus intermedius (VIMT, mm) were obtained using A-mode ultrasound device (model: BodyMetrix BX-2000®, IntelaMetrix, Livermore, CA). A fixed 2.5 MHz probe was used for these analyses. Participants were in a supine position, and scanning protocols adhered to established anatomical landmarks. BMT was assessed at the midpoint between the anterior axillary fold and the cubital fossa, encompassing a scanning range 5 cm above and 5 cm below this midpoint. RFMT and VIMT were measured at the midpoint between the inguinal ligament and the superior border of the patella, covering an area extending 10 cm proximally and distally [ 23 ]. MRI scans MRI scans were conducted by a certified radiologic technologist specialized in magnetic resonance. Axial images were acquired at the level of the third lumbar vertebra (L3). All scans were saved in DICOM (Digital Imaging and Communications in Medicine) format and analyzed using SliceOmatic version 5.0 (TomoVision) by a single trained analyst with anatomical and radiological expertise. Anatomical segmentation was performed qualitatively, with tissue boundaries defined according to morphological features and signal intensity. Intensity thresholds were determined after visual inspection and exploratory analysis, respecting the inherent signal differences among tissue types. Statistics Data were analyzed using SPSS, version 20 (IBM® Inc., NY, USA). Continuous variables were described as mean ± standard deviation (SD) and compared using an independent Student’s t-test. Categorical variables were described as absolute ( N ) and relative (%) frequencies. Pearson’s correlation was used to assess the relationship between muscle from MRI and ultrasound. The strength of correlations was classified as follows: very strong (r = 0.90–1.00), strong (r = 0.70–0.90), moderate (r = 0.50–0.70), weak (r = 0.30–0.50), or negligible (r = 0.00–0.30). Subsequently, linear regression analysis was performed to determine the contribution of each variable in predicting the other, with β and R 2 coefficients reported. A bias-corrected and accelerated (BCa) bootstrapping method was applied to address assumptions. A significance level of 5% was used for all analyses. RESULTS A total of 95 individuals were included (60% females; mean age: 29 ± 6 years). Descriptive characteristics are presented in Table 1 . Table 2 shows sex-stratified analyses comparing nutritional characteristics (BMI, MRI, and ultrasound). Overall, males had significantly higher BMI values ( P < .001). In MRI assessments, they exhibited higher SMA. In ultrasound measurements, males had significantly higher BMT, RFMT, and VIMT (all P < .005). Table 1 Participants’ characteristics ( N = 95) Variables Data distribution Age, mean ± SD 28.6 ± 6.1 Sex, n (%) Males 38 (40.0) Females 57 (60.0) Self-reported skin color, n (%) White 45 (47.4) Non-white 50 (52.6) Marital status, n (%) No partner 66 (69.5) With partner 29 (30.5) Abbreviations: SD: standard deviation. Table 2 Nutritional characteristics by sex ( N = 95) Variables Males Females P BMI (kg/m 2 ) 27.5 ± 3.67 24.4 ± 3.87 .001 MRI results SMA (cm 2 ) 188.0 ± 34.0 113.1 ± 21.8 .001 Ultrasound results BMT (mm) 38.3 ± 6.29 25.1 ± 4.75 .001 RFMT (mm) 22.98 ± 4.05 19.90 ± 4.36 .003 VIMT (mm) 23.64 ± 5.23 20.77 ± 6.55 .030 Abbreviations: BMI: body mass index; BMT: biceps muscle thickness; MRI: magnetic resonance imaging; RFMT: rectus femoris muscle thickness; SMA: skeletal muscle cross-sectional area; VIMT: vastus intermedius muscle thickness. BMT from ultrasound demonstrated a strong correlation with SMA from MRI, while RFMT showed a moderate correlation, and VIMT a weak correlation (all P < .001, Table 3 ). Linear regression analyses (Table 3 ) demonstrated that, individually, BMT from ultrasound explained 68% of the variation in SMA from MRI (R 2 = 0.68), while RFMT and VIMT accounted for 26% (R 2 = 0.26) and 18% (R 2 = 0.18), respectively. The inclusion of sex and age as adjustment factors significantly increased the explanatory power of the models. For BMT, each 1 mm increase was independently associated with a 2.6 cm 2 increase in SMA from MRI. Table 3 Correlations and linear regression analysis: associations between muscle thickness from A-mode ultrasound with muscle cross-sectional area from MRI ( N = 95). Variables Coefficient (r) P R 2 Adjusted R 2 β (95% CI) P SMA from MRI BMT (mm) 0.82 < .001 .68 .76 2.60 (1.59;3.46) .001 RFMT (mm) 0.51 < .001 .26 .73 2.76 (1.23;4.55) .001 VIMT (mm) 0.42 < .001 .18 .71 1.69 (0.77;2.56) .001 Pearson’s correlation. All regression models (each line) were adjusted for age and sex. Abbreviations : BMT: biceps muscle thickness; MRI: magnetic resonance imaging; RFMT: rectus femoris muscle thickness; SMA: skeletal muscle cross-sectional area; VIMT: vastus intermedius muscle thickness. DISCUSSION Our study examined the relationship between muscle thickness from A-mode ultrasound and muscle cross-sectional area assessed by MRI. Our main findings demonstrated that BMT was more strongly correlated with MRI-derived skeletal muscle cross-sectional area, than thickness measurements taken at the thigh site, including RFMT and VIMT. To our knowledge, this is the first study to compare these two imaging modalities for skeletal muscle assessment. The superior correlations observed for the biceps may be partly explained by the anatomical simplicity of the biceps brachii compared with the quadriceps femoris group. Biceps brachii is a superficial, isolated muscle with fewer overlapping structures, producing clearer and more distinct ultrasound images that reduce technical artifacts and enhance measurement reliability [ 24 ]. In contrast, the quadriceps consists of multiple muscle bellies arranged in deeper layers [ 25 , 26 ], increasing the potential for signal interference and imaging variability. Additionally, lower subcutaneous adiposity in the upper arm, particularly among males, may have further improved ultrasound visualization, whereas excess adipose tissue in the thigh can attenuate ultrasound signals and degrade image quality [ 27 , 28 ]. These factors collectively help explain the weaker correlations observed in thigh-based measurements. Previous research described similar results, demonstrating that arm muscle thickness obtained with portable A-mode ultrasound is significantly associated with arm lean soft tissue measured by DXA, even after adjusting for sex and age, whereas lower-limb measurements (thigh and calf) did not remain significant predictors of leg lean soft tissue in adjusted models [ 29 ]. Our previous work in a cancer population also found that BMT, but not TMT, predicted poor survival outcomes [ 23 ]. Together, these findings reinforce the potential value of BMT as a practical bedside marker of skeletal muscle. Assessing the biceps region also offers practical advantages. As earlier mentioned, the site is easily accessible for ultrasound evaluation, particularly in clinical or field settings where full leg exposure or precise anatomical positioning may be difficult [ 30 , 31 ]. The strong association between BMT and SMA suggests that a simple, rapid arm measurement could provide a reliable estimate of global muscle mass, especially when advanced imaging technologies are not available [ 32 ]. This study has limitations to be acknowledged. Its cross-sectional design hindered the evaluation whether A-mode ultrasound can detect longitudinal changes in muscle following training, disease progression, or other interventions. Moreover, although the sample was sex-balanced, it consisted of healthy, physically active young adults, which may limit generalizability to clinical populations, older adults, or individuals with chronic conditions. Future longitudinal studies are needed to assess the sensitivity of A-mode ultrasound in detecting changes in skeletal muscle. Despite limitations, the use of MRI (i.e., the gold standard for muscle imaging) enhances the validity of our findings. Additionally, the inclusion of a relatively homogeneous sample of healthy young adults helped minimize variability related to comorbidities or age-related muscle loss. CONCLUSION Our study suggests that biceps muscle thickness measured with A-mode ultrasound may serve as a useful surrogate indicator of muscle cross-sectional area obtained from MRI, performing better than measurements taken at the thigh. Factors such as the biceps’ simpler anatomy, lower adiposity in the region, and ease of imaging access likely contribute to this stronger performance. These results indicate that biceps ultrasound could be a practical, lower-cost option for estimating skeletal muscle quantity in settings where more advanced imaging techniques are unavailable. Declarations Acknowledgments: The authors acknowledge the GEMEN ( Grupo de Estudos em Metabolismo Exercício e Nutrição ) group members for their collaboration. Author Contributions: APTF, RABR and JPCP contributed to the conception and design of the research; RABR, NAB, MKN, JOA and GOCM acquired data; JPCP contributed to the data analysis. RABR, JPCP and APTF wrote the manuscript. All the authors critically reviewed, interpreted, and approved the final version of the manuscript. Funding statement: RABR, NAB, MKN and JPCP were partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (Finance Code 001). APTF received a productivity scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq). The supporting sources have no involvement or restrictions on this publication. Conflict of interest JPCP has received travel support from Fresenius Kabi, travel fees for Speaker engagement from Danone Nutricia Brazil, and speaking honoraria from Prodiet Medical Nutrition. APTF receives a grant for research from Prodiet Medical Nutrition. References Holmes CJ, Racette SB. The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology. Nutrients 2021;13:2493. https://doi.org/10.3390/nu13082493. Fayh APT, de Sousa IM, Gonzalez MC. New insights on how and where to measure muscle mass. Curr Opin Support Palliat Care 2020;14:316–23. https://doi.org/10.1097/SPC.0000000000000524. Casey P, Alasmar M, McLaughlin J, Ang Y, McPhee J, Heire P, et al. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 14 Apr, 2026 Review # 2 received at journal 08 Apr, 2026 Reviewer # 2 agreed at journal 25 Mar, 2026 Review # 1 received at journal 08 Feb, 2026 Reviewer # 1 agreed at journal 02 Feb, 2026 Reviewers invited by journal 26 Jan, 2026 Editor assigned by journal 04 Jan, 2026 Submission checks completed at journal 04 Jan, 2026 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8398687","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580850959,"identity":"eb9ea71b-9ae7-42b5-b296-f5ea53891208","order_by":0,"name":"Ana Paula Fayh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYLCCCgaGBH4GxgdQbgIDM0EtZ4DKJBuYDUjUYnCAWC38s5sfMByoOZxnfCOZ8ePPHXYM/Ow5BsyFe3BrkbhzzIDhwLHDxWY3kpmlec8kM0j2vDFgnvEMjzU3EgyYP7AdTtx2I/8YM2MbM4PBDaAtPAdw65C/kf6B4cC/w4mbZySzMf5sq2ewJ6QFZCbDwbbDiRskktkYeNsOMxhIENBieCOn4MDBvvTEGWceg/xynEfizLOCwzPwaJG7kb7xwYFv1on97eAQq5bjb0/e+LgAjxYQAEo3Q1iMDQw8UBGCoA6uZRSMglEwCkYBBgAAHwZX8LOo14QAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Health Sciences Center of Porto Alegre","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"Paula","lastName":"Fayh","suffix":""},{"id":580850960,"identity":"c0ec25e5-0522-488a-9ac2-1292a70fdb6b","order_by":1,"name":"Rodrigo Albert Rüegg","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"Albert","lastName":"Rüegg","suffix":""},{"id":580850961,"identity":"c2158357-c471-4bea-980d-4e10d32ee6d0","order_by":2,"name":"Jarson P Costa-Pereira","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Jarson","middleName":"P","lastName":"Costa-Pereira","suffix":""},{"id":580850962,"identity":"584c48c6-551e-44f8-8176-3b2fe0f02d7f","order_by":3,"name":"Nithaela Bennemann","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Nithaela","middleName":"","lastName":"Bennemann","suffix":""},{"id":580850963,"identity":"ecc026ec-6ab2-4996-81f6-b3aaf830ae13","order_by":4,"name":"Galtieri Otavio de Medeiros","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Galtieri","middleName":"Otavio","lastName":"de Medeiros","suffix":""},{"id":580850964,"identity":"07bbd12f-3353-4ad6-a684-3fbcf4a83819","order_by":5,"name":"Janaína de Araújo","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Janaína","middleName":"","lastName":"de Araújo","suffix":""},{"id":580850965,"identity":"a242a287-6293-4dc3-b869-703a21d06eb4","order_by":6,"name":"Maria do Nascimento","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"do","lastName":"Nascimento","suffix":""}],"badges":[],"createdAt":"2025-12-18 20:20:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8398687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8398687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101751756,"identity":"291463c8-a176-4210-957a-8a63fdaa79c7","added_by":"auto","created_at":"2026-02-03 10:23:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":597870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8398687/v1/4bd21472-bef2-4c61-b5c1-d81a3574d1b5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Validity of A-mode ultrasound for muscle assessments: Comparisons with magnetic resonance imaging","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBody composition assessment is pivotal in both research and clinical settings, due to its relevance to overall health status, disease prevention, and treatment decision-making [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the various components of body composition, skeletal muscle has gained increasing attention due to its association with functional capacity, metabolic health, and prognosis in various conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Reductions in muscle mass, for instance, are linked to worse adverse outcomes [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, accurate measures of skeletal muscle tissue may support preventive and treatment decisions [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUltrasound scans has been increasingly used for such purpose due to its portability, non-radiation exposure, and lower operational cost [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Different modes can be used for ultrasound measures, including B-mode (brightness mode), and A-mode (amplitude mode) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. B-mode scans provide two-dimensional images, commonly used for measuring muscle thickness and cross-sectional area [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while A-mode devices offer one-dimensional signal based on the amplitude of echoes from tissue interfaces [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A-mode devices, such as the BodyMetrix\u0026trade; [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], were initially \u0026ldquo;validated\u0026rdquo; to estimate body fat percentage [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, despite its practicality and easy use, studies demonstrating BodyMetrix\u0026rsquo;s validity to assess skeletal muscle remain scarce.\u003c/p\u003e \u003cp\u003eOur previous study using computed tomography (CT) scans as the reference method, in a sample of patients with cancer, we have demonstrated that muscle thickness measures from A-mode ultrasound had weak-to-moderate correlations with skeletal muscle area (SMA in cm\u003csup\u003e2\u003c/sup\u003e) at the third lumbar vertebra (L3) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These results are partially explained by the interval between CT scans and A-mode ultrasound measures (\u0026plusmn;\u0026thinsp;3 mo), reinforcing the importance of additional studies using more gold standard methods, such as magnetic resonance imaging (MRI). Our hypothesis is that muscle thickness measures from A-mode ultrasound can be a good surrogate for MRI. Thus, this study aimed to explore the potential validity of muscle thickness measures from A-mode ultrasound against MRI among subject individuals.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a single-center observational study with cross-sectional data collection. Adult volunteers without diagnosed diseases were eligible. Data collection occurred between July 2023 and November 2024. Participants were recruited through announcements on social media platforms and printed flyers distributed across the university campus at the Federal University of Rio Grande do Norte, Brazil. The study adhered to the ethical principles outlined in the Declaration of Helsinki [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and national protocols (Resolution 466/2012 of the Ministry of Health) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Local Research Ethics Committee approved the study (CAAE: 68852423.5.0000.5292). All individuals signed the informed consent form.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe included individuals aged between 20 and 45 years, without physical limitations preventing anthropometric or functional assessments, and with ability to comprehend and adhere to the study protocols. Individuals were excluded if they presented with chronic wasting conditions, or exhibited abnormal findings on MRI, including ascites, subcutaneous edema, or anatomical anomalies.\u003c/p\u003e\n\u003ch3\u003eAnthropometric assessment\u003c/h3\u003e\n\u003cp\u003eBody weight (kg) and height (m) were measured using a calibrated Filizola\u0026reg; scale equipped with an integrated stadiometer, and then used to calculate body mass index (BMI, expressed in kg/m\u003csup\u003e2\u003c/sup\u003e). BMI categories followed the classification standards set by the World Health Organization (WHO): underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal range (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (\u0026ge;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eUltrasound evaluation\u003c/h3\u003e\n\u003cp\u003eMuscle thickness measurements for the biceps (BMT, mm), rectus femoris (RFMT, mm), and vastus intermedius (VIMT, mm) were obtained using A-mode ultrasound device (model: BodyMetrix BX-2000\u0026reg;, IntelaMetrix, Livermore, CA). A fixed 2.5 MHz probe was used for these analyses. Participants were in a supine position, and scanning protocols adhered to established anatomical landmarks. BMT was assessed at the midpoint between the anterior axillary fold and the cubital fossa, encompassing a scanning range 5 cm above and 5 cm below this midpoint. RFMT and VIMT were measured at the midpoint between the inguinal ligament and the superior border of the patella, covering an area extending 10 cm proximally and distally [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMRI scans\u003c/h3\u003e\n\u003cp\u003eMRI scans were conducted by a certified radiologic technologist specialized in magnetic resonance. Axial images were acquired at the level of the third lumbar vertebra (L3). All scans were saved in DICOM (Digital Imaging and Communications in Medicine) format and analyzed using SliceOmatic version 5.0 (TomoVision) by a single trained analyst with anatomical and radiological expertise. Anatomical segmentation was performed qualitatively, with tissue boundaries defined according to morphological features and signal intensity. Intensity thresholds were determined after visual inspection and exploratory analysis, respecting the inherent signal differences among tissue types.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS, version 20 (IBM\u0026reg; Inc., NY, USA). Continuous variables were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using an independent Student\u0026rsquo;s t-test. Categorical variables were described as absolute (\u003cem\u003eN\u003c/em\u003e) and relative (%) frequencies. Pearson\u0026rsquo;s correlation was used to assess the relationship between muscle from MRI and ultrasound. The strength of correlations was classified as follows: very strong (r\u0026thinsp;=\u0026thinsp;0.90\u0026ndash;1.00), strong (r\u0026thinsp;=\u0026thinsp;0.70\u0026ndash;0.90), moderate (r\u0026thinsp;=\u0026thinsp;0.50\u0026ndash;0.70), weak (r\u0026thinsp;=\u0026thinsp;0.30\u0026ndash;0.50), or negligible (r\u0026thinsp;=\u0026thinsp;0.00\u0026ndash;0.30). Subsequently, linear regression analysis was performed to determine the contribution of each variable in predicting the other, with β and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e coefficients reported. A bias-corrected and accelerated (BCa) bootstrapping method was applied to address assumptions. A significance level of 5% was used for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 95 individuals were included (60% females; mean age: 29\u0026thinsp;\u0026plusmn;\u0026thinsp;6 years). Descriptive characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows sex-stratified analyses comparing nutritional characteristics (BMI, MRI, and ultrasound). Overall, males had significantly higher BMI values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In MRI assessments, they exhibited higher SMA. In ultrasound measurements, males had significantly higher BMT, RFMT, and VIMT (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; characteristics (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData distribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.6 \u0026plusmn; 6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported skin color, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (47.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (52.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (30.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: SD: standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional characteristics by sex (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e188.0\u0026thinsp;\u0026plusmn;\u0026thinsp;34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e113.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUltrasound results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: BMI: body mass index; BMT: biceps muscle thickness; MRI: magnetic resonance imaging; RFMT: rectus femoris muscle thickness; SMA: skeletal muscle cross-sectional area; VIMT: vastus intermedius muscle thickness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMT from ultrasound demonstrated a strong correlation with SMA from MRI, while RFMT showed a moderate correlation, and VIMT a weak correlation (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Linear regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated that, individually, BMT from ultrasound explained 68% of the variation in SMA from MRI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.68), while RFMT and VIMT accounted for 26% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.26) and 18% (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18), respectively. The inclusion of sex and age as adjustment factors significantly increased the explanatory power of the models. For BMT, each 1 mm increase was independently associated with a 2.6 cm\u003csup\u003e2\u003c/sup\u003e increase in SMA from MRI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations and linear regression analysis: associations between muscle thickness from A-mode ultrasound with muscle cross-sectional area from MRI (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csub\u003eAdjusted\u003c/sub\u003e \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMA from MRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.60 (1.59;3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.76 (1.23;4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIMT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.69 (0.77;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePearson\u0026rsquo;s correlation. All regression models (each line) were adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: BMT: biceps muscle thickness; MRI: magnetic resonance imaging; RFMT: rectus femoris muscle thickness; SMA: skeletal muscle cross-sectional area; VIMT: vastus intermedius muscle thickness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study examined the relationship between muscle thickness from A-mode ultrasound and muscle cross-sectional area assessed by MRI. Our main findings demonstrated that BMT was more strongly correlated with MRI-derived skeletal muscle cross-sectional area, than thickness measurements taken at the thigh site, including RFMT and VIMT. To our knowledge, this is the first study to compare these two imaging modalities for skeletal muscle assessment.\u003c/p\u003e \u003cp\u003eThe superior correlations observed for the biceps may be partly explained by the anatomical simplicity of the biceps brachii compared with the quadriceps femoris group. Biceps brachii is a superficial, isolated muscle with fewer overlapping structures, producing clearer and more distinct ultrasound images that reduce technical artifacts and enhance measurement reliability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In contrast, the quadriceps consists of multiple muscle bellies arranged in deeper layers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], increasing the potential for signal interference and imaging variability. Additionally, lower subcutaneous adiposity in the upper arm, particularly among males, may have further improved ultrasound visualization, whereas excess adipose tissue in the thigh can attenuate ultrasound signals and degrade image quality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese factors collectively help explain the weaker correlations observed in thigh-based measurements. Previous research described similar results, demonstrating that arm muscle thickness obtained with portable A-mode ultrasound is significantly associated with arm lean soft tissue measured by DXA, even after adjusting for sex and age, whereas lower-limb measurements (thigh and calf) did not remain significant predictors of leg lean soft tissue in adjusted models [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our previous work in a cancer population also found that BMT, but not TMT, predicted poor survival outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Together, these findings reinforce the potential value of BMT as a practical bedside marker of skeletal muscle.\u003c/p\u003e \u003cp\u003eAssessing the biceps region also offers practical advantages. As earlier mentioned, the site is easily accessible for ultrasound evaluation, particularly in clinical or field settings where full leg exposure or precise anatomical positioning may be difficult [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The strong association between BMT and SMA suggests that a simple, rapid arm measurement could provide a reliable estimate of global muscle mass, especially when advanced imaging technologies are not available [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has limitations to be acknowledged. Its cross-sectional design hindered the evaluation whether A-mode ultrasound can detect longitudinal changes in muscle following training, disease progression, or other interventions. Moreover, although the sample was sex-balanced, it consisted of healthy, physically active young adults, which may limit generalizability to clinical populations, older adults, or individuals with chronic conditions. Future longitudinal studies are needed to assess the sensitivity of A-mode ultrasound in detecting changes in skeletal muscle. Despite limitations, the use of MRI (i.e., the gold standard for muscle imaging) enhances the validity of our findings. Additionally, the inclusion of a relatively homogeneous sample of healthy young adults helped minimize variability related to comorbidities or age-related muscle loss.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study suggests that biceps muscle thickness measured with A-mode ultrasound may serve as a useful surrogate indicator of muscle cross-sectional area obtained from MRI, performing better than measurements taken at the thigh. Factors such as the biceps\u0026rsquo; simpler anatomy, lower adiposity in the region, and ease of imaging access likely contribute to this stronger performance. These results indicate that biceps ultrasound could be a practical, lower-cost option for estimating skeletal muscle quantity in settings where more advanced imaging techniques are unavailable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors acknowledge the GEMEN (\u003cem\u003eGrupo de Estudos em Metabolismo Exerc\u0026iacute;cio e Nutri\u0026ccedil;\u0026atilde;o\u003c/em\u003e) group members for their collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e APTF, RABR and JPCP contributed to the conception and design of the research; RABR, NAB, MKN, JOA and GOCM acquired data; JPCP contributed to the data analysis. RABR, JPCP and APTF wrote the manuscript. All the authors critically reviewed, interpreted, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e RABR, NAB, MKN and JPCP were partially funded by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES), Brazil (Finance Code 001). APTF received a productivity scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq). The supporting sources have no involvement or restrictions on this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJPCP has received travel support from Fresenius Kabi, travel fees for Speaker engagement from Danone Nutricia Brazil, and speaking honoraria from Prodiet Medical Nutrition. APTF receives a grant for research from Prodiet Medical Nutrition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHolmes CJ, Racette SB. The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology. Nutrients 2021;13:2493. https://doi.org/10.3390/nu13082493.\u003c/li\u003e\n\u003cli\u003eFayh APT, de Sousa IM, Gonzalez MC. New insights on how and where to measure muscle mass. Curr Opin Support Palliat Care 2020;14:316\u0026ndash;23. https://doi.org/10.1097/SPC.0000000000000524.\u003c/li\u003e\n\u003cli\u003eCasey P, Alasmar M, McLaughlin J, Ang Y, McPhee J, Heire P, et al. The current use of ultrasound to measure skeletal muscle and its ability to predict clinical outcomes: a systematic review. J Cachexia Sarcopenia Muscle 2022;13:2298\u0026ndash;309. https://doi.org/10.1002/jcsm.13041.\u003c/li\u003e\n\u003cli\u003eAlves VA, Fayh APT, Queiroz SA, Gonzalez MC, de Sousa IM. 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J Cachexia Sarcopenia Muscle 2024;15:240\u0026ndash;54. https://doi.org/10.1002/jcsm.13397.\u003c/li\u003e\n\u003cli\u003eSousa IM, Burgel CF, Silva FM, Fayh APT. Prognostic Value of Isolated Sarcopenia or Malnutrition\u0026ndash;Sarcopenia Syndrome for Clinical Outcomes in Hospitalized Patients. Nutrients 2022;14:2207. https://doi.org/10.3390/nu14112207.\u003c/li\u003e\n\u003cli\u003eTrejo-Avila M, Bozada-Guti\u0026eacute;rrez K, Valenzuela-Salazar C, Herrera-Esquivel J, Moreno-Portillo M. Sarcopenia predicts worse postoperative outcomes and decreased survival rates in patients with colorectal cancer: a systematic review and meta-analysis. Int J Colorectal Dis 2021;36:1077\u0026ndash;96. https://doi.org/10.1007/s00384-021-03839-4.\u003c/li\u003e\n\u003cli\u003eLI R, XIA J, ZHANG X, GATHIRUA-MWANGI WG, GUO J, LI Y, et al. Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults. Med Sci Sports Exerc 2018;50:458\u0026ndash;67. https://doi.org/10.1249/MSS.0000000000001448.\u003c/li\u003e\n\u003cli\u003eLunt E, Ong T, Gordon AL, Greenhaff PL, Gladman JRF. The clinical usefulness of muscle mass and strength measures in older people: a systematic review. Age Ageing 2021;50:88\u0026ndash;95. https://doi.org/10.1093/ageing/afaa123.\u003c/li\u003e\n\u003cli\u003eRibeiro G, de Aguiar RA, Penteado R, Lisb\u0026ocirc;a FD, Raimundo JAG, Loch T, et al. A‐Mode Ultrasound Reliability in Fat and Muscle Thickness Measurement. J Strength Cond Res 2022;36:1610\u0026ndash;7. https://doi.org/10.1519/JSC.0000000000003691.\u003c/li\u003e\n\u003cli\u003eMar\u0026iacute;n Baselga R, Teigell-Mu\u0026ntilde;oz FJ, Porcel JM, Ramos L\u0026aacute;zaro J, Garc\u0026iacute;a Rubio S. Ultrasound for body composition assessment: a narrative review. Intern Emerg Med 2025;20:23\u0026ndash;34. https://doi.org/10.1007/s11739-024-03756-8.\u003c/li\u003e\n\u003cli\u003eFanet H. 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Clin Physiol Funct Imaging 2017;37:317\u0026ndash;21. https://doi.org/10.1111/cpf.12307.\u003c/li\u003e\n\u003cli\u003eSousa IM, Pereira JP da C, R\u0026uuml;egg RAB, Calado GCF, Xavier JG, Bennemann NA, et al. Comparing A‐mode ultrasound and computed tomography for assessing cancer‐related sarcopenia: A cross‐sectional study. Nutrition in Clinical Practice 2024. https://doi.org/10.1002/ncp.11234.\u003c/li\u003e\n\u003cli\u003eGANDEVIA B, TOVELL A. DECLARATION OF HELSINKI. Med J Aust 1964;2:320\u0026ndash;1.\u003c/li\u003e\n\u003cli\u003eConselho Nacional de Sa\u0026uacute;de (Brasil). Resolu\u0026ccedil;\u0026atilde;o n\u0026deg; 466, de 12 de dezembro de 2012. Bras\u0026iacute;lia: 2012.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. 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Magn Reson Med 2020;84:3146\u0026ndash;56. https://doi.org/10.1002/mrm.28360.\u003c/li\u003e\n\u003cli\u003eWest J, Romu T, Thorell S, Lindblom H, Berin E, Holm A-CS, et al. Precision of MRI-based body composition measurements of postmenopausal women. PLoS One 2018;13:e0192495. https://doi.org/10.1371/journal.pone.0192495.\u003c/li\u003e\n\u003cli\u003eYang T, Jin Y, Neogi A. Acoustic Attenuation and Dispersion in Fatty Tissues and Tissue Phantoms Influencing Ultrasound Biomedical Imaging. ACS Omega 2023;8:1319\u0026ndash;30. https://doi.org/10.1021/acsomega.2c06750.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nni O, Ruby L, Paverd C, Frauenfelder T, Rominger MB, Martin A. Confounders of Ultrasound Attenuation Imaging in a Linear Probe Using the Canon Aplio i800 System: A Phantom Study. Diagnostics 2024;14:271. https://doi.org/10.3390/diagnostics14030271.\u003c/li\u003e\n\u003cli\u003eFernandes LV, de Oliveira GB, Ripka WL, Chen XS, Andrade FCD, Vasques ACJ, et al. The use of portable A-mode ultrasound in appendicular lean mass measurements among older adults: a comparison study with dual-energy X-ray absorptiometry and handgrip strength. Eur J Clin Nutr 2025;79:136\u0026ndash;41. https://doi.org/10.1038/s41430-024-01521-w.\u003c/li\u003e\n\u003cli\u003eDuren DL, Sherwood RJ, Czerwinski SA, Lee M, Choh AC, Siervogel RM, et al. Body Composition Methods: Comparisons and Interpretation. J Diabetes Sci Technol 2008;2:1139\u0026ndash;46. https://doi.org/10.1177/193229680800200623.\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;nard MR, Becher JG, Harlaar J, Huijing PA, Jaspers RT. Anatomical information is needed in ultrasound imaging of muscle to avoid potentially substantial errors in measurement of muscle geometry. Muscle Nerve 2009;39:652\u0026ndash;65. https://doi.org/10.1002/mus.21287.\u003c/li\u003e\n\u003cli\u003eGazzotti S, Sassi R, Aparisi G\u0026oacute;mez MP, Guglielmi R, Vasilevska Nikodinovska V, Messina C, et al. Imaging of Body Composition. Semin Musculoskelet Radiol 2024;28:594\u0026ndash;609. https://doi.org/10.1055/s-0044-1788887.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"body composition, magnetic resonance imaging, skeletal muscle, ultrasound","lastPublishedDoi":"10.21203/rs.3.rs-8398687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground \u0026amp; aims\u003c/h2\u003e \u003cp\u003eSkeletal muscle assessments are essential in clinical and research contexts. Amplitude mode ultrasound (A-mode ultrasound) offers a practical alternative, but comparisons with the gold standard (i.e., magnetic resonance imaging, MRI) remain underexplored. This study aimed to evaluate the validity of skeletal muscle assessment from A-mode ultrasound in comparison to MRI among healthy young adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNinety-five physically active individuals (60% female, 29\u0026thinsp;\u0026plusmn;\u0026thinsp;6 years) had biceps and quadriceps muscle thicknesses measured using the BodyMetrix\u0026reg; device (i.e., A-mode ultrasound). MRI scans at the third lumbar vertebrae level were used to estimate skeletal muscle area (SMA in cm2). Pearson\u0026rsquo;s correlations and linear regressions were used to assess correlations and associations, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBiceps muscle thickness (BMT) showed a strong correlation with SMA from MRI (r\u0026thinsp;=\u0026thinsp;0.82; R2\u0026thinsp;=\u0026thinsp;0.68, all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001), outperforming rectus femoris (r\u0026thinsp;=\u0026thinsp;0.51, R2 0.26, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and vastus intermedius (r\u0026thinsp;=\u0026thinsp;0.42, R2 0.18, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results demonstrate the potential use of A-mode ultrasound for skeletal muscle assessments, particularly for the biceps area (i.e., measuring BMT), although with modest value for the thigh region. Our study supports the use of A-mode ultrasound due to its lower cost, portability and bed-side feasibility and potential performance against MRI.\u003c/p\u003e","manuscriptTitle":"Validity of A-mode ultrasound for muscle assessments: Comparisons with magnetic resonance imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 16:34:20","doi":"10.21203/rs.3.rs-8398687/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-04-14T08:53:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-08T10:11:21+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-25T04:01:27+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-08T18:09:48+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-03T01:21:35+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-27T03:36:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-04T15:03:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-04T15:01:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-12-18T20:16:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1e46d294-5f91-413a-aa76-65c9130f6096","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61789840,"name":"Health sciences/Risk factors"},{"id":61789841,"name":"Health sciences/Health care/Medical imaging"}],"tags":[],"updatedAt":"2026-05-10T13:05:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 16:34:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398687","identity":"rs-8398687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}