Development of continuous assessment of muscle quality and frailty in older subjects using multi-parametric omics based on combined ultrasound and blood biomarkers: a study protocol for a cluster randomised controlled trial

Development of continuous assessment of muscle quality and frailty in older subjects using multi-parametric omics based on combined ultrasound and blood biomarkers: a study protocol for a cluster randomised controlled trial | 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 Research Article Development of continuous assessment of muscle quality and frailty in older subjects using multi-parametric omics based on combined ultrasound and blood biomarkers: a study protocol for a cluster randomised controlled trial Naiara Virto, Xabier Río, Garazi Angulo, Rafael García, Almudena Avendaño Céspedes, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2648138/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Frailty derived from muscle quality loss can potentially be delayed through early detection and physical exercise interventions. There is a need for affordable tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessment. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional and microstructural muscle properties, while biological essays derived from blood samples are associated with functional information. The aim of this study is to evaluate multi-parametric combinations of ultrasound and blood-based biomarkers to provide a cross-sectional evaluation of the patient frailty phenotype and to monitor muscle quality changes associated with supervised exercise programs. Methods This is a prospective observational multi-center study including patients older than 70 years with ability to give informed consent. We will recruit 100 patients from hospital environments and 100 from primary care facilities. At least two exams per patient (baseline and follow-up), with a total of (400 > 300) exams. In the hospital environments, 50 patients will be measured pre/post a 16-week individualized and supervised exercise programme, and 50 patients will be followed-up after the same period without intervention. The primary care patients will undergo a one-year follow-up evaluation. The primary goal is to compare cross-sectional evaluations of physical performance, functional capacity, body composition and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based essays. We will analyze ultrasound raw data obtained with a point-of-care device, and a set of biomarkers previously associated with frailty by quantitative Real time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA). Secondly, we will analyze the sensitivity of these biomarkers to detect short-term muscle quality changes as well as functional improvement after a supervised exercise intervention with respect to usual care. Discussion The presented study protocol will combine portable technologies based on quantitative muscle ultrasound and blood biomarkers for objective cross-sectional assessment of muscle quality in both hospital and primary care settings. It aims to provide data to investigate associations between biomarker combinations with cross-sectional clinical assessment of frailty and sarcopenia, as well as musculoskeletal changes after multicomponent physical exercise programs. Trial Registration ClinicalTrials.gov Identifier: NCT05294757. Date recorded: 24/03/2022. 'retrospectively registered’ muscle ultrasound blood-based biomarkers sarcopenia frailty older adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Aging results in a progressive loss of muscle mass and functional decline, which leads to an increase in the incidence and prevalence of chronic diseases [ 1 , 2 ], which leads to situations of multimorbidity [ 3 ] and has an impact on functional autonomy [ 4 ]. The most severe expression is the condition of frailty, “a progressive decline in physiological systems that results in decreased reserves of intrinsic capacity, which confers extreme vulnerability to stressors and increases the risk of a range of adverse health outcomes”[ 5 ]. Frailty is associated with dependency, hospitalization, institutionalization, falls, poor quality of life, and mortality [ 6 – 10 ] and with increased healthcare costs [ 11 , 12 ]. Standardized diagnostic criteria are lacking, but the two most accepted ones [ 13 , 14 ] are based on the phenotype construct [ 4 ], in which frailty is diagnosed if three or more of the following criteria are present: unintentional weight loss, self-reported exhaustion, decreased grip strength, slow gait speed and low physical activity. Additionally, the "Cumulative Deficit Model - Frailty Index", includes cognitive, functional, emotional and nutritional status [ 15 , 16 ]. The frailty phenotype described by Fried involves muscle dysfunction at its core [ 17 ]. Given that weakness, slowness and impairment of the muscular system are hallmarks of frailty, sarcopenia is likely to be a key physiopathological contributor. [ 13 , 18 ]. Sarcopenia is a progressive skeletal muscle disease, whose prevalence increases with age. Sarcopenia is estimated to affect between 6% and 19% of the general population ≥ 60 years of age, differing according to the definition applied [ 19 ]. Currently most used definitions are from the European Working Group on Sarcopenia in Older People (EWGSOP-2)[ 20 ], the Definition and Outcomes Consortium (DOCS)[ 21 ], and the National Institute of Health Foundation (NIHF)[ 22 ]. According to EWGSOP-2, reduced muscle strength is the first criterion of probable sarcopenia, while reduced muscle mass and quality confirms the diagnosis. In addition, when low physical performance is detected sarcopenia is graded as severe [ 20 ]. The DOCS agreed that both weakness defined by low grip strength and slowness defined by low usual gait speed should be included in the definition of sarcopenia [ 21 ]. NIHF defines sarcopenia as a loss of strength, diagnosed by low grip strength, together with low muscle mass [ 22 ]. To assess muscle mass and quality in clinical care, a semi-quantitative assessment is performed with two-dimensional images by Dual-energy X-ray absorptiometry (DXA) and with a body composition estimate with Bioelectrical impedance analysis (BIA)[ 20 ]. These techniques may however be confounded by other variables such as skeletal mass and large body mass index [ 23 ]. Radiological imaging allows full-scale three-dimensional mapping of muscle composition and microstructure. Magnetic resonance imaging (MRI) and computed tomography (CT) sequences have been proposed, which allow assessment of adipose fraction and fibrous microstructure, among others [ 24 – 26 ]. However, due to their high cost and potential patient complications, these methods are currently only applied in research or as a supplementary examination for a different primary indication [ 27 ]. Sarcopenia and frailty are related but distinct conditions related to aging. While sarcopenia is mainly based on the musculoskeletal system, frailty is a more multifactorial condition [ 28 , 29 ]. Different studies have shown that the prevalence of sarcopenia among frail older adults is higher than the prevalence of frailty among those with sarcopenia. [ 28 , 30 – 32 ]. The adverse outcomes associated with muscular decline can be prevented, delayed or even reversed by early detection and interventions including nutritional support and physical exercise programs [ 13 , 18 , 33 ]. However, there is a need for simple and reliable tools that allow the assessment of muscle quality and its impact on frailty [ 28 , 34 ]. Ultrasound is a fast, non-invasive and affordable imaging modality which is rapidly emerging for musculoskeletal examination [ 35 , 36 ]. Current clinical ultrasound images (B-mode) allow for assessment of muscle mass and morphology. Common features include measures of muscle thickness, pennation angle, cross-sectional area, echo intensity and fascicle length [ 37 – 39 ]. Despite ongoing efforts for standardization, these measurements are highly dependent on the expertise and skills of the operator and do not show definite results for early staging of muscle quality loss [ 37 , 40 ]. Ultrasound morphometric measurements of sarcopenia in older adults have shown mild to moderate associations with frailty [ 41 ]. More recently, several quantitative ultrasound techniques have emerged based on the analysis of echogenicity, texture parameters, elastography and acoustic wave properties, with still limited translation to clinical practice [ 42 – 51 ]. Artificial intelligence is bringing new opportunities to objectivize musculoskeletal ultrasound, with recent works demonstrating automatic muscle segmentation and fiber angle detection and textural discrimination of muscle microstructures [ 52 – 55 ]. Biological biomarkers are valuable tools in the diagnosis and stratification of patients as well as in the understanding of the underlying pathophysiology of the disease. Oxidative stress, pro-inflammatory state and immune aging are relevant in the relationship between nonspecific biomarkers and specific biological systems and frailty and sarcopenia [ 56 – 59 ]. Recent studies have addressed the complex interrelationships among the different systems underlying frailty through multi-omics approaches [ 60 ]. For instance, the FRAILOMIC initiative used blood samples to describe a set of biological biomarkers, both protective and risk factors. In particular, oxidative stress, vitamin D, and cardiovascular system were associated with frailty [ 61 ]. Despite these advances, currently available biomarkers are individually poorly associated with the clinical outcomes of sarcopenia and frailty and their capability to detect changes after physical intervention is largely unknown. Only a limited number of works have explored combinations of ultrasound and blood-based biomarkers. A study identified circulating biomarker changes corresponding to a short-term resistance exercise intervention in older adults, which were significantly related with ultrasound leg cross-sectional area [ 62 ]. Associations were identified between combined genetic and methylation scores and ultrasound-derived skeletal muscle morphometry in elderly women [ 63 ]. Another cross-sectional study related ultrasound characteristics of the quadriceps femoris in sarcopenic patients to blood and urinary biomarkers [ 64 ]. Overall, simple and objective screening tools to diagnose frailty and sarcopenia are lacking [ 28 , 34 ]. B-mode image clinical standardization is necessary, but there is also a need for advances in ultrasound technology to create quantitative indicators for evaluating muscle quality [ 65 , 66 ]. This study is designed to evaluate objective muscle quality assessment methods using quantitative analysis techniques based on the analysis of ultrasound raw data combined with blood-based biomarkers. In addition, this study will examine the capability of these biomarkers to detect muscle quality changes as a result of a physical exercise intervention program in frail elderly people. Thus, the main objective of this study is to evaluate the feasibility of combinations of point-of-care quantitative ultrasound parameters with blood-based essays for assessment of muscle quality and frailty in older adults in both hospital setting and community care with respect to clinical evaluation. Methods And Analysis Study setting Prospective, experimental, multi-center, three-cohort study, conducted at Complejo Hospitalario Universitario de Albacete, Spain (coordinating Clinical Research Ethics Committee - CEIC) (Hospital 1), Hospital Universitario de Getafe, Getafe, Spain (Hospital 2), and Primary Care Units of Donostialdea, Osakidetza, Spain (Primary Care Units). The design of this study protocol has followed the Standard Protocol Items of the Recommendations for Interventional Trials (SPIRIT) 2013 guideline (Fig. 1 ) (Fig. 2 ). Study population recruitment Hospital exercise cohort: Participants will be randomly recruited from the scheduled patient list of the falls unit and the outpatient clinics of Hospital 1. After informed consent acquisition, patients will receive a baseline clinical evaluation. Ultrasound measurements and DXA scan will be conducted, and blood samples will be collected, processed, and stored. Patients will be included in the 16-week multicomponent physical exercise program described below. After the completion of the exercise program, at 16-week follow-up, clinical evaluation, ultrasound, DXA, and blood-sample testing will be repeated. Hospital control cohort: Participants will be randomly recruited from the scheduled patient list of the frailty unit, day hospital, and the outpatient clinic of the Geriatrics Department of Hospital 2. After obtaining informed consent, baseline study variables will be collected. Patients will be followed-up for a 16-week period, under usual care. After the follow-up period, baseline evaluation will be repeated. Primary care cohort: Participants will be randomly recruited from the scheduled patient list of the Primary Care Units. After informed consent acquisition, baseline study variables will be collected. Patients will be followed-up during one year, under usual care. After the one-year follow-up, the baseline evaluation will be repeated. Inclusion criteria: Age of at least 70 years old. Either gender. Ability to provide informed consent. Ability to perform all the functional tests. In the hospital exercise cohort, ability to perform the physical exercise program. Exclusion criteria: Expected survival inferior to one year. Barthel scale < 70. Moderate to severe cognitive impairment. Refuse to participate. Medical conditions that may condition or difficult the follow-up assessments. Older adults already included in regular physical exercise programmes will be excluded to participate in the hospital exercise cohort. Termination criteria: Refuse to continue participation. Complications during or in-between examinations and intervention. Interventions: In the hospital exercise cohort, a 16-week supervised multicomponent physical exercise programme will be realized. The 16-week multicomponent exercise intervention will be individualized based on the functional capacity, comorbidities and previous experience of the subject. On a weekly basis, subjects will perform two supervised sessions of 45 minutes in small groups (4-to-6 older adults). The sessions are divided into warm-up, main part (strength, power and coordination exercises) and cool down (flexibility and stretching). The main part contains 8 exercises which work the main muscle groups, starting with low intensity and high-volume sessions and progressing to higher intensities and lower volumes. Depending on the stage, patients will perform 2–4 sets, 4–15 repetitions with 30 seconds rest between sets and exercises. For every exercise, loads and intensities will be modulated to allow a total of 30 repeats. The exercise sessions are to be performed in the gym of the Geriatrics Department of the hospital. Participants will be recommended to take additional 90-minute walks per week. Finally, it should be noted that the program is based on the guidelines of the VIVIFRAIL program [ 67 ] aimed at preventing weakness and the risk of falls. No intervention will be performed in hospital control and primary care cohorts. If patients need medical care that interferes with the correct conduct of the study, they will be treated according to the clinical routine and will be excluded from the study. Safety monitoring: Every principal investigator at each data acquisition center will monitor and follow-up of the participants included in the respective cohort under usual care, and accordingly decide on patient exclusion, assign participants to inventions and monitor termination criteria. Randomization and blinding: Data analysis will be performed independently from cohort recruitment and follow-up. Data evaluation will be performed at the Deusto Institute for Technology (Ultrasound Data Evaluation Center) and Biodonostia (Blood-based Essays Evaluation Center). Initially, only anonymized data - ultrasound images and raw data and blood-based essays - will be transferred to data evaluation centers. For combination models, a training set of clinical variables will be transferred to the data evaluation centers. Reserved datasets will be reserved at the clinical acquisition centers for independent testing, both including data from different sites and data acquired in follow-up examinations. Sample size: A minimum population of 100 participants will be acquired in the Primary care cohort, 50 participants in the hospital exercise cohort and 50 participants in the hospital control cohort (ratio 2:1.1). Within each acquisition site, patient selection will be performed based on the clinical agenda up to the completion of the recruitment goals. Two examinations (baseline and follow-up) will be performed per participant, with a minimum total of 400 examinations. A distribution of 85% frail − 15% robust patients in the hospital cohorts, and a distribution of 15% frail − 85% robust in the Primary care cohort is estimated. Accordingly, an accrual of 139 robust participants and 86 frail participants is expected. It is assumed that up to 10% participants will be excluded in data evaluation due to failed measurements or logistical impossibility to to collect all variables. For patients where follow-up measurements are not feasible, the baseline measurement will be still included in the evaluation of the primary outcomes. With an 80% power, the planned population will allow detecting a biomarker medium effect size of E/S = 0.39 with a two-sided/alpha level of 0.05. The sample size was calculated with RiskCalc [ 68 ]. Outcomes: Primary outcome: To analyze the association between quantitative ultrasound biomarkers associated to muscle mass and quality and extracted from raw data and blood-based biomarkers and combinations thereof with clinical variables including frailty, sarcopenia, physical function, disability, nutritional status, body composition, and Quality of Life in three cohorts of older adults: hospital control cohort, hospital exercise cohort, and primary care cohort. Secondary outcomes: In the hospital exercise cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables after a 16-week multicomponent physical exercise program, and to find associations between ultrasound and blood-based biomarkers with the rest of clinical variables before and after the exercise program. In the hospital control cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables, after a 16-week follow-up period without intervention, and to find associations between quantitative ultrasound and blood-based biomarkers with the rest of clinical variables after the 16-week follow-up period. In the primary care cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables after a one-year follow-up period without intervention, and to find associations between quantitative ultrasound and blood-based biomarkers with the rest of clinical variables after one-year follow-up. To analyze differences in the changes of all the measurements between the three cohorts. Both hospital cohorts will be compared directly, and the primary care cohort will be used to determine changes in non-hospital populations. Outcome measurements: The list of clinical variables collected in baseline and follow-up is included in Table 1 . Table 1 Clinical variables in baseline and follow-up examination Clinical variables - Global Deterioration Scale (GDS) of Reisberg for assessment of cognitive function. - Charlson Comorbidity Index. - Barthel index of independence to perform basic activities of daily living. - Lawton and Brody index of indepence to perform instrumental activities of daily living. - Short Physical Performance Battery (SPPB) for physical function assessment. - SARC-F scale of sarcopenia screening. - FRAIL scale of frailty evaluation. - Frailty phenotype of frailty. - MNA-SF® for nutritional screening. - IPAQ international questionnaire of physical activity. - EQ 5D-5L for health-related quality of life assessment. - Gait speed test for physical function assessment. - Grip strength with Jamar dynamometer. Ultrasound imaging and raw data acquisition: The ultrasound equipment is a L7 HD3 portable linear scanner (Clarius Mobile Health Corp., Vancouver, BC, Canada) for Point of Care Ultrasound (POCUS) examination. The probe has a frequency range 4–13 MHz with a center frequency of 7 MHz. Anonymized digital ultrasound data is streamed from the scanner through a custom Wi-Fi network to a smart device for real-time B-mode navigation and data storage. The scanner includes an image processing package to optimize B-mode musculoskeletal image quality. The scanner also includes a research package to acquire raw beamformed backscattering ultrasound data after the beamformer. Raw data is provided in an In-Phase/Quadrature complex baseband representation - also known as IQ data. Raw data is used as a basis for the implementation of customized Quantitative Ultrasound algorithms [ 69 ]. All examinations will be performed in a depth range 0–60 mm at 50% of the maximum acoustic output provided by the scanner. Measurements will be carried out with minimum necessary skin-probe compression for acceptable image quality. After examination, data will be uploaded to a HIPAA-compliant cloud service provided by the scanner’s manufacturer, which allows centralization of data (Fig. 3 ). Ultrasound examination protocol: Mid thigh will be identified and marked as the half distance between from the superior border of the patella to the antero-superior iliac crest. The midpoint of both thighs will be localized in transverse view with the help of the femur, and the different vastus of the quadriceps will be identified (rectus femoris, vastus intermedius, vastus medialis and vastus lateralis) (Fig. 4 ). A minimum of 12 co-registered B-scan and raw data frames will be acquired in each examination. Three transverse and three longitudinal views will be recorded for both thighs. Longitudinal examination will be performed in a plane where fasciculations are visible. Evaluation of quantitative ultrasound biomarkers: Morphometric ultrasound measures: During examination, the sonographer will register morphometric ultrasound measurements using the online ultrasound scanner’s interface, including (Fig. 5 ): thickness of rectus femoris in transverse view at mid-section point, cross-sectional surface of rectus femoris in transverse view, and pennation angle in longitudinal view. Raw data evaluation: Quantitative ultrasound biomarkers will be evaluated offline based on the raw IQ data acquired. Regions of interest (ROI)s comprising the rectus femoris will be defined in the raw data domain by automatically synthesizing B-mode images out of the raw data, and using the co-registered clinical B-mode images and sonographer annotations as a reference. Table 2 includes a list of state-of-the-art quantitative ultrasound biomarkers, which hyperparameters will be experimentally adapted to musculoskeletal examination and evaluated based on the raw data collected by the ultrasound probes. Table 2 Quantitative ultrasound biomarkers based on raw data. Biomarker Description Literature references Automatic morphometric measurements Automatic muscle morphometric analysis based on neural network segmentation models trained with respect to sonographer annotations of rectus femoris cross-section, and 2D Fourier analysis of pennation angle. [ 70 , 71 ]. Attenuation coefficient Measurement of loss of signal intensity with depth. [ 47 , 48 , 50 ] Backscattering coefficient Measurement of tissue reflectivity after attenuation compensation. [ 72 , 73 ] Power spectrum, Lizzi-Feleppa parameters Spectroscopy measurement of backscattered signal variation with frequency, including parametrizations such as spectral slope, spectral intercept and mid-band fit [ 49 ] Speckle statistics Fitting of raw envelope signal to speckle statistical distribution models, including Rayleigh, homodyned, Nakagami distributions. Estimation of scatterer concentration, spacing and coherence from fitted model parameters. [ 49 , 74 , 75 ] Statistical moments Non-parametric statistical moments capturing scatterer distribution and concentration, such as entropy, kurtosis, variance, anisotropy and signal-to-noise ratio. [ 76 , 77 ] Coherence, speed of sound Generalized Power Spectrum analysis and estimation of coherence, mean scatterer spacing and speed-of-sound in muscle. [ 78 , 79 ] Textural radiomics First and second-order texture features extracted from both B-mode (e.g., based on gray scale co-occurrence matrices) and raw data (e.g., based on wavelet and Laplacian transformations) and combined with machine learning models trained with respect to clinical outcomes. [ 51 , 53 , 54 ]. Artificial intelligence radiomics RF data and B-mode features extracted automatically with end-to-end neural network models trained with respect to clinical outcomes [ 52 , 80 – 82 ] Evaluation of blood-based biomarkers At basal and follow-up acquisitions, one 10 mL serum blood tube and two 5 mL EDTA blood tubes will be collected by venipuncture. The serum sample will be centrifuged and stored in four aliquots. One of the EDTA samples will be immediately frozen and stored at -80ºC, and the other one will be centrifuged for plasma and buffy coat extraction. Samples will be stored at -80ºC at the clinical sites, before transportation with dry ice to the Blood-Essay Evaluation Center for subsequent processing and storage (Fig. 6 ). The expression of previously described biomarkers such as Vitamin D, Lutein zeaxanthin, Troponin T, Pro-BNP, sRAGE [ 83 ], miRNAs, as well as some related to relevant pathways associated to frailty such as inflammation as Interleukin 6, senescence as p16 INK4A and p21 CIP [ 56 ] will be measured in cells obtained from a blood sample from a subsample of patients in basal condition and after intervention at transcriptional and protein level (Table 3 ). Erythrocytes will be lysed with Buffer EL (Qiagen) and total RNA from leukocytes will be isolated with the miRNeasy Mini Kit (Qiagen). First, RNA samples will be purified using the RNeasy Kit (Qiagen N.V., Hilden, Germany). Then, the RNA will be retro-transcribed and the expressions of genes will be determined by means of quantitative PCR (qPCR) using specific primers or probes and the equipment ABI Prism® SDS 7300 Real Time PCR System from Applied Biosystems. Expression levels will be standardized with those for expression of the enzyme Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) [ 84 ]. Additionally, Protein levels will be measured in serum samples with Quantikine ELISAs and Luminex as previously described [ 85 ]. Table 3 Blood-based biomarkers Biomarker Method of measurement Vitamin D ELISA Lutein zeaxanthin ELISA Troponin T ELISA Pro-BNP ELISA sRAGE ELISA microRNA 125 qRT-PCR microRNA 194 qRT-PCR microRNA 454 qRT-PCR IL6 ELISA and Luminex P16 INK4A qRT-PCR P21 CIP qRT-PCR New unpublished qRT-PCR and ELISA Statistical analysis Correlation of quantitative biomarkers will be assessed for each multi-factorial clinical evaluation parameter [ 86 ]. Concordance measurements (kappa for categorical variables and concordance correlation coefficient for continuous variables) will be used to test agreement between model variables. Unpaired tests (for instance, Student’s t-test) will be used for comparison of means of biomarkers between different acquired patient cohorts and stratified patient subgroups according to the frailty scales of Table 1 . Paired statistics will be used in statistical analysis for longitudinal monitoring both for interventions and clinical follow-up. For multi-parametric data science models, unsupervised learning models will be evaluated to cluster patient populations according to biomarker expression. Additionally, supervised multi-omics models will be trained with respect to a training set of clinical variables. Stratified cross-validation will be utilized to extract model performance statistics, with separate patient data in training and validation folds and a balanced distribution of robust and frail subjects in both training and validation. Random sample imputation will be used for missing data in training, and missing reference variables will be excluded from validation and testing. Reserve datasets, including data from different sites and follow-up examination data, will be used for model testing. Biomarker reproducibility will be assessed with inter-class correlation coefficient (ICC) and Bland-Altmann method based on repeated measurements within an examination. Discussion The expected outcomes of the present study are associations and combination models between quantitative ultrasound and blood-based essays with multi-factorial clinical assessment of muscle quality and frailty. Non-invasive portable technologies allow execution of the clinical protocol in both hospital and primary care environments [ 87 ]. Recent literature suggests that not only changes in skeletal muscle mass, but other factors underpinning muscle quality play a role in impaired mobility associated with aging. For instance, changes in muscle tissue composition, based on excessive levels of intramuscular adipose tissue and intramyocellular lipids have been found to adversely impact muscle functional capacity [ 17 , 65 , 88 ]. We hypothesize that quantitative ultrasound biomarkers for raw data may show superior discriminative performance, be more reproducible and easier to perform than current state-of-the-art assessment from B-mode images. B-mode morphological ultrasound parameters are primarily associated with muscle mass and show a limited sensitivity in the diagnosis of sarcopenia. Ultrasound quantitative biomarkers based on raw data have been previously generally shown to capture both tissue composition and microstructural properties, and have shown to encode a richer information content than ultrasound B-mode images in artificial intelligence models [ 52 , 54 , 81 , 82 ]. Ultrasound spectroscopy parameters and tissue acoustic properties such as speed-of-sound and attenuation have been linked to tissue composition [ 47 ] and viscoelastic changes in muscle [ 49 ] in elderly subjects with sarcopenia. Particularly, speed-of-sound showed correlations with MRI adiposity estimates in the calf muscles [ 89 ], CT assessment of the psoas muscle [ 90 ] and short-term changes in muscle due to immobilization [ 91 ]. Ultrasound statistical analysis of the envelope signal in soft tissues has been linked with concentration, spacing and directionality of microstructural scatterers [ 76 ]. Texture features based on radiomics analysis also capture musculoskeletal composition and microstructure, being applied to differentiate muscle spasticity, subjects with dynapenia, myositis, fibromyalgia, Duchenne muscular dystrophy, and exercise-induced muscle damage [ 55 , 92 ]. Being acquired at early stages of the ultrasound image formation, ultrasound raw data may also contribute to reducing equipment- and operator-dependent bias and facilitate data-based guidance for examiners with low sonographic acquisition expertise. Molecular biomarkers extracted from blood essays are associated with functional changes between robust and frail individuals, whereas there are currently no single established predictors biomarkers. This is mainly attributable to the heterogeneity and limitations of the scales and/or indices to detect sarcopenia and frailty, the different age, sex, and characteristics across different populations, small sample sizes, limited longitudinal clinical studies, no characterization on interventions to test their potential reversibility, or the different techniques and cut-offs used for biomarker measurement. In this study we include a panel of biomarkers rather than the assessment of individual molecules. This can contribute to better reflect the accumulation of damage associated with age-related syndromes [ 93 ]. Finally the potential of the biomarkers to evaluate reversibility, a critical characteristic of frailty, will be evaluated after the completion of an intervention based on an exercise program and a 16-week follow-up. Sarcopenia and frailty are related but distinct phenotypes of aging. The prevalence of sarcopenia among frail elderly people is higher than the frailty prevalence among sarcopenic [ 28 – 32 ]. As an exploratory outcome, we will take into account the coexistence of sarcopenia and frailty, redefining the frailty prognosis based on the presence or absence of sarcopenia. To our knowledge this cohort study is one of the few combining raw data ultrasound measurements with blood samples to extract non-invasive biomarkers of frailty and sarcopenia in older adults. This is the first cohort study to link quantitative ultrasound and blood biomarkers to cross-sectional evaluation of frailty and sarcopenia in both hospital and Primary care environments. This is the first study to combine quantitative ultrasound and blood-based biomarkers to assess musculoskeletal changes after multicomponent physical exercise programs. The study also has some limitations, the lack of access to a gold standard for muscle quality assessment. Established radiological techniques, such as CT and MRI, provide a reference to muscle composition and microstructure but are associated with patient complications and are not widely accessible in the investigated clinical environments. Instead we use a multi-factorial clinical assessment, patient stratification, and well-controlled interventions to assess muscle quality changes. The exploration of ultrasound technology is restricted to backscattering ultrasound data based on beamformed raw data. Other ultrasound quantitative technologies such as shear wave elastography [ 43 ] and blood flow measurements based on Doppler sequences [ 94 ] fall out of scope, since they are not routinely available in POCUS devices and add complexity to the protocol execution. The monitoring period in hospital (16 weeks) and primary care environments (1 year) is different to adapt to the different follow-up workflows in both environments. The primary care follow-up is dimensioned in agreement with periods defined in previous studies for the general population [ 95 – 96 ] . In conclusion, the presented study protocol will combine portable technologies based on quantitative muscle ultrasound and blood-based biomarkers for objective cross-sectional evaluation of muscle quality in both hospital and primary care environments. The study will provide data to investigate associations between biomarker combinations with cross-sectional clinical evaluation of frailty and sarcopenia, as well as musculoskeletal changes after multicomponent physical exercise programs. Trial Status: At the time of manuscript submission, the enrollment of volunteers is still ongoing. Recruitment started on 01/03/2022 and ends on 31/12/2023. Abbreviations Magnetic resonance imaging (MRI), computed tomography (CT) and Enzyme-linked immunosorbent assay (ELISA). Declarations Ethics and dissemination: This study protocol was approved by the Research Ethics Committee of Albacete (Spain) with reference CEIm-2021-51. For the other research sites, ethical approval was obtained from the local Ethics Committees of Getafe, IIS Biodonostia (CEIC-PI2022069), and Deusto. The study will be conducted in accordance with the principles of the Declaration of Helsinki [97]. Written informed consent will be obtained from all participants, and their data will be managed according to HIPAA guidelines. Consent for publication: Not applicable. Availability of data and materials: The results will be published in an academic journal. At least two interdisciplinary workshops with geriatrics and imaging specialists will be organized. We will publish digitalized datasets, including ultrasound data and biomarkers, digitized blood-based biomarkers and multi-factorial clinical evaluation in open-access repositories (e.g., Zenodo). Biological samples will be stored in the Basque Biomarker Center (Biobanco). Software models derived from the ultrasound raw data will be made available in open-source software repositories (e.g., GitLab). Data management: The study information will be stored in Microsoft Excel 365 in an electronic database. The database will record all subject data to include the baseline characteristics, pre- and post-assessments, and possible adverse events.The database will only be accessible to the study investigators. To ensure the confidentiality of the data, all subjects will be provided with identification numbers. All researchers will have access to the final trial data. Competing interest statement: The authors declare they have no competing interests. Funding: This project has received a public grant for its development in the call for RIS3 Basque Government Health Department (project numbers: 2021333036). Funded by the University of Deusto through a grant from the researcher education program (Ref: FPI UD_2022_10). The funders had no role in the design of this study and will not have any role during its execution, analyses, interpretation of data, or submission of outcomes. Authors’ contributions: SJS and AC contributed to study conceptualization. XR, NV, GA, RG, AA, EBC, EG, PAS, LRM, AMF, IV, AC and SJS contributed to the methodology and investigation. XR, NV, GA, RG, AC, RA, AC, UL, MS, LA and SJS contributed to the writing of the original draft. SJS, AC, PAS, LRM, IV, AC and SJS contributed to the project administration. RG, AA, EBC and EG contributed to the data curation. PAS, LRM, AC and SJS contributed to study supervision. All authors have read, provided feedback, and agreed to the final version of the manuscript for publication. Acknowledgements: Not applicable. References Roser M, Ortiz-Ospina E, Ritchie H. Life expectancy. Our world in data 2013 López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-1217. https://doi.org/10.1016/j.cell.2013.05.039. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing research reviews. 2011;10(4):430-439. https://doi.org/10.1016/j.arr.2011.03.003. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001;56(3):146-157. https://doi.org/10.1093/gerona/56.3.M146. Rodríguez-Laso A, Caballero Mora MA, García Sánchez I, Alonso Bouzón C, Rodríguez Mañas L, Bernabei R, et al. Updated state of the art report on the prevention and management of frailty. European Union. 2019. Hoogendijk EO, Romero L, Sánchez-Jurado PM, Ruano TF, Viña J, Rodríguez-Mañas L, et al. A new functional classification based on frailty and disability stratifies the risk for mortality among older adults: the FRADEA study. Journal of the American Medical Directors Association. 2019;20(9):1105-1110. https://doi.org/10.1016/j.jamda.2019.01.129. Kojima G. Frailty as a predictor of future falls among community-dwelling older people: a systematic review and meta-analysis. Journal of the American Medical Directors Association. 2015;16(12):1027-1033.https://doi.org/10.1016/j.jamda.2015.06.018 Kojima G, Iliffe S, Jivraj S, Walters K. Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):716-721. http://dx.doi.org/10.1136/jech-2015-206717 Kojima G. Frailty as a predictor of hospitalisation among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):722-729. http://dx.doi.org/10.1136/jech-2015-206978 Kojima G. Frailty as a predictor of nursing home placement among community-dwelling older adults: a systematic review and meta-analysis. Journal of geriatric physical therapy. 2018;41(1):42-48.https://doi.org/10.1519/JPT.0000000000000097 Kojima G. Increased healthcare costs associated with frailty among community-dwelling older people: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2019;84:103898. https://doi.org/10.1016/j.archger.2019.06.003 García-Nogueras I, Aranda-Reneo I, Peña-Longobardo LM, Oliva-Moreno J, Abizanda P. Use of health resources and healthcare costs associated with frailty: the FRADEA study. J Nutr Health Aging. 2017;21(2):207-214. https://doi.org/10.1007/s12603-016-0727-9 Chen X, Mao G, Leng SX. Frailty syndrome: an overview. Clinical interventions in aging. 2014;9:433 https://doi.org/10.2147%2FCIA.S45300 Faller JW, Pereira DdN, de Souza S, Nampo FK, Orlandi FdS, Matumoto S. Instruments for the detection of frailty syndrome in older adults: a systematic review. PloS one. 2019;14(4):e0216166. https://doi.org/10.1371/journal.pone.0216166 Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. TheScientificWorldJournal.2001;1:323-336. https://doi.org/10.1100/tsw.2001.58 Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489-495. https://doi.org/10.1503/cmaj.050051 Bartley JM, Studenski SA. Muscle ultrasound as a link to muscle quality and frailty in the clinic. J Am Geriatr Soc. 2017;65(12):2562-2563. https://doi.org/10.1111/jgs.15075 Landi F, Calvani R, Cesari M, Tosato M, Martone AM, Bernabei R, et al. Sarcopenia as the biological substrate of physical frailty. Clin Geriatr Med. 2015;31(3):367-374. https://doi.org/10.1016/j.cger.2015.04.005 Cruz-Jentoft AJ, Landi F, Schneider SM, Zúñiga C, Arai H, Boirie Y, et al. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing. 2014;43(6):748-759. https://doi.org/10.1093/ageing/afu115 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31.https://doi.org/10.1093/ageing/afy169 Bhasin S, Travison TG, Manini TM, Patel S, Pencina KM, Fielding RA, et al. Sarcopenia definition: the position statements of the sarcopenia definition and outcomes consortium. J Am Geriatr Soc. 2020;68(7):1410-1418. https://doi.org/10.1111/jgs.16372 Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2014;69(5):547-558. https://doi.org/10.1093/gerona/glu010 Gonzalez MC, Barbosa-Silva TG, Heymsfield SB. Bioelectrical impedance analysis in the assessment of sarcopenia. Current Opinion in Clinical Nutrition & Metabolic Care. 2018;21(5):366-374. https://doi.org/10.1097/mco.0000000000000496 Huber FA, Del Grande F, Rizzo S, Guglielmi G, Guggenberger R. MRI in the assessment of adipose tissues and muscle composition: how to use it. Quantitative Imaging in Medicine and Surgery. 2020;10(8):1636. https://doi.org/10.21037%2Fqims.2020.02.06 Giraudo C, Cavaliere A, Lupi A, Guglielmi G, Quaia E. Established paths and new avenues: a review of the main radiological techniques for investigating sarcopenia. Quantitative imaging in medicine and surgery. 2020;10(8):1602. https://doi.org/10.21037%2Fqims.2019.12.15 Grimm A, Nickel MD, Chaudry O, Uder M, Jakob F, Kemmler W, et al. Feasibility of Dixon magnetic resonance imaging to quantify effects of physical training on muscle composition—a pilot study in young and healthy men. Eur J Radiol. 2019;114:160-166. https://doi.org/10.1016/j.ejrad.2019.03.019 Sergi G, Trevisan C, Veronese N, Lucato P, Manzato E. Imaging of sarcopenia. Eur J Radiol. 2016;85(8):1519-1524.https://doi.org/10.1016/j.ejrad.2016.04.009 Davies B, García F, Ara I, Artalejo FR, Rodriguez-Mañas L, Walter S. Relationship between sarcopenia and frailty in the toledo study of healthy aging: a population based cross-sectional study. Journal of the American Medical Directors Association. 2018;19(4):282-286. https://doi.org/10.1016/j.jamda.2017.09.014 Reijnierse EM, Trappenburg MC, Blauw GJ, Verlaan S, de van der Schueren, Marian AE, Meskers CG, et al. Common ground? The concordance of sarcopenia and frailty definitions. Journal of the American Medical Directors Association. 2016;17(4):371-12. https://doi.org/10.1016/j.jamda.2016.01.013 Mijnarends DM, Schols JM, Meijers JM, Tan FE, Verlaan S, Luiking YC, et al. Instruments to assess sarcopenia and physical frailty in older people living in a community (care) setting: similarities and discrepancies. Journal of the American Medical Directors Association. 2015;16(4):301-308. https://doi.org/10.1016/j.jamda.2014.11.011 Nishiguchi S, Yamada M, Fukutani N, Adachi D, Tashiro Y, Hotta T, et al. Differential association of frailty with cognitive decline and sarcopenia in community-dwelling older adults. Journal of the American Medical Directors Association. 2015;16(2):120-124. https://doi.org/10.1016/j.jamda.2014.07.010 Spira D, Buchmann N, Nikolov J, Demuth I, Steinhagen-Thiessen E, Eckardt R, et al. Association of low lean mass with frailty and physical performance: a comparison between two operational definitions of sarcopenia—data from the Berlin Aging Study II (BASE-II). Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2015;70(6):779-784. https://doi.org/10.1093/gerona/glu246 Casas-Herrero Á, de Asteasu MLS, Antón-Rodrigo I, Sánchez-Sánchez JL, Montero-Odasso M, Marín-Epelde I, et al. Effects of Vivifrail multicomponent intervention on functional capacity: a multicentre, randomized controlled trial. Journal of cachexia, sarcopenia and muscle. 2022;13(2):884-893. https://doi.org/10.1002/jcsm.12925 Oviedo-Briones M, Laso ÁR, Carnicero JA, Cesari M, Grodzicki T, Gryglewska B, et al. A comparison of frailty assessment instruments in different clinical and social care settings: the frailtools project. Journal of the American Medical Directors Association. 2021;22(3):607-12. https://doi.org/10.1016/j.jamda.2020.09.024 Patil P, Dasgupta B. Role of diagnostic ultrasound in the assessment of musculoskeletal diseases. Therapeutic advances in musculoskeletal disease. 2012;4(5):341-355.https://doi.org/10.1177/1759720X12442112 Sconfienza LM, Albano D, Allen G, Bazzocchi A, Bignotti B, Chianca V, et al. Clinical indications for musculoskeletal ultrasound updated in 2017 by European Society of Musculoskeletal Radiology (ESSR) consensus. Eur Radiol. 2018;28(12):5338-5351. https://doi.org/10.1007/s00330-018-5474-3 Perkisas S, Baudry S, Bauer J, Beckwée D, De Cock A, Hobbelen H, et al. Application of ultrasound for muscle assessment in sarcopenia: towards standardized measurements. European geriatric medicine. 2018;9(6):739-757. https://doi.org/10.1007/s41999-018-0104-9 Ramírez-Fuentes C, Mínguez-Blasco P, Ostiz F, Sánchez-Rodríguez D, Messaggi-Sartor M, Macías R, et al. Ultrasound assessment of rectus femoris muscle in rehabilitation patients with chronic obstructive pulmonary disease screened for sarcopenia: correlation of muscle size with quadriceps strength and fat-free mass. European geriatric medicine. 2019;10(1):89-97.https://doi.org/10.1007/s41999-018-0130-7 Neira M, Ramírez R, Romero L, et al. Description and preliminary results from Project ECOSARC: Sarcopenia measured with echography in hospitalized elderly. Ticinesi A, Meschi T, Narici MV, Lauretani F, Maggio M. Muscle ultrasound and sarcopenia in older individuals: a clinical perspective. Journal of the American Medical Directors Association. 2017;18(4):290-300. https://doi.org/10.1016/j.jamda.2016.11.013 Benton E, Liteplo AS, Shokoohi H, et al. A pilot study examining the use of ultrasound to measure sarcopenia, frailty and fall in older patients, Am J Emerg Med. 2021;46:310-6. https://doi.org/10.1016/j.ajem.2020.07.081 Harris-Love MO, Monfaredi R, Ismail C, Blackman MR, Cleary K. Quantitative ultrasound: measurement considerations for the assessment of muscular dystrophy and sarcopenia. Frontiers in Aging Neuroscience. 2014;6:172. https://doi.org/10.3389/fnagi.2014.00172 Özçakar L, Ata AM, Kaymak B, Kara M, Kumbhare D. Ultrasound imaging for sarcopenia, spasticity and painful muscle syndromes. Current opinion in supportive and palliative care. 2018;12(3):373-381. https://doi.org/10.1097/spc.0000000000000354 Matsumoto H, Tanimura C, Tanishima S, Hagino H. Association between speed of sound of calcaneal bone assessed by quantitative ultrasound and sarcopenia in a general older adult population: A cross-sectional study. Journal of Orthopaedic Science. 2019;24(5):906-911. https://doi.org/10.1016/j.jos.2019.01.003 Sanabria SJ, Martini K, Freystätter G, Ruby L, Goksel O, Frauenfelder T, et al. Speed of sound ultrasound: a pilot study on a novel technique to identify sarcopenia in seniors. Eur Radiol.2019;29(1):3-12.https://doi.org/10.1007/s00330-018-5742-2 Ou L, Chang Y, Chang C, Chiu C, Chao T, Sun Z, et al. Epidemiological survey of the feasibility of broadband ultrasound attenuation measured using calcaneal quantitative ultrasound to predict the incidence of falls in the middle aged and elderly. BMJ open. 2017;7(1):e013420.http://dx.doi.org/10.1136/bmjopen-2016-013420 Berger G, Laugier P, Leroy A, Fink M, Roucayrol JC, Perrin J. Correlation between ultrasound attenuation in muscle and pathological fatty infiltration. Ultrason Imaging. 1987;9(1):66. Shore D, Miles CA. Experimental estimation of the viscous component of ultrasound attenuation in suspensions of bovine skeletal muscle myofibrils. Ultrasonics. 1988;26(1):31-36. https://doi.org/10.1016/0041-624X(88)90046-7 Thomson H, Yang S, Cochran S. Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics. 2022;49(4):517-528. https://doi.org/10.1007/s10396-022-01230-6 Weng W, Lin C, Shen H, Chang C, Tsui P. Instantaneous frequency as a new approach for evaluating the clinical severity of Duchenne muscular dystrophy through ultrasound imaging. Ultrasonics. 2019;94:235-241.https://doi.org/10.1016/j.ultras.2018.09.004 Paris MT, Mourtzakis M. Muscle composition analysis of ultrasound images: a narrative review of texture analysis. Ultrasound Med Biol. 2021;47(4):880-895. https://doi.org/10.1016/j.ultrasmedbio.2020.12.012 Shin Y, Yang J, Lee YH, Kim S. Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography. 2021;40(1):30. https://doi.org/10.14366%2Fusg.20080 Zhang Y, Du G, Zhan Y, Guo K, Zheng Y, Tang L, et al. Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images: A Cohort Data Study. IEEE Transactions on Biomedical Engineering. 2022;69(10):3163-3174. https://doi.org/10.1109/TBME.2022.3162223 Liu P, Wei T, Ching CT. Quantitative ultrasound texture analysis to assess the spastic muscles in stroke patients. Applied Sciences. 2020;11(1):11. https://doi.org/10.3390/app11010011 Yang K, Liao Y, Chang K, Huang K, Han D. The quantitative skeletal muscle ultrasonography in elderly with dynapenia but not sarcopenia using texture analysis. Diagnostics. 2020;10(6):400. https://doi.org/10.3390/diagnostics10060400 Álvarez-Satta M, Berna-Erro A, Carrasco-Garcia E, Alberro A, Saenz-Antoñanzas A, Vergara I, et al. Relevance of oxidative stress and inflammation in frailty based on human studies and mouse models. Aging (Albany NY). 2020;12(10):9982. https://doi.org/10.18632%2Faging.103295 Cardoso AL, Fernandes A, Aguilar-Pimentel JA, et al. Towards frailty biomarkers: candidates from genes and pathways regulated in aging and age-related diseases, Ageing research reviews. 2018;47:214-77. https://doi.org/10.1016/j.arr.2018.07.004 Kwak JY, Hwang H, Kim S, Choi JY, Lee S, Bang H, et al. Prediction of sarcopenia using a combination of multiple serum biomarkers. Scientific reports. 2018;8(1):1-7. https://doi.org/10.1038/s41598-018-26617-9 Picca A, Calvani R, Cesari M, Landi F, Bernabei R, Coelho-Júnior HJ, et al. Biomarkers of physical frailty and sarcopenia: Coming up to the place? International Journal of Molecular Sciences. 2020;21(16):5635. https://doi.org/10.3390/ijms21165635 Duan X, Wang B, Zhu J, Shao W, Wang H, Shen J, et al. Assessment of patient-based real-time quality control algorithm performance on different types of analytical error. Clinica Chimica Acta. 2020;511:329-335. https://doi.org/10.1016/j.cca.2020.10.006 Gomez-Cabrero D, Walter S, Abugessaisa I, Miñambres-Herraiz R, Palomares LB, Butcher L, et al. A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. Geroscience. 2021;43(3):1317-1329. https://doi.org/10.1007/s11357-021-00334-0 Fragala MS, Jajtner AR, Beyer KS, Townsend JR, Emerson NS, Scanlon TC, et al. Biomarkers of muscle quality: N-terminal propeptide of type III procollagen and C-terminal agrin fragment responses to resistance exercise training in older adults. Journal of cachexia, sarcopenia and muscle. 2014;5(2):139-148. https://doi.org/10.1007/s13539-013-0120-z He L, Khanal P, Morse CI, Williams A, Thomis M. Associations of combined genetic and epigenetic scores with muscle size and muscle strength: a pilot study in older women. Journal of cachexia, sarcopenia and muscle. 2020;11(6):1548-1561. https://doi.org/10.1002/jcsm.12585 Fuentes-Abolafio IJ, Ricci M, Bernal-López MR, Gómez-Huelgas R, Cuesta-Vargas AI, Pérez-Belmonte LM. Biomarkers and the quadriceps femoris muscle architecture assessed by ultrasound in older adults with heart failure with preserved ejection fraction: a cross-sectional study, Aging Clinical and Experimental Research. 2022;34:2493-2504. https://doi.org/10.1007/s40520-022-02189-7 Correa-de-Araujo R, Harris-Love MO, Miljkovic I, Fragala MS, Anthony BW, Manini TM. The need for standardized assessment of muscle quality in skeletal muscle function deficit and other aging-related muscle dysfunctions: a symposium report. Frontiers in physiology. 2017;8:87. https://doi.org/10.3389/fphys.2017.00087 Wang J, Wu W, Chang K, Chen L, Chi S, Kara M, et al. Ultrasound Imaging for the Diagnosis and Evaluation of Sarcopenia: An Umbrella Review. Life. 2021;12(1):9. https://doi.org/10.3390/life12010009 Izquierdo M. Multicomponent physical exercise program: Vivifrail, Nutricion Hospitalaria. 2019;36:50-6. https://doi.org/10.20960/nh.02680 Wang X, Ji X. Sample size estimation in clinical research: from randomized controlled trials to observational studies, Chest. 2020;158(1):12-20. https://doi.org/10.1016/j.chest.2020.03.010 Mamou J, Oelze ML. Quantitative ultrasound in soft tissues: Springer. 2013; 443-639. Jabbar SI, Day C, Chadwick E. Automated measurements of morphological parameters of muscles and tendons. Biomedical Physics & Engineering Express. 2021;7(2):025002. https://doi.org/10.1088/2057-1976/abd3de Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, et al. No title. Deep learning in medical ultrasound analysis: a review.Engineering. 2019; 5 (2): 261–75. Roy B, Darras BT, Zaidman CM, Wu JS, Kapur K, Rutkove SB. Exploring the relationship between electrical impedance myography and quantitative ultrasound parameters in Duchenne muscular dystrophy. Clinical Neurophysiology. 2019;130(4):515-520. https://doi.org/10.1016/j.clinph.2019.01.018 Young H, Jenkins NT, Zhao Q, Mccully KK. Measurement of intramuscular fat by muscle echo intensity. Muscle Nerve. 2015;52(6):963-971. https://doi.org/10.1002/mus.24656 Nillesen MM, Lopata RG, Gerrits IH, Kapusta L, Thijssen JM, de Korte CL. Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images. Ultrasound Med Biol. 2008;34(4):674-680. https://doi.org/10.1016/j.ultrasmedbio.2007.10.008 Harris‐Love MO, Gonzales TI, Wei Q, et al. Association between muscle strength and modeling estimates of muscle tissue heterogeneity in young and old adults, Journal of Ultrasound in Medicine. 2019;38:1757-68. https://doi.org/10.1002/jum.14864 Dubois GJ, Bachasson D, Lacourpaille L, Benveniste O, Hogrel J. Local texture anisotropy as an estimate of muscle quality in ultrasound imaging. Ultrasound Med Biol. 2018;44(5):1133-1140. https://doi.org/10.1016/j.ultrasmedbio.2017.12.017 Matta TTd, Pereira WCdA, Radaelli R, Pinto RS, Oliveira LFd. Texture analysis of ultrasound images is a sensitive method to follow‐up muscle damage induced by eccentric exercise. Clinical Physiology and Functional Imaging. 2018;38(3):477-482. https://doi.org/10.1111/cpf.12441 Papadacci C, Tanter M, Pernot M, Fink M. Ultrasound backscatter tensor imaging (BTI): analysis of the spatial coherence of ultrasonic speckle in anisotropic soft tissues. IEEE Trans Ultrason Ferroelectr Freq Control. 2014;61(6):986-996. https://doi.org/10.1109/TUFFC.2014.2994 Kari M, Feltovich H, Hall TJ. Correlation length ratio as a parameter for determination of fiber-like structures in soft tissues. Physics in Medicine & Biology. 2021;66(5):055017. https://doi.org/10.1088/1361-6560/abe0fb Burlina P, Billings S, Joshi N, et al. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods, PloS one. 2017;12(8):0184059. https://doi.org/10.1371/journal.pone.0184059 Brausch L, Hewener H, Lukowicz P. Towards a wearable low-cost ultrasound device for classification of muscle activity and muscle fatigue. Proceedings of the 23rd International Symposium on Wearable Computers; 2019:20-2. https://doi.org/10.1145/3341163.3347749 Sanabria SJ, Pirmoazen AM, Dahl J, Kamaya A, El Kaffas A. Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis. Ultrasound Med Biol. 2022;48(10):2060-2078. https://doi.org/10.1016/j.ultrasmedbio.2022.05.031 Polidori MC, Mecocci P. Modeling the dynamics of energy imbalance: The free radical theory of aging and frailty revisited. Free Radical Biology and Medicine. 2022. https://doi.org/10.1016/j.freeradbiomed.2022.02.009 Fernández-Torrón R, García-Puga M, Emparanza J, Maneiro M, Cobo A, Poza J, et al. Cancer risk in DM1 is sex-related and linked to miRNA-200/141 downregulation. Neurology. 2016;87(12):1250-1257. https://doi.org/10.1212/WNL.0000000000003124 Alberro A, Iribarren-Lopez A, Sáenz-Cuesta M, Matheu A, Vergara I, Otaegui D. Inflammaging markers characteristic of advanced age show similar levels with frailty and dependency. Scientific reports. 2021;11(1):1-10. https://doi.org/10.1038/s41598-021-83991-7 Stewart A. Basic statistics and epidemiology: a practical guide: CRC Press 2018. Ponti F, De Cinque A, Fazio N, Napoli A, Guglielmi G, Bazzocchi A. Ultrasound imaging, a stethoscope for body composition assessment. Quantitative Imaging in Medicine and Surgery. 2020;10(8):1699. https://doi.org/10.21037%2Fqims-19-1048 Newman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, et al. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2006;61(1):72-77. https://doi.org/10.1093/gerona/61.1.72 Ruby L, Kunut A, Nakhostin DN, Huber FA, Finkenstaedt T, Frauenfelder T, et al. Speed of sound ultrasound: comparison with proton density fat fraction assessed with Dixon MRI for fat content quantification of the lower extremity. Eur Radiol. 2020;30(10):5272-5280. https://doi.org/10.1007/s00330-020-06885-8 Ruby L, Sanabria SJ, Saltybaeva N, Frauenfelder T, Alkadhi H, Rominger MB. Comparison of ultrasound speed-of-sound of the lower extremity and lumbar muscle assessed with computed tomography for muscle loss assessment. Medicine. 2021;100(21):25947. https://doi.org/10.1097/md.0000000000025947 Ruby L, Sanabria SJ, Martini K, Frauenfelder T, Jukema GN, Goksel O, et al. Quantification of immobilization-induced changes in human calf muscle using speed-of-sound ultrasound: An observational pilot study. Medicine. 2021;100(11):23576. https://doi.org/10.1097/MD.0000000000023576 Wong V, Spitz RW, Bell ZW, Viana RB, Chatakondi RN, Abe T, et al. Exercise induced changes in echo intensity within the muscle: a brief review. Journal of ultrasound. 2020;23(4):457-472. https://doi.org/10.1007/s40477-019-00424-y Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC medicine. 2015;13(1):1-9.https://doi.org/10.1186/s12916-015-0400-x Holloszy JO, McCully KK, Posner JD. The application of blood flow measurements to the study of aging muscle. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 1995;50(Special_Issue):130-136. https://doi.org/10.1093/gerona/50A.Special_Issue.130 Checa-López M, Oviedo-Briones M, Pardo-Gómez A, Gonzales-Turín J, Guevara-Guevara T, Carnicero JA, et al. FRAILTOOLS study protocol: a comprehensive validation of frailty assessment tools to screen and diagnose frailty in different clinical and social settings and to provide instruments for integrated care in older adults. BMC geriatrics. 2019;19(1):1-8 .https://doi.org/10.1186/s12877-019-1042-1 Chicoulaa B, Escourrou E, Durrieu F, Milon V, Savary L, Gelibert M, et al. Challenges in management of frailty by primary healthcare teams: From identification to follow-up. La Presse Médicale Open. 2022;3:100032. https://doi.org/10.1016/j.lpmope.2022.100032 World Medical Association. 64thWMAGeneral Assembly, Fortaleza Brazil, October 2013. World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects, JAMA. 2013;310:2191-4. https://doi.org/10.1001/jama.2013.281053 Supplementary Files SPIRITFigure.docx SPIRITCHECKLISTFORTRIALS.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2648138","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":191268300,"identity":"b105b6aa-dfaf-464a-b0a2-4e243f7a6cf7","order_by":0,"name":"Naiara Virto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYFACxgYwxc+QQKoWyQaQFuK1AYHBAWK1mLMfbvxcUcOQuPl48jHJnz9sGPj5D+DXYtmT2Cx55hhD4rYzz9KkeRLSGCRnELDK4EBig2QDG4Ox2Y0cM2mGhMMMBjcIeeH8w+afDf8YjI1n5H+T/AHUYn+egMMMbiS2STa2McgZSOSwSfCAbCEUBgY3HrZZNvZJyEmceWZszZOWxiNxg5CW8+mPbzZ8s+Hhb09+ePOHjY0cfz8Bh0GBBJzFQ5T6UTAKRsEoGAX4AQBCGkEfyw9YCAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2850-8717","institution":"University of Deusto: Universidad de Deusto","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Naiara","middleName":"","lastName":"Virto","suffix":""},{"id":191268301,"identity":"23b281cc-d885-4749-a05a-7e0f790f7c4b","order_by":1,"name":"Xabier Río","email":"","orcid":"","institution":"University of Deusto: Universidad de Deusto","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xabier","middleName":"","lastName":"Río","suffix":""},{"id":191268302,"identity":"ec28c864-9f97-4cce-86b3-03aabc194a6f","order_by":2,"name":"Garazi Angulo","email":"","orcid":"","institution":"University of Deusto: Universidad de Deusto","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Garazi","middleName":"","lastName":"Angulo","suffix":""},{"id":191268303,"identity":"33651b9e-a8cf-47a2-8bad-668599618e78","order_by":3,"name":"Rafael García","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"García","suffix":""},{"id":191268304,"identity":"1d34c255-00a4-49ce-8d30-0335323d385d","order_by":4,"name":"Almudena Avendaño Céspedes","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Almudena","middleName":"Avendaño","lastName":"Céspedes","suffix":""},{"id":191268305,"identity":"7baada84-8c34-4f78-b7c0-9796c22d7c65","order_by":5,"name":"Elisa Belen Cortes Zamora","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"Belen Cortes","lastName":"Zamora","suffix":""},{"id":191268306,"identity":"4160ce0e-3343-4abc-b9e5-a7ecef470b5d","order_by":6,"name":"Elena Gómez Jiménez","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elena","middleName":"Gómez","lastName":"Jiménez","suffix":""},{"id":191268307,"identity":"096fbf66-f927-435a-bb11-0657cb929d70","order_by":7,"name":"Ruben Alcantud","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruben","middleName":"","lastName":"Alcantud","suffix":""},{"id":191268308,"identity":"82802212-3ad1-4691-8483-e6dbf467487a","order_by":8,"name":"Pedro Abizanda","email":"","orcid":"","institution":"Albacete University Hospital: Hospital General Universitario de Albacete","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Abizanda","suffix":""},{"id":191268309,"identity":"290eb6e0-dc3d-42df-afc5-57d1e27686fa","order_by":9,"name":"Leocadio Rodriguez Mañas","email":"","orcid":"","institution":"Getafe University Hospital: Hospital Universitario de Getafe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Leocadio","middleName":"Rodriguez","lastName":"Mañas","suffix":""},{"id":191268310,"identity":"05bdac42-031d-4cab-b538-9c952cb95960","order_by":10,"name":"Alba Costa","email":"","orcid":"","institution":"Getafe University Hospital: Hospital Universitario de Getafe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alba","middleName":"","lastName":"Costa","suffix":""},{"id":191268311,"identity":"3589713e-2cc7-43f2-a0b4-c3d96419abad","order_by":11,"name":"Ander Matheu","email":"","orcid":"","institution":"Hospital de Donostia: Hospital Universitario de Donostia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ander","middleName":"","lastName":"Matheu","suffix":""},{"id":191268312,"identity":"b62cce30-2e68-4cfc-a75e-70ac9e665d41","order_by":12,"name":"Uxue Lazcano","email":"","orcid":"","institution":"Hospital de Donostia: Hospital Universitario de Donostia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Uxue","middleName":"","lastName":"Lazcano","suffix":""},{"id":191268313,"identity":"d45e8b87-6fea-473b-8494-58a8d7fc543c","order_by":13,"name":"Itziar Vergara","email":"","orcid":"","institution":"Hospital de Donostia: Hospital Universitario de Donostia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Itziar","middleName":"","lastName":"Vergara","suffix":""},{"id":191268314,"identity":"443c4d11-22e9-4d6e-a368-da8f25fe4f06","order_by":14,"name":"Laura Arjona","email":"","orcid":"","institution":"University of Deusto: Universidad de Deusto","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Arjona","suffix":""},{"id":191268315,"identity":"b316f986-8758-4480-889a-65ace72a0a08","order_by":15,"name":"Morelva Saeteros","email":"","orcid":"","institution":"University of Deusto: Universidad de Deusto","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Morelva","middleName":"","lastName":"Saeteros","suffix":""},{"id":191268316,"identity":"e2b71c63-30f4-4265-9d2f-f20f25705567","order_by":16,"name":"Aitor Coca","email":"","orcid":"","institution":"EUNEIZ University: Universidad EUNEIZ","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aitor","middleName":"","lastName":"Coca","suffix":""},{"id":191268317,"identity":"8cae3be4-7d35-4e44-94ca-1ddaec2c2b2d","order_by":17,"name":"Sergio Sanabria","email":"","orcid":"https://orcid.org/0000-0003-4786-4597","institution":"Stanford University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Sanabria","suffix":""}],"badges":[],"createdAt":"2023-03-02 15:39:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2648138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2648138/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35783195,"identity":"e5327b2e-75dd-4cf6-93c4-af919ed8b94e","added_by":"auto","created_at":"2023-04-14 19:20:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71480,"visible":true,"origin":"","legend":"\u003cp\u003eSPIRIT figure\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/8f9336e8dface026804353bb.jpg"},{"id":35783197,"identity":"6bb1cb25-d627-4bbc-abc2-9baffa038be4","added_by":"auto","created_at":"2023-04-14 19:20:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69479,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study protocol.\u003c/p\u003e","description":"","filename":"figura1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/a78387c213c5b6fbf1a85c15.jpg"},{"id":35783657,"identity":"093c3b63-4923-4868-aaea-033da07058d1","added_by":"auto","created_at":"2023-04-14 19:28:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84358,"visible":true,"origin":"","legend":"\u003cp\u003eUltrasound data flow and management from clinical acquisition centers to ultrasound data evaluation center.\u003c/p\u003e","description":"","filename":"figura2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/05ca324344eb5ff11ba662c8.jpg"},{"id":35783198,"identity":"c7dddb0c-8847-416c-a247-a711aff2d8e6","added_by":"auto","created_at":"2023-04-14 19:20:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58116,"visible":true,"origin":"","legend":"\u003cp\u003eEchographic B-mode appearance of mid-thigh in a) transverse and b) longitudinal views.\u003c/p\u003e","description":"","filename":"figura3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/faa3011d4fe2b0f30726c573.jpg"},{"id":35783658,"identity":"c6c65665-7bd0-4c93-b27f-18e509bb50e6","added_by":"auto","created_at":"2023-04-14 19:28:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53175,"visible":true,"origin":"","legend":"\u003cp\u003eMorphometric ultrasound measurements. Legend:\u003c/p\u003e\n\u003cp\u003ea) Rectus femoris thickness, cross-sectional surface and perimeter (transverse view). b) Pennation angle (longitudinal view).\u003c/p\u003e","description":"","filename":"figura4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/518afae729acd0e6b366cce5.jpg"},{"id":35783199,"identity":"d21a0230-4509-4e00-a75c-2f563e722a33","added_by":"auto","created_at":"2023-04-14 19:20:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63557,"visible":true,"origin":"","legend":"\u003cp\u003eBiomarkers data flow and management from clinical centers to blood-essay evaluation center.\u003c/p\u003e","description":"","filename":"Figura5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/569aac637550d8aa60d54912.jpg"},{"id":40446816,"identity":"58632459-8d49-4e02-ad8d-019d9d51f08d","added_by":"auto","created_at":"2023-07-24 04:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":780378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/ec5c2a94-cd0b-4f94-a6d7-ad42df730fcb.pdf"},{"id":35783194,"identity":"37c24dde-bdeb-4d73-b9ae-c8cb29c4d325","added_by":"auto","created_at":"2023-04-14 19:20:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16095,"visible":true,"origin":"","legend":"","description":"","filename":"SPIRITFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/af4f52563c887e6b7e888033.docx"},{"id":35783201,"identity":"dbf3c3fb-a7fa-421f-892a-8241165496bf","added_by":"auto","created_at":"2023-04-14 19:20:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1059508,"visible":true,"origin":"","legend":"","description":"","filename":"SPIRITCHECKLISTFORTRIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-2648138/v1/f833b9e193619e1710230f7d.docx"}],"financialInterests":"","formattedTitle":"Development of continuous assessment of muscle quality and frailty in older subjects using multi-parametric omics based on combined ultrasound and blood biomarkers: a study protocol for a cluster randomised controlled trial","fulltext":[{"header":"Background","content":"\u003cp\u003eAging results in a progressive loss of muscle mass and functional decline, which leads to an increase in the incidence and prevalence of chronic diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which leads to situations of multimorbidity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and has an impact on functional autonomy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The most severe expression is the condition of frailty, \u0026ldquo;a progressive decline in physiological systems that results in decreased reserves of intrinsic capacity, which confers extreme vulnerability to stressors and increases the risk of a range of adverse health outcomes\u0026rdquo;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Frailty is associated with dependency, hospitalization, institutionalization, falls, poor quality of life, and mortality [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and with increased healthcare costs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Standardized diagnostic criteria are lacking, but the two most accepted ones [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] are based on the phenotype construct [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], in which frailty is diagnosed if three or more of the following criteria are present: unintentional weight loss, self-reported exhaustion, decreased grip strength, slow gait speed and low physical activity. Additionally, the \"Cumulative Deficit Model - Frailty Index\", includes cognitive, functional, emotional and nutritional status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe frailty phenotype described by Fried involves muscle dysfunction at its core [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that weakness, slowness and impairment of the muscular system are hallmarks of frailty, sarcopenia is likely to be a key physiopathological contributor. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Sarcopenia is a progressive skeletal muscle disease, whose prevalence increases with age. Sarcopenia is estimated to affect between 6% and 19% of the general population\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age, differing according to the definition applied [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Currently most used definitions are from the European Working Group on Sarcopenia in Older People (EWGSOP-2)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the Definition and Outcomes Consortium (DOCS)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and the National Institute of Health Foundation (NIHF)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. According to EWGSOP-2, reduced muscle strength is the first criterion of probable sarcopenia, while reduced muscle mass and quality confirms the diagnosis. In addition, when low physical performance is detected sarcopenia is graded as severe [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The DOCS agreed that both weakness defined by low grip strength and slowness defined by low usual gait speed should be included in the definition of sarcopenia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. NIHF defines sarcopenia as a loss of strength, diagnosed by low grip strength, together with low muscle mass [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo assess muscle mass and quality in clinical care, a semi-quantitative assessment is performed with two-dimensional images by Dual-energy X-ray absorptiometry (DXA) and with a body composition estimate with Bioelectrical impedance analysis (BIA)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These techniques may however be confounded by other variables such as skeletal mass and large body mass index [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Radiological imaging allows full-scale three-dimensional mapping of muscle composition and microstructure. Magnetic resonance imaging (MRI) and computed tomography (CT) sequences have been proposed, which allow assessment of adipose fraction and fibrous microstructure, among others [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, due to their high cost and potential patient complications, these methods are currently only applied in research or as a supplementary examination for a different primary indication [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSarcopenia and frailty are related but distinct conditions related to aging. While sarcopenia is mainly based on the musculoskeletal system, frailty is a more multifactorial condition [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Different studies have shown that the prevalence of sarcopenia among frail older adults is higher than the prevalence of frailty among those with sarcopenia. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The adverse outcomes associated with muscular decline can be prevented, delayed or even reversed by early detection and interventions including nutritional support and physical exercise programs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, there is a need for simple and reliable tools that allow the assessment of muscle quality and its impact on frailty [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUltrasound is a fast, non-invasive and affordable imaging modality which is rapidly emerging for musculoskeletal examination [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Current clinical ultrasound images (B-mode) allow for assessment of muscle mass and morphology. Common features include measures of muscle thickness, pennation angle, cross-sectional area, echo intensity and fascicle length [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Despite ongoing efforts for standardization, these measurements are highly dependent on the expertise and skills of the operator and do not show definite results for early staging of muscle quality loss [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Ultrasound morphometric measurements of sarcopenia in older adults have shown mild to moderate associations with frailty [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. More recently, several quantitative ultrasound techniques have emerged based on the analysis of echogenicity, texture parameters, elastography and acoustic wave properties, with still limited translation to clinical practice [\u003cspan additionalcitationids=\"CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Artificial intelligence is bringing new opportunities to objectivize musculoskeletal ultrasound, with recent works demonstrating automatic muscle segmentation and fiber angle detection and textural discrimination of muscle microstructures [\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiological biomarkers are valuable tools in the diagnosis and stratification of patients as well as in the understanding of the underlying pathophysiology of the disease. Oxidative stress, pro-inflammatory state and immune aging are relevant in the relationship between nonspecific biomarkers and specific biological systems and frailty and sarcopenia [\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Recent studies have addressed the complex interrelationships among the different systems underlying frailty through multi-omics approaches [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. For instance, the FRAILOMIC initiative used blood samples to describe a set of biological biomarkers, both protective and risk factors. In particular, oxidative stress, vitamin D, and cardiovascular system were associated with frailty [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Despite these advances, currently available biomarkers are individually poorly associated with the clinical outcomes of sarcopenia and frailty and their capability to detect changes after physical intervention is largely unknown.\u003c/p\u003e \u003cp\u003eOnly a limited number of works have explored combinations of ultrasound and blood-based biomarkers. A study identified circulating biomarker changes corresponding to a short-term resistance exercise intervention in older adults, which were significantly related with ultrasound leg cross-sectional area [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Associations were identified between combined genetic and methylation scores and ultrasound-derived skeletal muscle morphometry in elderly women [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Another cross-sectional study related ultrasound characteristics of the quadriceps femoris in sarcopenic patients to blood and urinary biomarkers [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, simple and objective screening tools to diagnose frailty and sarcopenia are lacking [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. B-mode image clinical standardization is necessary, but there is also a need for advances in ultrasound technology to create quantitative indicators for evaluating muscle quality [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This study is designed to evaluate objective muscle quality assessment methods using quantitative analysis techniques based on the analysis of ultrasound raw data combined with blood-based biomarkers. In addition, this study will examine the capability of these biomarkers to detect muscle quality changes as a result of a physical exercise intervention program in frail elderly people.\u003c/p\u003e \u003cp\u003eThus, the main objective of this study is to evaluate the feasibility of combinations of point-of-care quantitative ultrasound parameters with blood-based essays for assessment of muscle quality and frailty in older adults in both hospital setting and community care with respect to clinical evaluation.\u003c/p\u003e"},{"header":"Methods And Analysis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy setting\u003c/h2\u003e\n\u003cp\u003eProspective, experimental, multi-center, three-cohort study, conducted at Complejo Hospitalario Universitario de Albacete, Spain (coordinating Clinical Research Ethics Committee - CEIC) (Hospital 1), Hospital Universitario de Getafe, Getafe, Spain (Hospital 2), and Primary Care Units of Donostialdea, Osakidetza, Spain (Primary Care Units). The design of this study protocol has followed the Standard Protocol Items of the Recommendations for Interventional Trials (SPIRIT) 2013 guideline (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003eStudy population recruitment\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eHospital exercise cohort: Participants will be randomly recruited from the scheduled patient list of the falls unit and the outpatient clinics of Hospital 1. After informed consent acquisition, patients will receive a baseline clinical evaluation. Ultrasound measurements and DXA scan will be conducted, and blood samples will be collected, processed, and stored. Patients will be included in the 16-week multicomponent physical exercise program described below. After the completion of the exercise program, at 16-week follow-up, clinical evaluation, ultrasound, DXA, and blood-sample testing will be repeated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHospital control cohort: Participants will be randomly recruited from the scheduled patient list of the frailty unit, day hospital, and the outpatient clinic of the Geriatrics Department of Hospital 2. After obtaining informed consent, baseline study variables will be collected. Patients will be followed-up for a 16-week period, under usual care. After the follow-up period, baseline evaluation will be repeated.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePrimary care cohort: Participants will be randomly recruited from the scheduled patient list of the Primary Care Units. After informed consent acquisition, baseline study variables will be collected. Patients will be followed-up during one year, under usual care. After the one-year follow-up, the baseline evaluation will be repeated.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eAge of at least 70 years old.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEither gender.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbility to provide informed consent.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAbility to perform all the functional tests.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the hospital exercise cohort, ability to perform the physical exercise program.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExclusion criteria:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eExpected survival inferior to one year.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBarthel scale\u0026thinsp;\u0026lt;\u0026thinsp;70.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eModerate to severe cognitive impairment.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRefuse to participate.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMedical conditions that may condition or difficult the follow-up assessments.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOlder adults already included in regular physical exercise programmes will be excluded to participate in the hospital exercise cohort.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTermination criteria:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eRefuse to continue participation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComplications during or in-between examinations and intervention.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eInterventions:\u003c/h2\u003e\n\u003cp\u003eIn the hospital exercise cohort, a 16-week supervised multicomponent physical exercise programme will be realized. The 16-week multicomponent exercise intervention will be individualized based on the functional capacity, comorbidities and previous experience of the subject. On a weekly basis, subjects will perform two supervised sessions of 45 minutes in small groups (4-to-6 older adults). The sessions are divided into warm-up, main part (strength, power and coordination exercises) and cool down (flexibility and stretching). The main part contains 8 exercises which work the main muscle groups, starting with low intensity and high-volume sessions and progressing to higher intensities and lower volumes. Depending on the stage, patients will perform 2\u0026ndash;4 sets, 4\u0026ndash;15 repetitions with 30 seconds rest between sets and exercises. For every exercise, loads and intensities will be modulated to allow a total of 30 repeats. The exercise sessions are to be performed in the gym of the Geriatrics Department of the hospital. Participants will be recommended to take additional 90-minute walks per week. Finally, it should be noted that the program is based on the guidelines of the VIVIFRAIL program [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e] aimed at preventing weakness and the risk of falls.\u003c/p\u003e\n\u003cp\u003eNo intervention will be performed in hospital control and primary care cohorts. If patients need medical care that interferes with the correct conduct of the study, they will be treated according to the clinical routine and will be excluded from the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eSafety monitoring:\u003c/h2\u003e\n\u003cp\u003eEvery principal investigator at each data acquisition center will monitor and follow-up of the participants included in the respective cohort under usual care, and accordingly decide on patient exclusion, assign participants to inventions and monitor termination criteria.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eRandomization and blinding:\u003c/h2\u003e\n\u003cp\u003eData analysis will be performed independently from cohort recruitment and follow-up. Data evaluation will be performed at the Deusto Institute for Technology (Ultrasound Data Evaluation Center) and Biodonostia (Blood-based Essays Evaluation Center). Initially, only anonymized data - ultrasound images and raw data and blood-based essays - will be transferred to data evaluation centers. For combination models, a training set of clinical variables will be transferred to the data evaluation centers. Reserved datasets will be reserved at the clinical acquisition centers for independent testing, both including data from different sites and data acquired in follow-up examinations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eSample size:\u003c/h2\u003e\n\u003cp\u003eA minimum population of 100 participants will be acquired in the Primary care cohort, 50 participants in the hospital exercise cohort and 50 participants in the hospital control cohort (ratio 2:1.1). Within each acquisition site, patient selection will be performed based on the clinical agenda up to the completion of the recruitment goals. Two examinations (baseline and follow-up) will be performed per participant, with a minimum total of 400 examinations.\u003c/p\u003e\n\u003cp\u003eA distribution of 85% frail \u0026minus;\u0026thinsp;15% robust patients in the hospital cohorts, and a distribution of 15% frail \u0026minus;\u0026thinsp;85% robust in the Primary care cohort is estimated. Accordingly, an accrual of 139 robust participants and 86 frail participants is expected. It is assumed that up to 10% participants will be excluded in data evaluation due to failed measurements or logistical impossibility to to collect all variables. For patients where follow-up measurements are not feasible, the baseline measurement will be still included in the evaluation of the primary outcomes. With an 80% power, the planned population will allow detecting a biomarker medium effect size of E/S\u0026thinsp;=\u0026thinsp;0.39 with a two-sided/alpha level of 0.05. The sample size was calculated with RiskCalc [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eOutcomes:\u003c/h2\u003e\n\u003cp\u003ePrimary outcome:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTo analyze the association between quantitative ultrasound biomarkers associated to muscle mass and quality and extracted from raw data and blood-based biomarkers and combinations thereof with clinical variables including frailty, sarcopenia, physical function, disability, nutritional status, body composition, and Quality of Life in three cohorts of older adults: hospital control cohort, hospital exercise cohort, and primary care cohort.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSecondary outcomes:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIn the hospital exercise cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables after a 16-week multicomponent physical exercise program, and to find associations between ultrasound and blood-based biomarkers with the rest of clinical variables before and after the exercise program.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the hospital control cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables, after a 16-week follow-up period without intervention, and to find associations between quantitative ultrasound and blood-based biomarkers with the rest of clinical variables after the 16-week follow-up period.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn the primary care cohort, to analyze changes in quantitative ultrasound and blood-based biomarkers and clinical variables after a one-year follow-up period without intervention, and to find associations between quantitative ultrasound and blood-based biomarkers with the rest of clinical variables after one-year follow-up.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo analyze differences in the changes of all the measurements between the three cohorts. Both hospital cohorts will be compared directly, and the primary care cohort will be used to determine changes in non-hospital populations.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOutcome measurements:\u003c/p\u003e\n\u003cp\u003eThe list of clinical variables collected in baseline and follow-up is included in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical variables in baseline and follow-up examination\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClinical variables\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e- Global Deterioration Scale (GDS) of Reisberg for assessment of cognitive function.\u003c/p\u003e\n\u003cp\u003e- Charlson Comorbidity Index.\u003c/p\u003e\n\u003cp\u003e- Barthel index of independence to perform basic activities of daily living.\u003c/p\u003e\n\u003cp\u003e- Lawton and Brody index of indepence to perform instrumental activities of daily living.\u003c/p\u003e\n\u003cp\u003e- Short Physical Performance Battery (SPPB) for physical function assessment.\u003c/p\u003e\n\u003cp\u003e- SARC-F scale of sarcopenia screening.\u003c/p\u003e\n\u003cp\u003e- FRAIL scale of frailty evaluation.\u003c/p\u003e\n\u003cp\u003e- Frailty phenotype of frailty.\u003c/p\u003e\n\u003cp\u003e- MNA-SF\u0026reg; for nutritional screening.\u003c/p\u003e\n\u003cp\u003e- IPAQ international questionnaire of physical activity.\u003c/p\u003e\n\u003cp\u003e- EQ 5D-5L for health-related quality of life assessment.\u003c/p\u003e\n\u003cp\u003e- Gait speed test for physical function assessment.\u003c/p\u003e\n\u003cp\u003e- Grip strength with Jamar dynamometer.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eUltrasound imaging and raw data acquisition:\u003c/h2\u003e\n\u003cp\u003eThe ultrasound equipment is a L7 HD3 portable linear scanner (Clarius Mobile Health Corp., Vancouver, BC, Canada) for Point of Care Ultrasound (POCUS) examination. The probe has a frequency range 4\u0026ndash;13 MHz with a center frequency of 7 MHz. Anonymized digital ultrasound data is streamed from the scanner through a custom Wi-Fi network to a smart device for real-time B-mode navigation and data storage. The scanner includes an image processing package to optimize B-mode musculoskeletal image quality. The scanner also includes a research package to acquire raw beamformed backscattering ultrasound data after the beamformer. Raw data is provided in an In-Phase/Quadrature complex baseband representation - also known as IQ data. Raw data is used as a basis for the implementation of customized Quantitative Ultrasound algorithms [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAll examinations will be performed in a depth range 0\u0026ndash;60 mm at 50% of the maximum acoustic output provided by the scanner. Measurements will be carried out with minimum necessary skin-probe compression for acceptable image quality. After examination, data will be uploaded to a HIPAA-compliant cloud service provided by the scanner\u0026rsquo;s manufacturer, which allows centralization of data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eUltrasound examination protocol:\u003c/h2\u003e\n\u003cp\u003eMid thigh will be identified and marked as the half distance between from the superior border of the patella to the antero-superior iliac crest. The midpoint of both thighs will be localized in transverse view with the help of the femur, and the different vastus of the quadriceps will be identified (rectus femoris, vastus intermedius, vastus medialis and vastus lateralis) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA minimum of 12 co-registered B-scan and raw data frames will be acquired in each examination. Three transverse and three longitudinal views will be recorded for both thighs. Longitudinal examination will be performed in a plane where fasciculations are visible.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eEvaluation of quantitative ultrasound biomarkers:\u003c/h2\u003e\n\u003cp\u003eMorphometric ultrasound measures:\u003c/p\u003e\n\u003cp\u003eDuring examination, the sonographer will register morphometric ultrasound measurements using the online ultrasound scanner\u0026rsquo;s interface, including (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e): thickness of rectus femoris in transverse view at mid-section point, cross-sectional surface of rectus femoris in transverse view, and pennation angle in longitudinal view.\u003c/p\u003e\n\u003cp\u003eRaw data evaluation:\u003c/p\u003e\n\u003cp\u003eQuantitative ultrasound biomarkers will be evaluated offline based on the raw IQ data acquired. Regions of interest (ROI)s comprising the rectus femoris will be defined in the raw data domain by automatically synthesizing B-mode images out of the raw data, and using the co-registered clinical B-mode images and sonographer annotations as a reference. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e includes a list of state-of-the-art quantitative ultrasound biomarkers, which hyperparameters will be experimentally adapted to musculoskeletal examination and evaluated based on the raw data collected by the ultrasound probes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eQuantitative ultrasound biomarkers based on raw data.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBiomarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLiterature references\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAutomatic morphometric measurements\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAutomatic muscle morphometric analysis based on neural network segmentation models trained with respect to sonographer annotations of rectus femoris cross-section, and 2D Fourier analysis of pennation angle.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAttenuation coefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeasurement of loss of signal intensity with depth.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBackscattering coefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeasurement of tissue reflectivity after attenuation compensation.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePower spectrum,\u003c/p\u003e\n\u003cp\u003eLizzi-Feleppa parameters\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpectroscopy measurement of backscattered signal variation with frequency, including parametrizations such as spectral slope, spectral intercept and mid-band fit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpeckle statistics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFitting of raw envelope signal to speckle statistical distribution models, including Rayleigh, homodyned, Nakagami distributions. Estimation of scatterer concentration, spacing and coherence from fitted model parameters.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStatistical moments\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-parametric statistical moments capturing scatterer distribution and concentration, such as entropy, kurtosis, variance, anisotropy and signal-to-noise ratio.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoherence, speed of sound\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeneralized Power Spectrum analysis and estimation of coherence, mean scatterer spacing and speed-of-sound in muscle.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTextural radiomics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst and second-order texture features extracted from both B-mode (e.g., based on gray scale co-occurrence matrices) and raw data (e.g., based on wavelet and Laplacian transformations) and combined with machine learning models trained with respect to clinical outcomes.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArtificial intelligence radiomics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRF data and B-mode features extracted automatically with end-to-end neural network models trained with respect to clinical outcomes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eEvaluation of blood-based biomarkers\u003c/h2\u003e\n\u003cp\u003eAt basal and follow-up acquisitions, one 10 mL serum blood tube and two 5 mL EDTA blood tubes will be collected by venipuncture. The serum sample will be centrifuged and stored in four aliquots. One of the EDTA samples will be immediately frozen and stored at -80\u0026ordm;C, and the other one will be centrifuged for plasma and buffy coat extraction. Samples will be stored at -80\u0026ordm;C at the clinical sites, before transportation with dry ice to the Blood-Essay Evaluation Center for subsequent processing and storage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe expression of previously described biomarkers such as Vitamin D, Lutein zeaxanthin, Troponin T, Pro-BNP, sRAGE [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e], miRNAs, as well as some related to relevant pathways associated to frailty such as inflammation as Interleukin 6, senescence as p16\u003csup\u003eINK4A\u003c/sup\u003e and p21\u003csup\u003eCIP\u003c/sup\u003e[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e] will be measured in cells obtained from a blood sample from a subsample of patients in basal condition and after intervention at transcriptional and protein level (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Erythrocytes will be lysed with Buffer EL (Qiagen) and total RNA from leukocytes will be isolated with the miRNeasy Mini Kit (Qiagen). First, RNA samples will be purified using the RNeasy Kit (Qiagen N.V., Hilden, Germany). Then, the RNA will be retro-transcribed and the expressions of genes will be determined by means of quantitative PCR (qPCR) using specific primers or probes and the equipment ABI Prism\u0026reg; SDS 7300 Real Time PCR System from Applied Biosystems. Expression levels will be standardized with those for expression of the enzyme Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]. Additionally, Protein levels will be measured in serum samples with Quantikine ELISAs and Luminex as previously described [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBlood-based biomarkers\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBiomarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethod of measurement\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVitamin D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLutein zeaxanthin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTroponin T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePro-BNP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esRAGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emicroRNA 125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emicroRNA 194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emicroRNA 454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eELISA and Luminex\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP16\u003csup\u003eINK4A\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP21\u003csup\u003eCIP\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew unpublished\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eqRT-PCR and ELISA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eCorrelation of quantitative biomarkers will be assessed for each multi-factorial clinical evaluation parameter [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]. Concordance measurements (kappa for categorical variables and concordance correlation coefficient for continuous variables) will be used to test agreement between model variables. Unpaired tests (for instance, Student\u0026rsquo;s t-test) will be used for comparison of means of biomarkers between different acquired patient cohorts and stratified patient subgroups according to the frailty scales of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Paired statistics will be used in statistical analysis for longitudinal monitoring both for interventions and clinical follow-up.\u003c/p\u003e\n\u003cp\u003eFor multi-parametric data science models, unsupervised learning models will be evaluated to cluster patient populations according to biomarker expression. Additionally, supervised multi-omics models will be trained with respect to a training set of clinical variables. Stratified cross-validation will be utilized to extract model performance statistics, with separate patient data in training and validation folds and a balanced distribution of robust and frail subjects in both training and validation. Random sample imputation will be used for missing data in training, and missing reference variables will be excluded from validation and testing. Reserve datasets, including data from different sites and follow-up examination data, will be used for model testing. Biomarker reproducibility will be assessed with inter-class correlation coefficient (ICC) and Bland-Altmann method based on repeated measurements within an examination.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe expected outcomes of the present study are associations and combination models between quantitative ultrasound and blood-based essays with multi-factorial clinical assessment of muscle quality and frailty. Non-invasive portable technologies allow execution of the clinical protocol in both hospital and primary care environments [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent literature suggests that not only changes in skeletal muscle mass, but other factors underpinning muscle quality play a role in impaired mobility associated with aging. For instance, changes in muscle tissue composition, based on excessive levels of intramuscular adipose tissue and intramyocellular lipids have been found to adversely impact muscle functional capacity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. We hypothesize that quantitative ultrasound biomarkers for raw data may show superior discriminative performance, be more reproducible and easier to perform than current state-of-the-art assessment from B-mode images. B-mode morphological ultrasound parameters are primarily associated with muscle mass and show a limited sensitivity in the diagnosis of sarcopenia. Ultrasound quantitative biomarkers based on raw data have been previously generally shown to capture both tissue composition and microstructural properties, and have shown to encode a richer information content than ultrasound B-mode images in artificial intelligence models [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Ultrasound spectroscopy parameters and tissue acoustic properties such as speed-of-sound and attenuation have been linked to tissue composition [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and viscoelastic changes in muscle [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] in elderly subjects with sarcopenia. Particularly, speed-of-sound showed correlations with MRI adiposity estimates in the calf muscles [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], CT assessment of the psoas muscle [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] and short-term changes in muscle due to immobilization [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Ultrasound statistical analysis of the envelope signal in soft tissues has been linked with concentration, spacing and directionality of microstructural scatterers [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Texture features based on radiomics analysis also capture musculoskeletal composition and microstructure, being applied to differentiate muscle spasticity, subjects with dynapenia, myositis, fibromyalgia, Duchenne muscular dystrophy, and exercise-induced muscle damage [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Being acquired at early stages of the ultrasound image formation, ultrasound raw data may also contribute to reducing equipment- and operator-dependent bias and facilitate data-based guidance for examiners with low sonographic acquisition expertise.\u003c/p\u003e \u003cp\u003eMolecular biomarkers extracted from blood essays are associated with functional changes between robust and frail individuals, whereas there are currently no single established predictors biomarkers. This is mainly attributable to the heterogeneity and limitations of the scales and/or indices to detect sarcopenia and frailty, the different age, sex, and characteristics across different populations, small sample sizes, limited longitudinal clinical studies, no characterization on interventions to test their potential reversibility, or the different techniques and cut-offs used for biomarker measurement. In this study we include a panel of biomarkers rather than the assessment of individual molecules. This can contribute to better reflect the accumulation of damage associated with age-related syndromes [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Finally the potential of the biomarkers to evaluate reversibility, a critical characteristic of frailty, will be evaluated after the completion of an intervention based on an exercise program and a 16-week follow-up.\u003c/p\u003e \u003cp\u003eSarcopenia and frailty are related but distinct phenotypes of aging. The prevalence of sarcopenia among frail elderly people is higher than the frailty prevalence among sarcopenic [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As an exploratory outcome, we will take into account the coexistence of sarcopenia and frailty, redefining the frailty prognosis based on the presence or absence of sarcopenia.\u003c/p\u003e \u003cp\u003eTo our knowledge this cohort study is one of the few combining raw data ultrasound measurements with blood samples to extract non-invasive biomarkers of frailty and sarcopenia in older adults. This is the first cohort study to link quantitative ultrasound and blood biomarkers to cross-sectional evaluation of frailty and sarcopenia in both hospital and Primary care environments. This is the first study to combine quantitative ultrasound and blood-based biomarkers to assess musculoskeletal changes after multicomponent physical exercise programs.\u003c/p\u003e \u003cp\u003eThe study also has some limitations, the lack of access to a gold standard for muscle quality assessment. Established radiological techniques, such as CT and MRI, provide a reference to muscle composition and microstructure but are associated with patient complications and are not widely accessible in the investigated clinical environments. Instead we use a multi-factorial clinical assessment, patient stratification, and well-controlled interventions to assess muscle quality changes. The exploration of ultrasound technology is restricted to backscattering ultrasound data based on beamformed raw data. Other ultrasound quantitative technologies such as shear wave elastography [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and blood flow measurements based on Doppler sequences [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] fall out of scope, since they are not routinely available in POCUS devices and add complexity to the protocol execution. The monitoring period in hospital (16 weeks) and primary care environments (1 year) is different to adapt to the different follow-up workflows in both environments. The primary care follow-up is dimensioned in agreement with periods defined in previous studies for the general population [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eIn conclusion, the presented study protocol will combine portable technologies based on quantitative muscle ultrasound and blood-based biomarkers for objective cross-sectional evaluation of muscle quality in both hospital and primary care environments. The study will provide data to investigate associations between biomarker combinations with cross-sectional clinical evaluation of frailty and sarcopenia, as well as musculoskeletal changes after multicomponent physical exercise programs.\u003c/p\u003e\n\u003ch3\u003eTrial Status:\u003c/h3\u003e\n\u003cp\u003eAt the time of manuscript submission, the enrollment of volunteers is still ongoing. Recruitment started on 01/03/2022 and ends on 31/12/2023.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMagnetic resonance imaging (MRI), computed tomography (CT) and Enzyme-linked immunosorbent assay (ELISA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and dissemination:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Research Ethics Committee of Albacete (Spain) with reference CEIm-2021-51. For the other research sites, ethical approval was obtained from the local Ethics Committees of Getafe, IIS Biodonostia (CEIC-PI2022069), and Deusto. The study will be conducted in accordance with the principles of the Declaration of Helsinki [97]. Written informed consent will be obtained from all participants, and their data will be managed according to HIPAA guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results will be published in an academic journal. At least two interdisciplinary workshops with geriatrics and imaging specialists will be organized. We will publish digitalized datasets, including ultrasound data and biomarkers, digitized blood-based biomarkers and multi-factorial clinical evaluation in open-access repositories (e.g., Zenodo). Biological samples will be stored in the Basque Biomarker Center (Biobanco). Software models derived from the ultrasound raw data will be made available in open-source software repositories (e.g., GitLab).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study information will be stored in Microsoft Excel 365 in an electronic database. The database will record all subject data to include the baseline characteristics, pre- and post-assessments, and possible adverse events.The database will only be accessible to the study investigators. To ensure the confidentiality of the data, all subjects will be provided with identification numbers. All researchers will have access to the final trial data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received a public grant for its development in the call for RIS3 Basque Government Health Department (project numbers: 2021333036). Funded by the University of Deusto through a grant from the researcher education program (Ref: FPI UD_2022_10). The funders had no role in the design of this study and will not have any role during its execution, analyses, interpretation of data, or submission of outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSJS and AC contributed to study conceptualization. XR, NV, GA, RG, AA, EBC, EG, PAS, LRM, AMF, IV, AC and SJS contributed to the methodology and investigation. XR, NV, GA, RG, AC, RA, AC, UL, MS, LA and SJS contributed to the writing of the original draft. SJS, AC, PAS, LRM, IV, AC and SJS contributed to the project administration. RG, AA, EBC and EG contributed to the data curation. PAS, LRM, AC and SJS contributed to study supervision. All authors have read, provided feedback, and agreed to the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRoser M, Ortiz-Ospina E, Ritchie H. Life expectancy. Our world in data 2013\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Ot\u0026iacute;n C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-1217. https://doi.org/10.1016/j.cell.2013.05.039.\u003c/li\u003e\n\u003cli\u003eMarengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing research reviews. 2011;10(4):430-439. https://doi.org/10.1016/j.arr.2011.03.003.\u003c/li\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001;56(3):146-157. https://doi.org/10.1093/gerona/56.3.M146.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Laso A, Caballero Mora MA, Garc\u0026iacute;a S\u0026aacute;nchez I, Alonso Bouz\u0026oacute;n C, Rodr\u0026iacute;guez Ma\u0026ntilde;as L, Bernabei R, et al. Updated state of the art report on the prevention and management of frailty. European Union. 2019.\u003c/li\u003e\n\u003cli\u003eHoogendijk EO, Romero L, S\u0026aacute;nchez-Jurado PM, Ruano TF, Vi\u0026ntilde;a J, Rodr\u0026iacute;guez-Ma\u0026ntilde;as L, et al. A new functional classification based on frailty and disability stratifies the risk for mortality among older adults: the FRADEA study. Journal of the American Medical Directors Association. 2019;20(9):1105-1110. https://doi.org/10.1016/j.jamda.2019.01.129.\u003c/li\u003e\n\u003cli\u003eKojima G. Frailty as a predictor of future falls among community-dwelling older people: a systematic review and meta-analysis. Journal of the American Medical Directors Association. 2015;16(12):1027-1033.https://doi.org/10.1016/j.jamda.2015.06.018\u003c/li\u003e\n\u003cli\u003eKojima G, Iliffe S, Jivraj S, Walters K. Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):716-721. http://dx.doi.org/10.1136/jech-2015-206717\u003c/li\u003e\n\u003cli\u003eKojima G. Frailty as a predictor of hospitalisation among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health. 2016;70(7):722-729. http://dx.doi.org/10.1136/jech-2015-206978\u003c/li\u003e\n\u003cli\u003eKojima G. Frailty as a predictor of nursing home placement among community-dwelling older adults: a systematic review and meta-analysis. Journal of geriatric physical therapy. 2018;41(1):42-48.https://doi.org/10.1519/JPT.0000000000000097\u003c/li\u003e\n\u003cli\u003eKojima G. Increased healthcare costs associated with frailty among community-dwelling older people: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2019;84:103898. https://doi.org/10.1016/j.archger.2019.06.003\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Nogueras I, Aranda-Reneo I, Pe\u0026ntilde;a-Longobardo LM, Oliva-Moreno J, Abizanda P. Use of health resources and healthcare costs associated with frailty: the FRADEA study. J Nutr Health Aging. 2017;21(2):207-214. https://doi.org/10.1007/s12603-016-0727-9\u003c/li\u003e\n\u003cli\u003eChen X, Mao G, Leng SX. Frailty syndrome: an overview. Clinical interventions in aging. 2014;9:433 https://doi.org/10.2147%2FCIA.S45300\u003c/li\u003e\n\u003cli\u003eFaller JW, Pereira DdN, de Souza S, Nampo FK, Orlandi FdS, Matumoto S. Instruments for the detection of frailty syndrome in older adults: a systematic review. PloS one. 2019;14(4):e0216166. https://doi.org/10.1371/journal.pone.0216166\u003c/li\u003e\n\u003cli\u003eMitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. TheScientificWorldJournal.2001;1:323-336. https://doi.org/10.1100/tsw.2001.58\u003c/li\u003e\n\u003cli\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489-495. https://doi.org/10.1503/cmaj.050051\u003c/li\u003e\n\u003cli\u003eBartley JM, Studenski SA. Muscle ultrasound as a link to muscle quality and frailty in the clinic. J Am Geriatr Soc. 2017;65(12):2562-2563. https://doi.org/10.1111/jgs.15075\u003c/li\u003e\n\u003cli\u003eLandi F, Calvani R, Cesari M, Tosato M, Martone AM, Bernabei R, et al. Sarcopenia as the biological substrate of physical frailty. Clin Geriatr Med. 2015;31(3):367-374. https://doi.org/10.1016/j.cger.2015.04.005\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Landi F, Schneider SM, Z\u0026uacute;\u0026ntilde;iga C, Arai H, Boirie Y, et al. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing. 2014;43(6):748-759. https://doi.org/10.1093/ageing/afu115\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruy\u0026egrave;re O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31.https://doi.org/10.1093/ageing/afy169\u003c/li\u003e\n\u003cli\u003eBhasin S, Travison TG, Manini TM, Patel S, Pencina KM, Fielding RA, et al. Sarcopenia definition: the position statements of the sarcopenia definition and outcomes consortium. J Am Geriatr Soc. 2020;68(7):1410-1418. https://doi.org/10.1111/jgs.16372\u003c/li\u003e\n\u003cli\u003eStudenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2014;69(5):547-558. https://doi.org/10.1093/gerona/glu010\u003c/li\u003e\n\u003cli\u003eGonzalez MC, Barbosa-Silva TG, Heymsfield SB. Bioelectrical impedance analysis in the assessment of sarcopenia. Current Opinion in Clinical Nutrition \u0026amp; Metabolic Care. 2018;21(5):366-374. https://doi.org/10.1097/mco.0000000000000496\u003c/li\u003e\n\u003cli\u003eHuber FA, Del Grande F, Rizzo S, Guglielmi G, Guggenberger R. MRI in the assessment of adipose tissues and muscle composition: how to use it. Quantitative Imaging in Medicine and Surgery. 2020;10(8):1636. https://doi.org/10.21037%2Fqims.2020.02.06\u003c/li\u003e\n\u003cli\u003eGiraudo C, Cavaliere A, Lupi A, Guglielmi G, Quaia E. Established paths and new avenues: a review of the main radiological techniques for investigating sarcopenia. Quantitative imaging in medicine and surgery. 2020;10(8):1602. https://doi.org/10.21037%2Fqims.2019.12.15\u003c/li\u003e\n\u003cli\u003eGrimm A, Nickel MD, Chaudry O, Uder M, Jakob F, Kemmler W, et al. Feasibility of Dixon magnetic resonance imaging to quantify effects of physical training on muscle composition\u0026mdash;a pilot study in young and healthy men. Eur J Radiol. 2019;114:160-166. https://doi.org/10.1016/j.ejrad.2019.03.019\u003c/li\u003e\n\u003cli\u003eSergi G, Trevisan C, Veronese N, Lucato P, Manzato E. Imaging of sarcopenia. Eur J Radiol. 2016;85(8):1519-1524.https://doi.org/10.1016/j.ejrad.2016.04.009\u003c/li\u003e\n\u003cli\u003eDavies B, Garc\u0026iacute;a F, Ara I, Artalejo FR, Rodriguez-Ma\u0026ntilde;as L, Walter S. Relationship between sarcopenia and frailty in the toledo study of healthy aging: a population based cross-sectional study. Journal of the American Medical Directors Association. 2018;19(4):282-286. https://doi.org/10.1016/j.jamda.2017.09.014\u003c/li\u003e\n\u003cli\u003eReijnierse EM, Trappenburg MC, Blauw GJ, Verlaan S, de van der Schueren, Marian AE, Meskers CG, et al. Common ground? The concordance of sarcopenia and frailty definitions. Journal of the American Medical Directors Association. 2016;17(4):371-12. https://doi.org/10.1016/j.jamda.2016.01.013\u003c/li\u003e\n\u003cli\u003eMijnarends DM, Schols JM, Meijers JM, Tan FE, Verlaan S, Luiking YC, et al. Instruments to assess sarcopenia and physical frailty in older people living in a community (care) setting: similarities and discrepancies. Journal of the American Medical Directors Association. 2015;16(4):301-308. https://doi.org/10.1016/j.jamda.2014.11.011\u003c/li\u003e\n\u003cli\u003eNishiguchi S, Yamada M, Fukutani N, Adachi D, Tashiro Y, Hotta T, et al. Differential association of frailty with cognitive decline and sarcopenia in community-dwelling older adults. Journal of the American Medical Directors Association. 2015;16(2):120-124. https://doi.org/10.1016/j.jamda.2014.07.010\u003c/li\u003e\n\u003cli\u003eSpira D, Buchmann N, Nikolov J, Demuth I, Steinhagen-Thiessen E, Eckardt R, et al. Association of low lean mass with frailty and physical performance: a comparison between two operational definitions of sarcopenia\u0026mdash;data from the Berlin Aging Study II (BASE-II). Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2015;70(6):779-784. https://doi.org/10.1093/gerona/glu246\u003c/li\u003e\n\u003cli\u003eCasas-Herrero \u0026Aacute;, de Asteasu MLS, Ant\u0026oacute;n-Rodrigo I, S\u0026aacute;nchez-S\u0026aacute;nchez JL, Montero-Odasso M, Mar\u0026iacute;n-Epelde I, et al. Effects of Vivifrail multicomponent intervention on functional capacity: a multicentre, randomized controlled trial. Journal of cachexia, sarcopenia and muscle. 2022;13(2):884-893. https://doi.org/10.1002/jcsm.12925\u003c/li\u003e\n\u003cli\u003eOviedo-Briones M, Laso \u0026Aacute;R, Carnicero JA, Cesari M, Grodzicki T, Gryglewska B, et al. A comparison of frailty assessment instruments in different clinical and social care settings: the frailtools project. Journal of the American Medical Directors Association. 2021;22(3):607-12. https://doi.org/10.1016/j.jamda.2020.09.024\u003c/li\u003e\n\u003cli\u003ePatil P, Dasgupta B. Role of diagnostic ultrasound in the assessment of musculoskeletal diseases. Therapeutic advances in musculoskeletal disease. 2012;4(5):341-355.https://doi.org/10.1177/1759720X12442112\u003c/li\u003e\n\u003cli\u003eSconfienza LM, Albano D, Allen G, Bazzocchi A, Bignotti B, Chianca V, et al. Clinical indications for musculoskeletal ultrasound updated in 2017 by European Society of Musculoskeletal Radiology (ESSR) consensus. Eur Radiol. 2018;28(12):5338-5351. https://doi.org/10.1007/s00330-018-5474-3\u003c/li\u003e\n\u003cli\u003ePerkisas S, Baudry S, Bauer J, Beckw\u0026eacute;e D, De Cock A, Hobbelen H, et al. Application of ultrasound for muscle assessment in sarcopenia: towards standardized measurements. European geriatric medicine. 2018;9(6):739-757. https://doi.org/10.1007/s41999-018-0104-9\u003c/li\u003e\n\u003cli\u003eRam\u0026iacute;rez-Fuentes C, M\u0026iacute;nguez-Blasco P, Ostiz F, S\u0026aacute;nchez-Rodr\u0026iacute;guez D, Messaggi-Sartor M, Mac\u0026iacute;as R, et al. Ultrasound assessment of rectus femoris muscle in rehabilitation patients with chronic obstructive pulmonary disease screened for sarcopenia: correlation of muscle size with quadriceps strength and fat-free mass. European geriatric medicine. 2019;10(1):89-97.https://doi.org/10.1007/s41999-018-0130-7\u003c/li\u003e\n\u003cli\u003eNeira M, Ram\u0026iacute;rez R, Romero L, et al. Description and preliminary results from Project ECOSARC: Sarcopenia measured with echography in hospitalized elderly.\u003c/li\u003e\n\u003cli\u003eTicinesi A, Meschi T, Narici MV, Lauretani F, Maggio M. Muscle ultrasound and sarcopenia in older individuals: a clinical perspective. Journal of the American Medical Directors Association. 2017;18(4):290-300. https://doi.org/10.1016/j.jamda.2016.11.013\u003c/li\u003e\n\u003cli\u003eBenton E, Liteplo AS, Shokoohi H, et al. A pilot study examining the use of ultrasound to measure sarcopenia, frailty and fall in older patients, Am J Emerg Med. 2021;46:310-6. https://doi.org/10.1016/j.ajem.2020.07.081\u003c/li\u003e\n\u003cli\u003eHarris-Love MO, Monfaredi R, Ismail C, Blackman MR, Cleary K. Quantitative ultrasound: measurement considerations for the assessment of muscular dystrophy and sarcopenia. Frontiers in Aging Neuroscience. 2014;6:172. https://doi.org/10.3389/fnagi.2014.00172\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;z\u0026ccedil;akar L, Ata AM, Kaymak B, Kara M, Kumbhare D. Ultrasound imaging for sarcopenia, spasticity and painful muscle syndromes. Current opinion in supportive and palliative care. 2018;12(3):373-381. https://doi.org/10.1097/spc.0000000000000354\u003c/li\u003e\n\u003cli\u003eMatsumoto H, Tanimura C, Tanishima S, Hagino H. Association between speed of sound of calcaneal bone assessed by quantitative ultrasound and sarcopenia in a general older adult population: A cross-sectional study. Journal of Orthopaedic Science. 2019;24(5):906-911. https://doi.org/10.1016/j.jos.2019.01.003\u003c/li\u003e\n\u003cli\u003eSanabria SJ, Martini K, Freyst\u0026auml;tter G, Ruby L, Goksel O, Frauenfelder T, et al. Speed of sound ultrasound: a pilot study on a novel technique to identify sarcopenia in seniors. Eur Radiol.2019;29(1):3-12.https://doi.org/10.1007/s00330-018-5742-2\u003c/li\u003e\n\u003cli\u003eOu L, Chang Y, Chang C, Chiu C, Chao T, Sun Z, et al. Epidemiological survey of the feasibility of broadband ultrasound attenuation measured using calcaneal quantitative ultrasound to predict the incidence of falls in the middle aged and elderly. BMJ open. 2017;7(1):e013420.http://dx.doi.org/10.1136/bmjopen-2016-013420\u003c/li\u003e\n\u003cli\u003eBerger G, Laugier P, Leroy A, Fink M, Roucayrol JC, Perrin J. Correlation between ultrasound attenuation in muscle and pathological fatty infiltration. Ultrason Imaging. 1987;9(1):66.\u003c/li\u003e\n\u003cli\u003eShore D, Miles CA. Experimental estimation of the viscous component of ultrasound attenuation in suspensions of bovine skeletal muscle myofibrils. Ultrasonics. 1988;26(1):31-36. https://doi.org/10.1016/0041-624X(88)90046-7\u003c/li\u003e\n\u003cli\u003eThomson H, Yang S, Cochran S. Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics. 2022;49(4):517-528. https://doi.org/10.1007/s10396-022-01230-6\u003c/li\u003e\n\u003cli\u003eWeng W, Lin C, Shen H, Chang C, Tsui P. Instantaneous frequency as a new approach for evaluating the clinical severity of Duchenne muscular dystrophy through ultrasound imaging. Ultrasonics. 2019;94:235-241.https://doi.org/10.1016/j.ultras.2018.09.004\u003c/li\u003e\n\u003cli\u003eParis MT, Mourtzakis M. Muscle composition analysis of ultrasound images: a narrative review of texture analysis. Ultrasound Med Biol. 2021;47(4):880-895. https://doi.org/10.1016/j.ultrasmedbio.2020.12.012\u003c/li\u003e\n\u003cli\u003eShin Y, Yang J, Lee YH, Kim S. Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography. 2021;40(1):30. https://doi.org/10.14366%2Fusg.20080\u003c/li\u003e\n\u003cli\u003eZhang Y, Du G, Zhan Y, Guo K, Zheng Y, Tang L, et al. Muscle Atrophy Evaluation via Radiomics Analysis Using Ultrasound Images: A Cohort Data Study. IEEE Transactions on Biomedical Engineering. 2022;69(10):3163-3174. https://doi.org/10.1109/TBME.2022.3162223\u003c/li\u003e\n\u003cli\u003eLiu P, Wei T, Ching CT. Quantitative ultrasound texture analysis to assess the spastic muscles in stroke patients. Applied Sciences. 2020;11(1):11. https://doi.org/10.3390/app11010011\u003c/li\u003e\n\u003cli\u003eYang K, Liao Y, Chang K, Huang K, Han D. The quantitative skeletal muscle ultrasonography in elderly with dynapenia but not sarcopenia using texture analysis. Diagnostics. 2020;10(6):400. https://doi.org/10.3390/diagnostics10060400\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-Satta M, Berna-Erro A, Carrasco-Garcia E, Alberro A, Saenz-Anto\u0026ntilde;anzas A, Vergara I, et al. Relevance of oxidative stress and inflammation in frailty based on human studies and mouse models. Aging (Albany NY). 2020;12(10):9982. https://doi.org/10.18632%2Faging.103295\u003c/li\u003e\n\u003cli\u003eCardoso AL, Fernandes A, Aguilar-Pimentel JA, et al. Towards frailty biomarkers: candidates from genes and pathways regulated in aging and age-related diseases, Ageing research reviews. 2018;47:214-77. https://doi.org/10.1016/j.arr.2018.07.004\u003c/li\u003e\n\u003cli\u003eKwak JY, Hwang H, Kim S, Choi JY, Lee S, Bang H, et al. Prediction of sarcopenia using a combination of multiple serum biomarkers. Scientific reports. 2018;8(1):1-7. https://doi.org/10.1038/s41598-018-26617-9\u003c/li\u003e\n\u003cli\u003ePicca A, Calvani R, Cesari M, Landi F, Bernabei R, Coelho-J\u0026uacute;nior HJ, et al. Biomarkers of physical frailty and sarcopenia: Coming up to the place? International Journal of Molecular Sciences. 2020;21(16):5635. https://doi.org/10.3390/ijms21165635\u003c/li\u003e\n\u003cli\u003eDuan X, Wang B, Zhu J, Shao W, Wang H, Shen J, et al. Assessment of patient-based real-time quality control algorithm performance on different types of analytical error. Clinica Chimica Acta. 2020;511:329-335. https://doi.org/10.1016/j.cca.2020.10.006\u003c/li\u003e\n\u003cli\u003eGomez-Cabrero D, Walter S, Abugessaisa I, Mi\u0026ntilde;ambres-Herraiz R, Palomares LB, Butcher L, et al. A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. Geroscience. 2021;43(3):1317-1329. https://doi.org/10.1007/s11357-021-00334-0\u003c/li\u003e\n\u003cli\u003eFragala MS, Jajtner AR, Beyer KS, Townsend JR, Emerson NS, Scanlon TC, et al. Biomarkers of muscle quality: N-terminal propeptide of type III procollagen and C-terminal agrin fragment responses to resistance exercise training in older adults. Journal of cachexia, sarcopenia and muscle. 2014;5(2):139-148. https://doi.org/10.1007/s13539-013-0120-z\u003c/li\u003e\n\u003cli\u003eHe L, Khanal P, Morse CI, Williams A, Thomis M. Associations of combined genetic and epigenetic scores with muscle size and muscle strength: a pilot study in older women. Journal of cachexia, sarcopenia and muscle. 2020;11(6):1548-1561. https://doi.org/10.1002/jcsm.12585\u003c/li\u003e\n\u003cli\u003eFuentes-Abolafio IJ, Ricci M, Bernal-L\u0026oacute;pez MR, G\u0026oacute;mez-Huelgas R, Cuesta-Vargas AI, P\u0026eacute;rez-Belmonte LM. Biomarkers and the quadriceps femoris muscle architecture assessed by ultrasound in older adults with heart failure with preserved ejection fraction: a cross-sectional study, Aging Clinical and Experimental Research. 2022;34:2493-2504. https://doi.org/10.1007/s40520-022-02189-7\u003c/li\u003e\n\u003cli\u003eCorrea-de-Araujo R, Harris-Love MO, Miljkovic I, Fragala MS, Anthony BW, Manini TM. The need for standardized assessment of muscle quality in skeletal muscle function deficit and other aging-related muscle dysfunctions: a symposium report. Frontiers in physiology. 2017;8:87. https://doi.org/10.3389/fphys.2017.00087\u003c/li\u003e\n\u003cli\u003eWang J, Wu W, Chang K, Chen L, Chi S, Kara M, et al. Ultrasound Imaging for the Diagnosis and Evaluation of Sarcopenia: An Umbrella Review. Life. 2021;12(1):9. https://doi.org/10.3390/life12010009\u003c/li\u003e\n\u003cli\u003eIzquierdo M. Multicomponent physical exercise program: Vivifrail, Nutricion Hospitalaria. 2019;36:50-6. https://doi.org/10.20960/nh.02680\u003c/li\u003e\n\u003cli\u003eWang X, Ji X. Sample size estimation in clinical research: from randomized controlled trials to observational studies, Chest. 2020;158(1):12-20. https://doi.org/10.1016/j.chest.2020.03.010\u003c/li\u003e\n\u003cli\u003eMamou J, Oelze ML. Quantitative ultrasound in soft tissues: Springer. 2013; 443-639.\u003c/li\u003e\n\u003cli\u003eJabbar SI, Day C, Chadwick E. Automated measurements of morphological parameters of muscles and tendons. Biomedical Physics \u0026amp; Engineering Express. 2021;7(2):025002. https://doi.org/10.1088/2057-1976/abd3de\u003c/li\u003e\n\u003cli\u003eLiu S, Wang Y, Yang X, Lei B, Liu L, Li SX, et al. No title. Deep learning in medical ultrasound analysis: a review.Engineering. 2019; 5 (2): 261\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eRoy B, Darras BT, Zaidman CM, Wu JS, Kapur K, Rutkove SB. Exploring the relationship between electrical impedance myography and quantitative ultrasound parameters in Duchenne muscular dystrophy. Clinical Neurophysiology. 2019;130(4):515-520. https://doi.org/10.1016/j.clinph.2019.01.018\u003c/li\u003e\n\u003cli\u003eYoung H, Jenkins NT, Zhao Q, Mccully KK. Measurement of intramuscular fat by muscle echo intensity. Muscle Nerve. 2015;52(6):963-971. https://doi.org/10.1002/mus.24656\u003c/li\u003e\n\u003cli\u003eNillesen MM, Lopata RG, Gerrits IH, Kapusta L, Thijssen JM, de Korte CL. Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images. Ultrasound Med Biol. 2008;34(4):674-680. https://doi.org/10.1016/j.ultrasmedbio.2007.10.008\u003c/li\u003e\n\u003cli\u003eHarris‐Love MO, Gonzales TI, Wei Q, et al. Association between muscle strength and modeling estimates of muscle tissue heterogeneity in young and old adults, Journal of Ultrasound in Medicine. 2019;38:1757-68. https://doi.org/10.1002/jum.14864\u003c/li\u003e\n\u003cli\u003eDubois GJ, Bachasson D, Lacourpaille L, Benveniste O, Hogrel J. Local texture anisotropy as an estimate of muscle quality in ultrasound imaging. Ultrasound Med Biol. 2018;44(5):1133-1140. https://doi.org/10.1016/j.ultrasmedbio.2017.12.017\u003c/li\u003e\n\u003cli\u003eMatta TTd, Pereira WCdA, Radaelli R, Pinto RS, Oliveira LFd. Texture analysis of ultrasound images is a sensitive method to follow‐up muscle damage induced by eccentric exercise. Clinical Physiology and Functional Imaging. 2018;38(3):477-482. https://doi.org/10.1111/cpf.12441\u003c/li\u003e\n\u003cli\u003ePapadacci C, Tanter M, Pernot M, Fink M. Ultrasound backscatter tensor imaging (BTI): analysis of the spatial coherence of ultrasonic speckle in anisotropic soft tissues. IEEE Trans Ultrason Ferroelectr Freq Control. 2014;61(6):986-996. https://doi.org/10.1109/TUFFC.2014.2994\u003c/li\u003e\n\u003cli\u003eKari M, Feltovich H, Hall TJ. Correlation length ratio as a parameter for determination of fiber-like structures in soft tissues. Physics in Medicine \u0026amp; Biology. 2021;66(5):055017. https://doi.org/10.1088/1361-6560/abe0fb\u003c/li\u003e\n\u003cli\u003eBurlina P, Billings S, Joshi N, et al. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods, PloS one. 2017;12(8):0184059. https://doi.org/10.1371/journal.pone.0184059\u003c/li\u003e\n\u003cli\u003eBrausch L, Hewener H, Lukowicz P. Towards a wearable low-cost ultrasound device for classification of muscle activity and muscle fatigue. Proceedings of the 23rd International Symposium on Wearable Computers; 2019:20-2. https://doi.org/10.1145/3341163.3347749\u003c/li\u003e\n\u003cli\u003eSanabria SJ, Pirmoazen AM, Dahl J, Kamaya A, El Kaffas A. Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis. Ultrasound Med Biol. 2022;48(10):2060-2078. https://doi.org/10.1016/j.ultrasmedbio.2022.05.031\u003c/li\u003e\n\u003cli\u003ePolidori MC, Mecocci P. Modeling the dynamics of energy imbalance: The free radical theory of aging and frailty revisited. Free Radical Biology and Medicine. 2022. https://doi.org/10.1016/j.freeradbiomed.2022.02.009\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Torr\u0026oacute;n R, Garc\u0026iacute;a-Puga M, Emparanza J, Maneiro M, Cobo A, Poza J, et al. Cancer risk in DM1 is sex-related and linked to miRNA-200/141 downregulation. Neurology. 2016;87(12):1250-1257. https://doi.org/10.1212/WNL.0000000000003124\u003c/li\u003e\n\u003cli\u003eAlberro A, Iribarren-Lopez A, S\u0026aacute;enz-Cuesta M, Matheu A, Vergara I, Otaegui D. Inflammaging markers characteristic of advanced age show similar levels with frailty and dependency. Scientific reports. 2021;11(1):1-10. https://doi.org/10.1038/s41598-021-83991-7\u003c/li\u003e\n\u003cli\u003eStewart A. Basic statistics and epidemiology: a practical guide: CRC Press 2018.\u003c/li\u003e\n\u003cli\u003ePonti F, De Cinque A, Fazio N, Napoli A, Guglielmi G, Bazzocchi A. Ultrasound imaging, a stethoscope for body composition assessment. Quantitative Imaging in Medicine and Surgery. 2020;10(8):1699. https://doi.org/10.21037%2Fqims-19-1048\u003c/li\u003e\n\u003cli\u003eNewman AB, Kupelian V, Visser M, Simonsick EM, Goodpaster BH, Kritchevsky SB, et al. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2006;61(1):72-77. https://doi.org/10.1093/gerona/61.1.72\u003c/li\u003e\n\u003cli\u003eRuby L, Kunut A, Nakhostin DN, Huber FA, Finkenstaedt T, Frauenfelder T, et al. Speed of sound ultrasound: comparison with proton density fat fraction assessed with Dixon MRI for fat content quantification of the lower extremity. Eur Radiol. 2020;30(10):5272-5280. https://doi.org/10.1007/s00330-020-06885-8\u003c/li\u003e\n\u003cli\u003eRuby L, Sanabria SJ, Saltybaeva N, Frauenfelder T, Alkadhi H, Rominger MB. Comparison of ultrasound speed-of-sound of the lower extremity and lumbar muscle assessed with computed tomography for muscle loss assessment. Medicine. 2021;100(21):25947. https://doi.org/10.1097/md.0000000000025947\u003c/li\u003e\n\u003cli\u003eRuby L, Sanabria SJ, Martini K, Frauenfelder T, Jukema GN, Goksel O, et al. Quantification of immobilization-induced changes in human calf muscle using speed-of-sound ultrasound: An observational pilot study. Medicine. 2021;100(11):23576. https://doi.org/10.1097/MD.0000000000023576\u003c/li\u003e\n\u003cli\u003eWong V, Spitz RW, Bell ZW, Viana RB, Chatakondi RN, Abe T, et al. Exercise induced changes in echo intensity within the muscle: a brief review. Journal of ultrasound. 2020;23(4):457-472. https://doi.org/10.1007/s40477-019-00424-y\u003c/li\u003e\n\u003cli\u003eMitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC medicine. 2015;13(1):1-9.https://doi.org/10.1186/s12916-015-0400-x \u003c/li\u003e\n\u003cli\u003eHolloszy JO, McCully KK, Posner JD. The application of blood flow measurements to the study of aging muscle. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 1995;50(Special_Issue):130-136. https://doi.org/10.1093/gerona/50A.Special_Issue.130\u003c/li\u003e\n\u003cli\u003eCheca-L\u0026oacute;pez M, Oviedo-Briones M, Pardo-G\u0026oacute;mez A, Gonzales-Tur\u0026iacute;n J, Guevara-Guevara T, Carnicero JA, et al. FRAILTOOLS study protocol: a comprehensive validation of frailty assessment tools to screen and diagnose frailty in different clinical and social settings and to provide instruments for integrated care in older adults. BMC geriatrics. 2019;19(1):1-8 .https://doi.org/10.1186/s12877-019-1042-1\u003c/li\u003e\n\u003cli\u003eChicoulaa B, Escourrou E, Durrieu F, Milon V, Savary L, Gelibert M, et al. Challenges in management of frailty by primary healthcare teams: From identification to follow-up. La Presse M\u0026eacute;dicale Open. 2022;3:100032. https://doi.org/10.1016/j.lpmope.2022.100032\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. 64thWMAGeneral Assembly, Fortaleza Brazil, October 2013. World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects, JAMA. 2013;310:2191-4. https://doi.org/10.1001/jama.2013.281053\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"muscle, ultrasound, blood-based biomarkers, sarcopenia, frailty, older adults","lastPublishedDoi":"10.21203/rs.3.rs-2648138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2648138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrailty derived from muscle quality loss can potentially be delayed through early detection and physical exercise interventions. There is a need for affordable tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessment. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional and microstructural muscle properties, while biological essays derived from blood samples are associated with functional information. The aim of this study is to evaluate multi-parametric combinations of ultrasound and blood-based biomarkers to provide a cross-sectional evaluation of the patient frailty phenotype and to monitor muscle quality changes associated with supervised exercise programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective observational multi-center study including patients older than 70 years with ability to give informed consent. We will recruit 100 patients from hospital environments and 100 from primary care facilities. At least two exams per patient (baseline and follow-up), with a total of (400 \u0026gt; 300) exams. In the hospital environments, 50 patients will be measured pre/post a 16-week individualized and supervised exercise programme, and 50 patients will be followed-up after the same period without intervention. The primary care patients will undergo a one-year follow-up evaluation. The primary goal is to compare cross-sectional evaluations of physical performance, functional capacity, body composition and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based essays. We will analyze ultrasound raw data obtained with a point-of-care device, and a set of biomarkers previously associated with frailty by quantitative Real time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA). Secondly, we will analyze the sensitivity of these biomarkers to detect short-term muscle quality changes as well as functional improvement after a supervised exercise intervention with respect to usual care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe presented study protocol will combine portable technologies based on quantitative muscle ultrasound and blood biomarkers for objective cross-sectional assessment of muscle quality in both hospital and primary care settings. It aims to provide data to investigate associations between biomarker combinations with cross-sectional clinical assessment of frailty and sarcopenia, as well as musculoskeletal changes after multicomponent physical exercise programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov Identifier: NCT05294757. Date recorded: 24/03/2022. 'retrospectively registered’\u003c/p\u003e","manuscriptTitle":"Development of continuous assessment of muscle quality and frailty in older subjects using multi-parametric omics based on combined ultrasound and blood biomarkers: a study protocol for a cluster randomised controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-14 19:20:01","doi":"10.21203/rs.3.rs-2648138/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cec4e178-3fe6-43ab-894f-5183ef5162c2","owner":[],"postedDate":"April 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-24T04:32:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-14 19:20:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2648138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2648138","identity":"rs-2648138","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}