14. Measures of Sarcopenia: The Utility of Ultrasound, Bioelectrical Impedance Analysis and Single-Slice Cross-Sectional Imaging
KeywordsBody compositionSkeletal muscleComputed tomographyBioelectrical impedance analysisUltrasonography
The Importance of Body Composition Analysis in Liver Cirrhosis
A number of precise and accurate tools and techniques have evolved to measure the prevalence and implications of sarcopenia in liver cirrhosis; however, the future utility of these tools is contingent on their practicality and feasibility for use in clinical settings. New technologies and novel applications of existing diagnostic equipment are emerging for use in research as well as clinics. For example, computed tomography (CT) imaging and ultrasound can provide complex and progressive information about the composition and distribution of diverse tissues, such as skeletal muscle and adipose tissue. These technologies offer non-invasive strategies to advance our knowledge of the role of body composition in understanding and managing illness and malnutrition . With the enhancement of body composition technologies, we can identify deviations in features of muscle and adipose tissue from normal healthy values and explore how these deviations relate to clinical outcomes [2–7]. Using tools like CT, ultrasound and magnetic resonance imaging (MRI), skeletal muscle can be quantified with a high degree of accuracy and precision; further, these quantifications may be coupled with measurements of muscle quality or integrity, which are representative of the metabolic, physiological and physical function of skeletal muscle [2, 8]. While research has elevated our knowledge of and perspectives on body composition, there is an increasing demand to translate these laboratory-based methods into feasible and practical approaches that assist clinicians in identifying patients who may be at risk of malnutrition . Researchers and clinicians have yet to agree on a single consensus definition of sarcopenia; and numerous skeletal muscle cut-points have been developed and published using both diverse populations (e.g. healthy, as well as various clinical populations including cancer and intensive care unit patients) and body composition tools (e.g. CT and DXA). Thus, meticulous attention is needed when reviewing the methodologies used in published studies and interpreting the results of these investigations. In other words, different methods and body composition tools reference different cut-points for identifying individuals with low muscle mass. Classification of liver cirrhosis patients as sarcopenic or non-sarcopenic is essential because low muscularity is associated with numerous clinical complications and deleterious outcomes. Critically, patients who are correctly identified as sarcopenic may benefit from targeted nutrition, exercise and/or pharmacological treatment that is tailored to their specific needs.
Practical Need for Identifying Sarcopenia in Liver Cirrhosis
Find and use a protocol that is clearly written and provides sufficient detail about the methodological approach
Be vigilant in how you implement and execute the protocol
Understand the limitations of your tools and the protocol
Interpret your results with caution
Choosing the Right Modality
Currently, weight and, more specifically, body mass index (BMI) are most commonly used in clinic to assess a patient’s nutritional status and help clinicians identify patients at risk of malnutrition. Weight (or, more accurately, body mass) is useful in tracking gross changes in total tissue weight (i.e. mass) over time; BMI is weight normalized to height squared (kg/m2) and is a representation of overall body size. Weight and BMI are cost- and time-efficient methods requiring only access to a scale (for weight) and stadiometer (for height). While weight and BMI are both relatively crude assessment tools, a notable advantage of BMI overweight is that patients can be classified according to World Health Organization standards as underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2) or obese (BMI ≥ 30.0 kg/m2) for their frame . Although BMI is regularly reported in nutrition and epidemiological studies on healthy and clinical populations, BMI is not capable of distinguishing between different tissues (e.g. differentiating skeletal muscle from adipose tissue), nor does it provide information on the distribution of these distinct tissues within the body; as such, BMI provides only a very limited amount of information on the body composition, and thus nutritional status, of patients. Critically, it is inappropriate to use BMI as a surrogate measure for skeletal muscle mass in clinic or in research, because it has a poor capacity for identifying malnourished or sarcopenic individuals [2, 13, 14]. Baracos and colleagues  examined a group of non-small cell lung cancer patients with CT imaging performed at the third lumbar (L3) vertebra and observed that although ~47% were classified as sarcopenic using CT (a direct measure of skeletal muscle mass), only ~7.5% were identified as being underweight according to BMI (BMI < 18.5 kg/m2). Of particular interest, almost half of the patients in this study were classified as overweight or obese based on BMI, suggesting that a high proportion of patients present with, or are at risk of, sarcopenic obesity. This pattern has also been observed in other studies in cancer [13–16], critical illness  and liver cirrhosis [1, 22], where although sarcopenia was noted in as much as 40–71% of patients, only 1–12% were underweight according to BMI [1, 2, 13–16, 22, 25].
Objectives for Measuring Skeletal Muscle Health
To identify individuals who present with, or who are at risk of developing, low muscle mass (a key component of sarcopenia or cachexia)
To monitor changes in skeletal muscle in individuals who are likely to experience muscle atrophy (i.e. critically ill, cancer or liver cirrhosis patients)
To evaluate the effectiveness of nutrition/exercise/pharmacological intervention(s) over time
Identifying Individuals at Risk of Low Muscle Mass (with Sarcopenia)
Clinicians and researchers have traditionally used BMI to identify potentially malnourished individuals; however, BMI is a simplistic method that does not distinguish between tissues like skeletal muscle and adipose tissue. Several alternative, more sophisticated modalities have the ability to isolate fat-free mass (BIA, DXA) or even skeletal muscle (ultrasound, CT), and a range of cut-points for identifying sarcopenic patients is available in the literature [12, 13, 15, 22]. Over the last decade, this range has widened, creating some confusion around which cut-points are best to employ in research and in practice.
Two key factors should be considered when selecting cut-points. First, understand the population(s) in which your cut-points of interest were derived. Consider the sex and age distribution of the individuals in this reference population, as well as any details about their clinical or body composition characteristics that may introduce bias when using these cut-points. Second, identify the analytical methods the authors of these papers used to derive the cut-points. For example, some authors will have defined their cut-points for sarcopenia as two standard deviations below a healthy reference group , whereas others will have generated their cut-points using a receiver operator curve ; the latter approach would be based on a clinical outcome like mortality or a health outcome like a functional test. Given that there is no universal consensus on how we use cut-points, choose the approach in the literature that best fits your population and the objectives for what you are trying to learn from your patient data.
Monitoring Changes in Skeletal Muscle
Longitudinal measurement of skeletal muscle is fundamental to monitoring the magnitude and rate of muscle loss occurring in patients. Acquiring these measures can provide a guide for understanding the nutritional needs of the patient. In research, there is particular interest in longitudinal evaluation as a foundational measure for comparing any future interventions. However, one needs to consider a few factors regarding their modality of choice. Evaluate the precision, accuracy and specificity of the instrument relative to the changes you will be seeking. If you anticipate relatively small changes compared with the precision of the instrument, then you need to consider whether your skeletal muscle measurements will be confounded by artefact or error. For example, if you expect a 1% change but the error of your instrument is as great as 2% for a single scan, and given that you are comparing two scans for potential changes, you may need to account for error up to 4%. In addition, ensure that you are measuring what you think you are measuring. For example, thickness of muscle using ultrasound will include intramuscular fat; keep this in mind as you interpret your results (further details found in section “Quality of the Measurement: Considering Precision, Accuracy and Specificity”).
Evaluating the Effectiveness of Nutrition/Exercise/Pharmacological Interventions
One of the most important reasons for using body composition modalities to measure skeletal muscle mass is to assess the success or failure of a given intervention, the goal of which is to attenuate or prevent muscle loss. However, similar to the objective described above (section “Monitoring Changes in Skeletal Muscle”), awareness of the precision, accuracy and specificity of your instrument is vital and will significantly affect how you interpret your results.
Quality of the Measurement: Considering Precision, Accuracy and Specificity
Body composition terminology related to interpretation of results
The degree of agreement between multiple measurements made by a given instrument/method. This definition may not account for inter- and intra-analyst error, which introduces variability and affects the reproducibility of a measurement
The variability of multiple measures acquired by the same analyst, with the same instrument/method, on the same participant under the same conditions
The ability to generate the same (similar) multiple measures by different analysts with the same instrument
The variability between measurements taken by different analysts using the same instrument/method (i.e. analyst 1 vs. analyst 2)
The variability between measurements taken by the same analyst using the same instrument/method (i.e. analyst 1 vs. analyst 1)
The agreement between two measures/methods whereby one of the methods is a reference instrument/method. Does your method measure what it is supposed to be measuring?
The systematic error in a measures/method relative to the reference method
For an in-depth discussion on utilizing body composition tools at the bedside and correctly interpreting your results, please refer to Dr. Carrie Earthman’s tutorial . Measures of precision and accuracy are particularly important in determining the ability to measure change longitudinally. Here, ‘minimal detectable difference’, which infers that a meaningful change in a body tissue needs to exceed the precision of an instrument or method, should be considered. This concept may influence the choice of method one uses to measure skeletal muscle change over time. If you are expecting relatively large changes, then you may use an instrument that is less precise. However, if you are anticipating a relatively small change over time, a highly precise instrument is needed to ensure that the changes measured are not confounded by instrument or methodological error. To improve specificity, an instrument/method should have the optimal combination of precision (repeatability and reproducibility), inter- and intra-analyst reliability as well as accuracy.
Measuring Skeletal Muscle Health: Muscle Quantity vs. Muscle Quality
Measurements of muscle quantity include – but are not limited to – muscle mass, volume, cross-sectional area and thickness . Conversely, muscle quality refers to the evaluation of features of muscle that influence its metabolic and physical function, such as tissue composition (i.e. fatty infiltration), the presence of myonecrosis or fibrosis, as well as indicators of strength (i.e. pennation angle, fascicle length) . Historically, clinicians and researchers have focused on measurements of muscle quantity alone [8, 13]; however, the combination of measures of muscle quantity and quality may be more clinically valuable. For example, Martin et al.  demonstrated that the combination of accelerated weight loss, CT-based low muscle index as well as low muscle attenuation (low muscle quality) were predictive of survival in overweight and obese lung or gastrointestinal cancer patients. On the other hand, ultrasound-based measures of accelerated muscle atrophy are observed in critically ill patients with multi-organ failure compared with those who have single-organ failure, potentially predicting poor prognosis with severity of complications . Features of muscle quality, such as fascicle length and pennation angle, have also been associated with whole body physical function in critically ill patients . Muscle quantity has been extensively evaluated in liver cirrhosis; however, muscle quality using CT imaging has been evaluated to a limited extent, and ultrasound measures of muscle quality in this population are even more limited. When muscle quantity and quality measurements are combined, these may support the definition of sarcopenia, which is based on muscle quantity and function. Thus, exploring these measures in future investigations may help better define sarcopenia. Muscle quantity and muscle quality will be discussed to a greater extent in the sections corresponding to each body composition modality.
Clinically Friendly Modalities: BIA, CT Imaging and Ultrasonography
Despite our growing knowledge of the importance of muscle metabolism and function for positive clinical outcomes, a universal, standardized approach to specifically measure skeletal muscle in a clinically feasible fashion has yet to emerge. We understand that the most practical solutions, such as BMI, do not provide accurate, tissue-specific information. Fortunately, more sophisticated modalities, such as BIA, CT imaging and ultrasonography, are available in clinic and offer important advantages over BMI and skinfold estimates of percent body fat. BIA can efficiently identify changes in fat-free mass, while CT (or MRI) imaging and ultrasound are increasingly used to quantify skeletal muscle and characterize features of this tissue. These modalities are becoming increasingly available in clinic and provide details on the body composition of patients (rather than simply weight and height) that may influence prognosis.
Bioelectrical Impedance Analysis
Summary of the merits and limitations of three clinically accessible body composition modalities
Quick measurement (approximately 15 min)
No medical technician required to operate device (anyone can be trained)
Feasible for use at bedside (no burden is placed on patient)
Measurement highly affected by hydration status and fluid shifts
Prediction equations specific to population being measured are required, and most equations are only appropriate for use in healthy adults
Not accurate in obese populations due to violations of body geometry assumptions and tissue distribution
Highly precise, accurate and direct measurements of skeletal muscle and various adipose tissue depots (subcutaneous adipose tissue, SAT; visceral adipose tissue, VAT; and intramuscular adipose tissue IMAT)
Exposes patient to large doses of radiation
Trained medical radiation technologist required to perform scan
May not be feasible or accessible for every patient/institution
CSA analysis is time-consuming (approximately 30–40 min per scan)
Precise, reliable and accurate
Directly measures skeletal muscle
No medical technician required to operate device (anyone can be trained)
Feasible for use at bedside (no burden is placed on patient)
Proper training required to correctly identify landmarks and acquire good quality images
Reporting on methods and reliability testing is minimal or lacking
No universal protocol
Obese and clinical populations may present challenges during landmarking (due to difficulty palpating bony structures), imaging (due to indistinct fascial borders) and analysis (fatty infiltration might artificially inflate muscle thicknesses)
No normative data available
How Does BIA Work?
BIA involves passing a small electrical current at one or more frequencies through the body to estimate total body water (TBW) and body composition. BIA primarily distinguishes between fat-free mass (FFM) and fat mass by estimating TBW. The low-voltage current is neither dangerous to nor felt by the patient. Electrolyte-rich tissues, such as blood and muscle, conduct the current easily, whereas fat and bone are poor conductors. BIA instruments measure the change in voltage (i.e. impedance, Z) of the current across the body. The two components of impedance, resistance (R) and reactance (X), are applied to validated, population-specific prediction equations to estimate TBW and FFM. Certain instruments, such as multi-frequency BIA and bioelectrical impedance spectroscopy, can further distinguish intracellular water (ICW) from extracellular water (ECW).
BIA Instruments: What Do They Measure?
The three main categories of instruments are single-frequency BIA (SF-BIA), multi-frequency BIA (MF-BIA) and bioimpedance spectroscopy (BIS). The SF-BIA utilizes a current of a single frequency (usually 50 kHz), which passes through the water-soluble components of the body . As a result, SF-BIA devices can estimate FFM and TBW, using specific predictive equations. There are three main types of SF-BIA: hand-to-hand, foot-to-foot and hand-to-foot. When applying your patient’s data to existing predictive equations, it is important to be mindful of the type and model of BIA system that was used to derive the given equation. Whenever possible, you should attempt to match the BIA system to account for variance in results between instruments.
The MF-BIA uses several (usually 2–6) predefined frequencies between 5 and 500 kHz, which pass through the water-soluble components of the body (e.g. muscle and blood) . The use of multiple frequencies permits differentiation between different body water compartments. For example, ECW is predicted from the lower 5 kHz frequency, and TBW is predicted from the higher frequencies. ICW, which represents body cell mass (i.e. metabolically active tissue), can then be calculated by subtracting ECW from TBW.
In contrast to SF-BIA and MF-BIA, BIS does not use linear prediction equations. Rather, many currents (range: 50–250 individual currents) across the entire spectrum of frequencies (5–1200 kHz) are passed through the body, and complex modeling is applied to the data to generate estimates of ICW, ECW and FFM [26, 29]. BIS measures both ECW and ICW directly, from the lower and higher ends of the spectrum, respectively.
(Note: ht = height in m; wt = weight in kg; sex values include female = 0; male = 1)
In cancer patients, SPA values below −1.65 have been shown to indicate malnutrition. Age, sex and BMI all influence PA measurements.
Using BIA with Standardized Protocols
Environmental temperature: Choose a testing location with normal ambient temperature (not excessively cool or warm). Temperature may affect conductance of the current, hydration status (e.g. sweat loss with warmer temperatures) or involuntary muscle contraction (e.g. shivering with cooler room temperatures).
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