Methods for evaluating muscle-related conditions

Abstract

The present invention relates to a method (100) and system for assessing muscle-related conditions in an individual subject. The method comprises the following steps: obtaining (101) a muscle mass value (20) of the individual subject; obtaining (102) a data parameter value (10) of the individual subject, wherein the data parameter value is related to a quantitative parameter of the body composition of the individual subject; selecting (103) a certain number of individuals from a database (30), the database comprising at least one data parameter value (31) of a plurality of individuals and muscle mass values ​​(32) of the plurality of individuals, wherein the selection is based on comparing the at least one data parameter value with at least one data parameter value of the individual subject, thereby creating a virtual control group VCG (40); calculating (104) a predicted value (50) of the muscle mass value (42) of the individuals in the VCG (40); and comparing (105) the muscle mass value (20) of the individual subject with the determined predicted value (50) of the VCG (40).

Description

Technical Field

[0001] The present disclosure relates to a method for assessing a muscle-related condition in an individual subject, and more particularly to a method that uses muscle mass as part of the input data. Background Art

[0002] There are many different ways to identify health-related conditions in individuals. Some muscle-related conditions, such as sarcopenia, develop slowly over time, which can make common methods where specific tests are repeated difficult to use.

[0003] For example, sarcopenia, a condition characterized by the progressive loss of muscle mass and function over time, is associated with adverse outcomes in several different disease areas.

[0004] To identify low muscle mass, dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are widely used to estimate appendicular skeletal muscle mass (ASM) and provide a basis for identifying thresholds that are sensitive to subjects with particularly low muscle mass.

[0005] A major challenge in understanding sarcopenia and its consequences is the wide variation in normal physiology that exists in the general population. The causes of low muscle mass are universal in nature; certain phenotypes of low muscle mass may be associated with longevity, as a healthy lifestyle combined with a low caloric intake may lead to low muscle mass. From the perspective of the specificity of the definition of sarcopenia, identifying such individuals as having sarcopenia is problematic. From an individual perspective, what is increasingly problematic about current definitions of sarcopenia is that the growing obesity epidemic complicates early diagnosis. With a higher body mass index (BMI), it becomes more difficult to identify individuals with sarcopenia by virtue of low muscle mass, as individuals become heavier and have more muscle mass. This has led to the methods that have been disclosed for applying a range of body size adjustments to measured muscle mass, namely by dividing, for example, the ASM by height. 2 , weight, or BMI. However, debate remains about what the preferred adjustment is, and the recently updated European consensus on the definition and diagnosis of sarcopenia (EWGSOP2) does not make specific recommendations. Another challenge in detecting sarcopenic obesity is that prolonged loss of fat mass due to wasting over time may be masked by the patient's desire to lose weight. In addition to these challenges, different DXA instrument brands do not give consistent results, and muscle mass measurements can be affected by body thickness and body water status. BIA has similar limitations.

[0006] In order to identify low function, several measurements have been proposed and commonly used, including hand grip strength, chair stand, gait speed, 400-meter walk test, timed up and go test, and short-term physical activity ability. These are obtained at low cost, but are not muscle-specific and are therefore insensitive to the cause of low function. Factors that may affect these tests are, for example, the motivation for performing the test, the patient's general fitness level, neurological causes, pain, or arthritis. In addition, patients in the late stages of the disease may find it increasingly difficult to perform each of these tests.

[0007] The current consensus on sarcopenia is to use a combination of functional measures and muscle mass or quality to assess and confirm sarcopenia. A combination is needed to increase the specificity of sarcopenia diagnosis: low muscle strength may be due to, for example, depression, stroke, balance disorders, or peripheral vascular disease, while low muscle volume may simply be due to smaller body size (compared to the general population).

[0008] Therefore, there is a need for a method for assessing muscle-related disorders (such as sarcopenia, cachexia, muscle degenerative diseases, or muscle changes due to, for example, exercise or weight loss) that is also applicable to overweight and obese individuals and is more efficient and predictive than previously known methods. Summary of the Invention

[0009] Magnetic resonance imaging (MRI) is considered to be the gold standard for non-invasive assessment of muscle mass together with computed tomography (CT). Body composition analysis is a concept that has utilized standardized MRI examinations, which can simultaneously assess traditional body composition (total volume of lean muscle tissue and total volume of adipose tissue) and characterize adipose tissue distribution and ectopic fat accumulation in detail, such as visceral adipose tissue (VAT), liver fat and muscle fat infiltration (MFI). Muscle fat infiltration has previously been used as a quantitative measure of muscle quality and is a mature biomarker in the description of different muscular dystrophy. EWGSOP2 anticipates that the assessment of muscle quality will help guide treatment selection and monitor future responses to treatment.

[0010] The object of the present invention is to provide an improved solution that mitigates the above-mentioned disadvantages of existing methods. In addition, the object is to provide a method for obtaining an individualized (e.g. BMI invariant) threshold value for muscle-related conditions such as sarcopenia or cachexia, for example for identifying abnormally low muscle mass.

[0011] The present invention is defined by the accompanying independent claims, embodiments of which are set forth in the accompanying dependent claims, in the following description and in the accompanying drawings.

[0012] According to a first aspect of the present invention, a method for assessing muscle-related conditions in an individual subject is provided. The method comprises the following steps: obtaining a muscle mass value of a first muscle of the individual subject; obtaining at least one data parameter value of the individual subject, wherein the data parameter value is related to a quantitative parameter of the body composition of the individual subject; selecting a certain number of individuals from a database, wherein the database comprises at least one data parameter value of a plurality of individuals and the muscle mass values ​​of the first muscle of the plurality of individuals, wherein the selection of the certain number of individuals from the database is based on comparing the at least one data parameter value with the at least one data parameter value of the individual subject, thereby creating a virtual control group (VCG) including the selected individuals; calculating a predicted value of the muscle mass value of the individuals in the VCG; and comparing the muscle mass value of the individual subject with the determined predicted value of the VCG.

[0013] The at least one data parameter value may be a value of a parameter selected from the group consisting of: BMI (body mass index), gender, age (e.g., within a certain range), race, total fat mass, total muscle mass, or another weight- and / or height-related parameter representing an individual's body composition. The type of parameter may be selected to provide a basis for selecting individuals from the database, with the goal of finding individuals similar to the subject individual. In one embodiment, two data parameter values ​​may be obtained for the subject individual, and the database may include two corresponding data parameter values ​​for the individuals therein. In one embodiment, these two parameters may be gender and body composition, where body composition represents a parameter including data regarding the individual's weight and / or height. In other embodiments, the two parameters may be, for example, BMI and gender, total fat mass and gender, total muscle mass and gender, BMI and total fat mass, BMI and age, BMI and race, age and total fat mass, or BMI and total muscle mass. When selecting individuals from the VCG database using two parameters, the first data parameter may be used first to exclude a portion of the database cohort from further selection. The second data parameter may then be applied to the remaining portion of the database cohort to select individuals for the VCG. Such a first data parameter may be, for example, gender, wherein if the subject individual is female, the male individuals that were first screened out are excluded. Among the female individuals in the database, a second data parameter (e.g., BMI) may then be used to select individuals by comparing the BMI values ​​of the female individuals in the database with the BMI of the subject individual. In another embodiment, more data parameters may be used to select individuals from the database to be included in the VCG. For example, three, four, or more data parameters may be obtained for the subject individual, and the database may include corresponding three, four, or more data parameters for the individuals therein, and the individuals selected for the VCG may be based on the three, four, or more data parameters. As an illustrative example, the selection may be based on the gender, race, age, and BMI of the subject individual and the individuals in the database.

[0014] The muscle mass of the first muscle can be further determined using MRI scanning and image analysis. That is, a magnetic resonance imaging device can be used to obtain the muscle mass of the first muscle. The first muscle can be a thigh muscle, because the size of the thigh muscle may be closely related to the individual's body shape.

[0015] Muscle mass can refer to a quantification of muscle based on muscle volume, muscle mass, muscle cross-sectional area, or a combination thereof.

[0016] The predicted value can be a value determined based on the muscle mass values ​​of the first muscle of the selected individual from the database, which provides a prediction of the common value of the first muscle of this group. Such a predicted value can be calculated or modeled in several different ways. The purpose of the predicted value is to provide a numerical representation of the muscle mass values ​​of the first muscle of the entire group of individuals in the VCG. This representation in the form of the predicted value is then compared with the muscle mass values ​​of the individual subjects. Thus, when determining muscle-related disorders, the predicted value can provide an individual reference value for the individual subjects.

[0017] By creating a virtual control group using data parameter values ​​describing individual data parameters, a determination using the muscle mass value of a first muscle can be made using a normalized relationship between the data parameter and muscle mass, thereby providing an individualized threshold (predicted value) adjusted for, for example, body shape. Therefore, the present invention provides a systematic method for providing a reference value for muscle mass that is corrected for body shape, such as height and weight. Thus, muscle mass is normalized with respect to height and weight.

[0018] The muscle-related condition for which the assessment in this method may provide a basis can be any type of muscle degenerative disease, sarcopenia, cachexia, growth disorder, muscle-related metabolic disease, or neurological disease. This method can further be used to assess an individual's exercise-related condition, such as exercise results, rehabilitation measures, or other muscle-related changes, such as during weight loss. In any case, this method can be used to assess a muscle-related condition in an individual subject using normalized reference values ​​that conform to different body compositions.

[0019] In one embodiment, the predicted value can be the average, median, or model predicted value of the muscle mass values ​​of the individuals in the VCG. By determining the average, median, or model predicted value of the individuals in the VCG, a value that is predicted to represent the common value of the muscle mass of the group can be provided. Such determination can provide a predicted value that is suitable for comparison with the muscle mass values ​​of the individual subjects.

[0020] In one embodiment, the muscle mass value may be a fat-free muscle volume (FFMV) value. When assessing sarcopenia or other muscle-related conditions, fat-free muscle volume may be a suitable biomarker for use in the assessment. Additionally, fat-free thigh muscle volume may be used as a biomarker because thigh muscle size is closely related to body shape, thereby compensating for an individual's body shape.

[0021] In one embodiment, a muscle mass value may represent the effective volume of a first portion of a first muscle, the first portion having a level of muscle fat infiltration less than a predetermined threshold level T1, wherein the effective volume is determined by multiplying the volume of the first portion of the first muscle by 1-(1 / T1)*MFI1, where MFI1 is the level of muscle fat infiltration in the first portion of the first muscle. The first muscle may be partially damaged or non-functional due to high levels of fat infiltration. For this method, when providing a muscle mass value, a second portion of the muscle with a level of fat infiltration greater than the threshold level may be ignored. The term "effective" may be used to identify the volume of the first portion of the first muscle as a muscle volume that remains effective in terms of muscle function. The first portion of the first muscle, i.e., the portion with muscle fat infiltration less than the threshold level, may be used as the basis for the muscle mass value. For the first portion of the muscle, the effective volume is determined based on the muscle fat infiltration in the first portion. By multiplying the volume of the first portion by 1-(1 / T1)*MFI1, an effective volume value can be provided. This effective volume value is such that: when the muscle fat infiltration is zero, the entire first portion volume is used, and as the muscle fat infiltration level increases toward a predetermined threshold, the effective volume of the first portion toward zero is used. This provides the effective volume of the first muscle, enabling an assessment based on the residual function of the first muscle. In one embodiment, the predetermined threshold level for muscle fat infiltration can be between 30% and 80%. In another embodiment, the predetermined threshold level can be between 40% and 70%. In another embodiment, the predetermined threshold level can be between 45% and 55%. In another embodiment, the predetermined threshold level can be approximately 50%. The muscle mass value for an individual in the database can be a muscle mass value representing the corresponding effective volume of the corresponding individual, serving as the muscle mass value for the individual subject.

[0022] In another embodiment, the step of comparing the muscle mass value of the first muscle of the individual subject with the determined predicted value of the VCG comprises the following steps: determining a measure of the deviation of the muscle mass value from the predicted value. The deviation of the muscle mass value relative to the predicted value can provide a numerical representation of the deviation of the individual muscle mass value from the predicted value. The predicted value that provides an individualized reference value for the subject's muscle mass value can be used as a basis, and comparison with it can provide a measure of the deviation of the muscle mass value from it. In another embodiment, the deviation measure can be the number of standard deviations that the individual subject's muscle mass value is less than or greater than the predicted value. When the muscle mass value of the first muscle of the individual subject is compared with the predicted value, the number of standard deviations that the muscle mass value is less than or greater than the predicted value can provide an effective analysis of the relationship between the muscle mass value and the VCG predicted value. Therefore, this can provide an effective determination or assessment of muscle-related disorders.

[0023] In one embodiment, the method may further include the step of determining a muscle-related condition in the individual subject based on the comparison by determining whether the determined deviation measure is greater than or less than a predetermined threshold. Depending on the evaluation conditions, a threshold may be selected at which the evaluated outcome may indicate a certain condition.

[0024] In another embodiment, the step of selecting a certain number of individuals from the database may include the following steps: selecting individuals whose at least one data parameter value is within a predetermined range of the data parameter value of the individual subject. The data parameter value of the individual in the database may be a numerical value such as BMI. When selecting individuals for the VCG, the data parameter value of the individual in the database can be compared with the data parameter value of the individual subject, and if the data parameter value of the individual is within the predetermined range, it can be selected for the VCG. The predetermined range can be fixed or depend on another parameter of the individual subject, such as gender, age, etc. If two or more data parameter values ​​are present and used for the selection, the selection based on the predetermined range of the data parameter value of the individual subject can apply to one of the data parameter values, a portion of the available data parameter values, or all of the data parameter values.

[0025] As an example, if a subject has a BMI within ±2 kg / m2 of the subject's individual BMI 2 Within, individuals in the database can be selected for VCG.

[0026] In one embodiment, the step of selecting a certain number of individuals from the database may include the following steps: if the predetermined number of individuals meeting the criteria are not found in the database, then the range is extended from the data parameter value of the subject individual. Following the above example, if the predetermined number of individuals meeting the criteria is not reached, then the range may be increased by 0.1 kg / m 2 Gradually expand until the predetermined number is reached.

[0027] In another embodiment, the method may further include the following steps: obtaining a parameter value of a second biomarker for the individual subject. The second biomarker can improve the assessment of muscle-related conditions. The parameter value of the second biomarker can be used in the assessment in conjunction with the results of comparing the muscle mass value of a first muscle of the individual subject with the predicted value of the VCG. The second biomarker can be a muscle-related biomarker for the first muscle. Alternatively, the second biomarker can be a muscle-related biomarker for a second muscle or total muscle mass. In yet another embodiment, the second biomarker can be a non-muscle-related biomarker, such as visceral fat mass, organ fat content, etc. The parameter value of the second biomarker can be obtained based on an MRI scan of the individual subject. In one embodiment, the method may include the following steps: performing an MRI scan, and obtaining the parameter value of the second biomarker based on the performed MRI scan. If, in an embodiment, the muscle mass value is obtained from the MRI scan, the same MRI scan can be used to obtain the parameter value of the second biomarker.

[0028] In another embodiment, the parameter value of the second biomarker can be compared with a predetermined threshold value. The parameter value of the second biomarker can be compared with a predetermined threshold value. The threshold value can be fixed or depend on the data parameter value of the individual subject, such as gender, weight, BMI, total fat mass, total muscle mass, etc.

[0029] In one embodiment, the comparison of the parameter value of the second biomarker with the predetermined threshold is combined with the comparison of the muscle mass value with the VCG predicted value. Thus, a condition assessment can be performed by comparing the parameter values ​​of muscle mass and the second biomarker with the predicted value and the predetermined threshold, respectively. Such an operation can provide an effective analysis for assessing the condition of an individual subject.

[0030] In other embodiments, the second biomarker can be muscle fatty infiltration. In embodiments where the presence or risk of sarcopenia is determined by this method, the second biomarker can be muscle fatty infiltration (MFI), preferably of the same muscle or muscle type as muscle mass. Combining MFI with muscle mass or FFMV can provide a more complete picture of muscle composition, further providing additional indicators of unhealthy conditions. MFI can also be referred to as intramuscular adipose tissue (IMAT).

[0031] In one embodiment, the muscle mass value of the first muscle of individual subject can be an MRI scan based on individual subject. MRI scan to individual subject can provide effective and accurate quantization of muscle mass. In order to obtain the muscle mass value of the first muscle of individual subject by MRI scan, any known method for quantifying such biomarker can be used. A kind of such method is disclosed in EP 2283376 B1. In one embodiment, the method may comprise the following steps: performing an MRI scan to individual subject, and muscle mass value can be obtained from the MRI scan. Accordingly, the above-mentioned description of muscle mass value can also be applied to the parameter value of the second biomarker.

[0032] According to a second aspect of the present invention, a system is provided, comprising a device configured to perform a method according to any of the above embodiments. Such a device may be a computer configured to perform the method. The computer may be provided with a computer program product configured to perform the method.

[0033] According to a third aspect of the present invention, there is provided an evaluation device configured to evaluate muscle-related conditions of an individual subject, the evaluation device comprising: an acquisition device configured to acquire a muscle mass value of a first muscle of the individual subject, wherein the muscle mass value represents an effective volume of a first portion of the first muscle, the first portion having a muscle fat infiltration level less than a predetermined threshold level T1, wherein the effective volume is determined by multiplying the volume of the first portion of the first muscle by 1-(1 / T1)*MFI1, wherein MFI1 is the muscle fat infiltration level in the first portion of the first muscle, and wherein the data parameter value is related to the body composition of the individual subject. ; a selection device configured to select a certain number of individuals from a database, wherein the database includes at least one data parameter value of a plurality of individuals and muscle mass values ​​of the first muscle of the plurality of individuals, wherein the selection of a certain number of individuals from the database is based on comparing the at least one data parameter value with at least one data parameter value of the subject individual, thereby creating a virtual control group VCG including the selected individuals; a calculation device configured to calculate a predicted value of the muscle mass value of the individual in the VCG; and a comparison device configured to compare the muscle mass value of the subject individual with the determined predicted value of the VCG. In one embodiment, these different devices in the evaluation device can be provided by corresponding acquisition units, selection units, calculation units, and comparison units. These units can be provided in one or more computer units or processing units. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be described in more detail hereinafter with reference to the accompanying drawings, in which:

[0035] Figure 1 A flow chart of a method according to an embodiment of the present invention is shown;

[0036] Figure 2 A block diagram of a method according to an embodiment of the present invention is shown;

[0037] Figure 3 FFMVi is shown VCG and MFI joint and its distribution plot;

[0038] Figure 4 Is with FFMVi VCG to compare the effects of applying thresholds to FFMVi on the proportion of participants within sex-specific BMI categories who failed the muscular assessment;

[0039] Figure 5 Shows that FFMVi compares to other body size adjustments commonly used to assess muscle mass for sarcopenia detection VCG Association with BMI; and

[0040] Figure 6 FFMVi is shown compared with ASMi, FFMV, FFMVi, ASMi / BMI, FFMV / BMI, and FFMVi / BMI. VCG and the relationship between MFI and age. DETAILED DESCRIPTION

[0041] The present invention will be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. However, the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the drawings, like numbers represent like elements.

[0042] Figure 1An overview of a disclosed method 100 for assessing a muscle-related condition in an individual subject is presented. Method 100 includes the following steps: obtaining 101 a muscle mass value for a first muscle in the individual subject. This may be, for example, a fat-free muscle volume (FFMV) value for the thigh muscle of the individual subject. Furthermore, obtaining 102 at least one data parameter value that provides a quantitative parameter of the individual subject's body composition. Such data parameters may be, for example, gender and BMI. Next, selecting 103 individuals from a database comprising data values ​​for at least one parameter and muscle mass values ​​for the first muscle for a plurality of individuals. The data parameter(s) for which values ​​exist for the individual in the database are preferably the same data parameter(s) obtained for the individual subject. A certain number of individuals in the database are selected based on a comparison of at least one data parameter value for the data parameter with at least one data parameter value for the individual subject. Thus, a virtual control group (VCG) is created for the individual subject. After the VCG is created, a predicted value for the muscle mass value of the first muscle for the individuals in the VCG is determined 104. The predicted value is compared 105 with the muscle mass value of the individual subject. This comparison 105 can provide an assessment of the muscle-related condition of the individual subject. Accordingly, the condition of the individual subject can be determined 106.

[0043] Figure 2 Further illustrating an embodiment of the present invention. Two data parameters 10 for an individual subject are provided, namely, sex and BMI. These values ​​are compared with corresponding data parameter values ​​31a, 31b for n individuals in a database 30. Data parameter 31a indicates the sex (male or female) of the individual in the database 30. The database 30 further includes a muscle mass value 32 for each individual therein. Based on the comparison between the data parameters 10 for the individual and the data parameters 31a, 31b for the individuals in the database 30, a selection is made 103 to form a virtual control group VCG 40, which includes m individuals selected from the n individuals in the database 30. For each individual in the VCG 40, a predicted value 50 is calculated 104 using the muscle mass value 42. As an example, the predicted value 50 can be the average of the muscle mass values ​​42 in the VCG 40. Finally, the predicted value 50 is compared with the muscle mass value (e.g., fat-free muscle volume FFMV value) 20 of the individual subject. Thus, an assessment of a muscle-related condition in an individual subject is provided, wherein an individualized threshold value is provided, and the assessment is thereby normalized to the body size of the individual subject.

[0044] The muscle mass 50 of the individual subject may be obtained by an MRI scan 60, thereby providing a quantitative value of muscle mass. The data parameters 10 of the individual subject are typically obtained from previous data or measurements.

[0045] The assessment may further include the additional steps of obtaining a second biomarker value, exemplified here as a muscle fatty infiltration MFI value 70, and comparing this value 107 to a predetermined threshold 80. This may provide improved efficiency and accuracy in assessing muscle-related conditions in individual subjects, particularly when combined with comparison of muscle mass values ​​using individualized thresholds.

[0046] Figure 3 An assessment is shown using a combined comparison 105, 107 of the muscle mass value 50 to a predicted value and the MFI value 70 to a predetermined threshold 80. The graph can be used to visualize a muscle-related condition 200 in an individual subject based on the assessment.

[0047] Below, in an example of determining the presence of sarcopenia, a method for using fat-free muscle mass (FFMV) as a biomarker for an individual subject is described. The results of this method are also discussed in comparison with previously known muscle assessments for determining low muscle mass to understand the correlation between these methods. It should be noted that in this example, as discussed above, another muscle mass representation besides FFMV can also be used.

[0048] To provide a basis for the determination of sarcopenia using an individualized sarcopenia threshold to identify abnormally low muscle mass, a virtual control group VCG was created for each subject and the index FFMV / height was used. 2 (FFMVi). By applying a filter, the same sex and within ±2 kg / m2 of the individual subject’s BMI were included. 2 If this filter does not select 150 individuals for the VCG, the BMI intervals can be symmetrically and incrementally increased by 0.1 kg / m 2 The number of virtual controls was gradually increased until at least 150 virtual controls were classified. To measure the degree to which each individual subject deviated from their predicted FFMVi, the individual FFMVi z-score (the number of standard deviations (SD) relative to the mean (predicted value)) was calculated based on the VCG distribution. This is called FFMVi VCG The application of FFMVi can be applied to studying the functional performance of these individuals within gender-specific BMI categories who have not been assessed for muscle mass. VCG The thresholds for BMI and FFMVi were compared. The basis for identifying an individualized sarcopenia threshold for low muscle mass was created by presenting FFMVi values ​​corresponding to different numbers of SD below the mean for a range of BMI values.

[0049] FFMVi VCGPlaced in the context of other body size adjustments for muscle measurements commonly used to assess muscle mass for sarcopenia detection, its association with BMI can be compared to ASM / height. 2 The associations of (appendicular skeletal muscle mass ASM), ASM / body weight, ASM / BMI, FFMVi, FFMV / body weight, and FFMV / BMI with BMI were compared.

[0050] Modular Muscle Assessment for Sarcopenia

[0051] Combined muscle assessment and age

[0052] To study FFMVi VCG The relationship between muscle fatty infiltration (MFI) and age can be compared with the associations of ASMi, FFMV, FFMVi, ASMi / BMI, FFMV / BMI, and FFMVi / BMI by visualizing and calculating the 5-year mean difference and 5-year effect size.

[0053] Composite muscle assessment, healthcare burden, and functional outcomes

[0054] To further assess the potential value of a combined muscle assessment (including both FFMV and MFI) for sarcopenia, MFI was investigated as a potential biomarker of muscle mass to predict function and mobility. The following variables were included in the study:

[0055] Health care burden, defined as the number of hospital nights in the 10 years before the scan, excluding pregnancy-related nights (ICD10 codes under O and P), and truncated at 30 nights.

[0056] Handgrip strength: Compare subjects below and above the sex-specific threshold for sarcopenia detection (thresholds for women / men: 16 / 27 kg). Measurements of right / left hand grip strength less than 10 kg may be excluded. The handgrip strength used for analysis is the handgrip strength reported as dominant. If handedness information is not available, the average of the right and left hands may be used.

[0057] Usual walking speed, comparing participants who reported a "slow pace" to those who reported a "steady average pace" or "fast pace."

[0058] Stair climbing, comparing participants who reported climbing zero stairs (approximately 10 steps) in a day to participants who reported climbing one or more stairs a day.

[0059] Number of falls, comparing subjects who reported having more than one fall in the past year to subjects who reported no falls. Subjects who reported only one fall were excluded from the analysis (N = 1,223).

[0060] You can use FFMVi VCG Multivariable logistic regression modeling was performed using VAT and MFI as predictors. Due to the wide range of diseases and complications included in the measurement of healthcare burden, abdominal fat distribution (described by VAT and ASAT) can also be included as a predictor. The model was further adjusted for sex and age. The association between healthcare burden and body composition was studied in the entire cohort (N=9,615), in subjects below the ASMi threshold for sarcopenia detection (N=797), and in subjects below the handgrip strength threshold for sarcopenia detection (N=612). FFMVi can also be used instead of FFMVi VCG To repeat this analysis.

[0061] Multivariable logistic regression models for predicting handgrip strength, usual walking speed, stair climbing, and number of falls included only MFI and FFMVi VCG , and adjusted for sex, age, and BMI.

[0062] Detecting low functional performance through a combined muscle assessment

[0063] To investigate the value of a combined muscle assessment measuring FFMV and MFI, fitted values ​​for predicting the four functional outcomes described above (failed the handgrip strength test, usually slow walking speed, did not climb stairs, and fell more than once in the past year) were extracted from four logistic regressions using the following predictors: (1) FFMVi, (2) FFMVi VCG , (3) MFI, (4) FFMVi VCG The fitted values ​​were used as predictors in receiver operating characteristic (ROC) analysis, and the area under the ROC curve (AUC) and the corresponding confidence intervals were calculated to compare the diagnostic performance.

[0064] Sarcopenia thresholds for combined muscle assessments

[0065] Provides a basis for the application of thresholds for sarcopenia detection based on combined muscle assessment. Calculation of FFMVi VCG The proportion of female / male subjects with MFI values ​​less than different thresholds, as well as the proportion of subjects with low functional performance (i.e., low handgrip strength, slow walking speed, no stair climbing, and more than one fall in the past year) in these groups. VCG The threshold is converted to FFMVi value.

[0066] The results of the studies performed are used below to further illustrate the present approach.

[0067] Subjects

[0068] Table 1 summarizes the characteristics of the full cohort, women, and men, respectively.

[0069] Muscle mass assessment in sarcopenia

[0070] Table 2 shows the difference in BMI between the groups without muscle assessment (ASM / height assessed by DXA) and the group with the highest BMI. 2 ), handgrip strength test, and a combination of the two. The number of subjects failing the muscle assessment decreased with increasing BMI: among obese subjects, 0.0% of women and 0.2% of men failed, compared with 11.1% and 36.8% of normal-weight women and men, respectively.

[0071] Individualized muscle volume assessment

[0072] Figure 4 FFMVi is shown VCG The combination of FFMVi and MFI and its distribution. VCG and the coefficient of determination (R 2 ) is 0.13 / 0.17 (female / male).

[0073] Figure 5 Shown with FFMVi VCG The effect of applying a threshold to FFMVi on the proportion of subjects who failed the muscle assessment within a specific BMI category for a particular sex was compared. Applying a threshold to FFMVi resulted in different percentages of subjects being stratified according to BMI category, whereas VCG The percentage of subjects stratified by applied thresholds was independent of BMI category.

[0074] Figure 6 Use the corresponding R in Table 3 2 The values ​​show that FFMVi is significantly better than other body size adjustments commonly used to assess muscle mass for sarcopenia detection. VCG For women, the association between ASM and weight or BMI was 1.7 times and 2.4 times higher than that between FFMV and weight or BMI, respectively. 2 To adjust ASM to make the correlation with BMI (R 2 =0.640) than the initial correlation observed between ASM and BMI (R 2 =0.378). ASM / weight introduced a negative correlation with BMI and also an amount greater than the initial observation (R 2=0.397), and ASM / BMI introduced a similar negative correlation (R 2 =0.315). The association between muscle volume and BMI was effectively established using the sham control group (R 2 =0.002 / 0.006 (female / male)) normalized FFMVi VCG .

[0075] Modular Muscle Assessment for Sarcopenia

[0076] Combined muscle assessment and age

[0077] Figure 7 FFMVi is shown compared with ASMi, FFMV, FFMVi, ASMi / BMI, FFMV / BMI, and FFMVi / BMI. VCG and the relationship between MFI and age. VCG The MFI was shown to be negatively correlated with age, with a mean 5-year difference of -0.19 SD relative to the mean VCG between 47 and 77 years old. The MFI was shown to be positively correlated with age, with a mean 5-year difference of 0.40 pp. The mean 5-year difference and the corresponding SD and 5-year effect size can be found in Table 4. VCG ), the 5-year-old effect size of FFMV increased slightly, and the 5-year-old effect size of MFI was found to be the highest.

[0078] Combined muscle assessment and health burden

[0079] The results of statistical modeling of health care burden can be found in Table 5. For all groups (the entire cohort and subjects with low ASMi (p < 0.001) and subjects with low handgrip strength (p < 0.05)), higher MFI was significantly associated with higher health care burden. Lower FFMVi was significantly associated with lower health care burden for the entire cohort (p < 0.01) and subjects with low ASMi (p < 0.05). VCG For subjects with low handgrip strength, FFMVi VCG The association with health care burden was not significant. The association between VAT and health care burden was positive for the entire cohort (p < 0.05), negative for subjects with low ASMi (p < 0.05), and not significant for subjects with low handgrip strength. The association with ASAT was not significant for all groups. FFMVi was included instead of FFMVi VCG The model shows that for all groups (whole cohort and ASM / height 2Higher MFI was significantly associated with higher health care burden in subjects with low ASMi (p < 0.001) and those with low handgrip strength (p < 0.01). The association between FFMVi and health care burden was significant only for subjects with low ASMi. The association between health care burden and VAT and ASAT remained.

[0080] Composite muscle assessment and functional outcomes

[0081] The results of multivariate statistical modeling for the remaining functional outcomes of the study (handgrip strength, stair climbing, walking speed, and number of falls) can be found in Table 6. VCG Associations with handgrip strength, stair climbing, and walking speed were significant in the entire cohort and for women and men separately. VCG The association with the number of falls was not significant in any group. The associations between MFI and all functional outcomes were significant in all groups except men, in whom the associations with stair climbing and the number of falls were not significant.

[0082] Detecting low functional performance through a combined muscle assessment

[0083] Table 7 shows the use of FFMVi, FFMVi VCG and MFI, and combined use of FFMVi VCG Results of ROC analysis with MFI as a predictor of low functional performance. VCG The diagnostic performance of replacing FFMVi as a predictor was higher for all functional outcomes and in all groups (whole cohort, women, and men). VCG In comparison, the diagnostic performance using MFI was higher in all groups for stair climbing and number of falls, of the same magnitude for walking speed, and slightly lower for handgrip strength. VCG The combination of RT-PCR and MFI yielded the highest diagnostic performance for detecting subjects with low functional performance.

[0084] Sarcopenia thresholds for combined muscle assessments

[0085] FFMVi can be found in the sex-specific Tables 8 to 11 VCG The proportion of female / male subjects with MFI values ​​less than different thresholds, as well as the proportion of subjects with low functional performance in these groups. VCG Lookup table of BMI values ​​corresponding to thresholds and FFMVi values ​​for each gender.

[0086] in conclusion

[0087] The proposed method provides a basis for BMI-invariant muscle mass assessment for sarcopenia detection and reveals the value of combined muscle assessment (fat-free volume and fatty infiltration) performed by MRI and advanced image analysis techniques for segmentation and quantification. Three main findings from the above can be provided. First, FFMVi obtained by sham control group VCG Effectively correlate muscle mass with BMI (R 2 = 0.002 / 0.006) normalized (female / male). Secondly, muscle fat infiltration (MFI) and fat-free muscle volume (FFMVi) based on virtual control VCG ) has a very low correlation (R 2 =0.13 / 0.17 (female / male), and predicted hospitalization, muscle function (hand grip strength), and activity function (stair climbing, walking speed, and number of falls), respectively. VCG The combination of RT-PCR and MFI improved the functional association between imaging biomarkers and functional outcomes, and the combination of these two had the highest diagnostic performance for predicting low function.

[0088] The above provides that the previously proposed adjustment (divided by height 2 Neither weight nor BMI) effectively normalized the association between muscle mass (ASM or FFMV) and body size (Table 3). In fact, in large cohort studies, ASM / height 2 This effectively normalizes the association between ASM and height, but also introduces a correlation with BMI (R 2 =0.640), which is significantly higher than the correlation initially observed between ASM and BMI (R 2 =0.378). In addition, the other two adjustments (dividing by weight or BMI) introduced inverse associations with BMI that were larger or similar in magnitude to those initially observed. The few, if any, previous studies on sarcopenia have controlled whether the application of these adjustments actually normalizes measured muscle mass for body size as intended, which could lead to misinterpretation of the results. In the context of the other suggested adjustments (Table 3), FFMVi VCG The low correlation with height, weight, and BMI shows effective normalization for body shape. VCG The reason for this efficiency is that the specific distribution of FFMVi for each BMI value is taken into account when adjusting muscle measurements. This enables BMI-invariant assessment for sarcopenia detection, opening up the possibility of correctly assessing sarcopenia in overweight and obese people.

[0089] In addition to the effective body size normalization achieved by the use of a virtual control group in this method, the adjustment of FFMV (to obtain FFMViVCG ) strengthens the association between FFMV and both hospitalization and functional outcomes, indicating that assessing the extent to which an individual deviates from their expected muscle mass (rather than using directly measured muscle mass) and comparing with population-based thresholds is more clinically relevant. The rationale behind adjusting for muscle mass for body size is the fundamental correlation between the two due to the body's natural response to increasing muscle mass in response to weight gain, i.e., more muscle is needed to carry the body and increases in muscle mass enable individuals to better maintain their mobility and function during periods of weight gain. Although MFI also correlates with BMI, MFI was not adjusted for body size in this study. The reason is that, in contrast to higher muscle mass, higher MFI (perhaps caused by weight gain) is associated with lower function and worse outcomes.

[0090] MFI and FFMVi VCG The correlation is very low ( Figure 4 ) and showed differences in the results of multivariable statistical modeling of hospitalization and functional outcomes. Most notable was the association with the number of falls (which was significantly associated with FFMVi VCG The association between muscle mass loss and increased MFI was not significant, while MFI was positively associated with more than one fall (Table 6). Furthermore, among participants with low handgrip strength, only MFI predicted hospitalization (Table 5). The combined description of decreased muscle mass and increased MFI in the elderly population provides a more complete, muscle-specific picture of functional decline.

[0091] FFMV and MFI adjusted for the sham control group predicted hospitalization, low muscle function (hand grip strength), and low activity function (stair climbing, walking speed, and number of falls), respectively (Tables 5 and 6), indicating that there is additional value in measuring MFI as a descriptor of muscle quality in addition to muscle volume. This is further strengthened by the diagnostic performance results for predicting functional outcomes (Table 7), where the MFI was significantly better than the FFMVi or FFMVi alone. VCG In comparison, FFMVi VCG The combination of FRET and MFI resulted in higher diagnostic performance for all outcomes (although not all significantly).

[0092] Combined muscle assessment for sarcopenia (MFI and FFMVi) can be performed using a 6-minute MRI scan, combined with automated image analysis (e.g., including quantification of visceral fat, subcutaneous fat, and liver fat). VCGquantification of muscle mass), thereby allowing a complete assessment of wasting. Today, this is a solution that is also available outside the Institute of Image Processing. This assessment results in quantifiable, muscle-specific imaging biomarkers that are directly related to functional outcomes, which can allow objective sarcopenia assessment results to be obtained. Standardization and high accuracy and precision enable close tracking of longitudinal changes and comparisons to be made on and between large cohorts. However, today's MRI is not easy to use for detecting sarcopenia on a population scale and is not suitable as a preliminary assessment of potential sarcopenia. Screening is needed to determine which patients can benefit from MRI examinations to detect and track the progression of sarcopenia.

[0093] In the above, the basis for individualized thresholds is provided for identifying individuals with low muscle function due to abnormally low muscle volume and poor muscle quality. This is achieved by presenting FFMVi that links a range of thresholds to functional outcomes. VCG and MFI gender-specific tables (Tables 8 to 11). VCG is the FFMV value adjusted for body size, so a lookup table is also provided, which shows the VCG The thresholds correspond to what is in the FFMV (for each BMI value) (Table 12). This enables the use of Tables 7 and 9 without having to apply a dummy control group adjustment to the obtained FFMV values.

[0094] In the drawings and specification, there have been disclosed preferred embodiments of, and examples for, the invention, and although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention being set forth in the following claims.

[0095] surface

[0096]

[0097]

[0098] Table 1. Summary of characteristics of the complete cohort, women, and men, respectively. For continuous variables, means and standard deviations are shown.

[0099]

[0100] Table 2. Proportion of subjects failing current tests for sarcopenia: muscle assessment (appendicular skeletal muscle mass index (ASMi) assessed by dual-energy X-ray absorptiometry (DXA)) for sex-specific BMI categories, handgrip strength test, and a combination of the two: underweight (BMI < 20 kg / m 2 ), normal weight (20≤BMI<25kg / m2 ), overweight (25≤BMI<30kg / m 2 ), and obesity (BMI>=30kg / m 2 ).

[0101]

[0102]

[0103] Table 3. Determination coefficient (R 2 ), with adjusted signs of correlations between measures of muscle mass and body size. ASM, appendicular skeletal muscle mass; BMI, body mass index; DXA, dual-energy X-ray absorptiometry; FFMV, fat-free muscle volume; MRI, magnetic resonance imaging; VCG, sham control group.

[0104]

[0105] Table 4. Percent differences at 5 years of age and corresponding standard deviations and effect sizes for the fat-free muscle volume (FFMV) index commonly used to assess muscle mass / volume for sarcopenia testing. VCG , muscle fat infiltration, and body size adjustment for muscle measurements. Standard deviations are given as mean ± standard deviation across sex and age. ASM, appendicular skeletal muscle mass; VCG, sham control group; SD VCG , the number of standard deviations relative to the mean sham control group.

[0106]

[0107] Table 5. Results of multivariable statistical modeling of health care burden (health care burden was defined as the number of hospital nights in the 10 years before the scan, excluding pregnancy-related nights (ICD10 codes under O and P), and truncated at 30 nights). Low appendicular skeletal muscle mass index (ASMi) was defined as less than the sarcopenia threshold of 6.0 / 7.0 kg / m for women / men. 2 Low handgrip strength was defined as less than the sarcopenia threshold of 16 kg for women and 27 kg for men. Values ​​are odds ratios and associated confidence intervals. Models were adjusted for sex and age. Significance levels: *p < 0.05, **p < 0.01, ***p < 0.001, ns, not significant. VCG, sham control group.

[0108]

[0109] Table 6. Results of multivariable statistical modeling of functional outcomes (handgrip strength: comparing subjects above and below the sarcopenia threshold for women / men: 16 / 27 kg; usual walking speed: comparing slow gait speed with steady average gait speed and fast gait speed; stairs climbed: stairs climbed in the past 4 weeks, comparing no stairs climbed in a day with stairs climbed 1 or more times; and number of falls: number of falls in the past year, comparing more than one fall with no falls). Values ​​are odds ratios and associated confidence intervals. Models were adjusted for age and BMI; models included all subjects who were additionally adjusted for sex. Significance levels: *p < 0.05, **p < 0.01, ***p < 0.001, ns, not significant. VCG, sham control group.

[0110]

[0111]

[0112] Table 7. Diagnostic performance of muscle measurements for predicting functional outcomes (handgrip strength: comparing subjects above and below the sarcopenia threshold for women / men: 16 / 27 kg, usual walking speed: comparing slow gait speed to steady average gait speed and fast gait speed, stairs climbed: stairs climbed in the past 4 weeks, comparing no stairs climbed in a day to stairs climbed 1 or more times; and number of falls: number of falls in the past year, comparing more than 1 fall to no falls). Values ​​are the area under the receiver operating characteristic curve (AUC) with associated confidence intervals. Predictors are fitted values ​​extracted from 4 logistic regressions using (1) fat-free muscle mass index, (2) fat-free muscle mass index VCG , (3) muscle fat infiltration, and (4) fat-free muscle volume index VCG The combination of skeletal muscle mass and muscle fatty infiltration was used to predict functional outcome. VCG, sham control group.

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] Table 12. Corresponding to FFMVi VCGThe threshold values ​​of fat-free muscle volume index (FFMVi) are presented in Table 7 (for women) and Table 9 (for men). VCG, sham control group; SD, standard deviation.

Claims

1. A method (100) for assessing a muscle-related condition in an individual subject, the method comprising the steps of: Obtaining (101) a muscle mass value (20) of a first muscle of the subject individual; Obtaining (102) at least one data parameter value (10) of the individual subject, wherein the data parameter value is related to a quantitative parameter of the body composition of the individual subject; Obtaining a muscle fat infiltration value of the first muscle (70); comparing the muscle fat infiltration value (70) with a predetermined threshold value (107); selecting (103) a certain number of individuals from a database (30), wherein the database comprises at least one data parameter value (31) for a plurality of individuals and muscle mass values ​​(32) of the first muscle for the plurality of individuals, wherein the selection of the certain number of individuals from the database is based on comparing the at least one data parameter value with at least one data parameter value for the subject individual, thereby creating a virtual control group VCG (40) comprising the selected individuals; Calculating (104) predicted values ​​(50) of muscle mass values ​​(42) for individuals in the VCG (40); comparing (105) the subject's individual muscle mass value (20) with the determined predicted value (50) of the VCG (40); The comparison of the muscle fat infiltration value (70) with the predetermined threshold is combined with the comparison (105) of the muscle mass value (20) with the predicted value (50) of the VCG (40).

2. The method according to claim 1, wherein The predicted value (50) is the mean, median or model-predicted value of the muscle mass values ​​of the individuals in the VCG (40).

3. The method according to claim 1 or 2, wherein: The muscle mass values ​​(20, 32, 42) are fat-free muscle volume (FFMV) values.

4. The method according to claim 1 or 2, wherein: The muscle magnitude (20, 32, 42) represents an effective volume of a first portion of the first muscle, the first portion having a muscle fat infiltration level less than a predetermined threshold level T1, and wherein the effective volume is determined by multiplying the volume of the first portion of the first muscle by 1-(1 / T1)*MFI1, wherein MFI1 is the muscle fat infiltration level in the first portion of the first muscle.

5. The method according to claim 4, wherein The predetermined threshold level T1 of muscle fat infiltration is between 30% and 80%.

6. The method according to claim 1, wherein The step of comparing (105) the muscle mass value (20) of the first muscle of the individual subject with the determined predicted value (50) of the VCG (40) comprises the step of determining a measure of the deviation of the muscle mass value (20) from the predicted value (50).

7. The method according to claim 6, wherein: The measure of the deviation of the determined muscle mass value (20) from the predicted value is the number of standard deviations by which the subject's individual muscle mass value is less than or greater than the VCG's predicted value.

8. The method according to claim 6 or 7, further comprising the steps of: A muscle-related condition is determined (106) for the individual subject based on the comparison (105) by determining whether the determined deviation measure is less than a predetermined threshold.

9. The method according to claim 1, wherein The step of selecting (103) a number of individuals from the database (30) comprises the steps of selecting individuals whose at least one data parameter value (31b) is within a predetermined range of the subject's individual data parameter value (10).

10. The method according to claim 9, wherein: The step of selecting (103) a certain number of individuals from the database (30) includes the following steps: if the predetermined number of individuals meeting the criteria are not found in the database, then the range is extended from the subject individual data parameter value (10).

11. The method according to claim 1, wherein The muscle mass value (20) of the first muscle of the individual subject is based on a magnetic resonance imaging (MRI) scan (60) of the individual subject.

12. A system comprising means configured to perform the method according to any one of the preceding claims.

13. An evaluation device configured to perform the method according to claim 1, comprising: An acquisition device is configured to acquire (101) a muscle mass value (20) of a first muscle of the subject, at least one data parameter value (10) of the subject, and a muscle fat infiltration value (70) of the first muscle, wherein: The data parameter value is related to a quantitative parameter of the body composition of the subject individual; a selection device configured to select (103) a certain number of individuals from a database (30), wherein the database comprises at least one data parameter value (31) of a plurality of individuals and muscle mass values ​​(32) of the first muscle of the plurality of individuals, wherein the selection of the certain number of individuals from the database is based on comparing the at least one data parameter value with at least one data parameter value of the individual subject, thereby creating a virtual control group VCG (40) including the selected individuals; a computing device (104) configured to calculate predicted values ​​(50) of muscle mass values ​​(42) for individuals in the VCG (40); and Comparison means is configured to compare (105) the subject's individual muscle mass value (20) with the determined predicted value (50) of the VCG (40).

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