Intermuscular adipose tissue or intramuscular adipose tissue content, which affects skeletal muscle density more? -a study based on abdominal opportunistic CT

Intermuscular adipose tissue or intramuscular adipose tissue content, which affects skeletal muscle density more? -a study based on abdominal opportunistic CT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Intermuscular adipose tissue or intramuscular adipose tissue content, which affects skeletal muscle density more? -a study based on abdominal opportunistic CT Xinyi Guo, Nana Cao, Xin Deng, Nan Wang, Rui Li, Song Ren, Fei Fu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4643186/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract To explore intermuscular adipose tissue (IMAT) and intramuscular adipose tissue content (IMAC) which affect skeletal muscle density (SMD) more and elucidate its underlying causes. 292 inpatients without definite musculoskeletal system disease were recruited. All the patients performed abdominal CT. Muscle parameters which included skeletal muscle area (SMA), skeletal muscle index (SMI), SMD, IMAC and IMAT, and fat parameters which included area of subcutaneous fat tissue in abdominal wall were measured by two musculoskeletal radiologists with Image J software at the level of L3 vertebrae. Multiple regression analysis was used to identify the factors which affected SMD, and compared the extent of influence. SMD was highly correlated with IMAT and IMAC (p < 0.05), moderately correlated with gender, age and area of subcutaneous fat tissue in abdominal wall (p < 0.05), and slightly correlated with BMI (p < 0.05). Multiple linear regression analysis showed that IMAC, IMAT and age are influencing factors of SMD (p 0.05). Age, IMAT, and IMAC exert an influence on SMD. Notably, the impact of IMAT on SMD is much more significant. Health sciences/Medical research Health sciences/Health care/Medical imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia, a concept first introduced in 1984 by Rosenberg [ 1 ], refers to age-related loss of skeletal muscle mass and muscle quality. In 2016, sarcopenia was classified by the International Classification of Diseases (ICD-10-CM), with the code M62.84[ 2 ]. Many studies have confirmed its relationship with aging [ 3 , 4 ], atherosclerosis, diabetes, malignant tumors and cognitive disorders [ 5 ]. It often leads to serious consequences, such as increased morbidity, mortality, and health care costs. Early studies focused mainly on the association between reduction in muscle performance and the loss of muscle mass [ 6 – 8 ]. However, muscle quality has gradually attracted people's attention because substantial decreases in skeletal muscle function with ageing can occur with only minimal loss of skeletal muscle mass [ 9 – 13 ]. Skeletal muscle density (SMD) has already widely used to assess muscle quality in most studies, which reflects the density of all the components in the region of interest (ROI) of muscle and can be measured directly on computed tomography (CT) images. It has been consistently observed that there is a strong relationship between low muscle density and adverse health outcomes, but what affects the loss of muscle density? Skeletal muscle fat infiltration, known as myosteatosis, defined as an accumulation of intramuscular and intermuscular fat in skeletal muscle, is considered to be an indicator of poor muscle quality[ 14 ], with CT attenuation decreasing by 1 Hounsfield unit (HU) for every 1 g/100ml increase in lipid concentration [ 14 ].It is different from sarcopenia, as they do not frequently occur meanwhile; consequent changes in body composition differ; associated clinical factors are not the same; and they have additive effects on survival outcomes[ 15 ]. The parameters assessing muscle fat infiltration by CT are intermuscular adipose tissue (IMAT) and intramuscular adipose tissue content (IMAC). IMAT is the fat adipose accumulation between muscles and muscle fiber bundles, and quantified by CT attenuation ranging from − 190HU to -30HU [ 16 , 17 ]. It has already been widely used to assess muscle fat infiltration. Amini et al. [ 18 ] pointed out that 191 of 388 studies adopted IMAT as evaluation index in their review. IMAC is the extra-myocyte fat located in muscle fiber bundle, first proposed by Kitajima in 2010, which was the ratio of CT attenuation of multifidus muscle to CT attenuation of abdominal wall subcutaneous fat. It is standardized SMD that eliminate the influence of CT equipment, scanning parameters and individual differences of patients. Reports on IMAC mainly focused on disease prognosis and thought patients with high IMAC indicating poor prognosis [ 19 , 20 ]. Theoretically, the increase of IMAT and IMAC will lead to the decrease of SMD. However, it is not clear which factor has greater impact on SMD. Basing on the above background, we intend to make a cross-sectional analysis to explore the factors which influence SMD of the abdominal wall and paravertebral muscles group at L3 level in males over 50 years old and postmenopausal females utilizing opportunistic abdominal CT of inpatients. Results Baseline characteristics Figure 4 illustrates the recruitment of study participants. After the review of the clinical information and the CT images, 207 cases of the 499 inpatients were excluded. A total of 292 inpatients were finally enrolled, including 146 males (62.25 ± 9.99 years old) and 146 females (69.16 ± 9.95 years old), with an average age of 65.71 ± 10.54 years old. In each group, there was no gender difference in age (Table 1 ) and BMI (Table 2 ). Muscle parameters of male and female were showed in Table 3 . The ICC of inter-observer reliability was > 0.75, which indicated excellent repeatability (Table 4 ). Table 1 Age comparison of male and female in each age group. Significant statistical difference* ( p < 0.05). Age group Gender Number \(\stackrel{-}{\varvec{x}}\) ± s(y) t-value p ≤ 59 years old male 65 53.58 ± 3.47 -1.919 0.058 female 28 55.25 ± 4.59 60 ~ 70 years old male 50 64.14 ± 2.92 -1.551 0.124 female 49 65.02 ± 2.72 ≥ 70 years old male 31 77.35 ± 6.17 -0.322 0.748 female 69 77.75 ± 5.52 Table 2 BMI comparison of male and female in each age group (x ̅±s). BMI, body mass index. Significant statistical difference* ( p < 0.05). Age group Gender Number BMI (kg/m 2 ) t-value p ≤ 59 years old male 65 23.99 ± 3.38 -1.013 0.314 female 28 24.76 ± 3.37 60 ~ 70 years old male 50 24.48 ± 3.05 -0.391 0.697 female 49 24.77 ± 4.22 ≥ 70 years old male 31 23.62 ± 3.44 0.429 0.669 female 69 23.26 ± 4.06 Table 3 Muscle parameters of male and female in each age group ( \(\stackrel{-}{x}\) ±s). SMA, skeletal muscle area; SMI, skeletal muscle index; SAT, subcutaneous fat tissue in abdominal wall; SMD, skeletal muscle density; IMAT, intermuscular adipose tissue; IMAC, intramuscular adipose tissue content. Age group Gender SMA (cm 2 ) SMI SMD (HU) IMAT (%) IMAC SMA SAT (cm 2 ) SMD SAT (HU) ≤ 59 years old male 136.24 ± 24.66 46.37 ± 8.22 37.31 ± 7.16 6.89 ± 3.40 -0.49 ± 0.31 116.68 ± 49.73 -86.74 ± 19.02 female 91.96 ± 12.19 36.13 ± 4.85 31.07 ± 8.37 10.39 ± 4.86 -0.32 ± 0.11 184.00 ± 72.42 -99.77 ± 9.73 60 ~ 70 years old male 128.73 ± 21.45 44.19 ± 6.69 35.14 ± 8.02 7.89 ± 4.09 -0.45 ± 0.23 128.00 ± 46.61 -86.58 ± 17.15 female 89.70 ± 17.38 35.28 ± 6.40 26.15 ± 8.65 13.70 ± 5.95 -0.27 ± 0.10 184.87 ± 82.20 -99.86 ± 18.44 ≥ 70 years old male 105.60 ± 25.57 36.68 ± 8.70 29.67 ± 9.10 10.88 ± 4.85 -0.46 ± 0.39 119.27 ± 53.16 -83.97 ± 30.53 female 84.58 ± 18.65 33.64 ± 6.90 21.66 ± 10.15 16.06 ± 8.19 -0.26 ± 0.17 154.23 ± 69.12 -90.71 ± 15.74 Table 4 The ICC of Muscle parameters between the two observers ( \(\stackrel{-}{x}\) ±s). WM, the whole muscles of abdominal wall and paravertebral muscles. Parameters of muscle Observer 1 Observer 2 ICC 95% CI Lower limit Upper limit SMA WM (cm 2 ) 102.86 ± 24.57 109.41 ± 28.62 0.910 0.820 0.956 SMD WM (HU) 33.32 ± 8.17 27.94 ± 9.31 0.902 0.804 0.952 IMAT WM (%) 8.68 ± 3.23 10.28 ± 4.06 0.793 0.610 0.896 SMA SAT (cm 2 ) 127.09 ± 80.54 113.79 ± 83.06 0.946 0.891 0.974 SMD SAT (HU) -86.39 ± 18.87 -84.35 ± 19.19 0.917 0.833 0.960 Correlation analysis and influencing factors of SMD The correlation analysis of SMD with age, gender, BMI and other muscle parameters at L3 level are shown in Table 5 . Pearson linear correlation analysis shows that SMD is highly correlated with IMAT and IMAC (p < 0.05), moderately with gender, age, SMA, SMI and abdominal subcutaneous fat area (p < 0.05), and slightly with BMI (p < 0.05). Table 5 Correlation between SMD and age, gender, BMI and other muscle parameters. Significant statistical difference* ( p < 0.05). Age Gender BMI SMA SMI IMAC IMAT SMA SAT r -0.441 -0.478 -0.117 0.377 0.309 -0.628 -0.850 -0.370 p < 0.001* < 0.001* 0.046* < 0.001* < 0.001* < 0.001* < 0.001* < 0.001* The impact of SMI, IMAC, IMAT, abdominal subcutaneous fat area, gender and age on SMD are shown in Table 6 . Multiple linear regression analysis shows that IMAC, IMAT and age are influencing factors of SMD (p < 0.05), the order of influence degree is IMAT (Stbβ=-0.616), IMAC (Stbβ=-0.429), and age (Stbβ=-0.098); While IMAT increases by 1 unit, SMD decreases by 0.968HU; IMAC increases by 1 unit, SMD decreases by 17.524HU; and age increases by 1 year, SMD decreases by 0.097HU. Subcutaneous fat area and gender were not influential factors of SMD (p > 0.05). Table 6 Multiple linear regression analysis about influencing factors of SMD of abdominal wall and paravertebral muscle group at L3 level. Significant statistical difference ( p < 0.05) *. Factor B SE Stb β t p SMI 0.066 0.044 0.056 1.515 0.131 IMAC -17.524 1.936 -0.429 -9.052 < 0.001* IMAT -0.968 0.057 -0.616 -16.943 < 0.001* SMA SAT -0.003 0.006 -0.108 -0.490 0.625 Age -0.097 0.031 -0.098 -3.084 0.002* Female -0.402 0.770 -0.019 -0.521 0.602 Discussion Based on the analysis of 292 inpatients by abdominal opportunistic CT from our hospital, our study demonstrated that IMAT had greater effect on SMD than IMAC and age. For all we know, the impact of IMAT vs IMAC on SMD has yet to be investigated. Most previous studies have focused on evaluating healthy individuals [ 25 , 26 ]. However, it is noteworthy that a significant proportion of participants were elderly and presented with underlying medical conditions. Jong Hyuk Lee et al. assessed 2720 patients for annual physical examination, of whom 12% had a previous history of cancer, 27% hypertension, 14% diabetes, 11% cardiovascular disease and 5.4% chronic liver disease and kidney disease [ 27 ]. Consequently, relying solely on direct assessment of muscle quality often led to inaccuracies. We choose inpatients as our subjects for their clear and thorough medical histories, which facilitated the exclusion of diseases affecting muscles and minimized experimental errors, and enhanced the reliability. The measurement of muscle quality is also affected by CT equipment, tube voltage, slicer thickness and iodine contrast. Lamba et al. recruited 48 patients older than 18 years who underwent unenhanced CT performed both on GE and Siemens 64-MDCT scanner within 12 months, and found Hounsfield unit measurements for unenhanced abdominal soft tissues of the same patient vary between two common MDCT manufacturers [ 11 ]. Lortie et al. retrospectively analyzed the effects of iodine contrast and tube voltage on the evaluation of muscle mass and quality, found that the use of iodine contrast significantly increased the muscle density and area, lower tube voltage resulted in higher muscle density and lower muscle area, and the effect on muscle density significantly greater than on muscle area [ 10 ]. Additionally, Fuchs et al. noted an 11.64% decrease in muscle density (p < 0.0001) and a 1.11% in-crease in muscle area (p < 0.0001) measured on 5mm layer thickness images com-pared to 2mm layer thickness [ 28 ]. Therefore, it is necessary to control the above conditions to ensure the stability and reproducibility of the measurement results and avoid errors. In this research, the same CT scanner was used by the same scan parameters which were tube voltage, 120 kV; scanned slice thickness, 10 mm; re-constructed slice thickness, 0.625 mm; reconstruction window width, 350HU; and reconstruction window level was 40HU. For the measurement of muscle quality on CT, there are also inter-observer, intra-observer, and inter-measurement software variability. Barbalho et al. [ 29 ] showed that muscle parameters measured by both Slice-O-Matic and OsiriX tripartite software had excellent consistency (ICC ≥ 0.954, p 0.75), which were consistent with a couple of other studies [ 30 , 31 ]. Muscle quality, usually evaluated by SMD, refers to the microscopic and macroscopic changes in muscle structure and composition [ 32 ]. SMD reflects the density of all the components in the ROI of muscle, including muscle fibers, easily recognizable crude fat components between muscles or muscle bundles, indiscernible intrafascicular fat, and intracellular lipids in muscle cells [ 33 , 34 ]. Skeletal muscle fat infiltration is the main reason for the change of muscle density. Skeletal muscle adipose tissue can be divided into IMAT, IMAC, and intramyocellular lipids (IMCL) [ 14 , 35 – 37 ]. IMCL is a lipid component deposited in a single muscle cell, which cannot be measured separately by CT. IMCL can be obtained by magnetic resonance spectrum (MRS), but it is limited due to its lengthy scanning time. With the rapid advancement in technology, IMCL will emerge as a prominent research area in the future [ 38 ]. IMAT typically measured by the third-party software to quantitatively evaluate muscle and interfascicular fat, utilizing CT values ranging from − 190HU to -30HU. The distinctive adipose tissue reservoir exhibits a robust correlation with several diseases and the process of aging. A large number of studies have shown that high IMAT in the thigh is associated with abnormal blood glucose [ 39 – 43 ], and increases cardiovascular risk factors. IMAT plays a significant role in the age-related alterations in muscle metabolism. Muscle fat infiltration increases with aging. It is directly associated with the decline in muscle mass, flexibility and quality. To some degree, it can also predict the onset of activity limitations in older people. IMAT is used as a robust indicator for assessing muscle fat infiltration, benefiting from well-established and stable measurement methods [ 44 ]. In 2010, the ratio of CT attenuation of the multifidus muscle to the abdominal wall subcutaneous fat was proposed by Kitajima as a potential marker for non-alcoholic fatty liver disease [ 36 ]. This parameter was referred to intramuscular adipose tissue content (IMAC), and it was subsequently reported by multiple studies [ 45 – 47 ]. The researchers perceived IMAC as the extra-myocyte fat within muscle fiber bundle. Compared with IMAT, it controlled the influence of CT equipment, scanning conditions and individual differences. The higher IMAC is, the more the muscle's fat content is [ 17 ]. The distribution of fat infiltration in the abdominal wall and paravertebral muscles at the L3 level is imbalanced. The abdominal wall muscles of some individuals showed atrophy, while others displayed multifidus muscle atrophy. Kitajima's study only assessed multifidus muscle, but the IMAC of single muscle could not solely reflect extra-myocyte fat within the muscle fiber bundle and fully indicate overall fat content [ 36 ]. In our study, IMAC utilized represented the ratio of CT attenuation of abdominal wall and paravertebral whole muscles to the subcutaneous fat at L3 level. There are numerous reports about IMAC of multifidus muscle, most of which are used to evaluate the prognosis of different diseases [ 39 ]. However, there are few studies to compare IMAC of each composition muscle of abdominal wall and paravertebral muscles at the L3 level. Ashida conducted that iliopsoas IMAC offered more advantages in predicting skeletal muscle loss com-paring to the multifidus muscle [ 48 ]. This could be attributed to the presence of numerous clefts which might be the site of extra-muscular adipose tissue within the multifidus muscle, complicating the accurately determination of intramuscular adipose tissue levels. In contrast, the iliopsoas muscle which has smooth nature and tightly packed muscle fibers is less likely to contain extra-muscular adipose tissue, enabling a more accurate reflection of intramuscular adipose tissue. Therefore, the IMAC of different muscles at L3 level still deserves further study. The amount of intramuscular adipose within a specific muscle is dependent on its anatomical characteristics [ 49 ]. For instance, it is due to greater cracks and infiltrated fats be-tween muscles and bundles that IMAC for abdominal wall and posterior vertebral extensor muscles reflects both intra-muscle and inter-muscle adipose. Conversely, the IMAC of psoas major and quadratus lumborum which has exhibit denser muscle fibers with less interfascicular fat infiltration primarily reflects muscular adiposity. Our study demonstrated a strong negative correlation between IMAC and IMAT with SMD, establishing both as influential factors for SMD. Multiple linear regression analysis revealed standardized regression coefficients of -0.616 for IMAT and − 0.429 for IMAC, indicating that an increase in one unit of IMAT led to a de-crease in SMD by 0.968HU, while an increase in one unit of IMAC resulted in a decrease in SMD by 17.524HU. These findings highlight the significantly greater impact of IMAT on SMD comparing to that of IMAC. Brennan et al. demonstrated a significant association (p < 0.01) between lower muscle density in the elderly and higher intracellular lipid concentrations, but SMD exhibited a weaker correlation with intracellular lipid concentrations, being more influenced by extra-myocyte lipids and independent of age, gender, race, and obesity [ 34 ]. IMAT refers to the lipid storage in adipose cells located beneath the deep muscle fascia, encompassing visible lipids stored within intermuscular fibers and intermuscular adipocytes [ 39 ]. Therefore, we think that muscle density is predominantly influenced by the crude fat component observed amidst muscles and fascicles, while being minimally affected by the visible fat component within fascicles and muscle cells. This aligns with Brennan's findings [ 35 ]. Whether muscle function and metabolism are related to the location of muscle fat infiltration needs further study. In addition, our study demonstrated a moderate association between age and SMD, while gender showed no significant influence. Aging was found to significantly impact muscle quality decline in both males and females. Multiple linear regression analysis revealed a standard regression coefficient of -0.098 for age, indicating that SMD decreased by 0.097HU with aging annually. Previous studies have consistently reported IMAT increased with advancing age. Furthermore, our results are consistent with Tetsuro Hida [ 50 ], who observed that aging is associated with reduced muscle area and increased muscular fat deposition even after con-trolling for variables such as gender and ethnicity. Thus, it can be concluded that the effect of age on muscle quality is universal regardless of spinal disease presence or absence, muscle type, gender, or ethnicity. There are a few limitations in this study. Firstly, we choose the whole abdominal wall and paraspinal muscles for evaluation. It had a good linear correlation with the whole body muscles. However, the delineation of the whole muscles took a relatively long time, future research should analyze separately abdominal wall and single paraspinal muscle in order to find which one could reflect the changes of muscle mass and muscle quality well. Secondly, due to the inherent difficulty in distinguishing between adipose tissue within muscle bundles and intramuscular lipids, the CT attenuation of the muscle only reflects the comprehensive composition of all components within the muscle. But with the rapid advancement in technology of MRS, IMCL could be further studied in future research. Thirdly, although the same CT scanner was utilized and the same scanning parameters were standardized in our study, the measurement of muscle quality could be influenced by the scanning mode, and varying acquisition schemes which might potentially impact the experimental outcomes. Finally, the sample size of this study was relatively small, necessitating further confirmation of the conclusions through larger-scale studies. Conclusions In conclusion, our findings suggest that IMAT has greater effect on SMD than IMAC and age, suggesting a different causal mechanism for the decrease of SMD. This maybe attract a stronger focus on the prevention of the increase of IMAT. Materials and Methods Study design and participants This was a retrospective study. We recruited a total of 499 inpatients in Tianjin hospital from July 2019 to July 2022 for this study. Clinical information and abdominal CT were collected for every inpatient routinely. Inclusion criteria: (1) males ≥ 50 years old and females in postmenopausal state. (2) abdominal CT images were clear and the image quality met the standard [ 21 , 22 ]. Exclusion criteria: Patients with diseases seriously affecting muscle quality, such as musculoskeletal system diseases, neuromuscular system diseases, malignant tumors and chronic wasting diseases (severe diabetes, chronic obstructive pulmonary disease, chronic liver disease, etc.). The study was conducted in accordance with the Helsinki Declaration and approved by the Ethics Committee of Tianjin Hospital (2024YLS061). Due to the retrospective nature of the study, Tianjin hospital waived the need of obtaining informed consent. Computed tomography acquisition Spiral CT imaging of full abdomen was performed for all participants using a 128-slice GE revolution ES CT scanner (GE Medical Systems, LLC*). Scans were ac-quired from the upper edge of T12 vertebrae to the lower edge of L5 vertebrae. Scan parameters were: tube voltage, 120 kV [ 4 , 23 ]; pitch, 0.948:1 and slice thickness, 10 mm. Cross-sectional abdominal images were reconstructed at L3 level on GE AW4.7 workstation, which parallelled to the upper endplate of L3 vertebrae and showed the longest slice of transverse process (Fig. 1 ). Reconstruction parameters were: reconstruction algorithm, standard; reconstruction thickness, 0.625mm; window width, 350HU; window level, 40HU; and DFOV is 45cm. Muscle quality assessments Cross-sectional muscular parameters of the abdominal wall and paraspinal muscles at L3 level were measured. Image J 1.53e (Wayne Rasband and contributors National Institutes of Health, USA) was used for analysis. Muscle segmentation was performed manually using “Polygon selections” to out-line muscle contours (Fig. 2 ). Within the region of interest muscles, “Analyze-Measure” was used to calculate skeletal muscle area (SMA) and CT attenuation. And the threshold of -190 HU ~ -30 HU [ 4 , 24 ] was set to obtain IMAT by “Image-Adjust-Threshold” (Fig. 2 ). The same method was used for contouring subcutaneous fat to obtain its cross-sectional area and CT attenuation (Fig. 2 ). The images from all patients included were analyzed independently by two musculoskeletal radiologists with 10 and 20 years of experience and the average values were recorded (Fig. 3 ). The skeletal muscle index (SMI) and IMAC was calculated using the following formula: SMI = SMA / Height 2 , (1) IMAC = CT attenuation of abdominal wall and paravertebral muscles / CT attenuation of abdominal wall subcutaneous fat. (2) Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics Version 21 (USA). Continuous variables are shown as mean ± standard deviation (SD). All measurement data obeyed normal distribution. Significance level was α = 0.05. Intraclass correlation coefficient (ICC) was tested to assess inter-observer reliability between the two observers. An ICC > 0.75 was considered indicative of good agreement. Multiple linear regression analysis was used to analyze the influencing factors of SMD and compare their extent. Correlation coefficient r value: |r|<0.3 indicates low correlation between variables; 0.3≤|r|<0.5 indicates moderate correlation between variables; 0.5≤|r|≤1 indicates a high correlation between variables. Declarations Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to H.Z. Author Contribution Conceptualization, X.G. and N.C.; methodology, X.G. and N.C.; software, X.D., N.W., R.L. and F.F.; formal analysis, X.G., N.C. and Z.H.; investigation, X.G.; resources, X.G.; data curation, X.G.; writing—original draft preparation, X.G. and N.C.; writing—review and editing, X.G., N.C., S.R., L.G., L.K., Z.H.; visualization, X.G.; supervision, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets used during the current study available from the corresponding author on reasonable request References Rosenberg I H. Sarcopenia: origins and clinical relevance[J]. The Journal of Nutrition , 1997, 127(5 Suppl): 990S-991S. Anker S D, Morley J E, Von Haehling S. Welcome to the ICD-10 code for sarcopenia[J]. Journal of Cachexia, Sarcopenia and Muscle , 2016, 7(5): 512-514. Von Haehling S, Morley J E, Anker S D. From muscle wasting to sarcopenia and myopenia: update 2012[J]. 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Contribution of Intramyocellular Lipids to Decreased Computed Tomography Muscle Density With Age[J]. Frontiers In Physiology , 2021, 12: 632-642. Kitajima Y, Eguchi Y, Ishibashi E, et al. Age-related fat deposition in multifidus muscle could be a marker for nonalcoholic fatty liver disease[J]. Journal of Gastroenterology , 2010, 45(2): 218-224. Ahn H, Kim D W, Ko Y, et al. Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: A new paradigm beyond sarcopenia[J]. Ageing Research Reviews , 2021, 70: 101398. Han G, Jiang Y, Zhang B, et al. Imaging Evaluation of Fat Infiltration in Paraspinal Muscles on MRI: A Systematic Review with a Focus on Methodology[J]. Orthopaedic Surgery , 2021, 13(4): 1141-1148. Goodpaster B H, Thaete F L, Kelley D E. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus[J]. The American Journal of Clinical Nutrition , 2000, 71(4): 885-892. Boettcher M, Machann J, Stefan N, et al. Intermuscular adipose tissue (IMAT): association with other adipose tissue compartments and insulin sensitivity[J]. Journal of Magnetic Resonance Imaging : JMRI , 2009, 29(6): 1340-1345. Dubé M-C, Lemieux S, Piché M-E, et al. The contribution of visceral adiposity and mid-thigh fat-rich muscle to the metabolic profile in postmenopausal women[J]. Obesity (Silver Spring, Md.) , 2011, 19(5): 953-959. Goodpaster B H, Thaete F L, Simoneau J A, et al. Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat[J]. Diabetes , 1997, 46(10): 1579-1585. Albu J B, Kovera A J, Allen L, et al. Independent association of insulin resistance with larger amounts of intermuscular adipose tissue and a greater acute insulin response to glucose in African American than in white nondiabetic women[J]. The American Journal of Clinical Nutrition , 2005, 82(6): 1210-1217. Goodpaster B H, Bergman B C, Brennan A M, et al. Intermuscular adipose tissue in metabolic disease[J]. Nature Reviews. Endocrinology , 2023, 19(5): 285-298. Kitajima Y, Hyogo H, Sumida Y, et al. Severity of non-alcoholic steatohepatitis is associated with substitution of adipose tissue in skeletal muscle[J]. Journal of Gastroenterology and Hepatology , 2013, 28(9): 1507-1514. Kaibori M, Ishizaki M, Iida H, et al. Effect of Intramuscular Adipose Tissue Content on Prognosis in Patients Undergoing Hepatocellular Carcinoma Resection[J]. Journal of Gastrointestinal Surgery: Official Journal of the Society For Surgery of the Alimentary Tract , 2015, 19(7): 1315-1323. Aleixo G F P, Shachar S S, Nyrop K A, et al. Myosteatosis and prognosis in cancer: Systematic review and meta-analysis[J]. Critical Reviews In Oncology/hematology , 2020, 145: 102839. Ashida R, Yamamoto Y, Aramaki T, et al. Preoperative skeletal muscle fat infiltration is a strong predictor of poorer survival in gallbladder cancer underwent surgery[J]. Clin Nutr ESPEN , 2022, 52: 60-67. Fujii H, Kodani E, Kaneko T, et al. Sarcopenia and coexistent risk factors detected using the 'Yubi-wakka' (finger-ring) test in adults aged over 65 years in the public annual health check-up in Tama City, Tokyo: a cross-sectional study[J]. BMJ Open , 2022, 12(12): e061613. Hida T, Eastlack R K, Kanemura T, et al. Effect of race, age, and gender on lumbar muscle volume and fat infiltration in the degenerative spine[J]. Journal of Orthopaedic Science: Official Journal of the Japanese Orthopaedic Association , 2021, 26(1): 69-74. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 11 Aug, 2024 Editor assigned by journal 04 Aug, 2024 Editor invited by journal 02 Jul, 2024 Submission checks completed at journal 28 Jun, 2024 First submitted to journal 26 Jun, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4643186","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328670576,"identity":"c717dee9-1705-403c-adfa-580210d7fe2f","order_by":0,"name":"Xinyi Guo","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Guo","suffix":""},{"id":328670579,"identity":"74b26b9c-e859-45e0-895f-76ac750e95fb","order_by":1,"name":"Nana Cao","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Nana","middleName":"","lastName":"Cao","suffix":""},{"id":328670580,"identity":"1d95ff6d-a044-499c-973d-bd1de730f80d","order_by":2,"name":"Xin Deng","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Deng","suffix":""},{"id":328670582,"identity":"8255b46b-1dbd-4d7d-aa91-aa5e9e8757b8","order_by":3,"name":"Nan Wang","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Wang","suffix":""},{"id":328670583,"identity":"0b38fcfb-1e99-4bf7-9370-61b45310c4c1","order_by":4,"name":"Rui Li","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":328670586,"identity":"f83bb37c-b415-42c6-ae63-328234f62d66","order_by":5,"name":"Song Ren","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Ren","suffix":""},{"id":328670588,"identity":"09135022-0fe5-4829-8d3d-b6f3c5c94829","order_by":6,"name":"Fei Fu","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Fu","suffix":""},{"id":328670589,"identity":"c2442073-897c-4ed2-a37f-c4ab8990efbb","order_by":7,"name":"Liqing Kang","email":"","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Liqing","middleName":"","lastName":"Kang","suffix":""},{"id":328670591,"identity":"e2b0092f-d8a2-4a40-8485-daf531a0f24d","order_by":8,"name":"Zhen He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmUlEQVRIiWNgGAWjYBACAwYGZoYPBjZ2pGlhnFGQlkyaFmaeD4cYG4jWYs5/+LCxjcEBZgb2w0c3EKXFsuFYcnKOwR0+Bp60tBvEOexgj/HhHINnzAwSPGZEajnMY3zYwuAwYwPxWo7xGCczkKblDFuyYY9BWjIb8X45f/iwxI8/Nnb87IePEacFDthIUz4KRsEoGAWjAC8AADi0K8SM7LOLAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Hospital of Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-06-26 13:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4643186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4643186/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-85946-8","type":"published","date":"2025-03-10T15:58:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60851713,"identity":"34c75601-8e2a-459e-ba05-83fbfef76b52","added_by":"auto","created_at":"2024-07-22 21:04:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239799,"visible":true,"origin":"","legend":"\u003cp\u003eReferring to abdominal coronal and sagittal CT images, transverse images at L3 level were obtained which parallelled to upper endplate of L3 vertebrae and showed the longest slice of transverse process.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4643186/v1/a4581b988a6448eae5cf8207.png"},{"id":60850934,"identity":"4fcb687d-fddb-42cc-a717-b139b6d5702d","added_by":"auto","created_at":"2024-07-22 20:56:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1066129,"visible":true,"origin":"","legend":"\u003cp\u003ea. The muscle groups at L3 level included abdominal muscles (1 \u0026amp; 2), psoas major (3 \u0026amp; 4), quadratus lumborum (5 \u0026amp; 6), posterior vertebral extensor muscle group (7 \u0026amp; 8) and the entire muscle group (9). b. Measurement of cross-sectional area (mm2), mean CT attenuation (HU) and IMAT (%) of the whole abdominal wall and paraspinal muscle groups at L3 level by Image J. c. Measurement of cross-sectional area (mm2) and mean CT attenuation (HU) of the abdominal wall subcutaneous fat at L3 level by Image J.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4643186/v1/800143c10e0d9b985f210385.png"},{"id":60850933,"identity":"ba9376ea-0346-4880-9b2f-208bd1a8c0c4","added_by":"auto","created_at":"2024-07-22 20:56:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":914040,"visible":true,"origin":"","legend":"\u003cp\u003eAbdominal wall and the whole paraspinal muscles at the level of L3 with different extent of fat infiltration in postmenopausal females and males over 50 years old. a. 53-year-old female, BMI is 27.34kg/m2, mean CT attenuation of the whole paraspinal muscles is 34.26HU, mean CT attenuation of abdominal wall subcutaneous fat is -101.11HU, IMAC is -0.34 and IMAT is 9.45%. b. 79-year-old female, BMI 24.61kg/m2, muscle density 8.31HU, subcutaneous fat density -89.26HU, IMAC -0.09 and IMAT 18.03%. c. 51-year-old male, BMI 21.3kg/m2, muscle density 42.59HU, subcutaneous fat density -89.06HU, IMAC -0.48 and IMAT 2.46%. d. 76-year-old female, BMI 26.72kg/m2, muscle density 3.9HU, subcutaneous fat density -114.8HU, IMAC -0.03 and IMAT 27.95%. The higher IMAC and IMAT were, and the lower skeletal muscle density was, which meant the more fat deposition between and within muscles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4643186/v1/6e4a88ca3ce746b997ee5b7f.png"},{"id":60850931,"identity":"9272d916-ca14-47f2-bbff-2dc59aa87f78","added_by":"auto","created_at":"2024-07-22 20:56:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106529,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection for the study, with a total of 292 subjects enrolled.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4643186/v1/4366bc57dd37420a54c7634f.png"},{"id":78689060,"identity":"ea3bf499-3e3b-4f48-acd1-50ed9eac87ca","added_by":"auto","created_at":"2025-03-17 16:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3967452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4643186/v1/bf4e9fad-4112-430c-aa97-1c758c328c55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intermuscular adipose tissue or intramuscular adipose tissue content, which affects skeletal muscle density more? -a study based on abdominal opportunistic CT","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, a concept first introduced in 1984 by Rosenberg [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], refers to age-related loss of skeletal muscle mass and muscle quality. In 2016, sarcopenia was classified by the International Classification of Diseases (ICD-10-CM), with the code M62.84[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many studies have confirmed its relationship with aging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], atherosclerosis, diabetes, malignant tumors and cognitive disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It often leads to serious consequences, such as increased morbidity, mortality, and health care costs.\u003c/p\u003e \u003cp\u003eEarly studies focused mainly on the association between reduction in muscle performance and the loss of muscle mass [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, muscle quality has gradually attracted people's attention because substantial decreases in skeletal muscle function with ageing can occur with only minimal loss of skeletal muscle mass [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Skeletal muscle density (SMD) has already widely used to assess muscle quality in most studies, which reflects the density of all the components in the region of interest (ROI) of muscle and can be measured directly on computed tomography (CT) images. It has been consistently observed that there is a strong relationship between low muscle density and adverse health outcomes, but what affects the loss of muscle density?\u003c/p\u003e \u003cp\u003eSkeletal muscle fat infiltration, known as myosteatosis, defined as an accumulation of intramuscular and intermuscular fat in skeletal muscle, is considered to be an indicator of poor muscle quality[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with CT attenuation decreasing by 1 Hounsfield unit (HU) for every 1 g/100ml increase in lipid concentration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].It is different from sarcopenia, as they do not frequently occur meanwhile; consequent changes in body composition differ; associated clinical factors are not the same; and they have additive effects on survival outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe parameters assessing muscle fat infiltration by CT are intermuscular adipose tissue (IMAT) and intramuscular adipose tissue content (IMAC). IMAT is the fat adipose accumulation between muscles and muscle fiber bundles, and quantified by CT attenuation ranging from \u0026minus;\u0026thinsp;190HU to -30HU [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It has already been widely used to assess muscle fat infiltration. Amini et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] pointed out that 191 of 388 studies adopted IMAT as evaluation index in their review. IMAC is the extra-myocyte fat located in muscle fiber bundle, first proposed by Kitajima in 2010, which was the ratio of CT attenuation of multifidus muscle to CT attenuation of abdominal wall subcutaneous fat. It is standardized SMD that eliminate the influence of CT equipment, scanning parameters and individual differences of patients. Reports on IMAC mainly focused on disease prognosis and thought patients with high IMAC indicating poor prognosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTheoretically, the increase of IMAT and IMAC will lead to the decrease of SMD. However, it is not clear which factor has greater impact on SMD.\u003c/p\u003e \u003cp\u003eBasing on the above background, we intend to make a cross-sectional analysis to explore the factors which influence SMD of the abdominal wall and paravertebral muscles group at L3 level in males over 50 years old and postmenopausal females utilizing opportunistic abdominal CT of inpatients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the recruitment of study participants. After the review of the clinical information and the CT images, 207 cases of the 499 inpatients were excluded. A total of 292 inpatients were finally enrolled, including 146 males (62.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.99 years old) and 146 females (69.16\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95 years old), with an average age of 65.71\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54 years old.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn each group, there was no gender difference in age (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Muscle parameters of male and female were showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The ICC of inter-observer reliability was \u0026gt;\u0026thinsp;0.75, which indicated excellent repeatability (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge comparison of male and female in each age group. Significant statistical difference* (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{\\varvec{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; s(y)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e53.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e55.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e64.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e65.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e77.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e77.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBMI comparison of male and female in each age group (x ̅\u0026plusmn;s). BMI, body mass index. Significant statistical difference* (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMuscle parameters of male and female in each age group (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s). SMA, skeletal muscle area; SMI, skeletal muscle index; SAT, subcutaneous fat tissue in abdominal wall; SMD, skeletal muscle density; IMAT, intermuscular adipose tissue; IMAC, intramuscular adipose tissue content.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMD (HU)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIMAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIMAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSMA\u003csub\u003eSAT\u003c/sub\u003e (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMD\u003csub\u003eSAT\u003c/sub\u003e (HU)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;59 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e136.24\u0026thinsp;\u0026plusmn;\u0026thinsp;24.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e46.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e37.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e6.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e116.68\u0026thinsp;\u0026plusmn;\u0026thinsp;49.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-86.74\u0026thinsp;\u0026plusmn;\u0026thinsp;19.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e91.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e31.07\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e184.00\u0026thinsp;\u0026plusmn;\u0026thinsp;72.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-99.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e128.73\u0026thinsp;\u0026plusmn;\u0026thinsp;21.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e44.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e35.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e128.00\u0026thinsp;\u0026plusmn;\u0026thinsp;46.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-86.58\u0026thinsp;\u0026plusmn;\u0026thinsp;17.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e89.70\u0026thinsp;\u0026plusmn;\u0026thinsp;17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.28\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e13.70\u0026thinsp;\u0026plusmn;\u0026thinsp;5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e184.87\u0026thinsp;\u0026plusmn;\u0026thinsp;82.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-99.86\u0026thinsp;\u0026plusmn;\u0026thinsp;18.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e105.60\u0026thinsp;\u0026plusmn;\u0026thinsp;25.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e119.27\u0026thinsp;\u0026plusmn;\u0026thinsp;53.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-83.97\u0026thinsp;\u0026plusmn;\u0026thinsp;30.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e84.58\u0026thinsp;\u0026plusmn;\u0026thinsp;18.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e33.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e21.66\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e16.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e-0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e154.23\u0026thinsp;\u0026plusmn;\u0026thinsp;69.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e-90.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ICC of Muscle parameters between the two observers (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s). WM, the whole muscles of abdominal wall and paravertebral muscles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters of muscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserver 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserver 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA\u003csub\u003eWM\u003c/sub\u003e (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e102.86\u0026thinsp;\u0026plusmn;\u0026thinsp;24.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e109.41\u0026thinsp;\u0026plusmn;\u0026thinsp;28.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMD\u003csub\u003eWM\u003c/sub\u003e (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e33.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMAT\u003csub\u003eWM\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA\u003csub\u003eSAT\u003c/sub\u003e (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e127.09\u0026thinsp;\u0026plusmn;\u0026thinsp;80.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e113.79\u0026thinsp;\u0026plusmn;\u0026thinsp;83.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMD\u003csub\u003eSAT\u003c/sub\u003e (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-86.39\u0026thinsp;\u0026plusmn;\u0026thinsp;18.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-84.35\u0026thinsp;\u0026plusmn;\u0026thinsp;19.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis and influencing factors of SMD\u003c/h2\u003e \u003cp\u003eThe correlation analysis of SMD with age, gender, BMI and other muscle parameters at L3 level are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Pearson linear correlation analysis shows that SMD is highly correlated with IMAT and IMAC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), moderately with gender, age, SMA, SMI and abdominal subcutaneous fat area (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and slightly with BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between SMD and age, gender, BMI and other muscle parameters. Significant statistical difference* (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIMAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIMAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMA\u003csub\u003eSAT\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe impact of SMI, IMAC, IMAT, abdominal subcutaneous fat area, gender and age on SMD are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Multiple linear regression analysis shows that IMAC, IMAT and age are influencing factors of SMD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the order of influence degree is IMAT (Stbβ=-0.616), IMAC (Stbβ=-0.429), and age (Stbβ=-0.098); While IMAT increases by 1 unit, SMD decreases by 0.968HU; IMAC increases by 1 unit, SMD decreases by 17.524HU; and age increases by 1 year, SMD decreases by 0.097HU. Subcutaneous fat area and gender were not influential factors of SMD (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis about influencing factors of SMD of abdominal wall and paravertebral muscle group at L3 level. Significant statistical difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) *.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStb β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-17.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-16.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMA\u003csub\u003eSAT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the analysis of 292 inpatients by abdominal opportunistic CT from our hospital, our study demonstrated that IMAT had greater effect on SMD than IMAC and age. For all we know, the impact of IMAT vs IMAC on SMD has yet to be investigated.\u003c/p\u003e \u003cp\u003eMost previous studies have focused on evaluating healthy individuals [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, it is noteworthy that a significant proportion of participants were elderly and presented with underlying medical conditions. Jong Hyuk Lee et al. assessed 2720 patients for annual physical examination, of whom 12% had a previous history of cancer, 27% hypertension, 14% diabetes, 11% cardiovascular disease and 5.4% chronic liver disease and kidney disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, relying solely on direct assessment of muscle quality often led to inaccuracies. We choose inpatients as our subjects for their clear and thorough medical histories, which facilitated the exclusion of diseases affecting muscles and minimized experimental errors, and enhanced the reliability.\u003c/p\u003e \u003cp\u003eThe measurement of muscle quality is also affected by CT equipment, tube voltage, slicer thickness and iodine contrast. Lamba et al. recruited 48 patients older than 18 years who underwent unenhanced CT performed both on GE and Siemens 64-MDCT scanner within 12 months, and found Hounsfield unit measurements for unenhanced abdominal soft tissues of the same patient vary between two common MDCT manufacturers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Lortie et al. retrospectively analyzed the effects of iodine contrast and tube voltage on the evaluation of muscle mass and quality, found that the use of iodine contrast significantly increased the muscle density and area, lower tube voltage resulted in higher muscle density and lower muscle area, and the effect on muscle density significantly greater than on muscle area [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, Fuchs et al. noted an 11.64% decrease in muscle density (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and a 1.11% in-crease in muscle area (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) measured on 5mm layer thickness images com-pared to 2mm layer thickness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, it is necessary to control the above conditions to ensure the stability and reproducibility of the measurement results and avoid errors. In this research, the same CT scanner was used by the same scan parameters which were tube voltage, 120 kV; scanned slice thickness, 10 mm; re-constructed slice thickness, 0.625 mm; reconstruction window width, 350HU; and reconstruction window level was 40HU.\u003c/p\u003e \u003cp\u003eFor the measurement of muscle quality on CT, there are also inter-observer, intra-observer, and inter-measurement software variability. Barbalho et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] showed that muscle parameters measured by both Slice-O-Matic and OsiriX tripartite software had excellent consistency (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.954, p\u0026lt;0.001). Our results showed the inter-observer consistency reached excellent (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75), which were consistent with a couple of other studies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMuscle quality, usually evaluated by SMD, refers to the microscopic and macroscopic changes in muscle structure and composition [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. SMD reflects the density of all the components in the ROI of muscle, including muscle fibers, easily recognizable crude fat components between muscles or muscle bundles, indiscernible intrafascicular fat, and intracellular lipids in muscle cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Skeletal muscle fat infiltration is the main reason for the change of muscle density. Skeletal muscle adipose tissue can be divided into IMAT, IMAC, and intramyocellular lipids (IMCL) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. IMCL is a lipid component deposited in a single muscle cell, which cannot be measured separately by CT. IMCL can be obtained by magnetic resonance spectrum (MRS), but it is limited due to its lengthy scanning time. With the rapid advancement in technology, IMCL will emerge as a prominent research area in the future [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIMAT typically measured by the third-party software to quantitatively evaluate muscle and interfascicular fat, utilizing CT values ranging from \u0026minus;\u0026thinsp;190HU to -30HU. The distinctive adipose tissue reservoir exhibits a robust correlation with several diseases and the process of aging. A large number of studies have shown that high IMAT in the thigh is associated with abnormal blood glucose [\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and increases cardiovascular risk factors. IMAT plays a significant role in the age-related alterations in muscle metabolism. Muscle fat infiltration increases with aging. It is directly associated with the decline in muscle mass, flexibility and quality. To some degree, it can also predict the onset of activity limitations in older people. IMAT is used as a robust indicator for assessing muscle fat infiltration, benefiting from well-established and stable measurement methods [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2010, the ratio of CT attenuation of the multifidus muscle to the abdominal wall subcutaneous fat was proposed by Kitajima as a potential marker for non-alcoholic fatty liver disease [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This parameter was referred to intramuscular adipose tissue content (IMAC), and it was subsequently reported by multiple studies [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The researchers perceived IMAC as the extra-myocyte fat within muscle fiber bundle. Compared with IMAT, it controlled the influence of CT equipment, scanning conditions and individual differences. The higher IMAC is, the more the muscle's fat content is [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distribution of fat infiltration in the abdominal wall and paravertebral muscles at the L3 level is imbalanced. The abdominal wall muscles of some individuals showed atrophy, while others displayed multifidus muscle atrophy. Kitajima's study only assessed multifidus muscle, but the IMAC of single muscle could not solely reflect extra-myocyte fat within the muscle fiber bundle and fully indicate overall fat content [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our study, IMAC utilized represented the ratio of CT attenuation of abdominal wall and paravertebral whole muscles to the subcutaneous fat at L3 level. There are numerous reports about IMAC of multifidus muscle, most of which are used to evaluate the prognosis of different diseases [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, there are few studies to compare IMAC of each composition muscle of abdominal wall and paravertebral muscles at the L3 level. Ashida conducted that iliopsoas IMAC offered more advantages in predicting skeletal muscle loss com-paring to the multifidus muscle [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This could be attributed to the presence of numerous clefts which might be the site of extra-muscular adipose tissue within the multifidus muscle, complicating the accurately determination of intramuscular adipose tissue levels. In contrast, the iliopsoas muscle which has smooth nature and tightly packed muscle fibers is less likely to contain extra-muscular adipose tissue, enabling a more accurate reflection of intramuscular adipose tissue. Therefore, the IMAC of different muscles at L3 level still deserves further study. The amount of intramuscular adipose within a specific muscle is dependent on its anatomical characteristics [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. For instance, it is due to greater cracks and infiltrated fats be-tween muscles and bundles that IMAC for abdominal wall and posterior vertebral extensor muscles reflects both intra-muscle and inter-muscle adipose. Conversely, the IMAC of psoas major and quadratus lumborum which has exhibit denser muscle fibers with less interfascicular fat infiltration primarily reflects muscular adiposity.\u003c/p\u003e \u003cp\u003eOur study demonstrated a strong negative correlation between IMAC and IMAT with SMD, establishing both as influential factors for SMD. Multiple linear regression analysis revealed standardized regression coefficients of -0.616 for IMAT and \u0026minus;\u0026thinsp;0.429 for IMAC, indicating that an increase in one unit of IMAT led to a de-crease in SMD by 0.968HU, while an increase in one unit of IMAC resulted in a decrease in SMD by 17.524HU. These findings highlight the significantly greater impact of IMAT on SMD comparing to that of IMAC. Brennan et al. demonstrated a significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between lower muscle density in the elderly and higher intracellular lipid concentrations, but SMD exhibited a weaker correlation with intracellular lipid concentrations, being more influenced by extra-myocyte lipids and independent of age, gender, race, and obesity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. IMAT refers to the lipid storage in adipose cells located beneath the deep muscle fascia, encompassing visible lipids stored within intermuscular fibers and intermuscular adipocytes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, we think that muscle density is predominantly influenced by the crude fat component observed amidst muscles and fascicles, while being minimally affected by the visible fat component within fascicles and muscle cells. This aligns with Brennan's findings [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Whether muscle function and metabolism are related to the location of muscle fat infiltration needs further study.\u003c/p\u003e \u003cp\u003eIn addition, our study demonstrated a moderate association between age and SMD, while gender showed no significant influence. Aging was found to significantly impact muscle quality decline in both males and females. Multiple linear regression analysis revealed a standard regression coefficient of -0.098 for age, indicating that SMD decreased by 0.097HU with aging annually. Previous studies have consistently reported IMAT increased with advancing age. Furthermore, our results are consistent with Tetsuro Hida [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], who observed that aging is associated with reduced muscle area and increased muscular fat deposition even after con-trolling for variables such as gender and ethnicity. Thus, it can be concluded that the effect of age on muscle quality is universal regardless of spinal disease presence or absence, muscle type, gender, or ethnicity.\u003c/p\u003e \u003cp\u003eThere are a few limitations in this study. Firstly, we choose the whole abdominal wall and paraspinal muscles for evaluation. It had a good linear correlation with the whole body muscles. However, the delineation of the whole muscles took a relatively long time, future research should analyze separately abdominal wall and single paraspinal muscle in order to find which one could reflect the changes of muscle mass and muscle quality well. Secondly, due to the inherent difficulty in distinguishing between adipose tissue within muscle bundles and intramuscular lipids, the CT attenuation of the muscle only reflects the comprehensive composition of all components within the muscle. But with the rapid advancement in technology of MRS, IMCL could be further studied in future research. Thirdly, although the same CT scanner was utilized and the same scanning parameters were standardized in our study, the measurement of muscle quality could be influenced by the scanning mode, and varying acquisition schemes which might potentially impact the experimental outcomes. Finally, the sample size of this study was relatively small, necessitating further confirmation of the conclusions through larger-scale studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that IMAT has greater effect on SMD than IMAC and age, suggesting a different causal mechanism for the decrease of SMD. This maybe attract a stronger focus on the prevention of the increase of IMAT.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and participants\u003c/h2\u003e\n \u003cp\u003eThis was a retrospective study. We recruited a total of 499 inpatients in Tianjin hospital from July 2019 to July 2022 for this study. Clinical information and abdominal CT were collected for every inpatient routinely. Inclusion criteria: (1) males\u0026thinsp;\u0026ge;\u0026thinsp;50 years old and females in postmenopausal state. (2) abdominal CT images were clear and the image quality met the standard [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Exclusion criteria: Patients with diseases seriously affecting muscle quality, such as musculoskeletal system diseases, neuromuscular system diseases, malignant tumors and chronic wasting diseases (severe diabetes, chronic obstructive pulmonary disease, chronic liver disease, etc.). The study was conducted in accordance with the Helsinki Declaration and approved by the Ethics Committee of Tianjin Hospital (2024YLS061). Due to the retrospective nature of the study, Tianjin hospital waived the need of obtaining informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eComputed tomography acquisition\u003c/h2\u003e\n \u003cp\u003eSpiral CT imaging of full abdomen was performed for all participants using a 128-slice GE revolution ES CT scanner (GE Medical Systems, LLC*). Scans were ac-quired from the upper edge of T12 vertebrae to the lower edge of L5 vertebrae. Scan parameters were: tube voltage, 120 kV [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]; pitch, 0.948:1 and slice thickness, 10 mm.\u003c/p\u003e\n \u003cp\u003eCross-sectional abdominal images were reconstructed at L3 level on GE AW4.7 workstation, which parallelled to the upper endplate of L3 vertebrae and showed the longest slice of transverse process (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Reconstruction parameters were: reconstruction algorithm, standard; reconstruction thickness, 0.625mm; window width, 350HU; window level, 40HU; and DFOV is 45cm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eMuscle quality assessments\u003c/h2\u003e\n \u003cp\u003eCross-sectional muscular parameters of the abdominal wall and paraspinal muscles at L3 level were measured. Image J 1.53e (Wayne Rasband and contributors National Institutes of Health, USA) was used for analysis.\u003c/p\u003e\n \u003cp\u003eMuscle segmentation was performed manually using \u0026ldquo;Polygon selections\u0026rdquo; to out-line muscle contours (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWithin the region of interest muscles, \u0026ldquo;Analyze-Measure\u0026rdquo; was used to calculate skeletal muscle area (SMA) and CT attenuation. And the threshold of -190 HU ~ -30 HU [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] was set to obtain IMAT by \u0026ldquo;Image-Adjust-Threshold\u0026rdquo; (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The same method was used for contouring subcutaneous fat to obtain its cross-sectional area and CT attenuation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The images from all patients included were analyzed independently by two musculoskeletal radiologists with 10 and 20 years of experience and the average values were recorded (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe skeletal muscle index (SMI) and IMAC was calculated using the following formula:\u003c/p\u003e\n \u003cp\u003eSMI\u0026thinsp;=\u0026thinsp;SMA / Height\u003csup\u003e2\u003c/sup\u003e, (1)\u003c/p\u003e\n \u003cp\u003eIMAC\u0026thinsp;=\u0026thinsp;CT attenuation of abdominal wall and paravertebral muscles / CT attenuation of abdominal wall subcutaneous fat. (2)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics Version 21 (USA). Continuous variables are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). All measurement data obeyed normal distribution. Significance level was \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eIntraclass correlation coefficient (ICC) was tested to assess inter-observer reliability between the two observers. An ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 was considered indicative of good agreement. Multiple linear regression analysis was used to analyze the influencing factors of SMD and compare their extent.\u003c/p\u003e\n \u003cp\u003eCorrelation coefficient r value: |r|\u0026lt;0.3 indicates low correlation between variables; 0.3\u0026le;|r|\u0026lt;0.5 indicates moderate correlation between variables; 0.5\u0026le;|r|\u0026le;1 indicates a high correlation between variables.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to H.Z.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, X.G. and N.C.; methodology, X.G. and N.C.; software, X.D., N.W., R.L. and F.F.; formal analysis, X.G., N.C. and Z.H.; investigation, X.G.; resources, X.G.; data curation, X.G.; writing\u0026mdash;original draft preparation, X.G. and N.C.; writing\u0026mdash;review and editing, X.G., N.C., S.R., L.G., L.K., Z.H.; visualization, X.G.; supervision, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used during the current study available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRosenberg I H. Sarcopenia: origins and clinical relevance[J]. \u003cem\u003eThe Journal of Nutrition\u003c/em\u003e, 1997, 127(5 Suppl): 990S-991S.\u003c/li\u003e\n\u003cli\u003eAnker S D, Morley J E, Von Haehling S. Welcome to the ICD-10 code for sarcopenia[J]. \u003cem\u003eJournal of Cachexia, Sarcopenia and Muscle\u003c/em\u003e, 2016, 7(5): 512-514.\u003c/li\u003e\n\u003cli\u003eVon Haehling S, Morley J E, Anker S D. From muscle wasting to sarcopenia and myopenia: update 2012[J]. \u003cem\u003eJournal of Cachexia, Sarcopenia and Muscle\u003c/em\u003e, 2012, 3(4): 213-217.\u003c/li\u003e\n\u003cli\u003eFaulkner J A, Larkin L M, Claflin D R, et al. 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Effect of race, age, and gender on lumbar muscle volume and fat infiltration in the degenerative spine[J]. \u003cem\u003eJournal of Orthopaedic Science: Official Journal of the Japanese Orthopaedic Association\u003c/em\u003e, 2021, 26(1): 69-74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4643186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4643186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo explore intermuscular adipose tissue (IMAT) and intramuscular adipose tissue content (IMAC) which affect skeletal muscle density (SMD) more and elucidate its underlying causes. 292 inpatients without definite musculoskeletal system disease were recruited. All the patients performed abdominal CT. Muscle parameters which included skeletal muscle area (SMA), skeletal muscle index (SMI), SMD, IMAC and IMAT, and fat parameters which included area of subcutaneous fat tissue in abdominal wall were measured by two musculoskeletal radiologists with Image J software at the level of L3 vertebrae. Multiple regression analysis was used to identify the factors which affected SMD, and compared the extent of influence. SMD was highly correlated with IMAT and IMAC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), moderately correlated with gender, age and area of subcutaneous fat tissue in abdominal wall (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and slightly correlated with BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multiple linear regression analysis showed that IMAC, IMAT and age are influencing factors of SMD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The order of influence degree is IMAT(Stbβ=-0.616), IMAC(Stbβ=-0.429), and age (Stbβ=-0.098). Area of subcutaneous fat tissue in abdominal wall and gender were not influence factors of SMD (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Age, IMAT, and IMAC exert an influence on SMD. Notably, the impact of IMAT on SMD is much more significant.\u003c/p\u003e","manuscriptTitle":"Intermuscular adipose tissue or intramuscular adipose tissue content, which affects skeletal muscle density more? -a study based on abdominal opportunistic CT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 20:56:45","doi":"10.21203/rs.3.rs-4643186/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-02T10:34:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-30T21:28:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-19T16:11:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328645504123001504837835148039519346988","date":"2024-08-14T19:48:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116990428640006957309618287545011931444","date":"2024-08-13T11:08:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-11T19:25:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-04T08:33:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-02T14:28:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T09:14:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-26T13:49:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f34af460-cc32-4c5e-9669-d5c52c588312","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34788227,"name":"Health sciences/Medical research"},{"id":34788228,"name":"Health sciences/Health care/Medical imaging"}],"tags":[],"updatedAt":"2025-03-17T16:04:42+00:00","versionOfRecord":{"articleIdentity":"rs-4643186","link":"https://doi.org/10.1038/s41598-025-85946-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-10 15:58:31","publishedOnDateReadable":"March 10th, 2025"},"versionCreatedAt":"2024-07-22 20:56:45","video":"","vorDoi":"10.1038/s41598-025-85946-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-85946-8","workflowStages":[]},"version":"v1","identity":"rs-4643186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4643186","identity":"rs-4643186","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}