Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis

Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis | 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 Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis Toshikazu Kohira, Satoshi Oeda, Erina Eto, Yoshihito Kubotsu, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891676/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background/Aims Liver steatosis can be measured with ultrasound techniques such as the controlled attenuation parameter (CAP) on an equipped FibroScan. For more widespread screening and quantitative evaluation of liver steatosis, a predictive model using body composition data obtained by body bioelectrical impedance analysis (BIA) was developed. Methods In the training cohort including 365 patients suspected of having metabolic dysfunction-associated steatotic liver disease, a stepwise selection method was used to determine the BIA-related variables associated with CAP. Using the significant variables, a predictive formula was developed, and the estimated CAP (eCAP) was obtained. The diagnostic performance of eCAP was tested to predict liver steatosis with receiver operating characteristic (ROC) curve analysis in the training, validation (n = 408) and liver biopsy (n = 158) cohorts. Results The body fat mass of the trunk, skeletal muscle index and age were significant variables associated with CAP. eCAP was obtained as 219.1 − 0.4479 × age + 3.476 × BFM of trunk + 7.045 × SMI. The area under the ROC curve was 0.814 in the training cohort and 0.808 in the validation cohort. The sensitivity and specificity were 72.5% and 82.1% with a cut-off value of eCAP = 281 dB/m. For sensitivity ≥ 90%, the cut-off of eCAP was 266 dB/m. In the liver biopsy cohort, the presence of pathological steatosis was predicted with eCAP as an area under the ROC curve = 0.826, which was not statistically different from CAP (0.871). Conclusions Completely non-invasive BIA-based eCAP could predict liver steatosis. Health sciences/Gastroenterology Health sciences/Medical research bioelectrical impedance analysis body composition controlled attenuation parameter metabolic dysfunction-associated steatotic liver disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Study Highlights To utilize body composition measurements obtained by bioelectrical impedance analysis (BIA) for the prediction of liver steatosis, a predictive formula with BIA-related variables was developed to obtain the estimated controlled attenuation parameter (eCAP) in subjects suspected of having metabolic dysfunction-associated steatotic liver disease who underwent CAP measurement with FibroScan. eCAP correlated with CAP and pathological steatosis and demonstrated enough diagnostic performance to predict liver steatosis. eCAP is a completely non-invasive and quantitative parameter to predict liver steatosis and its severity. Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, is associated with overweight and lifestyle-related diseases; the cardiometabolic criteria for diagnosis of MASLD have been defined [ 1 ]. Metabolic dysfunction-associated steatohepatitis (MASH) is pathologically diagnosed on the basis of steatosis, inflammation and hepatocyte ballooning [ 2 ]. MASH with significant liver fibrosis is known as at-risk MASH and is considered to have a high risk of disease progression [ 3 ]. MASLD/MASH is globally prevalent and increasingly becoming an etiology of liver cancer [ 4 ]. Therefore, efficient surveillance of the general public is an urgent issue. Steatosis is the accumulation of lipid droplets within hepatocytes. For the diagnosis of MASLD, steatosis should be observed in more than 5% of hepatocytes [ 5 ]. Obesity is one risk factor for MASLD. The hepatic steatosis of MASLD is one manifestation of overweight and is defined as ectopic fat accumulation that is associated with visceral fat accumulation and insulin resistance [ 6 , 7 ]. Skeletal muscle plays an important role in metabolic homeostasis, and skeletal muscle mass, function and myosteatosis are associated with the pathological severity of the liver and the prognosis of MASLD [ 8 , 9 , 10 ]. Therefore, in the management of MASLD, the patient’s body composition provides beneficial information regarding systemic adiposity and skeletal muscle mass. Indeed, a recent study indicates that impaired body composition, such as visceral fat accumulation and reduced skeletal muscle mass, is associated with severe steatosis, inflammation and fibrosis of the liver in MASLD [ 11 ]. Accumulating evidence also indicates that bioelectrical impedance analysis (BIA) is a useful and accessible method to measure body composition and shows a good correlation with the dual-energy X-ray absorptiometry method [ 12 ]. BIA is a noninvasive, simple and low-cost procedure and can be applied broadly and repeatedly in health check screenings and clinical settings. In clinical practice, imaging modalities rather than liver biopsies are generally performed to evaluate hepatic steatosis because of their noninvasiveness, reliability and quantitativity [ 3 ]. B-mode ultrasound is the most common examination procedure to detect liver steatosis. However, it is impractical to perform ultrasound on all people with MASLD risk. Ultrasound technique-based examination measuring the attenuation of ultrasound is used for quantitative evaluation of hepatic steatosis [ 3 , 13 ]. FibroScan is one of the most widely used transient elastography techniques and can be used to perform controlled attenuation parameter (CAP) measurement and liver stiffness measurement (LSM) [ 14 ]. Using the CAP and LSM values, the FibroScan-Based Score (FAST Score) was also developed to predict at-risk MASH [ 15 ]. According to recent guidelines, FibroScan is included in the surveillance flow chart as the 2nd -step examination following the Fibrosis-4 index to identify advanced liver fibrosis [ 3 , 16 ]. CAP is simultaneously measured in this step. Therefore, in general, quantitative evaluation of liver steatosis such as CAP measurement is not provided to health checkup examinees and patients in the screening step, whereas information about the presence of and severity of liver steatosis would encourage health checkup examinees, patients and medical providers to perform further evaluation of MASLD. Considering the connective pathogenesis between MASLD and body composition, we hypothesized that BIA-related variables are able to predict the severity of liver steatosis. In this study, we aim to develop a completely noninvasive formula using the variables obtained from BIA to predict CAP. In addition, the prediction of LSM and the FAST Score using BIA-related variables was attempted. Materials and Methods Study design and patients For the training cohort, 365 patients who visited Saga University Hospital from December 2018 to December 2021 were included (Fig. 1 ). All the patients were suspected of having MASLD and received BIA and FibroScan examinations. In this cohort, formulas to predict the CAP, LSM and FAST Score were developed, and the estimated CAP (eCAP), estimated LSM (eLSM) and estimated FAST Score (eFAST Score) were obtained. The diagnostic performances of the eCAP, eLSM and eFAST Score were tested using the actual CAP, LSM and FAST Score measured with FibroScan as the gold standard. Four hundred eight patients who visited Saga University Hospital from January 2022 to December 2024 were included in the validation cohort. The diagnostic accuracy of eCAP was similarly tested as in the training cohort. The liver biopsy cohort was independently organized and included 158 patients who received liver biopsy, FibroScan examination and BIA within a month from December 2018 to December 2024. The diagnostic performances of the eCAP and CAP were tested using the pathological steatosis score as the gold standard. All patients were adult and older than 20 years old. No patients in any of the cohorts had other liver disease etiologies, including habitual alcohol intake (daily ethanol consumption of < 30 g in men and < 20 g in women), positivity for hepatitis B surface antigen or hepatitis C virus antibody and abnormal serum thyroid hormone levels. Additionally, no patients had autoimmune liver disease, drug-induced hepatotoxicity, hemochromatosis or Wilson’s disease. The study protocol was approved by the Clinical Research Ethics Review Committee of Saga University Hospital and was performed in accordance with the principles of the 1975 Declaration of Helsinki (revised in 2013). The participants provided informed consent to participate in the study. Physical examination and serum biochemical measurements The body mass and height of the participants were measured, and body mass index (BMI) was calculated as body mass (kg) divided by height squared (m 2 ). Venous blood samples were obtained after an overnight fast and were used to measure albumin, aspartate aminotransferase (AST), alanine transaminase (ALT), γ-glutamyl transpeptidase activity (GGT), total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride and fasting plasma glucose concentrations, platelet count and hemoglobin A1c (HbA1c) using conventional laboratory techniques. Body composition measurement For body composition measurement, after an overnight fast, BIA was performed using Inbody 770® (Biospace Co, Seoul, Republic of Korea). Parameters included body fat mass (BFM), fat-free mass (FFM), skeletal muscle mass (SMM), total body water (TBW), intracellular water (ICW), extracellular water (ECW) and whole-body phase angle (50 kHz). Percent body fat (PBF) was obtained by dividing BFM by body weight (kg). The skeletal muscle index (SMI) was obtained by dividing appendicular muscle mass by height (m) 2 . Detail explanations for individual parameters are available in the product manual at https://uk.inbody.com/about-inbody/result-sheet-interpretation/ . Liver stiffness measurement and controlled attenuation parameter measurement Experienced operators who had performed at least 500 examinations assessed the LSM and CAP in the right liver lobe using a FibroScan® 502. Patients were examined after an overnight fast using the M or XL probes within three months before and after BIA. The probe was selected based on the skin–liver capsule distance (SCD): XL probe for patients with SCD ≥ 25 mm and M probe for patients with SCD < 25 mm [ 17 , 18 ]. After we measured the SCD using ultrasound B-mode, the LSM and CAP measurements were performed using FibroScan® until 10 valid measurements were obtained for each patient. The median values were used to quantify liver fibrosis and steatosis. Based on previous reports, we defined measurement failure as examinations in which 10 valid LSMs were not obtained after 10 or more attempts. In patients with 10 valid LSMs, LSM ≥ 7.1 kPa and interquartile range-to-median ratio > 30% were defined as unreliable values [ 19 ]. Patients who had measurement failure or unreliable values were excluded from this study. Patients with CAP ≥ 248 dB/m were considered to have liver steatosis (pathological steatosis ≥ S1) [ 20 ], and patients with LSM ≥ 8.9 kPa were considered to have advanced fibrosis (pathological fibrosis stage ≥ 3) [ 21 ]. The FAST Score was calculated in accordance with the following previously reported formula [ 15 ]: FAST = (e – 1.65 + 1.07 × In (LSM) + 2.66*10‾⁸ × CAP³ – 63.3 × AST‾¹)/(1 + e – 1.65 + 1.07 × In (LSM) + 2.66*10‾⁸ × CAP³ – 63.3 × AST‾¹). Patients with a FAST Score of > 0.67 were considered to have at-risk MASH (MASH with fibrosis stage ≥ 2) [ 15 ]. Evaluation of liver biopsy Ultrasonography-guided liver biopsy was performed using a 16-gauge biopsy needle. All liver biopsies were approximately ≥ 20 mm in length. Liver biopsy slides, stained with hematoxylin–eosin and Azan or Masson stain, were independently evaluated by experienced central pathologist (S.A.) specializing in liver pathology. The central pathologists were blinded to the clinical data. Hepatic steatosis, lobular inflammation and hepatocyte ballooning were evaluated based on the NAFLD activity score [ 22 ]. Statistical analysis For the prediction of CAP, LSM and FAST Score using BIA, statistically independent predictive variables were selected by the forward–backward stepwise selection method from the variables obtained from BIA, age, sex and height. The predictive formula was developed with the selected variables. The variables tested in the stepwise selection method were age, sex, height, body weight, BMI and BIA-related variables (Supplementary Table S1 ). Correlations were tested using the Spearman’s rank correlation coefficient. The Steel–Dwass test was used for multiple comparisons. The diagnostic performance of individual tests was determined using receiver operating characteristic (ROC) curves. Optimal cut-off values were chosen to maximize the sum of the sensitivity and specificity, known as the Youden index [ 23 ]. Cut-off values with at least 90% sensitivity and specificity were also individually chosen. Comparisons of the area under the ROC curve (AUROC) between the tests were performed using the DeLong test [ 24 ]. We considered a p-value < 0.05 to be statistically significant. All statistical analyses were performed using JMP ver. 14 (SAS Institute Japan; Tokyo, Japan). Results Patient characteristic s The patient characteristics of the training cohort are summarized in Supplementary Table S2, and the BIA-related variables are summarized in Supplementary Table S1 . Regarding the BIA-related variables, the mean PBF and mean SMI were 36.7% and 7.2 kg/m 2 . Regarding FibroScan examinations, the mean LSM, CAP and FAST Scores were 6.1 kPa, 299 dB/m and 0.322, respectively. The patient characteristics of the validation cohort and liver biopsy cohort are summarized in Supplementary Table S3 and S4. Formula to predict CAP, LSM and FAST Score Among the 34 variables including age, sex, height, body weight, BMI and 29 BIA-related variables, significant variables were selected by stepwise analysis (Table 1 ). For the prediction of CAP, age (p = 0.008), BFM of trunk (p < 0.0001) and SMI (p = 0.0012) were selected. The predictive formula is as follows: eCAP = 219.1 − 0.4479 × age + 3.476 × BFM of trunk + 7.045 × SMI. For the prediction of LSM, ECW/TBW (p < 0.0001), BFM Control (body fat mass normalized to the appropriate body composition, p < 0.0001), TBW/FFM (p = 0.0034), whole-body phase angle (p < 0.0001) and SMI (p = 0.0001) were significant. The predictive formula is as follows: eLSM = 20.75 + 967.9 × ECW/TBW − 0.2932 × BFM Control − 5.478 × TBW/FFM + 9.942 × whole-body phase angle − 2.436 × SMI. For the prediction of FAST Score, BFM% of trunk (p < 0.0001) was solely significant, and the predictive formula is as follows: eFAST = 0.1754 + 0.0006315 × BFM% of trunk. Table 1 Stepwise analysis to predict CAP, LSM and at risk MASH Variable to be predicted Factor Regression coefficient p value CAP Age -0.4479 0.0079 BFM of Trunk 3.476 < 0.0001 SMI 7.045 0.0012 LSM ECW/TBW 967.9 < 0.0001 BFM Control -0.2932 < 0.0001 TBW/FFM -5.748 0.0034 Whole Body Phase Angle 9.942 < 0.0001 SMI -2.436 0.0001 FAST-Score BFM% of Trunk 0.0006315 < 0.0001 Regression coefficient and p value were obtained by stepwise analysis. Regression coefficient was shown as four significant digits. Abbreviations: CAP controlled attenuation parameter; LSM, liver stiffness measurement; FAST Score, FibroScan-Based Score; BFM, body fat mass; SMI, skeletal muscle mass index; TBW, total body water; ECW, extracellular water; FFM, fat free mass. Correlation between predicted and actual CAP, LSM and FAST Score values Using the formulas developed above, the predicted values, that is, eCAP, eLSM and eFAST Score, were obtained and correlated with the actual measured values of CAP, LSM and FAST Score (Fig. 2 ). A significant positive correlation was identified between CAP and eCAP (ρ = 0.5589, p < 0.0001). (Fig. 2 A). The correlations between LSM and eLSM (ρ = 0.2677, p < 0.0001) and between FAST Score and eFAST Score (ρ = 0.2577, p < 0.0001) were weak (Fig. 2 B, 2 C). ROC analysis of the training cohort The diagnostic performances to detect the presence of liver steatosis (CAP ≥ 248 dB/m), advanced fibrosis (LSM ≥ 8.9 kPa) and at-risk MASH (FAST Score > 0.67) were tested by ROC analysis (Fig. 3 and Table 2 ). The AUROCs of eCAP, eLSM and eFAST Score were 0.814, 0.700 and 0.657. On the basis of the Youden index, the optimal cut-off values for the diagnosis of steatosis, advanced fibrosis and at-risk MASH were eCAP = 281 db/m, eLSM = 10.69 kPa and eFAST Score = 0.381. Based on this study’s aim to contribute to the surveillance of MASLD, cut-off values with a sensitivity ≥ 90% were also obtained: eCAP = 266 db/m (sensitivity = 90.3%), eLSM = 6.67 kPa (sensitivity = 90%) and eFAST Score = 0.455 (sensitivity = 90.1%). Table 2 Diagnostic performance of eCAP, eLSM and eFAST Score eCAP eLSM eFAST Score AUROC 0.814 0.700 0.657 Optimal cut-off (Youden index) 281 db/m 10.69 kPa 0.381 Sensitivity 72.5% 50.9% 68.3% Specificity 82.1% 79.2% 57.4% Optimal cut-off (Sensitivity ≥ 90%) 266 db/m 6.67 kPa 0.455 Sensitivity 90.3% 90% 90.1% Specificity 44.8% 32.2% 29.5% Abbreviations: AUROC, area under the receiver operating characteristics curve; eCAP, estimated controlled attenuation parameter; eLSM estimated liver stiffens measurement; eFAST, estimated FibroScan-Based Score. ROC analysis of the validation cohort In accordance with the ROC analysis in the training cohort, the diagnostic performances of eLSM and eFAST Score were poor. Therefore, further analysis was performed to test eCAP. In the validation cohort, eCAP showed a significant positive correlation with CAP (ρ = 0.563, p < 0.0001) (Fig. 4 A). The AUROC of eCAP was 0.808 (Fig. 4 B). Using the cut-off value of eCAP obtained in the training cohort analysis, the sensitivity was 65.9% and the specificity was 79% with a cut-off value = 281 db/m, and the sensitivity was 86.4% and the specificity was 58% with a cut-off value = 266 dB/m. Correlation with pathological steatosis and ROC analysis in the liver biopsy cohort In the liver biopsy cohort, the correlation of eCAP or CAP with the pathological steatosis score was tested. The diagnostic performances of eCAP and CAP were also tested and compared. eCAP showed a significant positive correlation with the pathological steatosis score (ρ = 0.379, p < 0.0001), and a significant difference in eCAP was identified between the pathological scores (score 0 vs. score 1, p = 0.0061; score 0 vs. score 2, p = 0.0044; score 0 vs. score 3, p = 0.0007; score 1 vs. score 3, p = 0.0052) (Fig. 5 A). The AUROC of eCAP for the diagnosis of pathological steatosis score ≥ 1 was 0.826 (Fig. 5 B). The correlation between CAP and the pathological steatosis score was also significant (ρ = 0.429, p < 0.0001), and a significant difference in CAP was identified between the pathological scores (score 0 vs. score 1, p = 0.0015; score 0 vs. score 2, p = 0.0002; score 0 vs. score 3, p = 0.0002; score 1 vs. score 3, p = 0.0017) (Fig. 5 C). The AUROC of CAP for the diagnosis of pathological steatosis score ≥ 1 was 0.871 (Fig. 5 D). The AUROC of CAP was greater than that of eCAP, but it was not statistically significant (p = 0.4426). Discussion In this study, CAP was successfully predicted using BIA-related parameters and denoted as eCAP. The predictive performance of eCAP was validated in the validation cohort, and eCAP could predict the presence of pathological steatosis. eCAP is completely noninvasive and does not require liver biopsy, blood tests, radiation exposure or imaging examination. Emulating CAP, the most popular and well-validated parameter of FibroScan, eCAP enables the collection of intuitive and quantitative information regarding liver steatosis. According to previous reports, there are several indexes for predicting steatotic liver, including the fatty liver index (FLI), hepatic steatosis index, Zhejiang University index, NAFLD liver fat score and Framingham steatosis index [ 25 – 29 ]. All these indexes require blood tests such as liver function tests, fasting glucose levels and HbA1c for calculation. Regarding the imaging modalities, B-mode ultrasound has been the most common procedure to identify liver steatosis. Ultrasonographical findings of liver–kidney contrast, vascular blurring and deep attenuation enable the identification of liver steatosis but are not quantitative [ 30 ]. Recent developments in ultrasound-based techniques measuring the attenuation of ultrasound in the liver with steatosis can provide quantitative results representing the severity of liver steatosis. Attenuation imaging (ATI) and the attenuation coefficient (ATT) represent the application of conventional ultrasound machines to quantitatively evaluate liver steatosis [ 31 , 32 ]. Clinically, CAP is the most widely studied algorithm for measuring the attenuation of A-mode ultrasound beams [ 33 ]. Magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) is a reliable and representative method for measuring liver steatosis and evaluating treatment efficacy [ 34 , 35 , 36 ]. However, the low availability and high costs of these imaging modalities limit their wide use for screening [ 3 ]. eCAP, developed in this study, is a completely noninvasive and low-cost procedure to evaluate liver steatosis. In our study, BFM of trunk and SMI were independently and positively associated with liver steatosis and used in the predictive formula. The value of the regression coefficient was positive and highest for SMI, suggesting that liver steatosis was strongly affected by the SMI. This means that a higher SMI results in severe liver steatosis. This paradoxical association was observed in previous studies. Wan et al. performed computed tomography (CT) imaging-based body composition analysis and identified that the SMI, visceral fat index, visceral fat-to-muscle ratio and visceral fat-to-subcutaneous fat ratio were independently and positively associated with the degree of pathological liver steatosis in adult obesity [ 37 ]. Schmitz et al. reported that the SMI measured by BIA was significantly higher in patients with biopsy-proven MASH than in non-MASH patients [ 38 ]. These studies and our study indicate the undesirable effect of the SMI on liver steatosis. In contrast, Yodoshi et al. reported that skeletal muscle mass measured by BIA negatively correlated with liver steatosis in a pediatric population [ 39 ]. The association between skeletal muscle mass and liver steatosis varies in accordance with the study cohort, but a possible explanation for the unpreferable effect of skeletal muscle mass on liver steatosis is myosteatosis. Myosteatosis is fat infiltration in skeletal muscle, and it negatively affects the pathogenesis of MASLD [ 40 , 41 ]. By detecting the difference in the signal intensities of triglycerides and adipose tissue, MRI and CT scans can be used to measure myosteatosis separately from the muscle area [ 40 , 41 , 42 ]. However, BIA and simple measurement of the muscle area in a CT or MRI image cannot exclude myosteatosis, resulting in a higher muscle mass and SMI in obese patients, and thus, the SMI can be a risk factor for liver steatosis. The detection and prediction of liver steatosis can contribute to the management of lifestyle-related diseases as well as the diagnosis of MASLD. People with liver steatosis have a higher incidence of type 2 diabetes than that of people without liver steatosis [ 43 ]. Moreover, the risk of cardiovascular disease [ 44 ], development of chronic kidney disease [ 45 ] and risk of extrahepatic cancer [ 46 ] are higher in the population with liver steatosis than in those without liver steatosis. Therefore, the earlier detection and prediction of liver steatosis using BIA in health checkup and primary care settings could contribute to improving the outcome of lifestyle-related diseases and promoting awareness of the associations between liver steatosis and the risk of extrahepatic disease. As shown in this study, body composition is associated with MASH and liver fibrosis, but actual predictions of these conditions are challenging. In our study, BIA-related variables including the SMI were significantly associated with LSM, and BFM% of trunk was significantly associated with the FAST Score. According to recent reports, less skeletal muscle mass is independently associated with severe liver fibrosis and the presence of MASH [ 37 , 47 , 48 ]. Visceral fat mass is also associated with the presence of MASH [ 37 ]. Moreover, sarcopenia aggravates the prognosis of MASLD [ 49 ]. Taken together, body composition is consistently associated with the pathophysiological features and prognosis of MASLD. However, to our knowledge, no study has tried to develop a predictive model using body composition parameters. Recently, the development of biomarkers to predict liver fibrosis and the presence of MASH has become an emerging topic [ 3 ]. The combined use of BIA-related variables and blood tests or imaging examination might represent a future avenue for identifying reliable biomarkers to predict liver fibrosis and MASH. There are several limitations in this study. The study cohort consisted of patients who were suspected of having MASLD. Therefore, the relatively high prevalence of MASLD resulted in a low negative predictive value of eCAP. In terms of the study concept, investigation of the general population should be carried out. Differences in the version of the BIA modality might affect the values of the BIA-related parameters, and the representability should be tested using different modalities. In conclusion, BIA-related parameters are able to predict liver steatosis as well as the CAP value. BIA is a useful examination to evaluate liver steatosis as well as body composition. Abbreviations ALT alanine transaminase AST aspartate aminotransferase ATI attenuation imaging ATT attenuation coefficient AUROC area under the ROC curve BFM body fat mass BIA bioelectrical impedance analysis BMI body mass index CAP controlled attenuation parameter CT computed tomography eCAP estimated CAP ECW extracellular water eFAST Score estimated FAST Score eLSM estimated LSM FAST Score FibroScan-Based Score FFM fat-free mass GGT γ-glutamyl transpeptidase HbA1c hemoglobin A1c HDL cholesterol high-density lipoprotein cholesterol ICW intracellular water LDL cholesterol low-density lipoprotein cholesterol LSM liver stiffness measurement MASH metabolic dysfunction-associated steatohepatitis MASLD metabolic dysfunction-associated steatotic liver disease MRI-PDFF magnetic resonance imaging-estimated proton density fat fraction PBF percent body fat ROC receiver operating characteristic SCD skin–liver capsule distance SMI skeletal muscle index SMM skeletal muscle mass TBW total body water. Declarations Conflict of Interest The authors have no conflicts of interest associated with this manuscript. Funding information This research was supported by the Research Program on Hepatitis from Japan Agency for Medical Research and Development (AMED) [grant numbers 24015731 and 23808721] and JSPS KAKENHI, Grant-in-Aid for Scientific Research (C) [grant number 24K11171]. Author Contribution T.K. , M.M and H.T. worked on the conceptualization, visualization and methodology of the study and wrote the original draft. Data curation and formal analysis were performed by T.K., S.O., Y.K., N.O. and H.T. H.T. and Y.E. contributed to funding acquisition. E.E., M.N., K.I., N.H., S.N., K.T. and S.Y. contributed to data acquisition. S.A. contributed to pathological data acquisition. A.K. and S.O supervised the statistical analysis. H.T. was the project administrator. This study was supervised by H.I. and T.K. The final draft of the manuscript was reviewed by K.A., S.Y. D.H and H.T. Acknowledgments We sincerely thank Maki Miyahara and Akiko Komine for their support with the BIA measurement and data collection. We thank Jenna MacArthur, PhD, from Edanz ( https://jp.edanz.com/ac ) for editing a draft of this manuscript. Data Availability The data that support the findings of this study are available from the corresponding author on reasonable request. References Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J. Hepatol. 79 , 1542–1556 (2023). Bedossa, P. et al. Histopathological algorithm and scoring system for evaluation of liver lesions in morbidly obese patients. Hepatology 56 , 1751–1759 (2012). European Association for the Study of the Liver (EASL). European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J. 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Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut 66 , 1138–1153 (2017). Koo, B. K. et al. Sarcopenia is an independent risk factor for non-alcoholic steatohepatitis and significant fibrosis. J. Hepatol. 66 , 123–131 (2017). Pan, X. Y. et al. Low skeletal muscle mass is associated with more severe histological features of non-alcoholic fatty liver disease in male. Hepatol. Int. 16 , 1085–1093 (2022). Moon, J. H., Koo, B. K. & Kim, W. Non-alcoholic fatty liver disease and sarcopenia additively increase mortality: a Korean nationwide survey. J. Cachexia Sarcopenia Muscle . 12 , 964–972 (2021). Additional Declarations No competing interests reported. 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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-6891676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":481520968,"identity":"924edc5a-931d-4c5c-b7c1-fd4b717f6926","order_by":0,"name":"Toshikazu Kohira","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Toshikazu","middleName":"","lastName":"Kohira","suffix":""},{"id":481520970,"identity":"78dfe382-edc3-482a-ae34-af7f2018fc08","order_by":1,"name":"Satoshi Oeda","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Oeda","suffix":""},{"id":481520974,"identity":"00aa472f-b125-4af8-a5e9-87e67d52499c","order_by":2,"name":"Erina Eto","email":"","orcid":"","institution":"Saga-Ken Medical Centre Koseikan","correspondingAuthor":false,"prefix":"","firstName":"Erina","middleName":"","lastName":"Eto","suffix":""},{"id":481520975,"identity":"6b601211-ae06-4c4d-8a0e-fbdefd78813b","order_by":3,"name":"Yoshihito Kubotsu","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Yoshihito","middleName":"","lastName":"Kubotsu","suffix":""},{"id":481520976,"identity":"089caec1-53ae-4304-a024-d8208ed65c8e","order_by":4,"name":"Misa Norita","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Misa","middleName":"","lastName":"Norita","suffix":""},{"id":481520977,"identity":"63310a53-9747-4b5e-b62f-68b1f9a0cb77","order_by":5,"name":"Kaori Inoue","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kaori","middleName":"","lastName":"Inoue","suffix":""},{"id":481520978,"identity":"5eec206b-5595-40f0-96e0-1b469415892a","order_by":6,"name":"Nagisa Hara","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nagisa","middleName":"","lastName":"Hara","suffix":""},{"id":481520979,"identity":"1a31556e-035b-4d75-83a9-a770534527ff","order_by":7,"name":"Shotaro Noge","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Shotaro","middleName":"","lastName":"Noge","suffix":""},{"id":481520982,"identity":"b441b589-dcf8-4fe1-9746-6af7c64e0877","order_by":8,"name":"Kenichi Tanaka","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Kenichi","middleName":"","lastName":"Tanaka","suffix":""},{"id":481520983,"identity":"135d1065-115d-46e3-a902-7fc87da2ba0f","order_by":9,"name":"Shigenobu Yoshimura","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Shigenobu","middleName":"","lastName":"Yoshimura","suffix":""},{"id":481520984,"identity":"bd80741b-7485-422d-a35d-b0a02eff275f","order_by":10,"name":"Noriko Oza","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Oza","suffix":""},{"id":481520985,"identity":"abdf6871-d653-43a8-a8b1-53242d2add69","order_by":11,"name":"Keizo Anzai","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Keizo","middleName":"","lastName":"Anzai","suffix":""},{"id":481520986,"identity":"1117fab2-de74-429b-9e34-3e0d53bf03cf","order_by":12,"name":"Yuichiro Eguchi","email":"","orcid":"","institution":"Loco Medical General Institute","correspondingAuthor":false,"prefix":"","firstName":"Yuichiro","middleName":"","lastName":"Eguchi","suffix":""},{"id":481520987,"identity":"45ecafd1-2f37-4513-9556-b8b65b31294d","order_by":13,"name":"Cheng Han Ng","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"Han","lastName":"Ng","suffix":""},{"id":481520988,"identity":"d6412643-440c-4b1e-a267-df5175c680be","order_by":14,"name":"Daniel Q. 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Muthiah","email":"","orcid":"","institution":"National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"D.","lastName":"Muthiah","suffix":""},{"id":481520990,"identity":"a314ee52-a5f6-4b91-b7a8-f8142ec3781e","order_by":16,"name":"Atsushi Kawaguchi","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Kawaguchi","suffix":""},{"id":481520991,"identity":"25097b87-ac6e-4b5b-9198-784edf1ba443","order_by":17,"name":"Shinichi Aishima","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Shinichi","middleName":"","lastName":"Aishima","suffix":""},{"id":481520992,"identity":"ce2297d8-2641-40f6-9f1a-370a2da43513","order_by":18,"name":"Hiroshi Isoda","email":"","orcid":"","institution":"Saga University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Isoda","suffix":""},{"id":481520994,"identity":"29e8a49a-f37c-4b14-b351-8e8deba2f6a1","order_by":19,"name":"Takuya Kuwashiro","email":"","orcid":"","institution":"Saga University","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Kuwashiro","suffix":""},{"id":481520997,"identity":"1b0223d4-a4b7-4595-8719-df7fb8e8d3f2","order_by":20,"name":"Hirokazu Takahashi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYFACNgZmICnHjyGMC/BAtRhLNpCqJXHDAWKdZc9+LPFzQU0d4+bjhx8w/qiplWeQSGD88IOBLw+nLTxph6VnHDvMbHYmzYCZ59hxwwaJBGbJHga2YtwOS2+Q5mE7wGZ2gwfoQrZjjPtvJDBIA/2S2IBLC//z5t88/+p4jGfwMDD++HfMHmTLb7xaJNKOSfO2MUsYSPAwMPC21SQCtbDht+XGszRr3r7DBhJAvxzm7TuQ3MDzsM2yxwC3X9j704xv83yrq+9vP/zw4Y9vdbYN7MmHb/yoOIYzxFDAAQaGw0CKEegkg2MJRGkBgjoYo4ZoLaNgFIyCUTDsAQC5XU7HtNFbjQAAAABJRU5ErkJggg==","orcid":"","institution":"Saga University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hirokazu","middleName":"","lastName":"Takahashi","suffix":""}],"badges":[],"createdAt":"2025-06-14 04:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891676/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-17396-1","type":"published","date":"2025-08-30T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86628434,"identity":"bb9c369b-3938-4997-9535-fa39f8cabdb3","added_by":"auto","created_at":"2025-07-14 05:44:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":414504,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design. Using BIA parameters, a formula to predict CAP, LSM and FAST Score was developed in the training cohort, and eCAP, eLSM and eFAST Score were obtained. The diagnostic performance of these factors was tested in the training cohort. The diagnostic performance of eCAP was also tested in the validation cohort and liver biopsy cohort. Abbreviations: BIA, bioelectrical impedance analysis; CAP, controlled attenuation parameter; eCAP, estimated controlled attenuation parameter; LSM, liver stiffness measurement; eLSM, estimated liver stiffness measurement; FAST Score, FibroScan-Based Score; eFAST Score, estimated FibroScan-Based Score.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/5979f2b98aa140d454bb6cec.png"},{"id":86628432,"identity":"b89f9e59-0f8f-463f-a6a5-3597c0f92af9","added_by":"auto","created_at":"2025-07-14 05:44:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":465513,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plot with 95% confidence intervals showing the correlation between estimated values based on BIA- and FibroScan-related data: eCAP and CAP (A), eLSM and LSM (B), and eFAST and FAST (C). Abbreviations: CAP, controlled attenuation parameter; eCAP, estimated controlled attenuation parameter; LSM, liver stiffness measurement; eLSM, estimated liver stiffness measurement; FAST Score, FibroScan-Based Score; eFAST Score, estimated FibroScan-Based Score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/dd40f34ea32d7c2a611560e3.png"},{"id":86628439,"identity":"68088186-5b31-4098-be61-a17d32ccdc5b","added_by":"auto","created_at":"2025-07-14 05:44:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":432716,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves demonstrating the diagnostic performance of eCAP (A), eLSM (B) and eFAST Score (C).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/ae89470400969ce6a8d28fea.png"},{"id":86628450,"identity":"95ea9da3-1b0f-4d11-ae58-bbc1e1eb89de","added_by":"auto","created_at":"2025-07-14 05:44:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":403256,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the validation cohort. Linear regression plot with 95% confidence intervals showing the correlation between eCAP and CAP (A) and receiver operating characteristic (ROC) curves demonstrating the diagnostic performance of eCAP (B) in the validation cohort. Abbreviations: CAP, controlled attenuation parameter; eCAP, estimated controlled attenuation parameter.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/2fe4e79d3a583973dbfe6f74.png"},{"id":86628445,"identity":"b247378a-e0a9-4b6d-b252-1d5cd27359cf","added_by":"auto","created_at":"2025-07-14 05:44:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":466071,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the liver biopsy cohort. Box plots showing the association between the pathological steatosis score and eCAP (A) or CAP (C). Receiver operating characteristic (ROC) curves demonstrating the diagnostic performance of eCAP (B) and CAP (D). Error bars in the box plots represent the quantiles. *p \u0026lt; 0.05, **p \u0026lt; 0.001 in comparison with steatosis score = 0. \u003csup\u003e§\u003c/sup\u003ep \u0026lt; 0.05, \u003csup\u003e§§\u003c/sup\u003ep \u0026lt; 0.001 in comparison with steatosis score = 1. Abbreviations: CAP, controlled attenuation parameter; eCAP, estimated controlled attenuation parameter.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/54e43dfd401cfb399ec8f5a0.png"},{"id":90344869,"identity":"88f32f43-8851-437a-ba7f-d9b45507003e","added_by":"auto","created_at":"2025-09-01 16:06:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3151427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/fb5111ec-72a6-4c0f-8124-ef3167088ff4.pdf"},{"id":86630193,"identity":"52b41275-dd5c-4d9f-a9dc-e17b6606d9ae","added_by":"auto","created_at":"2025-07-14 06:10:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28459,"visible":true,"origin":"","legend":"","description":"","filename":"Kohiraetal.ScientificReportsSupple.docx","url":"https://assets-eu.researchsquare.com/files/rs-6891676/v1/ce2e29e5f536915f9304b71c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis","fulltext":[{"header":"Study Highlights","content":"\u003cp\u003eTo utilize body composition measurements obtained by bioelectrical impedance analysis (BIA) for the prediction of liver steatosis, a predictive formula with BIA-related variables was developed to obtain the estimated controlled attenuation parameter (eCAP) in subjects suspected of having metabolic dysfunction-associated steatotic liver disease who underwent CAP measurement with FibroScan. eCAP correlated with CAP and pathological steatosis and demonstrated enough diagnostic performance to predict liver steatosis. eCAP is a completely non-invasive and quantitative parameter to predict liver steatosis and its severity.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, is associated with overweight and lifestyle-related diseases; the cardiometabolic criteria for diagnosis of MASLD have been defined [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metabolic dysfunction-associated steatohepatitis (MASH) is pathologically diagnosed on the basis of steatosis, inflammation and hepatocyte ballooning [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MASH with significant liver fibrosis is known as at-risk MASH and is considered to have a high risk of disease progression [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MASLD/MASH is globally prevalent and increasingly becoming an etiology of liver cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, efficient surveillance of the general public is an urgent issue.\u003c/p\u003e\u003cp\u003eSteatosis is the accumulation of lipid droplets within hepatocytes. For the diagnosis of MASLD, steatosis should be observed in more than 5% of hepatocytes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Obesity is one risk factor for MASLD. The hepatic steatosis of MASLD is one manifestation of overweight and is defined as ectopic fat accumulation that is associated with visceral fat accumulation and insulin resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Skeletal muscle plays an important role in metabolic homeostasis, and skeletal muscle mass, function and myosteatosis are associated with the pathological severity of the liver and the prognosis of MASLD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, in the management of MASLD, the patient\u0026rsquo;s body composition provides beneficial information regarding systemic adiposity and skeletal muscle mass. Indeed, a recent study indicates that impaired body composition, such as visceral fat accumulation and reduced skeletal muscle mass, is associated with severe steatosis, inflammation and fibrosis of the liver in MASLD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Accumulating evidence also indicates that bioelectrical impedance analysis (BIA) is a useful and accessible method to measure body composition and shows a good correlation with the dual-energy X-ray absorptiometry method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. BIA is a noninvasive, simple and low-cost procedure and can be applied broadly and repeatedly in health check screenings and clinical settings.\u003c/p\u003e\u003cp\u003eIn clinical practice, imaging modalities rather than liver biopsies are generally performed to evaluate hepatic steatosis because of their noninvasiveness, reliability and quantitativity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. B-mode ultrasound is the most common examination procedure to detect liver steatosis. However, it is impractical to perform ultrasound on all people with MASLD risk. Ultrasound technique-based examination measuring the attenuation of ultrasound is used for quantitative evaluation of hepatic steatosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. FibroScan is one of the most widely used transient elastography techniques and can be used to perform controlled attenuation parameter (CAP) measurement and liver stiffness measurement (LSM) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Using the CAP and LSM values, the FibroScan-Based Score (FAST Score) was also developed to predict at-risk MASH [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to recent guidelines, FibroScan is included in the surveillance flow chart as the 2nd -step examination following the Fibrosis-4 index to identify advanced liver fibrosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. CAP is simultaneously measured in this step. Therefore, in general, quantitative evaluation of liver steatosis such as CAP measurement is not provided to health checkup examinees and patients in the screening step, whereas information about the presence of and severity of liver steatosis would encourage health checkup examinees, patients and medical providers to perform further evaluation of MASLD.\u003c/p\u003e\u003cp\u003eConsidering the connective pathogenesis between MASLD and body composition, we hypothesized that BIA-related variables are able to predict the severity of liver steatosis. In this study, we aim to develop a completely noninvasive formula using the variables obtained from BIA to predict CAP. In addition, the prediction of LSM and the FAST Score using BIA-related variables was attempted.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patients\u003c/h2\u003e\u003cp\u003eFor the training cohort, 365 patients who visited Saga University Hospital from December 2018 to December 2021 were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All the patients were suspected of having MASLD and received BIA and FibroScan examinations. In this cohort, formulas to predict the CAP, LSM and FAST Score were developed, and the estimated CAP (eCAP), estimated LSM (eLSM) and estimated FAST Score (eFAST Score) were obtained. The diagnostic performances of the eCAP, eLSM and eFAST Score were tested using the actual CAP, LSM and FAST Score measured with FibroScan as the gold standard. Four hundred eight patients who visited Saga University Hospital from January 2022 to December 2024 were included in the validation cohort. The diagnostic accuracy of eCAP was similarly tested as in the training cohort. The liver biopsy cohort was independently organized and included 158 patients who received liver biopsy, FibroScan examination and BIA within a month from December 2018 to December 2024. The diagnostic performances of the eCAP and CAP were tested using the pathological steatosis score as the gold standard. All patients were adult and older than 20 years old. No patients in any of the cohorts had other liver disease etiologies, including habitual alcohol intake (daily ethanol consumption of \u0026lt;\u0026thinsp;30 g in men and \u0026lt;\u0026thinsp;20 g in women), positivity for hepatitis B surface antigen or hepatitis C virus antibody and abnormal serum thyroid hormone levels. Additionally, no patients had autoimmune liver disease, drug-induced hepatotoxicity, hemochromatosis or Wilson\u0026rsquo;s disease. The study protocol was approved by the Clinical Research Ethics Review Committee of Saga University Hospital and was performed in accordance with the principles of the 1975 Declaration of Helsinki (revised in 2013). The participants provided informed consent to participate in the study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhysical examination and serum biochemical measurements\u003c/h3\u003e\n\u003cp\u003eThe body mass and height of the participants were measured, and body mass index (BMI) was calculated as body mass (kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e). Venous blood samples were obtained after an overnight fast and were used to measure albumin, aspartate aminotransferase (AST), alanine transaminase (ALT), γ-glutamyl transpeptidase activity (GGT), total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglyceride and fasting plasma glucose concentrations, platelet count and hemoglobin A1c (HbA1c) using conventional laboratory techniques.\u003c/p\u003e\n\u003ch3\u003eBody composition measurement\u003c/h3\u003e\n\u003cp\u003eFor body composition measurement, after an overnight fast, BIA was performed using Inbody 770\u0026reg; (Biospace Co, Seoul, Republic of Korea). Parameters included body fat mass (BFM), fat-free mass (FFM), skeletal muscle mass (SMM), total body water (TBW), intracellular water (ICW), extracellular water (ECW) and whole-body phase angle (50 kHz). Percent body fat (PBF) was obtained by dividing BFM by body weight (kg). The skeletal muscle index (SMI) was obtained by dividing appendicular muscle mass by height (m)\u003csup\u003e2\u003c/sup\u003e. Detail explanations for individual parameters are available in the product manual at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uk.inbody.com/about-inbody/result-sheet-interpretation/\u003c/span\u003e\u003cspan address=\"https://uk.inbody.com/about-inbody/result-sheet-interpretation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eLiver stiffness measurement and controlled attenuation parameter measurement\u003c/h3\u003e\n\u003cp\u003eExperienced operators who had performed at least 500 examinations assessed the LSM and CAP in the right liver lobe using a FibroScan\u0026reg; 502. Patients were examined after an overnight fast using the M or XL probes within three months before and after BIA. The probe was selected based on the skin\u0026ndash;liver capsule distance (SCD): XL probe for patients with SCD\u0026thinsp;\u0026ge;\u0026thinsp;25 mm and M probe for patients with SCD\u0026thinsp;\u0026lt;\u0026thinsp;25 mm [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. After we measured the SCD using ultrasound B-mode, the LSM and CAP measurements were performed using FibroScan\u0026reg; until 10 valid measurements were obtained for each patient. The median values were used to quantify liver fibrosis and steatosis. Based on previous reports, we defined measurement failure as examinations in which 10 valid LSMs were not obtained after 10 or more attempts. In patients with 10 valid LSMs, LSM\u0026thinsp;\u0026ge;\u0026thinsp;7.1 kPa and interquartile range-to-median ratio\u0026thinsp;\u0026gt;\u0026thinsp;30% were defined as unreliable values [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients who had measurement failure or unreliable values were excluded from this study. Patients with CAP\u0026thinsp;\u0026ge;\u0026thinsp;248 dB/m were considered to have liver steatosis (pathological steatosis\u0026thinsp;\u0026ge;\u0026thinsp;S1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and patients with LSM\u0026thinsp;\u0026ge;\u0026thinsp;8.9 kPa were considered to have advanced fibrosis (pathological fibrosis stage\u0026thinsp;\u0026ge;\u0026thinsp;3) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The FAST Score was calculated in accordance with the following previously reported formula [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]: FAST = (e \u0026ndash; 1.65\u0026thinsp;+\u0026thinsp;1.07 \u0026times; In (LSM)\u0026thinsp;+\u0026thinsp;2.66*10\u0026oline;⁸ \u0026times; CAP\u0026sup3; \u0026ndash; 63.3 \u0026times; AST\u0026oline;\u0026sup1;)/(1\u0026thinsp;+\u0026thinsp;e \u0026ndash; 1.65\u0026thinsp;+\u0026thinsp;1.07 \u0026times; In (LSM)\u0026thinsp;+\u0026thinsp;2.66*10\u0026oline;⁸ \u0026times; CAP\u0026sup3; \u0026ndash; 63.3 \u0026times; AST\u0026oline;\u0026sup1;). Patients with a FAST Score of \u0026gt;\u0026thinsp;0.67 were considered to have at-risk MASH (MASH with fibrosis stage\u0026thinsp;\u0026ge;\u0026thinsp;2) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEvaluation of liver biopsy\u003c/h3\u003e\n\u003cp\u003eUltrasonography-guided liver biopsy was performed using a 16-gauge biopsy needle. All liver biopsies were approximately\u0026thinsp;\u0026ge;\u0026thinsp;20 mm in length. Liver biopsy slides, stained with hematoxylin\u0026ndash;eosin and Azan or Masson stain, were independently evaluated by experienced central pathologist (S.A.) specializing in liver pathology. The central pathologists were blinded to the clinical data. Hepatic steatosis, lobular inflammation and hepatocyte ballooning were evaluated based on the NAFLD activity score [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor the prediction of CAP, LSM and FAST Score using BIA, statistically independent predictive variables were selected by the forward\u0026ndash;backward stepwise selection method from the variables obtained from BIA, age, sex and height. The predictive formula was developed with the selected variables. The variables tested in the stepwise selection method were age, sex, height, body weight, BMI and BIA-related variables (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Correlations were tested using the Spearman\u0026rsquo;s rank correlation coefficient. The Steel\u0026ndash;Dwass test was used for multiple comparisons. The diagnostic performance of individual tests was determined using receiver operating characteristic (ROC) curves. Optimal cut-off values were chosen to maximize the sum of the sensitivity and specificity, known as the Youden index [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cut-off values with at least 90% sensitivity and specificity were also individually chosen. Comparisons of the area under the ROC curve (AUROC) between the tests were performed using the DeLong test [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We considered a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to be statistically significant. All statistical analyses were performed using JMP ver. 14 (SAS Institute Japan; Tokyo, Japan).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatient characteristic\u003c/em\u003es\u003c/p\u003e\u003cp\u003eThe patient characteristics of the training cohort are summarized in Supplementary Table S2, and the BIA-related variables are summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Regarding the BIA-related variables, the mean PBF and mean SMI were 36.7% and 7.2 kg/m\u003csup\u003e2\u003c/sup\u003e. Regarding FibroScan examinations, the mean LSM, CAP and FAST Scores were 6.1 kPa, 299 dB/m and 0.322, respectively. The patient characteristics of the validation cohort and liver biopsy cohort are summarized in Supplementary Table S3 and S4.\u003c/p\u003e\n\u003ch3\u003eFormula to predict CAP, LSM and FAST Score\u003c/h3\u003e\n\u003cp\u003eAmong the 34 variables including age, sex, height, body weight, BMI and 29 BIA-related variables, significant variables were selected by stepwise analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the prediction of CAP, age (p\u0026thinsp;=\u0026thinsp;0.008), BFM of trunk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and SMI (p\u0026thinsp;=\u0026thinsp;0.0012) were selected. The predictive formula is as follows: eCAP\u0026thinsp;=\u0026thinsp;219.1\u0026thinsp;\u0026minus;\u0026thinsp;0.4479 \u0026times; age\u0026thinsp;+\u0026thinsp;3.476 \u0026times; BFM of trunk\u0026thinsp;+\u0026thinsp;7.045 \u0026times; SMI. For the prediction of LSM, ECW/TBW (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), BFM Control (body fat mass normalized to the appropriate body composition, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TBW/FFM (p\u0026thinsp;=\u0026thinsp;0.0034), whole-body phase angle (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and SMI (p\u0026thinsp;=\u0026thinsp;0.0001) were significant. The predictive formula is as follows: eLSM\u0026thinsp;=\u0026thinsp;20.75\u0026thinsp;+\u0026thinsp;967.9 \u0026times; ECW/TBW\u0026thinsp;\u0026minus;\u0026thinsp;0.2932 \u0026times; BFM Control\u0026thinsp;\u0026minus;\u0026thinsp;5.478 \u0026times; TBW/FFM\u0026thinsp;+\u0026thinsp;9.942 \u0026times; whole-body phase angle\u0026thinsp;\u0026minus;\u0026thinsp;2.436 \u0026times; SMI. For the prediction of FAST Score, BFM% of trunk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was solely significant, and the predictive formula is as follows: eFAST\u0026thinsp;=\u0026thinsp;0.1754\u0026thinsp;+\u0026thinsp;0.0006315 \u0026times; BFM% of trunk.\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\u003eStepwise analysis to predict CAP, LSM and at risk MASH\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable to be predicted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegression\u003c/p\u003e\u003cp\u003ecoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.4479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBFM of Trunk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECW/TBW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e967.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBFM Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.2932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTBW/FFM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhole Body Phase Angle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAST-Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBFM% of Trunk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0006315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eRegression coefficient and p value were obtained by stepwise analysis. Regression coefficient was shown as four significant digits. Abbreviations: CAP controlled attenuation parameter; LSM, liver stiffness measurement; FAST Score, FibroScan-Based Score; BFM, body fat mass; SMI, skeletal muscle mass index; TBW, total body water; ECW, extracellular water; FFM, fat free mass.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between predicted and actual CAP, LSM and FAST Score values\u003c/h2\u003e\u003cp\u003eUsing the formulas developed above, the predicted values, that is, eCAP, eLSM and eFAST Score, were obtained and correlated with the actual measured values of CAP, LSM and FAST Score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant positive correlation was identified between CAP and eCAP (ρ\u0026thinsp;=\u0026thinsp;0.5589, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The correlations between LSM and eLSM (ρ\u0026thinsp;=\u0026thinsp;0.2677, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and between FAST Score and eFAST Score (ρ\u0026thinsp;=\u0026thinsp;0.2577, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were weak (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eROC analysis of the training cohort\u003c/h2\u003e\u003cp\u003eThe diagnostic performances to detect the presence of liver steatosis (CAP\u0026thinsp;\u0026ge;\u0026thinsp;248 dB/m), advanced fibrosis (LSM\u0026thinsp;\u0026ge;\u0026thinsp;8.9 kPa) and at-risk MASH (FAST Score\u0026thinsp;\u0026gt;\u0026thinsp;0.67) were tested by ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The AUROCs of eCAP, eLSM and eFAST Score were 0.814, 0.700 and 0.657. On the basis of the Youden index, the optimal cut-off values for the diagnosis of steatosis, advanced fibrosis and at-risk MASH were eCAP\u0026thinsp;=\u0026thinsp;281 db/m, eLSM\u0026thinsp;=\u0026thinsp;10.69 kPa and eFAST Score\u0026thinsp;=\u0026thinsp;0.381. Based on this study\u0026rsquo;s aim to contribute to the surveillance of MASLD, cut-off values with a sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;90% were also obtained: eCAP\u0026thinsp;=\u0026thinsp;266 db/m (sensitivity\u0026thinsp;=\u0026thinsp;90.3%), eLSM\u0026thinsp;=\u0026thinsp;6.67 kPa (sensitivity\u0026thinsp;=\u0026thinsp;90%) and eFAST Score\u0026thinsp;=\u0026thinsp;0.455 (sensitivity\u0026thinsp;=\u0026thinsp;90.1%).\u003c/p\u003e\u003cp\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\u003eDiagnostic performance of eCAP, eLSM and eFAST Score\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eeCAP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eeLSM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eeFAST Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAUROC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimal cut-off (Youden index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e281 db/m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.69 kPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimal cut-off (Sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266 db/m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.67 kPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: AUROC, area under the receiver operating characteristics curve; eCAP, estimated controlled attenuation parameter; eLSM estimated liver stiffens measurement; eFAST, estimated FibroScan-Based Score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eROC analysis of the validation cohort\u003c/h2\u003e\u003cp\u003eIn accordance with the ROC analysis in the training cohort, the diagnostic performances of eLSM and eFAST Score were poor. Therefore, further analysis was performed to test eCAP. In the validation cohort, eCAP showed a significant positive correlation with CAP (ρ\u0026thinsp;=\u0026thinsp;0.563, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The AUROC of eCAP was 0.808 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Using the cut-off value of eCAP obtained in the training cohort analysis, the sensitivity was 65.9% and the specificity was 79% with a cut-off value\u0026thinsp;=\u0026thinsp;281 db/m, and the sensitivity was 86.4% and the specificity was 58% with a cut-off value\u0026thinsp;=\u0026thinsp;266 dB/m.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation with pathological steatosis and ROC analysis in the liver biopsy cohort\u003c/h2\u003e\u003cp\u003eIn the liver biopsy cohort, the correlation of eCAP or CAP with the pathological steatosis score was tested. The diagnostic performances of eCAP and CAP were also tested and compared. eCAP showed a significant positive correlation with the pathological steatosis score (ρ\u0026thinsp;=\u0026thinsp;0.379, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and a significant difference in eCAP was identified between the pathological scores (score 0 vs. score 1, p\u0026thinsp;=\u0026thinsp;0.0061; score 0 vs. score 2, p\u0026thinsp;=\u0026thinsp;0.0044; score 0 vs. score 3, p\u0026thinsp;=\u0026thinsp;0.0007; score 1 vs. score 3, p\u0026thinsp;=\u0026thinsp;0.0052) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The AUROC of eCAP for the diagnosis of pathological steatosis score\u0026thinsp;\u0026ge;\u0026thinsp;1 was 0.826 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The correlation between CAP and the pathological steatosis score was also significant (ρ\u0026thinsp;=\u0026thinsp;0.429, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and a significant difference in CAP was identified between the pathological scores (score 0 vs. score 1, p\u0026thinsp;=\u0026thinsp;0.0015; score 0 vs. score 2, p\u0026thinsp;=\u0026thinsp;0.0002; score 0 vs. score 3, p\u0026thinsp;=\u0026thinsp;0.0002; score 1 vs. score 3, p\u0026thinsp;=\u0026thinsp;0.0017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The AUROC of CAP for the diagnosis of pathological steatosis score\u0026thinsp;\u0026ge;\u0026thinsp;1 was 0.871 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The AUROC of CAP was greater than that of eCAP, but it was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.4426).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, CAP was successfully predicted using BIA-related parameters and denoted as eCAP. The predictive performance of eCAP was validated in the validation cohort, and eCAP could predict the presence of pathological steatosis. eCAP is completely noninvasive and does not require liver biopsy, blood tests, radiation exposure or imaging examination. Emulating CAP, the most popular and well-validated parameter of FibroScan, eCAP enables the collection of intuitive and quantitative information regarding liver steatosis.\u003c/p\u003e\u003cp\u003eAccording to previous reports, there are several indexes for predicting steatotic liver, including the fatty liver index (FLI), hepatic steatosis index, Zhejiang University index, NAFLD liver fat score and Framingham steatosis index [\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All these indexes require blood tests such as liver function tests, fasting glucose levels and HbA1c for calculation. Regarding the imaging modalities, B-mode ultrasound has been the most common procedure to identify liver steatosis. Ultrasonographical findings of liver\u0026ndash;kidney contrast, vascular blurring and deep attenuation enable the identification of liver steatosis but are not quantitative [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Recent developments in ultrasound-based techniques measuring the attenuation of ultrasound in the liver with steatosis can provide quantitative results representing the severity of liver steatosis. Attenuation imaging (ATI) and the attenuation coefficient (ATT) represent the application of conventional ultrasound machines to quantitatively evaluate liver steatosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Clinically, CAP is the most widely studied algorithm for measuring the attenuation of A-mode ultrasound beams [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) is a reliable and representative method for measuring liver steatosis and evaluating treatment efficacy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the low availability and high costs of these imaging modalities limit their wide use for screening [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. eCAP, developed in this study, is a completely noninvasive and low-cost procedure to evaluate liver steatosis.\u003c/p\u003e\u003cp\u003eIn our study, BFM of trunk and SMI were independently and positively associated with liver steatosis and used in the predictive formula. The value of the regression coefficient was positive and highest for SMI, suggesting that liver steatosis was strongly affected by the SMI. This means that a higher SMI results in severe liver steatosis. This paradoxical association was observed in previous studies. Wan et al. performed computed tomography (CT) imaging-based body composition analysis and identified that the SMI, visceral fat index, visceral fat-to-muscle ratio and visceral fat-to-subcutaneous fat ratio were independently and positively associated with the degree of pathological liver steatosis in adult obesity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Schmitz et al. reported that the SMI measured by BIA was significantly higher in patients with biopsy-proven MASH than in non-MASH patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These studies and our study indicate the undesirable effect of the SMI on liver steatosis. In contrast, Yodoshi et al. reported that skeletal muscle mass measured by BIA negatively correlated with liver steatosis in a pediatric population [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The association between skeletal muscle mass and liver steatosis varies in accordance with the study cohort, but a possible explanation for the unpreferable effect of skeletal muscle mass on liver steatosis is myosteatosis. Myosteatosis is fat infiltration in skeletal muscle, and it negatively affects the pathogenesis of MASLD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. By detecting the difference in the signal intensities of triglycerides and adipose tissue, MRI and CT scans can be used to measure myosteatosis separately from the muscle area [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, BIA and simple measurement of the muscle area in a CT or MRI image cannot exclude myosteatosis, resulting in a higher muscle mass and SMI in obese patients, and thus, the SMI can be a risk factor for liver steatosis.\u003c/p\u003e\u003cp\u003eThe detection and prediction of liver steatosis can contribute to the management of lifestyle-related diseases as well as the diagnosis of MASLD. People with liver steatosis have a higher incidence of type 2 diabetes than that of people without liver steatosis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Moreover, the risk of cardiovascular disease [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], development of chronic kidney disease [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and risk of extrahepatic cancer [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] are higher in the population with liver steatosis than in those without liver steatosis. Therefore, the earlier detection and prediction of liver steatosis using BIA in health checkup and primary care settings could contribute to improving the outcome of lifestyle-related diseases and promoting awareness of the associations between liver steatosis and the risk of extrahepatic disease.\u003c/p\u003e\u003cp\u003eAs shown in this study, body composition is associated with MASH and liver fibrosis, but actual predictions of these conditions are challenging. In our study, BIA-related variables including the SMI were significantly associated with LSM, and BFM% of trunk was significantly associated with the FAST Score. According to recent reports, less skeletal muscle mass is independently associated with severe liver fibrosis and the presence of MASH [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Visceral fat mass is also associated with the presence of MASH [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, sarcopenia aggravates the prognosis of MASLD [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Taken together, body composition is consistently associated with the pathophysiological features and prognosis of MASLD. However, to our knowledge, no study has tried to develop a predictive model using body composition parameters. Recently, the development of biomarkers to predict liver fibrosis and the presence of MASH has become an emerging topic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The combined use of BIA-related variables and blood tests or imaging examination might represent a future avenue for identifying reliable biomarkers to predict liver fibrosis and MASH.\u003c/p\u003e\u003cp\u003eThere are several limitations in this study. The study cohort consisted of patients who were suspected of having MASLD. Therefore, the relatively high prevalence of MASLD resulted in a low negative predictive value of eCAP. In terms of the study concept, investigation of the general population should be carried out. Differences in the version of the BIA modality might affect the values of the BIA-related parameters, and the representability should be tested using different modalities. In conclusion, BIA-related parameters are able to predict liver steatosis as well as the CAP value. BIA is a useful examination to evaluate liver steatosis as well as body composition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealanine transaminase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003easpartate aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eATI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eattenuation imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eATT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eattenuation coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under the ROC curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBFM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody fat mass\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebioelectrical impedance analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econtrolled attenuation parameter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecomputed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eeCAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestimated CAP\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eextracellular water\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eeFAST Score\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestimated FAST Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eeLSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestimated LSM\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFAST Score\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFibroScan-Based Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFFM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efat-free mass\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eγ-glutamyl transpeptidase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehemoglobin A1c\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL cholesterol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintracellular water\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL cholesterol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eliver stiffness measurement\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMASH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emetabolic dysfunction-associated steatohepatitis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emetabolic dysfunction-associated steatotic liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRI-PDFF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emagnetic resonance imaging-estimated proton density fat fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epercent body fat\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eskin\u0026ndash;liver capsule distance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eskeletal muscle index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eskeletal muscle mass\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal body water.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest associated with this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding information\u003c/h2\u003e\u003cp\u003eThis research was supported by the Research Program on Hepatitis from Japan Agency for Medical Research and Development (AMED) [grant numbers 24015731 and 23808721] and JSPS KAKENHI, Grant-in-Aid for Scientific Research (C) [grant number 24K11171].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.K. , M.M and H.T. worked on the conceptualization, visualization and methodology of the study and wrote the original draft. Data curation and formal analysis were performed by T.K., S.O., Y.K., N.O. and H.T. H.T. and Y.E. contributed to funding acquisition. E.E., M.N., K.I., N.H., S.N., K.T. and S.Y. contributed to data acquisition. S.A. contributed to pathological data acquisition. A.K. and S.O supervised the statistical analysis. H.T. was the project administrator. This study was supervised by H.I. and T.K. The final draft of the manuscript was reviewed by K.A., S.Y. D.H and H.T.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe sincerely thank Maki Miyahara and Akiko Komine for their support with the BIA measurement and data collection. We thank Jenna MacArthur, PhD, from Edanz (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jp.edanz.com/ac\u003c/span\u003e\u003cspan address=\"https://jp.edanz.com/ac\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for editing a draft of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 1542\u0026ndash;1556 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBedossa, P. et al. 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Magnetic Resonance Imaging More Accurately Classifies Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease Than Transient Elastography. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cb\u003e150\u003c/b\u003e, 626\u0026ndash;637e7 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndersson, A. et al. Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis. \u003cem\u003eClin. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 2451\u0026ndash;2461e3 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJayakumar, S. et al. Longitudinal correlations between MRE, MRI-PDFF, and liver histology in patients with non-alcoholic steatohepatitis: Analysis of data from a phase II trial of selonsertib. \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 133\u0026ndash;141 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWan, Q. et al. Body Composition and Progression of Biopsy-Proven Non-Alcoholic Fatty Liver Disease in Patients With Obesity. \u003cem\u003eJ. Cachexia Sarcopenia Muscle\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 2608\u0026ndash;2617 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmitz, S. M. et al. Association of Body Composition and Sarcopenia with NASH in Obese Patients. \u003cem\u003eJ. Clin. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 3445 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYodoshi, T. et al. Impedance-based measures of muscle mass can be used to predict severity of hepatic steatosis in pediatric nonalcoholic fatty liver disease. \u003cem\u003eNutrition\u003c/em\u003e ;91\u0026ndash;92 :111447. (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, M. J. et al. Association between metabolic dysfunction-associated steatotic liver disease and myosteatosis measured by computed tomography. \u003cem\u003eJ. Cachexia Sarcopenia Muscle\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 1942\u0026ndash;1952 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKitajima, Y. et al. Severity of non-alcoholic steatohepatitis is associated with substitution of adipose tissue in skeletal muscle. \u003cem\u003eJ. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1507\u0026ndash;1514 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurakiewicz, J. et al. Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy. \u003cem\u003eJ. Neurol.\u003c/em\u003e \u003cb\u003e264\u003c/b\u003e, 2053\u0026ndash;2067 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLonardo, A., Nascimbeni, F., Mantovani, A. \u0026amp; Targher, G. Hypertension, diabetes, atherosclerosis and NASH: Cause or consequence? \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 335\u0026ndash;352 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon, T. G., Roelstraete, B., Hagstr\u0026ouml;m, H., Sundstr\u0026ouml;m, J. \u0026amp; Ludvigsson, J. F. Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: results from a nationwide histology cohort. \u003cem\u003eGut\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 1867\u0026ndash;1875 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark, H., Dawwas, G. K., Liu, X. \u0026amp; Nguyen, M. H. Nonalcoholic fatty liver disease increases risk of incident advanced chronic kidney disease: a propensity-matched cohort study. \u003cem\u003eJ. Intern. Med.\u003c/em\u003e \u003cb\u003e286\u003c/b\u003e, 711\u0026ndash;722 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams, L. A., Anstee, Q. M., Tilg, H. \u0026amp; Targher, G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. \u003cem\u003eGut\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e, 1138\u0026ndash;1153 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoo, B. K. et al. Sarcopenia is an independent risk factor for non-alcoholic steatohepatitis and significant fibrosis. \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e, 123\u0026ndash;131 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, X. Y. et al. Low skeletal muscle mass is associated with more severe histological features of non-alcoholic fatty liver disease in male. \u003cem\u003eHepatol. Int.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1085\u0026ndash;1093 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoon, J. H., Koo, B. K. \u0026amp; Kim, W. Non-alcoholic fatty liver disease and sarcopenia additively increase mortality: a Korean nationwide survey. \u003cem\u003eJ. Cachexia Sarcopenia Muscle\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 964\u0026ndash;972 (2021).\u003c/span\u003e\u003c/li\u003e\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":"bioelectrical impedance analysis, body composition, controlled attenuation parameter, metabolic dysfunction-associated steatotic liver disease","lastPublishedDoi":"10.21203/rs.3.rs-6891676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground/Aims\u003c/h2\u003e\u003cp\u003eLiver steatosis can be measured with ultrasound techniques such as the controlled attenuation parameter (CAP) on an equipped FibroScan. For more widespread screening and quantitative evaluation of liver steatosis, a predictive model using body composition data obtained by body bioelectrical impedance analysis (BIA) was developed.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn the training cohort including 365 patients suspected of having metabolic dysfunction-associated steatotic liver disease, a stepwise selection method was used to determine the BIA-related variables associated with CAP. Using the significant variables, a predictive formula was developed, and the estimated CAP (eCAP) was obtained. The diagnostic performance of eCAP was tested to predict liver steatosis with receiver operating characteristic (ROC) curve analysis in the training, validation (n\u0026thinsp;=\u0026thinsp;408) and liver biopsy (n\u0026thinsp;=\u0026thinsp;158) cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe body fat mass of the trunk, skeletal muscle index and age were significant variables associated with CAP. eCAP was obtained as 219.1\u0026thinsp;\u0026minus;\u0026thinsp;0.4479 \u0026times; age\u0026thinsp;+\u0026thinsp;3.476 \u0026times; BFM of trunk\u0026thinsp;+\u0026thinsp;7.045 \u0026times; SMI. The area under the ROC curve was 0.814 in the training cohort and 0.808 in the validation cohort. The sensitivity and specificity were 72.5% and 82.1% with a cut-off value of eCAP\u0026thinsp;=\u0026thinsp;281 dB/m. For sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;90%, the cut-off of eCAP was 266 dB/m. In the liver biopsy cohort, the presence of pathological steatosis was predicted with eCAP as an area under the ROC curve\u0026thinsp;=\u0026thinsp;0.826, which was not statistically different from CAP (0.871).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCompletely non-invasive BIA-based eCAP could predict liver steatosis.\u003c/p\u003e","manuscriptTitle":"Quantification of liver steatosis of metabolic dysfunction-associated steatotic liver disease based on body composition analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 05:44:42","doi":"10.21203/rs.3.rs-6891676/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-18T15:31:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T07:17:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40652271581511698695325879316324436864","date":"2025-07-07T06:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196423405629576439621172046131349673291","date":"2025-07-07T02:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217602850468058881157825460527219959539","date":"2025-07-07T02:05:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T13:05:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242619896407314460168845429652610969793","date":"2025-07-06T12:25:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-06T11:17:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-06T11:12:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-26T16:55:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T12:14:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-25T12:10:38+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":"6f740093-d7d5-42ee-8d4d-207bedb9622a","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51123117,"name":"Health sciences/Gastroenterology"},{"id":51123118,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-09-01T16:00:24+00:00","versionOfRecord":{"articleIdentity":"rs-6891676","link":"https://doi.org/10.1038/s41598-025-17396-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-30 15:57:21","publishedOnDateReadable":"August 30th, 2025"},"versionCreatedAt":"2025-07-14 05:44:42","video":"","vorDoi":"10.1038/s41598-025-17396-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-17396-1","workflowStages":[]},"version":"v1","identity":"rs-6891676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6891676","identity":"rs-6891676","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}