New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis

New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis Haruki Uojima, Kazumi Yamasaki, Masaya Sugiyama, Masayoshi Kage, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3781087/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/purpose of the study: Mac-2-binding protein glycosylation isomer (M2BPGi), a biomarker for liver fibrosis, is influenced by various etiologies. Here, we aimed to investigate clinical factors that improve the accuracy of liver cirrhosis (LC) diagnosis based on quantitative M2BPGi (M2BPGi-Qt), regardless of etiology. Methods: In total, 1,373 patients with chronic liver disease (CLD) were recruited. Weassessed the correlation between fibrosis stage and M2BPGi-Qt levels among CLD etiologies. If there was no correlation between the fibrosis stage and M2BPGi level in a specific etiology of CLD, we evaluated the clinical factors influencing the M2BPGi-Qt level in that specific etiology. Subsequently, we created an algorithm to detect LC based on M2BPGi-Qt, considering an influencing factor other than fibrosis. Results: In virus hepatitis, non-alcoholic fatty liver disease, and primary biliary cholangitis, the M2BPGi-Qt levels increased liver fibrosis progression. In autoimmune hepatitis, no significant association was observed between the fibrosis stage and M2BPGi-Qt level. However, liver inflammation positively correlated with the M2BPGi-Qt levels. Considering liver inflammation, we established an algorithm, M2BPGi-Qt, to determine the alanine aminotransferase-to-platelet ratio (MAP-R) in LC. The area under the receiver operating characteristic curve (AUC) of the MAP-R index was 0.840. The AUC of MAP-R was higher than that of the M2BPGi-Qt for detecting LC. Conclusions: New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis. The algorithm based on the M2BPGi-Qt level demonstrates a high accuracy for LC diagnosis. chronic liver disease liver biopsy liver fibrosis noninvasive marker liver inflammation noninvasive method quantitative measurement liver biopsy platelet immunoassay Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Liver cirrhosis (LC) is the final stage of liver fibrosis, which leads to severe complications including ascites, encephalopathy, and hepatocellular carcinoma [ 1 ]. LC diagnosis can be confirmed through microscopic pathological examination of liver tissue obtained from biopsy. However, this approach poses the risk of bleeding and damages the surrounding organs. Additionally, the procedure is subject to sampling errors and different pathologists may interpret the same tissue sample differently [ 2 ]. In this milieu, noninvasive methods have become increasingly important to liver fibrosis assessment. Mac-2-binding protein glycosylation isomer (M2BPGi) is a biomarker for liver fibrosis [ 3 ]. Research on M2BPGi has focused extensively on its utility in assessing the degree of hepatitis C virus (HCV)-related liver fibrosis. The diagnostic accuracy of M2BPGi is higher than that of other commonly used markers, such as type 4 collagen and aspartate aminotransferase (AST)-to-platelet ratio index (APRI), in patients with HCV infection [ 4 ]. A unique characteristic of M2BPGi is its potential to have different cutoff values for LC, based on the etiology of fibrosis [ 5 , 6 ]. The variations in the cutoff values of the biomarker may be attributed to the presence of a factor other than fibrosis that influences the M2BPGi levels. If M2BPGi is used for assessing LC of various etiologies, several factors should be considered to ensure accurate and reliable results. To resolve this problem, we tried to generate an algorithm for detecting LC, regardless of the etiology. However, the current qualitative measurement method for M2BPGi may have limitations when evaluating its levels across various etiologies of liver disease. Qualitative measurement typically involves categorizing results into predefined ranges (e.g., low, moderate, and high) rather than providing a precise numerical value [ 7 ]. Therefore, we developed a new quantitative system, quantitative M2BPGi (M2BPGi-Qt), which minimizes the variability in cutoff values among different etiologies of CLD [ 8 ]. Previously, we validated the clinical utility of the new quantitative method and proved its advantages over qualitative methods when assessing M2BPGi for liver fibrosis [ 8 ]. Our final goal is to establish a noninvasive method for predicting LC as an alternative to liver biopsy. Therefore, the aim of this study was to investigate factors in addition to liver fibrosis that influence M2BPGi-Qt levels and improve the accuracy of predicting LC of various etiologies. Materials and Methods Ethics approval This study was performed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki after obtaining approval from the Institutional Review Board of the National Center for Global Health and Medicine (number: NCGM-G-004185-02). Informed consent was obtained from all participants via the “opt-out” method on each hospital website. Patients and study design Study population This study was conducted in 13 hospitals in Japan. The inclusion criterion was a confirmed diagnosis of CLD using liver biopsy and laboratory tests between January 2015 and December 2020. Patients with severe inflammatory diseases or malignancies unrelated to the study focus were excluded from the study. CLD etiologies included HCV infection, hepatitis B virus (HBV) infection, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and autoimmune liver disease. The absence of detectable HCV RNA for an extended period after the end of treatment was defined as HCV-sustained virological response (SVR). NAFLD was diagnosed when the intrahepatic triglyceride level was > 5% of the liver weight and was not influenced by other factors. The diagnosis of autoimmune liver diseases requires a combination of clinical, serological (autoantibodies), and histological assessments. Study design To investigate factors other than liver fibrosis that influence M2BPGi-Qt levels, we assessed the correlation between the fibrosis stage and M2BPGi-Qt levels among various CLD etiologies. As there was no correlation between the fibrosis stage and M2BPGi level in a specific etiology of CLD, we evaluated the clinical factors influencing the M2BPGi-Qt level in that specific etiology. Subsequently, we created an algorithm to detect LC using M2BPGi-Qt through logistic regression analysis, considering an influencing factor other than fibrosis. For model development, the enrolled patients were randomly divided into two groups for training and validation at a ratio of 7:3. After developing the model using the training dataset, it was validated using the validation dataset. Furthermore, we validated the new algorithm by different evaluators working at each hospital (hospital-based pathologists). M2BPGi-Qt measurement Serum samples were obtained to evaluate M2BPGi-Qt and transferred to the central institute. M2BPGi-Qt was measured using a fully automated HISCL-5000 immunoassay kit (Sysmex Co., Japan). M2BPGi was quantitatively measured via a five-point calibration. Immunoassays use a series of reagents and instruments [ 8 ]. They detect changes in the glyco-chain structure of M2BP, reflecting the status of liver fibrosis. For detection, the target glyco-chain was captured on lectin-coated magnetic particles, and after washing the particles, the anti-M2BP antibody (enzyme conjugated) was bound to M2BPGi on the magnetic particle. Pathological findings Pathological slides were collected from the Research Institute of Junshin Gakuen University and re-evaluated by a central reviewer to independently evaluate the fibrosis and activity stages based on biopsy samples. To minimize potential bias, the central reviewer was intentionally blinded to the patients' clinical information. Assessment of viral hepatitis and autoimmune hepatitis (AIH) was conducted using the new Inuyama classification for the grade of fibrosis and inflammation [ 9 ]. Fibrosis and activity stages of NAFLD and ALD were classified based on steatosis, activity, and fibrosis (SAF) scoring [ 10 ]. The stages of primary biliary cholangitis (PBC) fibrosis were determined using the Nakanuma classification [ 11 ], details of which are presented in Online Resource 1. Hospital-based pathologists had access to the patients’ clinical information, including medical history and other relevant data. The assessment of fibrosis stage by the hospital-based pathologists was not uniform. Clinical factors Blood samples and patient data are collected simultaneously. The general characteristics included information about age, sex, ethnicity, height, weight, alcohol consumption, and medical history. The laboratory data included white blood cell count; hemoglobin, platelet, total bilirubin, alanine aminotransferase (ALT), AST, albumin, and creatinine levels; and prothrombin time-to-international normalized ratio. Algorithm-based score models for detecting liver fibrosis are presented in Online Resource 2. Statistical evaluation The software used for data analysis was SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Differences between data points or groups were considered statistically significant if p < 0.05. Baseline characteristics are presented as mean ± standard deviation. M2BPGi-Qt levels were compared at each fibrosis stage using Jonckheere–Terpstra test. By constructing receiver operating characteristic (ROC) curves and determining cutoff values using the Youden index, we selected the most suitable threshold for a diagnostic test. Logistic regression models were employed to model the relationship between the independent variables and binary outcomes, providing odds ratios (ORs) as a measure of strength and direction of the association. An algorithm for detecting LC based on M2BPGi-Qt levels was developed using logistic regression analysis. A multivariate logistic regression model was used to account for the potential interactive effects of multiple variables. The ORs and 95% confidence intervals (CIs) were determined for each variable. To assess the performance of the new model in comparison to existing models, the area under the receiver operating characteristic (ROC) curve (AUC) values of different models were statistically compared using DeLong’s method. We also evaluated the integrated discrimination improvement (IDI) and net reclassification index (NRI) to provide a more comprehensive understanding of how the new model improved the classification and discrimination of individuals at different risk levels. STATISTA Corporation (Kyoto, Japan) supported the statistical analyses. Results Patient flow A total of 1,373 patients with CLD were recruited; 265 of them were excluded because of missing serum samples, the presence of inflammatory diseases, or unmatched diagnoses between the central reviewers and hospital-based pathologists. Discordant diagnoses were observed in patients with NAFLD (n = 20), AIH (5), and PBC (4). The remaining 1,108 patients were included in the final analysis (Fig. 1). Fig. 1 Study flow chart. M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer Baseline characteristics The mean age of the patients was 58.8 ± 13.7 years; 480 participants (43.3%) were men, and the mean BMI was 23.6 ± 3.86 kg/m 2 . There were 506, 163, 158, 153, 111, and 17 patients with HCV infection, HBV infection, NAFLD, AIH, PBC, and ALD, respectively. Among patients with HCV infection, 159 achieved SVR. As confirmed by the central reviewer, the number and frequency of each fibrosis stage were as follows: 145 (13.1), 395 (35.6), 248 (22.4), 193 (17.4), and 144 (13.0) for F0, F1, F2, F3, and F4, respectively (Table 1). M2BPGi-Qt level in each liver fibrosis stage An association was observed between increasing M2BPGi-Qt levels and liver fibrosis progression in patients with HCV and HBV infections. Specifically, a significant difference in M2BPGi-Qt levels was observed between patients with early- (F2) and advanced-stage (F3) liver fibrosis. In patients with NAFLD, the mean M2BPGi-Qt level increased as fibrosis progressed. However, ALD was not evaluated because of an insufficient number of cases. In patients with PBC, higher M2BPGi levels were associated with more severe fibrosis. In contrast to the other patient groups, M2BPGi-Qt levels did not appear to be strongly associated with the degree of fibrosis in patients with AIH (Fig 2). Fig. 2 Correlations between M2BPGi-Qt levels and fibrosis stage. Data are expressed as median. The M2BPGi-Qt levels were compared among the fibrosis stages for each etiology using Jonckheere–Terpstra test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama classification system; NAFLD was based on the SAF score and PBC was based on the Nakanuma classification. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer; PBC, primary biliary cholangitis; SVR, sustained virological response The AUC values of M2BPGi for predicting ≥F1, ≥F2, ≥F3 and ≥F4 in NAFLD were 0.815, 0.837, 0.784, and 0.768, respectively, which were better than those for other etiologies. A positive correlation between the M2BPGi-Qt cutoff values and fibrosis severity was observed in patients with various liver disease etiologies, excluding AIH (Online Resource 3). Clinical factors other than liver fibrosis influence M2BPGi levels in patients with AIH The degree of activity classification was more severe in patients with high M2BPGi-Qt levels (≥ 3.0 AU/mL) than in those with low levels (< 3.0 AU/mL) (p < 0.001) (Online Resource 4). The logistic regression analysis revealed whether there was a significant relationship or association between the groups (Online Resource 5). Activity stage; AST, ALT, total bilirubin, and albumin levels; platelet count; and prothrombin time were identified as significant variables in the univariate analyses. The multivariate analyses confirmed that activity stage; ALT, total bilirubin, and albumin levels; and platelet count were independent factors differentiating the two groups. Activity stage had the highest OR (7.663; 95% CI, 2.478–23.67; p < 0.001). Influence of liver activity stage on M2BPGi levels The M2BPGi-Qt levels increased with the progression of liver activity in patients with HCV infection, HBV infection, or AIH, indicating that higher M2BPGi-Qt levels are associated with more advanced stages of liver activity. Similarly, in patients with NAFLD, the M2BPGi levels increased with the progression of the activity stage based on the NAS score. Fig. 3 Correlations between M2BPGi-Qt levels and activity stages.Data are expressed as median. M2BPGi-Qt levels were compared among the activity stages for each etiology using Tukey’s honest significant difference test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama Classification system and NAFLD based on the NAFLD activity score. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative M2BPGi measurement; SVR, sustained virological response Model establishment using M2BPGi-Qt for LC The number of training and validation datasets was 775 and 333, respectively. No significant difference was observed between the groups overall; however, the frequency of men in the training dataset tended to be relatively higher than that in the validation dataset (p = 0.064) (Online Resource 6). From the regression analysis results, five variables (activity stage, platelet count, albumin, prothrombin time and total bilirubin levels) were selected as the optimal variables out of the original 19 variables (Online Resource 5). Subsequently, we used these variables in the multivariate logistic regression analysis of the training cohort to establish a new algorithm for diagnosing LC using the M2BPGi-Qt level (Table 2). The M2BPGi-Qt level, ALT level, and platelet count were identified as significant independent predictors. This set of predictors was combined to establish the following predictive equation. MAP-R:M2BPGi-Qt-to-ALT and platelet ratio = [M2BPGi-Qt (AU/mL)/[ALT (IU/L) 1/2 × platelets (10 3 /μL)]. MAP-R had a high AUC value for predicting LC in the training and validation datasets (0.759, 95% CI 0.709–0.810 and 0.702, 95% CI 0.595–0.796, respectively), and the AUC values of MAP-R were significantly different from those of M2BPGi-Qt (all p < 0.001) (Online Resource 7). Validation through the assessment of fibrosis stage by hospital-based pathologists A total of 144 (13.0%) and 148 (13.3%) patients were diagnosed with LC by a central reviewer and hospital-based pathologists, respectively. The percentage of concordance in LC diagnosis among the pathologists was 75.2%. MAP-R had the highest AUC value for predicting LC by hospital-based pathologists (0.840, 95% CI 0.806–0.874) (Fig 4), and the AUC values of MAP-R were significantly different from those of M2BPGi-Qt, FIB4 index, ALBI score, and APRI (all p < 0.05) (Online Resource 8). We observed an 83.2% sensitivity and a 71.4% specificity for the MAP-R, with a cutoff value of 1.59. Fig. 4 Predictive equation using M2BPGi-Qt for the diagnosis of liver cirrhosis by hospital-based pathologists. MAP-R; quantitative measurement of Mac-2-binding protein glycosylation isomer-to-ALT and platelet ratio, APRI; AST-to-platelet ratio index (APRI), FIB4; fibrosis 4, ALBI; albumin-bilirubin The IDI values of MAP-R were significantly different from those of the M2BPGi-Qt level, FIB4 index, ALBI score, and APRI (all p < 0.001). The NRI values were significantly different between MAP-R and the M2BPGi-Qt level, ALBI score, and APRI (p < 0.001). In contrast, there was no significant difference in the NRI values between the MAP-R and FIB 4 indices (p = 0.150). Discussion Before conducting the clinical study, we assumed the influence of liver inflammation on M2BPGi levels as previously reported [ 12 ]. Kimura et al. reported a positive correlation between M2BPGi level and necroinflammatory activity and stated that M2BPGi can be used to assess the degree of liver inflammation in patients with liver diseases [ 12 ]. These findings are consistent with the relationship between M2BPGi-Qt levels and inflammatory activity in various etiologies of CLD in our study. The interpretation of M2BPGi results when assessing the severity of liver disease can be complicated by variations in M2BPGi levels, associated with different etiologies and inflammation levels. Here, we emphasize the lack of correlation between M2BPGi levels and fibrosis progression in patients with severe hepatitis, consistent with the results of a previous study [ 13 ]. We assessed M2BPGi levels in patients with AIH with different activity stages. However, no significant association was observed between the M2BPGi-Qt levels and fibrosis stage, indicating that elevated inflammatory levels in the liver may lead to higher blood M2BPGi levels, potentially obscuring the relationship between M2BPGi levels and fibrosis progression. M2BPGi-Qt levels can vary with the underlying cause of liver disease and may reflect the severity of liver inflammation associated with that etiology. M2BPGi levels are higher in patients with HCV infection than in those with HBV infection or NAFLD [ 14 , 15 ]. In HCV infection, the M2BPGi-Qt levels may be elevated because of a higher degree of liver inflammation than that observed in NAFLD and PBC. The dependence of M2BPGi levels on both fibrosis and inflammation, regardless of etiology, is a major finding that supports the development of algorithms for detecting LC. A logistic regression analysis was conducted to create a new algorithm for diagnosing LC while considering the influence of liver inflammation by including ALT level and platelet count. The close association between liver inflammation and ALT level is a well-established and clinically significant finding. Platelets also perform inflammatory functions and play a role in coordinating immune responses by interacting with various cell types and immune system components [ 16 ]. The use of the index involving ALT levels and platelet counts for assessing liver inflammation, particularly because of its cost-effectiveness and ease of use, is valuable in liver disease assessment [ 17 ]. Although AST and ALT levels are important markers for assessing CLD, their use in combination with other predictors can improve the accuracy of diagnosing LC and other liver diseases [ 18 ]. We developed a robust and reliable model for LC detection, focusing on its accuracy, stability, and superiority to previous models using three metrics. MAP-R was a more accurate and effective model for predicting LC than other established models such as FIB-4, APRI, and ALBI, as well as M2BPGi-Qt. This finding implies that the MAP-R model is a valuable addition to clinical practice for the early detection of LC. The present study showed that MAP-R is clinically useful for the diagnosis of LC, especially in the context of different interpretations by pathologists when examining the same set of tissue samples. In particular, hospital-based pathologists demonstrated high accuracy, likely because of their access to patients' clinical information, which may contribute to the variability in diagnoses when provided to the pathologists. Collaboration and communication between clinicians and pathologists are crucial for an accurate diagnosis [ 20 ]. We plan to characterize M2BPGi subtypes based on their glycan isomers and their association with fibrosis and inflammation in patients with CLD. Glycan isomers in M2BP may have implications in these pathological processes, potentially serving as biomarkers, regulators, or mediators of liver health and disease. By identifying specific subtypes or structural variants that are particularly associated with liver inflammation and fibrosis, we will improve the accuracy and precision of M2BPGi as a biomarker for assessing liver health and disease progression. This study has some limitations. First, the etiology of CLD was not uniform among the enrolled patients. Notably, the number of patients with CLD and ALD was small. Second, the activity stage of PBC was not evaluated because appropriate criteria could not be identified. Third, M2BPGi levels in viral hepatitis and other causes of CLD may not be directly comparable, even if fibrosis is present at the same stage. Finally, all participants were recruited from an ethnically homogeneous Japanese population. Therefore, a re-examination of the data for other or more diverse ethnic groups is desirable. Our quantitative measurement system demonstrated that M2BPGi levels were dependent on both fibrosis and inflammation in the liver. The interpretation of M2BPGi-Qt for assessing the severity of liver disease should be based on the specific etiology, inflammation level, and fibrosis stage. The new algorithm is promising for improving LC diagnosis accuracy. Declarations Funding This study was supported by Sysmex Corporation. Conflicts of interest Takeji Umemura received lecture fees from AbbVie GK and Gilead Sciences and research grants from AbbVie GK, Eisai Co., Otsuka Pharmaceutical Co., and Tosoh Co. Masayuki Kurosaki received lecture fees from AbbVie, Eisai, Chugai, AstraZeneca, Lilly, and Takeda Co. Masashi Mizokami received lecture fees from Sysmec Co. Yasuhiro Asahina received lecture fees from Fujirebi Inc. and Abbott Japan LLC. Takumi Kawaguchi received lecture fees from Taisho Pharmaceutical Co., Kowa Company, Otsuka Pharmaceutical Co., Eisai Co., Janssen Pharmaceutical K.K., AbbVie GK., ASKA Pharmaceutical Co., and EA Pharma Co., and a research grant from Eisai Co., Ltd. Author contributions HU, KY, NI, MM, TK, TS, SJ, and HT collected and analyzed the data; HU and MM drafted the manuscript; KS designed and supervised the study; KY, TK, and HY offered technical or material support. All authors have read and approved the final version of the manuscript. 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B aseline clinical characteristics HCV ＊ 1 (n = 347) HCV-SVR ＊ 1 (n = 159) HBV ＊ 1 (n = 163) AIH ＊ 1 (n = 153) NAFLD ＊ 2 (n = 158) ALD ＊ 2 (N = 17) PBC ＊ 3 (n = 111) Age, years 57.9 ± 11.9 67.2 ± 10.2 50.1 ± 16.3 60.1 ± 14.2 59.1 ± 14.1 60.6 ± 12.9 59.4 ± 10.2 Male sex, n (%) 163 (47.0) 84 (52.8) 107 (65.6) 26 (17.1) 62 (39.2) 13 (76.5) 24 (21.6) BMI, kg/m 2 23.4 ± 4.07 23.1 ± 2.94 23.4 ± 4.07 22.7 ± 3.59 23.1 ± 4.41 24.7 ± 5.31 22.1 ± 3.35 FIB-4 index 2.93 ± 2.64 3.51 ± 2.46 3.08 ± 2.25 4.86 ± 4.01 2.89 ± 2.93 4.62 ± 3.48 2.02 ± 1.12 Platelet , 10 4 /μ 16.4 ± 5.72 16.3 ± 6.26 19.0 ± 7.50 22.9 ± 28.4 19.1 ± 6.45 15.4 ± 5.86 23.6 ± 7.43 AST, IU/L 60.2 ± 46.5 45.5 ± 34.2 40.7 ± 54.1 260 ± 359 60.0 ± 37.9 96.0 ± 127 53.4 ± 44. ALT, IU/L 83.1 ± 65.9 42.0 ± 37.1 125 ± 290 310 ± 464 70.3 ± 54.9 118 ± 265 55.3 ± 38.0 M2BPGi-Qt, AU/ ｍ L 2.39 ± 3.48 2.35 ± 2.85 1.91 ± 2.52 4.57 ± 2.76 1.89 ± 2.39 3.91 ± 5.06 1.67 ± 1.44 Fibrosis Stage, n (%) 0 1 2 3 4 55 (15.9) 130 (37.5) 72 (20.7) 48 (13.8) 42 (12.1) 11 (6.9) 49 (30.8) 49 (30.6) 23 (14.4) 27 (17.1) 22 (13.5) 70 (42.9) 30 (18.4) 28 (17.2) 13 (8.0) 15 (9.9) 59 (38.8) 39 (25.5) 28 (18.3) 12 (7.8) Fibrosis Scoring n (%) 0 1 2 3 4 26 (15.4) 33 (18.9) 27 (18.4) 43 (24.6) 29 (16.6) 2 (11.8) 5 (29.4) 0 (0) 6 (35.3) 4 (23.5) Score of Fibrosis n (%) 0 1 2 3 14 (12.6) 49 (44.1) 31 (27.9) 17 (15.3) Activity stage, n (%) 0 1 2 3 4 (1.2) 278 (80.1) 60 (17.3) 5 (1.4) 12 (7.5) 132 (83.1) 12 (7.5) 3 (1.9) 22 (13.5) 92 (56.4) 37 (22.7) 12 (7.4) 4 (2.6) 48 (31.6) 65 (42.8) 35 (23.0) Activity Scoring n (%) 0 1 2 3 4 23 (13.1) 35 (19.8) 45 (25.7) 38 (21.7) 17 (9.7) 4 (23.5) 5 (29.4) 3 (17.6) 2 (11.8) 3 (17.6) AIH, autoimmune hepatitis; ALD, alanine aminotransferase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; FIB-4, fibrosis 4 index; M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer; NAFLD, non-alcoholic fatty liver disease ; PBC, primary biliary cholangitis;＊1 AIH, HBV and HCV was divide fibrosis and activity stage by the new Inuyama classification.＊2 NAFLD was divided by the NAFLD activity score.＊3 PBC was divided by the Nakanuma classification. Table 2 Logistic regression analysis of estimating the liver cirrhosis in the training cohort Univariate analysis Multivariate analysis Variable B OR (95% CI) P value B OR (95% CI) P value M2BPGi-Qt (AU/mL) 0.120 1.128 (1.065-1.194) <0.001 0.136 1.146 (1.062-1.236) <0.001 Albumin (mg/dL) -1.094 0.335 (0.219–0.511) <0.001 Total bilirubin (mg/dL) -0.014 0.986 (0.881–1.104) 0.808 ALT (IU/L) -0.002 0.998 (0.901–1.000) 0.054 -0.008 0.995 (0.991–0.999) 0.009 √ALT (IU/L) -0.054 0.948 (0.901-0.997) 0.039 Platelets ( × 10 5 / μL) -0.141 0.868 (0.833-0.905) <0.001 -0.124 0.883 (0.845-0.923) <0.001 Supplementary Files Supplementallyfile20231101.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-3781087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":262819922,"identity":"0ee879a1-71af-4bc7-ae7a-c4bb5b08df4b","order_by":0,"name":"Haruki Uojima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACCWYGBiCyMWBDFechqCXNgA2sJ4EYLQxgLYcNGFC14AGS7bzPpAtqzhvzyTc//vDzhw2DwQHmhx8YZO7g1CLNzG4mPePYbTM2NjYzyZ6ENKAWNmMJBp5nOLXIMbOxSfOw3bYB+sWMgSfhcP2GA0AGA89hAlr+nQNqYf/88U/CYaAt7N/wapEGaeFtOwB0GI+BNA9YCw9+WySb2ZitefuSjdnYcsqkZdLSGCQP8xRLJODxi8T5Y4y3eb7ZGc5vPr754xsbGwa+4+0bP3zswR1iWAAwmhgSew6QogUMfpCuZRSMglEwCoYtAADKLULlYBYgJQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1719-1352","institution":"NCGM: Kokuritsu Kenkyu Kaihatsu Hojin Kokuritsu Kokusai Iryo Kenkyu Center","correspondingAuthor":true,"prefix":"","firstName":"Haruki","middleName":"","lastName":"Uojima","suffix":""},{"id":262819923,"identity":"642f372a-d8a9-4bd9-95f2-c5a7409153bf","order_by":1,"name":"Kazumi Yamasaki","email":"","orcid":"","institution":"Nagasaki Medical Center Clinical Research Center: Kokuritsu Byoin Kiko Nagasaki Iryo Center Rinsho Kenkyu Center","correspondingAuthor":false,"prefix":"","firstName":"Kazumi","middleName":"","lastName":"Yamasaki","suffix":""},{"id":262819924,"identity":"2f1c93d5-b250-473b-90ee-4ee2deb8b1c1","order_by":2,"name":"Masaya Sugiyama","email":"","orcid":"","institution":"National Center for Global Health and Medicine: Kokuritsu Kenkyu Kaihatsu Hojin Kokuritsu Kokusai Iryo Kenkyu Center","correspondingAuthor":false,"prefix":"","firstName":"Masaya","middleName":"","lastName":"Sugiyama","suffix":""},{"id":262819925,"identity":"149bbdf2-80c2-46dd-a0e0-1cb2fbd4245c","order_by":3,"name":"Masayoshi Kage","email":"","orcid":"","institution":"Junshin Gakuen University: Junshin Gakuen Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Masayoshi","middleName":"","lastName":"Kage","suffix":""},{"id":262819926,"identity":"7d0425a5-b082-4d07-96b3-a8185479d879","order_by":4,"name":"Norihiro Ishii","email":"","orcid":"","institution":"Gunma University Hospital: Gunma Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Norihiro","middleName":"","lastName":"Ishii","suffix":""},{"id":262819927,"identity":"980f9f8c-1af3-4856-a728-47325db1a4c8","order_by":5,"name":"Ken Shirabe","email":"","orcid":"","institution":"Gunma University Hospital: Gunma Daigaku Igakubu Fuzoku Byoin","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Shirabe","suffix":""},{"id":262819928,"identity":"71e771a8-5f0b-4ba8-bf60-3bdf54c443d0","order_by":6,"name":"Hisashi Hidaka","email":"","orcid":"","institution":"Kitasato University - 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Hakodate Campus: Hokkaido Daigaku - Hakodate Campus","correspondingAuthor":false,"prefix":"","firstName":"Akinobu","middleName":"","lastName":"Taketomi","suffix":""},{"id":262819943,"identity":"765f2258-ef61-43bf-857a-eb5e56582578","order_by":21,"name":"Satoru Joshita","email":"","orcid":"","institution":"Shinshu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Satoru","middleName":"","lastName":"Joshita","suffix":""},{"id":262819944,"identity":"ad57fecd-6500-4876-a44c-682dd8152e45","order_by":22,"name":"Takeji Umemura","email":"","orcid":"","institution":"Shinshu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takeji","middleName":"","lastName":"Umemura","suffix":""},{"id":262819945,"identity":"b0a8dc9c-59b2-4b67-a525-1da426f9c627","order_by":23,"name":"Sohji Nishina","email":"","orcid":"","institution":"Kawasaki Ika Daigaku Fuzoku Kawasaki Byoin","correspondingAuthor":false,"prefix":"","firstName":"Sohji","middleName":"","lastName":"Nishina","suffix":""},{"id":262819946,"identity":"aab23f89-cbe8-4521-ba3d-d6e1ae64093e","order_by":24,"name":"Keisuke Hino","email":"","orcid":"","institution":"Kawasaki Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"Hino","suffix":""},{"id":262819947,"identity":"3781d587-dece-4973-8ef1-fa58dbd50fae","order_by":25,"name":"Hidenori Toyoda","email":"","orcid":"","institution":"Ogaki Shimin Byoin","correspondingAuthor":false,"prefix":"","firstName":"Hidenori","middleName":"","lastName":"Toyoda","suffix":""},{"id":262819948,"identity":"468b26db-f02e-4ec4-868c-97924551fb39","order_by":26,"name":"Hiroshi Yatsuhashi","email":"","orcid":"","institution":"Nagasaki Medical Center Clinical Research Center: Kokuritsu Byoin Kiko Nagasaki Iryo Center Rinsho Kenkyu Center","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Yatsuhashi","suffix":""},{"id":262819949,"identity":"7b26167f-418a-427e-8fa8-ef580fbd1edf","order_by":27,"name":"Masashi Mizokami","email":"","orcid":"","institution":"NCGM: Kokuritsu Kenkyu Kaihatsu Hojin Kokuritsu Kokusai Iryo Kenkyu Center","correspondingAuthor":false,"prefix":"","firstName":"Masashi","middleName":"","lastName":"Mizokami","suffix":""}],"badges":[],"createdAt":"2023-12-20 09:33:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3781087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3781087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49071865,"identity":"e3a1a70d-5866-4f75-a4b2-808b6a65d261","added_by":"auto","created_at":"2024-01-02 17:14:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115942,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart.\u003c/p\u003e\n\u003cp\u003eM2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/22f0c57ecc506922d987468e.jpg"},{"id":49071863,"identity":"3fb3ae68-651d-43d8-a241-09eda6ad3f08","added_by":"auto","created_at":"2024-01-02 17:14:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122139,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between M2BPGi-Qt levels and fibrosis stage. Data are expressed as median.\u003c/p\u003e\n\u003cp\u003eThe M2BPGi-Qt levels were compared among the fibrosis stages for each etiology using Jonckheere–Terpstra test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama classification system; NAFLD was based on the SAF score and PBC was based on the Nakanuma classification. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer; PBC, primary biliary cholangitis; SVR, sustained virological response\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/2fe3fd0bee4bc8cd095193a3.jpg"},{"id":49071862,"identity":"9b3f5818-ad62-4440-b12e-30097b295e1c","added_by":"auto","created_at":"2024-01-02 17:14:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96313,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between M2BPGi-Qt levels and activity stages.\u003cstrong\u003e \u003c/strong\u003eData are expressed as median. M2BPGi-Qt levels were compared among the activity stages for each etiology using Tukey’s honest significant difference test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama Classification system and NAFLD based on the NAFLD activity score. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative M2BPGi measurement; SVR, sustained virological response\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/2d2bca419fca224ae08f1fd0.jpg"},{"id":49071864,"identity":"955ec8f2-8be4-4eed-bacf-4061c6ae614f","added_by":"auto","created_at":"2024-01-02 17:14:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43333,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive equation using M2BPGi-Qt for the diagnosis of liver cirrhosis by hospital-based pathologists. MAP-R; quantitative measurement of Mac-2-binding protein glycosylation isomer-to-ALT and platelet ratio, APRI; AST-to-platelet ratio index (APRI), FIB4; fibrosis 4, ALBI; albumin-bilirubin\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/0c46a760dc761f798ec81f9d.jpg"},{"id":49574787,"identity":"daec6b1a-94c2-4eaa-8d73-5aa648983097","added_by":"auto","created_at":"2024-01-13 22:47:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":801099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/c982ed42-715b-4c62-9e9c-b817e56501cb.pdf"},{"id":49071867,"identity":"9360497e-4621-4e83-8e33-15ca6707a38f","added_by":"auto","created_at":"2024-01-02 17:14:47","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":311727,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementallyfile20231101.docx","url":"https://assets-eu.researchsquare.com/files/rs-3781087/v1/7fa84d735b4427a6dbd27841.docx"}],"financialInterests":"","formattedTitle":"New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cirrhosis (LC) is the final stage of liver fibrosis, which leads to severe complications including ascites, encephalopathy, and hepatocellular carcinoma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. LC diagnosis can be confirmed through microscopic pathological examination of liver tissue obtained from biopsy. However, this approach poses the risk of bleeding and damages the surrounding organs. Additionally, the procedure is subject to sampling errors and different pathologists may interpret the same tissue sample differently [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this milieu, noninvasive methods have become increasingly important to liver fibrosis assessment. Mac-2-binding protein glycosylation isomer (M2BPGi) is a biomarker for liver fibrosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Research on M2BPGi has focused extensively on its utility in assessing the degree of hepatitis C virus (HCV)-related liver fibrosis. The diagnostic accuracy of M2BPGi is higher than that of other commonly used markers, such as type 4 collagen and aspartate aminotransferase (AST)-to-platelet ratio index (APRI), in patients with HCV infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA unique characteristic of M2BPGi is its potential to have different cutoff values for LC, based on the etiology of fibrosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The variations in the cutoff values of the biomarker may be attributed to the presence of a factor other than fibrosis that influences the M2BPGi levels. If M2BPGi is used for assessing LC of various etiologies, several factors should be considered to ensure accurate and reliable results.\u003c/p\u003e \u003cp\u003eTo resolve this problem, we tried to generate an algorithm for detecting LC, regardless of the etiology. However, the current qualitative measurement method for M2BPGi may have limitations when evaluating its levels across various etiologies of liver disease. Qualitative measurement typically involves categorizing results into predefined ranges (e.g., low, moderate, and high) rather than providing a precise numerical value [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, we developed a new quantitative system, quantitative M2BPGi (M2BPGi-Qt), which minimizes the variability in cutoff values among different etiologies of CLD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreviously, we validated the clinical utility of the new quantitative method and proved its advantages over qualitative methods when assessing M2BPGi for liver fibrosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our final goal is to establish a noninvasive method for predicting LC as an alternative to liver biopsy. Therefore, the aim of this study was to investigate factors in addition to liver fibrosis that influence M2BPGi-Qt levels and improve the accuracy of predicting LC of various etiologies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eThis study was performed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki after obtaining approval from the Institutional Review Board of the National Center for Global Health and Medicine (number: NCGM-G-004185-02). Informed consent was obtained from all participants via the \u0026ldquo;opt-out\u0026rdquo; method on each hospital website.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePatients and study design\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study was conducted in 13 hospitals in Japan. The inclusion criterion was a confirmed diagnosis of CLD using liver biopsy and laboratory tests between January 2015 and December 2020. Patients with severe inflammatory diseases or malignancies unrelated to the study focus were excluded from the study. CLD etiologies included HCV infection, hepatitis B virus (HBV) infection, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and autoimmune liver disease. The absence of detectable HCV RNA for an extended period after the end of treatment was defined as HCV-sustained virological response (SVR). NAFLD was diagnosed when the intrahepatic triglyceride level was \u0026gt;\u0026thinsp;5% of the liver weight and was not influenced by other factors. The diagnosis of autoimmune liver diseases requires a combination of clinical, serological (autoantibodies), and histological assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eTo investigate factors other than liver fibrosis that influence M2BPGi-Qt levels, we assessed the correlation between the fibrosis stage and M2BPGi-Qt levels among various CLD etiologies. As there was no correlation between the fibrosis stage and M2BPGi level in a specific etiology of CLD, we evaluated the clinical factors influencing the M2BPGi-Qt level in that specific etiology.\u003c/p\u003e \u003cp\u003eSubsequently, we created an algorithm to detect LC using M2BPGi-Qt through logistic regression analysis, considering an influencing factor other than fibrosis. For model development, the enrolled patients were randomly divided into two groups for training and validation at a ratio of 7:3. After developing the model using the training dataset, it was validated using the validation dataset. Furthermore, we validated the new algorithm by different evaluators working at each hospital (hospital-based pathologists).\u003c/p\u003e \u003cp\u003eM2BPGi-Qt measurement\u003c/p\u003e \u003cp\u003eSerum samples were obtained to evaluate M2BPGi-Qt and transferred to the central institute. M2BPGi-Qt was measured using a fully automated HISCL-5000 immunoassay kit (Sysmex Co., Japan). M2BPGi was quantitatively measured via a five-point calibration. Immunoassays use a series of reagents and instruments [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. They detect changes in the glyco-chain structure of M2BP, reflecting the status of liver fibrosis. For detection, the target glyco-chain was captured on lectin-coated magnetic particles, and after washing the particles, the anti-M2BP antibody (enzyme conjugated) was bound to M2BPGi on the magnetic particle.\u003c/p\u003e \u003cp\u003ePathological findings\u003c/p\u003e \u003cp\u003ePathological slides were collected from the Research Institute of Junshin Gakuen University and re-evaluated by a central reviewer to independently evaluate the fibrosis and activity stages based on biopsy samples. To minimize potential bias, the central reviewer was intentionally blinded to the patients' clinical information.\u003c/p\u003e \u003cp\u003eAssessment of viral hepatitis and autoimmune hepatitis (AIH) was conducted using the new Inuyama classification for the grade of fibrosis and inflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Fibrosis and activity stages of NAFLD and ALD were classified based on steatosis, activity, and fibrosis (SAF) scoring [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The stages of primary biliary cholangitis (PBC) fibrosis were determined using the Nakanuma classification [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], details of which are presented in Online Resource 1.\u003c/p\u003e \u003cp\u003eHospital-based pathologists had access to the patients\u0026rsquo; clinical information, including medical history and other relevant data. The assessment of fibrosis stage by the hospital-based pathologists was not uniform.\u003c/p\u003e \u003cp\u003eClinical factors\u003c/p\u003e \u003cp\u003eBlood samples and patient data are collected simultaneously. The general characteristics included information about age, sex, ethnicity, height, weight, alcohol consumption, and medical history. The laboratory data included white blood cell count; hemoglobin, platelet, total bilirubin, alanine aminotransferase (ALT), AST, albumin, and creatinine levels; and prothrombin time-to-international normalized ratio. Algorithm-based score models for detecting liver fibrosis are presented in Online Resource 2.\u003c/p\u003e \u003cp\u003eStatistical evaluation\u003c/p\u003e \u003cp\u003eThe software used for data analysis was SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Differences between data points or groups were considered statistically significant if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Baseline characteristics are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. M2BPGi-Qt levels were compared at each fibrosis stage using Jonckheere\u0026ndash;Terpstra test. By constructing receiver operating characteristic (ROC) curves and determining cutoff values using the Youden index, we selected the most suitable threshold for a diagnostic test. Logistic regression models were employed to model the relationship between the independent variables and binary outcomes, providing odds ratios (ORs) as a measure of strength and direction of the association.\u003c/p\u003e \u003cp\u003eAn algorithm for detecting LC based on M2BPGi-Qt levels was developed using logistic regression analysis. A multivariate logistic regression model was used to account for the potential interactive effects of multiple variables. The ORs and 95% confidence intervals (CIs) were determined for each variable. To assess the performance of the new model in comparison to existing models, the area under the receiver operating characteristic (ROC) curve (AUC) values of different models were statistically compared using DeLong\u0026rsquo;s method. We also evaluated the integrated discrimination improvement (IDI) and net reclassification index (NRI) to provide a more comprehensive understanding of how the new model improved the classification and discrimination of individuals at different risk levels. STATISTA Corporation (Kyoto, Japan) supported the statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003ePatient flow\u003c/h2\u003e\n\u003cp\u003eA total of 1,373 patients with CLD were recruited; 265 of them were excluded because of missing serum samples, the presence of inflammatory diseases, or unmatched diagnoses between the central reviewers and hospital-based pathologists. Discordant diagnoses were observed in patients with NAFLD (n = 20), AIH (5), and PBC (4). The remaining 1,108 patients were included in the final analysis (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1 \u003c/strong\u003eStudy flow chart. \u003c/p\u003e\n\u003cp\u003eM2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer\u003c/p\u003e\n\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n\u003cp\u003eThe mean age of the patients was 58.8 \u0026plusmn; 13.7 years; 480 participants (43.3%) were men, and the mean BMI was 23.6 \u0026plusmn; 3.86 kg/m\u003csup\u003e2\u003c/sup\u003e. There were 506, 163, 158, 153, 111, and 17 patients with HCV infection, HBV infection, NAFLD, AIH, PBC, and ALD, respectively. Among patients with HCV infection, 159 achieved SVR. As confirmed by the central reviewer, the number and frequency of each fibrosis stage were as follows: 145 (13.1), 395 (35.6), 248 (22.4), 193 (17.4), and 144 (13.0) for F0, F1, F2, F3, and F4, respectively (Table 1).\u003c/p\u003e\n\u003ch2\u003eM2BPGi-Qt level in each liver fibrosis stage \u003c/h2\u003e\n\u003cp\u003eAn association was observed between increasing M2BPGi-Qt levels and liver fibrosis progression in patients with HCV and HBV infections. Specifically, a significant difference in M2BPGi-Qt levels was observed between patients with early- (F2) and advanced-stage (F3) liver fibrosis. In patients with NAFLD, the mean M2BPGi-Qt level increased as fibrosis progressed. However, ALD was not evaluated because of an insufficient number of cases. In patients with PBC, higher M2BPGi levels were associated with more severe fibrosis. In contrast to the other patient groups, M2BPGi-Qt levels did not appear to be strongly associated with the degree of fibrosis in patients with AIH (Fig 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2 \u003c/strong\u003eCorrelations between M2BPGi-Qt levels and fibrosis stage. Data are expressed as median. \u003c/p\u003e\n\u003cp\u003eThe M2BPGi-Qt levels were compared among the fibrosis stages for each etiology using Jonckheere\u0026ndash;Terpstra test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama classification system; NAFLD was based on the SAF score and PBC was based on the Nakanuma classification. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer; PBC, primary biliary cholangitis; SVR, sustained virological response\u003c/p\u003e\n\u003cp\u003eThe AUC values of M2BPGi for predicting \u0026ge;F1, \u0026ge;F2, \u0026ge;F3 and \u0026ge;F4 in NAFLD were 0.815, 0.837, 0.784, and 0.768, respectively, which were better than those for other etiologies. A positive correlation between the M2BPGi-Qt cutoff values and fibrosis severity was observed in patients with various liver disease etiologies, excluding AIH (Online Resource 3).\u003c/p\u003e\n\u003ch2\u003eClinical factors other than liver fibrosis influence M2BPGi levels in patients with AIH \u003c/h2\u003e\n\u003cp\u003eThe degree of activity classification was more severe in patients with high M2BPGi-Qt levels (\u0026ge; 3.0 AU/mL) than in those with low levels (\u0026lt; 3.0 AU/mL) (p \u0026lt; 0.001) (Online Resource 4). \u003c/p\u003e\n\u003cp\u003eThe logistic regression analysis revealed whether there was a significant relationship or association between the groups (Online Resource 5). Activity stage; AST, ALT, total bilirubin, and albumin levels; platelet count; and prothrombin time were identified as significant variables in the univariate analyses. The multivariate analyses confirmed that activity stage; ALT, total bilirubin, and albumin levels; and platelet count were independent factors differentiating the two groups. Activity stage had the highest OR (7.663; 95% CI, 2.478\u0026ndash;23.67; p \u0026lt; 0.001).\u003c/p\u003e\n\u003ch2\u003eInfluence of liver activity stage on M2BPGi levels\u003c/h2\u003e\n\u003cp\u003eThe M2BPGi-Qt levels increased with the progression of liver activity in patients with HCV infection, HBV infection, or AIH, indicating that higher M2BPGi-Qt levels are associated with more advanced stages of liver activity. Similarly, in patients with NAFLD, the M2BPGi levels increased with the progression of the activity stage based on the NAS score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 3 \u003c/strong\u003eCorrelations between M2BPGi-Qt levels and activity stages.Data are expressed as median. M2BPGi-Qt levels were compared among the activity stages for each etiology using Tukey\u0026rsquo;s honest significant difference test. HCV infection, HBV infection, and AIH were determined based on the new Inuyama Classification system and NAFLD based on the NAFLD activity score. AIH, autoimmune hepatitis; M2BPGi-Qt, quantitative M2BPGi measurement; SVR, sustained virological response\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel establishment using M2BPGi-Qt for LC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of training and validation datasets was 775 and 333, respectively. No significant difference was observed between the groups overall; however, the frequency of men in the training dataset tended to be relatively higher than that in the validation dataset (p = 0.064) (Online Resource 6).\u003c/p\u003e\n\u003cp\u003eFrom the regression analysis results, five variables (activity stage, platelet count, albumin, prothrombin time and total bilirubin levels) were selected as the optimal variables out of the original 19 variables (Online Resource 5). Subsequently, we used these variables in the multivariate logistic regression analysis of the training cohort to establish a new algorithm for diagnosing LC using the M2BPGi-Qt level (Table 2). The M2BPGi-Qt level, ALT level, and platelet count were identified as significant independent predictors. This set of predictors was combined to establish the following predictive equation. \u003c/p\u003e\n\u003cp\u003eMAP-R:M2BPGi-Qt-to-ALT and platelet ratio \u003cem\u003e= [M2BPGi-Qt (AU/mL)/[ALT (IU/L)\u003csup\u003e1/2\u003c/sup\u003e \u0026times; platelets (10\u003csup\u003e3\u003c/sup\u003e/\u0026mu;L)].\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMAP-R had a high AUC value for predicting LC in the training and validation datasets (0.759, 95% CI 0.709\u0026ndash;0.810 and 0.702, 95% CI 0.595\u0026ndash;0.796, respectively), and the AUC values of MAP-R were significantly different from those of M2BPGi-Qt (all p \u0026lt; 0.001) (Online Resource 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation through the assessment of fibrosis stage by hospital-based pathologists\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 144 (13.0%) and 148 (13.3%) patients were diagnosed with LC by a central reviewer and hospital-based pathologists, respectively. The percentage of concordance in LC diagnosis among the pathologists was 75.2%.\u003c/p\u003e\n\u003cp\u003eMAP-R had the highest AUC value for predicting LC by hospital-based pathologists (0.840, 95% CI 0.806\u0026ndash;0.874) (Fig 4), and the AUC values of MAP-R were significantly different from those of M2BPGi-Qt, FIB4 index, ALBI score, and APRI (all p \u0026lt; 0.05) (Online Resource 8). We observed an 83.2% sensitivity and a 71.4% specificity for the MAP-R, with a cutoff value of 1.59.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 4 \u003c/strong\u003ePredictive equation using M2BPGi-Qt for the diagnosis of liver cirrhosis by hospital-based pathologists. MAP-R; quantitative measurement of Mac-2-binding protein glycosylation isomer-to-ALT and platelet ratio, APRI; AST-to-platelet ratio index (APRI), FIB4; fibrosis 4, ALBI; albumin-bilirubin\u003c/p\u003e\n\u003cp\u003eThe IDI values of MAP-R were significantly different from those of the M2BPGi-Qt level, FIB4 index, ALBI score, and APRI (all p \u0026lt; 0.001). The NRI values were significantly different between MAP-R and the M2BPGi-Qt level, ALBI score, and APRI (p \u0026lt; 0.001). In contrast, there was no significant difference in the NRI values between the MAP-R and FIB 4 indices (p = 0.150).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBefore conducting the clinical study, we assumed the influence of liver inflammation on M2BPGi levels as previously reported [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Kimura et al. reported a positive correlation between M2BPGi level and necroinflammatory activity and stated that M2BPGi can be used to assess the degree of liver inflammation in patients with liver diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings are consistent with the relationship between M2BPGi-Qt levels and inflammatory activity in various etiologies of CLD in our study. The interpretation of M2BPGi results when assessing the severity of liver disease can be complicated by variations in M2BPGi levels, associated with different etiologies and inflammation levels.\u003c/p\u003e \u003cp\u003eHere, we emphasize the lack of correlation between M2BPGi levels and fibrosis progression in patients with severe hepatitis, consistent with the results of a previous study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We assessed M2BPGi levels in patients with AIH with different activity stages. However, no significant association was observed between the M2BPGi-Qt levels and fibrosis stage, indicating that elevated inflammatory levels in the liver may lead to higher blood M2BPGi levels, potentially obscuring the relationship between M2BPGi levels and fibrosis progression.\u003c/p\u003e \u003cp\u003eM2BPGi-Qt levels can vary with the underlying cause of liver disease and may reflect the severity of liver inflammation associated with that etiology. M2BPGi levels are higher in patients with HCV infection than in those with HBV infection or NAFLD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In HCV infection, the M2BPGi-Qt levels may be elevated because of a higher degree of liver inflammation than that observed in NAFLD and PBC.\u003c/p\u003e \u003cp\u003eThe dependence of M2BPGi levels on both fibrosis and inflammation, regardless of etiology, is a major finding that supports the development of algorithms for detecting LC. A logistic regression analysis was conducted to create a new algorithm for diagnosing LC while considering the influence of liver inflammation by including ALT level and platelet count. The close association between liver inflammation and ALT level is a well-established and clinically significant finding. Platelets also perform inflammatory functions and play a role in coordinating immune responses by interacting with various cell types and immune system components [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of the index involving ALT levels and platelet counts for assessing liver inflammation, particularly because of its cost-effectiveness and ease of use, is valuable in liver disease assessment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although AST and ALT levels are important markers for assessing CLD, their use in combination with other predictors can improve the accuracy of diagnosing LC and other liver diseases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We developed a robust and reliable model for LC detection, focusing on its accuracy, stability, and superiority to previous models using three metrics. MAP-R was a more accurate and effective model for predicting LC than other established models such as FIB-4, APRI, and ALBI, as well as M2BPGi-Qt. This finding implies that the MAP-R model is a valuable addition to clinical practice for the early detection of LC.\u003c/p\u003e \u003cp\u003eThe present study showed that MAP-R is clinically useful for the diagnosis of LC, especially in the context of different interpretations by pathologists when examining the same set of tissue samples. In particular, hospital-based pathologists demonstrated high accuracy, likely because of their access to patients' clinical information, which may contribute to the variability in diagnoses when provided to the pathologists. Collaboration and communication between clinicians and pathologists are crucial for an accurate diagnosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe plan to characterize M2BPGi subtypes based on their glycan isomers and their association with fibrosis and inflammation in patients with CLD. Glycan isomers in M2BP may have implications in these pathological processes, potentially serving as biomarkers, regulators, or mediators of liver health and disease. By identifying specific subtypes or structural variants that are particularly associated with liver inflammation and fibrosis, we will improve the accuracy and precision of M2BPGi as a biomarker for assessing liver health and disease progression.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the etiology of CLD was not uniform among the enrolled patients. Notably, the number of patients with CLD and ALD was small. Second, the activity stage of PBC was not evaluated because appropriate criteria could not be identified. Third, M2BPGi levels in viral hepatitis and other causes of CLD may not be directly comparable, even if fibrosis is present at the same stage. Finally, all participants were recruited from an ethnically homogeneous Japanese population. Therefore, a re-examination of the data for other or more diverse ethnic groups is desirable.\u003c/p\u003e \u003cp\u003eOur quantitative measurement system demonstrated that M2BPGi levels were dependent on both fibrosis and inflammation in the liver. The interpretation of M2BPGi-Qt for assessing the severity of liver disease should be based on the specific etiology, inflammation level, and fibrosis stage. The new algorithm is promising for improving LC diagnosis accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Sysmex Corporation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTakeji Umemura received lecture fees from AbbVie GK and Gilead Sciences and research grants from AbbVie GK, Eisai Co., Otsuka Pharmaceutical Co., and Tosoh Co.\u003c/p\u003e\n\u003cp\u003eMasayuki Kurosaki received lecture fees from AbbVie, Eisai, Chugai, AstraZeneca, Lilly, and Takeda Co.\u003c/p\u003e\n\u003cp\u003eMasashi Mizokami received lecture fees from Sysmec Co.\u003c/p\u003e\n\u003cp\u003eYasuhiro Asahina received lecture fees from Fujirebi Inc. and Abbott Japan LLC.\u003c/p\u003e\n\u003cp\u003eTakumi Kawaguchi received lecture fees from Taisho Pharmaceutical Co., Kowa Company, Otsuka Pharmaceutical Co., Eisai Co., Janssen Pharmaceutical K.K., AbbVie GK., ASKA Pharmaceutical Co., and EA Pharma Co., and a research grant from Eisai Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHU, KY, NI, MM, TK, TS, SJ, and HT collected and analyzed the data; HU and MM drafted the manuscript; KS designed and supervised the study; KY, TK, and HY offered technical or material support. All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGin\u0026egrave;s P, Krag A, Abraldes JG, Sol\u0026agrave; E, Fabrellas N, Kamath PS. Liver cirrhosis. Lancet 2021;398:1359-1376, Oct 9, 2021 \u003c/li\u003e\n\u003cli\u003eVilar-Gomez E, Chalasani N. Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. J Hepatol 2018;68:305-315, Dec 2, 2017\u003c/li\u003e\n\u003cli\u003eToshima T, Shirabe K, Ikegami T, Yoshizumi T, Kuno A, Togayachi A, et al. A novel serum marker, glycosylated \u003cem\u003eWisteria floribunda\u003c/em\u003e agglutinin-positive Mac-2 binding protein (WFA\u003csup\u003e+\u003c/sup\u003e-M2BP), for assessing liver fibrosis. J Gastroenterol 2015;50:76-84, Mar 7, 2015\u003c/li\u003e\n\u003cli\u003eNah EH, Cho S, Kim S, Kim HS, Cho HI. 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Clinical implications of serum \u003cem\u003eWisteria floribunda\u003c/em\u003e agglutinin-positive Mac-2-binding protein in treatment-na\u0026iuml;ve chronic hepatitis B. Hepatol Res 2017;47:204-215, May 2, 2017 \u003c/li\u003e\n\u003cli\u003eSingh T, Allende DS, McCullough AJ. Assessing liver fibrosis without biopsy in patients with HCV or NAFLD. Cleve Clin J Med 2019;86:179-186, Mar 1, 2019\u003c/li\u003e\n\u003cli\u003eThomas MR, Storey RF. The role of platelets in inflammation. Thromb Haemost 2015;114:449-458, Aug 13, 2015\u003c/li\u003e\n\u003cli\u003eItakura J, Kurosaki M, Setoyama H, Simakami T, Oza N, Korenaga M, et al. Applicability of Apri and FIB-4 as a transition indicator of liver fibrosis in patients with chronic viral hepatitis. J Gastroenterol 2021;56:470-478, Mar 31, 2021\u003c/li\u003e\n\u003cli\u003eZhang D, Cao Y, Sun Y, Zhao X, Peng C, Zhao J, et al. Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: A single-center retrospective study. Eur Radiol 2023;33:1653-1667, Sep 23, 2023\u003c/li\u003e\n\u003cli\u003eNoro E, Matsuda A, Kyoutou T, Sato T, Tomioka A, Nagai M, et al. N-glycan structures of \u003cem\u003eWisteria floribunda\u003c/em\u003e agglutinin-positive Mac2 binding protein in the serum of patients with liver fibrosis. Glycobiology 2021;31:1268-1278, Nov 1, 2021 \u003c/li\u003e\n\u003cli\u003eNagaoka K, Tanaka M, Tanaka Y. Mac-2 binding protein and its glycan isomer: Where does it come from? Where is it going? Hepatol Res 2021;51:1026-1028, Oct 1, 2021\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;B\u003c/strong\u003e\u003cstrong\u003easeline clinical characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"992\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCV\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 347)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCV-SVR\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 159)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBV\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIH\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 153)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAFLD\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 158)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALD\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e＊\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 111)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e57.9 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e67.2 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e50.1 \u0026plusmn; 16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e60.1 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e59.1 \u0026plusmn; 14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e60.6 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e59.4 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale sex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e163 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e84 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e107 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e26 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e62 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e13 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e24 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e23.4 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e23.4 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e22.7 \u0026plusmn; 3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e24.7 \u0026plusmn; 5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e22.1 \u0026plusmn; 3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIB-4 index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e2.93 \u0026plusmn; 2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e3.51 \u0026plusmn; 2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e3.08 \u0026plusmn; 2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e4.86 \u0026plusmn; 4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e2.89 \u0026plusmn; 2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e4.62 \u0026plusmn; 3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e2.02 \u0026plusmn; 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet , 10\u003csup\u003e4\u003c/sup\u003e/\u0026mu;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e16.4 \u0026plusmn; 5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e16.3 \u0026plusmn; 6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e19.0 \u0026plusmn; 7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e22.9 \u0026plusmn; 28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e19.1 \u0026plusmn; 6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e15.4 \u0026plusmn; 5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e23.6 \u0026plusmn; 7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST, IU/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e60.2 \u0026plusmn; 46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e45.5 \u0026plusmn; 34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e40.7 \u0026plusmn; 54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e260 \u0026plusmn; 359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e60.0 \u0026plusmn; 37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e96.0 \u0026plusmn; 127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e53.4 \u0026plusmn; 44.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;ALT, IU/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e83.1 \u0026plusmn; 65.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e42.0 \u0026plusmn; 37.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e125 \u0026plusmn; 290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e310 \u0026plusmn; 464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e70.3 \u0026plusmn; 54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e118 \u0026plusmn; 265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e55.3 \u0026plusmn; 38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2134944612286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2BPGi-Qt, AU/\u003c/strong\u003e\u003cstrong\u003eｍ\u003c/strong\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e2.39 \u0026plusmn; 3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e2.35 \u0026plusmn; 2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e1.91 \u0026plusmn; 2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e4.57 \u0026plusmn; 2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.4662638469285%\"\u003e\n \u003cp\u003e1.89 \u0026plusmn; 2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566968781470292%\"\u003e\n \u003cp\u003e3.91 \u0026plusmn; 5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.473313192346424%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.559919436052366%\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.306451612903226%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibrosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStage,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9233870967741935%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e55 (15.9)\u003c/p\u003e\n \u003cp\u003e130 (37.5)\u003c/p\u003e\n \u003cp\u003e72 (20.7)\u003c/p\u003e\n \u003cp\u003e48 (13.8)\u003c/p\u003e\n \u003cp\u003e42 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.576612903225806%\"\u003e\n \u003cp\u003e11 (6.9)\u003c/p\u003e\n \u003cp\u003e49 (30.8)\u003c/p\u003e\n \u003cp\u003e49 (30.6)\u003c/p\u003e\n \u003cp\u003e23 (14.4)\u003c/p\u003e\n \u003cp\u003e27 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e22 (13.5)\u003c/p\u003e\n \u003cp\u003e70 (42.9)\u003c/p\u003e\n \u003cp\u003e30 (18.4)\u003c/p\u003e\n \u003cp\u003e28 (17.2)\u003c/p\u003e\n \u003cp\u003e13 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e15 (9.9)\u003c/p\u003e\n \u003cp\u003e59 (38.8)\u003c/p\u003e\n \u003cp\u003e39 (25.5)\u003c/p\u003e\n \u003cp\u003e28 (18.3)\u003c/p\u003e\n \u003cp\u003e12 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.560483870967742%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFibrosis Scoring\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.8225806451612905%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475806451612904%\"\u003e\n \u003cp\u003e26 (15.4)\u003c/p\u003e\n \u003cp\u003e33 (18.9)\u003c/p\u003e\n \u003cp\u003e27 (18.4)\u003c/p\u003e\n \u003cp\u003e43 (24.6)\u003c/p\u003e\n \u003cp\u003e29 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.576612903225806%\"\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003cp\u003e5 (29.4)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e6 (35.3)\u003c/p\u003e\n \u003cp\u003e4 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.661290322580645%\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore of\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFibrosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.8225806451612905%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e14 (12.6)\u003c/p\u003e\n \u003cp\u003e49 (44.1)\u003c/p\u003e\n \u003cp\u003e31 (27.9)\u003c/p\u003e\n \u003cp\u003e17 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.306451612903226%\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003estage,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9233870967741935%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e4 (1.2)\u003c/p\u003e\n \u003cp\u003e278 (80.1)\u003c/p\u003e\n \u003cp\u003e60 (17.3)\u003c/p\u003e\n \u003cp\u003e5 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.576612903225806%\"\u003e\n \u003cp\u003e12 (7.5)\u003c/p\u003e\n \u003cp\u003e132 (83.1)\u003c/p\u003e\n \u003cp\u003e12 (7.5)\u003c/p\u003e\n \u003cp\u003e3 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e22 (13.5)\u003c/p\u003e\n \u003cp\u003e92 (56.4)\u003c/p\u003e\n \u003cp\u003e37 (22.7)\u003c/p\u003e\n \u003cp\u003e12 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.568548387096774%\"\u003e\n \u003cp\u003e4 (2.6)\u003c/p\u003e\n \u003cp\u003e48 (31.6)\u003c/p\u003e\n \u003cp\u003e65 (42.8)\u003c/p\u003e\n \u003cp\u003e35 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.560483870967742%\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity Scoring\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.8225806451612905%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475806451612904%\"\u003e\n \u003cp\u003e23 (13.1)\u003c/p\u003e\n \u003cp\u003e35 (19.8)\u003c/p\u003e\n \u003cp\u003e45 (25.7)\u003c/p\u003e\n \u003cp\u003e38 (21.7)\u003c/p\u003e\n \u003cp\u003e17 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.576612903225806%\"\u003e\n \u003cp\u003e4 (23.5)\u003c/p\u003e\n \u003cp\u003e5 (29.4)\u003c/p\u003e\n \u003cp\u003e3 (17.6)\u003c/p\u003e\n \u003cp\u003e2 (11.8)\u003c/p\u003e\n \u003cp\u003e3 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.052419354838708%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAIH, autoimmune hepatitis; ALD, alanine aminotransferase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; FIB-4, fibrosis 4 index; M2BPGi-Qt, quantitative measurement of Mac-2-binding protein glycosylation isomer; NAFLD, non-alcoholic fatty liver disease ; PBC, primary biliary cholangitis;＊1 AIH, HBV and HCV was divide fibrosis and activity stage by the new Inuyama classification.＊2 NAFLD was divided by the NAFLD activity score.＊3 PBC was divided by the Nakanuma classification.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 2\u003c/strong\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLogistic regression analysis of estimating the liver cirrhosis in the training cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"851\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.494712103407757%\" colspan=\"2\" style=\"width: 26.4454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.24559341950646%\" colspan=\"3\" style=\"width: 36.9814%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2BPGi-Qt (AU/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e1.128 (1.065-1.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e1.146 (1.062-1.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e-1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e0.335 (0.219\u0026ndash;0.511)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal bilirubin\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e0.986 (0.881\u0026ndash;1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e0.998 (0.901\u0026ndash;1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e0.995 (0.991\u0026ndash;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026radic;ALT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e0.948 (0.901-0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.38895417156287%\" style=\"width: 17.3844%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelets \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u0026times;\u003c/strong\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003e\u0026mu;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.403055229142185%\" style=\"width: 6.6377%;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.329024676850764%\" style=\"width: 18.2273%;\"\u003e\n \u003cp\u003e0.868 (0.833-0.905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165687426556993%\" style=\"width: 8.2181%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.873090481786134%\" style=\"width: 7.0591%;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.151586368977675%\" style=\"width: 18.9648%;\"\u003e\n \u003cp\u003e0.883 (0.845-0.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.220916568742656%\" style=\"width: 10.9575%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic liver disease, liver biopsy, liver fibrosis, noninvasive marker, liver inflammation, noninvasive method, quantitative measurement, liver biopsy, platelet, immunoassay","lastPublishedDoi":"10.21203/rs.3.rs-3781087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3781087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/purpose of the study: \u003c/strong\u003eMac-2-binding protein glycosylation isomer (M2BPGi), a biomarker for liver fibrosis, is influenced by various etiologies. Here, we aimed to investigate clinical factors that improve the accuracy of liver cirrhosis (LC) diagnosis based on quantitative M2BPGi (M2BPGi-Qt), regardless of etiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn total, 1,373 patients with chronic liver disease (CLD) were recruited.\u003cstrong\u003e \u003c/strong\u003eWeassessed the correlation between fibrosis stage and M2BPGi-Qt levels among CLD etiologies. If there was no correlation between the fibrosis stage and M2BPGi level in a specific etiology of CLD, we evaluated the clinical factors influencing the M2BPGi-Qt level in that specific etiology. Subsequently, we created an algorithm to detect LC based on M2BPGi-Qt, considering an influencing factor other than fibrosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn virus hepatitis, non-alcoholic fatty liver disease, and primary biliary cholangitis, the M2BPGi-Qt levels increased liver fibrosis progression. In autoimmune hepatitis, no significant association was observed between the fibrosis stage and M2BPGi-Qt level. However, liver inflammation positively correlated with the M2BPGi-Qt levels. Considering liver inflammation, we established an algorithm, M2BPGi-Qt, to determine the alanine aminotransferase-to-platelet ratio (MAP-R) in LC. The area under the receiver operating characteristic curve (AUC) of the MAP-R index was 0.840. The AUC of MAP-R was higher than that of the M2BPGi-Qt for detecting LC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eNew quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis. The algorithm based on the M2BPGi-Qt level demonstrates a high accuracy for LC diagnosis.\u003c/p\u003e","manuscriptTitle":"New quantitative measurement system for M2BPGi reveals liver inflammation complicates liver cirrhosis diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 17:14:42","doi":"10.21203/rs.3.rs-3781087/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c95ba1e2-384b-4fc9-b18b-a2446b7ed1d7","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-13T22:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 17:14:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3781087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3781087","identity":"rs-3781087","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}