Diagnostic performance of serum mac-2-binding protein glycosylation isomer as a fibrosis biomarker in non-obese and obese patients with MASLD

Diagnostic performance of serum mac-2-binding protein glycosylation isomer as a fibrosis biomarker in non-obese and obese patients with MASLD | 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 Diagnostic performance of serum mac-2-binding protein glycosylation isomer as a fibrosis biomarker in non-obese and obese patients with MASLD Prooksa Ananchuensook, Kamonchanok Moonlisarn, Bootsakorn Boonkaew, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5473146/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 Aim Serum mac-2-binding protein glycosylation isomer (M2BPGi) is a new biomarker for liver fibrosis. However, its performance in metabolic dysfunction-associated steatotic liver disease (MASLD), particularly in obese patients, remains to be explored. Methods This study evaluated the role of M2BPGi in predicting liver fibrosis in 205 patients with MASLD using magnetic resonance elastography (MRE) as a reference. The performance of M2BPGi was compared to vibration-controlled transient elastography (VCTE), FIB-4, APRI, and NFS. The PNPLA3, TM6SF2 , and HSD17B13 polymorphisms were assessed by allelic discrimination assays. Results The area under the ROC curves for VCTE, M2BPGi FIB-4, APRI, and NFS in differentiating significant fibrosis were 0.95 (95% CI; 0.91–0.98), 0.85 (0.79–0.92), 0.81 (0.74–0.89), 0.79 (0.71–0.87) and 0.80 (0.72–0.87) (all P < 0.001), respectively. The optimal cut-off values of M2BPGi in predicting significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.95, and 1.23 cut-off index (COI), yielding satisfactory sensitivity, specificity, and diagnostic accuracy. The performance of M2BPGi was consistent among subgroups according to BMI, while the AUROCs of FIB-4, APRI, and NFS were remarkably declined in patients with BMI ≥ 30 kg/m 2 . Patients with the PNPLA3 GG genotype had significantly higher M2BPGi than those with the CC/CG genotypes. In multivariate analysis, the independent factors associated with significant liver fibrosis were VCTE, M2BPGi, and PNPLA3 rs738409. Conclusion Our data demonstrated that serum M2BPGi accurately assessed liver fibrosis across different BMI, indicating that this biomarker could apply to non-obese and obese patients with MASLD in clinical settings. Liver fibrosis Metabolic dysfunction-associated steatotic liver disease M2BPGi Magnetic resonance elastography PNPLA3 Figures Figure 1 Figure 2 INTRODUCTION Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease (CLD) worldwide, affecting approximately 30% of the global population[ 1 ]. The continuing liver injury of this disorder can lead to more severe forms, including steatohepatitis with or without liver fibrosis, cirrhosis, and, ultimately, hepatocellular carcinoma (HCC)[ 1 ]. Increasing evidence also indicates that progressive MASLD is an independent risk for developing cardiovascular disease, particularly in individuals with obesity and advanced liver disease [ 2 ]. Thus, accurate assessment of disease severity, particularly the extent of liver fibrosis, is essential for the management of patients with MASLD. At present, liver biopsy is the gold standard for diagnosis; however, this method is invasive, prone to sampling errors, and has poor acceptability, which limits its use in routine clinical settings[ 3 ]. Accordingly, several imaging has been applied to measure the severity of liver fibrosis, including vibration-controlled transient elastography (VCTE) and magnetic resonance elastography (MRE). Currently, MRE and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) are considered the most reliable non-invasive procedure for evaluating liver fibrosis and steatosis in patients with MASLD, respectively[ 4 , 5 ]. Serum-based biomarkers have also been developed as an alternative to liver biopsy for evaluating the severity of liver fibrosis in CLD. The widely used fibrosis models in patients with MASLD that include a combination of indirect biomarkers are the fibrosis-4 (FIB-4) index, aspartate aminotransferase (AST)/platelet ratio index (APRI), and NAFLD fibrosis score (NFS)[ 6 ]. Although these fibrosis models are easy to use in routine practice, the tests are not liver-specific, and their performance may be affected by some clinical parameters, particularly high body mass index (BMI) [ 7 ]. Recently, serum mac-2-binding protein glycosylation isomer (M2BPGi) measured by glycan-based immunoassays has emerged as an accurate liver-specific biomarker for assessing liver fibrosis stages resulting from various CLDs, particularly chronic hepatitis C and B [ 8 ]. This novel biomarker has also helped monitor disease progression and predict HCC development in patients with chronic viral hepatitis [ 8 ]. Additionally, the clinical utility of M2BPGi as a fibrosis marker has been explored in patients with MASLD, and the results suggest that this novel biomarker is reliable for distinguishing liver fibrosis stages[ 9 ], as well as cirrhosis[ 10 , 11 ]. Moreover, a recent study showed that higher serum M2BPGi levels were significantly related to an increased risk of diabetes in Japanese individuals[ 12 ]. Despite these findings, the available data are still limited and have inconsistencies among reports that need further investigation. This cross-sectional study aimed to examine the clinical utility of serum M2BPGi in a well-characterized cohort of Thai patients with MASLD. Using MRE as the reference for assessing fibrosis staging, the diagnostic performance of serum M2BPGi was directly compared with VCTE and conventional fibrosis models, including FIB-4, APRI, and NFS. In this study, we also investigated various clinical characteristics that might impact the severity of fibrosis, including obesity, type 2 diabetes (T2DM), and single nucleotide polymorphisms (SNPs), which are important factors associated with the development and progression of MASLD [ 13 ]. METHODS Patients Between 2022 and 2024, 205 Thai patients diagnosed with MASLD were enrolled in this cross-sectional cohort at the King Chulalongkorn Memorial Hospital, Thailand. The Institute Ethics Committee approved the study (IRB No. 981 − 64), and the study was conducted following the Declaration of Helsinki and the principles of Good Clinical Practice. Written informed consent was obtained from patients, and their medical records were reviewed before enrollment. For all participants, anthropometric variables, including weight, height, and BMI, were measured. According to the Asian-BMI categorization, individuals with BMI of ≤ 24.9, 25-29.9, and ≥ 30 kg/m 2 were classified as non-obese, obese class I, and obese class II, respectively [ 14 ]. The inclusion criteria were patients aged ≥ 18 years diagnosed with liver steatosis based on MRI-PDFF grade ≥ 1 (defined as MRI-PDFF ≥ 5.4%) [ 5 ]. Exclusion criteria were (1) concomitant other chronic liver diseases such as chronic viral hepatitis, Wilson's disease, autoimmune hepatitis, and primary biliary cirrhosis; (2) presence of cirrhotic complications (e.g., ascites) or evidence of HCC; (3) presence of other conditions causing secondary steatosis such as human immunodeficiency virus (HIV) infection; (4) known active malignancies or severe health conditions; (5) current significant alcohol misuse or history of alcohol consumption (≥ 30 g for men and ≥ 20 g for women). Laboratory analyses Serum biochemical parameters, including AST, alanine aminotransferase (ALT), alkaline phosphatase (ALP), albumin, total cholesterol, triglyceride, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, fasting plasma glucose (FPG), hemoglobin A1C (HbA1C) test and platelet count were measured using a conventional automated analyzer. The FIB-4 index was calculated using the following formula: age (years) × AST [U/L]/platelets [10 9 /L] × ALT [U/L]) 1/2 . The APRI score was calculated by the formula: AST [U/L] × 100/platelets [10 9 /L] × ALT [upper limit of normal] [U/L]). The NFS was based on the following calculation: −1.675 + 0.037 × Age (years) + 0.094 × BMI (kg/m 2 ) + 1.13 × impaired glucose tolerance/diabetes (yes = 1, no = 0) + 0.99 × (AST/ALT ratio) − 0.013 × platelets (×10 9 /L) − 0.66 × serum albumin (g/dl). Serum samples for M2BPGi collected from the patients were stored at -80 0 C until analysis. The biomarker was measured by lectin-antibody sandwich immunoassay using a fully automatic immune analyzer (HISCL-2000i, Sysmex, Hyogo, Japan). M2BPGi level was then calculated by the following equation: cutoff index (COI)=([M2BPGi]sample-[M2BPGi]negative controls)/[M2BPGi]positive controls - [M2BPGi]negative control]), as previously described [ 15 ]. Imaging studies for liver stiffness and steatosis Liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) were measured using VCTE (Echosens, Paris, France) with M-probe and XL-probe as appropriate. The procedure was based on at least 10 validated measurements with a success rate of over 60% and an interquartile range of less than 30%.[ 16 ] MRE and MRI-PDFF were conducted by the MRI system Philips Ingenia at 3.0 T (Philips Healthcare, Best, the Netherlands). The cut-off values for fibrosis ≥ F1 ≥ F2, ≥F3, and F4 by MRE measurement were 2.6, 3.0, 3.6, and 4.7 kPa, respectively, based on a systematic review and meta-analysis in MASLD [ 4 ]. For the measurement of MRI-PDFF, the cut-off values for diagnosing steatosis grades ≥ 1, ≥2, and ≥ 3 were 5.4%, 15.4%, and 20.4% respectively [ 5 ]. DNA extraction and SNP genotyping DNA was extracted from peripheral blood mononuclear cells (PBMCs) using the phenol-chloroform isoamyl alcohol technique, and its quantity and quality measurement was performed by a DeNovix™ UV-Vis spectrophotometer. The DNA samples were then stored at -80°C until analysis. For genotyping of the patatin-like phospholipase domain containing 3 (PNPLA3) rs738409, transmembrane 6 superfamily member 2 (TM6SF2) rs58542926 , and 17β-hydroxysteroid dehydrogenase 13 (HSD17B13) rs6834314 genes, allelic discrimination with TaqMan Probe Real-Time PCR Assays (ThermoFisher Scientific, US), and fluorescent signals (FAM and VIC) detection were applied as previously described [ 17 ]. To confirm the accuracy of interpretation, positive and negative controls were involved in each experiment, and the allelic discrimination plot was assessed by the QuantStudio™ 3 Real-Time PCR System (ThermoFisher Scientific, US). Statistical analyses Statistical analyses were performed using the IBM SPSS software version 23.0 (IBM, Chicago, IL, USA). Data were presented as percentages or mean ± standard deviation (SD). Comparisons between groups were assessed by analysis of variance and the Student's t-test or nonparametric Mann–Whitney U test when appropriate. Based on MRE as the reference, the diagnostic performances of VCTE, M2BPGi, FIB-4, APRI, and NFS were calculated using the receiver operator characteristics (ROC) curves. The area under the ROC (AUROC) was compared, and the cut-off values were determined to predict the fibrosis stage. The diagnostic performance was also determined regarding sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Spearman's rank test was applied to evaluate the correlation of serum M2BPGi with other parameters. Linear regression analysis was applied to test variables associated with M2BPGi. Univariate and multivariable analyses were evaluated using binary logistic regression to determine parameters related to F2-F4 fibrosis. P- value < 0.05 was considered as statistically significant. RESULTS Patient characteristics A total of 205 patients with MASLD were recruited in this study. Their clinical and laboratory characteristics are shown in Table 1 . The mean age of the patients was 57.0 ± 13.4 years, and 105(51.2%) were men. There were 65 (31.7%), 89 (43.4%), and 72 (35.1%) patients with a history of type 2 diabetes (T2DM), hypertension, and dyslipidemia, respectively. There were 150 (73.2%) patients classified as obese, and the average BMI of all patients was 27.8 ± 4.6 kg/m2. The mean values of MRE and MRI-PDFF were 2.9 ± 1.2 kPa and 12.2 ± 7.5%. Sixty-two (30.2%) patients had significant fibrosis (F2) or more, defined as MRE ≥ 3.0 kPa. Table 1 Clinical characteristics of patients in this study Characteristics MALSD (n = 205) Age (years) 57.0 ± 13.4 Gender (Male/Female) 51.2(51.2)/48.8(48.8) Body mass index (kg/m 2 ) (< 25.0/25.0-29.9/≥30.0) 55(26.8)/98(47.8)/52(25.4) Presence of type 2 diabetes 65(31.7) Presence of hypertension 89(43.4) Presence of dyslipidemia 72(35.1) Creatinine (mg/dL) 1.0 ± 1.2 Hemoglobin (g/dL) 13.8 ± 1.6 White blood count (10 3 /µL) 7.0 ± 2.0 Platelet count (10 3 /µL) 248.2 ± 72.1 Total bilirubin (mg/dL) 0.7 ± 0.3 Serum albumin (g/dL) 4.4 ± 0.3 Aspartate aminotransferase (IU/L) 28.5 ± 13.8 Alanine aminotransferase (IU/L) 36.4 ± 22.4 Alkaline phosphatase (IU/L) 73.6 ± 27.1 Magnetic resonance elastography (kPa) 2.9 ± 1.2 Proton density fat fraction (%) 12.2 ± 7.5 Liver fibrosis stage (F0-1/F2/F3/F4) 143(69.8)/21(10.2)/18(8.8)/23(11.2) Liver steatosis grade (S1/S2/S3) 140(68.3)/30(14.6)/35(17.1) Data are presented as n (%)/ mean ± SD. Diagnostic performance of VCTE and serum biomarkers The diagnostic performance of VCTE, M2BPGi, FIB-4, APRI, and NFS was calculated by the AUROCs using MRE as the reference method for defining fibrosis stages (Fig. 1 ). The AUROCs for VCTE in distinguishing significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4) were 0.95 [95% confident interval (CI); 0.91–0.98, P < 0.001], 0.94 (0.90–0.98, P < 0.001) and 0.98 (0.95-1.00, P < 0.001), respectively. The corresponding figures for M2BPGi were 0.85 (0.79–0.92, P < 0.001), 0.91 (0.87–0.96, P < 0.001), and 0.93 (0.88–10.98, P < 0.001), respectively. For FIB-4, the corresponding values were 0.81 (0.74–0.89, P < 0.001), 0.86 (0.79–0.93, P < 0.001), and 0.92 (0.88–0.97, P < 0.001), respectively. The corresponding figures for APRI were 0.79 (0.71–0.87, P < 0.001), 0.84 (0.76–0.92, P < 0.001), and 0.87 (0.78–10.96, P < 0.001), respectively. Regarding NFS, the corresponding values were 0.80 (0.72–0.87, P < 0.001), 0.83 (0.76–0.91, P < 0.001), and 0.91 (0.84–0.98, P < 0.001), respectively. The performance of M2BPGi in assessing fibrosis stages Figure 2 demonstrates the serum M2BPGi values for each fibrosis stage. There were significant differences in the mean levels of M2BPGi between the F0-F1 and F2-F4 stages (0.66 ± 0.40 vs 1.31 ± 0.76 COI, P < 0.001), between F0-F2 and F3-F4 (0.69 ± 0.44 vs 1.52 ± 0.73 COI, P < 0.001) and between F0-F3 and F4 (0.73 ± 0.45 vs 1.84 ± 0.82 COI, P < 0.001). Table 2 demonstrates the optimal cut-off value, sensitivity, specificity, PPV, and NPV of M2BPGi for each fibrosis stage. The AUROCs were 0.85, 0.91, and 0.93 for ≥ F2, ≥F3, and F4, respectively. The optimal cut-off values that best predicted the corresponding fibrosis stages were 0.82, 0.95, and 1.23, respectively. Overall, M2BPGi was considered a reliable single biomarker for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis. Table 2 M2BPGi values for the assessment of fibrosis stages Fibrosis Stage AUROCs COI Sensitivity (%) Specificity (%) PPV (%) NPV (%) Accuracy (%) ≥F2 0.85 0.82 74.2 79.0 60.5 87.6 77.6 ≥F3 0.91 0.95 80.5 86.6 60.0 94.7 85.4 F4 0.93 1.23 87.0 92.3 58.8 98.3 91.7 Abbreviations: AUROCs, area under the receiver operator curves; COI, Optimal cut-off; PPV, positive predictive value; NPV, negative predictive value. Relationship between M2BPGi levels and clinical parameters The relationship between serum M2BPGi levels and clinical parameters was also assessed. There was a positive correlation found between M2BPGi and age (r = 0.305, P < 0.001) and AST (r = 0.300, P < 0.001). In contrast, a negative correlation was found between M2BPGi and platelet counts (r=-0.274, P < 0.001) and albumin (r=-0.348, P < 0.001). There was no significant correlation between M2BPGi and other clinical parameters (sex, BMI, FPG, lipid profiles, creatinine, bilirubin, ALT, and ALP). Among fibrosis markers, M2BPGi level was positively correlated with MRE (r = 0.558, P < 0.001), VCTE (r = 0.391, P < 0.001), FIB-4 (r = 0.439, P < 0.001), APRI (r = 0.383, P < 0.001) and NFS (r = 0.483, P < 0.001). However, there was no correlation between M2BPGi level and MRI-PDFF (r=-0.116, P = 0.102). Performance of VCTE and serum biomarkers according to age and BMI We further determined the AUROCs for diagnosing significant fibrosis for VCTE and serum biomarkers compared with the reference method based on MRE in subgroups of patients according to age and BMI (Table 3 ). With VCTE, the lowest AUROC was found for obese patients with BMI ≥ 30 kg/m 2 . Similar patterns among these patients with obese class II were observed for FIB-4, APRI, and NFS. However, the AUROCs for M2BPGi were relatively consistent among subgroups regardless of the patient’s age and BMI. Table 3 AUROCs of fibrosis biomarkers for significant fibrosis (≥ F2) according to age and BMI VCTE M2BPGi FIB-4 APRI NFS Age < 60 0.95 (0.89-1.00) 0.85 (0.76–0.94) 0.78 (0.65–0.92) 0.74 (0.58–0.90) 0.88 (0.80–0.96) Age ≥ 60 0.94 (0.89–0.99) 0.84 (0.75–0.93) 0.80 (0.70–0.90) 0.80 (0.69–0.90) 0.70 (0.58–0.83) BMI < 25 0.98 (0.95-1.00) 0.85 (0.74–0.97) 0.86 (0.75–0.97) 0.76 (0.60–0.93) 0.85 (0.73–0.97) BMI = 25.0-29.9 0.95 (0.89-1.00) 0.86 (0.75–0.96) 0.84 (0.75–0.94) 0.83 (0.73–0.93) 0.82 (0.72–0.92) BMI ≥ 30 0.89 (0.77-1.00) 0.88 (0.77–0.99) 0.74 (0.56–0.92) 0.74 (0.54–0.93) 0.70 (0.53–0.88) AUROCs (95% confidence interval) Abbreviations: AUROCs, area under the receiver operator curves; BMI, body mass index; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase /platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score. Distributions of SNPs according to fibrosis stages In this cohort, the genotype frequencies of SNPs, including PNPLA3 rs738409, TM6SF2 rs58542926 , and HSD17B13 rs6834314 , were also investigated. The frequencies of PNPLA3 (CC/CG/GG) were 53(25.9%)/74 (36.1%)/78(38.0%), while the distributions of TM6SF2 (CC/CT/TT) were 156(76.1%)/40(19.5%)/9(4.4%), and HSD17B13 (AA/AG/GG) were 82(40.0%)/87(42.4%)/22(10.7%) and 14(6.8%) samples were unclassified. Patients with F2-F4 fibrosis had a higher frequency of PNPLA3 GG genotype than patients with F0-F1 (56.5% vs. 30.1%, P = 0.001]. However, the frequencies of TM6SF2 CT + TT in the F2-F4 vs. F0-F1 groups were not significant (27.4% vs. 22.4%, P = 0.477], which was similar to the distributions of HSD17B13 AG + GG between the corresponding groups (64.2% vs. 54.3%, P = 0.255]. Interestingly, patients harboring the PNPLA3 GG genotype had a significantly higher M2BPGi than those with PNPLA3 CC + CG genotypes (0.99 ± 0.81 vs. 0.77 ± 0.43, P = 0.029). Similar trends regarding the PNPLA3 GG vs. CC + CG genotypes were also observed among the imaging modalities and other serum fibrosis biomarkers; MRE (3.1 ± 1.3 vs. 2.7 ± 1.1 kPa, P = 0.023), VCTE (11.2 ± 11.5 vs. 9.3 ± 7.1 kPa, P = 0.188), FIB-4 (1.73 ± 1.64 vs. 1.30 ± 1.06, P = 0.044), APRI (0.40 ± 0.37 vs. 0.35 ± 0.32, P = 0.334), and NFS (-1.17 ± 1.88 vs. -1.76 ± 1.64, P = 0.045). Factors predicted significant fibrosis. We further investigated whether the parameters in our cohort were independently associated with significant fibrosis (≥ F2). The factors included age, gender, BMI, T2DM, HT, DLP, AST, ALT, platelet count, liver steatosis grade, PNPLA3 rs738409, TM6SF2 rs58542926 , and HSD17B13 rs6834314 as well as VCTE, M2BPGi, FIB-4, APRI, and NFS. In univariate analysis, parameters associated with significant fibrosis were age, the presence of T2DM and HT, AST, platelet, steatosis grade, PNPLA3 rs738409 , VCTE, and all serum fibrosis biomarkers. In multivariate analysis, only PNPLA3 rs738409 , VCTE, and serum M2BPGi were independently associated with significant fibrosis (Table 4 ). Table 4 Factors associated with significant fibrosis (≥ F2) Factors Category Univariate analysis Multivariate analysis OR (95%CI) p- value OR (95%CI) p- value Age (years) ≥ 60 vs. < 60 2.19 (1.19–4.03) 0.012* 1.07 (0.18–6.38) 0.943 Gender Male vs. Female 1.29 (0.71–2.35) 0.402 BMI (kg/m 2 ) ≥ 25 vs. < 25 1.08 (0.55–2.12) 0.828 Diabetes Yes vs. No 3.56 (1.89–6.69) < 0.001* 1.39 (0.33–5.74) 0.653 Hypertension Yes vs. No 3.15 (1.70–5.86) < 0.001* 1.52 (0.36–6.36) 0.568 Dyslipidemia Yes vs. No 0.83 (0.44–1.57) 0.572 Aspartate aminotransferase (IU/L) ≥ 40 vs. < 40 8.94 (3.51–22.75) < 0.001* 1.66 (0.09–30.36) 0.733 Alanine aminotransferase (IU/L) ≥ 40 vs. < 40 1.29 (0.68–2.43) 0.432 Platelet count (10 9 /L) < 150 vs. ≥ 150 6.10 (1.97–18.84) 0.002* 2.12 (0.13–34.64) 0.599 Liver steatosis grade S2 + S3 vs. S1 2.07 (1.03–4.18) 0.042* 1.09 (0.24–4.97) 0.909 PNPLA3 rs738409 GG vs. CC + CG 3.02 (1.63–5.58) < 0.001* 5.00 (1.15–21.81) 0.032* TM6SF2 rs58542926 CT + TT vs. CC 1.31 (0.66–2.59) 0.438 HSD17B13 rs6834314 AA vs. AG + GG 1.50 (0.78–2.89) 0.222 VCTE (kPa) ≥ 7.6 vs. < 7.6 28.39 (11.76–68.53) < 0.001* 21.78 (4.97–95.47) < 0.001* M2BPGi (COI) ≥ 0.82 vs. < 0.82 25.19 (10.49–60.47) < 0.001* 11.16 (2.55–48.95) 0.001* FIB-4 ≥ 1.30 vs. < 1.30 8.36 (4.20-16.62) < 0.001* 3.06 (0.49–19.22) 0.233 APRI ≥ 0.50 vs. < 0.50 16.95 (6.83–42.06) < 0.001* 0.87 (0.04–16.95) 0.925 NFS ≥ 1.455 vs. < 1.455 4.20 (2.03–8.70) < 0.001* 1.01 (0.23–4.52) 0.987 Data expressed as odds ratio (OR) and 95% confidence intervals (CI) * p -value < 0.05 Abbreviations: BMI, body mass index; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase /platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score; COI, Optimal cut-off. DISCUSSION MASLD, characterized by excess intrahepatic fat accumulation accompanied by metabolic dysregulation, is now considered a multisystem disease and a leading global health concern. As the degree of liver fibrosis is a major determining factor for disease outcomes and overall survival, it is essential to identify appropriate noninvasive biomarkers for accurate assessment in patients with MASLD, particularly differentiating F2-F4 from mild fibrosis stages [ 18 ]. In this study, our data demonstrated that serum M2BPGi performed well in distinguishing liver fibrosis stages F0-F1 vs. F2-F4. Although the AUROC of serum M2BPGi in detecting fibrosis ≥ F2 was inferior to that of VCTE, its diagnostic performance was superior to those of FIB-4, APRI, and NFS. Additionally, multivariate regression analysis showed that serum M2BPGi levels were independently associated with the fibrosis stage (F2-F4). In particular, the performance of M2BPGi was not influenced by the differing BMI as patients within the non-obese, obese class I, and obese class II groups exhibited similar AUROCs. Our data suggest that M2BPGi could be a suitable serum-based biomarker to differentiate early from significant fibrosis in patients with MASLD, regardless of different BMI. Although previous reports indicated that BMI was not an independent factor influencing M2BPGi levels [ 10 , 19 ], these data did not directly address the diagnostic accuracy of this biomarker compared to other simple serum algorithms across different BMIs, as demonstrated in our study. Current evidence has indicated that MRE is the best alternative to liver biopsy due to its excellent accuracy in diagnosing and stratifying liver fibrosis; thus, this imaging modality was selected as the reference in our study. Based on a pooled data analysis from individual participants with biopsy-proven MASLD, MRE displays a significantly higher accuracy than VCTE in detecting each fibrosis stage.[ 4 ] For example, MRE has AUROCs of 0.92, 0.93, and 0.94 for the prediction of ≥ F2, ≥F3, and F4 fibrosis stages, respectively, while the corresponding figures for VCTE are 0.87, 0.84, and 0.84, respectively. Furthermore, using MRE allows the visualization and assessment of the entire liver rather than sampling small hepatic regions as applied by VCTE. Operator skills might also affect the diagnostic success rate of VCTE, indicating its limitations, particularly in examining individuals with obesity. Additionally, a meta-analysis indicates that BMI minimally influences MRE cut-off values for assessing liver fibrosis stage [ 20 ]. On the contrary, it has been shown that VCTE with either M and XL probes displays lower diagnostic accuracy for F2–F4 and F3–F4 fibrosis in patients with BMI ≥ 30kg/m 2 [ 21 ]. In agreement with these observations, our results also demonstrated that the AUROCs for detecting F2–F4 fibrosis for VCTE were comparable between patients with BMI ≤ 24.9 and 25-29.9 kg/m 2 . Still, they noticeably declined in patients with BMI ≥ 30kg/m 2 . M2BPGi is a highly glycosylated form of the mac-2-binding protein (M2BP) secreted by hepatic stellate cells (HSCs), and its expression is closely related to HSC activation[ 22 ]. Thus, measuring this extracellular matrix protein in the serum could reflect the fibrogenic process irrespective of the etiologic factors of CLD[ 22 , 23 ]. Current evidence has shown that serum M2BPGi is a promising biomarker that correlates well with the severity of liver fibrosis in various CLDs, such as chronic viral hepatitis, primary biliary cholangitis, and autoimmune hepatitis [ 8 ]. Regarding MASLD, previous studies also showed that serum M2BPGi increased with the progression of fibrosis stage, particularly in significant fibrosis to cirrhosis [ 9 – 11 , 19 , 24 ]. For instance, a Japanese study of patients with biopsy-proven MASLD demonstrated a stepwise increase of serum M2BPGi values with progressive fibrosis, which displayed a superior AUROC for the diagnosis compared with the FIB-4, APRI, and NFS, among other biomarkers [ 10 ]. In that study, the AUROCs for detecting ≥ F2 and ≥ F3 fibrosis were 0.84 and 0.88, respectively. These data aligned with our results, indicating that serum M2BPGi could predict F2–F4 fibrosis in patients with MASLD, as the AUROCs in our study for ≥ F2, ≥F3 and F4 fibrosis were 0.85, 0.91, and 0.93, respectively. Moreover, a meta-analysis indicated that the overall AUROCs of serum M2BPGi for detecting significant fibrosis, advanced fibrosis, and cirrhosis from any etiologic factor of CLD were 0.79, 0.82 and 0.88, respectively [ 25 ]. A recent report also demonstrated that serum M2BPGi was precise in estimating severe liver stiffness assessed by MRE in patients with MASLD with a single cut-off value unrelated to patients’ age.[ 26 ] In this context, our study showed comparable AUROCs, demonstrating that age was not a confounder for M2BPGi measurement. We further explored the impact of BMI in association with the performance of each serum biomarker in predicting significant fibrosis. Our data displayed that the AUROCs for serum M2BPGi in differentiating F0-F1 vs. F2-F4 did not change with the BMI altered. In contrast, the AUROCs for the FIB-4, APRI, and NFS remarkably decreased in patients with BMI ≥ 30 kg/m 2 , which agreed with previous data indicating that these serum fibrosis models appear to be less accurate in patients with obesity [ 7 ]. These findings are considered necessary given that obesity is highly prevalent in patients with MASLD and could be a significant limitation of their use in clinical practice. In our study, for example, approximately 75% and 25% of patients were classified as obese in class I and II, respectively. Indeed, the proportion of patients with BMI ≥ 25 kg/m 2 (75%) was slightly higher than that of the Asian prevalence in a recent meta-analysis (66%) [ 27 ], probably due to the older mean age of patients in our cohort (57.0 vs. 52.1 years). Of note, serum M2BPGi levels might vary significantly depending on the underlying etiologies of CLD. For example, the cut-off levels of M2BPGi in patients with chronic viral hepatitis C are mostly higher than those of patients with MASLD in the same fibrosis stages.[ 28 ] In the meta-analysis of serum M2BPGi covered broad etiologies of CLD, the optimal cut-off levels for ≥ F2, ≥F3, and F4 fibrosis stages in MASLD were 0.90-1.00, 0.94–1.10, and 1.46–1.60 COI, respectively [ 28 ]. The corresponding cut-off values in our study were 0.82, 0.95, and 1.23 COI, respectively. In fact, the cut-off level for ≥ F2 fibrosis in our study was very similar to a previous Japanese report (0.83 COI) based on histopathological-proven MASLD [ 29 ]. Thus, it appeared that the cut-off threshold for each fibrosis stage might differ from study to study, which could probably be related to several factors, such as the populations in studied cohorts and the methods for defining the severity of liver fibrosis. Our study also highlighted the role of the PNPLA3 rs738409 genotype in association with significant fibrosis. In contrast, TM6SF2 rs58542926 and HSD17B13 rs6834314 did not relate to progressive fibrosis. The PNPLA3 polymorphism has been documented from genome-wide association studies as increased steatosis susceptibility and is currently the most robust genetic determinant in MASLD [ 30 ]. Indeed, PNPLA3 GG and CG accounted for most patients in our cohort (38.0% and 36.1%, respectively), reflecting the enrichment of the risk genotypes in individuals with progressive liver disease. These results agreed with previous data indicating an increased risk of this polymorphism on fibrosis progression in patients with MASLD [ 31 ]. Interestingly, our data also demonstrated that serum levels of M2BPGi in patients with the PNPLA3 GG genotype were significantly higher than those with the CC/CG genotypes. The mechanisms by which this polymorphism links to increased M2BPGi levels are unclear. However, it has been shown in vitro that the function of the PNPLA3 protein is necessary for HSC activation, and this variant could, in turn, promote the profibrogenic characteristics of HSCs, resulting in an increased risk of fibrosis progression [ 32 ]. As HSCs are the primary source of M2BP production, it is speculated that high M2BP expression might be linked to enhanced HSC activation in patients carrying the GG genotype. This study has some limitations. First, we evaluated the usefulness of the serum M2BPGi in a cross-sectional study. However, the role of this biomarker in monitoring natural history and predicting clinical outcomes of MASLD remains unknown. In this context, a previous study indicated that the biomarker could predict HCC development in patients with MASLD unrelated to its levels as a fibrosis biomarker [ 33 ]. Another limitation was this serum M2BPGi positively correlated, albeit weakly, with serum AST level. This finding was in line with a recent report demonstrating that quantitative measurement of serum M2BPGi might depend on liver inflammation and fibrosis, regardless of the etiologic causes of CLD [ 34 ]. Thus, falsely increased M2BPGi levels might be affected by the activity of liver inflammation in some patients with MASLD. Finally, the sample size of individuals with F2-F4 was relatively small, which could reflect the lesser distribution of significant fibrosis to cirrhosis typically found in real-life circumstances. For example, recent real-world data from a large European report revealed that the prevalence of significant fibrosis defined by FIB-4 was approximately 30–35%, similar to our cohort [ 35 ]. In conclusion, as the global incidence of MASLD increases, finding non-invasive biomarkers for accurately predicting the severity of liver fibrosis is becoming critically important. Although fibrosis stages based on simple serum algorithms are practical and not expensive, their results could be negatively affected by obesity. Our data demonstrated that measuring serum M2BPGi levels could accurately assess liver fibrosis in patients with MASLD, particularly in patients with F2-F4 fibrosis independently of BMI. Since obesity and metabolic disturbance are closely related to MASLD development, serum M2BPGi could be used as a promising fibrosis biomarker for MASLD in clinical settings. However, additional prospective studies with a larger sample size on the diagnostic role of serum M2BPGi, especially among obese patients with MASLD, are warranted. Declarations CONFLICT OF INTEREST STATEMENT PT has received research grants from Sysmex Asia Pacific. PA, KM, BB, and CB have nothing to disclose. ETHICS STATEMENTS The study was reviewed and approved by the Ethics Committee and the Institutional Review Board (IRB) at the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand (IRB 981 − 64), and the study was conducted following the Declaration of Helsinki. All patients provided written informed consent. Author Contribution P.A. and P.T. conceptualized the study design. K.M. performed the experiments. C.B. and P.T. supplied the specimens and clinical data. P.T. provided recommendations for data analysis. P.A. and K.M. carried out the statistical analysis. B.B. completed the data visualization. P.A. prepared the initial manuscript draft. P.T. reviewed, edited, and supervised the manuscript. All authors reviewed and approved the final version of the manuscript. ACKNOWLEDGMENTS This work was supported by the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B, grant number B36G660010) and the Center of Excellence in Hepatitis and Liver Cancer, Chulalongkorn University, and Sysmex Asia Pacific Pte Ltd. DATA AVAILABILITY STATEMENT The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Rinella ME, Lazarus JV, Ratziu Vet al.. A multisociety Delphi consensus statement on new fatty liver disease nomenclature J Hepatol. 2023;79:1542–1556. Simon TG, Roelstraete B, Hagstrom H, Sundstrom J, Ludvigsson JF. Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: results from a nationwide histology cohort Gut. 2022;71:1867–1875. Chalasani N, Younossi Z, Lavine JEet al.. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases Hepatology. 2018;67:328–357. Hsu C, Caussy C, Imajo Ket al.. Magnetic Resonance vs Transient Elastography Analysis of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Pooled Analysis of Individual Participants Clin Gastroenterol Hepatol. 2019;17:630–637 e638. Gu Q, Cen L, Lai Jet al.. A meta-analysis on the diagnostic performance of magnetic resonance imaging and transient elastography in nonalcoholic fatty liver disease Eur J Clin Invest. 2021;51:e13446. Wattacheril JJ, Abdelmalek MF, Lim JK, Sanyal AJ. AGA Clinical Practice Update on the Role of Noninvasive Biomarkers in the Evaluation and Management of Nonalcoholic Fatty Liver Disease: Expert Review Gastroenterology. 2023;165:1080–1088. Chan WK, Petta S, Noureddin M, Goh GBB, Wong VW. Diagnosis and non-invasive assessment of MASLD in type 2 diabetes and obesity Aliment Pharmacol Ther. 2024;59 Suppl 1:S23-S40. Shirabe K, Bekki Y, Gantumur Det al.. Mac-2 binding protein glycan isomer (M2BPGi) is a new serum biomarker for assessing liver fibrosis: more than a biomarker of liver fibrosis J Gastroenterol . 2018;53:819–826. Kamada Y, Ono M, Hyogo Het al.. Use of Mac-2 binding protein as a biomarker for nonalcoholic fatty liver disease diagnosis Hepatol Commun. 2017;1:780–791. Abe M, Miyake T, Kuno Aet al.. Association between Wisteria floribunda agglutinin-positive Mac-2 binding protein and the fibrosis stage of non-alcoholic fatty liver disease J Gastroenterol . 2015;50:776–784. Atsukawa M, Tsubota A, Okubo Tet al.. Serum Wisteria floribunda agglutinin-positive Mac-2 binding protein more reliably distinguishes liver fibrosis stages in non-alcoholic fatty liver disease than serum Mac-2 binding protein Hepatol Res . 2018;48:424–432. Higashioka M, Hirakawa Y, Hata Jet al.. Serum Mac-2 Binding Protein Glycosylation Isomer Concentrations Are Associated With Incidence of Type 2 Diabetes J Clin Endocrinol Metab. 2023;108:e425-e433. Trepo E, Valenti L. Update on NAFLD genetics: From new variants to the clinic J Hepatol. 2020;72:1196–1209. World Health O. International Association for the Study of Obesity,International Obesity Taskforce The Asia-Pacific perspective:redefining obesity and its treatment . 2000:15–21. Chuaypen N, Chittmittrapap S, Avihingsanon Aet al.. Liver fibrosis improvement assessed by magnetic resonance elastography and Mac-2-binding protein glycosylation isomer in patients with hepatitis C virus infection receiving direct-acting antivirals Hepatol Res . 2021;51:528–537. Boursier J, Zarski JP, de Ledinghen Vet al.. Determination of reliability criteria for liver stiffness evaluation by transient elastography Hepatology (Baltimore, Md . 2013;57:1182–1191. Raksayot M, Chuaypen N, Khlaiphuengsin Aet al.. Independent and additive effects of PNPLA3 and TM6SF2 polymorphisms on the development of non-B, non-C hepatocellular carcinoma J Gastroenterol . 2019;54:427–436. Hagstrom H, Nasr P, Ekstedt Met al.. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD J Hepatol . 2017;67:1265–1273. Lai LL, Chan WK, Sthaneshwar P, Nik Mustapha NR, Goh KL, Mahadeva S. Serum Wisteria floribunda agglutinin-positive Mac-2 binding protein in non-alcoholic fatty liver disease PLoS One. 2017;12:e0174982. Ajmera V, Kim BK, Yang Ket al.. Liver Stiffness on Magnetic Resonance Elastography and the MEFIB Index and Liver-Related Outcomes in Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Individual Participants Gastroenterology. 2022;163:1079–1089 e1075. Wong VW, Irles M, Wong GLet al.. Unified interpretation of liver stiffness measurement by M and XL probes in non-alcoholic fatty liver disease Gut. 2019;68:2057–2064. Yamada N, Katano T, Hirata Yet al.. Serum Mac-2 binding protein glycosylation isomer predicts the activation of hepatic stellate cells after liver transplantation J Gastroenterol Hepatol. 2019;34:418–424. Gantumur D, Harimoto N, Muranushi Ret al.. Hepatic stellate cell as a Mac-2-binding protein-producing cell in patients with liver fibrosis Hepatol Res . 2021;51:1058–1063. Seko Y, Takahashi H, Toyoda Het al.. Diagnostic accuracy of enhanced liver fibrosis test for nonalcoholic steatohepatitis-related fibrosis: Multicenter study Hepatol Res. 2023;53:312–321. Feng S, Wang Z, Zhao Y, Tao C. Wisteria floribunda agglutinin-positive Mac-2-binding protein as a diagnostic biomarker in liver cirrhosis: an updated meta-analysis Sci Rep. 2020;10:10582. Tamaki N, Higuchi M, Kurosaki Met al.. Wisteria floribunda agglutinin-positive mac-2 binding protein as an age-independent fibrosis marker in nonalcoholic fatty liver disease Sci Rep. 2019;9:10109. Kam LY, Huang DQ, Teng MLPet al.. Clinical Profiles of Asians with NAFLD: A Systematic Review and Meta-Analysis Dig Dis. 2022;40:734–744. Ito K, Murotani K, Nakade Yet al.. Serum Wisteria floribunda agglutinin-positive Mac-2-binding protein levels and liver fibrosis: A meta-analysis J Gastroenterol Hepatol. 2017;32:1922–1930. Ogawa Y, Honda Y, Kessoku Tet al.. Wisteria floribunda agglutinin-positive Mac-2-binding protein and type 4 collagen 7S: useful markers for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease J Gastroenterol Hepatol . 2018;33:1795–1803. Eslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: Clinical impact J Hepatol. 2018;68:268–279. Singal AG, Manjunath H, Yopp ACet al.. The effect of PNPLA3 on fibrosis progression and development of hepatocellular carcinoma: a meta-analysis Am J Gastroenterol. 2014;109:325–334. Bruschi FV, Claudel T, Tardelli Met al.. The PNPLA3 I148M variant modulates the fibrogenic phenotype of human hepatic stellate cells Hepatology. 2017;65:1875–1890. Kawanaka M, Tomiyama Y, Hyogo Het al.. Wisteria floribunda agglutinin-positive Mac-2 binding protein predicts the development of hepatocellular carcinoma in patients with non-alcoholic fatty liver disease Hepatol Res . 2018;48:521–528. Uojima H, Yamasaki K, Sugiyama Met al.. Quantitative measurements of M2BPGi depend on liver fibrosis and inflammation J Gastroenterol. 2024;59:598–608. Alexander M, Loomis AK, Fairburn-Beech Jet al.. Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease BMC Med . 2018;16:130. Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5473146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383171983,"identity":"69707ee7-6589-4c4e-b4a8-8890c55c99dd","order_by":0,"name":"Prooksa Ananchuensook","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Prooksa","middleName":"","lastName":"Ananchuensook","suffix":""},{"id":383171984,"identity":"15449f37-a500-42b4-b39e-124ceccd7d5b","order_by":1,"name":"Kamonchanok Moonlisarn","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Kamonchanok","middleName":"","lastName":"Moonlisarn","suffix":""},{"id":383171985,"identity":"2f673cbb-0387-47e2-ac0d-339c22c08d75","order_by":2,"name":"Bootsakorn Boonkaew","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Bootsakorn","middleName":"","lastName":"Boonkaew","suffix":""},{"id":383171986,"identity":"a0abdb15-c419-4f3d-a158-1909b2cd3cfb","order_by":3,"name":"Chalermarat Bunchorntavakul","email":"","orcid":"","institution":"Rajavithi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chalermarat","middleName":"","lastName":"Bunchorntavakul","suffix":""},{"id":383171987,"identity":"7c486059-48ba-4d3b-b527-fb1127bc21b2","order_by":4,"name":"Pisit Tangkijvanich","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3PMQrCMBTG8U8K7RLsGrDYK7zirtfoWCnoUsGx4KAgONW9x6g3iGTo0gM4WlwdHB0ETdXBqenokP8UwvuRF8Bk+sMmW0AAz6Gboyc+d6ydkFREgI/46W1Vto40Y4pMi7AzKZ1a3sDnxSAjccfYh78Xmr8wahZbFF5FxwxxsLH7UfsrFsOH8IQEgxWpxUhDnHND5qTI8YF1F4L3YhGFCUkG2YWov1TEg/w0W0qPymBnJxrilpdbmnLfzeNDfU1XvmtV7eQLfw52h3mTyWQyaXoB3+NA7oGEO44AAAAASUVORK5CYII=","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":true,"prefix":"","firstName":"Pisit","middleName":"","lastName":"Tangkijvanich","suffix":""}],"badges":[],"createdAt":"2024-11-18 06:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5473146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5473146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72300901,"identity":"866b9619-ea29-4619-afd5-5173433ebc3a","added_by":"auto","created_at":"2024-12-25 01:25:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244688,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AUROCs between VCTE and serum biomarkers (A) F0-F1 vs. F2-F4 (B) F0-F2 vs. F3-F4 (C) F0-F3 vs. F4. AUROCs, area under the receiver operator curves; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase /platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score; COI, Optimal cut-off.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5473146/v1/78aff87d2cf7e82d6b40404e.jpg"},{"id":72300083,"identity":"1b4e28b6-bf36-47e6-a46c-d016b0367340","added_by":"auto","created_at":"2024-12-25 01:17:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158582,"visible":true,"origin":"","legend":"\u003cp\u003eSerum M2BPGi values for each fibrosis stage. Data are presented as mean ± S.E.M. ***\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5473146/v1/1e671a9748eb52fc2d5d452e.jpg"},{"id":72301549,"identity":"7f1d273a-0563-49ae-9d5c-8422d5ddd019","added_by":"auto","created_at":"2024-12-25 01:41:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1138122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5473146/v1/a9daad1c-0dfe-428e-ac7b-63c7a4e6a935.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic performance of serum mac-2-binding protein glycosylation isomer as a fibrosis biomarker in non-obese and obese patients with MASLD","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease (CLD) worldwide, affecting approximately 30% of the global population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The continuing liver injury of this disorder can lead to more severe forms, including steatohepatitis with or without liver fibrosis, cirrhosis, and, ultimately, hepatocellular carcinoma (HCC)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Increasing evidence also indicates that progressive MASLD is an independent risk for developing cardiovascular disease, particularly in individuals with obesity and advanced liver disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, accurate assessment of disease severity, particularly the extent of liver fibrosis, is essential for the management of patients with MASLD. At present, liver biopsy is the gold standard for diagnosis; however, this method is invasive, prone to sampling errors, and has poor acceptability, which limits its use in routine clinical settings[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accordingly, several imaging has been applied to measure the severity of liver fibrosis, including vibration-controlled transient elastography (VCTE) and magnetic resonance elastography (MRE). Currently, MRE and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) are considered the most reliable non-invasive procedure for evaluating liver fibrosis and steatosis in patients with MASLD, respectively[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSerum-based biomarkers have also been developed as an alternative to liver biopsy for evaluating the severity of liver fibrosis in CLD. The widely used fibrosis models in patients with MASLD that include a combination of indirect biomarkers are the fibrosis-4 (FIB-4) index, aspartate aminotransferase (AST)/platelet ratio index (APRI), and NAFLD fibrosis score (NFS)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although these fibrosis models are easy to use in routine practice, the tests are not liver-specific, and their performance may be affected by some clinical parameters, particularly high body mass index (BMI) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recently, serum mac-2-binding protein glycosylation isomer (M2BPGi) measured by glycan-based immunoassays has emerged as an accurate liver-specific biomarker for assessing liver fibrosis stages resulting from various CLDs, particularly chronic hepatitis C and B [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This novel biomarker has also helped monitor disease progression and predict HCC development in patients with chronic viral hepatitis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, the clinical utility of M2BPGi as a fibrosis marker has been explored in patients with MASLD, and the results suggest that this novel biomarker is reliable for distinguishing liver fibrosis stages[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], as well as cirrhosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, a recent study showed that higher serum M2BPGi levels were significantly related to an increased risk of diabetes in Japanese individuals[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite these findings, the available data are still limited and have inconsistencies among reports that need further investigation.\u003c/p\u003e \u003cp\u003eThis cross-sectional study aimed to examine the clinical utility of serum M2BPGi in a well-characterized cohort of Thai patients with MASLD. Using MRE as the reference for assessing fibrosis staging, the diagnostic performance of serum M2BPGi was directly compared with VCTE and conventional fibrosis models, including FIB-4, APRI, and NFS. In this study, we also investigated various clinical characteristics that might impact the severity of fibrosis, including obesity, type 2 diabetes (T2DM), and single nucleotide polymorphisms (SNPs), which are important factors associated with the development and progression of MASLD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eBetween 2022 and 2024, 205 Thai patients diagnosed with MASLD were enrolled in this cross-sectional cohort at the King Chulalongkorn Memorial Hospital, Thailand. The Institute Ethics Committee approved the study (IRB No. 981\u0026thinsp;\u0026minus;\u0026thinsp;64), and the study was conducted following the Declaration of Helsinki and the principles of Good Clinical Practice. Written informed consent was obtained from patients, and their medical records were reviewed before enrollment. For all participants, anthropometric variables, including weight, height, and BMI, were measured. According to the Asian-BMI categorization, individuals with BMI of \u0026le;\u0026thinsp;24.9, 25-29.9, and \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as non-obese, obese class I, and obese class II, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inclusion criteria were patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years diagnosed with liver steatosis based on MRI-PDFF grade\u0026thinsp;\u0026ge;\u0026thinsp;1 (defined as MRI-PDFF\u0026thinsp;\u0026ge;\u0026thinsp;5.4%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Exclusion criteria were (1) concomitant other chronic liver diseases such as chronic viral hepatitis, Wilson's disease, autoimmune hepatitis, and primary biliary cirrhosis; (2) presence of cirrhotic complications (e.g., ascites) or evidence of HCC; (3) presence of other conditions causing secondary steatosis such as human immunodeficiency virus (HIV) infection; (4) known active malignancies or severe health conditions; (5) current significant alcohol misuse or history of alcohol consumption (\u0026ge;\u0026thinsp;30 g for men and \u0026ge;\u0026thinsp;20 g for women).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory analyses\u003c/h3\u003e\n\u003cp\u003eSerum biochemical parameters, including AST, alanine aminotransferase (ALT), alkaline phosphatase (ALP), albumin, total cholesterol, triglyceride, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, fasting plasma glucose (FPG), hemoglobin A1C (HbA1C) test and platelet count were measured using a conventional automated analyzer. The FIB-4 index was calculated using the following formula: age (years) \u0026times; AST [U/L]/platelets [10\u003csup\u003e9\u003c/sup\u003e/L] \u0026times; ALT [U/L])\u003csup\u003e1/2\u003c/sup\u003e. The APRI score was calculated by the formula: AST [U/L] \u0026times; 100/platelets [10\u003csup\u003e9\u003c/sup\u003e/L] \u0026times; ALT [upper limit of normal] [U/L]). The NFS was based on the following calculation: \u0026minus;1.675\u0026thinsp;+\u0026thinsp;0.037 \u0026times; Age (years)\u0026thinsp;+\u0026thinsp;0.094 \u0026times; BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;+\u0026thinsp;1.13 \u0026times; impaired glucose tolerance/diabetes (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;+\u0026thinsp;0.99 \u0026times; (AST/ALT ratio)\u0026thinsp;\u0026minus;\u0026thinsp;0.013 \u0026times; platelets (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u0026thinsp;\u0026minus;\u0026thinsp;0.66 \u0026times; serum albumin (g/dl).\u003c/p\u003e \u003cp\u003eSerum samples for M2BPGi collected from the patients were stored at -80\u003csup\u003e0\u003c/sup\u003eC until analysis. The biomarker was measured by lectin-antibody sandwich immunoassay using a fully automatic immune analyzer (HISCL-2000i, Sysmex, Hyogo, Japan). M2BPGi level was then calculated by the following equation: cutoff index (COI)=([M2BPGi]sample-[M2BPGi]negative controls)/[M2BPGi]positive controls - [M2BPGi]negative control]), as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImaging studies for liver stiffness and steatosis\u003c/h3\u003e\n\u003cp\u003eLiver stiffness measurement (LSM) and controlled attenuation parameter (CAP) were measured using VCTE (Echosens, Paris, France) with M-probe and XL-probe as appropriate. The procedure was based on at least 10 validated measurements with a success rate of over 60% and an interquartile range of less than 30%.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] MRE and MRI-PDFF were conducted by the MRI system Philips Ingenia at 3.0 T (Philips Healthcare, Best, the Netherlands). The cut-off values for fibrosis\u0026thinsp;\u0026ge;\u0026thinsp;F1\u0026thinsp;\u0026ge;\u0026thinsp;F2, \u0026ge;F3, and F4 by MRE measurement were 2.6, 3.0, 3.6, and 4.7 kPa, respectively, based on a systematic review and meta-analysis in MASLD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For the measurement of MRI-PDFF, the cut-off values for diagnosing steatosis grades\u0026thinsp;\u0026ge;\u0026thinsp;1, \u0026ge;2, and \u0026ge;\u0026thinsp;3 were 5.4%, 15.4%, and 20.4% respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDNA extraction and SNP genotyping\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from peripheral blood mononuclear cells (PBMCs) using the phenol-chloroform isoamyl alcohol technique, and its quantity and quality measurement was performed by a DeNovix\u0026trade; UV-Vis spectrophotometer. The DNA samples were then stored at -80\u0026deg;C until analysis. For genotyping of the \u003cem\u003epatatin-like phospholipase domain containing 3 (PNPLA3) rs738409, transmembrane 6 superfamily member 2 (TM6SF2) rs58542926\u003c/em\u003e, and \u003cem\u003e17β-hydroxysteroid dehydrogenase 13 (HSD17B13) rs6834314\u003c/em\u003e genes, allelic discrimination with TaqMan Probe Real-Time PCR Assays (ThermoFisher Scientific, US), and fluorescent signals (FAM and VIC) detection were applied as previously described [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To confirm the accuracy of interpretation, positive and negative controls were involved in each experiment, and the allelic discrimination plot was assessed by the QuantStudio\u0026trade; 3 Real-Time PCR System (ThermoFisher Scientific, US).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using the IBM SPSS software version 23.0 (IBM, Chicago, IL, USA). Data were presented as percentages or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Comparisons between groups were assessed by analysis of variance and the Student's t-test or nonparametric Mann\u0026ndash;Whitney U test when appropriate. Based on MRE as the reference, the diagnostic performances of VCTE, M2BPGi, FIB-4, APRI, and NFS were calculated using the receiver operator characteristics (ROC) curves. The area under the ROC (AUROC) was compared, and the cut-off values were determined to predict the fibrosis stage. The diagnostic performance was also determined regarding sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Spearman's rank test was applied to evaluate the correlation of serum M2BPGi with other parameters. Linear regression analysis was applied to test variables associated with M2BPGi. Univariate and multivariable analyses were evaluated using binary logistic regression to determine parameters related to F2-F4 fibrosis. \u003cem\u003eP-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 205 patients with MASLD were recruited in this study. Their clinical and laboratory characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the patients was 57.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years, and 105(51.2%) were men. There were 65 (31.7%), 89 (43.4%), and 72 (35.1%) patients with a history of type 2 diabetes (T2DM), hypertension, and dyslipidemia, respectively. There were 150 (73.2%) patients classified as obese, and the average BMI of all patients was 27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 kg/m2. The mean values of MRE and MRI-PDFF were 2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 kPa and 12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5%. Sixty-two (30.2%) patients had significant fibrosis (F2) or more, defined as MRE\u0026thinsp;\u0026ge;\u0026thinsp;3.0 kPa.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMALSD (n\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2(51.2)/48.8(48.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e) (\u0026lt;\u0026thinsp;25.0/25.0-29.9/\u0026ge;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(26.8)/98(47.8)/52(25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of type 2 diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(43.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of dyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(35.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood count (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248.2\u0026thinsp;\u0026plusmn;\u0026thinsp;72.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;27.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnetic resonance elastography (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProton density fat fraction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver fibrosis stage (F0-1/F2/F3/F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143(69.8)/21(10.2)/18(8.8)/23(11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver steatosis grade (S1/S2/S3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(68.3)/30(14.6)/35(17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eData are presented as n (%)/ mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnostic performance of VCTE and serum biomarkers\u003c/h3\u003e\n\u003cp\u003eThe diagnostic performance of VCTE, M2BPGi, FIB-4, APRI, and NFS was calculated by the AUROCs using MRE as the reference method for defining fibrosis stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The AUROCs for VCTE in distinguishing significant fibrosis (\u0026ge;\u0026thinsp;F2), advanced fibrosis (\u0026ge;\u0026thinsp;F3), and cirrhosis (F4) were 0.95 [95% confident interval (CI); 0.91\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], 0.94 (0.90\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.98 (0.95-1.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The corresponding figures for M2BPGi were 0.85 (0.79\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.91 (0.87\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.93 (0.88\u0026ndash;10.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. For FIB-4, the corresponding values were 0.81 (0.74\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.86 (0.79\u0026ndash;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.92 (0.88\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The corresponding figures for APRI were 0.79 (0.71\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.84 (0.76\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.87 (0.78\u0026ndash;10.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Regarding NFS, the corresponding values were 0.80 (0.72\u0026ndash;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.83 (0.76\u0026ndash;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.91 (0.84\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe performance of M2BPGi in assessing fibrosis stages\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the serum M2BPGi values for each fibrosis stage. There were significant differences in the mean levels of M2BPGi between the F0-F1 and F2-F4 stages (0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 vs 1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 COI, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), between F0-F2 and F3-F4 (0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 vs 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 COI, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between F0-F3 and F4 (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 vs 1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 COI, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the optimal cut-off value, sensitivity, specificity, PPV, and NPV of M2BPGi for each fibrosis stage. The AUROCs were 0.85, 0.91, and 0.93 for \u0026ge;\u0026thinsp;F2, \u0026ge;F3, and F4, respectively. The optimal cut-off values that best predicted the corresponding fibrosis stages were 0.82, 0.95, and 1.23, respectively. Overall, M2BPGi was considered a reliable single biomarker for diagnosing significant fibrosis, advanced fibrosis, and cirrhosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eM2BPGi values for the assessment of fibrosis stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrosis Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUROCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: AUROCs, area under the receiver operator curves; COI, Optimal cut-off; PPV, positive predictive value; NPV, negative predictive value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between M2BPGi levels and clinical parameters\u003c/h2\u003e \u003cp\u003eThe relationship between serum M2BPGi levels and clinical parameters was also assessed. There was a positive correlation found between M2BPGi and age (r\u0026thinsp;=\u0026thinsp;0.305, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AST (r\u0026thinsp;=\u0026thinsp;0.300, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, a negative correlation was found between M2BPGi and platelet counts (r=-0.274, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and albumin (r=-0.348, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no significant correlation between M2BPGi and other clinical parameters (sex, BMI, FPG, lipid profiles, creatinine, bilirubin, ALT, and ALP).\u003c/p\u003e \u003cp\u003eAmong fibrosis markers, M2BPGi level was positively correlated with MRE (r\u0026thinsp;=\u0026thinsp;0.558, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VCTE (r\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FIB-4 (r\u0026thinsp;=\u0026thinsp;0.439, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), APRI (r\u0026thinsp;=\u0026thinsp;0.383, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NFS (r\u0026thinsp;=\u0026thinsp;0.483, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no correlation between M2BPGi level and MRI-PDFF (r=-0.116, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.102).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of VCTE and serum biomarkers according to age and BMI\u003c/h2\u003e \u003cp\u003eWe further determined the AUROCs for diagnosing significant fibrosis for VCTE and serum biomarkers compared with the reference method based on MRE in subgroups of patients according to age and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). With VCTE, the lowest AUROC was found for obese patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Similar patterns among these patients with obese class II were observed for FIB-4, APRI, and NFS. However, the AUROCs for M2BPGi were relatively consistent among subgroups regardless of the patient\u0026rsquo;s age and BMI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAUROCs of fibrosis biomarkers for significant fibrosis (\u0026ge;\u0026thinsp;F2) according to age and BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVCTE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM2BPGi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFIB-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAPRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.89-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.76\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.65\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.58\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.80\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.89\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.75\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.70\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80 (0.69\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.58\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.95-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.74\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.75\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.60\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.73\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;25.0-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.89-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.75\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.75\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.73\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82 (0.72\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.77-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.77\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.56\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.54\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.53\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAUROCs (95% confidence interval)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: AUROCs, area under the receiver operator curves; BMI, body mass index; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase /platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDistributions of SNPs according to fibrosis stages\u003c/h2\u003e \u003cp\u003eIn this cohort, the genotype frequencies of SNPs, including \u003cem\u003ePNPLA3 rs738409, TM6SF2 rs58542926\u003c/em\u003e, and \u003cem\u003eHSD17B13 rs6834314\u003c/em\u003e, were also investigated. The frequencies of \u003cem\u003ePNPLA3\u003c/em\u003e (CC/CG/GG) were 53(25.9%)/74 (36.1%)/78(38.0%), while the distributions of \u003cem\u003eTM6SF2\u003c/em\u003e (CC/CT/TT) were 156(76.1%)/40(19.5%)/9(4.4%), and \u003cem\u003eHSD17B13\u003c/em\u003e (AA/AG/GG) were 82(40.0%)/87(42.4%)/22(10.7%) and 14(6.8%) samples were unclassified. Patients with F2-F4 fibrosis had a higher frequency of \u003cem\u003ePNPLA3\u003c/em\u003e GG genotype than patients with F0-F1 (56.5% vs. 30.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001]. However, the frequencies of \u003cem\u003eTM6SF2\u003c/em\u003e CT\u0026thinsp;+\u0026thinsp;TT in the F2-F4 vs. F0-F1 groups were not significant (27.4% vs. 22.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477], which was similar to the distributions of \u003cem\u003eHSD17B13\u003c/em\u003e AG\u0026thinsp;+\u0026thinsp;GG between the corresponding groups (64.2% vs. 54.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.255].\u003c/p\u003e \u003cp\u003eInterestingly, patients harboring \u003cem\u003ethe PNPLA3 GG\u003c/em\u003e genotype had a significantly higher M2BPGi than those with \u003cem\u003ePNPLA3\u003c/em\u003e CC\u0026thinsp;+\u0026thinsp;CG genotypes (0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 vs. 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). Similar trends regarding the \u003cem\u003ePNPLA3\u003c/em\u003e GG vs. CC\u0026thinsp;+\u0026thinsp;CG genotypes were also observed among the imaging modalities and other serum fibrosis biomarkers; MRE (3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 vs. 2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 kPa, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), VCTE (11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 vs. 9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 kPa, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.188), FIB-4 (1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 vs. 1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), APRI (0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 vs. 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.334), and NFS (-1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88 vs. -1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors predicted significant fibrosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe further investigated whether the parameters in our cohort were independently associated with significant fibrosis (\u0026ge;\u0026thinsp;F2). The factors included age, gender, BMI, T2DM, HT, DLP, AST, ALT, platelet count, liver steatosis grade, \u003cem\u003ePNPLA3 rs738409, TM6SF2 rs58542926\u003c/em\u003e, and \u003cem\u003eHSD17B13 rs6834314\u003c/em\u003e as well as VCTE, M2BPGi, FIB-4, APRI, and NFS. In univariate analysis, parameters associated with significant fibrosis were age, the presence of T2DM and HT, AST, platelet, steatosis grade, \u003cem\u003ePNPLA3 rs738409\u003c/em\u003e, VCTE, and all serum fibrosis biomarkers. In multivariate analysis, only \u003cem\u003ePNPLA3 rs738409\u003c/em\u003e, VCTE, and serum M2BPGi were independently associated with significant fibrosis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with significant fibrosis (\u0026ge;\u0026thinsp;F2)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 vs. \u0026lt; 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19 (1.19\u0026ndash;4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.18\u0026ndash;6.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale vs. Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29 (0.71\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 vs. \u0026lt; 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.55\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.56 (1.89\u0026ndash;6.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.39 (0.33\u0026ndash;5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15 (1.70\u0026ndash;5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52 (0.36\u0026ndash;6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes vs. No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.44\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40 vs. \u0026lt; 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.94 (3.51\u0026ndash;22.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.66 (0.09\u0026ndash;30.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40 vs. \u0026lt; 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29 (0.68\u0026ndash;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150 vs. \u0026ge; 150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.10 (1.97\u0026ndash;18.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.12 (0.13\u0026ndash;34.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver steatosis grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u0026thinsp;+\u0026thinsp;S3 vs. S1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07 (1.03\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.24\u0026ndash;4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePNPLA3\u003c/em\u003e rs738409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG vs. CC\u0026thinsp;+\u0026thinsp;CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.02 (1.63\u0026ndash;5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00 (1.15\u0026ndash;21.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTM6SF2\u003c/em\u003e rs58542926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT vs. CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31 (0.66\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e rs6834314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA vs. AG\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 (0.78\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVCTE (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7.6 vs. \u0026lt; 7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.39 (11.76\u0026ndash;68.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.78 (4.97\u0026ndash;95.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2BPGi (COI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.82 vs. \u0026lt; 0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.19 (10.49\u0026ndash;60.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.16 (2.55\u0026ndash;48.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.30 vs. \u0026lt; 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.36 (4.20-16.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.06 (0.49\u0026ndash;19.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.50 vs. \u0026lt; 0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.95 (6.83\u0026ndash;42.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.04\u0026ndash;16.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.455 vs. \u0026lt; 1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20 (2.03\u0026ndash;8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.23\u0026ndash;4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData expressed as odds ratio (OR) and 95% confidence intervals (CI) *\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: BMI, body mass index; VCTE, vibration-controlled transient elastography; M2BPGi, serum mac-2-binding protein glycosylation isomer; FIB-4, fibrosis-4 index; APRI, aspartate aminotransferase /platelet ratio index; NFS, non-alcoholic fatty liver disease fibrosis score; COI, Optimal cut-off.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMASLD, characterized by excess intrahepatic fat accumulation accompanied by metabolic dysregulation, is now considered a multisystem disease and a leading global health concern. As the degree of liver fibrosis is a major determining factor for disease outcomes and overall survival, it is essential to identify appropriate noninvasive biomarkers for accurate assessment in patients with MASLD, particularly differentiating F2-F4 from mild fibrosis stages [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, our data demonstrated that serum M2BPGi performed well in distinguishing liver fibrosis stages F0-F1 vs. F2-F4. Although the AUROC of serum M2BPGi in detecting fibrosis\u0026thinsp;\u0026ge;\u0026thinsp;F2 was inferior to that of VCTE, its diagnostic performance was superior to those of FIB-4, APRI, and NFS. Additionally, multivariate regression analysis showed that serum M2BPGi levels were independently associated with the fibrosis stage (F2-F4). In particular, the performance of M2BPGi was not influenced by the differing BMI as patients within the non-obese, obese class I, and obese class II groups exhibited similar AUROCs. Our data suggest that M2BPGi could be a suitable serum-based biomarker to differentiate early from significant fibrosis in patients with MASLD, regardless of different BMI. Although previous reports indicated that BMI was not an independent factor influencing M2BPGi levels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], these data did not directly address the diagnostic accuracy of this biomarker compared to other simple serum algorithms across different BMIs, as demonstrated in our study.\u003c/p\u003e \u003cp\u003eCurrent evidence has indicated that MRE is the best alternative to liver biopsy due to its excellent accuracy in diagnosing and stratifying liver fibrosis; thus, this imaging modality was selected as the reference in our study. Based on a pooled data analysis from individual participants with biopsy-proven MASLD, MRE displays a significantly higher accuracy than VCTE in detecting each fibrosis stage.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] For example, MRE has AUROCs of 0.92, 0.93, and 0.94 for the prediction of \u0026ge;\u0026thinsp;F2, \u0026ge;F3, and F4 fibrosis stages, respectively, while the corresponding figures for VCTE are 0.87, 0.84, and 0.84, respectively. Furthermore, using MRE allows the visualization and assessment of the entire liver rather than sampling small hepatic regions as applied by VCTE. Operator skills might also affect the diagnostic success rate of VCTE, indicating its limitations, particularly in examining individuals with obesity. Additionally, a meta-analysis indicates that BMI minimally influences MRE cut-off values for assessing liver fibrosis stage [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the contrary, it has been shown that VCTE with either M and XL probes displays lower diagnostic accuracy for F2\u0026ndash;F4 and F3\u0026ndash;F4 fibrosis in patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In agreement with these observations, our results also demonstrated that the AUROCs for detecting F2\u0026ndash;F4 fibrosis for VCTE were comparable between patients with BMI\u0026thinsp;\u0026le;\u0026thinsp;24.9 and 25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e. Still, they noticeably declined in patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eM2BPGi is a highly glycosylated form of the mac-2-binding protein (M2BP) secreted by hepatic stellate cells (HSCs), and its expression is closely related to HSC activation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, measuring this extracellular matrix protein in the serum could reflect the fibrogenic process irrespective of the etiologic factors of CLD[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Current evidence has shown that serum M2BPGi is a promising biomarker that correlates well with the severity of liver fibrosis in various CLDs, such as chronic viral hepatitis, primary biliary cholangitis, and autoimmune hepatitis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Regarding MASLD, previous studies also showed that serum M2BPGi increased with the progression of fibrosis stage, particularly in significant fibrosis to cirrhosis [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For instance, a Japanese study of patients with biopsy-proven MASLD demonstrated a stepwise increase of serum M2BPGi values with progressive fibrosis, which displayed a superior AUROC for the diagnosis compared with the FIB-4, APRI, and NFS, among other biomarkers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In that study, the AUROCs for detecting\u0026thinsp;\u0026ge;\u0026thinsp;F2 and \u0026ge;\u0026thinsp;F3 fibrosis were 0.84 and 0.88, respectively. These data aligned with our results, indicating that serum M2BPGi could predict F2\u0026ndash;F4 fibrosis in patients with MASLD, as the AUROCs in our study for \u0026ge;\u0026thinsp;F2, \u0026ge;F3 and F4 fibrosis were 0.85, 0.91, and 0.93, respectively. Moreover, a meta-analysis indicated that the overall AUROCs of serum M2BPGi for detecting significant fibrosis, advanced fibrosis, and cirrhosis from any etiologic factor of CLD were 0.79, 0.82 and 0.88, respectively [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA recent report also demonstrated that serum M2BPGi was precise in estimating severe liver stiffness assessed by MRE in patients with MASLD with a single cut-off value unrelated to patients\u0026rsquo; age.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] In this context, our study showed comparable AUROCs, demonstrating that age was not a confounder for M2BPGi measurement. We further explored the impact of BMI in association with the performance of each serum biomarker in predicting significant fibrosis. Our data displayed that the AUROCs for serum M2BPGi in differentiating F0-F1 vs. F2-F4 did not change with the BMI altered. In contrast, the AUROCs for the FIB-4, APRI, and NFS remarkably decreased in patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e, which agreed with previous data indicating that these serum fibrosis models appear to be less accurate in patients with obesity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings are considered necessary given that obesity is highly prevalent in patients with MASLD and could be a significant limitation of their use in clinical practice. In our study, for example, approximately 75% and 25% of patients were classified as obese in class I and II, respectively. Indeed, the proportion of patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e (75%) was slightly higher than that of the Asian prevalence in a recent meta-analysis (66%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], probably due to the older mean age of patients in our cohort (57.0 vs. 52.1 years).\u003c/p\u003e \u003cp\u003eOf note, serum M2BPGi levels might vary significantly depending on the underlying etiologies of CLD. For example, the cut-off levels of M2BPGi in patients with chronic viral hepatitis C are mostly higher than those of patients with MASLD in the same fibrosis stages.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] In the meta-analysis of serum M2BPGi covered broad etiologies of CLD, the optimal cut-off levels for \u0026ge;\u0026thinsp;F2, \u0026ge;F3, and F4 fibrosis stages in MASLD were 0.90-1.00, 0.94\u0026ndash;1.10, and 1.46\u0026ndash;1.60 COI, respectively [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The corresponding cut-off values in our study were 0.82, 0.95, and 1.23 COI, respectively. In fact, the cut-off level for \u0026ge;\u0026thinsp;F2 fibrosis in our study was very similar to a previous Japanese report (0.83 COI) based on histopathological-proven MASLD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, it appeared that the cut-off threshold for each fibrosis stage might differ from study to study, which could probably be related to several factors, such as the populations in studied cohorts and the methods for defining the severity of liver fibrosis.\u003c/p\u003e \u003cp\u003eOur study also highlighted the role of the \u003cem\u003ePNPLA3\u003c/em\u003e rs738409 genotype in association with significant fibrosis. In contrast, \u003cem\u003eTM6SF2 rs58542926\u003c/em\u003e and \u003cem\u003eHSD17B13\u003c/em\u003e rs6834314 did not relate to progressive fibrosis. The \u003cem\u003ePNPLA3\u003c/em\u003e polymorphism has been documented from genome-wide association studies as increased steatosis susceptibility and is currently the most robust genetic determinant in MASLD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Indeed, \u003cem\u003ePNPLA3\u003c/em\u003e GG and CG accounted for most patients in our cohort (38.0% and 36.1%, respectively), reflecting the enrichment of the risk genotypes in individuals with progressive liver disease. These results agreed with previous data indicating an increased risk of this polymorphism on fibrosis progression in patients with MASLD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interestingly, our data also demonstrated that serum levels of M2BPGi in patients with the \u003cem\u003ePNPLA3 GG\u003c/em\u003e genotype were significantly higher than those with the CC/CG genotypes. The mechanisms by which this polymorphism links to increased M2BPGi levels are unclear. However, it has been shown \u003cem\u003ein vitro\u003c/em\u003e that the function of the PNPLA3 protein is necessary for HSC activation, and this variant could, in turn, promote the profibrogenic characteristics of HSCs, resulting in an increased risk of fibrosis progression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As HSCs are the primary source of M2BP production, it is speculated that high M2BP expression might be linked to enhanced HSC activation in patients carrying the GG genotype.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we evaluated the usefulness of the serum M2BPGi in a cross-sectional study. However, the role of this biomarker in monitoring natural history and predicting clinical outcomes of MASLD remains unknown. In this context, a previous study indicated that the biomarker could predict HCC development in patients with MASLD unrelated to its levels as a fibrosis biomarker [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Another limitation was this serum M2BPGi positively correlated, albeit weakly, with serum AST level. This finding was in line with a recent report demonstrating that quantitative measurement of serum M2BPGi might depend on liver inflammation and fibrosis, regardless of the etiologic causes of CLD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, falsely increased M2BPGi levels might be affected by the activity of liver inflammation in some patients with MASLD. Finally, the sample size of individuals with F2-F4 was relatively small, which could reflect the lesser distribution of significant fibrosis to cirrhosis typically found in real-life circumstances. For example, recent real-world data from a large European report revealed that the prevalence of significant fibrosis defined by FIB-4 was approximately 30\u0026ndash;35%, similar to our cohort [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, as the global incidence of MASLD increases, finding non-invasive biomarkers for accurately predicting the severity of liver fibrosis is becoming critically important. Although fibrosis stages based on simple serum algorithms are practical and not expensive, their results could be negatively affected by obesity. Our data demonstrated that measuring serum M2BPGi levels could accurately assess liver fibrosis in patients with MASLD, particularly in patients with F2-F4 fibrosis independently of BMI. Since obesity and metabolic disturbance are closely related to MASLD development, serum M2BPGi could be used as a promising fibrosis biomarker for MASLD in clinical settings. However, additional prospective studies with a larger sample size on the diagnostic role of serum M2BPGi, especially among obese patients with MASLD, are warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e \u003cp\u003ePT has received research grants from Sysmex Asia Pacific. PA, KM, BB, and CB have nothing to disclose.\u003c/p\u003e \u003ch2\u003eETHICS STATEMENTS\u003c/h2\u003e \u003cp\u003e The study was reviewed and approved by the Ethics Committee and the Institutional Review Board (IRB) at the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand (IRB 981\u0026thinsp;\u0026minus;\u0026thinsp;64), and the study was conducted following the Declaration of Helsinki. All patients provided written informed consent.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.A. and P.T. conceptualized the study design. K.M. performed the experiments. C.B. and P.T. supplied the specimens and clinical data. P.T. provided recommendations for data analysis. P.A. and K.M. carried out the statistical analysis. B.B. completed the data visualization. P.A. prepared the initial manuscript draft. P.T. reviewed, edited, and supervised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThis work was supported by the NSRF via the Program Management Unit for Human Resources \u0026amp; Institutional Development, Research and Innovation (PMU-B, grant number B36G660010) and the Center of Excellence in Hepatitis and Liver Cancer, Chulalongkorn University, and Sysmex Asia Pacific Pte Ltd.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRinella ME, Lazarus JV, Ratziu Vet al.. A multisociety Delphi consensus statement on new fatty liver disease nomenclature J Hepatol. 2023;79:1542\u0026ndash;1556.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimon TG, Roelstraete B, Hagstrom H, Sundstrom J, Ludvigsson JF. Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: results from a nationwide histology cohort Gut. 2022;71:1867\u0026ndash;1875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalasani N, Younossi Z, Lavine JEet al.. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases Hepatology. 2018;67:328\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu C, Caussy C, Imajo Ket al.. Magnetic Resonance vs Transient Elastography Analysis of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Pooled Analysis of Individual Participants Clin Gastroenterol Hepatol. 2019;17:630\u0026ndash;637 e638.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Q, Cen L, Lai Jet al.. A meta-analysis on the diagnostic performance of magnetic resonance imaging and transient elastography in nonalcoholic fatty liver disease Eur J Clin Invest. 2021;51:e13446.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWattacheril JJ, Abdelmalek MF, Lim JK, Sanyal AJ. AGA Clinical Practice Update on the Role of Noninvasive Biomarkers in the Evaluation and Management of Nonalcoholic Fatty Liver Disease: Expert Review Gastroenterology. 2023;165:1080\u0026ndash;1088.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan WK, Petta S, Noureddin M, Goh GBB, Wong VW. Diagnosis and non-invasive assessment of MASLD in type 2 diabetes and obesity Aliment Pharmacol Ther. 2024;59 Suppl 1:S23-S40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirabe K, Bekki Y, Gantumur Det al.. Mac-2 binding protein glycan isomer (M2BPGi) is a new serum biomarker for assessing liver fibrosis: more than a biomarker of liver fibrosis \u003cem\u003eJ Gastroenterol\u003c/em\u003e. 2018;53:819\u0026ndash;826.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamada Y, Ono M, Hyogo Het al.. Use of Mac-2 binding protein as a biomarker for nonalcoholic fatty liver disease diagnosis Hepatol Commun. 2017;1:780\u0026ndash;791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbe M, Miyake T, Kuno Aet al.. Association between Wisteria floribunda agglutinin-positive Mac-2 binding protein and the fibrosis stage of non-alcoholic fatty liver disease \u003cem\u003eJ Gastroenterol\u003c/em\u003e. 2015;50:776\u0026ndash;784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtsukawa M, Tsubota A, Okubo Tet al.. Serum Wisteria floribunda agglutinin-positive Mac-2 binding protein more reliably distinguishes liver fibrosis stages in non-alcoholic fatty liver disease than serum Mac-2 binding protein \u003cem\u003eHepatol Res\u003c/em\u003e. 2018;48:424\u0026ndash;432.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHigashioka M, Hirakawa Y, Hata Jet al.. Serum Mac-2 Binding Protein Glycosylation Isomer Concentrations Are Associated With Incidence of Type 2 Diabetes J Clin Endocrinol Metab. 2023;108:e425-e433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrepo E, Valenti L. Update on NAFLD genetics: From new variants to the clinic J Hepatol. 2020;72:1196\u0026ndash;1209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health O. International Association for the Study of Obesity,International Obesity Taskforce \u003cem\u003eThe Asia-Pacific perspective:redefining obesity and its treatment\u003c/em\u003e. 2000:15\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuaypen N, Chittmittrapap S, Avihingsanon Aet al.. Liver fibrosis improvement assessed by magnetic resonance elastography and Mac-2-binding protein glycosylation isomer in patients with hepatitis C virus infection receiving direct-acting antivirals \u003cem\u003eHepatol Res\u003c/em\u003e. 2021;51:528\u0026ndash;537.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoursier J, Zarski JP, de Ledinghen Vet al.. Determination of reliability criteria for liver stiffness evaluation by transient elastography \u003cem\u003eHepatology (Baltimore, Md\u003c/em\u003e. 2013;57:1182\u0026ndash;1191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaksayot M, Chuaypen N, Khlaiphuengsin Aet al.. Independent and additive effects of PNPLA3 and TM6SF2 polymorphisms on the development of non-B, non-C hepatocellular carcinoma \u003cem\u003eJ Gastroenterol\u003c/em\u003e. 2019;54:427\u0026ndash;436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagstrom H, Nasr P, Ekstedt Met al.. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD \u003cem\u003eJ Hepatol\u003c/em\u003e. 2017;67:1265\u0026ndash;1273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai LL, Chan WK, Sthaneshwar P, Nik Mustapha NR, Goh KL, Mahadeva S. Serum Wisteria floribunda agglutinin-positive Mac-2 binding protein in non-alcoholic fatty liver disease PLoS One. 2017;12:e0174982.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjmera V, Kim BK, Yang Ket al.. Liver Stiffness on Magnetic Resonance Elastography and the MEFIB Index and Liver-Related Outcomes in Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Individual Participants Gastroenterology. 2022;163:1079\u0026ndash;1089 e1075.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong VW, Irles M, Wong GLet al.. Unified interpretation of liver stiffness measurement by M and XL probes in non-alcoholic fatty liver disease Gut. 2019;68:2057\u0026ndash;2064.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada N, Katano T, Hirata Yet al.. Serum Mac-2 binding protein glycosylation isomer predicts the activation of hepatic stellate cells after liver transplantation J Gastroenterol Hepatol. 2019;34:418\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGantumur D, Harimoto N, Muranushi Ret al.. Hepatic stellate cell as a Mac-2-binding protein-producing cell in patients with liver fibrosis \u003cem\u003eHepatol Res\u003c/em\u003e. 2021;51:1058\u0026ndash;1063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeko Y, Takahashi H, Toyoda Het al.. Diagnostic accuracy of enhanced liver fibrosis test for nonalcoholic steatohepatitis-related fibrosis: Multicenter study Hepatol Res. 2023;53:312\u0026ndash;321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng S, Wang Z, Zhao Y, Tao C. Wisteria floribunda agglutinin-positive Mac-2-binding protein as a diagnostic biomarker in liver cirrhosis: an updated meta-analysis Sci Rep. 2020;10:10582.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamaki N, Higuchi M, Kurosaki Met al.. Wisteria floribunda agglutinin-positive mac-2 binding protein as an age-independent fibrosis marker in nonalcoholic fatty liver disease Sci Rep. 2019;9:10109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKam LY, Huang DQ, Teng MLPet al.. Clinical Profiles of Asians with NAFLD: A Systematic Review and Meta-Analysis Dig Dis. 2022;40:734\u0026ndash;744.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto K, Murotani K, Nakade Yet al.. Serum Wisteria floribunda agglutinin-positive Mac-2-binding protein levels and liver fibrosis: A meta-analysis J Gastroenterol Hepatol. 2017;32:1922\u0026ndash;1930.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgawa Y, Honda Y, Kessoku Tet al.. Wisteria floribunda agglutinin-positive Mac-2-binding protein and type 4 collagen 7S: useful markers for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease \u003cem\u003eJ Gastroenterol Hepatol\u003c/em\u003e. 2018;33:1795\u0026ndash;1803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEslam M, Valenti L, Romeo S. Genetics and epigenetics of NAFLD and NASH: Clinical impact J Hepatol. 2018;68:268\u0026ndash;279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingal AG, Manjunath H, Yopp ACet al.. The effect of PNPLA3 on fibrosis progression and development of hepatocellular carcinoma: a meta-analysis Am J Gastroenterol. 2014;109:325\u0026ndash;334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruschi FV, Claudel T, Tardelli Met al.. The PNPLA3 I148M variant modulates the fibrogenic phenotype of human hepatic stellate cells Hepatology. 2017;65:1875\u0026ndash;1890.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawanaka M, Tomiyama Y, Hyogo Het al.. Wisteria floribunda agglutinin-positive Mac-2 binding protein predicts the development of hepatocellular carcinoma in patients with non-alcoholic fatty liver disease \u003cem\u003eHepatol Res\u003c/em\u003e. 2018;48:521\u0026ndash;528.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUojima H, Yamasaki K, Sugiyama Met al.. Quantitative measurements of M2BPGi depend on liver fibrosis and inflammation J Gastroenterol. 2024;59:598\u0026ndash;608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander M, Loomis AK, Fairburn-Beech Jet al.. Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease \u003cem\u003eBMC Med\u003c/em\u003e. 2018;16:130.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Liver fibrosis, Metabolic dysfunction-associated steatotic liver disease, M2BPGi, Magnetic resonance elastography, PNPLA3","lastPublishedDoi":"10.21203/rs.3.rs-5473146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5473146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eSerum mac-2-binding protein glycosylation isomer (M2BPGi) is a new biomarker for liver fibrosis. However, its performance in metabolic dysfunction-associated steatotic liver disease (MASLD), particularly in obese patients, remains to be explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study evaluated the role of M2BPGi in predicting liver fibrosis in 205 patients with MASLD using magnetic resonance elastography (MRE) as a reference. The performance of M2BPGi was compared to vibration-controlled transient elastography (VCTE), FIB-4, APRI, and NFS. The \u003cem\u003ePNPLA3, TM6SF2\u003c/em\u003e, and \u003cem\u003eHSD17B13\u003c/em\u003e polymorphisms were assessed by allelic discrimination assays.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe area under the ROC curves for VCTE, M2BPGi FIB-4, APRI, and NFS in differentiating significant fibrosis were 0.95 (95% CI; 0.91\u0026ndash;0.98), 0.85 (0.79\u0026ndash;0.92), 0.81 (0.74\u0026ndash;0.89), 0.79 (0.71\u0026ndash;0.87) and 0.80 (0.72\u0026ndash;0.87) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The optimal cut-off values of M2BPGi in predicting significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.95, and 1.23 cut-off index (COI), yielding satisfactory sensitivity, specificity, and diagnostic accuracy. The performance of M2BPGi was consistent among subgroups according to BMI, while the AUROCs of FIB-4, APRI, and NFS were remarkably declined in patients with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Patients with the \u003cem\u003ePNPLA3\u003c/em\u003e GG genotype had significantly higher M2BPGi than those with the CC/CG genotypes. In multivariate analysis, the independent factors associated with significant liver fibrosis were VCTE, M2BPGi, and \u003cem\u003ePNPLA3\u003c/em\u003e rs738409.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur data demonstrated that serum M2BPGi accurately assessed liver fibrosis across different BMI, indicating that this biomarker could apply to non-obese and obese patients with MASLD in clinical settings.\u003c/p\u003e","manuscriptTitle":"Diagnostic performance of serum mac-2-binding protein glycosylation isomer as a fibrosis biomarker in non-obese and obese patients with MASLD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 01:17:03","doi":"10.21203/rs.3.rs-5473146/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":"1d94382c-c358-4024-bc06-9d7f6d8a8a42","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-25T01:17:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-25 01:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5473146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5473146","identity":"rs-5473146","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}