F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.147153.1 Research Article Articles Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis: A Stepwise Diagnostic Analysis

[version 1; peer review: 2 approved with reservations]

 Bestari Muhammad Begawan Conceptualization Formal Analysis Methodology Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-6911-8213 a 1 Haryono Haryono Data Curation Investigation Methodology Validation 1 Wijaya Muhammad Palar Data Curation Project Administration Writing – Original Draft Preparation https://orcid.org/0000-0002-3990-9014 1 Girawan Dolvy Formal Analysis Investigation Methodology Supervision Validation 1 Agustanti Nenny Formal Analysis Investigation Methodology Supervision Validation 1 Nugraha Eka Surya Formal Analysis Investigation Methodology Validation 1 Internal Medicine, Padjadjaran University, Bandung, West Java, 40161, Indonesia begawan@unpad.ac.id

No competing interests were disclosed.

 16 4 2024 2024 13 280 23 3 2024 Copyright: © 2024 Bestari MB et al. 2024 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract* Background

The level of liver fibrosis is the basis for the treatment of chronic hepatitis B (CHB), and it is necessary to adapt non-invasive liver fibrosis modalities. We aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis.

 Methods

Cross-sectional data were taken from patients between October 2021 and August 2022. Demographic data, blood profile, liver function, and liver stiffness were measured in CHB patients over 18 years old, willing to take part in the research, and had complete data. APRI, FIB-4, and AAR were calculated using the well-known formulas. Serum M2BPGi-levels were converted into a cut-off index (COI). The patients were divided into low-risk (LR) and high-risk fibrosis (HR) groups. A cut-off for each predictor variable to differentiate between the LR and HR groups was determined. The obtained cut-off was assessed for its association with the grouping of liver elastography results. Models to diagnose the liver stiffness measurement (LSM) ≥8 kPa were created and compared through multivariate and ROC analyses.

 Results

The number of patients that met the inclusion and exclusion criteria was 143 (HR = 65, LR = 78). The cut-off for diagnosing LSM ≥8kPa was 0.311, 0.742, 0.635, and 1.434 for APRI, FIB-4, AAR, and M2BPGi, respectively. This cut-off was significantly associated with the results of the HR and LR groupings. A multivariate analysis found that FIB4, AAR, and M2BPGi added significantly to the model. Statistically, the most optimal use of M2BPGi was combined with FIB-4, with an AUC of 0.835.

 Conclusions

The optimal cut-off of M2BPGi for diagnosing high-risk liver fibrosis in this study was 1.434. M2BPGi should be used with FIB-4 as a diagnostic tool for diagnosing liver fibrosis, especially in the absence of a liver biopsy or elastography.

 M2BPGi chronic hepatitis B fibrosis diagnostic APRI FIB-4 AAR The author(s) declared that no grants were involved in supporting this work. Introduction

Globally, an estimated 296 million people, with 18 million in Southeast Asia, are projected to have a CHB infection by the World Health Organization (WHO). The annual rate of new infections is about 1.5 million. Hepatocellular carcinoma (HCC) and liver fibrosis caused by hepatitis B were responsible for 820,000 deaths in 2019. 1 In determining the severity of fibrosis or inflammation in the liver, a liver biopsy is the primary option, but it is an invasive procedure. The American Association for the Study of Liver Diseases (AASLD) has suggested several non-invasive techniques. 2

Mac-2-binding protein (M2BP) is a glycoprotein that, when changes are made to its N-glycan residue, forms M2BPGi. M2BPGi is produced by hepatic stellate cells (HSCs) and it induces profibrotic cytokine expression in Kupffer cells (KCs), namely Mac-2. Subsequently, Mac-2 activates HSCs and cause fibrogenesis. 3 , 4 M2BPGi has been widely used to predict liver fibrosis and cirrhosis in different chronic liver diseases. 4 – 17 In several previous studies, M2BPGi helped to diagnose liver fibrosis in a CHB population 5 , 10 and it could be used as a single predictor variable to diagnose liver fibrosis grade. 11 , 12 This marker could also complement and be used with other modalities. 13 , 14 The most accurate non-invasive methods to assess fibrosis are liver stiffness measurements (elastography), followed by several scoring methods such as the AST-to-platelet ratio index (APRI), the fibrosis index based on four factors (FIB-4), and the AST-to-ALT ratio (AAR). 2 , 18 – 20

Adapting non-invasive liver fibrosis modalities to each type of chronic liver disease and each region is necessary due to the heterogeneity of outcomes. A stepwise diagnostic analysis has yet to be conducted to determine whether M2BPGi should be utilized alone or in conjunction with modalities to assess liver fibrosis. Thus, we aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis.

 Methods Study design and patients

We obtained ethical approval from the Research Ethics Committee of Dr. Hasan Sadikin General Hospital Bandung (LB.02.01/X.6.5/299/2021) in order to protect the rights and welfare of research subjects, and to guarantee the study to be conducted according to ethical, legal, social implications, and other applicable regulations. This was a cross-sectional study; the subjects of this study were patients from the Gastroenterohepatology outpatient clinic, Hasan Sadikin General Hospital, Indonesia, between October 2021 and August 2022. All patients were older than 18 years and were positive for serum hepatitis B surface antigen (HBsAg) for at least six months. The criteria for exclusion were as follows: 1) acute hepatitis; 2) acute exacerbation of chronic hepatitis; 3) hepatitis C; 4) autoimmune liver disease; 5) hepatitis B co-infection with hepatitis C or HIV; 6) co-morbidities (type 2 diabetes mellitus, heart disease, chronic kidney disease, pulmonary tuberculosis, or cancer); 7) patients with a history of alcohol use (>20 grams of alcohol per day); 8) pregnant or breastfeeding woman; 9) body mass index (BMI) >27 kg/m 2; 10) hemoglobin <5 g/dL; and 11) pulmonary fibrosis, chronic pancreatitis, liver cancer, or pancreatic cancer.

There are several ways to determine the optimum sample size for a binary logistic regression analysis. First, using the rule of thumb method with N = number of independent variables multiplied by 10-50, the value for our sample size was between 30 and 150. Another method is by including the prevalence correction factor with the formula: N = 10 k/p, where k is the number of independent variables and p is the prevalence correction factor. 21 In our study subjects, the prevalence was 45%; thus, the number of efficient samples is 66.67 = 67 patients. Our study was conducted on 143 subjects.

 Clinical data and laboratory test

CHB patients who met the inclusion and exclusion criteria received information about the study. After obtaining written (informed) consent, their demographic data were collected. The research subjects underwent supporting examinations of liver elastography and routine laboratory investigations, including measurements of CBC, AST, ALT, PT, INR, and M2BPGi serum levels. All laboratory examinations were carried out in the clinical pathology laboratory of Hasan Sadikin General Hospital. The formulas used to calculate the non-invasive liver fibrosis scores are as follows 22 , 23 : APRI = [ AST ( U / L ) / ULN × 100 ] Platelet ( × 10 9 / L ) FIB − 4 = age ( years ) × AST ( U / L ) Platelet ( × 10 9 / L ) × ALT ( U / L ) AAR = AST ALT

Transient elastography

Transient elastography (TE) is a non-invasive examination to determine the level of liver fibrosis, with results given in the form of an LSM (in kPa). This examination was performed using the FibroScan ® 502 Series F00734 (Echosens, Paris, France) with the M or XL probe. Liver stiffness was expressed as the median value of more than ten valid examinations. The value of LSM can be trusted if the success rate is greater than 60% and the interquartile range (IQR) ratio to the median liver stiffness is below 30%. This examination was performed by a gastroenterohepatologist at Hasan Sadikin General Hospital.

 M2BPGi measurements

Serum M2BPGi levels were measured using the HISCL M2BPGi reagent kit (Sysmex, Hyogo, Japan, Catalogue number: CB090850) (Supplier: PT. Saba Indomedika) and an automatic immune analyzer HISCL 800 (Sysmex, Hyogo). In total, from the M2BPGi reagent kit, 50 μL of R1 reagent, 30 μL of R2 reagent, 600-4200 μL of washing solution, 100 μL of R3 reagent, 50 μL of R4 reagent, and 100 μL of R5 reagent, were used. The results of the M2BPGi serum level were expressed as a cut-off index (COI). The COI was calculated using the following formula 24 : COI = ( [ M 2 BPGi ] sample – [ M 2 BPGi ] negative control ) ( [ M 2 BPGi ] positive control [ M 2 BPGi ] negative control )

Statistical analysis

First, a normality test was conducted to determine the subsequent statistical analysis procedure. The patient characteristic data are presented in Table 1; those with a normal distribution are expressed as the mean and standard deviation, while those that are not normally distributed are presented as the median and minimum–maximum values. The results of the transient elastography were used to classify the subjects into two groups: high-risk fibrosis (HR) (LSM ≥8 kPa) and low-risk fibrosis (LR) (LSM < 8 kPa). 25

Baseline characteristics of 143 study participants.
Characteristic Value
Total number of patients (n) n = 143
Age (years) † 42 (20-76)
Sex (male/female) * 77/66
AST (IU/L) † 26 (12-143)
ALT (IU/L) † 35 (9-279)
Platelet (×10 9/L) ‡ 237.2 (±79.25)
M2BPGi (COI) † 1.04 (0.22-20)
APRI † 0.31 (0.1-2.70)
FIB-4 † 0.75 (0.19-8.43)
AAR † 0.73 (0.31-1.88)
Liver elastography (kPa) † 7.4 (2.5-70.6)

AST: aspartate aminotransferase; ALT: alanine aminotransferase; M2BPGi: Mac-2-binding protein glycosylation isomer; APRI: AST-to-platelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

Value is given as a proportion.

Values are the medians with ranges in parentheses.

Values are the means with standard deviations in parentheses.

Each predictor variable underwent an ROC analysis using SPSS version 20 (IBM Corp. 2011. Armonk, NY, USA, RRID:SCR_00286) to develop cut-off values based on Youden’s index to balance sensitivity and specificity in diagnosing high-risk fibrosis. STATA 17 (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC; RRID: SCR_012763) was used for the stepwise diagnostic analysis. Each predictor variable was grouped based on their cut-off value. The association between each grouped predictor variable and the HR and LR groupings was assessed using a chi-square analysis. Diagnostic models were created and subjected to stepwise logistic regression. The receiver operating characteristic (ROC) analysis determined each model’s accuracy. Then, ROC analysis comparisons were carried out between each model to evaluate whether there was a significant difference in adding predictors. A two-tailed p < 0.05 was considered statistically significant.

 Results Subject characteristics

The total number of CHB patients at Hasan Sadikin General Hospital during the research period was 157. All patients were entered into the Hasan Sadikin Chronic Hepatitis B Registry. After all examinations and data collection, 14 patients were excluded because of incomplete data. The number of patients who met the inclusion and exclusion criteria was 143. The patients’ baseline characteristics are summarized in Table 1. Based on the transient elastography results, the patients were split into two groups: high-risk fibrosis (HR) (n = 65) and low-risk fibrosis (LR) (n = 78) groups. The flowchart for the selection of the participants is shown in Figure 1.

Flowchart for selection of study subjects. Bivariate analysis for high-risk fibrosis

Each predictor underwent an ROC analysis to obtain the optimal cut-off point. It was found that the best cutoff values for M2BPGi, APRI, FIB-4, and AAR were 1.434, 0.311, 0.742, and 0.635, respectively. The results of the bivariate analysis between the predictor variables for the HR and LR groups are shown in Table 2. Based on the liver elastography results, M2BPGi, APRI, FIB-4, and AAR were significantly associated with the HR and LR groupings.

Bivariate analysis for high-risk fibrosis (LSM ≥8 kPa) amongst 143 patients.
Variable LSM < 8 (n = 78) LSM ≥ 8 (n = 65) p-value
APRI 53 (68%) 18 (28%) <0.001
25 (32%) 47 (72%)
FIB4 57 (73%) 12 (18%) <0.001
21 (27%) 53 (82%)
AAR 33 (42%) 12 (18%) 0.002
45 (58%) 53 (82%)
M2BPGi 70 (90%) 28 (43%) <0.001
8 (10%) 37 (57%)

p-value from the chi-square test. LSM: liver stiffness measurement; M2BPGi Mac-2-binding protein glycosylation isomer; APRI: AST-to-platelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

 Stepwise multivariate analysis for high-risk fibrosis

Through a Spearman analysis, APRI with FIB-4 had the strongest correlation, with a correlation coefficient of 0.78. Based on the correlation coefficient data, it was decided to exclude APRI from the diagnostic model to avoid violating the multicollinearity rule. The models used different combinations of M2BPGi, FIB-4, and AAR. Model 1 consisted of M2BPGi only, followed by the addition of other predictors one by one to produce Model 2, which consisted of M2BPGi and AAR; Model 3, which consisted of M2BPGi and FIB-4; and Model 4, which consisted of M2BPGi, FIB-4, and AAR. All variables were analyzed to assess the association. The results of the multivariate analysis are shown in Table 3. All models were statistically significant (p-value < 0.001) compared to the intercept-only model and had a good pseudo-R2 fit at 0.2, 0.23, 0.31, and 0.33 for Models 1, 2, 3, and 4, respectively.

Multivariate analysis for predictors of HR group (LSM ≥8kPa) amongst 143 patients.
Predictor variable Adjusted OR 95% CI p-value
Model 1 M2BPGi ≥1.434 11.562 4.791-27.903 <0.001
Model 2 M2BPGi ≥1.434 11.424 4.609-28.316 <0.001
AAR ≥0.635 3.162 1.3-7.6 0.011
Model 3 M2BPGi ≥1.434 6.297 2.409-16.461 <0.001
FIB-4 ≥0.742 7.44 3.161-17.511 <0.001
Model 4 M2BPGi ≥1.434 6.476 2.389-17.558 <0.001
FIB-4 ≥0.742 6.873 2.882-16.388 <0.001
AAR ≥0.635 2.685 1.048-6.88 0.04

The p-value is the result of the binary logistic regression analysis. M2BPGi: Mac-2-binding protein glycosylation isomer; APRI: AST-to-platelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

 Model’s accuracy for diagnosing high-risk fibrosis

The ROC analysis’s area under the curve (AUC) evaluates each model’s accuracy ( Table 4). The model utilizing M2BPGi with the best accuracy was in combination with FIB-4 and AAR. The abilities of each model were compared, and the results are shown in Table 5.

ROC analysis of the models for diagnosing high-risk fibrosis (LSM ≥8 kPa) amongst 143 patients.
Model AUC 95% CI
Model 1 0.733 0.664-0.803
Model 2 0.782 0.71-0.854
Model 3 0.835 0.77-0.9
Model 4 0.852 0.788-0.916

AUC: area under curve; Model 1: M2BPGi; Model 2: M2BPGi and AAR; Model 3: M2BPGi and FIB-4; Model 4: M2BPGI, FIB4, and AAR; M2BPGi: Mac-2-binding protein glycosylation isomer; APRI: AST-to-platelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

 ROC comparison of the models for diagnosing high-risk fibrosis (LSM ≥8 kPa) amongst 143 patients.
Model AUC difference p-value
Model 1 vs. Model 2 vs. Model 3 vs. Model 4 overall differences 0.0019 *
Model 1 vs. Model 2 4.9% (post hoc) 0.017 *
Model 1 vs. Model 3 10.2% (post hoc) 0.0007 *
Model 1 vs. Model 4 11.9% (post hoc) 0.0001 *
Model 2 vs. Model 3 5.3% (post hoc) 0.1013
Model 2 vs. Model 4 7% (post hoc) 0.01 *
Model 3 vs. Model 4 1.7% (post hoc) 0.1239

The p-value is the result of the ROC comparison.

Model 1: M2BPGi; Model 2: M2BPGi and AAR; Model 3: M2BPGi and FIB-4; Model 4: M2BPGI, FIB4, and AAR; M2BPGi: Mac-2-binding protein glycosylation isomer; APRI: AST-to-platelet ratio index; FIB4: fibrosis index based on 4 factors; AAR: AST-to-ALT ratio.

Statistically significant difference.

 Discussion

Non-invasive methods for assessing liver fibrosis are currently being developed. It has been demonstrated that M2BPGi is a good biomarker for evaluating liver fibrosis. 4 – 17 A stepwise diagnostic analysis has not yet been conducted to determine the value of M2BPGi in assessing liver fibrosis. Here, we aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis.

Currently, a liver biopsy is the gold standard for assessing liver inflammation and fibrosis. However, biopsy is an invasive procedure and has several risks, such as bleeding, hematoma, and mild discomfort to severe pain; hence, it is not suitable for routine use. 26 There are various non-invasive methods to assess liver fibrosis. Liver elastography is the primary alternative for assessing liver fibrosis. To determine liver stiffness in this study, we used the FibroScan ® tool and determined the fibrosis class based on the EASL recommendations in FibroScan ®. In the outside liver clinic settings, the results of LSM are divided into ≥8 kPa for high-risk fibrosis and <8 kPa for low-risk. 25 Operator skills and experience, the selection of appropriate probes, and special conditions such as obesity are challenges in applying the liver elastography method.

The ROC curve analysis obtained a new cut-off for M2BPGi, APRI, FIB-4, and AAR ( Table 2). This optimal cut-off is not too different from those of Zou’s study 14 for diagnosing METAVIR grade ≥F2 with an APRI cut-off of 0.51, a FIB-4 cut-off of 0.92, and an AAR cut-off of 0.55. In a study to distinguish LSM ≥7 kPa in Vietnam, cut-offs of 0.5 and 1.8 were obtained for APRI and FIB-4, respectively. 11 In patients with liver elastography results ≥9 kPa in Egypt, the cut-offs were at 0.256, 0.74, and 0.8 for APRI, FIB-4, and AAR, respectively. 27 For predicting Knodell histologic activity index (HAI) ≥F2 results, APRI and FIB-4 had the best cut-offs at 0.9 and 0.35, respectively. 10 The research regarding the ability of non-invasive liver fibrosis modalities can be broadly divided into two areas, using liver biopsy or TE as the comparison. We are among those who used TE results as the gold standard. While TE was rarely used, Bui et al. found an M2BPGi cut-off of 0.79 for diagnosing LSM ≥7 kPa. 11 The cut-off of M2BPGi that we obtained to diagnose LSM ≥8 kPa was 1.434. Our cut-off is quite close to the previous cut-off for diagnosing significant liver fibrosis using biopsy as the gold standard by Yeh et al. 13 and Ishii et al. 5 at 1.345 and 1.4, respectively.

Our M2BPGi cut-off is greater than 1, which we suspect is due to aging. There was a significant difference between the ages of the HR and LR groups in our study. In several previous studies, the cut-off for M2BPGi was around COI 1. 5 , 9 , 10 , 12 , 14 Cheng et al. found that aging increases M2BPGi levels in healthy patients. 17 This finding may explain why our cut-off results were more than one. However, the effects aging on M2BPGi levels require further research. Based on the cut-off found, the four predictor variables were divided into categorical data; all predictors were associated with the categorical classification of liver elastography with a cut-off of 8 kPa ( Table 2).

The highest bivariate correlation analysis results were found between APRI and FIB-4; this was based on the fact that both indices consist of AST and platelet counts as the primary variables. Four models were developed involving M2BPGi, FIB-4, and AAR to assess the performance of M2BPGi on its own. M2BPGi, FIB-4, and AAR (Model 4) were able to predict the HR group. Patients with any result equal to or more than the M2BPGi, FIB-4, and AAR cut-offs will result in a probability of 6.476, 6.873, and 2.685, respectively, for classification into the HR group. If used alone, each COI M2BPGi value ≥1.434 will produce a probability of 11.562 ( Table 4).

Model 4 had the best diagnostic ability with an AUC of 0.852 ( Table 4). The use of M2BPGi as a single modality (Model 1) in diagnosing high-risk liver fibrosis was quite good, with an AUC of 0.733. In diagnosing liver biopsy at ≥F2, Yeh et al. 13 and Zou et al. 14 obtained an AUC for M2BPGi of 0.78 and 0.753, respectively. Bui et al. found an AUC of 0.77 for diagnosing LSM ≥7 kPa. 11 Ichikawa et al., in determining F≥2 based on the revised Inuyama classification, found that M2BPGi had an AUC of 0.713. 9 In the group of patients with treatment-naïve CHB to diagnose portal fibrosis without septal involvement (F≥2), an AUC of 0.77 was obtained by Ishii et al. 5

There were significant differences in the diagnostic abilities of the models (p = 0.0019); a post hoc analysis was performed to determine whether the addition of a modality was statistically significant ( Table 5). In Model 2, M2BPGi was coupled with AAR, which increased the diagnostic capability compared to the M2BPGi-only model by around 4.9%. Model 3, which consisted of M2BPGi and FIB-4, was statistically the best model, with an AUC of 0.835. The addition of FIB-4 increased the AUC to 10.2%. Adding AAR to Model 3 to form Model 4 increased the diagnostic capability by 1.7% but this was not statistically significant. Therefore, Model 3 was the most efficient diagnostic model. M2BPGi can be used efficiently, and its application should be combined with FIB-4 to diagnose high-risk liver fibrosis.

In some earlier studies, the use of M2BPGi combined with other variables was proposed, as was performed by Yeh et al. 13 and Zou et al. 14 Yeh et al. supported using M2BPGi in models involving age and platelet counts to increase the specificity in the prediction of advanced fibrosis. 13 Zou et al. suggested measuring M2BPGi levels as a complementary method for liver biopsies and elastography. 14 However, both studies showed that the AUC value of M2BPGi was always superior to other scoring methods. 13 , 14 Bui et al. found that M2BPGi and APRI had the same AUC value (0.77) as a single indicator. However, in combining M2BPGi with other modalities, they only formed a single model to predict significant fibrosis using M2BPGi and APRI. By adding APRI to M2BPGi, the accuracy in detecting LSM ≥7 kPa was increased. Based on the high coefficient correlation between M2BPGi and liver elastography results, the paper stated that M2BPGi could be used as an alternative liver fibrosis test in CHB patients, especially in settings with limited resources. 11 Mak et al. performed an ROC analysis and created two predictive models for F3/F4 biopsy results. M2BPGi always produced statistically significant correlation in both models, while APRI, FIB-4, and AAR did not. They stated that M2BPGi was a potential marker for easily diagnosing F3/F4 without the need for a liver biopsy. 12

This study of serum M2BPGi levels in CHB patients aimed to aid its diagnostic application outside of liver clinic settings. The use of M2BPGi levels as part of a non-invasive method for diagnosing liver fibrosis outcomes based on liver elastography values was compared with several scoring methods. In our study, M2BPGi showed good diagnostic performance when used alone. However, our stepwise diagnostic analysis found that M2BPGi had a better result in diagnosing liver fibrosis when combined with FIB-4.

Since this is the first comprehensive statistical analysis performed on M2BPGi utilization, future studies should examine the use of serum M2BPGi levels by applying the stepwise diagnostic analysis method. In conclusion, after considering all the statistical comparisons and the stepwise diagnostic analysis, we believe that M2BPGi should be used with FIB-4 as a diagnostic tool for liver fibrosis, especially in the absence of liver biopsies or elastography.

Ethical considerations

The Research Ethics Committee of Dr. Hasan Sadikin General Hospital Bandung granted permission on 20 October 2021 to conduct this research (LB.02.01/X.6.5/299/2021). After obtaining written (informed) consent, their demographic data were collected.

 Data availability

Figshare: CHB for stepwise M2BPGi, https://doi.org/10.6084/m9.figshare.24971649.v1. 28

This project contains the following underlying data:

CHB for stepwise M2BPGi.sav (the raw data for the study)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Extended data

Figshare: Participant information sheets, https://doi.org/10.6084/m9.figshare.25383106.v1. 29

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Figshare: Informed consent form for sample, https://doi.org/10.6084/m9.figshare.25383157.v1. 30

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Reporting guidelines

STARD checklist for ‘Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis: A Stepwise Diagnostic Analysis’. https://doi.org/10.6084/m9.figshare.25383313.v1

Acknowledgements

The authors sincerely thank the dedicated staff at the Gastroenterohepatology Outpatient Clinic and the Clinical Pathology Laboratory of Hasan Sadikin General Hospital for their invaluable contributions and unwavering support in this research journey.

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Med. 2015;8:20876–20882. 26885013 Sha FR Pk MU Abuelezz NZ : Investigating the Efficiency of APRI, FIB-4, AAR and AARPRI as Noninvasive Markers for Predicting Hepatic Fibrosis in Chronic Hepatitis B Patients in Bangladesh. Open Microbiol. J. 2019;13:34–40. 10.2174/1874285801913010034 Peduzi P Concato J Kemper E : Practical methods for public health practitioners. J. Clin. Epidemiol. 1996;49:1373–1379. 15026108 10.1016/s0895-4356(96)00236-3 Wai CT Greenson JK Fontana RJ : A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38:518–526. 12883497 10.1053/jhep.2003.50346 Sterling RK Lissen E Clumeck N : Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43:1317–1325. 16729309 10.1002/hep.21178 Kuno A Sato T Shimazaki H : Reconstruction of a robust glycodiagnostic agent supported by multiple lectin-assisted glycan profiling. Proteomics Clin. Appl. 2013;7:642–647. 23640794 10.1002/prca.201300010 Berzigotti A Tsochatzis E Boursier J : EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J. Hepatol. 2021;75:659–689. 34166721 10.1016/j.jhep.2021.05.025 Maria Streba L Florin E Teodor C : Risks and Benefits of Liver Biopsy in Focal Liver Disease. Liver Biopsy - Indications, Procedures, Results. InTech;2012. 10.5772/52620 Badawi R Soliman H Ziada D : Serum Markers as a Predictor of Hepatic Fibrosis Compared to Fibroscan in chronic hepatitis B Infected Egyptian patients: A Cross-sectional Study. Open Biomark J. 2020;10:69–75. 10.2174/1875318302010010069 Wijaya MP : CHB for stepwise M2BPGi. figshare.Dataset.2024. 10.6084/m9.figshare.24971649.v1 Wijaya MP : Participant information sheets. figshare.Journal contribution.2024. 10.6084/m9.figshare.25383106.v1 Wijaya MP : Informed consent form for sample. figshare.Journal contribution.2024. 10.6084/m9.figshare.25383157.v1 10.5256/f1000research.161310.r286137 Reviewer response for version 1 Jasirwan Chyntia Olivia Maurine 1 Referee https://orcid.org/0000-0001-5950-8245 Universitas Indonesia and Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia

Competing interests: No competing interests were disclosed.

 27 6 2024 Copyright: © 2024 Jasirwan COM 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The manuscript investigates the use of Mac-2-binding protein glycosylation isomer (M2BPGi) as a diagnostic marker for liver fibrosis in chronic hepatitis B (CHB) patients. The study aims to determine the optimal cut-off values for M2BPGi and other non-invasive fibrosis markers (APRI, FIB-4, and AAR) and assess the diagnostic performance of these markers, both individually and in combination through a stepwise diagnostic analysis.

This manuscript clearly presents objectives, methods, results, and conclusions, includes a comprehensive literature review that situates the study within the context of existing research, and accurately and appropriately cites relevant studies.

Suggestions :

  - Abstract: Briefly mention the statistical methods used in the analysis.

  - Introduction: Add a few sentences highlighting the specific advantages of non-invasive methods over liver biopsies.

  - Discussion of Limitations: Provide a more detailed discussion of the study's limitations, such as the cross-sectional design, potential biases, and generalizability of the findings.

  - Future Directions: Suggest specific areas for future research, such as validating the findings in larger, diverse populations.

  - Analytical Code: Provide the exact scripts or commands used in SPSS and STATA for the analyses. This can be included as supplementary files.

  Quality Control Measures: Please mention any quality control measures or calibration procedures for the laboratory tests and liver elastography.

  - Supplementary Material: Include supplementary material detailing the step-by-step process of data processing and analysis.

  Confidence Intervals: Include confidence intervals for key statistics such as AUC values and cut-off points to provide a sense of the estimates' precision.

  - Validation: Discuss the potential for external validation of the findings in other populations or settings.

  - Subgroup Analysis: Perform and discuss subgroup analyses (e.g., based on age and gender) to provide additional insights.

  - Processed Data: Include processed data and the analytical code used for statistical analyses.

  - Detailed Data Files: Ensure data files are well-documented with clear labels and descriptions of each variable.

  - Reproducibility Checklist: Provide a checklist or protocol outlining all steps from data collection to final analysis.

  - Limitations: Expand on the limitations section to provide a balanced perspective.

  - Future Research Directions: Include specific suggestions for future research based on the study’s findings.

  - Confidence Intervals and Effect Sizes: Include these in the results to provide a clearer sense of the precision and reliability of the estimates.

Conclusion

The manuscript is scientifically sound, with a clear presentation, appropriate study design, and robust statistical analysis. Minor enhancements, such as providing detailed analytical code, discussing limitations more explicitly, and suggesting future research directions, will further strengthen the manuscript. Addressing these points will ensure the article meets high standards of scientific rigor and transparency.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

gastroenterology and hepatology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

BESTARI MUHAMMAD BEGAWAN Internal Medicine, Universitas Padjadjaran, Bandung, West Java, Indonesia

Competing interests: We accepted all reviews, comments, and suggestions and have revised our manuscript to improve the quality and meet the high standards of F1000 and the reviewers without conflict of interest.

 10 7 2024

The F1000 Research

Dear Editor and reviewer (Dr Chyntia Olivia Maurine Jasirwan),

Subject: Submission of Revised Manuscript

I hope this comment finds you well. I am writing to express my sincere gratitude for the feedback provided by the reviewer on our manuscript, “Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis: A Stepwise Diagnostic Analysis.” The insightful comments and suggestions have significantly enhanced the quality and clarity of our work.

In response to the reviewer's feedback, we have carefully addressed each comment and made substantial revisions to improve the overall quality of the manuscript. The critical revisions include:

Limitations and Future Directions:

We have added a discussion of potential limitations and future research directions from our research.

Research steps, analysis, and results:

We have provided additional files for research steps and analysis. The research results and discussion elaboration are presented more thoroughly.

Supplementary Files:

We have completed and added supplementary files from our paper to support reproducibility and understanding.

Enclosed is the revised manuscript with tracked changes marked up the specific changes made in response to the reviewer's suggestions. These revisions address the reviewer's concerns and improve the manuscript's quality. We value the reviewer's time and effort and believe our manuscript now meets the high standards of F1000 Research.

Thank you for considering our resubmission. We look forward to your response and the potential approval of our work for full publication.

Kind Regards,

Authors

Suggestions from the reviewer:

1. Abstract: Briefly mention the statistical methods used in the analysis.

Author response:

Thanks for the suggestion.

We have added the statistical methods used in the abstract section.

2. Introduction: Add a few sentences highlighting the specific advantages of non-invasive methods over liver biopsies.

Author response:

Thanks for the suggestion.

In the introduction section, We added a few sentences highlighting the advantages of non-invasive liver fibrosis assessment.

3. Discussion of Limitations: Provide a more detailed discussion of the study's limitations, such as the cross-sectional design, potential biases, and generalizability of the findings.

Author response:

Thanks for the suggestion.

In the discussion section, we have added a more detailed discussion regarding research limitations.

4. Future Directions: Suggest specific areas for future research, such as validating the findings in larger, diverse populations.

Author response:

Thanks for the suggestion.

We have completed the suggestions for future research in the last paragraph of the discussion section.

5. Analytical Code: Provide the exact scripts or commands used in SPSS and STATA for the analyses. This can be included as supplementary files.

Author response:

Thanks for the suggestion.

We have added a reproducibility checklist for the analytical code information to the extended data.

6. Quality Control Measures: Please mention any quality control measures or calibration procedures for the laboratory tests and liver elastography.

Author response:

Thanks for the suggestion.

In the methods section, We added information about the quality control and calibration for the laboratory tests and liver elastography.

7. Supplementary Material: Include supplementary material detailing the step-by-step process of data processing and analysis.

Author response:

Thanks for the suggestion.

We have added a reproducibility checklist to the extended data, detailing the step-by-step process of data processing and analysis.

8. Confidence Intervals: Include confidence intervals for key statistics such as AUC values and cut-off points to provide a sense of the estimates' precision.

Author response:

Thanks for the suggestion.

We have completed the confidence intervals and AUC data found and needed to interpret the research results as a whole.

9. Validation: Discuss the potential for external validation of the findings in other populations or settings.

Author response:

Thanks for the suggestion.

We have completed suggestions for validating our research results in the last paragraph of the discussion section.

10. Subgroup Analysis: Perform and discuss subgroup analyses (e.g., based on age and gender) to provide additional insights.

Author response:

Thanks for the suggestion.

We have added the results of the subgroup analysis in the discussion section to support and explain the overall research results.

  11. Processed Data: Include processed data and the analytical code used for statistical analyses.

Author response:

Thanks for the suggestion.

We have completed the processed data in the Data Availability section and the analytical code can be seen in the reproducibility checklist in the extended data.

12. Detailed Data Files: Ensure data files are well-documented with clear labels and descriptions of each variable.

Author response:

Thanks for the suggestion.

We have completed the underlying data with clear labels and descriptions.

13. Reproducibility Checklist: Provide a checklist or protocol outlining all steps from data collection to final analysis.

Author response:

Thanks for the suggestion.

We have added a reproducibility checklist in the extended data for all steps from data collection to final analysis for the Stepwise Diagnostic Analysis.

14. Limitations: Expand on the limitations section to provide a balanced perspective.

Author response:

Thanks for the suggestion.

In the final part of the discussion, we have dissected the potential limitations of our study.

15. Future Research Directions: Include specific suggestions for future research based on the study’s findings.

Author response:

Thanks for the suggestion.

We have completed suggestions for future research directions in the last paragraph of the discussion section.

16. Confidence Intervals and Effect Sizes: Include these in the results to provide a clearer sense of the precision and reliability of the estimates.

Author response:

Thanks for the suggestion.

We ran a categorical predictor variables analysis to form some models that would also predict categorical variables (HR or LR group). Through those analyses, the effect of the model was assessed through Nagelkerke R 2, which we have added in the results section. In addition, we have included all important confidence interval data, including CIs for each AUC, to find the cut-off value in the discussion section.

 10.5256/f1000research.161310.r273641 Reviewer response for version 1 Kalista Kemal Fariz 1 2 Referee Internal Medicine, Cipto Mangunkusumo Hospital, Jakarta, Jakarta, Indonesia Universitas Indonesia, Depok, West Java, Indonesia

Competing interests: No competing interests were disclosed.

 14 6 2024 Copyright: © 2024 Kalista KF 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

I think this is very good work. This study comparing M2BPGi with elastography and many scoring system to assess liver fibrosis like APRI, FIB-4 and AAR, so i think this is a strength of this study, because not many studies comparing M2BPGi with multiple scoring system like this study. I consider to add “patient with chronic hepatitis B” in study title, because population in this study only include patient with hepatitis B. In discussion, I also consider to elaborate further regarding why APRI cut off for diagnosing liver fibrosis (0.311) is lower than many previous studies. It would be appreciate if author more explain why APRI was excluded from the model, because I think APRI is quite easy way to do in daily practice. Regarding study title “stepwise diagnosis analysis” I would suggest the author to provide how to apply study result to real life practice, eg: when considering patient for further fibrosis assessment with elastography or liver biopsy or if the M2BPGi result < 1.4 and FIB-4 > 0.7 what author suggestion in this kind of situation. Minor revision maybe it would be better if in table 2, in variable APRI, FIB-4, AAR and M2BPGi row, add note which one is lower or higher than cut off level.

I think this study is approved with reservation, need to add further revision.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Hepatology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

BESTARI MUHAMMAD BEGAWAN Internal Medicine, Universitas Padjadjaran, Bandung, West Java, Indonesia

Competing interests: We accepted all reviews, comments, and suggestions and have revised our manuscript to improve the quality and meet the high standards of F1000 and the reviewers without conflict of interest.

 10 7 2024

The F1000 Research

Dear Editor and reviewer (Dr Kemal Fariz Kalista),

Subject: Submission of Revised Manuscript

I hope this comment finds you well. I am writing to express my sincere gratitude for the feedback provided by the reviewer on our manuscript, “Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis: A Stepwise Diagnostic Analysis.” The insightful comments and suggestions have significantly enhanced the quality and clarity of our work.

In response to the reviewer's feedback, we have carefully addressed each comment and made substantial revisions to improve the overall quality of the manuscript. The critical revisions include:

Clarification of Title:

We have added information to the title to better represent our manuscript's contents.

Clarification of Results and Discussion:

We have provided additional structural discussion regarding the results and analysis related to APRI and added considerations regarding finding similar conditions in clinical practice. The results table has also undergone adjustments to make it more straightforward.

Enclosed is the revised manuscript with tracked changes marked up the specific changes made in response to the reviewer's suggestions. These revisions address the reviewer's concerns and improve the manuscript's quality. We value the reviewer's time and effort and believe our manuscript now meets the high standards of F1000 Research.

Thank you for considering our resubmission. We look forward to your response and the potential approval of our work for full publication.

Kind Regards,

Authors

Suggestions from the reviewer:

1. Add “patient with chronic hepatitis B” in study title, because population in this study only include patient with hepatitis B.

Author response:

Thanks for the suggestion.

We have added information about Chronic Hepatitis B patients in the study title.

2. Elaborate further regarding why APRI cut off for diagnosing liver fibrosis (0.311) is lower than many previous studies

Author response:

Thanks for the suggestion.

After reviewing and comparing various articles, we have added arguments related to the APRI cut-off value from our analysis to the discussion section.

3. More explain why APRI was excluded from the model.

Author response:

Thanks for the suggestion.

The strong correlation with FIB-4 and weaker overall performance than FIB-4, obtained from the prefix analysis, would make APRI insignificant if included in the statistical analysis.

4. Regarding study title “stepwise diagnosis analysis” I would suggest the author to provide how to apply study result to real life practice

Author response:

Thanks for the suggestion.

We have added the steps to follow when applying our study results in real-life practice. This information is available at the end of the discussion section.

5. Minor revision maybe it would be better if in table 2, in variable APRI, FIB-4, AAR and M2BPGi row, add note which one is lower or higher than cut off level

Author response:

Thanks for the suggestion.

We added information to Table 2 to make it more straightforward.