Evaluating thresholds of Mac-2 binding protein glycosylation isomer in association with clinical outcomes in patients with cirrhosis

Abstract

BACKGROUND

Cirrhosis is a progressive condition characterized by fibrosis that can lead to severe complications and increased mortality. The mac-2 binding protein glycosylation isomer (M2BPGi) is a prominent biomarker for predicting hepatocellular carcinoma (HCC) and cirrhosis-induced esophageal varices (EV).

AIM

To investigate thresholds of M2BPGi associated with HCC, EV, and decompensation in patients with cirrhosis.

METHODS

This was a prospective study. A total of 153 patients with cirrhosis who met the inclusion criteria were enrolled. The patients were diagnosed with HCC and EV according to the Baveno VII and European Association for the Study of the Liver guidelines. Baseline serum M2BPGi levels were assessed along with other routine tests. The data analysis aimed to determine the cutoff values of M2BPGi for predicting EV and HCC.

RESULTS

In the study 85.6% of patients were Child-Pugh B and C. M2BPGi mean cutoff index was 7.1 ± 3.7, showing no significant etiological differences. However, M2BPGi levels varied significantly among Child-Pugh classes, EV classifications, and between patients with and without HCC (P < 0.01). M2BPGi cutoff values for predicting HCC, EV, and decompensated cirrhosis were 6.50, 6.64, and 5.25, respectively. Multivariate analysis confirmed M2BPGi as an independent risk factor for EV [adjusted odds ratio (aOR): 1.3, 95%CI: 1.08-1.64] and liver decompensation (aOR: 2.11, 95%CI: 1.37-3.83). Area under the curve of M2BPGi for HCC differentiation was 0.71. An algorithm combining alpha-fetoprotein (AFP) and M2BPGi detected 26 of 28 HCC cases with 98.04% accuracy vs 10 cases by AFP alone.

CONCLUSION

Serum M2BPGi predicted cirrhosis complications, including decompensation and varices, especially in HCC. Combined with AFP, it enhanced HCC detection. Future liver biopsy studies are needed for tissue confirmation.

Key Words: Cirrhosis; Hepatocellular carcinoma; Esophageal varices; High-risk esophageal varices; Liver decompensation; Mac-2 binding protein glycosylation isomer

Core Tip: This study introduces serum mac-2 binding protein glycosylation isomer (M2BPGi) as a novel biomarker for predicting complications in cirrhotic patients, particularly hepatocellular carcinoma (HCC), liver decompensation, and esophageal varices. The findings show that combining M2BPGi with alpha-fetoprotein significantly enhances diagnostic accuracy, achieving an impressive sensitivity of 92.8% for HCC detection. Importantly, M2BPGi levels correlate with disease severity as measured by the Child-Pugh classification, highlighting its potential role in clinical risk stratification.

INTRODUCTION

Cirrhosis represents an advanced stage in most chronic liver diseases. The 1-year mortality rate associated with cirrhosis exhibits significant variability, ranging from 1% to 57%, contingent on the degree of decompensation. Globally, it is the 14th leading cause of death and ranks 4^th in Central Europe[1]. According to a Global Burden of Disease study conducted in 2020, cirrhosis accounted for approximately 1.32 million deaths in 2017, with about 0.44 million occurring in females and 0.88 million in males[2]. The mortality rate due to cirrhosis is particularly high in low-income regions such as sub-Saharan Africa, Central Asia, and Southeast Asia[2]. Notably, in Viet Nam, statistics from 2018 indicated that cirrhosis was the seventh leading cause of death among the top ten causes[3].

Cirrhosis can result in severe complications. Hepatocellular carcinoma (HCC), esophageal varices (EV), and liver decompensation are the most significant concerns in such patients. Indeed, 80%-90% of patients with HCC are diagnosed with cirrhosis[4]. Given the high prevalence of cirrhosis in patients with HCC, international guidelines strongly recommend identifying the risk of HCC development in such patients. Several markers have been developed and utilized for early detection to identify risk factors for HCC. These include alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II, AFP-L3, liver stiffness assessed via ultrasound, and quantitative hepatitis B surface antigen in patients with hepatitis B infection. However, no optimal marker nor method has been established yet.

More specifically, previous studies demonstrated that the combination of AFP and ultrasound exhibited only 63% sensitivity for the early detection of HCC[5]. The limitation of the deployment of AFP in conjunction with ultrasound for HCC surveillance may lead to more than 40% of patients being misdiagnosed with early cases. Consequently, researchers are actively exploring new and more specific markers for the early detection of HCC in patients with chronic hepatitis, regardless of cirrhosis status. Protein induced by vitamin K absence or antagonist-II, AFP-L3, and particularly the GAAD score, have recently attracted the attention of numerous researchers and clinicians because the combination of individual parameters has improved HCC diagnostic performance[6]. Despite their superior attributes the high-cost individual parameters contributing to the GAAD score may constrain their usage among patients.

While effective treatment of the underlying causes of cirrhosis is important, identifying factors that can predict future complications, such as EV and liver decompensation, is crucial for triggering strict follow-up. Traditionally, an invasive method, esophagogastroduodenoscopy-retrieved parameters, consisting of size and the presence of red signs, was the gold standard for determining the existence of EV. Recent advancements in scientific and technical knowledge have led to the investigation of noninvasive methods aimed at ruling out the presence of EV in patients with cirrhosis, both in general and in specific contexts. These indicators, including platelet count and liver stiffness, accurately exclude low-risk EV in patients with cirrhosis[7]. Nevertheless, FibroScan has been found to be affected by obesity and acute inflammation, which may result in a false elevation. In addition, limited installation and well-served personnel requirements limit accessibility in rural areas.

Mac-2 binding protein (M2BP) is a secreted glycoprotein (about 90 kDa) that contains seven N-glycans per monomer. M2BP exhibits high polymerization in serum, forming a large doughnut-like structure[8]. It is secreted by hepatic stellate cells (HSCs) in the liver and by macrophages from other organs. M2BP interacts with collagens IV, V, and VI, fibronectin, and nidogens[9]. Alterations in the glycan structure of M2BP reflect the degree of cell differentiation and carcinogenesis[10]. For instance, the N-glycosylation of M2BP changes during liver disease progression, with altered M2BP levels increasing in advanced fibrosis. Lectin Wisteria floribunda agglutinin specifically recognizes the altered M2BP produced by HSCs [M2BP glycosylation isomer (M2BPGi)][8]. HSCs are the primary producers of M2BPGi, which acts as a juxtacrine messenger enhancing mac-2 protein (galectin-3) expression in Kupffer cells. Mac-2 activates HSCs to become fibrogenic, reflecting HSC activation during fibrogenesis. Clinically, M2BPGi is recognized as a marker of fibrosis in various liver diseases[9].

Recent studies have highlighted the potential of M2BPGi as a biological marker for evaluating liver fibrosis. M2BPGi has been found to correlate with fibrosis stages in various etiologies, including viral hepatitis, metabolic dysfunction-associated steatotic liver disease, autoimmune hepatitis[11], and the prognosis of HCC in patients with chronic hepatitis B infection and chronic hepatitis C, regardless of treatment status[12,13]. Moreover, several studies have shown that M2BPGi is a promising short-term predictor of recurrent HCC in patients undergoing transarterial chemoembolization and hepatectomy[14]. Recently, M2BPGi has been found to be a potential marker to detect other cirrhosis-induced complications comprising EV, portal hypertension, and mortality[13,15].

Building on the existing literature that has explored the value of M2BPGi in liver fibrosis among patients with chronic hepatitis, this study further investigated its association with clinical outcomes in patients with cirrhosis. To address this concern we conducted a study titled “Assessing thresholds of M2BPGi in association with clinical outcomes in patients with cirrhosis”. This study aimed to investigate M2BPGi concentration in the association with HCC, EV, and decompensation in patients with all-cause cirrhosis. Additionally, we sought to improve diagnostic performance by combining M2BPGi with existing markers.

MATERIALS AND METHODS

Study design

This cross-sectional, prospective study was conducted at the Gastroenterology Department of Da Nang Hospital in Da Nang City, Viet Nam, from March 1, 2022 to September 31, 2024. A total of 153 patients with cirrhosis receiving treatment at the inpatient gastroenterology department were enrolled in the study and evaluated for HCC and EV. Blood sampling was performed on the same day as imaging diagnosis.

Patients

The diagnosis of cirrhosis was based on a comprehensive examination comprising the following: (1) Clinical signs and radiological findings, including morphological alterations in the liver (coarse liver echotexture with irregular margins), ascites, splenomegaly, and jaundice; (2) Laboratory test results: Aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio > 1, total bilirubin > 17.1 μmol/L, serum albumin < 35 g/L, international normalized ratio > 1.3, or prolonged prothrombin time; and (3) FibroScan of ≥ 11 kPa and/or an AST to platelet ratio index ≥ 2 for patients with ambiguous clinical findings[16]. The exclusion criteria were as follows: (1) Acute decompensation; (2) End-stage decompensated cirrhosis and limited expected survival time; (3) Declined participation in the study; and (4) Under 18 years of age.

HCC was defined according to the 2018 American Association for the Study of Liver Diseases Guidelines: (1) Lesion > 1 cm and AFP > 20 ng/mL; and/or (2) Criteria on multiphase imaging by multiphase CT and magnetic resonance imaging[17]. The presence of EV was determined according to the Japan Gastroenterological Endoscopy Society classification: F0, no varicose appearance; F1, straight, small-caliber varices; F2, moderately enlarged, beady varices; and F3, markedly enlarged, nodular or tumor-shaped varices[18]. High-risk EVs were defined as medium or large varices, small varices with red signs, or Child–Pugh C in accordance with the European Association for the Study of Liver guidelines[19].

Routine laboratory tests

Blood samples were collected from all patients on the same day as the imaging diagnosis. Routine laboratory data were collected, including hemoglobin, WBC, platelet count, AST, ALT, albumin, total bilirubin, ammonia, and total bilirubin. AFP was measured by a Cobas C6000 analyzer (Roche Diagnostics, Indianapolis, IN, United States), and viral tests including HBV surface antigen and hepatitis C antibody were measured on an Architect i2000SR immunoassay analyzer (Abbott, Chicago, IL, United States). Prothrombin time was measured on a Sysmex coagulation analyzer (Sysmex Corp., Kobe, Hyogo, Japan). AST > 40 IU/L was defined as the upper limit. AST to platelet ratio index was calculated as: AST level (IU/L)/AST (upper limit of normal) (IU/L)/platelet level × 100.

Liver elasticity measurement

Liver stiffness was measured using transient elastography (FibroScan; Echosens, Paris, France) by an experienced operator following established recommendations[20]. The results of at least 10 valid shots obtained in the session were expressed in kPa. A success rate of < 60% or an interquartile range/median value of > 30% was considered unreliable.

Computed tomography and magnetic resonance imaging

To confirm HCC cases all patients underwent computed tomography (CT) and/or magnetic resonance imaging (MRI) examinations on the same day as their clinical examination and blood sampling. A GE Optima 128-slice CT scanner (GE Healthcare, Chicago, IL, United States) and Philips 1.5T Multiva MRI system (Philips Healthcare, Best, Netherlands) with multiphase contrast enhancement were employed to detect HCC in patients with cirrhosis.

Esophagogastroduodenoscopy

Endoscopic procedures were conducted using the Olympus Evis X1, 170, and 190 systems (Olympus, Tokyo, Japan). The classification of variceal size was as follows: (1) Small, < 5 mm diameter; (2) Medium, 5-10 mm; and (3) Large, > 10 mm. Red signs were identified using narrow-band imaging technology, which enhances the contrast between blood vessels and surrounding tissues by employing different light wavelengths. Narrow-band imaging facilitates the visualization of red spots by increasing the visibility of vascular structures. Patients who did not exhibit these features were considered to be at low risk for variceal bleeding.

M2BPGi testing

Serum M2BPGi was measured in stored serum by employing a lectin-antibody sandwich immunoassay on an automated immunoassay system, HISCL-5000 (Sysmex), as previously reported[8]. Venous blood was collected prior to the endoscopy. Three milliliters of venous blood was centrifuged at 2000 rpm for 15 min at room temperature (25-30 °C) to obtain serum samples, which were then stored at -80 °C. The results were expressed as the cutoff index (C.O.I.).

Statistical analysis

Continuous variables were expressed as mean ± SD or median (interquartile range), and categorical data were reported as number (percentage). Differences between groups were evaluated using Student’s t-test or the χ² statistic, as appropriate. The predictability of M2BPGi for complications, including liver decompensation, the presence of EV, high-risk EV, and HCC, was expressed as the area under the receiver operating characteristic curve (AUROC). Multivariate logistic regression was applied to exam diagnostic performance of the combination of various parameters in association with the presence of EV and liver decompensation. The optimal cutoff M2BPGi value was obtained by maximizing Youden’s index, and statistical analysis was performed using GraphPad Prism v10 (GraphPad, La Jolla, CA, United States). Statistical significance was set at P < 0.05.

RESULTS

Baseline characteristics of patients

Table 1 shows the basic characteristics of the patients. The study population consisted predominantly of males (80.4%), with a mean age of 56.3 years. Liver function markers such as AST, ALT, and bilirubin levels indicated a significant degree of liver dysfunction, with notably elevated AST and bilirubin levels. Albumin levels decreased, consistent with impaired liver function. The platelet count also decreased, likely reflecting advanced liver disease or portal hypertension. A significant proportion of patients were classified as Child-Pugh class B (41.8%) or C (43.8%), indicating moderate-to-severe liver dysfunction. The mean FibroScan value of 36.4 kPa further corroborates the presence of significant liver fibrosis or cirrhosis. AFP levels are generally within a low-to-moderate range, while the M2BPGi marker also exhibited elevated values, which may indicate an underlying liver pathology.

Table 1 Baseline characteristics of the study participants, n (%)/mean ± SD/median (interquartile range).

Characteristic, n = 153	Finding
Male	123 (80.4)
Age (years)	56.3 ± 10.9
AST (U/L)	93.2 (49.4-149.5)
ALT (U/L)	48.0 (30.2-82.9)
Total bilirubin (μmol/L)	41.8 (20.7-69.8)
Albumin (g/L)	27.1 (22.0-33.1)
Hemoglobin (g/L)	111.0 (94.0-124.0)
Platelet as × 109/L	94.0 (59.0-145.0)
AFP (ng/Ml)	4.66 (2.4-13.0)
Child-Pugh
A	22 (14.4)
B	64 (41.8)
C	67 (43.8)
M2BPGi C.O.I.	7.1 ± 3.7
FibroScan in kPa, n = 77	36.4 ± 20.5

AST: Aspartate transaminase; ALT: Alanine transaminase; AFP: Alpha-fetoprotein; C.O.I.: Cutoff index; M2BPGi: Mac-2 binding protein glycosylation isomer.

Correlation between M2BPGi levels with severity of cirrhosis, model for end-stage liver disease score, and degree of EV and HCC

As shown in Table 2, serum M2BPGi levels significantly increased with the degree of cirrhosis (Child-Pugh) (P < 0.01), model for end-stage liver disease score (P = 0.0018), and severity of EV (P < 0.01). Notably in the group of patients with HCC, M2BPGi levels were also significantly higher than those in the group with cirrhosis without HCC (P < 0.01).

Table 2 Serum mac-2 binding protein glycosylation isomer according to the degree of cirrhosis, esophageal varices, and with or without hepatocellular carcinoma, mean ± SD.

Characteristic	n	M2BPGi C.O.I.	P value1
Child-Pugh
A	22	5.20 ± 2.65	< 0.001
B	64	6.14 ± 2.89
C	67	8.63 ± 4.08
MELD score
≤ 9	38	6.09	0.003
10-19	53	6.43
20-29	39	7.85
30–39	10	10.02
≥ 40	7	11.11
Esophageal varices
F0	22	5.3 ± 4.3	< 0.05
F1	10	5.0 ± 3.8
FII	21	7.6 ± 3.4
FIII	57	8.1 ± 3.8
HCC
Yes	28	8.8 ± 2.9	< 0.05
No	125	6.7 ± 3.8

¹Analysis of variance was performed to evaluate the difference amongst the group; P < 0.05 was considered statistically significant.

HCC: Hepatocellular carcinoma; C.O.I.: Cutoff index; M2BPGi: Mac-2 binding protein glycosylation isomer; MELD: Model for end-stage liver disease.

Association of M2BPGi with clinical outcomes in patients with cirrhosis

Analysis of the ROC curve was conducted to determine the cutoff value of M2BPGi levels for diagnosing HCC, liver decompensation, and the presence of EV (Figure 1). For HCC the ROC curve demonstrated an AUROC of 0.71 at the cutoff of 6.5 C.O.I., with sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of 89.3%, 58.4%, 96.1%, and 32.5%, respectively (Figure 1A). Further investigation was performed using an algorithm combining M2BPGi (> 6.5 C.O.I.) and AFP (> 400 IU/L) to detect HCC cases. The algorithm resulted in a significant improvement in HCC detection with an accuracy of 98.0%. This algorithm, illustrated in Figure 2, yielded a sensitivity of 92.8%, a specificity of 99.2%, an NPV of 98.4%, and a PPV of 96.3%.

Figure 1 Area under the receiver operating characteristic curves of mac-2 binding protein glycosylation isomer in predicting hepatocellular carcinoma, liver decompensation, presence of esophageal varices, and high-risk esophageal varices. A: Hepatocellular carcinoma; B: Liver decompensation; C: Esophageal varices; D: High-risk esophageal varices. AUC: Area under the curve.

Figure 2 Algorithm combining alpha-fetoprotein and mac-2 binding protein glycosylation isomer to detect hepatocellular carcinoma in patients with cirrhosis. Hepatocellular carcinoma was confirmed by computed tomography or magnetic resonance imaging. AFP: Alpha-fetoprotein; C.O.I.: Cutoff index; M2BPGi: Mac-2 binding protein glycosylation isomer; HCC: Hepatocellular carcinoma.

The results demonstrated that M2BPGi can distinguish liver decompensation at 5.25 C.O.I. with an AUROC of 0.69, balanced sensitivity (73.3%) and specificity (72.7%), and a high NPV of 94.1%, thus rendering it reliable for excluding decompensation (Figure 1B). Similarly, the model demonstrated moderate accuracy, with a specificity of 72.7% and a relatively low PPV of 30.8% for the presence of EV (cut-off: 6.64, AUROC: 0.68), indicating limited reliability in confirming positive cases (Figure 1C). For high-risk EV (cutoff: 6.86, AUROC: 0.63), the discriminatory power was weak, with fair sensitivity (60.7%) and specificity (65.3%), suggesting limited clinical utility (Figure 1D).

As shown in Table 3, the multivariate logistic regression analysis revealed that M2BPGi was an independent risk factor associated with decompensation with an adjusted odds ratio (aOR) of 2.11, 95%CI: 1.37-3.83 and the presence of EV (aOR: 1.3, 95%CI: 1.08-1.64).

Table 3 Univariate logistic regression and multivariate logistic regression in prediction of liver decompensation and esophageal varices.

Variable	Univariate analysis		Multivariate analysis
	OR	95%CI	aOR	95%CI
Liver decompensation
MELD score	1.19	1.10-1.32	1.18	1.04-1.41
M2BPGi	1.23	1.06-1.47	2.11	1.37-3.83
FibroScan	1.06	1.02-1.12	1.05	0.98-1.15
AAR	1.80	1.06-3.48	4.12	0.84-35.8
Esophageal varices
Platelets	0.99	0.99-1.00	1.00	0.99-1.02
FibroScan	0.99	0.97-1.02	0.98	0.95-1.00
M2BPGi	1.10	1.01-1.22	1.30	1.08-1.64
AAR	1.02	0.78-1.36	1.04	0.62-1.69
Child-Pugh
A	Reference		Reference
B	4.75	1.70-14.80	4.65	1.06-24.11
C	4.21	1.52-13.01	1.90	0.32-11.90

AAR: Aspartate aminotransferase to alanine aminotransferase ratio; aOR: Adjusted odds ratio; M2BPGi: Mac-2 binding protein glycosylation isomer; MELD: Model for end-stage liver disease; OR: Odds ratio.

DISCUSSION

Our study demonstrated that M2BPGi is associated with HCC, liver compensation, and the presence of EV in patients with cirrhosis. M2BPGi has been identified as a fibrosis biomarker in patients with various etiologies including HBV, hepatitis C virus, metabolic dysfunction-associated steatotic liver disease, and autoimmune hepatology[8,13]. Furthermore, M2BPGi has been extensively investigated in East Asian populations as a biomarker for HCC[21]. Numerous studies have established the efficacy of M2BPGi in predicting HCC in patients with chronic hepatitis irrespective of the presence of cirrhosis[9,22]. In recent years, researchers have investigated the association between M2BPGi and EV and portal hypertension in patients with cirrhosis[11,23].

As shown in Table 2, serum M2BPGi levels significantly increased with cirrhosis severity (Child-Pugh score). These findings align with those of previous studies, such as that by Eso et al[24], who reported M2BPGi levels in Child-Pugh A, B, and C cirrhosis of 0.94, 4.775, and 11.37, respectively. Similarly, Hanai et al[25] observed serum Wisteria floribunda agglutinin positive-M2BP levels in Child-Pugh classes A, B, and C with C.O.I.s of 2.90, 6.15, and 9.45, respectively.

The present study demonstrated that an M2BPGi concentration of 6.5 C.O.I. can diagnose HCC in patients with cirrhosis, achieving an AUROC of 0.71 and a significant NPV of 96.1% (P < 0.001). These findings align with the research conducted by Liu et al[22] in which M2BPGi was identified as a substantial short-term predictor of HCC in chronic hepatitis B patients, particularly within 1 year to 2 years before diagnosis, with AUROCs exceeding 0.80. Tseng et al[26] reported that elevated levels of M2BPGi independently predicted HCC risk and enhanced existing models such as PAGE-B. Moreover, Jun et al[21] demonstrated that M2BPGi outperformed AFP in predicting HCC risk, particularly in patients with HBV without advanced fibrosis. Su et al[9] noted that M2BPGi levels declined significantly in patients without HCC but remained elevated in HCC cases. These findings further support the use of M2BPGi as a dynamic surveillance tool.

This study also revealed that M2BPGi levels of ≥ 6.5 C.O.I. considerably increased the risk of HCC, underscoring its clinical significance for early detection. Despite the high NPV for ruling out HCC, M2BPGi exhibited an inferior PPV. While M2BPGi demonstrated high sensitivity and low specificity, AFP showed a contrasting pattern (data not shown). To enhance the diagnosis of HCC, the algorithm combining M2BPGi and AFP showed superior performance compared to either individual M2BPGi or AFP. Specifically, this algorithm significantly improved the sensitivity, specificity, and PPV for diagnosing HCC and mitigated the limitations of these assays. Indeed, 16 out of 28 HCC cases with AFP levels lower than 400 ng/L were greater than 6.5 C.O.I. Collectively, this algorithm suggests a novel approach combining these assays to improve the early detection of HCC and reduce misdiagnoses caused by the low sensitivity of AFP. However, this model requires validation in a larger population, owing to the small sample size.

Regarding liver decompensation, our study identified a C.O.I. of 5.25 for M2BPGi in predicting liver decompensation, yielding an AUROC of 0.69, sensitivity of 73.3%, and specificity of 72.7%. Multivariate regression analysis demonstrated that M2BPGi was an independent risk factor for liver decompensation (aOR: 2.11; 95%CI: 1.37-3.83). Previous studies have indicated that elevated M2BPGi levels are associated with worsening of fibrosis and decompensation. For instance, M2BPGi was identified as an independent predictor of clinical decompensation in patients with compensated cirrhosis with an OR of 11.958 (95%CI: 1.876-76.226, P = 0.009)[27]. Uojima et al[28] observed that patients with decompensated cirrhosis exhibited significantly higher M2BPGi levels (6.91 ± 5.04) than patients with compensated cirrhosis (2.22 ± 1.61, P < 0.0001), with a C.O.I. of 3.37 predicting decompensated cirrhosis with 77.8% sensitivity and 86.7% specificity. These findings demonstrated a relationship between M2BPGi levels and liver decompensation progression, although a universal cutoff value is lacking, necessitating larger studies for standardization.

The final clinical outcome examined in this study was the presence of EV. The M2BPGi demonstrated a fair discriminative ability. The sensitivity was moderate (59.1%), while the specificity was higher (72.7%) for identifying patients without EV. The PPV was 89.7%, indicating a strong correlation between positive test results and the presence of EV. However, its low NPV of 30.8% limits its efficacy in ruling out EV. Wu et al[29] demonstrated a positive correlation between M2BPGi and hepatic venous pressure gradient (HVPG) in patients with cirrhosis, reporting M2BPGi levels of 2.33 C.O.I. for clinically significant portal hypertension (HVPG > 10 mmHg) and 5.93 C.O.I. for severe portal hypertension (HVPG > 16 mmHg).

Kikukawa et al[15] also reported higher M2BPGi levels in patients with esophagogastric varices compared with those without esophagogastric varices. Furthermore, this study indicated that the M2BPGi value had predictive ability for high-risk EV, with an AUROC of 0.63 (P < 0.05). A previous study found higher M2BPGi levels in patients with high-risk EV than in those without (C.O.I. of 11.4 vs 3.7, P < 0.001), using a C.O.I. of 5; the sensitivity, specificity, PPV, and NPV were 92.6%, 70.1%, 55.6%, and 95.9%, respectively[23]. Our study demonstrated lower sensitivity and NPV (60.7% and 56.8%, respectively), which may indicate the limitation of this test in excluding patients with high-risk EV. Another factor contributing to this result was the sample size of the subset.

This study had several limitations. First, as this was a single-center study with a small sample size, the findings may not be generalizable to the entire target population. Second, the use of FibroScan for staging fibrosis may introduce bias owing to its inherent limitations. Additionally, the lack of liver histology deferred us from HCC confirmation. Hence, MRI/CT was used as a reference method due to its non-invasive nature. Third, given the cross-sectional nature of this study and the collection of data solely at baseline, the absence of longitudinal measurements constrained our ability to observe temporal changes in M2BPGi. Fourth, our algorithm combining AFP and M2BPGi was investigated in a small sample size as HCC cases diagnosed in our patient population may have resulted in biased data. Thus, we suggest a larger population to investigate this algorithm in an establishment and validation cohort before its implementation in practical procedures. Furthermore, the predominance of males and treatment history may contribute to biased cutoffs. Finally, the limitation of the subset sample sizes (e.g., EV, high-risk EV, and liver decompensation) precluded comprehensive data analysis. Future prospective multicenter studies should address and uncover the barriers. Nevertheless, the study results may provide a novel approach for diagnosing the aforementioned cirrhosis-induced complications in limited-resource settings.

CONCLUSION

Serum M2BPGi appears to be a valuable and accessible non-invasive biomarker for predicting clinically significant complications in patients with cirrhosis, such as hepatic decompensation and EV, and especially in cases with HCC. Although histological validation was not performed, the findings provided supportive clinical evidence for the utility of M2BPGi in risk stratification. Furthermore, the combined use of M2BPGi and AFP showed improved sensitivity in identifying HCC, highlighting its potential value in enhancing surveillance strategies in cirrhotic patients. Future studies incorporating liver biopsy are warranted to further confirm these associations exist at the tissue level.