Wisteria floribunda Agglutinin-Positive Mac-2 Binding Protein as a Screening Tool for Significant Liver Fibrosis in Health Checkup

SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD can progress to liver cirrhosis and hepatocellular carcinoma (HCC). Recently, the prognosis of NAFLD/NASH has been reported to be dependent on liver fibrosis degree. Liver biopsy remains the gold standard, but it has several issues that must be addressed, including its invasiveness, cost, and inter-observer diagnosis variability. To solve these issues, a variety of noninvasive tests (NITs) have been in development for the assessment of NAFLD progression, including blood biomarkers and imaging methods, although the use of NITs varies around the world. The aim of the Japan NASH NIT (JANIT) Forum organized in 2020 is to advance the development of various NITs to assess disease severity and/or response to treatment in NAFLD patients from a scientific perspective through multi-stakeholder dialogue with open innovation, including clinicians with expertise in NAFLD/NASH, companies that develop medical devices and biomarkers, and professionals in the pharmaceutical industry. In addition to conventional NITs, artificial intelligence will soon be deployed in many areas of the NAFLD landscape. To discuss the characteristics of each NIT, we conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis in this study with the 36 JANIT Forum members (16 physicians and 20 company representatives). Based on this SWOT analysis, the JANIT Forum identified currently available NITs able to accurately select NAFLD patients at high risk of NASH for HCC surveillance/therapeutic intervention and evaluate the effectiveness of therapeutic interventions.

Noninvasive assessment of liver fibrosis and its clinical significance in nonalcoholic fatty liver disease

Liver fibrosis is the most important prognostic factor in patients with nonalcoholic fatty liver disease (NAFLD). Several noninvasive markers for fibrosis, including blood-based markers and imaging based-markers have been developed. Indirect fibrosis markers (e.g., fibrosis-4 index and NAFLD fibrosis score) consist of standard laboratory data and clinical parameters. Given its availability and high negative predictive value for advanced fibrosis, these markers are suitable for screening at primary care. Blood-based fibrogenesis markers (enhanced liver fibrosis and N-terminal propeptide of type 3 collagen), ultrasound-based modalities (vibration-controlled transient elastography, point shear wave elastography [SWE], and two-dimensional SWE), and magnetic resonance elastography have high diagnostic accuracy for liver fibrosis and are suitable for diagnosing liver fibrosis at secondary care centers. Sequential use of these markers can increase diagnostic accuracy and reduce health care costs. Furthermore, combining noninvasive makers may assist in identifying candidates for pharmacological trials and reducing screening failure. Emerging data suggest that these noninvasive markers are associated with liver-related events (hepatocellular carcinoma and decompensation) and mortality. Furthermore, delta change in noninvasive markers over time is also associated with time-course change in fibrosis, liver-related event risk, and mortality risk. However, the association between liver fibrosis and cardiovascular disease (CVD) risk is still controversial. CVD risk may decrease in patients with decompensated liver disease and noninvasive markers may be useful for assessing CVD risk in these patients. Therefore, noninvasive markers may be utilized as measures of fibrosis as well as real-time prognostic tools, in place of liver biopsy.

Liver fibrosis and fatty liver as independent risk factors for cardiovascular disease

BACKGROUND AND AIM

The association between liver fibrosis, fatty liver, and cardiovascular disease (CVD) risk is unknown. Hence, this study aimed to investigate the association of liver fibrosis and fatty liver with CVD risk independent of already known CVD risk comorbidities.

METHODS

This is a prospective study registered with the University Hospital Medical Information Network clinical trial registry (UMIN000036175). Liver fibrosis was assessed by serum fibrosis markers including FIB-4, nonalcoholic fatty liver disease fibrosis score (NFS), and Wisteria floribunda agglutinin-positive Mac-2 binding protein (WFA⁺ -M2BP), whereas fatty liver was diagnosed by ultrasonography. CVD risk was evaluated using the Framingham risk score (FRS), and a high CVD risk was defined as an FRS ≥ 20%.

RESULTS

A total of 3512 subjects were enrolled, and high CVD risk (FRS ≥ 20%) was observed in 17.5%. Advanced fibrosis (FIB-4 ≥ 2.67, NFS ≥ 0.675, and WFA⁺ -M2BP ≥ 1.0) and the presence of fatty liver were significantly associated with high CVD risk independent of diabetes mellitus, dyslipidemia, and hypertension. When subjects were stratified by liver fibrosis and fatty liver, subjects with advanced fibrosis and fatty liver have the highest odds for high CVD risk (odds ratio [OR]: 5.90-35.6), followed by subjects with advanced fibrosis and without fatty liver (OR: 2.53-9.62) using subjects without advanced fibrosis and fatty liver as a reference.

CONCLUSIONS

Liver fibrosis and fatty liver were associated with CVD risk independent of already known CVD risk comorbidities. The assessment of liver fibrosis and fatty liver may be useful to identify high CVD risk subjects.

Attenuation coefficient (ATT) measurement for liver fat quantification in chronic liver disease

Liver fat is one of the main clinical features in chronic liver disease, and the number of fatty liver patients is increasing as the prevalence of obesity and metabolic syndrome increases globally. Noninvasive and quantitative assessment of liver fat content was made possible by recent technological advances. Attenuation coefficient (ATT) measurement is a noninvasive and quantitative liver fat measurement method used in clinical practice. The ATT value is significantly associated with histological steatosis grade. The diagnostic accuracy of ATT for histological steatosis grade is equivalent to controlled attenuation parameter (CAP), and ATT has a lower measurement failure rate than CAP because ATT can be measured on a B-mode image with the exact location of the region of interest. Furthermore, ATT measurement has high interobserver reproducibility. Since ATT measurement and other ultrasound-based modalities for liver fat quantification are easy to perform and inexpensive, these modalities are suitable for point-of-care and screening. Although emerging data suggest that quantitative liver fat content and its changes over time may be associated with disease progression in nonalcoholic fatty liver disease, the association between ATT and disease progression has not been evaluated yet. Therefore, further investigation and validation studies are necessary to strengthen the clinical significance of ATT measurement in chronic liver disease.

Measurements of Serum Mac-2-Binding Protein Glycosylation Isomer and Shear Wave Velocity in Health Checkups Are Useful in Screening for Non-Alcoholic Steatohepatitis

Liver-related mortality rates in patients with non-alcoholic steatohepatitis (NASH) increase with advancing liver fibrosis stage. The present study aimed to elucidate whether adding non-invasive liver fibrosis tests to a comprehensive health checkup system is useful for NASH screening. Both serum Mac-2-binding protein glycosylation isomer (M2BPGi) and point shear wave elastography (pSWE) using ultrasonography were performed for 483 health checkup subjects who consented to participate in this prospective study. Outcomes in positive subjects were surveyed 1 year later. Eighty-eight subjects (18%) showed positive results for at least one liver fibrosis test, with 63 subjects positive for pSWE, 33 subjects positive for M2BPGi, and 72 subjects showing no significant elevation of liver enzymes. The secondary consultation rate for positive subjects was 52% (46/88). However, as 15 of those 46 subjects visited a non-liver-specialist and could not undergo detailed examination, the secondary examination rate was only 35% (31/88). For the 31 subjects who received secondary examination, NASH was diagnosed in 14 subjects, other chronic liver disease (CLD) in 6 subjects, and no CLD in 11 subjects. Additional liver fibrosis tests using M2BPGi and pSWE appear useful in health checkups when screening for CLD, especially for NASH.

The role of mucosal barriers in human gut health

The intestinal mucosa is continuously exposed to a large number of commensal or pathogenic microbiota and foreign food antigens. The intestinal epithelium forms a dynamic physicochemical barrier to maintain immune homeostasis. To efficiently absorb nutrients from food, the epithelium in the small intestine has thin, permeable layers spread over a vast surface area. Epithelial cells are renewed from the crypt toward the villi, accompanying epithelial cell death and shedding, to control bacterial colonization. Tight junction and adherens junction proteins provide epithelial cell-cell integrity. Microbial signals are recognized by epithelial cells via toll-like receptors. Environmental signals from short-chain fatty acids derived from commensal microbiota metabolites, aryl hydrocarbon receptors, and hypoxia-induced factors fortify gut barrier function. Here we summarize recent findings regarding various environmental factors for gut barrier function. Further, we discuss the role of gut barriers in the pathogenesis of human intestinal disease and the challenges of therapeutic strategies targeting gut barrier restoration.

High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis

Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed a predictive model on the basis of 55 routine laboratory and clinical parameters by machine learning algorithms as a novel non-invasive method for liver fibrosis diagnosis. The model was further evaluated on the accuracy and rationality and proved to be highly accurate and efficient for the prediction of HBV-related fibrosis. In conclusion, we suggested a potential combination of high-dimensional clinical data and machine learning predictive algorithms for the liver fibrosis diagnosis.