Serum immunoglobulin A levels: Diagnostic utility in alcoholic liver disease and association with liver fibrosis in steatotic liver disease
- Authors:
- Published online on: August 1, 2024 https://doi.org/10.3892/br.2024.1830
- Article Number: 142
-
Copyright: © Ichikawa et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Immunoglobulin A (IgA) is a component of the balance between bacterial colonization and containment in the intestines (1,2). The importance of gut microbial metabolites in regulating IgA production has been reported previously (3).
The liver is a frontline organ that receives gut-derived products through the portal vein; thus, the liver can be severely affected by disrupted intestinal homeostasis (4). A retrospective analysis reported that advancing cirrhosis, irrespective of the underlying etiology or hepatocellular carcinoma, resulted in progressively increasing serum IgG and IgA levels (5). IgA secretion and Fc receptor γ signaling aggravate hepatic fibrosis in mice and patients with non-alcoholic steatohepatitis (NASH) (6). Additionally, the positive correlation between serum IgA levels and activated Fc receptor γ-positive hepatic myeloid cells, as well as the extent of liver fibrosis, has been reported (6). Moreover, the association between elevated serum IgA level and advanced liver disease was demonstrated in steatotic liver diseases (SLDs), including alcoholic liver disease (ALD) and metabolic dysfunction-associated SLD (MASLD) (5-8).
As ALD and MASLD have a heavy disease burden on a global basis, the diagnosis of advanced fibrosis in SLD is commonly required in primary medicine (9,10). Additionally, since the twin epidemics of obesity and type 2 diabetes mellitus (T2DM) also increase the incidence of MASLD, non-invasive tests (NITs) have been used to identify patients with non-alcoholic fatty liver disease (NAFLD) and those who are at risk of liver disease progression (11). Patients at risk for MASLD [those with T2DM, obesity or chronically elevated alanine aminotransferase (ALT) levels] have been screened for fibrosis-4 (FIB-4) (11,12). As a FIB-4 level >1.3 is related to a moderate-to-high risk for liver fibrosis, these patients should be assessed using second-line NITs (11,12). Liver stiffness (LS), measured using a FibroScan® device (Echosens), is the most useful second-line tool for assessing liver fibrosis in SLD (13). LS >8 kPa indicates an intermediate or high risk of advanced liver fibrosis (F2-F4 by biopsy) (11-13). Macrophage galactose-specific lectin-2 binding protein glycosylation isomer (M2BPGi) is also associated with advanced liver fibrosis in MASLD (14).
The association between IgA and metabolic syndrome is mediated via gut microbiota (15). Serum IgA may bind to these gut microbial antigens, restrict their toxicity and control gut microbial antigens in the circulation, thereby reducing systemic inflammation (15). Decreased IgG and IgM levels, and increased IgA levels are independently associated with T2DM prevalence in the adult population (16). Poor glycemic management may be associated with elevated serum IgA levels and IgG antibodies in patients with T2DM (17). Furthermore, the onset of T2DM is predicted by visceral fat mass and the ratio of visceral to subcutaneous fat mass evaluated using computed tomography (CT) (18). Visceral fat mass is an important prognostic marker of liver disease and sarcopenia (19).
The present study investigated the significance of serum IgA levels in patients with liver disease who were initially diagnosed in the Department of Gastroenterology in Nagasaki Harbor Medical Center (Nagasaki, Japan). As the association between NITs (LS, FIB-4 and M2BPGi) and IgA levels has not been reported, a focus was placed on such NITs Additionally, the associations between body composition and IgA levels were evaluated in patients who underwent CT.
Materials and methods
Patients
In total, 478 patients first diagnosed with liver disease in Nagasaki Harbor Medical Center between May 2017 and October 2023 were initially included in the present study (Table I; Fig. S1A). The median patient age was 68 years (range, 27-84 years). A total of 249 patients were female and 229 were male. Of them, clinically, 18 patients presented with autoimmune hepatitis, 64 patients presented with ALD and 54 patients presented with the treatment-naïve hepatitis B virus (HBV). Furthermore, 114 patients had a treatment-naïve hepatitis C virus (HCV) infection, 1 had a treatment-naïve HBV and HCV infection, 129 had MASLD and 24 had treatment-naïve primary biliary cholangitis. Another 2 patients had treatment-naïve primary sclerosing cholangitis. The diagnosis of fatty liver was obtained by ultrasound echography. ALD was diagnosed using the new nomenclature (20). Metabolic and alcohol related/associated liver disease Met-ALD (20) was included in the definition of MASLD in this study, whereas SLD included both ALD and MASLD. A further 72 patients had other treatment-naïve liver diseases (e.g., unknown cause or drug-induced liver damage). T2DM was defined as follows: Fasting serum glucose ≥100 mg/dl, 2-h post-load glucose levels ≥140 mg/dl, HbA1c ≥5.7%, diagnosed as T2DM at the first visit or receiving treatment for T2DM (20).
Of the 478 patients, 353 patients with liver disease were evaluated with the FibroScan device. The clinical characteristics of these patients are presented in Table SI. LS (kPa) was evaluated using vibration-controlled transient elastography, and liver fat content (dB/m) was evaluated using the controlled attenuation parameter (CAP), both functions of FibroScan. Of the 478 patients, 270 patients with liver disease were evaluated using CT for hepatoma screening. The clinical characteristics of these patients are presented in Table SII. Cross-sectional CT images of the third lumbar vertebrae (L3) were analyzed using Slice-O-Matic software (version 5.0; TomoVision) to determine the skeletal muscle (SM) mass, including the psoas, erector spinae, quadratus lumborum, transversus abdominis, external and internal obliques, and rectus abdominis muscles. Tissue Hounsfield unit (HU) thresholds were employed as follows: 29 to 150 HU for SM, 190 to 30 for subcutaneous adipose tissue and 150 to 50 for visceral adipose tissue (VAT) (21). The visceral-to-subcutaneous fat ratio (VSR) is an index of VAT divided by SAT.
The medical records of 478 patients were retrospectively reviewed, and all laboratory measurements were obtained from these records. Informed consent was obtained from each patient included in the study, and they were guaranteed the right to leave the study if desired. The study protocol conformed to the guidelines of the 1975 Declaration of Helsinki (22) and was approved by the Human Research Ethics Committee of Nagasaki Harbor Medical Center (approval no. H30-031).
Laboratory measurements
Laboratory data and anthropometric measurements were obtained from each participant during outpatient visits. The body mass index (BMI) of each patient was calculated by dividing their weight (kg) by the square of their height (m). The normal BMI range is 20-25 kg/m2. Grip strength was measured using a dynamometer (Smedley Dynamo Meter; Tsutsumi Co., Ltd.) with the participants standing in an erect position with both arms at their sides. The normal laboratory ranges used were as follows: Total bilirubin, 0.3-1.2 mg/dl; albumin, 3.8-5.2 g/dl; prothrombin time international normalized ratio, 0.85-1.15; creatinine (Cr) for male patients (M), 0.61-1.04 mg/dl, and for female patients (F), 0.47-0.79 mg/dl; Cr-estimated glomerular filtration rate (eGFR), <90 ml/min/1.73 m2; cystatin C (CysC) for M, 0.63-0.95 mg/l, and for F, 0.56-0.87 mg/l; CysC-eGFR, <90 ml/min/1.73 m2; platelets for M, 13.1-26.2x104/µl, and for F, 13.0-36.9x104/µl; aspartate aminotransferase (AST), 10-40 U/l; ALT, 5-40 U/l; M2BPGi, less than the cut-off index (C.O.I.) value of 1; α-fetoprotein (AFP), <10 ng/ml; protein induced by vitamin K absence or antagonist-II, <40 mAU/ml; IgG, <1,700 mg/dl; IgM for M, <190 mg/dl and for F, <260 mg/dl; and IgA <410 mg/dl (Fig. S1B). The Child-Pugh score (CPS) (23), model of end-stage liver disease (24), albumin-bilirubin score (ALBI) (25), FIB-4(26) and Fibroscan-AST score (FAST) (27) were calculated as previously reported. A normal FIB-4 score is <1.3 (11,12).
Statistical analysis
Data were analyzed using StatFlex (version 6.0; Artech LLC) and are presented as the median and 95% confidence interval (CI). Laboratory variables were compared using Mann-Whitney U tests (for differences between two groups) and Kruskal-Wallis tests (for differences between three groups). Multiple comparisons among independent groups were conducted using Dunn's post hoc test. A multiple regression analysis was performed, and a standardized partial regression coefficient, β, was employed. Univariate and multivariate analyses were performed using logistic regression. Correlations were evaluated using the Pearson's correlation coefficient (R). The detection level was analyzed using receiver operating characteristic (ROC) curves. P<0.05 was used to indicate a statistically significant difference.
Results
First, the associations between IgA levels and clinical factors were evaluated (Table II). If the clinical factors were continuous data, the correlation between the serum IgA titer and clinical factors was evaluated. If the clinical factors were grouped, a Mann-Whitney U analysis was performed. The results of the analysis showed that sex, ALD, SLD, CPG, ALBI, FIB-4, M2BPGi, BMI, T2DM, AFP, total protein, albumin and IgG levels were significantly associated with IgA levels (Table II). Of these factors, continuous data were then evaluated by multiple regression analysis for serum IgA levels (Fig. 1A), demonstrating that ALBI, AFP, CPS, IgG and BMI were significantly associated with serum IgA levels. The R values (P-values) in relation to IgA and LS were 0.4609 (<0.00001) and 0.5997 (<0.00001) in MASLD and ALD, respectively. Factors contributing to high serum IgA levels (high IgA; >410 mg/dl) were analyzed using logistic regression analysis. After including CPGBC, ALD, IgG 1,700 mg/dl (higher than normal range), M2BPGiH (higher than normal range), T2DM, sex, AFP 10 ng/ml (higher than normal range), BMI (>25 kg/m2) and FIB 2.67 [>2.67(28)] in the analysis, it was found that CPGBC, ALD, high IgG, high M2BPGiH and T2DM were contributing factors for high IgA levels (Fig. 1B). In the multivariate logistic model, SLD did not contribute to high IgA levels when ALD (Fig. 1B) was changed to SLD (odds ratio, 1.708; 95% CI, 0.962-3.031). The characteristics of patients with ALD were compared with those of patients with MASLD and non-SLD. In patients with ALD, serum IgG levels were lower compared with those in patients with non-SLD, but not compared with those in patients with MASLD (Fig. 2A). Serum IgA levels in patients with ALD were higher than those in patients with MASLD and non-SLD (Fig. 2B). The IgA/G ratio (serum IgA divided by IgG) was higher in the patients with ALD than that in the patients with MASLD and non-SLD (Fig. 2C). An attempt was made to determine the difference between ALD and non-ALD using serum IgG and IgA levels and IgA/G ratio by ROC analysis (Fig. 2D). The cutoff value was set at the point where sensitivity and specificity are equal. The cut-off point for IgG was 1,358.1 mg/dl (sensitivity, 0.5625), that for IgA was 305.7 mg/dl (sensitivity, 0.614) and the IgA/G ratio was 0.2 (sensitivity, 0.6715). The IgA/G ratio was therefore more valuable than IgG and IgA levels in distinguishing patients with ALD from those with non-ALD.
Next, the associations between IgA levels and LS were evaluated (Table SI; Fig. 3). LS was compared with NITs (M2BPGi and FIB-4), IgG and IgA levels, and IgA/G ratio. Multivariate regression analysis revealed that, in the entire cohort (478 cases), IgA levels, IgA/G ratio, M2BPGi and FIB-4 were associated with LS levels (Fig. 3A). In the SLD group (169 cases), IgA levels and the IgA/G ratio were associated with LS levels (Fig. 3B); however, in the non-SLD group (309 cases), only M2BPGi was significantly associated with LS levels (Fig. 3C). In the SLD group, IgA levels, M2BPGi and FIB-4 were compared for their association with high LS (>8 kPa) using ROC analysis. IgA levels (AUC, 0.79362), M2BPGi (AUC, 0.84439) and FIB-4 (AUC, 0.78391) were equally useful for diagnosing high LS (Fig. 3D). The associations between IgA levels and CAP were evaluated, but no significant association was found (Table SIII). CAP values were positively correlated with BMI and negatively correlated with age and ALBI (Table SIII). IgA showed a correlation with FAST in both males (Fig. S2A) and females (Fig. S2B). However, there was no correlation between IgA and CAP in males (Fig. S2A and C), while a weak correlation with CAP was observed in females (Fig. S2B and D)
Next, the associations between IgA levels and body composition were evaluated (Tables III and SII). IgA levels were associated with SM, VAT, VSR and BMI (Table III). In particular, a weak correlation was observed between VAT and IgA, and between VSR and IgA in females (Fig. S3). No association was found between IgA and SM or IgA and BMI in women (Fig. S3). Since body composition is influenced by sex differences (19), the cut-off value for detecting high IgA levels was evaluated using ROC analysis. In males, the cut-off value (sensitivity) for high IgA level was 23.3 (0.525) for BMI, 121.3 (0.536) for SM and 1.2 (0.552) for VSR. No significant difference was observed in the area under the curve (AUC) among the three groups (Table IV; Fig. S4A). In females, the cut-off value (sensitivity) for high IgA level was 23.25 (0.571) for BMI, 85.18 (0.504) for SM and 0.7 (0.741) for VSR. Similarly, no significant difference was observed in the AUC among the three groups (Table IV; Fig. S4B). In the multivariate logistic analysis, high VSR contributed to high IgA levels in females but not in males (Table V).
 | Table IVCut-off value for BMI, SM and VSR for high serum immunoglobulin A level as per the receiver operating characteristic analysis. |
| Table VAssociation between body composition and high immunoglobulin A level analyzed using multivariate logistic analysis. |
Discussion
The present study showed that in chronic liver disease (CLD), CPGBC, ALD, high IgG (>1,700 mg/dl), high M2BPGi (>1) and T2DM are associated with high IgA levels. The IgA/G ratio was the highest in patients with ALD, followed by those with MASLD and non-SLD. High LS was associated with high IgA levels, and IgA level was more strongly associated with LS than with M2BPGi and FIB-4. IgA level was associated with VSR and was particularly pronounced in females.
Previous reports have shown that ALD is associated with high serum IgA levels (5,6,29). High IgA levels are related to severe liver disease, including ALD, and high IgG levels are also associated with decompensated cirrhosis (5). IgA levels are elevated in ALD, and an increased IgA/IgG ratio is highly suggestive of ALD (29). IgA/G ratio >0.2 (sensitivity, 0.6715) was more valuable than IgG and IgA levels in distinguishing patients with ALD from those with non-ALD. We consider that IgA level, in combination with IgG level, can be used as a biomarker for ALD. By contrast, SLD, including ALD and MASLD, did not contribute to high IgA levels in the present study. Unlike pathogenic bacteria, commensal bacteria do not induce a systemic IgG response but only a mucosal IgA response, which is different from the response to non-invasive strains of Salmonella, which are treated differently compared with pathogenic strains, even if the commensal bacteria are non-invasive (30). Since IgA levels in patients with ALD were higher than those in patients with MASLD in the present study, we hypothesized that alcohol consumption and metabolic abnormalities may have different effects on the gut microbiota, which may be reflected in the differences in IgA and IgG levels.
Furthermore, elevated IgA levels reflect the severity of liver disease, regardless of the cause of the liver disease (5). In the present study, as CPGBC contributed to high IgA levels, there was no contradiction to this result. Notably, M2BPGi, a marker of liver fibrosis, also contributed to high IgA levels. When examining the associations between LS, typical second-line NIT and IgA level, an association between high IgA level and high LS as a high-risk factor for advanced fibrosis (11-13) was observed in SLD. In SLD, IgA level was equivalent to M2BPGi and FIB-4 as a marker for discriminating advanced fibrosis. In a previous study using a mouse NASH model and patients with NASH, the levels of serum IgA secreted by the plasma cells of secondary lymphoid organs was shown to be elevated in patients with NAFLD and was an independent predictor of advanced fibrosis (6). In the present study, high IgA levels were associated with liver fibrosis in patients with SLD, including ALD. There are a variety of common mechanisms that cause the elevated IgA underlying both diseases, including alcoholic liver disease and NAFLD (31). A previous review (31) explored the similar downstream signaling events involved in the onset and progression of the two entities, which are not completely different, predominantly focusing on the gut microbiome. We hypothesize that among the downstream events, lipopolysaccharide and bacterial migration are associated with increased blood IgA. Therefore, we hypohesize that IgA level (>312 mg/dl) is a useful marker of advanced liver fibrosis in SLD.
T2DM also contributed to high IgA levels in the present study; however, BMI was not associated with IgA levels. Therefore, the association between IgA levels and body composition was evaluated. Poor glycemic control is reportedly associated with high IgA levels (17). Elevated VSR (≥1 in males and ≥0.5 in females) is an independent risk factor for T2DM development (18). Notably, VSR (>1.33 in males and >0.93 in females) independently predicted the outcomes (mortality) of hepatocellular carcinoma (21). In the present study, high VSR (≥0.7 in females) contributed to high IgA, but not in males. Sex differences in VSR were detected in previous studies (18,19), and other reports have described that high VSR, but not sex differences, predicts advanced fibrosis in NAFLD (32,33). In a previous study, VSR evaluated using CT was independently associated with VAT inflammation, and VSR was significantly associated with histological VAT inflammation in cirrhotic males but not in females (34). In females, the association between high IgA and high VSR was close; however, such an association in males should be further evaluated in the future. In the present study, SM was not associated with high IgA. However, a limitation in the field of clinical investigation of sarcopenic patients is the lack of a generally accepted definition coupled with the difficulty of adopting common diagnostic criteria (35). The association between sarcopenia and IgA in liver disease is a future challenge.
The present study had several limitations. Differentiation between ALD and MASLD was performed using medical records, and met-ALD was included in MASLD. Therefore, the association between alcohol consumption and serum IgA levels should be examined in the future. Additionally, treatment for diabetes was not considered. Thus, although T2DM contributed to high IgA levels, the glycemic control levels could not be evaluated. Finally, this was a single-hospital, small, retrospective study, and body composition factors associated with IgA were unknown in males. These issues should to be further examined in the future.
In conclusion, the present study demonstrated the usefulness of serum IgA measurements in CLD. IgA levels, in combination with IgG levels, are useful for the differential diagnosis of ALD. In SLD, IgA level is comparable to known NITs (FIB-4 and M2BPGi) in its ability to discriminate patients with advanced LS. T2DM is associated with high IgA levels regardless of sex, and visceral obesity (high VSR) is associated with high IgA levels in females. In the current era of increasing SLD, the evaluation of serum IgA level as a new NIT is important for the assessment of liver disease.
Supplementary Material
Study design and IgA distribution. (A) Study design. (B) Frequency distribution chart of IgA. Ig, immunoglobulin; CT, computed tomography.
Correlations between CAP, FAST and IgA. The correlation between IgA and FAST in (A) males and (B) females. The correlation between IgA and CAP in (C) males and (D) females. R represents the correlation coefficient, and stdβ denotes the standard partial regression coefficient. CAP, controlled attenuation parameter; FAST, Fibroscan-aspartate aminotransferase score; Ig, immunoglobulin.
Correlations between IgA level and body composition as distinguished by sex. Correlation between IgA and SM in (A) males and (B) females, between IgA and VAT in (C) males and (D) females, between IgA and VSR in (E) males and (F) females, and between IgA and BMI in (G) males and (H) females.r denotes the correlation coefficient. Ig, immunoglobulin; SM, skeletal muscle; VAT, visceral adipose tissue; VSR, visceral-to-subcutaneous fat ratio; BMI, body mass index.
Cut-off value for high IgA, BMI, SM and VSR were analyzed using ROC curves. The cut-off value had equal sensitivity and 1-specificity. The number of cases in the high IgA and control groups is shown numerically. ROC curves for (A) Males and (B) Females. Ig, immunoglobulin; BMI, body mass index; SM, skeletal muscle; VSR, visceral-tosubcutaneous fat ratio; ROC, receiver operating characteristic.
Clinical characteristics of 358 patients evaluated using FibroScan.
Clinical characteristics of 270 patients evaluated using computed tomography.
Controlled attenuation parameter and clinical characteristics of 358 patients evaluated using FibroScan.
Acknowledgements
Not applicable.
Funding
Funding: Not applicable.
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Author's contributions
TIc wrote the manuscript, analyzed the data and designed the study. TIc, MY, SY, MK, YN, HY, OM, TIk, TO, KNag, KS, KNi and KNak collected the data. TIc and MY, confirm the authenticity of all the raw data. All the authors have read and approved the final manuscript.
Ethics approval and consent to participate
The study protocol conformed to the guidelines of the 1975 Declaration of Helsinki, which was approved by the Human Research Ethics Committee of the Nagasaki Harbor Medical Center (Nagasaki, Japan; approval no. H30-031). Informed consent was obtained from each patient included in the study, and they were guaranteed the right to leave the study if desired.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
|
Pabst O, Cerovic V and Hornef M: Secretory IgA in the coordination of establishment and maintenance of the microbiota. Trends Immunol. 37:287–296. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Lycke NY and Bemark M: The regulation of gut mucosal IgA B-cell responses: Recent developments. Mucosal Immunol. 10:1361–1374. 2017.PubMed/NCBI View Article : Google Scholar | |
|
Takeuchi T, Miyauchi E, Kanaya T, Kato T, Nakanishi Y, Watanabe T, Kitami T, Taida T, Sasaki T, Negishi H, et al: Acetate differentially regulates IgA reactivity to commensal bacteria. Nature. 595:560–564. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Inamine T and Schnabl B: Immunoglobulin A and liver diseases. J Gastroenterol. 53:691–700. 2018.PubMed/NCBI View Article : Google Scholar | |
|
Doi H, Hayashi E, Arai J, Tojo M, Morikawa K, Eguchi J, Ito T, Kanto T, Kaplan DE and Yoshida H: Enhanced B-cell differentiation driven by advanced cirrhosis resulting in hyperglobulinemia. J Gastroenterol Hepatol. 2018.PubMed/NCBI View Article : Google Scholar : (Online ahead of print). | |
|
Kotsiliti E, Leone V, Schuehle S, Govaere O, Li H, Wolf MJ, Horvatic H, Bierwirth S, Hundertmark J, Inverso D, et al: Intestinal B-cells license metabolic T-cell activation in NASH microbiota/antigen-independently and contribute to fibrosis by IgA-FcR signalling. J Hepatol. 79:296–313. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Tomita K, Teratani T, Yokoyama H, Suzuki T, Irie R, Ebinuma H, Saito H, Hokari R, Miura S and Hibi T: Serum immunoglobulin A concentration is an independent predictor of liver fibrosis in nonalcoholic steatohepatitis before the cirrhotic stage. Dig Dis Sci. 56:3648–3654. 2011.PubMed/NCBI View Article : Google Scholar | |
|
Maleki I, Aminafshari MR, Taghvaei T, Hosseini V, Rafiei A, Torabizadeh Z, Barzin M and Orang E: Serum immunoglobulin A concentration is a reliable biomarker for liver fibrosis in non-alcoholic fatty liver. World J Gastroenterol. 20:12566–12573. 2014.PubMed/NCBI View Article : Google Scholar | |
|
Danpanichkul P, Ng CH, Muthiah MD, Duangsonk K, Yong JN, Tan DJH, Lim WH, Wong ZY, Syn N, Tsusumi T, et al: The silent burden of non-alcoholic fatty liver disease in the elderly : A global burden of disease analysis. Aliment Pharmacol Ther. 58:1062–1074. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E and Kamath PS: Global burden of liver disease: 2023 update. J Hepatol. 79:516–537. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Younossi ZM, Henry L, Isaacs S and Cusi K: Identification of high risk NAFLD patients in endocrinology clinics. Endocr Pract. 29:912–918. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Wattacheril JJ, Abdelmalek MF, Lim JK and 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. 165:1080–1088. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, Guha IN, Cobbold JF, Deeks JJ, Paradis V, et al: Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology. 156:1717–1730. 2019.PubMed/NCBI View Article : Google Scholar | |
|
Kiyoaki I, Sumida Y, Nakade Y, Okumura A, Nishimura S, Ibusuki M, Kitano R, Sakamoto K, Kimoto S, Inoue T, et al: Mac-2 binding protein glycosylation isomer, the FIB-4 index, and a combination of the two as predictors of non-alcoholic steatohepatitis. PLoS One. 17(e0277380)2022.PubMed/NCBI View Article : Google Scholar | |
|
Guo J, Han X, Huang W, You Y and Jicheng Z: Interaction between IgA and gut microbiota and its role in controlling metabolic syndrome. Obes Rev. 22(e13155)2021.PubMed/NCBI View Article : Google Scholar | |
|
Guo X, Meng G, Liu F, Zhang Q, Liu L, Wu H, Du H, Shi H, Xia Y, Liu X, et al: Serum levels of immunoglobulins in an adult population and their relationship with type 2 diabetes. Diabetes Res Clin Pract. 115:76–82. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Rafaqat S, Sattar A, Khalid A and Rafaqat S: Role of liver parameters in diabetes mellitus-a narrative review. Endocr Regul. 57:200–220. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Kim EH, Kim HK, Lee MJ, Bae SJ, Choe J, Jung CH, Kim CH, Park JY and Lee WJ: Sex differences of visceral fat area and visceral-to-subcutaneous fat ratio for the risk of incident type 2 diabetes mellitus. Diabetes Metab J. 46:486–498. 2022.PubMed/NCBI View Article : Google Scholar | |
|
Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D and Ross R: Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol (1985). 85:115–122. 1998.PubMed/NCBI View Article : Google Scholar | |
|
Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, Romero D, Abdelmalek MF, Anstee QM, Arab JP, et al: A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol. 79:1542–1556. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Fujiwara N, Nakagawa H, Kudo Y, Tateishi R, Taguri M, Watadani T, Nakagomi R, Kondo M, Nakatsuka T, Minami T, et al: Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma. J Hepatol. 63:131–140. 2015.PubMed/NCBI View Article : Google Scholar | |
|
Shephard DA: The 1975 declaration of helsinki and consent. Can Med Assoc J. 115:1191–1192. 1976.PubMed/NCBI | |
|
Tarantino G, Citro V, Esposit P, Giaquinto S, de Leone A, Milan G, Tripodi FS, Cirillo M and Lobello R: Blood ammonia levels in liver cirrhosis: A clue for the presence of portosystemic collateral veins. BMC Gastroenterol. 9(21)2009.PubMed/NCBI View Article : Google Scholar | |
|
Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, O'Beirne J, Fox R, Skowronska A, Palmer D, et al: Assessment of liver function in patients with hepatocellular carcinoma: A new evidence-based approach-the ALBI grade. J Clin Oncol. 33:550–558. 2015.PubMed/NCBI View Article : Google Scholar | |
|
Kamath P, Wiesner RH, Malinchoc M, Kremers W, Therneau TM, Kosberg CL, D'Amico G, Dickson ER and Kim WR: A model to predict survival in patients with end-stage liver disease. Hepatology. 33:464–470. 2001.PubMed/NCBI View Article : Google Scholar | |
|
Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, Fontaine H and Pol S: FIB-4: An inexpensive and accurate marker of fibrosis in HCV infection. Comparison with liver biopsy and FibroTest. Hepatology. 46:32–36. 2007.PubMed/NCBI View Article : Google Scholar | |
|
Newsome PN, Sasso M, Deeks JJ, Paredes A, Boursier J, Chan WK, Yilmaz Y, Czernichow S, Zheng MH, Wong VW, et al: FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: A prospective derivation and global validation study. Lancet Gastroenterol Hepatol. 5:362–373. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Mózes FE, Lee JA, Vali Y, Alzoubi O, Staufer K, Trauner M, Paternostro R, Stauber RE, Holleboom AG, van Dijk AM, et al: Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: An individual participant data meta-analysis. Lancet Gastroenterol Hepatol. 8:704–713. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Torruellas C, French SW and Medici V: Diagnosis of alcoholic liver disease. World J Gastroenterol. 20:11684–11699. 2014.PubMed/NCBI View Article : Google Scholar | |
|
Zagato E, Mazzini E and Rescigno M: The variegated aspects of immunoglobulin A. Immunol Lett. 178:45–49. 2016.PubMed/NCBI View Article : Google Scholar | |
|
Tarantino G and Citro V: What are the common downstream molecular events between alcoholic and nonalcoholic fatty liver? Lipids Health Dis. 23(41)2024.PubMed/NCBI View Article : Google Scholar | |
|
Jung CH, Rhee EJ, Kwon H, Chang Y, Ryu S and Lee WY: Visceral-to-subcutaneous abdominal fat ratio is associated with nonalcoholic fatty liver disease and liver fibrosis. Endocrinol Metab (Seoul). 35:165–176. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Hernández-Conde M, Llop E, Carrillo CF, Tormo B, Abad J, Rodriguez L, Perelló C, Gomez ML, Martínez-Porras JL, Puga NF, et al: Estimation of visceral fat is useful for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease. World J Gastroenterol. 26:6514–6705. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Ha NB, Cho SJ, Mohamad Y, Kent D, Jun G, Wong R, Swarnakar V, Lin S, Maher JJ and Lai JC: Visceral adipose tissue inflammation and radiographic visceral-to-subcutaneous adipose tissue ratio in patients with cirrhosis. Dig Dis Sci. 67:3436–3444. 2022.PubMed/NCBI View Article : Google Scholar | |
|
Tarantino G, Sinatti G, Citro V, Santini SJ and Balsano C: Sarcopenia, a condition shared by various diseases: Can we alleviate or delay the progression? Intern Emerg Med. 18:1887–1895. 2023.PubMed/NCBI View Article : Google Scholar |
