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Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models

  • Kailing Wang , Yong Li , Jianfeng Pan , Huifang He , Ziyi Zhao , Yiming Guo and Xiaomei Zhang EMAIL logo
Published/Copyright: September 28, 2022
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Open Medicine
From the journal Open Medicine Volume 17 Issue 1

Abstract

Autoimmune liver diseases (AILDs) are life-threatening chronic liver diseases, mainly including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and AIH–PBC overlap syndrome (OS), which are difficult to distinguish clinically at early stages. This study aimed to establish model to achieve the purpose of the diagnosis of AIH/PBC OS in a noninvasive way. A total of 201 AILDs patients were included in this retrospective study who underwent liver biopsy during January 2011 to December 2020. Serological factors significantly associated with OS were determined by the univariate analysis. Two multivariate models based on these factors were constructed to predict the diagnosis of AIH/PBC OS using logistic regression and random forest analysis. The results showed that immunoglobulins G and M had significant importance in both models. In logistic regression model, anti-Sp100, anti-Ro-52, anti-SSA, or antinuclear antibody positivity were risk factors for OS. In random forest model, activated partial thromboplastin time and ɑ-fetoprotein level were important. To distinguish PBC and OS, the sensitivity and specificity of logistic regression model were 0.889 and 0.727, respectively, and the sensitivity and specificity of random forest model were 0.944 and 0.818, respectively. In conclusion, we established two predictive models for the diagnosis of AIH/PBC OS in a noninvasive method and they showed better performance than Paris criteria for the definition of AIH/PBC OS.

Keywords: primary biliary cirrhosis; predictive model; immunoglobulin

1 Introduction

Autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis are main categories of autoimmune liver diseases (AILDs) caused by anomalous response of immune system to self-antigens on hepatocytes or bile ducts. AIH and PBC have different mechanisms and clinical manifestations. In AIH, autoimmune injury mainly affects the hepatocytes, leading to the presence of interface hepatitis in liver histology, and AIH is characterized by high levels of immunoglobulin G (IgG) or γ-globulin and some positive serum autoantibodies [1]. PBC is characterized by circulating anti-mitochondrial antibodies (AMAs), chronic cholestasis, and autoimmunity on the intrahepatic small bile ducts, and the liver biopsy findings show typical appearance of non-purulent granulomatous destructive cholangitis [2,3]. About 2–19% of patients share overlapping features of both PBC and AIH, and it is called PBC–AIH overlap syndrome (OS) [4,5,6,7]. OS may represent an important and unrecognized cause of resistance to ursodeoxycholic acid in patients with PBC [6]. If treated inappropriate, OS leads to the development of liver cirrhosis rapidly and even liver failure, which need liver transplantation [8]. Therefore, early diagnosis and proper treatment of OS are extremely important.

The sensitivity and specificity of the Paris criteria for OS were reported to be 92 and 97%, respectively [9]. However, patients with less severe forms of AIH–PBC OS may not be captured by the Paris criteria [10]. The gold standard for diagnosing AILDs is still liver pathology, which can evaluate the severity and prognosis and determine treatment options [11,12]. However, liver biopsy has some disadvantages. First, about one quarter of patients already develop cirrhosis at diagnosis [13], have poor blood clotting and a greater risk, and increase the possibility of complications. Second, sampling process errors occur in needle liver biopsies of AILDs, which may be affected by different extents of lesions. Finally, OS should not be over-diagnosed to avoid the risk of steroid side effects on PBC patients [14,15].

Therefore, a noninvasive method as a supplementation for biopsy is urgently needed to differentiate PBC and OS, which should be safe, convenient, accurate, and effective. This noninvasive approach helps integrate numerous clinical parameters, providing a reliable understanding of liver pathology features and prognosis. The artificial intelligence model is well suited to this challenge and has already been used to predict effects of different treatment options [16,17,18,19].

This study aimed to develop and validate a prediction model for predicting the diagnosis of AIH/PBC OS based on machine learning models. We utilized the AILD patient cohort and selected valuable parameters and weighed for their importance.

2 Patients and methods

2.1 Patient and public involvement

It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.

This study was approved by Ethics Committee of Xiangya Hospital of Central South University (Changsha, China, Approval ID: 20341), and all patients provided informed consent. A total of 201 patients with AILDs admitted to Xiangya Hospital between 2011 and 2020 were retrospectively analyzed.

Patients with AIH were selected based on the international group for the study of AIH simplified criteria [20]. These simplified diagnostic criteria for AIH have been validated in different countries including China [21,22,23]. PBC patients were classified according to the Paris criteria [10]. PBC–AIH OS was strictly defined by the association of PBC and AIH either simultaneously or consecutively [9]. In each patient, the absence of biliary obstruction was assessed by ultrasonography and hepatitis virus serology, and copper blue protein was negative. None had excessive alcohol consumption (<20 g/day), and there was no evidence of exposure to hepato- or bile duct toxicity. To ensure an accurate classification of AILDs, all patients had pathological results of liver biopsy. Among 201 patients with AILDs, 65 patients were excluded according to the exclusion criteria as follows: age <18 years (n = 2), virus hepatitis (n = 52), drug-induced liver injury (n = 3), liver cancer (n = 2), blood disease (n = 1), and incomplete information (n = 5).

2.2 Collection of clinical and pathology data

Information on each patient, including history as well as symptoms, clinical findings, and data from laboratory or other diagnostic investigations were obtained, including age and sex; clinical symptoms including pruritus, jaundice, and fatigue; red blood cell (RBC), white blood cell (WBC), platelet (PLT), lymphocyte count (L), eosinophil count (E), basophil count (B), monocyte count (M), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase, international normalized ratio (INR), activated partial thromboplastin time (APTT), IgG, immunoglobulin A (IgA), immunoglobulin M (IgM), and auto-antibody tests. All auto-antibody tests were performed using Western blot-based antibody detection kits (Oumeng Diagnostics Ltd., Germany).

2.3 Statistical analysis

Continuous variables were described as median and interquartile range (IQR), and categorical variables were described as absolute frequencies and percentages. SPSS 25.0 was utilized for statistical analysis. The chi-squared test or Fischer’s exact test was used to compare categorical data. For the parametric distribution, Student’s t-test was used to compare the mean values of two groups. For nonparametric variables, the Kruskal–Wallis test or Mann–Whitney U test was used. All P-values were two-sided, and P-values <0.05 were considered statistically significant.

2.4 Model establishment

A random forest model was constructed to distinguish PBC from OS using the statistical language R (version 3.3), with the packages random forest, pROC, and rms. The random forest creates multiple training sets for decision trees, wherein each tree is built based on a bootstrap sample drawn randomly from the original dataset using the classification and regression tree method and the decrease Gini impurity as the splitting criterion [24]. Of the final cohort included, 70% were selected for training and the remaining for validation. A 10-fold cross-validation strategy was performed during training. In the model, we determined 500 decision trees in the forest, and 5 variables were considered on each decision tree. The maximum depth of the trees was set to 3 [25]. The confidence intervals for area under the receiver-operating characteristic curve (AUC) and other evaluation criteria were constructed using bootstrapping. The one that produced an AUC closest to the average was considered the best trained model. The model was then verified in the testing dataset. We selected the AUC, accuracy, sensitivity, and specificity to evaluate the performance of the model. The input variables in our model were ranked by relative importance in diagnosing PBC based on the mean decrease in accuracy and the mean decrease in the Gini coefficient [26]. Moreover, we developed the logistic regression model using the same training data and testing data.

3 Results

3.1 Demographics and baseline features

The medical records of 201 AILD patients with liver biopsy were reviewed. Finally, 136 patients were enrolled in this study and divided into the PBC group (35, 25.74%), the AIH group (44, 32.35%), and the OS group (57, 41.91%) (Figure 1). We found no difference in the distribution of age or sex among the three groups. The general characteristics of the study participants are summarized in Table 1.

Figure 1 Brief scheme of the study design.
Figure 1

Brief scheme of the study design.

Table 1

Demographic characteristics of study subjects

Variables AIH (N = 44) PBC (N = 35) OS (N = 57) P*-value
Female 41 (93.2%) 27 (77.1%) 48 (84.2%) 0.129
Age, years 51.3 (10.1) 47.5 (12.0) 50.1 (9.6) 0.504
Symptoms
Jaundice 23 (52.3%) 22 (62.9%) 37 (64.9%) 0.406
Pruritus 0 (0%) 5 (14.3%) 7 (12.3%) 0.04
Fatigue 12 (27.3%) 7 (20%) 17 (29.8%) 0.584
Signs
Liver palms 5 (11.4%) 1 (2.9%) 9 (15.8%) 0.154
Spider angioma 4 (9.8%) 1 (2.9%) 7 (12.3%) 0.273
Lower limb edema 5 (11.4%) 6 (14.6%) 4 (7.0%) 0.325
Imaging results
Hepatomegaly 5 (11.4%) 6 (14.6%) 13 (22.8%) 0.319
Splenomegaly 20 (48.8%) 17 (48.6%) 45 (79.0%) 0.001
Ascites 10 (24.4%) 13 (31.7%) 12 (21.1%) 0.218
Complications
Other AIDs 8 (18.2%) 8 (22.9%) 14 (24.6%) 0.753
Gallbladder diseases 22 (50.0%) 14 (40.0%) 21 (36.8%) 0.412
Liver cirrhosis 17 (38.6%) 12 (34.3%) 33 (57.9%) 0.045
Varicose veins 4 (9.1%) 4 (11.4%) 15 (26.3%) 0.046
Infection 7 (15.9%) 6 (17.1%) 10 (17.5%) 0.976

Data are median (IQR) or n (%). A P-value of less than 0.05 is considered statistically significant among three groups.

*The P-value represents comparison between AIH, PBC, and OS within the test panel.

3.2 Comparison between AIH and other liver diseases

To explore the difference between AIH and other ALDs, we performed single-factor and multi-factor comparisons. The blood indexes of study subjects are shown in Table 2. In immunological index, more AIHs were positive for antibodies against M2-3E (BPO), while more PBC and OS were positive for AMA, and antinuclear antibody (ANA) showed significant differences. Sjogren’s syndrome (SS)-related SSA was associated with PBC [odds ratio (OR) 5.5, P = 0.02]. RO-52 was associated with OS (OR 0.26, P = 0.002).

Table 2

Blood indexes of patients studied

Parameters AIH (N = 44) PBC (N = 35) OS (N = 57) P*-value
WBC (109/L) 4.65 (1.67) 5.1 (2.3) 4.4 (2.4) 0.621
RBC (1,012/L) 4.07 (0.82) 3.9 (1.13) 3.7 (0.6) 0.114
Hb (g/L) 122.5 (33.5) 115.0 (32.0) 113 (28.5) 0.522
PLT (109/L) 161 (118.75) 166 (108.0) 166 (105.5) 0.727
N (109/L) 2.7 (1.13) 3 (2) 2.7 (1.5) 0.392
L (109/L) 1.3 (0.8) 2.3 (1.6) 1.78 (1.2) <0.001
E (109/L) 0.1 (1.18) 1.4 (0.8) 1.5 (0.7) <0.001
B (109/L) 0 (0) 0.1 (0.1) 0.1 (0.19) <0.001
M (109/L) 0.55 (0.3) 0.07 (0.10) 0.08 (0.11) <0.001
NLR (%) 178.63 (95.98) 231.82 (150) 178.26 (116.57) 0.337
ELR (%) 5 (11.11) 6.25 (10) 8 (10.55) 0.353
BLR (%) 0 (0) 0 (0) 0 (1.52) 0.42
MLR (%) 39.23 (27.74) 27.78 (21.43) 27.27 (0.213) 0.008
LPR (%) 0.88 (0.54) 0.82 (0.58) 0.91 (0.46) 0.451
Albumin (g/L) 35.5 (9.83) 38.2 (9.3) 38.5 (7.65) 0.066
Globulin (g/L) 38.65 (10.68) 34.6 (10.3) 43.8 (8.35) <0.001
TBIL (mol/L) 40.3 (84.68) 31.9 (54.6) 39.2 (50.1) 0.357
DBIL (mol/L) 24.25 (46.88) 19.8 (36.1) 20.4 (30) 0.467
Bile acid (mol/L) 37.6 (129.43) 52 (109.8) 41.5 (59.05) 0.989
ALT (U/L) 127.85 (322.55) 87.9 (121.8) 95.3 (96.65) 0.078
AST (U/L) 159.15 (327.58) 96 (95.99) 133.5 (102.75) 0.042
AAR (%) 129.74 (82.75) 120.19 (88.1) 139.41 (71.73) 0.487
ALP (U/L) 164.75 (167.85) 443 (430.3) 367.4 (518.3) <0.001
γ-GT (U/L) 151.8 (141.29) 381 (592.4) 348.8 (425.1) <0.001
5-Nucleotidase (U/L) 10.75 (14.65) 36.9 (95.7) 51.9 (69.1) <0.001
Fucosidase (U/L) 37.25 (16.82) 42.5 (25.8) 41.7 (27.35) 0.532
mAST (IU/L) 29.55 (40.64) 27.5 (30.4) 34.8 (24.8) 0.375
Triglicerides (mmol/L) 1.54 (0.82) 2.1 (0.98) 1.7 (0.89) 0.02
Cholesterol (mmol/L) 4.16 (1.48) 6.4 (3.84) 6.07 (2.39) <0.001
HDL (mmol/L) 1.005 (0.66) 1.56 (0.97) 1.63 (0.9) <0.001
LDL (mmol/L) 2.38 (0.86) 4.1 (2.1) 3.51 (1.46) <0.001
PT (s) 14.4 (3.25) 12.7 (1.6) 13.3 (2.25) <0.001
INR 1.13 (0.27) 0.99 (0.14) 1.05 (0.16) <0.001
APTT (s) 40.27 (7.1) 33.9 (9.0) 38.16 (7.8) 0.002
Fibrinogen (g/L) 2.22 (0.92) 3.15 (1.27) 2.9 (1.01) <0.001
D-dimer (mg/L) 0.25 (0.15) 0.16 (0.22) 0.18 (0.16) 0.238
K (mmol/L) 3.72 (0.7) 3.86 (0.42) 3.74 (0.41) 0.217
Na (mmol/L) 139.85 (3.05) 141.55 (3) 140.3 (4.6) 0.221
Cl (mmol/L) 104.17 (3.08) 104.27 (5) 103.7 (4.8) 0.621
Ca (mmol/L) 2.22 (0.15) 2.29 (0.2) 2.23 (0.16) 0.027
P (mmol/L) 1.12 (0.24) 1.17 (0.25) 1.12 (0.21) 0.373
Mg (mmol/L) 0.84 (0.09) 0.84 (0.11) 0.84 (0.08) 0.881
C3 (mg/L) 152 (75.5) 194 (118) 192 (79) 0.012
C4 (mg/L) 846.5 (346.5) 1,130 (564) 1,030 (335.5) <0.001
IgG (g/L) 22.25 (10.55) 14.3 (4.9) 22.4 (6.4) <0.001
IgA (mg/L) 3,220 (2,265) 2695.52 (1,840) 3740 (2,060) 0.002
IgM (mg/L) 1,570 (1102.5) 2,430 (2,220) 3,950 (2,635) <0.001

Data are median (IQR). Hb, hemoglobin count; N, neutrophil count; NLR, neutrophil lymphocyte ratio; ELR, eosinophil lymphocyte ratio; BLR, basophil lymphocyte ratio; MLR, monocyte lymphocyte ratio; LPR, lymphocyte platelet ratio; TBIL, total bilirubin; DBIL, direct bilirubin; AAR, AST and ALT ratio; γ-GT, γ-glutamyl transferase; mAST, mitochondrial aspartate aminotransferase; C4, complement C4; C3, complement C3.

*A P-value of less than 0.05 is considered statistically significant.

We put the variables with P-values less than 0.05 in the univariate analysis into the logistic regression analysis. The AUC between AIH and PBC and between AIH and OS was 1.

Comparison between PBC and OS showed that Sp100, RO-52, and SSA were associated with OS: Sp100 (OR [95% confidence interval (CI)]: 0.30 [0.09–0.99], P = 0.041), RO-52 (OR [95% CI]: 0.313 [0.13–0.77], P = 0.01), and SSA (OR [95% CI]: 0.12 [0.03–0.56], P = 0.002) (Table 3).

Table 3

Comparison between PBC and OS

Parameters PBC (N = 35) OS (N = 57) P-value
Sp100 (n, %) 4 (11.43%) 17 (29.82%) 0.041
RO-52 (n, %) 10 (28.57%) 32 (56.14%) 0.010
SSA (n, %) 2 (5.71%) 19 (33.33%) 0.002
ANA (n, %) 7 (20.00%) 0 (0.00%) 0.006
Globulin (g/L) 34.6 (10.3) 43.8 (8.35) 0.000
IgG (g/L) 14.3 (4.9) 22.4 (6.4) 0.000
IgA (mg/L) 2695.52 (1,840) 3,740 (2,060) 0.001
IgM (mg/L) 2,430 (2,220) 3,950 (2,635) 0.002

A P-value of less than 0.05 is considered statistically significant.

3.3 Construction of model to discriminate PBC and OS

To construct prediction model to discriminate PBC and OS, first we ranked the variables used to construct the model in the order of importance (Figure 2a). For the model based on random forest after over 50 replications, we chose a model which displayed the receiver-operating characteristic (ROC) under the selected training and testing cohort with the AUC of 0.93 (95% CI: 0.91–0.99), the closest to the average. According to the mean decrease in the Gini value, we found that the five strongest predictor variables used to differentiate PBC from OS were ɑ-fetoprotein (AFP), APTT, globulin (GLB), IgG, and IgM (Figure 2b). After validation in the remaining 30% of samples, the random forest model could predict the outcome with the sensitivity of 0.944 and the specificity of 0.818. The effects of these five variables on the outcome of diagnosing PBC are shown in Figure 3.

Figure 2 The importance ranking of variables predicting the diagnosis of PBC and OS. (a) OS-related variables ranked by logistic regression analysis (P＜0.05). (b) OS-related variables ranked by random forest analysis. SSA, anti-SSA antibody; Sp100, anti-Sp100 antibody; P, serum phosphorus level; K, serum potassium level; TG, triglyceride; C4, complement 4; BA, serum bile acid; DB, direct bilirubin; TB, total bilirubin; FIB, fibrin; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein.
Figure 2

The importance ranking of variables predicting the diagnosis of PBC and OS. (a) OS-related variables ranked by logistic regression analysis (P＜0.05). (b) OS-related variables ranked by random forest analysis. SSA, anti-SSA antibody; Sp100, anti-Sp100 antibody; P, serum phosphorus level; K, serum potassium level; TG, triglyceride; C4, complement 4; BA, serum bile acid; DB, direct bilirubin; TB, total bilirubin; FIB, fibrin; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein.

Figure 3 The effects of five important variables on the outcome of diagnosing OS. (a) GLB. (b) IgG. (c) IgM. (d) APTT. (e) AFP.
Figure 3

The effects of five important variables on the outcome of diagnosing OS. (a) GLB. (b) IgG. (c) IgM. (d) APTT. (e) AFP.

We also established prediction model applying binary logistic regression algorithms with the sensitivity of 0.889 and the specificity of 0.727 after testing in the same data. The AUC for this model was 0.78 (95% CI: 0.63–0.86), which was lower than that of the random forest model (Figure 4).

Figure 4 The ROC curve of predictive models for distinguish PBC with OS. RF, random forest; LR, logistic regression.
Figure 4

The ROC curve of predictive models for distinguish PBC with OS. RF, random forest; LR, logistic regression.

4 Discussion

In this retrospective study, we conducted single-factor and multi-factor analysis of patients with AILDs. When AIH was compared with PBC and OS, many variables showed significant differences, indicating that it is easy to distinguish them. However, it is difficult to distinguish PBC from OS, because the two diseases lack unique clinical characteristics [6]. In fact, there is no international consensus on the definition of OS in daily practice, especially on the histological level. The percentage of OS patients was higher than usual, which may be related to the fact that liver biopsy is not necessary for the diagnosis of PBC alone [11]. Moreover, we excluded some patients with comorbidities, which may have some selection bias. For AIH and OS, liver biopsy is necessary, and the early detection rate of OS patients has increased because of liver biopsy [12]. Therefore, we established two prediction models to reduce unnecessary operations and make effective early diagnosis. The results showed that the models had powerful competence to differentiate PBC and OS.

In our study, it is important to note that the magnitude of elevated IgG in OS was more than that in PBC. From a clinical point of view, hypergammaglobulinemia is one of the prominent clinical characteristics of AIH patients, and the decrease in IgG is an important aspect for disease control [27]. The presence of a combination of anti-double-stranded DNA (anti-dsDNA), elevated alanine aminotransferase (ALT), and elevated IgG levels should prompt a clinician to a potential diagnosis of OS [28]. We found similar result that OS is more prone to higher levels of immunoglobulins, IgG and IgM, in random forest model. Many autoantibodies have been detected in AILDs, and AMA has been recognized as specific targets of PBC [29]. However, in our study, we found no difference in AMA between PBC and OS, which may be related to the fact that OS has clinical traits of PBC. AMAs are not associated with disease progression, while ANAs are related to disease severity and clinical outcome, and are the markers of poor prognosis. In particular, ANAs are detected in up to 50% PBC patients. Two immunofluorescence patterns are considered PBC specific: the multiple nuclear dot patterns for antigens, such as Sp100 and promyelocytic leukemia protein, and rim-like/membranous patterns for antigens, such as gp210, nucleoporin p62, and the lamin B receptor [30,31]. In our study, the prevalence of ANA was 100% in AIH and 80% in PBC patients.

It was reported that sp100 had a sensitivity of 40% and a specificity of 97.3% for PBC [32]. OS also has the characteristics of PBC, and Sp100 and SSA may be potential autoantibodies to differentiate patients between PBC and OS. In fact, SSA and anticentromere antibody (ACA) are helpful for the diagnosis of PBC with SS, and SSA and ACA are recognized as serological markers of AMA negative PBC patients [33]. In our study, OS patients were more likely to have SS. Anti-dsDNA and anti-p53 have been suggested to be potential autoantibodies for identifying patients with OS [27,34].

Furthermore, gp210 antibodies in PBC are associated with severe prognosis [35]. One study found significantly higher frequency of anti-gp210 in patients with OS than in patients with PBC [36], indicating that OS has a worse prognosis. However, we did not detect significantly higher frequencies of gp210, dsDNA, and p53 in OS patients compared to PBC patients.

Logistic regression model showed that the combination of splenomegaly with Sp100, SSA, and IgG levels was able to differentiate patients with OS from those with PBC. In random forest model, APTT and AFP level were factors in the top 5. It has been reported that prothrombin time (PT) and APTT can be used as appropriate predictors of bleeding risk due to impaired liver synthesis and reduced procoagulant factors in cirrhosis [37]. However, other study showed that APTT abnormalities were poorly associated with bleeding [38]. Therefore, the importance of APTT is controversial and requires follow-up verification. Generally, the sensitivity and specificity of random forest model were better than those of logistic regression model, which is likely due to the strong generalization power of the random forest model [39].

Recently, Wang et al. developed a nice model based on limited sociodemographic and clinical parameters from routine health checkup to identify individuals at high risk for AILD [40]. In contrast, our model has unique advantages: it is noninvasive; it integrates multiple irrelevant variables simultaneously and evaluates the weight of each variable; it can be refined continuously as database enlarges and sensitive variables are constantly discovered. More importantly, our model could be used to distinguish PBC and OS. Similarly, Zhang et al. developed a scoring classification based on selected histologic features of AIH and PBC and modified biochemical and immunologic characteristics, and it showed a high sensitivity and specificity for the diagnosis of OS and may be better than current OS scoring systems to detect mild forms of OS [41].

It should be noted that our study has some imitations. First, we did not compare healthy control cohort. Second, our sample size is relatively small. Third, to improve the sensitivity and specificity of our model, we focused on the factors with importance ranking shown in Figure 1 and did not include other significant biomarkers or demographic factors, such as those used in previous study [40]. Further studies are needed to explore novel and sensitive parameters. With the enlargement of data, machine learning will gain power and become a promising approach to distinguishing PBC and OS.

In conclusion, we constructed two models with sufficient accuracy to predict the diagnosis of PBC and OS patients who probably benefit from early treatment based on readily available parameters. Following the use in clinical practice, these models help patients with early and effective treatment and reduce surveillance liver biopsies in the future.

  1. Funding information: This work was supported by the Natural Science Foundation of China (No. 81873575).

  2. Author contributions: Xiaomei Zhang conceived the study. Kailing Wang and Yong Li contributed to the conception and design of the study. All the authors contributed to the collection of data and the analysis and interpretation of the data. Kailing Wang and Xiaomei Zhang contributed to the drafting of the article, and the other authors contributed to the critical revision of the article for important intellectual content. Xiaomei Zhang supervised this study. All the authors approved the final draft of the article.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: All data are available upon request.

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Received: 2022-01-14
Revised: 2022-06-21
Accepted: 2022-06-24
Published Online: 2022-09-28

© 2022 Kailing Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/med-2022-0526
Keywords for this article
primary biliary cirrhosis; predictive model; immunoglobulin
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. AMBRA1 attenuates the proliferation of uveal melanoma cells
  3. A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma
  4. Differences in complications between hepatitis B-related cirrhosis and alcohol-related cirrhosis
  5. Effect of gestational diabetes mellitus on lipid profile: A systematic review and meta-analysis
  6. Long noncoding RNA NR2F1-AS1 stimulates the tumorigenic behavior of non-small cell lung cancer cells by sponging miR-363-3p to increase SOX4
  7. Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data
  8. Plasmapheresis: Is it a potential alternative treatment for chronic urticaria?
  9. The biomarkers of key miRNAs and gene targets associated with extranodal NK/T-cell lymphoma
  10. Gene signature to predict prognostic survival of hepatocellular carcinoma
  11. Effects of miRNA-199a-5p on cell proliferation and apoptosis of uterine leiomyoma by targeting MED12
  12. Does diabetes affect paraneoplastic thrombocytosis in colorectal cancer?
  13. Is there any effect on imprinted genes H19, PEG3, and SNRPN during AOA?
  14. Leptin and PCSK9 concentrations are associated with vascular endothelial cytokines in patients with stable coronary heart disease
  15. Pericentric inversion of chromosome 6 and male fertility problems
  16. Staple line reinforcement with nebulized cyanoacrylate glue in laparoscopic sleeve gastrectomy: A propensity score-matched study
  17. Retrospective analysis of crescent score in clinical prognosis of IgA nephropathy
  18. Expression of DNM3 is associated with good outcome in colorectal cancer
  19. Activation of SphK2 contributes to adipocyte-induced EOC cell proliferation
  20. CRRT influences PICCO measurements in febrile critically ill patients
  21. SLCO4A1-AS1 mediates pancreatic cancer development via miR-4673/KIF21B axis
  22. lncRNA ACTA2-AS1 inhibits malignant phenotypes of gastric cancer cells
  23. circ_AKT3 knockdown suppresses cisplatin resistance in gastric cancer
  24. Prognostic value of nicotinamide N-methyltransferase in human cancers: Evidence from a meta-analysis and database validation
  25. GPC2 deficiency inhibits cell growth and metastasis in colon adenocarcinoma
  26. A pan-cancer analysis of the oncogenic role of Holliday junction recognition protein in human tumors
  27. Radiation increases COL1A1, COL3A1, and COL1A2 expression in breast cancer
  28. Association between preventable risk factors and metabolic syndrome
  29. miR-29c-5p knockdown reduces inflammation and blood–brain barrier disruption by upregulating LRP6
  30. Cardiac contractility modulation ameliorates myocardial metabolic remodeling in a rabbit model of chronic heart failure through activation of AMPK and PPAR-α pathway
  31. Quercitrin protects human bronchial epithelial cells from oxidative damage
  32. Smurf2 suppresses the metastasis of hepatocellular carcinoma via ubiquitin degradation of Smad2
  33. circRNA_0001679/miR-338-3p/DUSP16 axis aggravates acute lung injury
  34. Sonoclot’s usefulness in prediction of cardiopulmonary arrest prognosis: A proof of concept study
  35. Four drug metabolism-related subgroups of pancreatic adenocarcinoma in prognosis, immune infiltration, and gene mutation
  36. Decreased expression of miR-195 mediated by hypermethylation promotes osteosarcoma
  37. LMO3 promotes proliferation and metastasis of papillary thyroid carcinoma cells by regulating LIMK1-mediated cofilin and the β-catenin pathway
  38. Cx43 upregulation in HUVECs under stretch via TGF-β1 and cytoskeletal network
  39. Evaluation of menstrual irregularities after COVID-19 vaccination: Results of the MECOVAC survey
  40. Histopathologic findings on removed stomach after sleeve gastrectomy. Do they influence the outcome?
  41. Analysis of the expression and prognostic value of MT1-MMP, β1-integrin and YAP1 in glioma
  42. Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm
  43. miR-223-3p alleviates TGF-β-induced epithelial-mesenchymal transition and extracellular matrix deposition by targeting SP3 in endometrial epithelial cells
  44. Clinical value of SIRT1 as a prognostic biomarker in esophageal squamous cell carcinoma, a systematic meta-analysis
  45. circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8
  46. miR-22-5p regulates the self-renewal of spermatogonial stem cells by targeting EZH2
  47. hsa-miR-340-5p inhibits epithelial–mesenchymal transition in endometriosis by targeting MAP3K2 and inactivating MAPK/ERK signaling
  48. circ_0085296 inhibits the biological functions of trophoblast cells to promote the progression of preeclampsia via the miR-942-5p/THBS2 network
  49. TCD hemodynamics findings in the subacute phase of anterior circulation stroke patients treated with mechanical thrombectomy
  50. Development of a risk-stratification scoring system for predicting risk of breast cancer based on non-alcoholic fatty liver disease, non-alcoholic fatty pancreas disease, and uric acid
  51. Tollip promotes hepatocellular carcinoma progression via PI3K/AKT pathway
  52. circ_0062491 alleviates periodontitis via the miR-142-5p/IGF1 axis
  53. Human amniotic fluid as a source of stem cells
  54. lncRNA NONRATT013819.2 promotes transforming growth factor-β1-induced myofibroblastic transition of hepatic stellate cells by miR24-3p/lox
  55. NORAD modulates miR-30c-5p-LDHA to protect lung endothelial cells damage
  56. Idiopathic pulmonary fibrosis telemedicine management during COVID-19 outbreak
  57. Risk factors for adverse drug reactions associated with clopidogrel therapy
  58. Serum zinc associated with immunity and inflammatory markers in Covid-19
  59. The relationship between night shift work and breast cancer incidence: A systematic review and meta-analysis of observational studies
  60. LncRNA expression in idiopathic achalasia: New insight and preliminary exploration into pathogenesis
  61. Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway
  62. Moscatilin suppresses the inflammation from macrophages and T cells
  63. Zoledronate promotes ECM degradation and apoptosis via Wnt/β-catenin
  64. Epithelial-mesenchymal transition-related genes in coronary artery disease
  65. The effect evaluation of traditional vaginal surgery and transvaginal mesh surgery for severe pelvic organ prolapse: 5 years follow-up
  66. Repeated partial splenic artery embolization for hypersplenism improves platelet count
  67. Low expression of miR-27b in serum exosomes of non-small cell lung cancer facilitates its progression by affecting EGFR
  68. Exosomal hsa_circ_0000519 modulates the NSCLC cell growth and metastasis via miR-1258/RHOV axis
  69. miR-455-5p enhances 5-fluorouracil sensitivity in colorectal cancer cells by targeting PIK3R1 and DEPDC1
  70. The effect of tranexamic acid on the reduction of intraoperative and postoperative blood loss and thromboembolic risk in patients with hip fracture
  71. Isocitrate dehydrogenase 1 mutation in cholangiocarcinoma impairs tumor progression by sensitizing cells to ferroptosis
  72. Artemisinin protects against cerebral ischemia and reperfusion injury via inhibiting the NF-κB pathway
  73. A 16-gene signature associated with homologous recombination deficiency for prognosis prediction in patients with triple-negative breast cancer
  74. Lidocaine ameliorates chronic constriction injury-induced neuropathic pain through regulating M1/M2 microglia polarization
  75. MicroRNA 322-5p reduced neuronal inflammation via the TLR4/TRAF6/NF-κB axis in a rat epilepsy model
  76. miR-1273h-5p suppresses CXCL12 expression and inhibits gastric cancer cell invasion and metastasis
  77. Clinical characteristics of pneumonia patients of long course of illness infected with SARS-CoV-2
  78. circRNF20 aggravates the malignancy of retinoblastoma depending on the regulation of miR-132-3p/PAX6 axis
  79. Linezolid for resistant Gram-positive bacterial infections in children under 12 years: A meta-analysis
  80. Rack1 regulates pro-inflammatory cytokines by NF-κB in diabetic nephropathy
  81. Comprehensive analysis of molecular mechanism and a novel prognostic signature based on small nuclear RNA biomarkers in gastric cancer patients
  82. Smog and risk of maternal and fetal birth outcomes: A retrospective study in Baoding, China
  83. Let-7i-3p inhibits the cell cycle, proliferation, invasion, and migration of colorectal cancer cells via downregulating CCND1
  84. β2-Adrenergic receptor expression in subchondral bone of patients with varus knee osteoarthritis
  85. Possible impact of COVID-19 pandemic and lockdown on suicide behavior among patients in Southeast Serbia
  86. In vitro antimicrobial activity of ozonated oil in liposome eyedrop against multidrug-resistant bacteria
  87. Potential biomarkers for inflammatory response in acute lung injury
  88. A low serum uric acid concentration predicts a poor prognosis in adult patients with candidemia
  89. Antitumor activity of recombinant oncolytic vaccinia virus with human IL2
  90. ALKBH5 inhibits TNF-α-induced apoptosis of HUVECs through Bcl-2 pathway
  91. Risk prediction of cardiovascular disease using machine learning classifiers
  92. Value of ultrasonography parameters in diagnosing polycystic ovary syndrome
  93. Bioinformatics analysis reveals three key genes and four survival genes associated with youth-onset NSCLC
  94. Identification of autophagy-related biomarkers in patients with pulmonary arterial hypertension based on bioinformatics analysis
  95. Protective effects of glaucocalyxin A on the airway of asthmatic mice
  96. Overexpression of miR-100-5p inhibits papillary thyroid cancer progression via targeting FZD8
  97. Bioinformatics-based analysis of SUMOylation-related genes in hepatocellular carcinoma reveals a role of upregulated SAE1 in promoting cell proliferation
  98. Effectiveness and clinical benefits of new anti-diabetic drugs: A real life experience
  99. Identification of osteoporosis based on gene biomarkers using support vector machine
  100. Tanshinone IIA reverses oxaliplatin resistance in colorectal cancer through microRNA-30b-5p/AVEN axis
  101. miR-212-5p inhibits nasopharyngeal carcinoma progression by targeting METTL3
  102. Association of ST-T changes with all-cause mortality among patients with peripheral T-cell lymphomas
  103. LINC00665/miRNAs axis-mediated collagen type XI alpha 1 correlates with immune infiltration and malignant phenotypes in lung adenocarcinoma
  104. The perinatal factors that influence the excretion of fecal calprotectin in premature-born children
  105. Effect of femoral head necrosis cystic area on femoral head collapse and stress distribution in femoral head: A clinical and finite element study
  106. Does the use of 3D-printed cones give a chance to postpone the use of megaprostheses in patients with large bone defects in the knee joint?
  107. lncRNA HAGLR modulates myocardial ischemia–reperfusion injury in mice through regulating miR-133a-3p/MAPK1 axis
  108. Protective effect of ghrelin on intestinal I/R injury in rats
  109. In vivo knee kinematics of an innovative prosthesis design
  110. Relationship between the height of fibular head and the incidence and severity of knee osteoarthritis
  111. lncRNA WT1-AS attenuates hypoxia/ischemia-induced neuronal injury during cerebral ischemic stroke via miR-186-5p/XIAP axis
  112. Correlation of cardiac troponin T and APACHE III score with all-cause in-hospital mortality in critically ill patients with acute pulmonary embolism
  113. LncRNA LINC01857 reduces metastasis and angiogenesis in breast cancer cells via regulating miR-2052/CENPQ axis
  114. Endothelial cell-specific molecule 1 (ESM1) promoted by transcription factor SPI1 acts as an oncogene to modulate the malignant phenotype of endometrial cancer
  115. SELENBP1 inhibits progression of colorectal cancer by suppressing epithelial–mesenchymal transition
  116. Visfatin is negatively associated with coronary artery lesions in subjects with impaired fasting glucose
  117. Treatment and outcomes of mechanical complications of acute myocardial infarction during the Covid-19 era: A comparison with the pre-Covid-19 period. A systematic review and meta-analysis
  118. Neonatal stroke surveillance study protocol in the United Kingdom and Republic of Ireland
  119. Oncogenic role of TWF2 in human tumors: A pan-cancer analysis
  120. Mean corpuscular hemoglobin predicts the length of hospital stay independent of severity classification in patients with acute pancreatitis
  121. Association of gallstone and polymorphisms of UGT1A1*27 and UGT1A1*28 in patients with hepatitis B virus-related liver failure
  122. TGF-β1 upregulates Sar1a expression and induces procollagen-I secretion in hypertrophic scarring fibroblasts
  123. Antisense lncRNA PCNA-AS1 promotes esophageal squamous cell carcinoma progression through the miR-2467-3p/PCNA axis
  124. NK-cell dysfunction of acute myeloid leukemia in relation to the renin–angiotensin system and neurotransmitter genes
  125. The effect of dilution with glucose and prolonged injection time on dexamethasone-induced perineal irritation – A randomized controlled trial
  126. miR-146-5p restrains calcification of vascular smooth muscle cells by suppressing TRAF6
  127. Role of lncRNA MIAT/miR-361-3p/CCAR2 in prostate cancer cells
  128. lncRNA NORAD promotes lung cancer progression by competitively binding to miR-28-3p with E2F2
  129. Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models
  130. lncRNA FAM230B is highly expressed in colorectal cancer and suppresses the maturation of miR-1182 to increase cell proliferation
  131. circ-LIMK1 regulates cisplatin resistance in lung adenocarcinoma by targeting miR-512-5p/HMGA1 axis
  132. LncRNA SNHG3 promoted cell proliferation, migration, and metastasis of esophageal squamous cell carcinoma via regulating miR-151a-3p/PFN2 axis
  133. Risk perception and affective state on work exhaustion in obstetrics during the COVID-19 pandemic
  134. lncRNA-AC130710/miR-129-5p/mGluR1 axis promote migration and invasion by activating PKCα-MAPK signal pathway in melanoma
  135. SNRPB promotes cell cycle progression in thyroid carcinoma via inhibiting p53
  136. Xylooligosaccharides and aerobic training regulate metabolism and behavior in rats with streptozotocin-induced type 1 diabetes
  137. Serpin family A member 1 is an oncogene in glioma and its translation is enhanced by NAD(P)H quinone dehydrogenase 1 through RNA-binding activity
  138. Silencing of CPSF7 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells by blocking the AKT/mTOR signaling pathway
  139. Ultrasound-guided lumbar plexus block versus transversus abdominis plane block for analgesia in children with hip dislocation: A double-blind, randomized trial
  140. Relationship of plasma MBP and 8-oxo-dG with brain damage in preterm
  141. Identification of a novel necroptosis-associated miRNA signature for predicting the prognosis in head and neck squamous cell carcinoma
  142. Delayed femoral vein ligation reduces operative time and blood loss during hip disarticulation in patients with extremity tumors
  143. The expression of ASAP3 and NOTCH3 and the clinicopathological characteristics of adult glioma patients
  144. Longitudinal analysis of factors related to Helicobacter pylori infection in Chinese adults
  145. HOXA10 enhances cell proliferation and suppresses apoptosis in esophageal cancer via activating p38/ERK signaling pathway
  146. Meta-analysis of early-life antibiotic use and allergic rhinitis
  147. Marital status and its correlation with age, race, and gender in prognosis of tonsil squamous cell carcinomas
  148. HPV16 E6E7 up-regulates KIF2A expression by activating JNK/c-Jun signal, is beneficial to migration and invasion of cervical cancer cells
  149. Amino acid profiles in the tissue and serum of patients with liver cancer
  150. Pain in critically ill COVID-19 patients: An Italian retrospective study
  151. Immunohistochemical distribution of Bcl-2 and p53 apoptotic markers in acetamiprid-induced nephrotoxicity
  152. Estradiol pretreatment in GnRH antagonist protocol for IVF/ICSI treatment
  153. Long non-coding RNAs LINC00689 inhibits the apoptosis of human nucleus pulposus cells via miR-3127-5p/ATG7 axis-mediated autophagy
  154. The relationship between oxygen therapy, drug therapy, and COVID-19 mortality
  155. Monitoring hypertensive disorders in pregnancy to prevent preeclampsia in pregnant women of advanced maternal age: Trial mimicking with retrospective data
  156. SETD1A promotes the proliferation and glycolysis of nasopharyngeal carcinoma cells by activating the PI3K/Akt pathway
  157. The role of Shunaoxin pills in the treatment of chronic cerebral hypoperfusion and its main pharmacodynamic components
  158. TET3 governs malignant behaviors and unfavorable prognosis of esophageal squamous cell carcinoma by activating the PI3K/AKT/GSK3β/β-catenin pathway
  159. Associations between morphokinetic parameters of temporary-arrest embryos and the clinical prognosis in FET cycles
  160. Long noncoding RNA WT1-AS regulates trophoblast proliferation, migration, and invasion via the microRNA-186-5p/CADM2 axis
  161. The incidence of bronchiectasis in chronic obstructive pulmonary disease
  162. Integrated bioinformatics analysis shows integrin alpha 3 is a prognostic biomarker for pancreatic cancer
  163. Inhibition of miR-21 improves pulmonary vascular responses in bronchopulmonary dysplasia by targeting the DDAH1/ADMA/NO pathway
  164. Comparison of hospitalized patients with severe pneumonia caused by COVID-19 and influenza A (H7N9 and H1N1): A retrospective study from a designated hospital
  165. lncRNA ZFAS1 promotes intervertebral disc degeneration by upregulating AAK1
  166. Pathological characteristics of liver injury induced by N,N-dimethylformamide: From humans to animal models
  167. lncRNA ELFN1-AS1 enhances the progression of colon cancer by targeting miR-4270 to upregulate AURKB
  168. DARS-AS1 modulates cell proliferation and migration of gastric cancer cells by regulating miR-330-3p/NAT10 axis
  169. Dezocine inhibits cell proliferation, migration, and invasion by targeting CRABP2 in ovarian cancer
  170. MGST1 alleviates the oxidative stress of trophoblast cells induced by hypoxia/reoxygenation and promotes cell proliferation, migration, and invasion by activating the PI3K/AKT/mTOR pathway
  171. Bifidobacterium lactis Probio-M8 ameliorated the symptoms of type 2 diabetes mellitus mice by changing ileum FXR-CYP7A1
  172. circRNA DENND1B inhibits tumorigenicity of clear cell renal cell carcinoma via miR-122-5p/TIMP2 axis
  173. EphA3 targeted by miR-3666 contributes to melanoma malignancy via activating ERK1/2 and p38 MAPK pathways
  174. Pacemakers and methylprednisolone pulse therapy in immune-related myocarditis concomitant with complete heart block
  175. miRNA-130a-3p targets sphingosine-1-phosphate receptor 1 to activate the microglial and astrocytes and to promote neural injury under the high glucose condition
  176. Review Articles
  177. Current management of cancer pain in Italy: Expert opinion paper
  178. Hearing loss and brain disorders: A review of multiple pathologies
  179. The rationale for using low-molecular weight heparin in the therapy of symptomatic COVID-19 patients
  180. Amyotrophic lateral sclerosis and delayed onset muscle soreness in light of the impaired blink and stretch reflexes – watch out for Piezo2
  181. Interleukin-35 in autoimmune dermatoses: Current concepts
  182. Recent discoveries in microbiota dysbiosis, cholangiocytic factors, and models for studying the pathogenesis of primary sclerosing cholangitis
  183. Advantages of ketamine in pediatric anesthesia
  184. Congenital adrenal hyperplasia. Role of dentist in early diagnosis
  185. Migraine management: Non-pharmacological points for patients and health care professionals
  186. Atherogenic index of plasma and coronary artery disease: A systematic review
  187. Physiological and modulatory role of thioredoxins in the cellular function
  188. Case Reports
  189. Intrauterine Bakri balloon tamponade plus cervical cerclage for the prevention and treatment of postpartum haemorrhage in late pregnancy complicated with acute aortic dissection: Case series
  190. A case of successful pembrolizumab monotherapy in a patient with advanced lung adenocarcinoma: Use of multiple biomarkers in combination for clinical practice
  191. Unusual neurological manifestations of bilateral medial medullary infarction: A case report
  192. Atypical symptoms of malignant hyperthermia: A rare causative mutation in the RYR1 gene
  193. A case report of dermatomyositis with the missed diagnosis of non-small cell lung cancer and concurrence of pulmonary tuberculosis
  194. A rare case of endometrial polyp complicated with uterine inversion: A case report and clinical management
  195. Spontaneous rupturing of splenic artery aneurysm: Another reason for fatal syncope and shock (Case report and literature review)
  196. Fungal infection mimicking COVID-19 infection – A case report
  197. Concurrent aspergillosis and cystic pulmonary metastases in a patient with tongue squamous cell carcinoma
  198. Paraganglioma-induced inverted takotsubo-like cardiomyopathy leading to cardiogenic shock successfully treated with extracorporeal membrane oxygenation
  199. Lineage switch from lymphoma to myeloid neoplasms: First case series from a single institution
  200. Trismus during tracheal extubation as a complication of general anaesthesia – A case report
  201. Simultaneous treatment of a pubovesical fistula and lymph node metastasis secondary to multimodal treatment for prostate cancer: Case report and review of the literature
  202. Two case reports of skin vasculitis following the COVID-19 immunization
  203. Ureteroiliac fistula after oncological surgery: Case report and review of the literature
  204. Synchronous triple primary malignant tumours in the bladder, prostate, and lung harbouring TP53 and MEK1 mutations accompanied with severe cardiovascular diseases: A case report
  205. Huge mucinous cystic neoplasms with adhesion to the left colon: A case report and literature review
  206. Commentary
  207. Commentary on “Clinicopathological features of programmed cell death-ligand 1 expression in patients with oral squamous cell carcinoma”
  208. Rapid Communication
  209. COVID-19 fear, post-traumatic stress, growth, and the role of resilience
  210. Erratum
  211. Erratum to “Tollip promotes hepatocellular carcinoma progression via PI3K/AKT pathway”
  212. Erratum to “Effect of femoral head necrosis cystic area on femoral head collapse and stress distribution in femoral head: A clinical and finite element study”
  213. Erratum to “lncRNA NORAD promotes lung cancer progression by competitively binding to miR-28-3p with E2F2”
  214. Retraction
  215. Expression and role of ABIN1 in sepsis: In vitro and in vivo studies
  216. Retraction to “miR-519d downregulates LEP expression to inhibit preeclampsia development”
  217. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part II
  218. Usefulness of close surveillance for rectal cancer patients after neoadjuvant chemoradiotherapy
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