Soluble programmed cell death 1 protein is a promising biomarker to predict severe liver inflammation in chronic hepatitis B patients

Soluble programmed cell death 1 protein is a promising biomarker to predict severe liver inflammation in chronic hepatitis B patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Soluble programmed cell death 1 protein is a promising biomarker to predict severe liver inflammation in chronic hepatitis B patients Mingrong Ou, Weiming Zhang, Jie Pan, Jianmin Guo, Rui Huang, Jian Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3324436/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Mar, 2024 Read the published version in ACS Omega → Version 1 posted You are reading this latest preprint version Abstract Background and Aims: Liver inflammation is important in guiding the initiation of antiviral treatment and affect the disease progression of chronic hepatitis B(CHB). Soluble programmed cell death 1 protein(sPD-1) was upregulated in inflammatory, infectious diseases and correlated with disease severity. We aimed to investigate the correlation between serum sPD-1 and liver inflammation in CHB patients and role in indicating liver inflammation. Methods: 241 CHB patients who underwent a liver biopsy were enrolled. Correlation between sPD-1 levels and the degree of liver inflammation was analyzed. Univariate and multivariate logistic regression were performed to analyze independent variables of severe liver inflammation. Binary logistic regression was conducted to construct the predictive model for severe liver inflammation, and receiver operator characteristic curve(ROC) was used to evaluate the diagnostic accuracy of the predictive model. Results: sPD-1 was the highest in CHB patients with severe liver inflammation, which was higher than that in CHB patients with mild or moderate liver inflammation(P<0.001). Besides, sPD-1 was weakly correlated with AST(r=0.278, P<0.001). Multivariable analysis showed that sPD-1 was an independent predictor of severe liver inflammation. The predictive model contained sPD-1 had an area under the ROC(AUROC) of 0.917 and 0.921 in predicting severe liver inflammation in CHB patients and CHB patients with ALT≤1×upper limit of normal(ULN), respectively. Conclusions: Serum sPD-1 is associated with liver inflammation in CHB patients, and high levels of sPD-1 reflect severe liver inflammation. Serum sPD-1 is an independent predictor of severe liver inflammation and shows improved diagnostic accuracy when combined with other clinical indicators. Chronic hepatitis B Inflammation Programmed Cell Death 1 Receptor Figures Figure 1 Figure 2 Figure 3 Introduction Hepatitis B virus (HBV) infection has brought a heavy burden to global public health. There are 240 million people estimated to suffer from chronic HBV infection. 1 During chronic HBV infection, CHB may progress to different liver diseases, including liver cirrhosis (LC), liver de-compensation and hepatocellular carcinoma (HCC). 2 CHB patients with severe liver inflammation are more susceptible to develop LC, liver de-compensation and HCC. Timely antiviral treatment is essential to impede the deterioration of the disease. 3 Indications for antiviral treatment in CHB patients include the level of HBV replication, the severity of liver inflammation and liver fibrosis. 1,4 The ALT level is one of the most commonly used indicators to evaluate liver inflammation in patients with CHB. 5 However, accumulating studies have shown that ALT has limited specificity in reflecting liver inflammation, while a part of CHB patients with normal ALT levels have severe liver inflammation. 6,7 Liver biopsy remains the gold standard for assessing liver pathohistology. 8 However, the invasiveness of liver biopsy has limited its wide application, and many patients refuse to undergo liver biopsy. Therefore, there is an urgent need to find new biomarkers to assess the degree of liver inflammation and better guide the initiation of antiviral treatment. Programmed cell death protein 1 (PD-1) is inducibly expressed on activated T cells and is also expressed on B cells, nature killer (NK) cells, activated monocytes and dendritic cells (DCs). 9 The expression of PD-1 can be upregulated by inflammatory cytokines such as IFN-α, IL-6, and IL-12. 10,11 When PD-1 binds to its ligand programmed cell death 1 ligand 1 (PD-L1)/programmed cell death 1 ligand 2 (PD-L2), it can suppress the proliferation and effector function of T cells. 12 Under physiological conditions, the PD-1/PD-L1( 2 ) immunosuppressive pathway contributes to the maintenance of immune homeostasis and peripheral immune tolerance. 13 PD-1 is upregulated in viral infections, such as human immunodeficiency virus (HIV) and HBV infection. PD-1 prevents virus from killing by T cells and induces the persistence of infection. 14 In addition to its membrane-bound form, PD-1 also exists in the extracellular space in a soluble form. sPD-1 is produced by the activated peripheral blood mononuclear cells (PBMCs) through the expression of alternative spliced PD1 mRNA transcript PD1Δex3. 15 Recent studies have shown that sPD-1 is elevated in infectious, inflammatory and autoimmune diseases and associated with the disease activity. 16–19 In chronic HBV infection, sPD-1 is also elevated and correlates with the levels of liver enzymes and virological response. Besides, high sPD-1 levels were demonstrated to associated with a high risk of developing HCC. 20–22 Those results suggest that sPD-1 may be associated with disease activity and involved in disease progression in chronic HBV infection. However, there are few relevant studies, and the ability of serum sPD-1 to predict liver inflammation in CHB patients is still unclear. In the present study, we detected sPD-1 in the serum of CHB patients and analyzed the association between sPD-1 and liver inflammation and other clinical parameters. Furthermore, we constructed a predictive model to evaluate the role of serum sPD-1 in predicting the liver inflammation in CHB patients. Materials and Methods Patient selection In total, 241 consecutive CHB patients who underwent liver biopsy from November 2017 to March 2022 in Nanjing Drum Tower Hospital, Nanjing, China, were enrolled in this study. CHB was defined as the persistence of serum hepatitis B surface antigen (HBsAg) > 6 months and significant inflammation necrosis and/or fibrosis (≥ G2 / S2) showing in the histological examination of the liver biopsy. The inclusion criteria were CHB patients with detectable HBV DNA at the time of liver biopsy. We excluded patients ( 1 ) co-infected with other viruses, including hepatitis A virus (HAV), hepatitis C virus (HCV), hepatitis D virus (HDV), hepatitis E virus (HEV), and HIV; ( 2 ) with HCC; ( 3 ) with cardiovascular diseases; ( 4 ) with diabetes; ( 5 ) with kidney disease; ( 6 ) who are pregnant; ( 7 ) with autoimmune disease. Whole blood samples were collected via phlebotomy in Gel&Clot Activator serum separator tubes from CHB patients and centrifuged at 3000 rpm for 5 min at room temperature to separate the serum. Sera samples were isolated and stored at -80℃ for further analysis. All enrolled patients were informed of the necessary information concerning this study and provided written informed consent. This study was approved by the Ethics Committee of Nanjing Drum Tower Hospital. Laboratory assay Routine blood includes white blood cell (WBC), red blood cell (RBC) and platelet (PLT) were measured by XN 1000 (SYSMEX, Kobe, Japan). The liver function test includes ALT, AST, and GGT was measured by AU5800 (Beckman Coulter, Inc., Brea, CA, USA). Virological tests including HBsAg and HBeAg were measured by commercial immunoassays (Abbott Gmbh & Co. KG). HBV DNA was measured by a real-time fluorescent quantitative polymerase chain reaction (RT-PCR; Aikang Biotechnology Co., Ltd). Liver biopsy Liver biopsy was performed under ultrasonic guidance and evaluated by two experienced pathologists without the clinical information. The histodiagnosis of liver tissues was determined according to the degree of liver inflammation and fibrosis. Liver inflammation and fibrosis were staged according to Scheuer’s classification. 23 G0-G1, G2 and G3-4 were defined as no or mild, moderate and severe liver inflammation, respectively. Liver fibrosis was categorized into no significant fibrosis (S0-S1), moderate fibrosis (S2-S3), and cirrhosis (S4). Serum sPD-1 analysis In the present study, we used the TOP sPD-1 immunoassay (ETHealthcare, Shanghai, China), an established high-throughput and automated assay, to quantitatively measure the level of sPD-1 in the serum of CHB patients. The detection principle and detailed procedures for the TOP sPD-1 immunoassay were described in our previous study. 24 Statistical analysis Data were analyzed using SPSS 25.0 software (SPSS Inc, Chicago, IL, the United States). Continuous variables were expressed as medians and interquartile ranges (IQR), and categorical variables were expressed as numbers and percentages. Continuous variables between two groups were compared using t test (normal distribution) or Mann-Whitney U test (nonnormal distribution), and categorical variables were compared using chi square test. The correlations between sPD-1 and other parameters were evaluated by Spearman’s rank correlation test. Univariate and multivariate logistic regression analysis were performed to analyze the determinants of severe liver inflammation and fibrosis. Binary logistic regression was conducted using the backward (conditional) method. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic ability of predictors for liver inflammation. The areas under the ROC curves (AUROCs) as well as 95% confidential interval (CI) of AUROC were calculated. All significance tests were two-sided, and P < 0.05 was considered statistically significant. Results Demographic and clinical features of the study cohort A total of 241 CHB patients who underwent liver biopsy were enrolled for subsequent analysis, including 84 women and 157 men, with a median age of 39 (range 33–48) years. Among the CHB patients 79 (32.8%) were HBeAg positive. The serum sPD-1 level in CHB patients was 131.09 (101.47,195.70) pg/ml. The enrolled CHB patients were divided into different groups according to the degrees of liver inflammation or fibrosis, including 211 (87.6%) CHB patients with G < 3 and 30 (12.4%) CHB patients with G ≥ 3. No significant difference was observed with regard to HBsAg (P = 0.863) between CHB patients with G < 3 and G ≥ 3. According to the degrees of liver fibrosis, 224 (92.9%) cases had S < 3 and 17 (7.1%) cases had S ≥ 3. Detailed demographic and clinical parameters are presented in Table 1 . Table 1 Clinical features of the enrolled patients and patients with different stages of inflammation Variables Total(n = 241) G < 3(n = 211) G ≥ 3(n = 30) P- value Gender, male: female 157/84 141/70 16/14 0.147 Age, years 39(33–48) 39(33–48) 40.5(33-51.75) 0.434 Platelet ×10 9 /L 168(143–208) 177(146–214) 147(111.5-166.5) < 0.001 Tbil, umol/ml 11.6(8.3–15.9) 11.2(8.1–15.4) 13.55(10.975–20.275) 0.009 Alb, g/L 41.7(39.2–43.9) 42(39.5–44) 39.6(37.65-42.425) 0.022 ALT, U/L 36.60(22.25–60.55) 32.3(21.5–55.7) 112.9(42.8-260.55) < 0.001 AST, U/L 23.60(19.65–34.80) 22.5(18.9–29.8) 67.05(30.85-135.875) < 0.001 GGT, U/L 27.00(16.25–42.50) 22.9(15.9–40.5) 56.05(36.05-95.575) < 0.001 HBsAg, lg IU/ml 3.431(2.829–4.0815) 3.424(2.802–4.109) 3.490(2.886–3.9711) 0.863 HBeAg, positive: negative 79/162 60/151 19/11 < 0.001 HBV DNA, lg IU/ml 3.892(2.993–6.552) 3.614(2.975–5.375) 6.044(4.124–7.375) 0.003 HBV RNA, lg IU/ml 3.952(3.205–7.105) 3.819(3.047–7.041) 6.044(4.122–7.245) 0.002 sPD-1, pg/ml 131.09(101.47–195.70) 126.059(101.284–171.05) 211.01(164.295-390.629) < 0.001 abbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; HBeAg, hepatitis B e antigen, sPD-1, soluble programmed cell death 1 protein Serum sPD-1 in CHB patients at different stages of inflammation and fibrosis We combined CHB patients in G3 and G4 into one group due to the small sample size in these two groups, as did S3 and S4 groups. Serum levels of sPD-1 in CHB patients at different stages of inflammation and fibrosis were shown in Fig. 1 . The results showed that the serum sPD-1 was the highest in CHB patients at G3 and G4 stages [211.01, (164.30,390.63) pg/ml], which was significantly higher than those in CHB patients at G0-G1 [128.07, (100.98-174.06) pg/ml, P < 0.001] and G2 stage [120.97 (100.73-165.37) pg/ml, P < 0.001]. However, serum sPD-1 was comparable between patients at G0-G1 and G2 stage ( P = 0.426). Furthermore, the serum sPD-1 level of CHB patients with G ≥ 3 was higher than that of CHB patients with G < 3 in both HBeAg- group and HBeAg + group (Fig. 1 . B, C), suggesting that either HBeAg + or HBeAg- CHB patients with higher serum sPD-1 may have severe liver inflammation. Besides, serum sPD-1 level was positively correlated with inflammation stage (r = 0.192, P = 0.003, Fig. 2 . A). In our study, we did not find differences in serum sPD-1 between patients with different stages of fibrosis [S0-S1, 133.03 (102.39-190.96) pg/ml; S2, 126.132 (101.284-196.061) pg/ml; S3-S4, 169.181(98.581-286.977) pg/ml, P = 0.289] (Fig. 1 . D, E, F). Correlation between serum sPD-1 with clinical parameters ALT and AST levels are the most common biochemical indicators to evaluate liver inflammation, hepatocyte damage, and disease fluctuation. Correlation analysis showed that serum sPD-1 levels were positively associated with ALT (r = 0.136, P = 0.035) and AST (r = 0.278, P < 0.001) (Fig. 2 . C, D). There was no obvious correlation between serum sPD-1 and other clinical parameters including PLT, HBsAg and HBV DNA. Our data suggested that serum sPD-1 may be a potential novel biomarker which is distinct from other traditional biomarkers such as ALT, AST and HBV DNA. sPD-1 is an independent predictor of severe liver inflammation Logistic regression was performed to analyze the independent predictors of severe liver inflammation in CHB patients. Univariate logistic regression analysis showed that sPD-1(odd ration (OR), 1.008; 95%CI, 1.005–1.012; P < 0.001 ), PLT (OR, 0.986; 95%CI, 0.978–0.995; P = 0.002 ), ALT (OR, 1.013; 95%CI, 1.007–1.019; P < 0.001 ), AST (OR, 1.037; 95%CI, 1.024–1.050; P < 0.001 ), GGT (OR, 1.030; 95%CI, 1.017–1.042; P < 0.001 ), HBeAg-positive (OR, 4.347; 95%CI, 1.952–9.680; P < 0.001), HBV DNA (OR, 1.323; 95%CI, 1.105–1.584; P = 0.002 ) and HBV RNA (OR, 1.305; 95%CI, 1.073–1.587; P = 0.008 ) were associated with severe liver inflammation (G ≥ 3). Those indicators were then selected for multivariate analysis. The results showed that serum sPD-1 (OR, 1.007; 95%CI, 1.001–1.012; P = 0.013), PLT (OR, 0.978; 95%CI, 0.965–0.991; P = 0.001), AST (OR, 1.025; 95%CI, 1.010–1.041; P = 0.001) and GGT (OR, 1.030; 95%CI, 1.012–1.048; P = 0.001) were independent predictors of severe liver inflammation (Table 2 ). Besides, we further divided CHB patients into HBeAg- group and HBeAg + group, and carried out logistics regression analysis for patients with G ≥ 3. Our results showed that serum sPD-1 still was an independent predictor of severe liver inflammation in both HBeAg- and HBeAg + CHB patients (Table 3 , 4 ). Table 2 Univariable and multivariable analyses of severe liver inflammation (G ≥ 3) in enrolled CHB patients Variable Univariate Multivariate OR (95% CI) P OR (95% CI) P Age 1.019(0.982,1.056) 0.320 Gender Female 1.000 1.000 Male 0.567(0.262,1.228) 0.150 Platelet ×10 9 /L 0.986(0.978,0.995) 0.002 0.978(0.965,0.991) 0.001 TBil, umol/ml 1.018(0.998,1.037) 0.074 ALB, g/L 0.996(0.945,1.051) 0.896 ALT, U/L 1.013(1.007,1.019) < 0.001 AST, U/L 1.037(1.024,1.050) < 0.001 1.025(1.010,1.041) 0.001 GGT, U/L 1.030(1.017,1.042) < 0.001 1.030(1.012,1.048) 0.001 HBsAg, lg IU/ml 1.028(0.714,1.481) 0.882 HBeAg Negaitive 1.000 1.000 Positive 4.347(1.952,9.680) < 0.001 2.825(0.796,10.025) 0.108 HBV DNA, lg IU/ml 1.323(1.105,1.584) 0.002 HBV RNA, lg IU/ml 1.305(1.073,1.587) 0.008 sPD-1, pg/ml 1.008(1.005,1.012) < 0.001 1.007(1.001,1.012) 0.013 Abbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; HBeAg, hepatitis B e antigen, sPD-1, soluble programmed cell death 1 protein Table 3 Univariable and multivariable analyses of severe liver inflammation (G ≥ 3) in HBeAg-Positive CHB patients Variable Univariate Multivariate OR (95% CI) P OR (95% CI) P Age 1.032(0.961,1.109) 0.385 Gender Female 1.000 1.000 Male 0.722(0.233,2.236) 0.573 Platelet ×10 9 /L 0.972(0.957,0.987) < 0.001 0.976(0.954,0.998) 0.033 TBil, umol/ml 1.010(0.991,1.030) 0.287 ALB, g/L 1.037(0.956,1.126) 0.382 ALT, U/L 1.010(1.004,1.016) 0.001 AST, U/L 1.029(1.013,1.045) < 0.001 1.036(1.012,1.061) 0.003 GGT, U/L 1.047(1.021,1.072) < 0.001 HBsAg, lg IU/ml 0.283(0.128,0.628) 0.002 0.209(0.067,0.649) 0.007 HBV DNA, lg IU/ml 0.851(0.638,1.135) 0.272 HBV RNA, lg IU/ml 0.840(0.613,1.150) 0.276 sPD-1, pg/ml 1.008(1.003,1.012) 0.001 1.008(1.001,1.015) 0.018 Abbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; sPD-1, soluble programmed cell death 1 protein Table 4 Univariable and multivariable analyses of severe liver inflammation (G ≥ 3) in HBeAg-Negaitive CHB patients Variable Univariate Multivariate OR (95% CI) P OR (95% CI) P Age 1.114(1.039,1.193) 0.002 1.164(1.016,1.334) 0.029 Gender Female 1.000 1.000 Male 0.215(0.055,0.844) 0.028 Platelet ×10 9 /L 0.993(0.980,1.006) 0.287 0.978(0.965,0.991) 0.001 TBil, umol/ml 1.028(0.945,1.119) 0.522 ALB, g/L 0.958(0.896,1.026) 0.219 ALT, U/L 1.018(1.006,1.031) 0.004 1.021(1.000,1.042) 0.051 AST, U/L 1.043(1.019,1.068) < 0.001 GGT, U/L 1.022(1.009,1.035) 0.001 HBsAg, lg IU/ml 0.935(0.506,1.729) 0.831 HBV DNA, lg IU/ml 1.975(1.168,3.337) 0.011 HBV RNA, lg IU/ml 3.587(1.569,8.201) 0.002 2.434(1.124,5.270) 0.024 sPD-1, pg/ml 1.008(1.002,1.013) 0.009 1.019(1.004,1.034) 0.011 Abbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; sPD-1, soluble programmed cell death 1 protein Construction of the predictive model and comparison with individual predictors Based on the multivariate logistic regression results, we combined sPD-1, AST, GGT, PLT and HBeAg to construct a novel predictive model to predict severe liver inflammation. The model (M) is as follows: M= \(\frac{1}{1+{e}^{-(0.025\ast AST+0.029\ast GGT+0.007\ast sPD-1+1.039\ast HBeAg-positive(Yes=1; No=0)-0.022\ast PLT-2.347)}}\) ROC analysis showed that the predictive model had an AUROCs of 0.917 (95%CI, 0.863–0.971) for predicting severe liver inflammation, which further improved the predictive performance compared with the individual predictors such as sPD-1[0.782 (95% CI,0.692–0.871)] and ALT [0.812 (95% CI,0.726–0.897)] (Figure.3). In addition, we also evaluated the predictive performance of the model in CHB patients with ALT ≤ 1×ULN. In the present study, there were 134 patients with ALT ≤ 1×ULN, of which 6 patients were diagnosed with severe liver inflammation by liver biopsy. The AUROCs of the predictive model, sPD-1 and ALT was 0.921 (95% CI,0.851–0.991), 0.707 (95% CI,0.503–0.911) and 0.711 (95% CI,0.517–0.905) respectively. In those CHB patients with ALT ≤ 1×ULN, the predictive model still had an excellent performance in predicting severe liver inflammation, and its AUROC was higher than those of sPD-1and ALT alone. Discussion During HBV infection, the host antiviral immune response can cause hepatocyte damage and liver inflammation while eliminating virus. Besides, viral proteins and nucleic acids also contribute to hepatocyte damage and liver inflammation. Persistent liver inflammation promotes the disease progression from CHB to cirrhosis and HCC. 25–28 Accumulating studies have shown that sPD-1 expression is upregulated in viral infections, such as HCV and HIV infection, and autoimmune diseases including rheumatoid arthritis and autoimmune hepatitis. In those diseases, sPD-1 was associated with disease activity and progression. 16–18,29 In chronic HBV infection, the expression of sPD-1 is also increased and correlates with liver disease progression. 21,30,31 In our study, the results showed that serum sPD-1 was highest in CHB patients with severe liver inflammation (G ≥ 3), and higher than in CHB patients with mild (G0) and moderate (G2) liver inflammation. Zhou et al. also found that the levels of sPD-1 in CHB patients with moderate-to-severe liver inflammation were higher than those in patients with mild inflammation. 31 Besides, we also found that serum sPD-1 was positively correlated with the inflammatory stages. Our results suggest that serum sPD-1 is associated with liver inflammation in CHB patients, higher serum sPD-1 levels may indicate severe inflammation in the liver of CHB patients. Our results showed that serum sPD-1 has a weak positive correlation with AST and ALT levels in CHB patients, this may result from the different production mechanisms between sPD-1 and ALT, AST in the condition of liver inflammation in CHB patients. And this weak correlation may indicate the uniqueness of sPD-1 in reflecting liver inflammation and may shed a new light on the understanding of liver inflammation. A previous study showed that serum sPD-1 levels were higher in CHB patients with moderate-to-severe liver fibrosis than in those with mild fibrosis. 31 However, in our study, there was no significant association between PD1 and hepatic fibrosis, which may be caused by the different distribution of patients with hepatic fibrosis in two studies. The correlation between serum sPD-1 and liver fibrosis in CHB patients should be validated a large clinical cohort study. The severity of liver inflammation is one of the indicators for initiating anti-viral treatment for chronic HBV infection. 1,4 Liver enzymes including ALT and AST are commonly used to reflect the activity of liver inflammation. 5 However, about 20% of CHB patients with normal ALT have significant liver inflammation. 33 Liver biopsy is the gold standard for the diagnose of liver inflammation, 8 but the invasiveness of liver biopsy procedure limits its application for mass screening. Therefore, it’s necessary to seek new non-invasive markers of liver inflammation. We used logistic regression to evaluate the role of sPD-1 as a potential biomarker for predicting severe liver inflammation in CHB patients. Multivariate analysis showed that serum sPD-1 was an independent predictor for severe liver inflammation (G ≥ 3) in CHB patients. In patients with chronic HBV infection, serum HBeAg is associated with viral replication, inflammation and disease activity, and response to antiviral therapy. 32 Therefore, we further analyzed the CHB patients with different HBeAg status, which revealed that serum sPD-1 in CHB patients with G ≥ 3 was higher than that of CHB patients with G < 3 and was an independent predictor of severe liver inflammation in both HBeAg- and HBeAg + CHB patients. Those results indicate serum sPD-1 was an independent risk factor for severe liver inflammation in CHB patients, and the patients with higher serum sPD-1 levels had a higher risk of severe liver inflammation. In CHB patients, researchers have found that liver enzymes (ALT, AST, and GGT), viral parameters (HBsAg, HBeAg, anti-HBc), and PLT are also independently associated with liver inflammation. 34–38 Consistent with those studies, we also found that AST, GGT, and PLT were independent predictors of liver inflammation in CHB patients. Since individual parameters are affected by patients’ demographics and other factors. We combined sPD-1, AST, GGT, HBeAg and PLT to construct a predictive model for liver inflammation in CHB patients. Compared with single parameter, the predictive model improved the predictive ability of liver inflammation (AUROC = 0.917, 95%CI, 0.863–0.971). In order not to miss the optimal opportunity to receive therapeutic intervention, it’s of great significance to monitor liver inflammation in CHB patients with normal ALT levels. 134 patients in our study cohort had normal ALT levels. In this subset of patients, the predictive model also showed a good predictive performance (AUROC = 0.921, 95%CI, 0.851–0.991) using liver biopsy as the gold standard for the diagnosis of liver inflammation. Those results suggest that serum sPD-1 can be used as a predictive biomarker for severe liver inflammation, and the predictive performance was further improved when combined with other indicators. Our study has some limitations. CHB patients enrolled in the present study was relatively small, furthermore, only 30 CHB patients with G ≥ 3. At the time of conceiving this study, we mainly want to focus on the ability of sPD-1 in reflecting liver inflammation in the early stages of liver damage, CHB patients with severe liver inflammation (G ≥ 3) is not our primary focus. Therefore, a large-scale cohort including CHB patients with varying degrees of liver inflammation status would be desirable for further confirm the findings in our study. In conclusion, our results suggest that the serum sPD-1 is associated with the degrees of liver inflammation in CHB patients, and high levels of serum sPD-1 reflect severe liver inflammation. Besides, we constructed a model based on the non-invasive biomarkers, sPD-1, AST, GGT, HBeAg, and PLT, which has a good performance in predicting CHB patients with severe liver inflammation regardless of their ALT levels, and may help to decide whether to initiate the antiviral treatment. Declarations Funding Information This work was supported by Medical Science and technology development Foundation，Nanjing Department of Health（YKK22073）and Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University（2022-LCYJ-PY-49）. Conflict of Interest The authors have no conflict of interests related to this publication. Author Contributions Study conception and design (YXC, CW), acquisition of data; analysis and interpretation of data (MRO, WMZ, RH, JMG), drafting of the manuscript (MRO, WMZ, JP), statistical analysis and analysis of data (MRO, JCL, JX), study supervision; critical revision of the manuscript for important intellectual content (YXC, CW, JP, JX). Data Sharing Statement The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics Statements N/A References European Association for the Study of the Liver. EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol. 2017;67(2):370–98. 10.1016/j.jhep.2017.03.021 . Trépo C, Chan HL, Lok A. Hepatitis B virus infection. Lancet. 2014;384(9959):2053–63. 10.1016/S0140-6736(14)60220-8 . Russo FP, Rodríguez-Castro K, Scribano L, Gottardo G, Vanin V, Farinati F. Role of antiviral therapy in the natural history of hepatitis B virus-related chronic liver disease. World J Hepatol. 2015;7(8):1097–104. 10.4254/wjh.v7.i8.1097 . Terrault NA, Lok A, McMahon BJ, Chang KM, Hwang JP, Jonas MM, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560–99. 10.1002/hep.29800 . 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Circulating level of sPD-1 and PD-1 genetic variants are associated with hepatitis B infection and related liver disease progression. Int J Infect Dis. 2022;115:229–36. 10.1016/j.ijid.2021.12.325 . Tan N, Luo H, Kang Q, Pan J, Cheng R, Xi H, et al. High levels of soluble programmed death-1 are associated with virological response in chronic hepatitis B patients after antiviral treatment. Virus Res. 2022;309:198660. 10.1016/j.virusres.2021.198660 . Scheuer PJ. Classification of chronic viral hepatitis: a need for reassessment. J Hepatol. 1991;13(3):372–4. 10.1016/0168-8278(91)90084-o . Zhang J, Chen L, Xu Q, Tao Y, Pan J, Guo J, et al. An automated, rapid fluorescent immunoassay to quantify serum soluble programmed death-1 (PD-1) protein using testing-on-a-probe biosensors. Clin Chem Lab Med. 2022;60(7):1073–80. 10.1515/cclm-2022-0166 . Wu J, Han M, Li J, Yang X, Yang D. Immunopathogenesis of HBV Infection. Adv Exp Med Biol. 2020;1179:71–107. 10.1007/978-981-13-9151-4_4 . Zeng C, Wang YL, Xie C, Sang Y, Li TJ, Zhang M, et al. Identification of a novel TGF-β-miR-122-fibronectin 1/serum response factor signaling cascade and its implication in hepatic fibrogenesis. Oncotarget. 2015;6(14):12224–33. 10.18632/oncotarget.3652 . Shi Y, Wang J, Wang Y, Wang A, Guo H, Wei F, et al. A novel mutant 10Ala/Arg together with mutant 144Ser/Arg of hepatitis B virus X protein involved in hepatitis B virus-related hepatocarcinogenesis in HepG2 cell lines. Cancer Lett. 2016;371(2):285–91. 10.1016/j.canlet.2015.12.008 . Yang G, Wan P, Zhang Y, Tan Q, Qudus MS, Yue Z, et al. Innate Immunity, Inflammation, and Intervention in HBV Infection. Viruses. 2022;14(10). 10.3390/v14102275 . Xu L, Jiang L, Nie L, Zhang S, Liu L, Du Y, et al. Soluble programmed death molecule 1 (sPD-1) as a predictor of interstitial lung disease in rheumatoid arthritis. Bmc Immunol. 2021;22(1):69. 10.1186/s12865-021-00460-6 . Xia J, Huang R, Chen Y, Liu Y, Wang J, Yan X, et al. Profiles of serum soluble programmed death-1 and programmed death-ligand 1 levels in chronic hepatitis B virus-infected patients with different disease phases and after anti-viral treatment. Aliment Pharmacol Ther. 2020;51(11):1180–7. 10.1111/apt.15732 . Zhou L, Li X, Huang X, Chen L, Gu L, Huang Y. Soluble programmed death-1 is a useful indicator for inflammatory and fibrosis severity in chronic hepatitis B. J Viral Hepat. 2019;26(7):795–802. 10.1111/jvh.13055 . Kramvis A, Kostaki EG, Hatzakis A, Paraskevis D. Immunomodulatory Function of HBeAg Related to Short-Sighted Evolution, Transmissibility, and Clinical Manifestation of Hepatitis B Virus. Front Microbiol. 2018;9:2521. 10.3389/fmicb.2018.02521 . Kennedy P, Sandalova E, Jo J, Gill U, Ushiro-Lumb I, Tan AT, et al. Preserved T-cell function in children and young adults with immune-tolerant chronic hepatitis B. Gastroenterology. 2012;143(3):637–45. 10.1053/j.gastro.2012.06.009 . Zeng DW, Dong J, Jiang JJ, Zhu YY, Liu YR. Ceruloplasmin, a reliable marker of fibrosis in chronic hepatitis B virus patients with normal or minimally raised alanine aminotransferase. World J Gastroenterol. 2016;22(43):9586–94. 10.3748/wjg.v22.i43.9586 . Li Q, Zhou Y, Huang C, Li W, Chen L. A novel diagnostic algorithm to predict significant liver inflammation in chronic hepatitis B virus infection patients with detectable HBV DNA and persistently normal alanine transaminase. Sci Rep. 2018;8(1):15449. 10.1038/s41598-018-33412-z . Li Q, Huang C, Xu W, Hu Q, Chen L. A simple algorithm for non-invasive diagnosis of significant liver histological changes in patients with CHB and normal or mildly elevated alanine transaminase levels. Med (Baltim). 2019;98(28):e16429. 10.1097/MD.0000000000016429 . Chen S, Huang H. Clinical Non-invasive Model to Predict Liver Inflammation in Chronic Hepatitis B With Alanine Aminotransferase ≤ 2 Upper Limit of Normal. Front Med (Lausanne). 2021;8:661725. 10.3389/fmed.2021.661725 . Xie Y, Yi W, Zhang L, Lu Y, Hao HX, Gao YJ, et al. Evaluation of a logistic regression model for predicting liver necroinflammation in hepatitis B e antigen-negative chronic hepatitis B patients with normal and minimally increased alanine aminotransferase levels. J Viral Hepat. 2019;26(Suppl 1):42–9. 10.1111/jvh.13163 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Mar, 2024 Read the published version in ACS Omega → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3324436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":235015920,"identity":"2f65b268-e0e3-480b-b09e-ce3e16ff99a1","order_by":0,"name":"Mingrong Ou","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingrong","middleName":"","lastName":"Ou","suffix":""},{"id":235015921,"identity":"874739ed-da5e-4233-882c-def42c21da17","order_by":1,"name":"Weiming Zhang","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Zhang","suffix":""},{"id":235015922,"identity":"2d4c89b1-fda2-44d4-9727-771f473645c4","order_by":2,"name":"Jie Pan","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Pan","suffix":""},{"id":235015923,"identity":"356d0fa9-bd59-4f85-9ea4-3bd25235d4c3","order_by":3,"name":"Jianmin Guo","email":"","orcid":"","institution":"ET HealthCare","correspondingAuthor":false,"prefix":"","firstName":"Jianmin","middleName":"","lastName":"Guo","suffix":""},{"id":235015924,"identity":"10a275cc-900c-452f-ab26-d2327368e05b","order_by":4,"name":"Rui Huang","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical 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Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJCCA0CcwMDAfIBBAsYlUgtbAvFaGCBaeAwQJuADBjdyDA8XMNjl8Uv3fHtg2cYgx3cjgfFzAV4taQmHZzAkF0vOObvdQLKNwVjyRgKz9Ay8WpIPHOZhOJC44UbuNgmgFiAjgY2ZB6+WxAawlv03cp6BtNQToQVmi0QOG0hLggEhLZJnniUc5jFITpxxI81MQuKchOHMMw+bpfFp4TueY/yZp8IusX9G8jNpiTIbeb7jyQc/49OicADsPAiHWQIcmYwNeDQwMMgjSzN+wKt2FIyCUTAKRioAAF9sTILrWiNsAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School","correspondingAuthor":true,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-09-04 12:29:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3324436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3324436/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1021/acsomega.4c00780","type":"published","date":"2024-03-29T10:19:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43772191,"identity":"001d43b3-41a3-4bb8-a4a2-45cabb84b5a9","added_by":"auto","created_at":"2023-09-27 14:37:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum sPD-1 levels in CHB patients with different stages of inflammation and fibrosis. \u003c/strong\u003e(A) Serum sPD-1 levels in CHB patients at G0-1, G2 and G3-4 stage. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(B, C) Serum sPD-1 levels in HBeAg\u003csup\u003e+\u003c/sup\u003e CHB patients and HBeAg\u003csup\u003e-\u003c/sup\u003e CHB patients at G0-1, G2 and G3-4 stages (D) Serum sPD-1 levels in CHB patients at S0-1, S2 and S3-4 stage. (E, F) Serum sPD-1 levels in HBeAg\u003csup\u003e+\u003c/sup\u003e CHB patients and HBeAg\u003csup\u003e-\u003c/sup\u003e CHB patients at S0-1, S2 and S3-4 stages. Mann-Whitney U test was used for comparison between groups.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3324436/v1/8c2d2cd95dd5c412cd3db01f.jpeg"},{"id":43772192,"identity":"18510511-6aaa-4fff-8eba-451900c2c933","added_by":"auto","created_at":"2023-09-27 14:37:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between serum sPD-1 and inflammation and fibrosis stages and ALT, AST levels. \u003c/strong\u003e(A, B) The association between serum sPD-1 and inflammation and fibrosis stages. (C, D) Correlation between serum sPD-1 and ALT, AST levels.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3324436/v1/cd0ce9a50138991dd1547195.png"},{"id":43772193,"identity":"7d96b843-bf3f-4309-9bea-137d7d502f70","added_by":"auto","created_at":"2023-09-27 14:37:44","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons of AUC between different independent predicting parameters (sPD-1，ALT) and the mode\u003c/strong\u003el. (A) Comparisons of AUC between sPD-1，ALT and the model in all enrolled CHB patients. (B) Comparisons of AUC between sPD-1，ALT and the model in CHB patients with normal ALT levels.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3324436/v1/d15b4c479e37a58c610ca03b.jpeg"},{"id":54358214,"identity":"70cb8942-eb6c-4e7e-b7c9-5e40d11ab9c2","added_by":"auto","created_at":"2024-04-09 10:19:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":765759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3324436/v1/5db0c835-2f0b-4c0d-a8c7-20adb8346da2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soluble programmed cell death 1 protein is a promising biomarker to predict severe liver inflammation in chronic hepatitis B patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis B virus (HBV) infection has brought a heavy burden to global public health. There are 240\u0026nbsp;million people estimated to suffer from chronic HBV infection.\u003csup\u003e1\u003c/sup\u003e During chronic HBV infection, CHB may progress to different liver diseases, including liver cirrhosis (LC), liver de-compensation and hepatocellular carcinoma (HCC).\u003csup\u003e2\u003c/sup\u003e CHB patients with severe liver inflammation are more susceptible to develop LC, liver de-compensation and HCC. Timely antiviral treatment is essential to impede the deterioration of the disease.\u003csup\u003e3\u003c/sup\u003e Indications for antiviral treatment in CHB patients include the level of HBV replication, the severity of liver inflammation and liver fibrosis.\u003csup\u003e1,4\u003c/sup\u003e The ALT level is one of the most commonly used indicators to evaluate liver inflammation in patients with CHB.\u003csup\u003e5\u003c/sup\u003e However, accumulating studies have shown that ALT has limited specificity in reflecting liver inflammation, while a part of CHB patients with normal ALT levels have severe liver inflammation.\u003csup\u003e6,7\u003c/sup\u003e Liver biopsy remains the gold standard for assessing liver pathohistology.\u003csup\u003e8\u003c/sup\u003e However, the invasiveness of liver biopsy has limited its wide application, and many patients refuse to undergo liver biopsy. Therefore, there is an urgent need to find new biomarkers to assess the degree of liver inflammation and better guide the initiation of antiviral treatment.\u003c/p\u003e \u003cp\u003eProgrammed cell death protein 1 (PD-1) is inducibly expressed on activated T cells and is also expressed on B cells, nature killer (NK) cells, activated monocytes and dendritic cells (DCs).\u003csup\u003e9\u003c/sup\u003e The expression of PD-1 can be upregulated by inflammatory cytokines such as IFN-α, IL-6, and IL-12.\u003csup\u003e10,11\u003c/sup\u003e When PD-1 binds to its ligand programmed cell death 1 ligand 1 (PD-L1)/programmed cell death 1 ligand 2 (PD-L2), it can suppress the proliferation and effector function of T cells.\u003csup\u003e12\u003c/sup\u003e Under physiological conditions, the PD-1/PD-L1(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) immunosuppressive pathway contributes to the maintenance of immune homeostasis and peripheral immune tolerance.\u003csup\u003e13\u003c/sup\u003e PD-1 is upregulated in viral infections, such as human immunodeficiency virus (HIV) and HBV infection. PD-1 prevents virus from killing by T cells and induces the persistence of infection.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition to its membrane-bound form, PD-1 also exists in the extracellular space in a soluble form. sPD-1 is produced by the activated peripheral blood mononuclear cells (PBMCs) through the expression of alternative spliced PD1 mRNA transcript PD1Δex3.\u003csup\u003e15\u003c/sup\u003e Recent studies have shown that sPD-1 is elevated in infectious, inflammatory and autoimmune diseases and associated with the disease activity.\u003csup\u003e16\u0026ndash;19\u003c/sup\u003e In chronic HBV infection, sPD-1 is also elevated and correlates with the levels of liver enzymes and virological response. Besides, high sPD-1 levels were demonstrated to associated with a high risk of developing HCC. \u003csup\u003e20\u0026ndash;22\u003c/sup\u003e Those results suggest that sPD-1 may be associated with disease activity and involved in disease progression in chronic HBV infection. However, there are few relevant studies, and the ability of serum sPD-1 to predict liver inflammation in CHB patients is still unclear.\u003c/p\u003e \u003cp\u003eIn the present study, we detected sPD-1 in the serum of CHB patients and analyzed the association between sPD-1 and liver inflammation and other clinical parameters. Furthermore, we constructed a predictive model to evaluate the role of serum sPD-1 in predicting the liver inflammation in CHB patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eIn total, 241 consecutive CHB patients who underwent liver biopsy from November 2017 to March 2022 in Nanjing Drum Tower Hospital, Nanjing, China, were enrolled in this study. CHB was defined as the persistence of serum hepatitis B surface antigen (HBsAg)\u0026thinsp;\u0026gt;\u0026thinsp;6 months and significant inflammation necrosis and/or fibrosis (\u0026ge;\u0026thinsp;G2 / S2) showing in the histological examination of the liver biopsy. The inclusion criteria were CHB patients with detectable HBV DNA at the time of liver biopsy. We excluded patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) co-infected with other viruses, including hepatitis A virus (HAV), hepatitis C virus (HCV), hepatitis D virus (HDV), hepatitis E virus (HEV), and HIV; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) with HCC; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) with cardiovascular diseases; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) with diabetes; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) with kidney disease; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) who are pregnant; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) with autoimmune disease. Whole blood samples were collected via phlebotomy in Gel\u0026amp;Clot Activator serum separator tubes from CHB patients and centrifuged at 3000 rpm for 5 min at room temperature to separate the serum. Sera samples were isolated and stored at -80℃ for further analysis. All enrolled patients were informed of the necessary information concerning this study and provided written informed consent. This study was approved by the Ethics Committee of Nanjing Drum Tower Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory assay\u003c/h2\u003e \u003cp\u003eRoutine blood includes white blood cell (WBC), red blood cell (RBC) and platelet (PLT) were measured by XN 1000 (SYSMEX, Kobe, Japan). The liver function test includes ALT, AST, and GGT was measured by AU5800 (Beckman Coulter, Inc., Brea, CA, USA). Virological tests including HBsAg and HBeAg were measured by commercial immunoassays (Abbott Gmbh \u0026amp; Co. KG). HBV DNA was measured by a real-time fluorescent quantitative polymerase chain reaction (RT-PCR; Aikang Biotechnology Co., Ltd).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLiver biopsy\u003c/h2\u003e \u003cp\u003eLiver biopsy was performed under ultrasonic guidance and evaluated by two experienced pathologists without the clinical information. The histodiagnosis of liver tissues was determined according to the degree of liver inflammation and fibrosis. Liver inflammation and fibrosis were staged according to Scheuer\u0026rsquo;s classification.\u003csup\u003e23\u003c/sup\u003e G0-G1, G2 and G3-4 were defined as no or mild, moderate and severe liver inflammation, respectively. Liver fibrosis was categorized into no significant fibrosis (S0-S1), moderate fibrosis (S2-S3), and cirrhosis (S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSerum sPD-1 analysis\u003c/h2\u003e \u003cp\u003eIn the present study, we used the TOP sPD-1 immunoassay (ETHealthcare, Shanghai, China), an established high-throughput and automated assay, to quantitatively measure the level of sPD-1 in the serum of CHB patients. The detection principle and detailed procedures for the TOP sPD-1 immunoassay were described in our previous study.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 25.0 software (SPSS Inc, Chicago, IL, the United States). Continuous variables were expressed as medians and interquartile ranges (IQR), and categorical variables were expressed as numbers and percentages. Continuous variables between two groups were compared using t test (normal distribution) or Mann-Whitney U test (nonnormal distribution), and categorical variables were compared using chi square test. The correlations between sPD-1 and other parameters were evaluated by Spearman\u0026rsquo;s rank correlation test. Univariate and multivariate logistic regression analysis were performed to analyze the determinants of severe liver inflammation and fibrosis. Binary logistic regression was conducted using the backward (conditional) method. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic ability of predictors for liver inflammation. The areas under the ROC curves (AUROCs) as well as 95% confidential interval (CI) of AUROC were calculated. All significance tests were two-sided, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical features of the study cohort\u003c/h2\u003e \u003cp\u003eA total of 241 CHB patients who underwent liver biopsy were enrolled for subsequent analysis, including 84 women and 157 men, with a median age of 39 (range 33\u0026ndash;48) years. Among the CHB patients 79 (32.8%) were HBeAg positive. The serum sPD-1 level in CHB patients was 131.09 (101.47,195.70) pg/ml. The enrolled CHB patients were divided into different groups according to the degrees of liver inflammation or fibrosis, including 211 (87.6%) CHB patients with G\u0026thinsp;\u0026lt;\u0026thinsp;3 and 30 (12.4%) CHB patients with G\u0026thinsp;\u0026ge;\u0026thinsp;3. No significant difference was observed with regard to HBsAg (P\u0026thinsp;=\u0026thinsp;0.863) between CHB patients with G\u0026thinsp;\u0026lt;\u0026thinsp;3 and G\u0026thinsp;\u0026ge;\u0026thinsp;3. According to the degrees of liver fibrosis, 224 (92.9%) cases had S\u0026thinsp;\u0026lt;\u0026thinsp;3 and 17 (7.1%) cases had S\u0026thinsp;\u0026ge;\u0026thinsp;3. Detailed demographic and clinical parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical features of the enrolled patients and patients with different stages of inflammation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;241)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;3(n\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u0026thinsp;\u0026ge;\u0026thinsp;3(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, male: female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157/84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141/70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(33\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(33\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5(33-51.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168(143\u0026ndash;208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177(146\u0026ndash;214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147(111.5-166.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTbil, umol/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6(8.3\u0026ndash;15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2(8.1\u0026ndash;15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.55(10.975\u0026ndash;20.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.7(39.2\u0026ndash;43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(39.5\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.6(37.65-42.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.60(22.25\u0026ndash;60.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3(21.5\u0026ndash;55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.9(42.8-260.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.60(19.65\u0026ndash;34.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5(18.9\u0026ndash;29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.05(30.85-135.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00(16.25\u0026ndash;42.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.9(15.9\u0026ndash;40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.05(36.05-95.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.431(2.829\u0026ndash;4.0815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.424(2.802\u0026ndash;4.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.490(2.886\u0026ndash;3.9711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBeAg,\u003c/p\u003e \u003cp\u003epositive: negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79/162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60/151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;DNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.892(2.993\u0026ndash;6.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.614(2.975\u0026ndash;5.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.044(4.124\u0026ndash;7.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;RNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.952(3.205\u0026ndash;7.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.819(3.047\u0026ndash;7.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.044(4.122\u0026ndash;7.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPD-1, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.09(101.47\u0026ndash;195.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.059(101.284\u0026ndash;171.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211.01(164.295-390.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eabbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; HBeAg, hepatitis B e antigen, sPD-1, soluble programmed cell death 1 protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSerum sPD-1 in CHB patients at different stages of inflammation and fibrosis\u003c/h2\u003e \u003cp\u003eWe combined CHB patients in G3 and G4 into one group due to the small sample size in these two groups, as did S3 and S4 groups. Serum levels of sPD-1 in CHB patients at different stages of inflammation and fibrosis were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results showed that the serum sPD-1 was the highest in CHB patients at G3 and G4 stages [211.01, (164.30,390.63) pg/ml], which was significantly higher than those in CHB patients at G0-G1 [128.07, (100.98-174.06) pg/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and G2 stage [120.97 (100.73-165.37) pg/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. However, serum sPD-1 was comparable between patients at G0-G1 and G2 stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.426). Furthermore, the serum sPD-1 level of CHB patients with G\u0026thinsp;\u0026ge;\u0026thinsp;3 was higher than that of CHB patients with G\u0026thinsp;\u0026lt;\u0026thinsp;3 in both HBeAg- group and HBeAg\u0026thinsp;+\u0026thinsp;group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. B, C), suggesting that either HBeAg\u0026thinsp;+\u0026thinsp;or HBeAg- CHB patients with higher serum sPD-1 may have severe liver inflammation. Besides, serum sPD-1 level was positively correlated with inflammation stage (r\u0026thinsp;=\u0026thinsp;0.192, P\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A). In our study, we did not find differences in serum sPD-1 between patients with different stages of fibrosis [S0-S1, 133.03 (102.39-190.96) pg/ml; S2, 126.132 (101.284-196.061) pg/ml; S3-S4, 169.181(98.581-286.977) pg/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.289] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. D, E, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between serum sPD-1 with clinical parameters\u003c/h2\u003e \u003cp\u003eALT and AST levels are the most common biochemical indicators to evaluate liver inflammation, hepatocyte damage, and disease fluctuation. Correlation analysis showed that serum sPD-1 levels were positively associated with ALT (r\u0026thinsp;=\u0026thinsp;0.136, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and AST (r\u0026thinsp;=\u0026thinsp;0.278, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. C, D). There was no obvious correlation between serum sPD-1 and other clinical parameters including PLT, HBsAg and HBV DNA. Our data suggested that serum sPD-1 may be a potential novel biomarker which is distinct from other traditional biomarkers such as ALT, AST and HBV DNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003esPD-1 is an independent predictor of severe liver inflammation\u003c/h2\u003e \u003cp\u003eLogistic regression was performed to analyze the independent predictors of severe liver inflammation in CHB patients. Univariate logistic regression analysis showed that sPD-1(odd ration (OR), 1.008; 95%CI, 1.005\u0026ndash;1.012; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), PLT (OR, 0.986; 95%CI, 0.978\u0026ndash;0.995; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 ), ALT (OR, 1.013; 95%CI, 1.007\u0026ndash;1.019; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), AST (OR, 1.037; 95%CI, 1.024\u0026ndash;1.050; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), GGT (OR, 1.030; 95%CI, 1.017\u0026ndash;1.042; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), HBeAg-positive (OR, 4.347; 95%CI, 1.952\u0026ndash;9.680; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HBV DNA (OR, 1.323; 95%CI, 1.105\u0026ndash;1.584; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 ) and HBV RNA (OR, 1.305; 95%CI, 1.073\u0026ndash;1.587; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008 ) were associated with severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3). Those indicators were then selected for multivariate analysis. The results showed that serum sPD-1 (OR, 1.007; 95%CI, 1.001\u0026ndash;1.012; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), PLT (OR, 0.978; 95%CI, 0.965\u0026ndash;0.991; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), AST (OR, 1.025; 95%CI, 1.010\u0026ndash;1.041; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and GGT (OR, 1.030; 95%CI, 1.012\u0026ndash;1.048; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were independent predictors of severe liver inflammation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Besides, we further divided CHB patients into HBeAg- group and HBeAg\u0026thinsp;+\u0026thinsp;group, and carried out logistics regression analysis for patients with G\u0026thinsp;\u0026ge;\u0026thinsp;3. Our results showed that serum sPD-1 still was an independent predictor of severe liver inflammation in both HBeAg- and HBeAg\u0026thinsp;+\u0026thinsp;CHB patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analyses of severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3) in enrolled CHB patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.019(0.982,1.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.567(0.262,1.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986(0.978,0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978(0.965,0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, umol/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018(0.998,1.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.996(0.945,1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.013(1.007,1.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.037(1.024,1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.025(1.010,1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.030(1.017,1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.030(1.012,1.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028(0.714,1.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBeAg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegaitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.347(1.952,9.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.825(0.796,10.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;DNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.323(1.105,1.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;RNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.305(1.073,1.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPD-1, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.008(1.005,1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007(1.001,1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; HBeAg, hepatitis B e antigen, sPD-1, soluble programmed cell death 1 protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analyses of severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3) in HBeAg-Positive CHB patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.032(0.961,1.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.722(0.233,2.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.972(0.957,0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976(0.954,0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, umol/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.010(0.991,1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.037(0.956,1.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.010(1.004,1.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029(1.013,1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.036(1.012,1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.047(1.021,1.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.283(0.128,0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209(0.067,0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;DNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.851(0.638,1.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;RNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840(0.613,1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPD-1, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.008(1.003,1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.008(1.001,1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; sPD-1, soluble programmed cell death 1 protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analyses of severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3) in HBeAg-Negaitive CHB patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.114(1.039,1.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.164(1.016,1.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.215(0.055,0.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.993(0.980,1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978(0.965,0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil, umol/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028(0.945,1.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958(0.896,1.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018(1.006,1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.021(1.000,1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.043(1.019,1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.022(1.009,1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBsAg, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.935(0.506,1.729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;DNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.975(1.168,3.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u0026nbsp;RNA, lg IU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.587(1.569,8.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.434(1.124,5.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esPD-1, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.008(1.002,1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.019(1.004,1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation/. Tbil, total bilirubin; Alb, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transpeptidase; HBsAg, hepatitis B surface antigen; sPD-1, soluble programmed cell death 1 protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the predictive model and comparison with individual predictors\u003c/h2\u003e \u003cp\u003eBased on the multivariate logistic regression results, we combined sPD-1, AST, GGT, PLT and HBeAg to construct a novel predictive model to predict severe liver inflammation. The model (M) is as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eM=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{1+{e}^{-(0.025\\ast AST+0.029\\ast GGT+0.007\\ast sPD-1+1.039\\ast HBeAg-positive(Yes=1; No=0)-0.022\\ast PLT-2.347)}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eROC analysis showed that the predictive model had an AUROCs of 0.917 (95%CI, 0.863\u0026ndash;0.971) for predicting severe liver inflammation, which further improved the predictive performance compared with the individual predictors such as sPD-1[0.782 (95% CI,0.692\u0026ndash;0.871)] and ALT [0.812 (95% CI,0.726\u0026ndash;0.897)] (Figure.3). In addition, we also evaluated the predictive performance of the model in CHB patients with ALT\u0026thinsp;\u0026le;\u0026thinsp;1\u0026times;ULN. In the present study, there were 134 patients with ALT\u0026thinsp;\u0026le;\u0026thinsp;1\u0026times;ULN, of which 6 patients were diagnosed with severe liver inflammation by liver biopsy. The AUROCs of the predictive model, sPD-1 and ALT was 0.921 (95% CI,0.851\u0026ndash;0.991), 0.707 (95% CI,0.503\u0026ndash;0.911) and 0.711 (95% CI,0.517\u0026ndash;0.905) respectively. In those CHB patients with ALT\u0026thinsp;\u0026le;\u0026thinsp;1\u0026times;ULN, the predictive model still had an excellent performance in predicting severe liver inflammation, and its AUROC was higher than those of sPD-1and ALT alone.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDuring HBV infection, the host antiviral immune response can cause hepatocyte damage and liver inflammation while eliminating virus. Besides, viral proteins and nucleic acids also contribute to hepatocyte damage and liver inflammation. Persistent liver inflammation promotes the disease progression from CHB to cirrhosis and HCC.\u003csup\u003e25\u0026ndash;28\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAccumulating studies have shown that sPD-1 expression is upregulated in viral infections, such as HCV and HIV infection, and autoimmune diseases including rheumatoid arthritis and autoimmune hepatitis. In those diseases, sPD-1 was associated with disease activity and progression. \u003csup\u003e16\u0026ndash;18,29\u003c/sup\u003e In chronic HBV infection, the expression of sPD-1 is also increased and correlates with liver disease progression. \u003csup\u003e21,30,31\u003c/sup\u003e In our study, the results showed that serum sPD-1 was highest in CHB patients with severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3), and higher than in CHB patients with mild (G0) and moderate (G2) liver inflammation. Zhou et al. also found that the levels of sPD-1 in CHB patients with moderate-to-severe liver inflammation were higher than those in patients with mild inflammation.\u003csup\u003e31\u003c/sup\u003e Besides, we also found that serum sPD-1 was positively correlated with the inflammatory stages. Our results suggest that serum sPD-1 is associated with liver inflammation in CHB patients, higher serum sPD-1 levels may indicate severe inflammation in the liver of CHB patients. Our results showed that serum sPD-1 has a weak positive correlation with AST and ALT levels in CHB patients, this may result from the different production mechanisms between sPD-1 and ALT, AST in the condition of liver inflammation in CHB patients. And this weak correlation may indicate the uniqueness of sPD-1 in reflecting liver inflammation and may shed a new light on the understanding of liver inflammation. A previous study showed that serum sPD-1 levels were higher in CHB patients with moderate-to-severe liver fibrosis than in those with mild fibrosis.\u003csup\u003e31\u003c/sup\u003e However, in our study, there was no significant association between PD1 and hepatic fibrosis, which may be caused by the different distribution of patients with hepatic fibrosis in two studies. The correlation between serum sPD-1 and liver fibrosis in CHB patients should be validated a large clinical cohort study.\u003c/p\u003e \u003cp\u003eThe severity of liver inflammation is one of the indicators for initiating anti-viral treatment for chronic HBV infection.\u003csup\u003e1,4\u003c/sup\u003e Liver enzymes including ALT and AST are commonly used to reflect the activity of liver inflammation.\u003csup\u003e5\u003c/sup\u003e However, about 20% of CHB patients with normal ALT have significant liver inflammation.\u003csup\u003e33\u003c/sup\u003e Liver biopsy is the gold standard for the diagnose of liver inflammation,\u003csup\u003e8\u003c/sup\u003e but the invasiveness of liver biopsy procedure limits its application for mass screening. Therefore, it\u0026rsquo;s necessary to seek new non-invasive markers of liver inflammation. We used logistic regression to evaluate the role of sPD-1 as a potential biomarker for predicting severe liver inflammation in CHB patients. Multivariate analysis showed that serum sPD-1 was an independent predictor for severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3) in CHB patients. In patients with chronic HBV infection, serum HBeAg is associated with viral replication, inflammation and disease activity, and response to antiviral therapy.\u003csup\u003e32\u003c/sup\u003e Therefore, we further analyzed the CHB patients with different HBeAg status, which revealed that serum sPD-1 in CHB patients with G\u0026thinsp;\u0026ge;\u0026thinsp;3 was higher than that of CHB patients with G\u0026thinsp;\u0026lt;\u0026thinsp;3 and was an independent predictor of severe liver inflammation in both HBeAg- and HBeAg\u0026thinsp;+\u0026thinsp;CHB patients. Those results indicate serum sPD-1 was an independent risk factor for severe liver inflammation in CHB patients, and the patients with higher serum sPD-1 levels had a higher risk of severe liver inflammation. In CHB patients, researchers have found that liver enzymes (ALT, AST, and GGT), viral parameters (HBsAg, HBeAg, anti-HBc), and PLT are also independently associated with liver inflammation.\u003csup\u003e34\u0026ndash;38\u003c/sup\u003e Consistent with those studies, we also found that AST, GGT, and PLT were independent predictors of liver inflammation in CHB patients.\u003c/p\u003e \u003cp\u003eSince individual parameters are affected by patients\u0026rsquo; demographics and other factors. We combined sPD-1, AST, GGT, HBeAg and PLT to construct a predictive model for liver inflammation in CHB patients. Compared with single parameter, the predictive model improved the predictive ability of liver inflammation (AUROC\u0026thinsp;=\u0026thinsp;0.917, 95%CI, 0.863\u0026ndash;0.971). In order not to miss the optimal opportunity to receive therapeutic intervention, it\u0026rsquo;s of great significance to monitor liver inflammation in CHB patients with normal ALT levels. 134 patients in our study cohort had normal ALT levels. In this subset of patients, the predictive model also showed a good predictive performance (AUROC\u0026thinsp;=\u0026thinsp;0.921, 95%CI, 0.851\u0026ndash;0.991) using liver biopsy as the gold standard for the diagnosis of liver inflammation. Those results suggest that serum sPD-1 can be used as a predictive biomarker for severe liver inflammation, and the predictive performance was further improved when combined with other indicators.\u003c/p\u003e \u003cp\u003eOur study has some limitations. CHB patients enrolled in the present study was relatively small, furthermore, only 30 CHB patients with G\u0026thinsp;\u0026ge;\u0026thinsp;3. At the time of conceiving this study, we mainly want to focus on the ability of sPD-1 in reflecting liver inflammation in the early stages of liver damage, CHB patients with severe liver inflammation (G\u0026thinsp;\u0026ge;\u0026thinsp;3) is not our primary focus. Therefore, a large-scale cohort including CHB patients with varying degrees of liver inflammation status would be desirable for further confirm the findings in our study.\u003c/p\u003e \u003cp\u003eIn conclusion, our results suggest that the serum sPD-1 is associated with the degrees of liver inflammation in CHB patients, and high levels of serum sPD-1 reflect severe liver inflammation. Besides, we constructed a model based on the non-invasive biomarkers, sPD-1, AST, GGT, HBeAg, and PLT, which has a good performance in predicting CHB patients with severe liver inflammation regardless of their ALT levels, and may help to decide whether to initiate the antiviral treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Medical Science and technology development Foundation，Nanjing Department of Health（YKK22073）and Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University（2022-LCYJ-PY-49）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests related to this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design (YXC, CW), acquisition of data; analysis and interpretation of data (MRO, WMZ, RH, JMG), drafting of the manuscript (MRO, WMZ, JP), statistical analysis and analysis of data (MRO, JCL, JX), study supervision; critical revision of the manuscript for important intellectual content (YXC, CW, JP, JX).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEuropean Association for the Study of the Liver. 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J Viral Hepat. 2019;26(Suppl 1):42\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jvh.13163\u003c/span\u003e\u003cspan address=\"10.1111/jvh.13163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic hepatitis B, Inflammation, Programmed Cell Death 1 Receptor","lastPublishedDoi":"10.21203/rs.3.rs-3324436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3324436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims: \u003c/strong\u003eLiver inflammation is important in guiding the initiation of antiviral treatment and affect the disease progression of chronic hepatitis B(CHB). Soluble programmed cell death 1 protein(sPD-1) was upregulated in inflammatory, infectious diseases and correlated with disease severity. We aimed to investigate the correlation between serum sPD-1 and liver inflammation in CHB patients and role in indicating liver inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e241 CHB patients who underwent a liver biopsy were enrolled. Correlation between sPD-1 levels and the degree of liver inflammation was analyzed. Univariate and multivariate logistic regression were performed to analyze independent variables of severe liver inflammation. Binary logistic regression was conducted to construct the predictive model for severe liver inflammation, and receiver operator characteristic curve(ROC) was used to evaluate the diagnostic accuracy of the predictive model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003esPD-1 was the highest in CHB patients with severe liver inflammation, which was higher than that in CHB patients with mild or moderate liver inflammation(P\u0026lt;0.001). Besides, sPD-1 was weakly correlated with AST(r=0.278, P\u0026lt;0.001). Multivariable analysis showed that sPD-1 was an independent predictor of severe liver inflammation. The predictive model contained sPD-1 had an area under the ROC(AUROC) of 0.917 and 0.921 in predicting severe liver inflammation in CHB patients and CHB patients with ALT≤1×upper limit of normal(ULN), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSerum sPD-1 is associated with liver inflammation in CHB patients, and high levels of sPD-1 reflect severe liver inflammation. Serum sPD-1 is an independent predictor of severe liver inflammation and shows improved diagnostic accuracy when combined with other clinical indicators.\u003c/p\u003e","manuscriptTitle":"Soluble programmed cell death 1 protein is a promising biomarker to predict severe liver inflammation in chronic hepatitis B patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-27 14:37:39","doi":"10.21203/rs.3.rs-3324436/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f209afc9-3673-49cd-82fa-c41ac638e7ea","owner":[],"postedDate":"September 27th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-09T10:19:09+00:00","versionOfRecord":{"articleIdentity":"rs-3324436","link":"https://doi.org/10.1021/acsomega.4c00780","journal":{"identity":"acs-omega","isVorOnly":true,"title":"ACS Omega"},"publishedOn":"2024-03-29 10:19:09","publishedOnDateReadable":"March 29th, 2024"},"versionCreatedAt":"2023-09-27 14:37:39","video":"","vorDoi":"10.1021/acsomega.4c00780","vorDoiUrl":"https://doi.org/10.1021/acsomega.4c00780","workflowStages":[]},"version":"v1","identity":"rs-3324436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3324436","identity":"rs-3324436","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}