Study population
427 patients met our inclusion criteria. 23.4% (n = 100) were included in CHC group, 55.3% (n = 236) had CHB, 11.9% (n = 51) were included in LC group and 9.4% (n = 40) had HCC. Demographics and clinical characteristics of different groups are described in Table 1. There were significant differences in sex, age and occupation among the four groups (all with p < 0.001). The prevalence of HBV-related diseases was significantly higher in men, in rural residents and in those with increased with age.
Characteristics
|
Chronic HBV carrier
(n = 100)
|
Chronic hepatitis B
(n = 236)
|
Liver Cirrhosis
(n = 51)
|
Hepatocellular Carcinoma
(n = 40)
|
p
|
Table 1
Baseline characteristics of patients*
Age (years)
|
37.5(26.0,48.0)
|
42.0(32.0,50.0)
|
54.0(47.8,62.0)
|
55.2 ± 9.1
|
< 0.001
|
Sex(%)
|
|
|
|
|
< 0.001
|
Male
|
36(36.0)
|
151(63.9)
|
34(66.6)
|
35(87.5)
|
|
Female
|
64(64.0)
|
85(36.1)
|
17(33.4)
|
5(12.5)
|
|
Occupation (%)˦
|
|
|
|
|
< 0.001
|
Worker
|
0
|
0
|
0
|
9(22.5)
|
|
Civil servant
|
11(11.0)
|
28(11.8)
|
4(7.8)
|
0
|
|
Teacher
|
0
|
0
|
0
|
1(2.5)
|
|
Peasant
|
28(28.0)
|
89(37.7)
|
34(66.6)
|
25(62.5)
|
|
Enterprise staff
|
10(10.0)
|
16(6.7)
|
3(5.8)
|
0
|
|
Others
|
51(51.0)
|
103(43.6)
|
10(19.6)
|
5(12.5)
|
|
Drinking(%)˦
|
|
|
|
|
0.022
|
Yes
|
16(16.0)
|
37(15.6)
|
2(3.9)
|
11(27.5)
|
|
No
|
84(84.0)
|
199(84.4)
|
49(96.1)
|
29(72.5)
|
|
Smoking(%)˦
|
|
|
|
|
< 0.001
|
Yes
|
20(20.0)
|
83(35.2)
|
27(52.9)
|
20(50.0)
|
|
No
|
80(80.0)
|
153(64.8)
|
24(47.1)
|
20(50.0)
|
|
HBV family history(%)˦
|
|
|
|
|
0.025
|
Yes
|
34(34.0)
|
91(38.5)
|
24(47.0)
|
24(60.0)
|
|
No
|
66(66.0)
|
145(61.5)
|
27(53.0)
|
16(40.0)
|
|
HCC family history(%)˦
|
|
|
|
|
0.330
|
Yes
|
89(89.0)
|
204(86.4)
|
41(80.3)
|
37(92.5)
|
|
No
|
11(11.0)
|
32(13.6)
|
10(19.7)
|
3(7.5)
|
|
HBeAg positive(%)#
|
|
|
|
|
< 0.001
|
Yes
|
50(50.0)
|
129(54.6)
|
5(9.8)
|
15(37.5)
|
|
No
|
50(50.0)
|
107(45.4)
|
46(90.2)
|
25(62.5)
|
|
Antiviral therapy(%)˦
|
|
|
|
|
< 0.001
|
Yes
|
0
|
136(57.6)
|
47(92.1)
|
40(100.0)
|
|
No
|
100(100.0)
|
100(42.4)
|
4(7.9)
|
0
|
|
Antiviral solutions(%)˦
|
|
|
|
|
|
Interferon
|
|
38(31.7)
|
0
|
0
|
|
Nucleoside(acid) analogues(NAs)
|
|
79(52.5)
|
45(95.7)
|
40(100.0)
|
|
Interferon + NAs
|
|
19(15.8)
|
2(4.3)
|
0
|
|
ALT(U/L)
|
25.9 ± 7.0a
|
55.6(38.4,91.2)a,b
|
41.2(19.0,79.5)a,c
|
46.0(36.0,73.5)a,d
|
< 0.001
|
AST(U/L)
|
23.7 ± 5.4a
|
36.0(26.0,55.1)a,b
|
34.2(27.0,66.0)a,c
|
46.0(32.5,81.5)a,d
|
< 0.001
|
TBIL(mmol/L)
|
15.8(12.1,20.2)a
|
18.0(14.6,21.9)b
|
24.1(16.7,29.1)a,b,c
|
21.9(13.1,30.9)d
|
< 0.001
|
DBIL(mmol/L)
|
5.9(5.0,7.3)a
|
6.9(5.9,8.3)a,b
|
8.9(6.0,11.9)a,b,c
|
4.1(2.8,8.8)c,d
|
< 0.001
|
ALB(g/L)
|
48.6(45.5,50.4)a,d
|
49.2(46.4,51.7)b,d
|
46.7 ± 4.8b,c,d
|
40.8 ± 4.7d
|
< 0.001
|
GGT(U/L)
|
16.6(13.0,22.4)a
|
29.5(18.1,51.6)a,b
|
35.5(23.6,60.7)a,c
|
55.0(29.0,102.0)a,b,d
|
< 0.001
|
AFP(ng/ml)
|
2.2(1.1,3.0)a,d
|
2.5(1.5,3.7)b,d
|
3.3(1.4,4.4)c,d
|
134.6(18.3,1382.5)d
|
< 0.001
|
LAM(kPa)
|
4.4(3.7,5.2)a
|
5.4(4.5,7.2)b
|
13.9(8.1,18.0)a,b,c
|
NA
|
< 0.001
|
CAP(db/m)
|
229.0(202.5,253.0)
|
223.0(189.8,261.0)
|
218.0(175.0,251.0)
|
NA
|
0.121
|
HBV DNA(log10IU/ml)
|
7.9(4.2,8.0)a,c
|
4.3(2.6,7.4)b,c
|
2.0(2.0,4.2)c
|
3.3 ± 1.4d
|
< 0.001
|
HBV RNA(log10copies/ml)
|
6.6(3.5,6.9)a
|
4.0(2.5,6.4)a,b
|
2.8(2.2,3.7)a,c
|
3.0 ± 1.4a,d
|
< 0.001
|
HBcrAg(log10U/ml)
|
8.5(7.0,8.7)a
|
6.3(3.6,8.3)a,b
|
5.3 ± 1.3a,c
|
5.2 ± 0.9a,d
|
0.029
|
*All patients who met the criteria were first divided into four outcomes, and we would gradually divided them into subgroups for more detailed analysis in the follow-up study. The statistical results of non-normally distributed data were represented by the median(Median(Q1,Q3)). Plus-minus values were means ± SD. The same letters meant that there was a statistical difference between the groups.
|
NA meant no data. Because of the particularity of HCC, fibroscan test couldn’t be used to cancer patients.
|
˦ Information were reported by the patient though the questionnaire.
|
# qualitative HBeAg was detected by ELISA kit.
|
Table 2. Multiplex Analysis of Serum Cytokines
Cytokine/ Chemokine
|
Detection Limit
|
Chronic HBV carrier (n=100)
|
Chronic hepatitis B (n=236)
|
Liver cirrhosis (n=51)
|
Hepatocellular carcinoma (n=40)
|
p
|
IL-6,pg/ml
|
1.36
|
4.8(3.8,6.3)
|
5.3(4.2,7.2)
|
5.5(4.2,7.8)
|
6.3(3.9,23.6)
|
0.136
|
IL-8,pg/ml
|
1.30
|
11.6(6.7,65.1)a
|
14.9(7.6,73.0)b
|
30.9(11.8,113.1)a,b,c,d
|
17.6(6.5,37.5)d
|
0.044
|
IL-21,pg/ml
|
10.40
|
33.1(25.6,46.6)a
|
40.0(28.7,56.1)a,b
|
37.9(32.1,45.0)c
|
43.80(27.1,59.0)d
|
0.046
|
IL-23,pg/ml
|
44.53
|
139.9(93.4,170.6)a
|
156.5(117.7,203.1)a,b
|
135.7(114.9,178.4)c
|
146.4(87.6,201.7)d
|
0.027
|
IL-33,pg/ml
|
5.31
|
9.9(8.0,12.7)a
|
11.7(9.0,14.4)a,b
|
10.5(9.6,11.7)c
|
11.5(8.2,15.0)d
|
0.030
|
IFN-γ,pg/ml
|
18.37
|
25.0(18.7,35.5)
|
29.0(22.7,37.1)
|
25.3(22.2,29.2)
|
27.7(20.7,35.8)
|
0.127
|
TNF-α,pg/ml
|
2.63
|
7.6(5.8,10.4)a
|
8.4(6.3,11.2)b
|
9.9(6.9,11.6)a,b,c
|
9.4(7.0,13.6)d
|
0.016
|
BAFF,pg/ml
|
5.14
|
702.9(564.1,893.8)a
|
798.4(634.8,965.2)b
|
810.4(597.5,959.6)c
|
645.6(578.0,828.2)b,d
|
0.035
|
TNFR1,pg/ml
|
16.50
|
994.0(743.6,1271.0)a
|
1142.0(897.3,1464.5)a,b
|
1253.0(1014.3,1530.0)a,c
|
1377.0(1089.8,1759.5)a,d
|
<0.001
|
CXCL9,pg/ml
|
236.38
|
662.1(408.1,894.5)a
|
718.4(524.2,941.0)b
|
589.8(47.0,899.2)b,c
|
52.5(44.2,63.3)a,b,c,d
|
<0.001
|
CXCL10,pg/ml
|
0.55
|
18.8(13.6,27.3)a
|
26.0(15.9,44.3)a,b
|
33.2(22.1,46.3)a,c
|
33.6(17.2,54.7)a,d
|
<0.001
|
CXCL11,pg/ml
|
8.16
|
30.5(23.1,43.5)
|
38.4(26.1,52.5)
|
36.1(25.7,52.5)
|
25.2(21.8,44.6)
|
0.221
|
CXCL13,pg/ml
|
5.41
|
48.2(34.8,64.1)
|
49.9(35.0,64.7)
|
45.4(30.4,63.2)
|
61.9(41.8,84.3)
|
0.204
|
CCL2,pg/ml
|
9.22
|
223.3(170.4,319.9)a
|
257.1(197.4,338.1)b
|
203.2(155.5,259.8)c
|
175.1(104.3,234.2)b,d
|
<0.001
|
MMP-1,pg/ml
|
14.91
|
2463.5(1430.3,4134.3)
|
2553.0(1598.5,5066.0)
|
2322.5(1454.3,4591.0)
|
2390.5(1156.5,5730.5)
|
0.729
|
MMP-2,pg/ml
|
74.75
|
17608.0(15377.8,21043.3)a
|
16996.0(14724.0,22158.8)b
|
18746.0(16514.3,21901.5)c
|
18231.5(17500.8,19050.5)b,d
|
0.016
|
MMP-3,pg/ml
|
24.75
|
10605.5(6795.0,15837.3)a
|
14196.5(8597.0,21814.0)a,b
|
16696.0(10173.8,25618.3)a,c
|
13663.5(9562.8,21597.5)d
|
0.001
|
Values expressed as median(Q1,Q3). Due to non-normality of CK data, independent sample nonparametric Kruskal-Wallis test was used. The same letters meant that there was a statistical difference between the groups.
|
Abbreviation: IL, interleukin; MMP, matrix metalloproteinases; TNF, tumor necrosis factor; BAFF, TNFSF13B, B cell activator.
|
p <0.05
|
There was no CHC case, 136 CHB patients, 47 patients with LC and 40 patients with HCC, received antiviral treatment. There were significant differences in liver function among the four groups. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were significantly lower in CHC compared to other groups (p < 0.001); total bilirubin (TB) and direct bilirubin (DB) levels were significantly higher in post-CHB phases including LC and HCC, especially in the LC group (p < 0.001). The level of albumin (13) in CHC and CHB groups were higher than that in LC and HCC groups (p < 0.001). The levels of glutamyl transpeptidase (GGT) and alpha fetoprotein (AFP) in the LC and HCC groups were higher than those in the CHC and CHB groups (all with p < 0.001). In the LC group, the degree of liver fibrosis was more severe than CHB and CHC groups (p < 0.001). Quantification of HBV DNA was higher in CHC compared to CHB, LC and HCC (p < 0.001). The level of novel markers including HBV RNA and HBcrAg were higher in CHC group compared to CHB, LC and HCC (p < 0.001).
Changes of cytokines highlight CD4 + T cellular activation and monocytes/macrophages in CHB patients is accompanied by passivated IFN-γ secretion
Compared with CHC, the levels of IL-21, IL-23, IL-33 and CXCL10 increased significantly in CHB patients. However, there was no significant increase of IFN-γ in CHB patients (Fig. 1A). There were also significant positive correlations between IL-21, IL-23 and IL-33, especially IL-21 and IL-33 (rIL−21/IL23=0.536, p < 0.001; rIL−21/IL−33=0.7514, p < 0.001; rIL−23/IL−33=0.3535, p < 0.001). Additionally, there was a positive correlation between CXCL10 and IL-21 (rIL−21/CXCL10=0.1464, p < 0.01). Please see Fig. 1B for further details.
Subtle Il-8 Increases In Lc Patients Exposed Active Neutrophils During Liver Fibrosis Progression
Compared with patients without cirrhosis, IL-8, TNF-α and MMP-3 levels in LC patients increased significantly. IL-8, in particular, showed a characteristic increase during cirrhosis (Fig. 2A). There was also a significant, positive correlation between IL-8 and TNF-α (rIL−8/TNF−α=0.2796, p < 0.001). The correlations between MMP-3 and IL-8, TNF-α were not remarkable (p > 0.05). See Fig. 2B for further details.
Cxcl9 Dramatically Decreased And Other Myeloid-related Markers Point To Innate Immunity Inhibition In Hcc Patients
CXCL9, CCL2 and BAFF levels in HCC patients were significantly lower than those in CHC, CHB and LC patients. IL-8 was also lower than in the LC group (Fig. 2A). Among them, the decrease in CXCL9 was most notable. TNFR1 levels in HCC patients was also higher than that in other groups (Fig. 3A). Correlation analysis with CKs showed that CXCL9 and CCL2 were significantly positively correlated, but not with BAFF (rCXCL9/CCL2=0.1696, p < 0.01). BAFF also positively correlated with CCL2 (rBAFF/CCL2=0.4131, p < 0.001), see Fig. 3B. Other non-specific cytokine level differences among CHC, CHB, LC and HCC are provided in Table 2.
Application Of Ck Combination Can Predict Early Hcc Based On Liver Cirrhosis
There were significant differences in CK expressions between LC and HCC groups, such as IL-8 and CXCL9. Therefore, we used an ROC model to predict the occurrence of early HCC through multiple CK combinations based on univariate logistic regression (supplementary Table 1). We tried different kinds of CK combinations, including pairwise combinations and multiple combinations (supplementary Table 2). CK combinations of IL-6, IL-8, CXCL9 and CXCL13 could effectively distinguish LC and HCC, with high sensitivity and specificity (the sensitivity of ROC was 87.5% and the specificity of ROC was 85.4%), and the AUCROC was 0.898. See Table 3 and Fig. 4A for further details. There were also remarkable differences in expressions of CKs between CHC and CHB populations.
Table 3. The prediction and analysis of immune status of statistically significant CK combinations
Cytokine combination
|
AUCROC
|
cut off value
|
sensitivity(%)
|
specificity(%)
|
95%CI
|
p
|
IL-6+IL-8+CXCL9+CXCL13 (LC vs HCC)
|
0.898
|
-
|
87.500
|
85.400
|
0.810-0.987
|
<0.001
|
TNFa+IL-33+CXCL10+IL-21+IL-23 (CHC vs CHB)
|
0.723
|
-
|
50.500
|
84.000
|
0.663-0.872
|
<0.001
|
We predicted CHB on the base of CHC using the same analytical method. Nine kinds of CKs showed characteristic differences in univariate analysis. However after analyzing the β value, we found that only TNF-α, IL-33, CXCL10, IL-21 and IL-23 contributed to the result of univariate logistic, so we chose these CKs with large contribution value for subsequent combination analysis (supplementary Table 3). Different kinds of CK combinations based on univariate logistic were shown in supplementary Table 4 and the hierarchical ROC showed that separating DNA into high and low levels were consistent with those without stratification (supplementary Table 5). The AUCROC of CK combination of TNF-α, IL-33, CXCL10, IL-21 and IL-23 was 0.723, and the sensitivity was 50.5% and the specificity was 84.0% (Table 3 and Fig. 4B). This indicated that the CK combination of TNF-α, IL-33, CXCL10, IL-21 and IL-23 could distinguish CHC and CHB.
Dynamic changes of cytokines infer progressively attenuating CD4 + T-cell responses and sequential activation pattern of macrophages during the natural courses of chronic HBV infection
Patients with chronic HBV infection and without antiviral treatment were divided into IT, IA, IC and ENEG according to the natural courses of disease.
IL-21 and IL-23 showed similar trends during natural courses without antiviral treatment. The levels in IA and ENEG phases were higher than in IT and IC, and the level in ENEG rose lower than that in IA. There was a significant decrease in IC when compared to IA (p < 0.01). IFN-γ increased in IA phase, however there showed no statistical difference compared with other phases (p > 0.05, Fig. 5A). The levels of CXCL9 and CXCL11 increased in IT phase, continued to decline significantly until IC, and increased again in ENEG. The level of CXCL9 in IC was significantly lower than that in IT and ENEG (both with p < 0.01) and the level of CXCL11 in IC was significantly lower than that in ENEG (p < 0.05). CXCL10 lagged (the level in IT phase was low), and significantly increased in IA phase and ENEG. The level of CXCL10 showed significant differences among four subgroups (p < 0.05, p < 0.01, p < 0.001, respectively, Fig. 5B). Level changes of MMP family see in Fig. 5C. There were no significant differences in IL-6, IL-8, IL-33, CXCL13, and MMP-1 in each natural course (supplementary Fig. 1).
In addition, we used the same method to distinguish different periods in natural course of chronic HBV infection using CKs. CXCL9 as well as CXCL11 were specifically selected for IT and IC, and MMP-2 were selected for IA as well as ENEG through univariate analysis (supplementary Table 6, 7). ROC curve showed that the AUCROC of CXCL9 was 0.687 and the cutoff value was 598.450 pg/ml, the sensitivity and the specificity of CXCL9 were respectively 63.8% and 74.5%. The AUCROC of CXCL11 was 0.662 and the cut-off value was 26.315 pg/ml, the sensitivity and the specificity of CXCL11 were respectively 51.1% and 80.9% (Table 4 and Fig. 5D). After the serial and parallel test, prediction efficiency would be improved. Besides, The AUCROC of MMP-2 was 0.657, the cut-off value was 17,691 pg/ml and the specificity of MMP-2 were respectively 68.0% and 67.3%. After the cut-off value was used for stratified ROC, the AUCROC was significantly improved, and so to was sensitivity and specificity (AUCROC=0.858), see Table 4 and Fig. 5E.
Table 4. Predictions and analysis of immune status of statistically significant CKs and combination forms
Cytokines/Chemokines
|
AUCROC
|
cut off value(pg/ml)
|
sensitivity(%)
|
specificity(%)
|
95%CI
|
p
|
IT vs IC
|
|
|
|
|
|
|
CXCL9
|
0.687
|
598.450
|
63.800
|
74.500
|
0.576-0.797
|
0.002
|
CXCL11
|
0.662
|
26.315
|
51.100
|
80.900
|
0.551-0.773
|
0.007
|
CXCL9+CXCL11
|
0.697
|
-
|
63.800
|
78.700
|
0.589-0.805
|
0.001
|
Serial test
|
|
|
|
|
|
|
CXC9+CXCL11
|
0.967
|
-
|
91.500
|
100.000
|
0.928-1.000
|
<0.001
|
Parallel test
|
|
|
|
|
|
|
CXCL9+CXCL11
|
0.943
|
-
|
100.000
|
85.100
|
0.890-0.996
|
<0.001
|
IA vs ENEG
|
|
|
|
|
|
|
MMP-2
|
0.657
|
17691.000
|
68.000
|
67.300
|
0.548-0.766
|
0.007
|
MMP-2(cut off)
|
0.858
|
-
|
100.000
|
75.400
|
0.785-0.930
|
<0.001
|
We compared the CKCs among natural course of chronic HBV infection including IT, IA, IC and ENEG phases and found the completely different immune status in chronic infection. (A) The differences in CK expression related to T cell response. (B) The differences in CK expression related to monocytes/macrophages response. (C) The differences in CK expression related to MMP family. (*)p < 0.05; (**)p < 0.01; (***)p < 0.001. IT: Immune tolerance phase; IA: Immune activation phase; IC: Immune control phase; ENEG: HBeAg-negative Hepatitis. (D) The ROC curve for predicting different phases of natural courses of chronic HBV infection. showed the ROC curve for distinguished between IT and IC phases and the joint tests, including serial and parallel tests. The joint test could increase the AUCROC and the corresponding the sensitivity and specificity. (E) The ROC curve for distinguished between IA and ENEG phases and the validation test. It could be seen that the AUCROC increased after the validation test, and the sensitivity and specificity also improved.
Cytokines Negatively Correlate With Virological Markers And When Paired Exhibit A Strong Positive Correlation
In order to answer whether these changes are related to serological and virological indicators, we conducted further correlation analysis.
We found that most CKs significantly negatively correlated with HBV DNA in IT, including IL-21, IL-23, IL-33, CXCL9 and CXCL11. The correlation with HBV RNA was weaker than HBV DNA. Among these, the strongest negative correlation was between IL-23 and HBV DNA (r=-0.377, p < 0.001). CKs showed a certain positive correlation among them. There was a significant positive correlation between proinflammatory factors IL-21, IL-23, IL-33 and IFN-γ, and the positive correlation between IL-21 and IL-23 was the strongest (r = 0.681, p < 0.001), see Fig. 6A.
Most CKs maintained a negative correlation with HBV DNA in IA phase, including IL-21, IL-23, IL-33 and IFN-γ. There was also a positive correlation between CKs and transaminase ALT and AST, especially CXCL10 (rALT=0.523, p < 0.001; rAST=0.709, p < 0.001). There was also a significant positive correlation among IL-21, IL-23, IL-33 and IFN-γ(Fig. 6B).
In IC phase, the negative correlation between most CKs and bilirubin were remarkable. IFN-γ and BAFF also were negatively correlated with total bilirubin (TB) and direct bilirubin (DB) and BAFF had strong negative correlations with HBV DNA in IC (r=-0.357, p < 0.001). While positive correlations between IL-21, IL-23, IL-33 and IFN-γ increased in IC than that in IA (Fig. 6C).
During the ENEG phase, correlations between CKs and HBV DNA decreased while negative correlations with HBV RNA increased, such as IFN-γ (r=-0.239, p < 0.001). The detection sensitivity of HBV RNA is higher than DNA, especially when the level of HBV DNA is low. In addition, CKs, including IL-21, IL-23, IL-33 and IFN-γ, maintained a significant positive correlation in ENEG phase than that in IC (Fig. 6D).
The correlation of CKCs and HBV serological and viral factors showed in the heat map. The red color meant positive correlation and the blue meant negative correlation. The darker the color, the stronger the correlation. (A) The correlation between CKs and serological and viral factors in natural course IT. (B) The correlations between CKs and serological and viral factors in natural course IA. (C) The correlations between CKs and serological and viral factors in natural course IC. (D) The correlations between CKs and serological and viral factors in natural course ENEG. logDNA: log10 quantification of HBV DNA; logRNA: log10 quantification of HBV RNA; HBcrAg: hepatitis B core-related antigen; ALT: alanine aminotransferase; AST: aspartate aminotransferase; TBIL: total bilirubin; DBIL: Direct bilirubin; GGT: γ-glutamyl transpeptidase; LSM: liver stiffness measured by Fibroscan. CAP: controlled attenuation parameters measured by Fibroscan.
Lack Of Antiviral Therapy Effect On Cytokine Levels May Result In Hbv Clearance Failure
Based on the natural courses of chronic HBV infection, the antiviral treatment population was included for analysis. We analyzed the serological and virological markers and found that ALT, HBV DNA, HBV RNA and HBcrAg decreased to varying degrees after antiviral therapy, especially HBV DNA. It showed that antiviral therapies promote liver function recovery and HBV load decline, see supplementary materials, Fig. 2.
We further analyzed the differences in the changes of CKs before and after receiving antiviral therapy. IL-21 levels in the HBeAg- group after antiviral treatment was higher than that in IC without antiviral treatment (p < 0.05). IL-33, CXCL9 and CXCL11 in HBeAg positive and negative groups after antiviral treatment were higher than those in IC without antiviral treatment (p < 0.05, p < 0.01, respectively). CXCL10 in HBeAg positive and negative groups after antiviral treatment was higher than that in IT without antiviral treatment (p < 0.01), see Fig. 7A, B. Compared to IA and ENEG patients (HBeAg positive/negative), there was no significant change in CKs in HBeAg positive/negative CHB antiviral treatment groups (Fig. 7A). Simultaneously, we analyzed the correlations between CKs and serological and virological markers in the antiviral group, and found that the correlation between CKs and HBV DNA decreased after treatment. Although there was a positive correlation between IL-21, IL-23, IL-33 and IFN-γ, the correlation was weaker than that in the non-antiviral group (Fig. 7C).