Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Platelet counts modulate the quantitative relationship between hepatitis B viral DNA and surface antigen concentrations: a cross-sectional study of hematological, histological and viral factors

BMC Infectious Diseases volume 17, Article number: 9 (2017) Cite this article

Abstract

Background

The concentrations of hepatitis B virus (HBV) DNA and surface antigen (HBsAg) are two critical virological variables to be monitored in chronic hepatitis B. HBsAg is derived from the HBV genome. Thus, higher HBV-DNA concentrations should implicate higher HBsAg levels. Nevertheless, the two variables do not manifest a simple linear relationship due to elusive host factor involvements. The aim of this study was to address the discrepancy of HBV DNA and HBsAg levels by a quantitative modeling of HBsAg concentrations.

Methods

Pretreatment hematological, histological and virus serological records of 327 chronic hepatitis B patients were reviewed. Two independent patient cohorts were used for validation.

Results

Univariate/multivariate analysis showed that ISHAK fibrosis stages, HBV-DNA levels and hepatitis e-antigen status were independently associated with HBsAg concentrations. In agreement with the natural history of chronic hepatitis B, HBsAg concentrations were negatively correlated with ISHAK fibrosis stages (adjusted P = 0.002). Subgroup analysis showed that significant HBsAg-DNA correlation existed in high-viral-titer patients with HBV-DNA > 6 log10 IU/mL (P < 0.001), but not in low-viral-titer patients with HBV-DNA ≤ 6 log10 IU/mL (P = 0.076). A backward stepwise linear regression analysis in the low-viral-titer subgroup revealed a significant correlation between HBsAg levels and a linear combination of HBV-DNA levels and platelet counts. A biphasic model was thus established to accommodate patients with high and low HBV-DNA titers:

$$ \mathrm{HB}\mathrm{sAg}=0.538\ast \mathrm{H}\mathrm{B}\mathrm{V}{\textstyle \hbox{-}}\mathrm{D}\mathrm{N}\mathrm{A}+0.001\ast \mathrm{platelet}\ast \left(\left|6{\textstyle \hbox{-}}\mathrm{H}\mathrm{B}\mathrm{V}{\textstyle \hbox{-}}\mathrm{D}\mathrm{N}\mathrm{A}\right|+6{\textstyle \hbox{-}}\mathrm{HB}\mathrm{V}\mathrm{D}\mathrm{N}\mathrm{A}\right){\textstyle \hbox{-} }0.321 $$

The estimated HBsAg concentrations correlated well with the measured HBsAg levels not only in the model construction cohort (N =327, P < 0.001), but also in two validation cohorts comprising respectively the patients who had received pretreatment liver biopsy assessments (N = 45, P = 0.001), and the treatment-naïve patients who had not received liver biopsy (N = 80, P < 0.001).

Conclusion

HBsAg concentrations can be quantitatively estimated by viral DNA concentrations and human platelet counts.

Peer Review reports

Background

Chronic hepatitis B virus (HBV) infection is an endemic disease with a global burden of 350 million patients [1]. This disease persists for multiple decades, and its natural history comprises the immune tolerance, immune clearance and inactive residual phases [2–6]. During the chronic infection, episodes of liver inflammation may occur which cause progressive liver fibrosis and cirrhosis, leading toward thrombocytopenia [7], hypoalbuminemia [8], portal hypertension, esophageal varices, ascites [9], liver decompensation and hepatocellular carcinoma (HCC) [10]. To prevent such devastating consequences, effective antiviral therapies were now vigorously used, with viral and host status carefully monitored [11]. Serum concentrations of HBV DNA and surface protein antigen (HBsAg) are both important viral markers [12]. HBsAg is derived from the HBV genome. Thus, higher HBV DNA concentrations should implicate higher HBsAg levels.

Despite the established molecular origin, serum HBV DNA and HBsAg did not manifested a simple linear relationship in the natural course. The HBV DNA and hepatitis B e-antigen (HBeAg) levels were drastically reduced in the immune clearance phase, while the HBsAg levels were further reduced continuously in the inactive residual phase [2–6]. The discrepancy between HBV DNA and HBsAg levels made them independent variables rather than confounding variables in clinical studies. For example, they played different roles in the prediction of subsequent HCC [13]. Medical guidelines suggested that anti-viral treatments should be given to patients in the immune clearance phase for the purpose of expediting the natural course into the inactive residual phase; and to patients with viral reactivation during the inactive residual phase [11, 12, 14]. HBV DNA was demonstrated to be effectively suppressed, often to undetectable levels, by treatments of approved nucltos(t)ide analogs including lamivudine [15], adefovir [16], entecavir [17], telbivudine [18] and tenofovir [19]. The HBsAg, however, remained positive for years for most of these treated patients. This was why HBsAg seroconversion (the disappearance of HBsAg and the production of anti-HBs antibody), rather than the HBV DNA undetectability, was regarded as the closest sign of cure [12]. On the other hand, patients with negative HBsAg but with positive HBV DNA were occasionally identified, and referred to as the occult hepatitis B patients [20–22]. These patients were still at risk of HBV reactivation [22].

The lack of linear relationship between HBV DNA and HBsAg may be partly explained by the viral life cycle. The covalently closed circular DNA (cccDNA) is the template for generating messenger RNAs, which are further translated to produce HBsAg, as well as the pregenomic RNAs which are reversely transcribed to viral DNA [23]. Since the HBV DNA can integrate into the human genome, the HBsAg may also be derived from the integrated HBV DNA in addition to cccDNA [24]. The viral life cycles occurred in the human hepatocytes, making them susceptible to host factors.

The discrepancy between serum HBV DNA and HBsAg levels remained to be quantitatively evaluated. Therefore, we employed a data-driven approach and conducted a systematic, multivariate evaluation of hematological, histological and viral factors to evaluate their effects on HBsAg concentrations.

Methods

Patients

This study was approved by the institutional review board of the Chang Gung Memorial Hospital, Taiwan, and conducted in accordance with the Declaration of Helsinki. All patients have given informed consent for the deposition of their clinical samples to the tissue bank of Chang Gung Memorial Hospital, Taiwan, for academic researches.

In the first stage, clinical records of 327 chronic hepatitis B patients who received pretreatment hematology, liver histology and viral serology assessments between years 2007–2009 were retrospectively retrieved for a quantitative modeling (Table 1). Liver histology was evaluated by the ISHAK hepatic activity indexes [25]. In the second stage, two independent cohorts were assessed for the validation purposes. The first cohort comprised 45 patients who also received liver biopsy for pretreatment evaluations between years 2007–2009. The second cohort comprised 80 anti-hepatitis B treatment-naïve patients evaluated between years 2010–2012. These patients did not receive liver biopsy.

Table 1 Baseline characteristics of patients in the model construction cohort
Full size table

Quantitation of HBV DNA and HBsAg concentrations

HBV DNA levels were measured by use of the COBAS AmpliPrep/COBAS TaqMan HBV Test, v2.0 assay (Roche Molecular Systems Inc, Pleasanton, CA) according to manufacturer’s protocols. HBsAg concentrations were measured by use of Elecsys HBsAg II assay (Roche Diagnostics GmbH, Mannheim, Germany).

Statistical analysis

The HBV DNA and HBsAg concentrations were consistently represented here in the logarithm scale due to their wide numerical ranges. Clinical associations were evaluated by univariate and multivariate linear regressions. Subgroup analysis was then performed to identify patient stratum where the significant HBsAg-HBV DNA correlation was lost. For this subgroup, modulating factors for the DNA-HBsAg relationships were then evaluated by the backward stepwise linear regression method, where the F-test were used to evaluate the model performance. The modulating factors were then introduced into a prediction model. The statistical analysis was performed using the SPSS software (IBM, Armonk, NY). P values smaller than 0.05 were considered statistically significant.

Results

HBV DNA levels, HBeAg positivity and ISHAK fibrosis stages were independently associated with HBsAg levels

The first cohort comprised 327 chronic hepatitis B patients (Table 1). Age, ISHAK fibrosis stages, HBV DNA levels, hepatitis B e-antigen (HBeAg) positivity, platelet counts and hemoglobin levels were significantly associated with HBsAg levels in the univariate analysis (Table 2). When these variables were entered into multivariate analysis, only three variables remained significantly associated (ISHAK fibrosis stages, HBV DNA levels and HBeAg positivity) (Table 2). Among them, HBV DNA is the most strongly associated variable (P < 0.001). An initial model of HBsAg by use of the three independent variables was therefore constructed as a benchmark using the multivariate linear regression as:

Table 2 Association of viral and host variables to quantitative HBsAg concentrations using linear regression
Full size table
$$ \mathrm{HBsAg} = 0.274\ *\ \mathrm{H}\mathrm{B}\mathrm{V}\ \mathrm{D}\mathrm{N}\mathrm{A}+0.314\ *\ \left(0\ \mathrm{if}\ \mathrm{HB}\mathrm{eAg}\ \mathrm{negative}\right)\ \hbox{-}\ 0.123\ *\ \mathrm{ISHAK}\ \mathrm{fibrosis}\ \mathrm{score}+1.858 $$

The estimated HBsAg levels by the three-variable model were highly correlated with the measured HBsAg levels (Pearson’s correlation r = 0.59; P < 0.001). The standard deviation of the regression residual is 0.79 log10 IU/ml.

Identification of a patient subgroup which lacked significant HBV DNA-HBsAg correlations

We further conducted the subgroup analysis of patients stratified by the above three variables. Significant HBsAg-DNA correlations remained in HBeAg positive and negative patient subgroups (both P < 0.001), in ISHAK score ≥ 4 or ≤ 3 subgroups (both P < 0.001), and in patients with HBV-DNA > 6 log10 IU/mL (P < 0.001). However, no significant association were found in the patient subgroup with HBV-DNA ≤ 6 log10 IU/mL (Fig. 1). A baseline comparison of the low- and high- HBV DNA titer subgroups, defined using the boundary threshold of 6 log10 IU/mL, showed that the low-titer subgroup has a significantly lower percentage of HBeAg positive patients (27.48%) than the high-titer subgroup (56.12%, Table 3).

Fig. 1
figure 1

Pearson’s correlation (r) of HBsAg and HBV DNA levels in patient subgroups stratified by HBeAg status, ISHAK fibrosis stages and HBV DNA levels

Full size image
Table 3 Baseline characteristics of patients with HBV DNA below or above 106 IU/mL
Full size table

A biphasic model of HBsAg concentrations using platelet counts and HBV DNA concentrations

A scatter plot was then produced to offer a visualization of the relationship between the HBV DNA and HBsAg identified in the previous subgroup analysis (Fig. 2a). Significant HBsAg-DNA correlation were found in patients with HBV-DNA > 6 log10 IU/mL but not in patients with HBV-DNA ≤ 6 log10 IU/mL, suggesting unknown modulating factors of the HBsAg levels in the HBV DNA low-titer subgroup. Therefore, a backward stepwise linear regression analysis was then performed in the subgroup when HBV-DNA ≤ 6 log10 IU/mL (N = 131). This was done by incorporating all the 16 clinical variables into a multivariate linear regression equation, then gradually removing irrelevant variables one at a time, and evaluating the statistical significance (Fig. 2b). At the end of the stepwise analysis, the linear combination of two variables, platelet counts and DNA levels, was found to be significantly correlated with HBsAg levels (F-test P = 0.048, degrees of freedom = 2).

Fig. 2
figure 2

a The scatter plot of HBV DNA and HBsAg levels in the model construction cohort. b Backward stepwise linear regression analysis in patients with HBV-DNA ≤ 6 log10 IU/mL. The x-axis showed the number of variables incorporated in the model, which also equated to the degrees of freedom in the F-test. The y-axis showed the P values calculated by the F-test. At the beginning, all 16 clinical variables were incorporated into a linear model. Less relevant variables were progressively removed. At the end of the stepwise process, a linear combination of platelet and HBV DNA levels showed significant association to HBsAg levels (P = 0.048, degrees of freedom = 2). c Estimated HBsAg levels is a function of HBV DNA levels and platelet counts in the constructed biphasic model. d A scatter plot of the measured and estimated HBsAg levels in two validation cohorts. Validation cohort 1: patients with biopsy-included pretreatment evaluations. Validation cohort 2: treatment naïve patients

Full size image

We continued to construct a biphasic model of HBsAg level using (i) HBV-DNA alone when HBV-DNA > 6 log10 IU/mL, and (ii) HBV-DNA and platelet counts together when HBV-DNA ≤ 6 log10 IU/mL.

$$ \mathrm{HB}\mathrm{sAg}=0.538\ast \mathrm{H}\mathrm{B}\mathrm{V}\mathrm{D}\mathrm{N}\mathrm{A}+0.001\ast \mathrm{platelet}\ast \left(\left|6{\textstyle \hbox{-}}\mathrm{HBVDNA}\right|+6{\textstyle \hbox{-}}\mathrm{HBVDNA}\right){\textstyle \hbox{-} }0.321 $$

Where |‧| represented the absolute-value function. The relationship between the HBV DNA levels, platelet counts and the estimated HBsAg levels was visualized in Fig. 2c. In the model construction cohort, the HBsAg levels calculated by the biphasic model were significantly correlated with the measured HBsAg levels (r = 0.60, P < 0.001). The standard deviation of the regression residual is 0.78 log10 IU/ml.

Clinical records of additional 45 patients with liver biopsy-included pretreatment evaluations were used for the first validation (Table 4). Significant positive correlations were found between estimated and measured HBsAg concentrations (r = 0.47, P = 0.001). The standard deviation of the residual is 0.82 log10 IU/mL. Furthermore, a cohort of 80 treatment-naïve patients (not receiving pretreatment liver biopsy) evaluated between 2010-2012 were recruited for the second validation (Table 5). Significant positive correlations were found again (r = 0.57, P < 0.001). The standard deviation of the residual is 0.88 log10 IU/mL. A visual presentation of the estimated and the measured HBsAg levels in the two validation cohorts were shown in Fig. 2d.

Table 4 Baseline characteristics of patients in the first validation cohort. The patients all received biopsy-included pretreatment evaluations between 2007-2009
Full size table
Table 5 Baseline characteristics of patients in the second validation cohort. The patients were treatment-naïve patients evaluated between 2010-2012
Full size table

Discussion

Chronic hepatitis B often lasted for decades, if not lifetime. The HBsAg level was high in the immune tolerance phase. It reduced gradually in the immune clearance phase and the inactive residual phases [12]. A strong positive linear relationship between age and the annual rate of HBsAg seroclearance has been demonstrated in a meta-analysis of 13 study cohorts [26]. The highest rate of HBsAg seroclearance occurred at 50 years old [26], an age when many patients have already developed mild or severe liver fibrosis. The negative correlations between fibrosis stages and HBsAg levels has also been demonstrated in previous univariate analyses [27, 28]. Significantly lower HBsAg levels were found in patients with ISHAK fibrosis score >1, compared with those with score ≤ 1 (P < 0.001) [27]. Baseline data from a multicenter, phase III trial of peginterferon alfa-2a and a phase IV NEPTUNE trial showed that lower HBsAg levels were associated with lower PS1 and PS2 scores, which indicated more severe fibrosis [28].

The necessity of multivariate analysis arises as multiple factors (age, HBsAg level, fibrosis stage) were shown to be involved in univariate analyses [27, 28]. Our systematical evaluation of hematological, histological and viral serological variables showed that the progression of liver fibrosis was accompanied by HBsAg reduction (Table 2 , adjusted regression coefficient of “ISHAK fibrosis stage” = -0.125, P = 0.002), independent of age, HBV DNA levels, HBeAg positivity, platelet counts and hemoglobin levels. Age on the other hand was negatively correlated with HBsAg concentrations only in the univariate analysis but not in the multivariate analysis.

The discrepancy between HBV DNA and HBsAg levels underlies the reason why HBsAg cannot play comparable roles on the estimation of subsequent HCC risks as what HBV DNA can do (except for patients with very low levels of HBV DNA). HBV DNA has been established as an important predictor of HCC risks [29]. A recent report showed that HBV DNA in general is a better predictor of HCC than HBsAg [13]. However, in a specific subgroup of HBeAg negative, HBV DNA < 2000 IU/mL patients, HBsAg rather than HBV DNA was a better predictor [13]. This conclusion was based on a study population of non-cirrhotic, relatively young patients (>50% patients were 28–39 years old at the time of enrolment). Considering the strong effect of fibrosis stages on the subsequent HCC occurrence [12, 30] and the negative correlations between HBsAg levels and fibrosis stages demonstrated here, it was reasonable to say that any potential positive correlations between HBsAg and HCC incidence can only be found in patients with similar fibrosis status, which however required liver biopsy to be assessed correctly. The predictive role of HBsAg on HCC reported in [13] may not be readily extrapolated to elder people with mild, moderate and severe fibrosis.

A model of HBsAg levels can be constructed straightforwardly using the three independent variables (HBV DNA, fibrosis stages and HBeAg status). This model was a benchmark in the search for a simpler model with fewer number of clinical variables. We continued to investigate patient subgroups stratified by the three independent variables. We found that the DNA remained significantly associated with HBsAg in all strata except when DNA < 6 log10 IU/mL. A backward stepwise linear regression analysis in the low-titer subgroup showed that, after the less relevant variables were removed gradually, platelet counts and HBV DNA remained, and their combination was synergistically associated with HBsAg levels. Thus, a biphasic model was constructed using HBV DNA alone when HBV-DNA > 6 log10 IU/mL, and platelet levels in conjunction with HBV DNA when HBV-DNA ≤ 6 log10 IU/mL. This new model is simpler, with fewer variables, yet the correlation (r = 0.60) is even higher and the standard deviation of the regression residual (e = 0.78 IU/mL) is even lower than those of the three-variable model (r = 0.59 and e = 0.79 IU/mL).

The reduction of platelet counts, i.e. thrombocytopenia, has been acknowledged to be associated with chronic liver diseases and cirrhosis [31, 32]. The correlation between platelet counts and ISHAK fibrosis stages made them both associated with HBsAg levels in our univariate analysis (Table 2). When they were both introduced into the multivariate analysis, only the ISHAK stage but not the platelet counts (P = 0.064) remained significantly associated. However, in the low-titer subgroup when HBV-DNA ≤ 6 log10 IU/mL, platelet counts rather than ISHAK stages were remained in the backward stepwise regression analysis. This showed that platelet counts and HBV DNA formed an effective combination for estimating HBsAg when HBV-DNA ≤ 6 log10 IU/mL.

Platelets were widely known for their roles in blood coagulation. In addition to this conventional role, its antimicrobial roles were gradually being noticed [33]. Platelets can secret chemokine ligand 5 (CCL5) so as to stimulate the production of megakaryocytes, forming a positive feedback loop of platelet activation [34]. It can also secrete hepatocyte growth factor (HGF) so as to protect against liver fibrosis [35]. The detailed mechanism on the interactions of platelets to the HBV life cycle warrants further investigations.

The quantitative modeling provided a numerical basis for our understanding on the relationship between HBsAg, HBV DNA, age, fibrosis stages and platelet counts. The estimated HBsAg concentrations correlated well with the measured HBsAg in the model construction cohort as well as the two independent validation cohorts (P ≤ 0.001 in all), supporting the use of the biphasic model in retrospective studies where the HBsAg was not measured at previous timepoints and no stored clinical samples were available. Since quantitative HBsAg measurement has become more and more available recently, patients’ HBsAg levels can now be measured directly without the help of this biphasic model.

Patients in the immune activation and the inactive residual phases were particularly required for quantitative HBsAg monitoring, and they were the major population of our study cohorts. Although we have analyzed a total of 452 patients, patients in the immune tolerance phases were not well represented. Therefore, the current analysis may only be applied to patients in the immune activation phase onward, but may not be extrapolated to patients in the immune tolerance phase.

In conclusion, serum HBsAg levels depended on HBV DNA titers, the liver fibrosis stages, and HBeAg positivity. Taking into consideration of all the above aspects, we constructed a noninvasive, biphasic quantitative model using two variables, HBV DNA and platelet levels, which can effectively estimate HBsAg concentrations.

Abbreviations

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

DNA:

Deoxyribonucleic acid

HBeAg:

Hepatitis B e-antigen

HBsAg:

Hepatitis B surface antigen

HBV:

Hepatitis B virus

HCC:

Hepatocellular carcinoma

References

  1. Lavanchy D. Hepatitis B virus epidemiology, disease burden, treatment, and current and emerging prevention and control measures. J Viral Hepat. 2004;11(2):97–107.

    Article  CAS  PubMed  Google Scholar 

  2. Liaw Y-F, Chu C-M. Hepatitis B virus infection. Lancet. 2009;373(9663):582–92.

    Article  CAS  PubMed  Google Scholar 

  3. Elgouhari HM, Abu-Rajab Tamimi TI, Carey WD. Hepatitis B virus infection: Understanding its epidemiology, course, and diagnosis. Cleve Clin J Med. 2008;75(12):881–9.

    Article  PubMed  Google Scholar 

  4. Yim HJ, Lok AS-F. Natural history of chronic hepatitis B virus infection: What we knew in 1981 and what we know in 2005. Hepatology. 2006;43(S1):S173–81.

    Article  CAS  PubMed  Google Scholar 

  5. Ganem D, Prince AM. Hepatitis B virus infection — natural history and clinical consequences. N Engl J Med. 2004;350(11):1118–29.

    Article  CAS  PubMed  Google Scholar 

  6. Pan CQ: Natural History and Clinical Consequences of Hepatitis B Virus Infection. International Journal of Medical Sciences 2005:36.

  7. Poordad F. Review article: thrombocytopenia in chronic liver disease. Aliment Pharmacol Ther. 2007;26:5–11.

    Article  CAS  PubMed  Google Scholar 

  8. Bernardi M, Maggioli C, Zaccherini G. Human Albumin in the Management of Complications of Liver Cirrhosis. Crit Care 2012;16:211.

    PubMed  PubMed Central  Google Scholar 

  9. Garcia–Tsao G. Current management of the complications of cirrhosis and portal hypertension: variceal hemorrhage, Ascites, and spontaneous bacterial peritonitis. Gastroenterology. 2001;3:726–48.

    Article  Google Scholar 

  10. Liang KH, Hsu CW, Chang ML, Chen YC, Lai MW, Yeh CT. Peginterferon is superior to nucleos(t)ide analogs for prevention of hepatocellular carcinoma in chronic hepatitis B. J Infect Dis. 2016;213(6):966–74.

    Article  PubMed  Google Scholar 

  11. Lok ASF, McMahon BJ. Chronic hepatitis B: update 2009. Hepatology. 2009;50(3):661–2.

    Article  PubMed  Google Scholar 

  12. Liaw Y-F, Kao J-H, Piratvisuth T, Chan HLY, Chien R-N, Liu C-J, Gane E, Locarnini S, Lim S-G, Han K-H, et al. Asian-Pacific consensus statement on the management of chronic hepatitis B: a 2012 update. Hepatol Int. 2012;6(3):531–61.

    Article  PubMed  Google Scholar 

  13. Tseng TC, Liu Cj Fau - Yang H-C, Yang Hc Fau - Su T-H, Su Th Fau - Wang C-C, Wang Cc Fau - Chen C-L, Chen Cl Fau - Kuo SF-T, Kuo Sf Fau - Liu C-H, Liu Ch Fau - Chen P-J, Chen Pj Fau - Chen D-S, Chen Ds Fau - Kao J-H, et al. High levels of hepatitis B surface antigen increase risk of hepatocellular carcinoma in patients with low HBV load. 2012;142(5):1140-49.

  14. Clinical Practice Guidelines EASL. Management of chronic hepatitis B virus infection. J Hepatol. 2012;57(1):167–85.

    Article  Google Scholar 

  15. Liaw Y-F, Sung JJY, Chow WC, Farrell G, Lee C-Z, Yuen H, Tanwandee T, Tao Q-M, Shue K, Keene ON, et al. Lamivudine for patients with chronic hepatitis B and advanced liver disease. N Engl J Med. 2004;351(15):1521–31.

    Article  CAS  PubMed  Google Scholar 

  16. Marcellin P, Heathcote EJ, Buti M, Gane E, de Man RA, Krastev Z, Germanidis G, Lee SS, Flisiak R, Kaita K, et al. Tenofovir disoproxil fumarate versus Adefovir dipivoxil for chronic hepatitis B. N Engl J Med. 2008;359(23):2442–55.

    Article  CAS  PubMed  Google Scholar 

  17. Chang T-T, Gish RG, de Man R, Gadano A, Sollano J, Chao Y-C, Lok AS, Han K-H, Goodman Z, Zhu J, et al. A comparison of entecavir and lamivudine for HBeAg-positive chronic hepatitis B. N Engl J Med. 2006;354(10):1001–10.

    Article  CAS  PubMed  Google Scholar 

  18. Liaw YF, Gane E, Leung N, Zeuzem S, Wang Y, Lai CL, Heathcote EJ, Manns M, Bzowej N, Niu J, et al. 2-year GLOBE trial results: telbivudine is superior to lamivudine in patients with chronic hepatitis B. Gastroenterology. 2009;136(2):486–95.

    Article  CAS  PubMed  Google Scholar 

  19. Pol S, Lampertico P. First-line treatment of chronic hepatitis B with entecavir or tenofovir in ‘real-life’ settings: from clinical trials to clinical practice. J Viral Hepat. 2012;19(6):377–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ocana S. Diagnostic strategy for occult hepatitis B virus infection. World J Gastroenterol. 2011;17(12):1553.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Said ZNA. An overview of occult hepatitis B virus infection. World J Gastroenterol. 2011;17(15):1927.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zobeiri M. Occult hepatitis B: clinical viewpoint and management. Hepat Res Treat. 2013;2013:1–7.

    Article  Google Scholar 

  23. Chan HL-Y, Thompson A, Martinot-Peignoux M, Piratvisuth T, Cornberg M, Brunetto MR, Tillmann HL, Kao J-H, Jia J-D, Wedemeyer H, et al. Hepatitis B surface antigen quantification: why and how to use it in 2011 – a core group report. J Hepatol. 2011;55(5):1121–31.

    Article  PubMed  Google Scholar 

  24. Liaw Y-F. Clinical utility of hepatitis B surface antigen quantitation in patients with chronic hepatitis B: A review. Hepatology. 2011;53(6):2121–9.

    Article  CAS  PubMed  Google Scholar 

  25. Ishak K, Baptista A, Bianchi L, Callea F, De Groote J, Gudat F, Denk H, Desmet V, Korb G, MacSween RNM, et al. Histological grading and staging of chronic hepatitis. J Hepatol. 1995;22(6):696–9.

    Article  CAS  PubMed  Google Scholar 

  26. Chu C-M, Liaw Y-F. Hepatitis B surface antigen seroclearance during chronic HBV infection. Antivir Ther. 2010;15(2):133–43.

    Article  PubMed  Google Scholar 

  27. Seto W-K, Wong DK-H, Fung J, Ip PPC, Yuen JC-H, Hung IF-N, Lai C-L, Yuen M-F. High hepatitis B surface antigen levels predict insignificant fibrosis in hepatitis B e antigen positive chronic hepatitis B. PLoS One. 2012;7(8):e43087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Marcellin P, Martinot-Peignoux M, Asselah T, Batrla R, Messinger D, Rothe V, Lau G, Liaw Y-F. Serum levels of hepatitis B surface antigen predict severity of fibrosis in patients with E antigen-positive chronic hepatitis B. Clin Gastroenterol Hepatol. 2015;13(8):1532–9. e1531.

    Article  CAS  PubMed  Google Scholar 

  29. Yang H-I, Yuen M-F, Chan HL-Y, Han K-H, Chen P-J, Kim D-Y, Ahn S-H, Chen C-J, Wong VW-S, Seto W-K. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol. 2011;12(6):568–74.

    Article  PubMed  Google Scholar 

  30. Bruix J, Sherman M. Management of hepatocellular carcinoma: an update. 2011. 1527-3350 (Electronic).

    Google Scholar 

  31. Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, Fontaine H, Pol S. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. Comparison with liver biopsy and fibrotest. Hepatology. 2007;46(1):32–6.

    Article  CAS  PubMed  Google Scholar 

  32. Wai C. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518–26.

    Article  PubMed  Google Scholar 

  33. Yeaman MR. Platelets: at the nexus of antimicrobial defence. Nat Rev Microbiol. 2014;12(6):426–37.

    Article  CAS  PubMed  Google Scholar 

  34. Machlus KR, Johnson KE, Kulenthirarajan R, Forward JA, Tippy MD, Soussou TS, El-Husayni SH, Wu SK, Wang S, Watnick RS, et al. CCL5 derived from platelets increases megakaryocyte proplatelet formation. Blood. 2015;127(7):921–6.

    Article  PubMed  Google Scholar 

  35. Kodama T, Takehara T, Hikita H, Shimizu S, Li W, Miyagi T, Hosui A, Tatsumi T, Ishida H, Tadokoro S, et al. Thrombocytopenia exacerbates cholestasis-induced liver fibrosis in mice. Gastroenterology. 2010;138(7):2487–98. e2487.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Chung-Yin Wu, Hui-Chin Chen and Chien-Chih Wang in the Liver Research Center, Chang Gung Memorial Hospital for the excellent assistance in organizing clinical data. The authors also thank Liver Disease Prevention and Treatment Research Foundation, Taiwan, for the spiritual support.

Funding

This study was supported by research funding from Chang Gung Memorial Hospital [grant numbers CMRPG3E0241, CLRPG3C0012, CRRPG3E0101, CMRPG392181, CMRPG2B0463 and CMRPG3A1193].

Availability of data and materials

De-linked data available to academic scientists upon request.

Authors’ contributions

CWH, CTY and KHL conceived the studies, analysed and interpreted the data, and wrote the manuscript; THW and CLL performed the research and contributed to the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the institutional review board of the Chang Gung Memorial Hospital, Taiwan, and conducted in accordance with the Declaration of Helsinki. All patients have given informed consent for the deposition of their clinical samples to the tissue bank of Chang Gung Memorial Hospital, Taiwan.

Author information

Authors and Affiliations

  1. Liver Research Center, Chang Gung Memorial Hospital, Linko, Taoyuan city, Taiwan

    Chao-Wei Hsu, Kung-Hao Liang, Chih-Lang Lin, Tong-Hong Wang & Chau-Ting Yeh

  2. Chang Gung University, College of Medicine, Taoyuan city, Taiwan

    Chao-Wei Hsu, Chih-Lang Lin & Chau-Ting Yeh

  3. Liver Research Unit, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

    Chih-Lang Lin

  4. Tissue Bank, Chang Gung Memorial Hospital, Taoyuan city, Taiwan

    Tong-Hong Wang

  5. Research Center for Industry of Human Ecology, Chang Gung University of Science and Technology, Taoyuan city, Taiwan

    Tong-Hong Wang

  6. Graduate Institute of Health Industry Technology, Chang Gung University of Science and Technology, Taoyuan city, Taiwan

    Tong-Hong Wang

  7. Molecular Medicine Research Center, Chang Gung University, Taoyuan city, Taiwan

    Chau-Ting Yeh

Authors
  1. Chao-Wei Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kung-Hao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chih-Lang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong-Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chau-Ting Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Kung-Hao Liang or Chau-Ting Yeh.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, CW., Liang, KH., Lin, CL. et al. Platelet counts modulate the quantitative relationship between hepatitis B viral DNA and surface antigen concentrations: a cross-sectional study of hematological, histological and viral factors. BMC Infect Dis 17, 9 (2017). https://doi.org/10.1186/s12879-016-2121-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12879-016-2121-y

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us