Table 1.
Baseline characteristics and follow-up in study populationa.
Figure 1.
Viral and clinical features in association with the four phases of chronic HBV infection.
The analysis was performed using data from a case-cohort study of participants with chronic HBV infection aged 30–65 y at recruitment in 1989–1992 (n = 1112), and followed to 2006. Parameters of viral genetic diversity were measured in 460 subjects with HBV/Ba and 95 with HBV/Ce (GenBank accession numbers KC792648 – KC793202). Data on BCP double mutations were available for 441 subjects with HBV/Ba and 91 with HBV/Ce. There are three trajectory classes for the time trend of viral load: “sustained low”, “steadily high” (consistently in the levels of 5–6 log10 copies/mL), and “extremely high to low” (gradually declining from the levels of 8–9 log10 copies/mL), as defined by our previous longitudinal viral-load study [13]. Disease-free survival rate (i.e. cumulative incidence) and hazard ratio (HR) for hepatocellular carcinoma (HCC) were estimated by the Kaplan-Meier method and Cox regression model, respectively, using the subcohort of 1054 participants. Odds ratios (ORs) and HRs were derived by multivariate models adjusted for age, cigarette smoking, alcohol consumption, and body mass index. aP<0.0001 for IT/IC vs. LR/ENH. bP<0.0340 for Ba vs. Ce subgenotype. cPtrend<0.0210 across phase of HBV-infection, determined by Mantel–Haenszel extension of the χ2 test for trend.
Figure 2.
Influence of viral genetic diversity on hepatitis B viral load by HBV subgenotypes.
The plot show the estimated impact (β estimates; regression coefficients) per 1-unit increment of dS (10−3 substitution per site), dN (10−3 substitution per site), or genetic distance (10−2 nucleotide substitution) on cross-sectional (solid circle and horizontal line) and longitudinal (empty circle and horizontal line) measures of viral load (log copies/mL) and 95% confidence intervals (CIs). All regression models include age as a covariate. The partial R2 values measure the marginal contribution of each parameter of viral genetic diversity to the variability in baseline viral load when age was already in the respective linear regression model.
Figure 3.
Map of viral SNPs associated with long-term hepatitis B viral load by HBV subgenotypes.
Shown are the estimated impacts of viral SNPs on longitudinal viral load (regression coefficients; bar) and their corresponding P values (red × symbol) in the entire subjects (A) and in the HBeAg-negative subjects (B) with sequence data, as well as the structure of the genes across a 2403-bp stretch in the sequence region covering HBV polymerase, pre-S1, pre-S2, and surface (S) (C). Viral SNPs are marked as colored bars if their locations fall in a region within or flanking (defined as occurring within 3 aa apart from the epitope) known HLA class I (blue)- or class II (yellow)-restricted epitopes (class I plus II, green) (source: http://www.immuneepitope.org/); otherwise viral SNPs are shown as gray bars.
Table 2.
Viral polymorphisms associated with increased viral load in HBeAg negative phase and progression to HCC and/or liver cirrhosis (LC) by HBV subgenotypes.
Table 3.
Effects of viral SNPs in relation to enhanced viral load and HCC on coding function by HBV genomic regions.