Clin Mol Hepatol > Volume 30(3); 2024 > Article
Hur, Lee, Lee, Kim, Park, Shin, Chung, Cho, Park, Jang, Lee, Yu, Lee, Jung, Kim, and Yoon: Extrahepatic malignancies and antiviral drugs for chronic hepatitis B: A nationwide cohort study

ABSTRACT

Background/Aims

Chronic hepatitis B (CHB) is related to an increased risk of extrahepatic malignancy (EHM), and antiviral treatment is associated with an incidence of EHM comparable to controls. We compared the risks of EHM and intrahepatic malignancy (IHM) between entecavir (ETV) and tenofovir disoproxil fumarate (TDF) treatment.

Methods

Using data from the National Health Insurance Service of Korea, this nationwide cohort study included treatment-naïve CHB patients who initiated ETV (n=24,287) or TDF (n=29,199) therapy between 2012 and 2014. The primary outcome was the development of any primary EHM. Secondary outcomes included overall IHM development. E-value was calculated to assess the robustness of results to unmeasured confounders.

Results

The median follow-up duration was 5.9 years, and all baseline characteristics were well balanced after propensity score matching. EHM incidence rate differed significantly between within versus beyond 3 years in both groups (P<0.01, Davies test). During the first 3 years, EHM risk was comparable in the propensity score-matched cohort (5.88 versus 5.84/1,000 person-years; subdistribution hazard ratio [SHR]=1.01, 95% confidence interval [CI]=0.88–1.17, P=0.84). After year 3, however, TDF was associated with a significantly lower EHM incidence compared to ETV (4.92 versus 6.91/1,000 person-years; SHR=0.70, 95% CI=0.60–0.81, P<0.01; E-value for SHR=2.21). Regarding IHM, the superiority of TDF over ETV was maintained both within (17.58 versus 20.19/1,000 person-years; SHR=0.88, 95% CI=0.81–0.95, P<0.01) and after year 3 (11.45 versus 16.20/1,000 person-years; SHR=0.68, 95% CI=0.62–0.75, P<0.01; E-value for SHR=2.30).

Conclusions

TDF was associated with approximately 30% lower risks of both EHM and IHM than ETV in CHB patients after 3 years of antiviral therapy.

Graphical Abstract

INTRODUCTION

Chronic hepatitis B (CHB) infection is the most prevalent chronic viral infection worldwide, affecting more than 250 million people and accounting for approximately 45% of hepatocellular carcinoma (HCC) cases [1,2]. Entecavir (ETV) and tenofovir disoproxil fumarate (TDF) have been the most commonly used nucleos(t)ide-analogues (NAs) for CHB patients and both are currently recommended as first-line antivirals because of their high potency and genetic barrier against the development of NA resistance [3-5].
It remains controversial which of the two antivirals, ETV or TDF, is superior for the prevention of HCC in CHB patients [6]. While some cohort studies and meta-analyses have shown a lower risk of HCC in CHB patients treated with TDF [7-10], other studies have demonstrated no significant differences between the two antivirals [11-15]. However, no study has proved the superiority of ETV over TDF. It has been suggested that more effective and earlier viral suppression with TDF may lead to better outcomes [7]. Some researchers suspect that the protumor or carcinogenic effects of ETV reported in a preclinical animal model [16] might be responsible for the inferiority of ETV, but none of these effects have not been confirmed in humans [17,18].
Meanwhile, our group recently reported that patients with CHB have a higher risk of developing a primary extrahepatic malignancy (EHM) than controls [19]. Furthermore, we demonstrated that complete viral suppression with long-term NA treatment was associated with a lower risk of EHM among CHB patients. There has been no study comparing ETV and TDF in terms of EHM prevention. Therefore, we aimed to compare the risk of EHM as well as intrahepatic malignancy (IHM) between patients treated with ETV and those treated with TDF; the results may reflect the antitumor or protumor effects of each antiviral.

MATERIALS AND METHODS

Data source

We established a retrospective cohort using nationwide claims in the National Health Insurance Service (NHIS) database of South Korea. NHIS is a health insurance policy that covers 97% of South Koreans; its utility for research purposes has been well-established [20]. The NHIS database uses the tenth revision of the International Classification of Diseases (ICD-10). The Institutional Review Boards of both the NHIS (No. NHIS-2021-1-804) and SMG–SNU Boramae Medical Center (No. 07-2019-23) approved this study. The requirement for informed consent was waived due to the retrospective nature of this study and because patient data within the NHIS database is coded anonymously.

Study populations and variables

The study cohort originally included 178,937 patients with CHB who initiated treatment with ETV or TDF between January 1, 2012 and December 31, 2014. The cohort entry date and index date were defined as the first day of NA treatment and the 90th day after initiating NA therapy, respectively. After applying exclusion criteria, the final study population included 53,486 patients with CHB (24,287 received ETV [ETV group] and 29,199 received TDF [TDF group]; Fig. 1). For each subject, we obtained demographic information, comorbidity data, and NA data, including the type and cumulative defined daily dose of antiviral used. The presence of liver cirrhosis and/or decompensation was identified using ICD-10 diagnosis codes, NHIS classification codes for specific procedures (e.g., abdominal paracentesis and endoscopic treatment of esophageal or gastric varices), and relevant prescriptions. Supplementary Table 1 shows the diagnosis, procedural, and prescription codes used in this study. In the subset of individuals who received health check-ups provided by the NHIS, anthropometric data, blood test results, and health-related behaviors (smoking, alcohol intake, and physical activity) were also collected. Further details regarding the study population are described in the Supplementary Methods.

Outcomes

The primary outcome was the development of any EHM. EHMs were defined according to ICD-10 codes for non-liver cancers, as well as cancer-specific insurance claim codes. Only the first diagnosed malignancy after the index date was counted as an event. Death and a new IHM were considered competing events. Secondary outcomes were the development of specific EHMs (the 10 most prevalent EHMs in South Korea) and overall IHM. The date of the first claim with the ICD-10 code was considered the date of cancer diagnosis. Further information is provided in the Supplementary Methods.

Statistical analysis

To compare categorical and continuous variables, the standardized difference was measured between the two groups. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were employed to balance the ETV and TDF groups; propensity scores were calculated using all covariates. Study subjects were followed from the index date to the date of EHM diagnosis, date of any competing event, or cut-off date (December 31, 2019), whichever occurred first. The log-log plot and Schoenfeld residual test were utilized to validate the proportional hazards assumption inside the Cox model. If the proportional hazards assumption was not satisfied, an extended Cox model with Heaviside functions was used.
Cumulative incidence of EHM was derived using the cumulative incidence function, and cumulative incidence curves were compared using the Gray test. Applying segmented linear regression, the cumulative incidence was fitted as a piecewise linear function, and change in slope was assessed using the Davies test. To estimate the effect of variables on the cumulative incidence, while taking competing events into account, we used the Fine–Gray model to calculate the subdistribution hazard ratio (SHR). P-value for interaction (Pinteraction) was calculated to assess whether NA therapy had differential impacts on EHMs according to subgroups. Various sensitivity analyses were performed to confirm the robustness of our findings (see Supplementary Methods). E-value was calculated to estimate the magnitude of an unadjusted confounding variable needed to mitigate the association between antiviral treatment and the incidence of EHM or IHM [21]. All statistical analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute Inc, Cary, NC, USA) and R 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). P-values were derived from two-tailed tests, with values <0.05 considered statistically significant.

RESULTS

Baseline characteristics

Table 1 shows the baseline characteristics of the ETV and TDF groups. Although the healthcare level differed slightly between groups in the crude population, all variables, including age, sex, and coexisting medical conditions, were well balanced after PSM or IPTW. Baseline characteristics of the NHIS Health Check-Up Database subcohort are summarized in Supplementary Table 2. Additional anthropometric, habitual, and laboratory variables were well balanced. Distributions of propensity scores for both the entire study population and the NHIS Health Check-Up Database subcohort showed good concordance between treatment groups, without extreme values (Supplementary Fig. 1).

Incidence of primary extrahepatic malignancies

Median follow-up durations of the ETV and TDF groups were 6.3 years (interquartile range [IQR]=3.7–7.2 years) and 5.7 years (IQR=5.1–6.4 years), respectively (Table 2). After PSM, there were 822 (3.4%) EHMs in the ETV group and 706 (2.9%) EHMs in the TDF group.
Figure 2 shows the cumulative incidence of primary EHM after PSM. During the entire study period, TDF was associated with a lower risk of EHM than ETV (P=0.001 by Gray test). Differences in risk of EHM between groups became more prominent after 3 years from the index date. The incidence rate of EHM decreased in the TDF group but increased in the ETV group after year 3 (both P<0.01 by Davies test; Supplementary Fig. 2). In the TDF group, the crude incidence of EHM decreased from 5.88/1,000 person-years in the first 3 years to 4.92/1,000 person-years after year 3 (Table 2). Conversely, in the ETV group, the incidence of EHM increased from 5.84/1,000 person-years in the first 3 years to 6.91/1,000 person-years after year 3. According to the Schoenfeld residual test, the Cox proportional hazards assumption was satisfied if the study period was divided as within 3 years and after 3 years from the index date (P=0.82) but not if the entire study period was considered (P<0.01). The log-log plot reproduced these results (Supplementary Fig. 3). Thus, we stratified the study duration into within the first 3 years and beyond the first 3 years in all analyses and applied the extended Cox model.
EHM incidence within the first 3 years did not differ between antivirals (TDF vs. ETV: SHR=1.01, 95% confidence interval [CI]=0.88–1.17, P=0.84), whereas the risk of EHM was significantly lower in the TDF group than in the ETV group after year 3 (SHR=0.70, 95% CI=0.60–0.81, P<0.01). Similar results were obtained in most subgroup analyses (age, socioeconomic status, and other medical conditions; all Pinteraction>0.05) except sex (Fig. 3). E-value analysis showed that an unexplained confounder would need to be associated with both NA type and EHM incidence at a risk ratio of 2.21 to mitigate the relationship between these variables and make the SHR=1, while controlling for other covariates in our model (Supplementary Table 3).

Sensitivity analyses

Various sensitivity analyses showed similar results (Table 3 and Supplementary Results). The main result was maintained in the study population balanced using IPTW (SHR=0.70, 95% CI=0.61–0.81, P<0.01). Supplementary Figure 4 presents the cumulative incidence of primary EHMs in both treatment groups before and after IPTW. Similar to the analysis of the study population balanced using PSM, the difference in EHM incidence between groups became more apparent after year 3. The superiority of TDF over ETV was also reproduced when analyzing the NHIS Health Check-Up Database subcohort (SHR=0.68, 95% CI=0.57–0.83, P<0.01). In this subcohort, the cumulative incidence of EHM differed significantly between groups (Supplementary Fig. 4). In subgroup analyses of the NHIS Health Check-Up Database subcohort, most subgroups had a Pinteraction>0.05, except for sex and presence of cirrhosis subgroups (Supplementary Fig. 5).

Incidence of specific extrahepatic malignancies

Supplementary Table 4 shows the incidence of specific EHM and IHM after PSM. After year 3, TDF was associated with a significantly lower risk of stomach cancer (SHR=0.57, 95% CI=0.38–0.86, P=0.01), breast cancer (SHR=0.53, 95% CI=0.33–0.85, P=0.01), and non-Hodgkin lymphoma (SHR=0.34, 95% CI=0.15–0.78, P=0.01) than ETV. Within the first 3 years, TDF was associated with a higher incidence of breast cancer than ETV (SHR=1.74, 95% CI=1.05–2.89, P=0.03).

Incidence of overall intrahepatic malignancies

Supplementary Figure 6 depicts the cumulative incidence of primary IHM in the entire population and the NHIS Health Check-Up Database subcohort after PSM. Similar to EHM, differences in IHM risk between groups became more prominent after approximately 3 years from the index date. However, unlike EHMs, the annual incidence rate of IHM decreased in the ETV group as well as the TDF group after year 3 (P<0.01 by Davies test; Supplementary Fig. 7). In the ETV group, the incidence of IHM decreased from 20.19/1,000 person-years to 16.20/1,000 person-years after year 3 (Supplementary Table 4). In the TDF group, the crude incidence of IHM decreased from 17.58/1,000 person-years in the first 3 years to 11.45/1,000 person-years after year 3. In terms of IHM incidence, the superiority of TDF over ETV was confirmed both within 3 years (SHR=0.88, 95% CI=0.81–0.95, P<0.01) and after 3 years (SHR=0.68, 95% CI=0.62–0.75, P<0.01), with the difference being more prominent after 3 years. According to the E-values for the IHM incidence, it seems less likely that there is an unmeasured confounder that could alter the superiority of TDF (Supplementary Table 3).

DISCUSSION

In this nationwide cohort study, CHB patients treated with TDF had a 30% lower risk of EHM than those treated with ETV after 3 years of antiviral therapy. During the first 3 years, the incidence of EHM did not differ between ETV and TDF groups. After year 3, however, EHM risk differed significantly between groups as the incidence of EHM accelerated in the ETV group but decelerated in the TDF group. These results were consistent across various sensitivity and subgroup analyses. Regarding IHMs, the superiority of TDF over ETV was observed during the entire study period, although the difference in IHM risk between groups also became more prominent after year 3. These findings collectively suggest the superiority of TDF over ETV in terms of both EHM and IHM prevention.
Superior virologic response of TDF compared to ETV might be responsible for the outcomes of this study. Although head-to-head clinical trials are limited, prior studies showed that TDF suppressed viral RNA [22,23], as well as DNA [7,24-27], more potently than ETV, and this suppression was associated with a reduced risk of HCC. Recent studies reported associations between hepatitis B virus (HBV) infection and the development of EHM [19,28-30], and chronic inflammation in HBV-infected extrahepatic tissues [31,32]. TDF might lower viral load below a certain threshold level more rapidly than ETV, resulting in decreased local inflammation and subsequent malignant transformation. Interferon lambda 3 induced by nucleotide analogues (e.g., TDF and adefovir), but not by nucleoside analogues (e.g., ETV and lamivudine), may also contribute to the antitumor effects of TDF [33]. As interferon lambda exhibited potent antitumor effects in animal malignancy models [34,35], this could provide another explanation for the superiority of TDF over ETV.
In this study, we used several statistical strategies to overcome the limitations of a retrospective design. The E-value represents the minimum strength of association that an unmeasured confounder must have with both the treatment and outcome to completely explain away a given treatment–outcome association [21]. Based on the calculated E-value for the EHM incidence, the observed SHR of 0.70 could be explained away by an unmeasured confounder that was associated with both the treatment and the outcome by a risk ratio of 2.21-fold each, above and beyond the measured confounders, which seems unfeasible. Most of the general risk factors for EHM, including age, sex, and comorbidity, were well balanced in both the entire cohort and the NHIS Health Check-Up subcohort, even before matching. In addition, the distribution of several disease-specific risk factors was similar between the two groups. For instance, Helicobacter pylori infection is a main cause of stomach cancer, which is still prevalent in Korea, and its risk factors, including elevated cholesterol, male gender, old age, and low socioeconomic status, were comparable [36]. The risk factors for non-Hodgkin lymphoma (e.g., obesity, low physical activity, smoking, and alcohol intake) were well balanced between the two groups [37,38], and the superiority of TDF was maintained after adjusting for surveillance intensity, a crucial factor in the diagnosis of thyroid cancer [39]. Considering the E-value for the IHM incidence, the superiority of TDF with respect to IHM risk is also unlikely to be overturned by unmeasured confounders, as baseline characteristics are evenly distributed between the two groups, including the proportion of liver cirrhosis, one of the most important risk factors for IHM. To minimize the effects of residual confounders, we also applied other statistical strategies, such as PSM, IPTW, multivariable adjustment, and various prespecified sensitivity and subgroup analyses, which produced similar results.
The lower incidence of both EHM and IHM in the TDF group compared to the ETV group became more prominent after year 3. This suggests that antitumor effects of antivirals may require a certain period of time to manifest as differences in EHM or IHM incidence. However, IHM incidence was also significantly different within the first 3 years as well, although EHM incidence was comparable between groups during this time. Since HBV is a hepatotropic virus, the viral load is much higher in the liver than in extrahepatic tissues; thus, differences in antitumor effects resulting from different efficacy of viral suppression may be more apparent when comparing the incidence of IHM, rather than EHM. The further decrease in incidence of IHM after year 3 in both groups may be attributed to regression of hepatic fibrosis induced by antiviral therapy [40]. A previous study reported that the increase in cumulative HCC incidence decelerated after 5 years of ETV or TDF treatment, compared to the first 5 years [41]. Furthermore, another Korean study using the NHIS database observed a similar trend of HCC incidence among ETV- or TDF-treated patients, supporting the validity of our current results [7].
It is notable that the absolute incidence of EHM increased in the ETV group but decreased in the TDF group after year 3. Although one may assume that this trend indicates the protumor effects of ETV, it should be interpreted with caution. Preclinical animal studies raised concerns about the potential carcinogenicity of ETV [16]. Although two real-world retrospective studies showed that ETV at usual clinical doses does not increase cancer risk [17,18], the number of patients analyzed may have been insufficient to avoid a false-negative result, considering the low incidence of EHM. In addition, the protumor effects of ETV were possibly masked by the overall antitumor effects for both IHM and EHM [19,41]. ETV can incorporate into the human genome [42,43], which may lead to carcinogenicity during subsequent replication cycles. It is possible to assume that the protumor effects of ETV became apparent after cumulative damage caused by ETV exceeded a certain threshold level (approximately 430 mg of ETV in this study). However, the doses of ETV at which its carcinogenic effects were confirmed in the animal experiments, were far higher compared to the approved dose for humans. In addition, since CHB itself increases the risk of both IHM and EHM, the true protumor effects of ETV can only be proven by comparing ETV versus no ETV over a long period time in healthy non-CHB subjects, which is unfeasible.
ETV appears to have potent antitumor effects, considering the decreasing incidence of IHM confirmed in previous studies [44,45]. Therefore, in conjunction with prior evidence suggesting that both ETV and TDF treatments are beneficial in CHB patients for reducing EHM [19], as well as IHM [41,44,45], our findings can be interpreted as follows: (i) the antitumor effects of TDF are greater than those of ETV; and (ii) even if ETV has protumor effects in humans, which cannot be proven with certainty in the current setting, these effects are unlikely to be strong enough to overpower its antitumor effects associated with suppressing HBV replication. However, it may be advised that clinicians should be more suspicious of the potential protumor effects of ETV.
This study had several limitations. First, NHIS database does not provide detailed individual laboratory data including serum HBV DNA levels. Instead, additional data on anthropometric measurements, health-related behaviors, and blood test results were collected in more than half of the entire cohort who received medical check-up provided by NHIS and same results were maintained. Second, statistical significance was not achieved for the incidence of most individual EHMs, except stomach cancer, breast cancer, and non-Hodgkin lymphoma. Despite the use of a large nationwide cohort, the low incidence of each cancer made it difficult to achieve statistical significance. However, a similar trend of higher risk with ETV was seen for most of the EHMs. Regarding the differences in breast cancer before versus after year 3, a high level of estradiol may have contributed to these results; this requires additional research (see Supplementary Discussion). Third, while an association between the use of ETV or TDF and the incidence of EHM or IHM was assessed, not all aspects of the antivirals, including adverse events, were evaluated. TDF is known to have a higher risk of renal and bone toxicity compared to ETV [46]. Although long-term use of TDF may reduce the risk of EHM, the superiority of TDF cannot be generalized to all patients, as the risks of TDF use may outweigh the benefits in patients with impaired renal function [47]. Fourth, drug modifications or discontinuations during follow-up were not considered. We excluded patients who switched regimens within 90 days of the index date. However, cases in which ETV or TDF was subsequently discontinued or switched were not excluded. Although the number of cases that switched regimens is relatively small, and changes occur in both directions, it may have affected the results of this study as another confounder. Fifth, the results of this study may have limited generalizability, as most CHB patients in South Korea are infected with genotype C HBV [48]. Although comparable overall virologic responses to NAs have been observed among patients with diverse HBV genotypes [49], further international investigations are required. Lastly, this is a retrospective study, which by its nature cannot show causality, only association. In addition, despite the use of multiple statistical strategies, it is still possible that the results were affected by unmeasured confounders as each cancer has different risk factors. Considering the low incidence of EHMs in patients with CHB, a large randomized controlled trial or prospective study with long-term follow-up is not feasible, and the results of this study need to be validated at least in an independent retrospective cohort.
In conclusion, TDF was associated with approximately 30% reduced risks of both EHM and IHM than ETV after 3 years of treatment. Although the results of this study need to be validated in an independent cohort, the antitumor effects of TDF appeared to be greater than those of ETV.

ACKNOWLEDGMENTS

This work was supported by National IT Industry Promotion Agency grant funded by the Korea Ministry of Science and ICT (S0252-21-1001), Liver Research Foundation of Korea as part of Bio Future Strategies Research Project, and Seoul National University Hospital Research Fund (04-2019-3090).

FOOTNOTES

Authors’ contribution
Conceptualization: Moon Haeng Hur, Dong Hyeon Lee, Jeong-Hoon Lee, Sang Hyub Lee, Yong Jin Jung, Yoon Jun Kim, and Jung-Hwan Yoon; Data Curation: Moon Haeng Hur, Dong Hyeon Lee, Jeong-Hoon Lee, Mi-Sook Kim, Jeayeon Park, Hyunjae Shin, Sung Won Chung, and Hee Jin Cho; Formal analysis: Moon Haeng Hur, Dong Hyeon Lee, Jeong-Hoon Lee, Mi-Sook Kim, Min Kyung Park, Heejoon Jang, Yun Bin Lee, and Su Jong Yu; Methodology: Moon Haeng Hur, Dong Hyeon Lee, Jeong-Hoon Lee, Mi-Sook Kim, Jeayeon Park, Hyunjae Shin, Sung Won Chung, and Hee Jin Cho; Supervision: Jeong-Hoon Lee, Sang Hyub Lee, Yong Jin Jung, Yoon Jun Kim, and Jung- Hwan Yoon; Writing - Original Draft: Moon Haeng Hur, Dong Hyeon Lee, and Jeong-Hoon Lee; Writing - Review & Editing: Mi-Sook Kim, Jeayeon Park, Hyunjae Shin, Sung Won Chung, Hee Jin Cho, Min Kyung Park, Heejoon Jang, Yun Bin Lee, Su Jong Yu, Sang Hyub Lee, Yong Jin Jung, Yoon Jun Kim, and Jung-Hwan Yoon.
Conflicts of Interest
Moon Haeng Hur: Nothing to declare; Dong Hyeon Lee: Nothing to declare; Jeong-Hoon Lee: Receives research grants from Yuhan Pharmaceuticals and GreenCross Cell, lecture fees from GreenCross Cell, Daewoong Pharmaceuticals, and Gilead Korea; Mi-Sook Kim: Nothing to declare; Jeayeon Park: Nothing to declare; Hyunjae Shin: Nothing to declare; Sung Won Chung: Nothing to declare; to declare; Heejoon Jang: Nothing to declare; Yun Bin Lee: Receives research grants from Samjin Pharmaceuticals and Yuhan Pharmaceuticals; Su Jong Yu: Receives research grants from Yuhan Pharmaceuticals and Daewoong Pharmaceuticals; Sang Hyub Lee: Nothing to declare; Yong Jin Jung: Nothing to declare; Yoon Jun Kim: Receives research grants from BTG, Boston Scientific, AstraZeneca, Gilead Sciences, Samjin, BL&H, and Bayer, and lecture fees from Roche, Abbvie, Eisai, Boston Scientific, BMS, BTG, Bayer, MSD, Novo Nordisk, Green Cross Cell, Boehringer Ingelheim, and Gilead; Jung-Hwan Yoon: Receives research grants from Bayer, Daewoong Pharmaceutical, and Bukwang Pharmaceutical.

SUPPLEMENTAL MATERIAL

Supplementary material is available at Clinical and Molecular Hepatology website (http://www.e-cmh.org).
SUPPLEMENTARY METHODS
SUPPLEMENTARY RESULTS
SUPPLEMENTARY DISCUSSION
SUPPLEMENTARY REFERENCES
cmh-2024-0055-Supplementary-Material.pdf
Supplementary Table 1.
Definition of diagnostic and procedural codes and measured variables
cmh-2024-0055-Supplementary-Table-1.pdf
Supplementary Table 2.
Baseline characteristics of patients in the NHIS health check-up database before and after propensity score matching or inverse probability of treatment weighting
cmh-2024-0055-Supplementary-Table-2.pdf
Supplementary Table 3.
E-value* for the effect of antiviral treatment on the incidence of extrahepatic and intrahepatic malignancies
cmh-2024-0055-Supplementary-Table-3.pdf
Supplementary Table 4.
Adjusted incidence of extrahepatic and intrahepatic malignancies in the propensity score-matched cohort
cmh-2024-0055-Supplementary-Table-4.pdf
Supplementary Figure 1.
Distribution of propensity scores. Propensity scores of the entire study population after (A) PSM and (B) IPTW and those of the NHIS Health Check-Up Database subcohort after (C) PSM and (D) IPTW. IPTW, inverse probability of treatment weighting; PSM, propensity score matching.
cmh-2024-0055-Supplementary-Fig-1.pdf
Supplementary Figure 2.
Changes in the Slope of Extrahepatic Malignancy Incidence in the (A) Entire Study Population and (B) Propensity Score-Matched Cohort.
cmh-2024-0055-Supplementary-Fig-2.pdf
Supplementary Figure 3.
The Log-Log plot to confirm the cox proportional hazard assumption.
cmh-2024-0055-Supplementary-Fig-3.pdf
Supplementary Figure 4.
Cumulative Incidence of Primary Extrahepatic Malignancies in the (A) Crude Population, (B) Balanced Cohort Using Inverse Probability of Treatment Weighting, and (C) NHIS Health Check-Up Database Subcohort.
cmh-2024-0055-Supplementary-Fig-4.pdf
Supplementary Figure 5.
Risk of extrahepatic malignancy in the propensity score-matched cohort with medical check-up data according to specified subgroups. BMI, body mass index; CI, confidence interval; ETV, entecavir; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LNL, lower normal limit; Pinteraction, P-value for interaction; SHR, subdistribution hazard ratio; TDF, tenofovir disoproxil fumarate; UNL, upper normal limit. aHigh, middle, and low socioeconomic statuses indicate socioeconomic status within the ≥75th, 25th–75th, and <25th percentiles, respectively. bAbdominal obesity was defined as a waist circumference greater than 90 cm in men or 80 cm in women.
cmh-2024-0055-Supplementary-Fig-5.pdf
Supplementary Figure 6.
Cumulative Incidence of Primary Intrahepatic Malignancy after Propensity Score Matching in the (A) Entire Population and (B) NHIS Health Check-Up Database Subcohort.
cmh-2024-0055-Supplementary-Fig-6.pdf
Supplementary Figure 7.
Changes in the Slope of Intrahepatic Malignancy Incidence in the (A) Entire Study Population and (B) Propensity Score-Matched Cohort.
cmh-2024-0055-Supplementary-Fig-7.pdf

Figure 1.
Patient flow diagram. Specific diagnostic and procedural codes are presented in Supplementary Table 1. CHB, chronic hepatitis B; ETV, entecavir; HCV, hepatitis C virus; HDV, hepatitis D virus; HIV, human immunodeficiency virus; NA, nucleos(t)ide-analogue; NHIS, National Health Insurance Service; TDF, tenofovir disoproxil fumarate.

cmh-2024-0055f1.jpg
Figure 2.
Cumulative incidence of extrahepatic malignancies in propensity score-matched cohort. Analysis was performed after propensity score matching. Intrahepatic malignancy development and death were treated as competing events. ETV, entecavir; TDF, tenofovir disoproxil fumarate.

cmh-2024-0055f2.jpg
Figure 3.
Risk of extrahepatic malignancy in the propensity score-matched cohort according to prespecified subgroups. SHR, subdistribution hazard ratio; CI, confidence interval; ETV, entecavir; Pinteraction, P-value for interaction; TDF, tenofovir disoproxil fumarate. *High, middle, and low socioeconomic statuses indicate socioeconomic status within the ≥75th, 25th–75th, and <25th percentiles, respectively.

cmh-2024-0055f3.jpg

cmh-2024-0055f4.jpg
Table 1.
Baseline characteristics of the study cohort before and after propensity score matching or inverse probability of treatment weighting
Characteristics Unmatched cohort
After propensity score matching*
After inverse probability of treatment weighting*
ETV (n=24,287) TDF (n=29,199) Standardized difference ETV (n=24,285) TDF (n=24,285) Standardized difference ETV (n=23,664) TDF (n=28,559) Standardized difference
Age, years 48.6±10.3 48.3±10.3 0.03 48.6±10.3 48.5±10.2 0.02 48.4±10.2 48.4±10.3 <0.01
Sex 0.02 0.01 <0.01
 Male 15,664 (64.5) 18,523 (63.4) 15,664 (64.5) 15,582 (64.2) 15,193 (64.2) 18,330 (64.2)
 Female 8,623 (35.5) 10,676 (36.6) 8,621 (35.5) 8,703 (35.8) 8,471 (35.8) 10,229 (35.8)
Socioeconomic status
 High 7,965 (32.8) 10,290 (35.2) –0.05 7,964 (32.8) 8,149 (33.6) –0.02 8,003 (33.8) 9,721 (34.1) <0.01
 Middle 10,941 (45.1) 13,025 (44.6) 0.01 10,940 (45.1) 11,016 (45.3) –0.01 10,747 (45.4) 12,940 (45.3) <0.01
 Low 3,880 (16.0) 4,443 (15.2) 0.02 3,880 (16.0) 3,805 (15.7) 0.01 3,719 (15.7) 4,462 (15.6) <0.01
 Medical aid 837 (3.4) 753 (2.6) 0.05 837 (3.4) 714 (2.9) 0.03 596 (2.5) 719 (2.5) <0.01
 Others 664 (2.7) 688 (2.4) 0.02 664 (2.7) 601 (2.5) 0.02 599 (2.6) 717 (2.5) <0.01
Level of healthcare
 Tertiary 6,663 (27.4) 10,067 (34.5) –0.15 6,661 (27.4) 6,620 (27.3) <0.01 7,280 (30.8) 8,855 (31.0) <0.01
 Secondary 11,299 (46.5) 11,240 (38.5) 0.16 11,299 (46.5) 11,155 (45.9) 0.01 9,943 (42.0) 11,938 (41.8) <0.01
 Primary 6,325 (26.1) 7,892 (27.0) –0.02 6,325 (26.1) 6,510 (26.8) –0.02 6,441 (27.2) 7,766 (27.2) <0.01
Coexisting medical conditions
 Cirrhosis 6,737 (27.7) 8,302 (28.4) –0.02 6,736 (27.7) 6,722 (27.7) <0.01 6,332 (26.8) 7,658 (26.8) <0.01
 Decompensated cirrhosis 2,129 (8.8) 2,151 (7.4) 0.05 2,129 (8.8) 1,944 (8.0) 0.03 1,711 (7.2) 2,016 (7.1) 0.01
 Ascites 987 (4.1) 913 (3.1) 0.05 987 (4.1) 863 (3.6) 0.03 669 (2.8) 757 (2.7) 0.01
 Varices 1,441 (5.9) 1,524 (5.2) 0.03 1,441 (5.9) 1,343 (5.5) 0.02 1,261 (5.3) 1,517 (5.3) <0.01
 Diabetes mellitus 4,571 (18.8) 5,124 (17.6) 0.03 4,570 (18.8) 4,376 (18.0) 0.02 4,250 (18.0) 5,112 (17.9) <0.01
 Hypertension 5,480 (22.6) 6,323 (21.7) 0.02 5,478 (22.6) 5,314 (21.9) 0.02 5,164 (21.8) 6,241 (21.8) <0.01
 CCI§point 1.2±1.4 1.2±1.3 –0.01 1.2±1.4 1.2±1.3 0.02 1.2±1.3 1.2±1.2 0.01

Data are expressed as number (%) or mean±standard deviation.

CCI, Charlson Comorbidity Index; ETV, entecavir; TDF, tenofovir disoproxil fumarate.

* Propensity scores were computed using following variables: age, sex, socioeconomic status, level of healthcare, cirrhosis, decompensated cirrhosis, ascites, varices, diabetes mellitus, hypertension, and Charlson Comorbidity Index.

High, middle, and low socioeconomic statuses indicate socioeconomic status within the ≥75th, 25th–75th, and <25th percentiles, respectively.

Patients with a special occupation such as military personnel or shipping labor union.

§ Charlson Comorbidity Index was based on data from 1 year before the cohort entry date.

Table 2.
Clinical outcomes in propensity score-matched cohort of chronic hepatitis B patients treated with entecavir or tenofovir disoproxil fumarate
cDDDs (per patient per year) Events, no. (%) Median follow-up, year (IQR) Crude incidence of extrahepatic malignancy, per 1000 person-year
Within 3 years
After 3 years
Within 3 years After 3 years SHR (95% CI) P-value SHR (95% CI) P-value
ETV 288.4 822 (3.4%) 6.3 (3.7–7.2) 5.84 (5.28–6.46) 6.91 (6.30–7.58) [reference] [reference]
TDF 293.4 706 (2.9%) 5.7 (5.1–6.4) 5.88 (5.33–6.48) 4.92 (4.40–5.51) 1.01 (0.88–1.17) 0.84 0.70 (0.60–0.81) <0.01

cDDDs, cumulative defined daily doses; CI, confidence interval; ETV, entecavir; IQR, interquartile range; NA, nucleos(t)ide analogue; SHR, subdistribution hazard ratio; TDF, tenofovir disoproxil fumarate.

Table 3.
Results of sensitivity analyses
cDDDs (per patient per year) Events, no. (%) Median follow-up, year (IQR) Crude incidence of extrahepatic malignancy, per 1000 person-year
Within 3 years
After 3 years
Within 3 years After 3 years SHR (95% CI) P-value aSHR (95% CI) P-value SHR (95% CI) P-value aSHR (95% CI) P-value
Analyses to minimize detection bias
Model 1A: Hospital visit-adjusted*,
 ETV 288.2 711 (3.2%) 6.5 (4.8–7.3) 4.14 (3.67–4.68) 6.91 (6.30–7.58) [reference] [reference]
 TDF 293.3 591 (2.5%) 5.8 (5.1–6.4) 4.23 (3.77–4.74) 4.92 (4.40–5.51) 1.02 (0.87–1.21) 0.78 0.71 (0.61–0.82) <0.01
Model 1B: Surveillance-adjusted*,
 ETV 288.2 711 (3.2%) 6.5 (4.8–7.3) 4.14 (3.67–4.68) 6.91 (6.30–7.58) [reference] [reference]
 TDF 293.3 591 (2.5%) 5.8 (5.1–6.4) 4.23 (3.77–4.74) 4.92 (4.40–5.51) 1.00 (0.84–1.18) 0.97 0.68 (0.59–0.79) <0.01
Different statistical approaches
Model 2A: Cause-specific analysis,§
 ETV 288.4 822 (3.4%) 6.3 (3.7–7.2) 5.84 (5.28–6.46) 6.91 (6.30–7.58) [reference] [reference]
 TDF 293.4 706 (2.9%) 5.7 (5.1–6.4) 5.88 (5.33–6.48) 4.92 (4.40–5.51) 1.01 (0.88–1.16) 0.90 0.69 (0.60–0.81) <0.01
Model 2B: After IPTW
 ETV 288.3 794 (3.4%) 6.4 (3.7–7.2) 5.78 (5.21–6.41) 6.8 (6.19–7.47) [reference] [reference]
 TDF 293.9 812 (2.8%) 5.7 (5.1–6.4) 5.62 (5.13–6.16) 4.92 (4.43–5.46) 0.98 (0.85–1.12) 0.76 0.70 (0.61–0.81) <0.01
Model 3: NHIS Health Check-Up Database*
 ETV 296.5 525 (3.4%) 6.3 (3.8–7.2) 6.05 (5.34–6.85) 6.78 (6.03–7.62) [reference] [reference]
 TDF 299.5 427 (2.8%) 5.8 (5.1–6.4) 5.58 (4.92–6.33) 4.73 (4.09–5.47) 0.93 (0.78–1.11) 0.41 0.68 (0.57–0.83) <0.01
Model 4: Without window period (including events within initial 3 months)*,
 ETV 289.8 822 (3.2%) 6.5 (3.4–7.4) 5.08 (4.57–5.65) 6.66 (6.09– 7.28) [reference] [reference] [reference] [reference]
 TDF 295.0 829 (2.7%) 6.0 (5.3–6.6) 4.99 (4.54–5.49) 4.83 (4.38–5.33) 0.98 (0.85–1.13) 0.83 0.98 (0.85–1.13) 0.79 0.71 (0.62–0.82) <0.01 0.71 (0.62–0.81) <0.01
Model 5: Crude population
 ETV 288.4 822 (3.4%) 6.3 (3.7–7.2) 5.84 (5.28–6.46) 6.91 (6.30–7.58) [reference] [reference] [reference] [reference]
 TDF 294.0 829 (2.8%) 5.8 (5.1–6.4) 5.59 (5.10–6.12) 4.94 (4.46–5.48) 0.96 (0.84–1.11) 0.61 0.97 (0.84–1.11) 0.63 0.70 (0.61–0.81) <0.01 0.70 (0.61–0.81) <0.01
Model 6: Treatment initiation between 2013–2014*
 ETV 288.2 250 (3.3%) 5.5 (4.4–6.2) 5.81 (4.86–6.95) 7.77 (6.54–9.23) [reference] [reference]
 TDF 293.9 232 (3.0%) 5.7 (5.0–6.3) 6.07 (5.12–7.20) 5.24 (4.31–6.39) 1.05 (0.82–1.34) 0.70 0.69 (0.53–0.90) 0.01

aSHR, adjusted subdistribution hazard ratio; cDDDs, cumulative defined daily doses; CI, confidence interval; ETV, entecavir; IPTW, inverse probability of treatment weighting; IQR, interquartile range; SHR, subdistribution hazard ratio; TDF, tenofovir disoproxil fumarate.

* Propensity score-matched cohort.

Additionally adjusted for the frequency of hospital visits.

Further adjusted for the frequency of surveillance test (alpha-fetoprotein, abdominal ultrasonography, or contrast-enhanced computed tomography).

§ Hazard ratios (instead of subdistribution hazard ratios) are provided for model 2A.

Adjusted for the level of healthcare.

Abbreviations

CHB
chronic hepatitis B
EHM
extrahepatic malignancy
IHM
intrahepatic malignancy
ETV
entecavir
TDF
tenofovir disoproxil fumarate
SHR
subdistribution hazard ratio
CI
confidence interval
HCC
hepatocellular carcinoma
NAs
nucleos(t)ide-analogues
IPTW
inverse probability of treatment weighting
PSM
propensity score matching
HBV
hepatitis B virus

REFERENCES

1. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1211-1259.
pmid pmc
2. Lin CL, Kao JH. Development of hepatocellular carcinoma in treated and untreated patients with chronic hepatitis B virus infection. Clin Mol Hepatol 2023;29:605-622.
crossref pmid pmc pdf
3. European Association For The Study Of The Liver. EASL 2017 clinical practice guidelines on the management of hepatitis B virus infection. J Hepatol 2017;67:370-398.
crossref pmid
4. Terrault NA, Lok ASF, 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;67:1560-1599.
crossref pmid pmc pdf
5. Korean Association for the Study of the Liver (KASL). KASL clinical practice guidelines for management of chronic hepatitis B. Clin Mol Hepatol 2022;28:276-331.
crossref pmid pmc pdf
6. Lee SW, Choi J, Kim SU, Lim YS. Entecavir versus tenofovir in patients with chronic hepatitis B: enemies or partners in the prevention of hepatocellular carcinoma. Clin Mol Hepatol 2021;27:402-412.
crossref pmid pmc pdf
7. Choi J, Kim HJ, Lee J, Cho S, Ko MJ, Lim YS. Risk of hepatocellular carcinoma in patients treated with entecavir vs tenofovir for chronic hepatitis B: a Korean nationwide cohort study. JAMA Oncol 2019;5:30-36.
crossref pmid pmc
8. Yip TCF, Wong VWS, Chan HLY, Tse YK, Lui GCY, Wong GLH. Tenofovir is associated with lower risk of hepatocellular carcinoma than entecavir in patients with chronic HBV infection in China. Gastroenterology 2020;158:215-225 e6.
crossref pmid
9. Dave S, Park S, Murad MH, Barnard A, Prokop L, Adams LA, et al. Comparative effectiveness of entecavir versus tenofovir for preventing hepatocellular carcinoma in patients with chronic hepatitis B: a systematic review and meta‐analysis. Hepatology 2021;73:68-78.
crossref pmid pmc pdf
10. Kim WR, Telep LE, Jump B, Lu M, Ramroth H, Flaherty J, et al. Risk of hepatocellular carcinoma in treatment-naïve chronic hepatitis B patients receiving tenofovir disoproxil fumarate versus entecavir in the United States. Aliment Pharmacol Ther 2022;55:828-835.
crossref pmid pdf
11. Kim SU, Seo YS, Lee HA, Kim MN, Lee YR, Lee HW, et al. A multicenter study of entecavir vs. tenofovir on prognosis of treatment-naïve chronic hepatitis B in South Korea. J Hepatol 2019;71:456-464.
crossref pmid
12. Tseng CH, Hsu YC, Chen TH, Ji F, Chen IS, Tsai YN, et al. Hepatocellular carcinoma incidence with tenofovir versus entecavir in chronic hepatitis B: a systematic review and metaanalysis. Lancet Gastroenterol Hepatol 2020;5:1039-1052.
crossref pmid
13. Oh H, Yoon EL, Jun DW, Ahn SB, Lee HY, Jeong JY, et al. No difference in incidence of hepatocellular carcinoma in patients with chronic hepatitis B virus infection treated with entecavir vs tenofovir. Clin Gastroenterol Hepatol 2020;18:2793-2802.e6.
crossref pmid
14. Lee SW, Kwon JH, Lee HL, Yoo SH, Nam HC, Sung PS, et al. Comparison of tenofovir and entecavir on the risk of hepatocellular carcinoma and mortality in treatment-naïve patients with chronic hepatitis B in Korea: a large-scale, propensity score analysis. Gut 2020;69:1301-1308.
crossref pmid pmc
15. Papatheodoridis GV, Dalekos GN, Idilman R, Sypsa V, Van Boemmel F, Buti M, et al. Similar risk of hepatocellular carcinoma during long-term entecavir or tenofovir therapy in Caucasian patients with chronic hepatitis B. J Hepatol 2020;73:1037-1045.
pmid
16. US Food and Drug Admistration (FDA). NDA Review pharmacology/toxicology review and evaluation: NDA No. 21-797. US FDA, <https://www.accessdata.fda.gov/drugsatfda_docs/nda/2005/21797_BARACLUDE_pharmr.PDF>. Accessed 30 Apr 2024.

17. Wong GLH, Tse YK, Yip TCF, Chan HLY, Tsoi KKF, Wong VWS. ong-term use of oral nucleos(t)ide analogues for chronic hepatitis B does not increase cancer risk - a cohort study of 44 494 subjects. Aliment Pharmacol Ther 2017;45:1213-1224.
crossref pmid pdf
18. Hou JL, Zhao W, Lee C, Hann HW, Peng CY, Tanwandee T, et al. Outcomes of long-term treatment of chronic HBV infection with entecavir or other agents from a randomized trial in 24 countries. Clin Gastroenterol Hepatol 2020;18:457-467 e21.
crossref pmid
19. Lee DH, Chung SW, Lee JH, Kim HY, Chung GE, Kim MS, et al. Association of chronic hepatitis B infection and antiviral treatment with the development of the extrahepatic malignancies: a nationwide cohort study. J Clin Oncol 2022;40:3394-3405.
crossref pmid
20. Seo HJ, Oh IH, Yoon SJ. A comparison of the cancer incidence rates between the national cancer registry and insurance claims data in Korea. Asian Pac J Cancer Prev 2012;13:6163-6168.
crossref pmid
21. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med 2017;167:268-274.
crossref pmid
22. Liu S, Deng R, Zhou B, Liang X, Liu Z, Peng J, et al. Association of serum hepatitis B virus RNA with hepatocellular carcinoma risk in chronic hepatitis B patients under nucleos(t)ide analogues therapy. J Infect Dis 2022;226:881-890.
crossref pmid pdf
23. Lim YS. New biomarkers of hepatitis B virus (HBV) infection: HBV RNA and HBV core-related antigen, new kids on the block? Clin Mol Hepatol 2023;29:118-119.
crossref pmid pmc pdf
24. Woo G, Tomlinson G, Nishikawa Y, Kowgier M, Sherman M, Wong DK, et al. Tenofovir and entecavir are the most effective antiviral agents for chronic hepatitis B: a systematic review and Bayesian meta-analyses. Gastroenterology 2010;139:1218-1229.
crossref pmid
25. Zuo SR, Zuo XC, Wang CJ, Ma YT, Zhang HY, Li ZJ, et al. A meta-analysis comparing the efficacy of entecavir and tenofovir for the treatment of chronic hepatitis B infection. J Clin Pharmacol 2015;55:288-297.
crossref pmid
26. Chen MB, Wang H, Zheng QH, Zheng XW, Fan JN, Ding YL, et al. Comparative efficacy of tenofovir and entecavir in nucleos(t)ide analogue-naive chronic hepatitis B: a systematic review and meta-analysis. PLoS One 2019;14:e0224773.
crossref pmid pmc
27. Gao L, Trinh HN, Li J, Nguyen MH. Tenofovir is superior to entecavir for achieving complete viral suppression in HBeAg-positive chronic hepatitis B patients with high HBV DNA. Aliment Pharmacol Ther 2014;39:629-637.
crossref pmid pmc
28. Kamiza AB, Su FH, Wang WC, Sung FC, Chang SN, Yeh CC. Chronic hepatitis infection is associated with extrahepatic cancer development: a nationwide population-based study in Taiwan. BMC Cancer 2016;16:861.
crossref pmid pmc pdf
29. Song C, Lv J, Liu Y, Chen JG, Ge Z, Zhu J, et al. Associations between hepatitis B virus infection and risk of all cancer types. JAMA Netw Open 2019;2:e195718.
crossref pmid pmc
30. Hur MH, Lee JH. The imitator of immune-tolerant chronic hepatitis B: a killer in disguise. Clin Mol Hepatol 2023;29:363-366.
crossref pmid pmc pdf
31. Chen NL, Bai L, Deng T, Zhang C, Kong QY, Chen H. Expression of hepatitis B virus antigen and Helicobacter pylori infection in gastric mucosa of patients with chronic liver disease. Hepatobiliary Pancreat Dis Int 2004;3:223-225.
pmid
32. Lai KN, Ho RT, Tam JS, Lai FM. Detection of hepatitis B virus DNA and RNA in kidneys of HBV related glomerulonephritis. Kidney Int 1996;50:1965-1977.
crossref pmid
33. Murata K, Asano M, Matsumoto A, Sugiyama M, Nishida N, Tanaka E, et al. Induction of IFN-λ3 as an additional effect of nucleotide, not nucleoside, analogues: a new potential target for HBV infection. Gut 2018;67:362-371.
crossref pmid pmc
34. Abushahba W, Balan M, Castaneda I, Yuan Y, Reuhl K, Raveche E, et al. Antitumor activity of type I and type III interferons in BNL hepatoma model. Cancer Immunol Immunother 2010;59:1059-1071.
crossref pmid pmc pdf
35. Sato A, Ohtsuki M, Hata M, Kobayashi E, Murakami T. Antitumor activity of IFN-λ in murine tumor models. J Immunol 2006;176:7686-7694.
crossref pmid pdf
36. Lim SH, Kwon JW, Kim N, Kim GH, Kang JM, Park MJ, et al. Prevalence and risk factors of Helicobacter pylori infection in Korea: nationwide multicenter study over 13 years. BMC Gastroenterol 2013;13:104.
crossref pmid pmc pdf
37. Thandra KC, Barsouk A, Saginala K, Padala SA, Barsouk A, Rawla P. Epidemiology of non-Hodgkin’s lymphoma. Med Sci (Basel) 2021;9:5.
crossref pmid pmc
38. Skibola CF. Obesity, diet and risk of non-Hodgkin lymphoma. Cancer Epidemiol Biomarkers Prev 2007;16:392-395.
crossref pmid pmc pdf
39. Vaccarella S, Dal Maso L. Challenges in investigating risk factors for thyroid cancer. Lancet Diabetes Endocrinol 2021;9:57-59.
crossref pmid
40. Rockey DC. Liver fibrosis reversion after suppression of hepatitis B virus. Clin Liver Dis 2016;20:667-679.
crossref pmid pmc
41. Papatheodoridis GV, Idilman R, Dalekos GN, Buti M, Chi H, van Boemmel F, et al. The risk of hepatocellular carcinoma decreases after the first 5 years of entecavir or tenofovir in Caucasians with chronic hepatitis B. Hepatology 2017;66:1444-1453.
crossref pmid pdf
42. Brown JA, Pack LR, Fowler JD, Suo Z. Presteady state kinetic investigation of the incorporation of anti-hepatitis B nucleotide analogues catalyzed by noncanonical human DNA polymerases. Chem Res Toxicol 2012;25:225-233.
crossref pmid pmc
43. Jiang L, Wu X, He F, Liu Y, Hu X, Takeda S, et al. Genetic evidence for genotoxic effect of entecavir, an anti-hepatitis B virus nucleotide analog. PLoS One 2016;11:e0147440.
crossref pmid pmc
44. Hosaka T, Suzuki F, Kobayashi M, Seko Y, Kawamura Y, Sezaki H, et al. Long-term entecavir treatment reduces hepatocellular carcinoma incidence in patients with hepatitis B virus infection. Hepatology 2013;58:98-107.
crossref pmid
45. Su TH, Hu TH, Chen CY, Huang YH, Chuang WL, Lin CC, et al. Four-year entecavir therapy reduces hepatocellular carcinoma, cirrhotic events and mortality in chronic hepatitis B patients. Liver Int 2016;36:1755-1764.
pmid
46. Kim J, Hur MH, Kim SU, Kim JW, Sinn DH, Lee HW, et al. Inverse propensity score-weighted analysis of entecavir and tenofovir disoproxil fumarate in patients with chronic hepatitis B: a large-scale multicenter study. Cancers (Basel) 2023;15:2936.
crossref pmid pmc
47. Hur MH, Park MK, Yip TCF, Chen CH, Lee HC, Choi WM, et al. Personalized antiviral drug selection in patients with chronic hepatitis B using a machine learning model: a multinational study. Am J Gastroenterol 2023;118:1963-1972.
crossref pmid
48. Song BC, Cui XJ, Kim H. Hepatitis B virus genotypes in Korea: an endemic area of hepatitis B virus infection. Intervirology 2005;48:133-137.
crossref pmid pdf
49. Kim BK, Revill PA, Ahn SH. HBV genotypes: relevance to natural history, pathogenesis and treatment of chronic hepatitis B. Antivir Ther 2011;16:1169-1186.
crossref pmid pdf

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