Abstract
Hepatitis B virus (HBV) infection is a leading risk factor for hepatocellular carcinoma (HCC), contributing to cancer development through direct genomic integration and chronic inflammation. N-acetylcysteine (NAC), known for its antioxidant properties, is widely utilized in cancer prevention. However, clinical evidence regarding its protective effect against HCC in HBV carriers remains sparse. In this retrospective cohort study spanning 2008 to 2018, we utilized Taiwan’s National Health Insurance Research Database (NHIRD) to include 1,061,174 chronic HBV carriers. Participants were stratified into NAC users and non-users using Propensity Score Matching. We assessed the incidence of HCC in both cohorts, examining the relationship between NAC usage duration and HCC incidence, and evaluating the dose-response effect. NAC users exhibited a significantly lower risk of developing HCC (adjusted hazard ratio [aHR]: 0.38; 95% confidence interval [CI]: 0.36-0.40; P < 0.0001). A dose-response relationship was evident, with higher cumulative defined daily doses (cDDDs) of NAC correlating with reduced HCC risk, revealing a significant trend (P < 0.0001). Notably, a daily NAC intensity of > 1.4 DDDs was associated with a decreased risk of HCC in HBV patients. Our results demonstrate that the use of NAC, in a dose-dependent manner, is intricately linked with a diminished incidence of HCC in individuals chronically infected with the HBV.
Keywords: Hepatocellular carcinoma, N-acetylcysteine, hepatitis B, reactive oxidase species, antioxidant agents
Introduction
Hepatocellular carcinoma (HCC) is the primary form of liver cancer and the fourth leading cause of cancer-related deaths globally [1]. While multiple risk factors exist, including exposure to liver-toxic agents, alcohol consumption, and metabolic-associated liver diseases, chronic infections with hepatitis B virus (HBV) and hepatitis C virus (HCV) remain the most significant contributors, accounting for approximately 80% of HCC cases [2]. In Eastern Asia and much of Africa, HBV is the predominant etiological agent for HCC. It is responsible for over half of HCC cases and ranks as the second leading cause of cancer deaths worldwide. Individuals with chronic HBV infection have a 10- to 25-fold higher risk of developing HCC compared to non-carriers [3]. In Taiwan, although the prevalence of HBV-related HCC in males decreased from 80% to 66% (and in females from 64% to 41%) over recent decades, HBV infection remains a principal risk factor for HCC in the region [4,5]. HBV infection directly participates in hepatic transformation by initiating both common and virus-specific oncogenic pathways, alongside stimulating the host’s immune response, leading to chronic liver necro-inflammation [6]. Among the complex interplay of HBV infection in the host, the role of HBV-induced mitochondrial reactive oxygen species (ROS) in promoting hepatocarcinogenesis has been extensively investigated [7,8].
N-acetylcysteine (NAC), a mucolytic agent traditionally used for bronchial airway clearance, also serves as a precursor to L-cysteine and reduced glutathione [9]. Recent studies have expanded its therapeutic scope, revealing NAC’s role as an antioxidant. Its clinical applications now extend beyond pulmonary diseases to include use as an antidote for acetaminophen overdose and in the prevention of contrast-induced nephropathy associated with imaging procedures. The hepatoprotective actions of NAC, attributed to its role as a precursor to glutathione (GSH), are believed to involve cytokine-mediated mechanisms in addition to glutathione replenishment [10]. Furthermore, NAC has garnered attention in the realm of oncology as a potential chemopreventive agent [11].
Numerous investigations have demonstrated the potential efficacy of NAC in cancer prevention and as an adjuvant in cancer treatment, particularly in the context of lung cancer [12] and triple-negative breast cancer [13]. A comprehensive literature review highlights pre-clinical studies illustrating NAC’s capacity to mitigate reactive oxygen species (ROS) and inhibit hepatocarcinogenesis [14,15]. Notably, clinical trials have indicated that intravenous administration of NAC, serving as a hepatic protector, reduces the incidence of post-embolization syndrome following trans-arterial chemoembolization in patients with HCC [16]. Despite these advancements, a notable gap in the existing data pertains to the protective effects of NAC use in individuals carrying the HBV against the development of HCC. This nationwide population-based study aims to analyze whether NAC exhibits a protective effect in preventing HCC development in HBV carriers and seeks to estimate the optimal dosage of NAC that yields the most robust protective effects.
Methods
Study population
Our cohort study, conducted from 2008 to 2018 using Taiwan’s National Health Insurance Research Database (NHIRD), included Chronic Hepatitis B Carriers (HBV). This database, covering over 99% of Taiwan’s population, provided encrypted patient data, including diagnoses, procedures, and prescriptions [17-21]. Linked to the Taiwan death registry, it enabled us to ascertain the patients’ mortality status and causes of death, offering a comprehensive view for our analysis [17-21].
This study assessed HBV patients aged 18 or older from the NHIRD, excluding those with missing age data. NAC use was defined as 28 or more cumulative defined daily doses (cDDDs) following HBV diagnosis. The observation period started from the initiation of NAC treatment ≥ 28 cDDD (the index date) and continued until HCC diagnosis, death, or December 31, 2021. The case group comprised patients prescribed at least 28 cDDDs of NAC, while the control group had no NAC prescription. Follow-up lasted one year post-initial NAC use or cohort entry. This study aims to clarify the link between NAC use and HCC risk in HBV patients.
In our study examining HBV patients, we implemented specific exclusion criteria to maintain the integrity of the data. Exclusions applied to individuals who: (1) were diagnosed with HCC within one year following the index date; (2) lacked complete data regarding sex and age, or were below 18 years; (3) had a follow-up period shorter than one year; (4) had a prior diagnosis of any cancer type before joining the cohort; or (5) used NAC prior to their HBV diagnosis. These criteria were essential to ensure the reliability of our findings regarding the relationship between NAC use and HCC risk among HBV patients.
The Institutional Review Board (IRB) of the Tzu-Chi Medical Foundation granted approval for our study protocols (IRB Approval No. IRB109-015-B).
NAC exposure
NAC use was operationalized as the administration of a minimum of 28 cDDDs. NAC, a versatile agent in medicine, primarily serves as an antidote to acetaminophen toxicity by replenishing glutathione and neutralizing detrimental metabolites [10]. It is also commonly used as a mucolytic in treating respiratory disorders like chronic bronchitis, aiding in mucus clearance and respiratory function enhancement [22]. Considering the potential variability in NAC consumption over the study period, we accounted for NAC use as a time-varying covariate in our Cox model.
The cumulative NAC dose was computed by dividing the prescribed dose by the days’ supply. We employed the World Health Organization’s defined daily dose (DDD) standard to quantify NAC dosage, where the DDD represents the average maintenance dose per day for an adult’s primary drug indication. To assess the impact of NAC daily intensity on HCC risk, we categorized usage into ≥ 1 DDD and < 1 DDD groups, with ≥ 1 DDD indicating substantial daily NAC use. The cDDDs were summed to verify a minimum intake of 28 cDDDs. NAC nonuse was classified as 0 cDDDs throughout the follow-up, excluding sporadic usage, while NAC use was defined as at least 28 cDDDs. We further stratified patients into four subgroups based on cDDD quartiles.
PSM and covariates
In our analysis, we controlled for potential confounders by including a range of covariates. We categorized study participants into four age groups at the index date: 18-30, 31-40, 41-50, and over 50 years. The index date for NAC users was the initiation of NAC at a minimum of 28 cDDDs. For matched non-users of NAC, the index date corresponded to the same variables’ assessment date.
To ensure accuracy in our multivariate analysis, we avoided duplication in comorbidity adjustments. Comorbidities, identified within one year of the index date, were based on the International Classification of Diseases (ICD) codes. These were either from a primary inpatient diagnosis or from at least two outpatient visits within the year.
We utilized both the Ninth Revision, Clinical Modification (ICD-9-CM) and the Tenth Revision, Clinical Modification (ICD-10-CM) for coding. We employed a time-varying Cox proportional hazards model to examine the association between NAC use and HCC development, accounting for potential confounders. To robustly compare HCC risk between NAC users and nonusers, patient matching was based on propensity scores. Matching criteria included age, sex, income level, urbanization, Charlson Comorbidity Index (CCI) scores, existing comorbidities, and specific medication usage (anti-HBV treatment, statins, metformin, aspirin).
Continuous variables are reported as either means ± standard deviations or medians with interquartile ranges, as appropriate. To align patient groups more closely, we utilized the greedy matching technique: Propensity Score Matching (PSM) with a caliper width of 0.1, achieving a 1:1 match [23]. This approach involves pairing controls with comparable covariates considered essential by the researchers for effective confounder control.
Primary endpoints
The primary endpoint of our study was the incidence of HCC, verified by the certification records in the Catastrophic Illness Patient Registry [24].
Statistical analysis
We gathered patient characteristics as covariates, detailed in Table 1. Age was categorized in decade intervals. To compare baseline characteristics between NAC users and nonusers, we employed the chi-squared test for categorical variables, the t-test for continuous variables, and the Wilcoxon rank-sum test for median comparisons. The cohort entry date was established as the baseline.
Table 1.
Comparative characteristics of chronic hepatitis B carriers before and after propensity score matching, with and without N-acetylcysteine use
| Before PSM | After PSM | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||
| Never-NAC use | NAC Use | ASMD | Never-NAC use | NAC Use | ASMD | |||||
| N=978,235 | N=82,939 | N=82,939 | N=82,939 | |||||||
|
|
|
|
|
|||||||
| N | % | N | % | N | % | N | % | |||
| Age, years-old (mean ± SD) | 41.60 ± 13.71 | 51.67 ± 15.73 | 50.79 ± 14.79 | 51.67 ± 15.73 | ||||||
| Age, median (IQR), years-old | 40.00 (31.00, 51.00) | 51.00 (40.00, 63.00) | 51.00 (40.00, 61.00) | 51.00 (40.00, 63.00) | ||||||
| Age group, years | 0.3363 | 0.0140 | ||||||||
| 18-30 | 233,516 | 23.87% | 8,192 | 9.88% | 8,074 | 9.73% | 8,192 | 9.88% | ||
| 31-40 | 256,632 | 26.23% | 13,400 | 16.16% | 13,139 | 15.84% | 13,400 | 16.16% | ||
| 41-50 | 240,352 | 24.57% | 18,183 | 21.92% | 17,994 | 21.70% | 18,183 | 21.92% | ||
| > 50 | 247,735 | 25.32% | 43,164 | 52.04% | 43,732 | 52.73% | 43,164 | 52.04% | ||
| Sex | 0.0507 | 0.0140 | ||||||||
| Female | 421,995 | 43.14% | 37,870 | 45.66% | 38,447 | 46.36% | 37,870 | 45.66% | ||
| Male | 556,240 | 56.86% | 45,069 | 54.34% | 44,492 | 53.64% | 45,069 | 54.34% | ||
| Income (NTD) | 0.2011 | 0.0010 | ||||||||
| Low income | 177,501 | 18.15% | 18,869 | 22.75% | 18,666 | 22.51% | 18,869 | 22.75% | ||
| ≤ 20,000 | 372,325 | 38.06% | 36,962 | 44.57% | 36,827 | 44.40% | 36,962 | 44.57% | ||
| 20,001-30,000 | 157,901 | 16.14% | 10,776 | 12.99% | 10,923 | 13.17% | 10,776 | 12.99% | ||
| 30,001-45,000 | 157,483 | 16.10% | 9,616 | 11.59% | 9,642 | 11.63% | 9,616 | 11.59% | ||
| > 45,000 | 113,025 | 11.55% | 6,716 | 8.10% | 6,881 | 8.30% | 6,716 | 8.10% | ||
| Urbanization | 0.1022 | 0.0060 | ||||||||
| Rural | 236,050 | 24.13% | 23,748 | 28.63% | 23,525 | 28.36% | 23,748 | 28.63% | ||
| Urban | 742,185 | 75.87% | 59,191 | 71.37% | 59,414 | 71.64% | 59,191 | 71.37% | ||
| CCI Scores | ||||||||||
| Mean (SD) | 1.34 ± 1.24 | 1.88 ± 1.60 | 1.77 ± 1.44 | 1.88 ± 1.60 | ||||||
| Median (Q1-Q3) | 2.00 (0.00, 2.00) | 2.00 (0.00, 3.00) | 2.00 (0.00, 2.00) | 2.00 (0.00, 3.00) | ||||||
| CCI Scores | 0.2663 | 0.0124 | ||||||||
| 0 | 382,208 | 39.07% | 22,118 | 26.67% | 22,580 | 27.22% | 22,118 | 26.67% | ||
| ≥ 1 | 596,027 | 60.93% | 60,821 | 73.33% | 60,359 | 72.78% | 60,821 | 73.33% | ||
| CCI | ||||||||||
| Congestive Heart Failure | 10,909 | 1.12% | 3,786 | 4.56% | 0.2366 | 3,294 | 3.97% | 3,786 | 4.56% | 0.0292 |
| Dementia | 1,986 | 0.20% | 1,235 | 1.49% | 0.1886 | 626 | 0.75% | 1,235 | 1.49% | 0.0704 |
| Chronic Pulmonary Disease | 70,853 | 7.24% | 21,290 | 25.67% | 0.5525 | 15,015 | 18.10% | 21,290 | 25.67% | 0.1839 |
| Rheumatic Disease | 13,625 | 1.39% | 2,303 | 2.78% | 0.0833 | 1,803 | 2.17% | 2,303 | 2.78% | 0.0393 |
| Liver Disease | 541,440 | 55.35% | 48,102 | 58.00% | 0.0044 | 51,121 | 61.64% | 48,102 | 58.00% | 0.0743 |
| Diabetes with complications | 13,295 | 1.36% | 3,030 | 3.65% | 0.1401 | 2,939 | 3.54% | 3,030 | 3.65% | 0.0059 |
| Hemiplegia and Paraplegia | 5,262 | 0.54% | 1,204 | 1.45% | 0.1088 | 845 | 1.02% | 1,204 | 1.45% | 0.0389 |
| Renal Disease | 15,950 | 1.63% | 3,694 | 4.45% | 0.1789 | 3,356 | 4.05% | 3,694 | 4.45% | 0.0198 |
| AIDS | 988 | 0.10% | 83 | 0.10% | 0.0896 | 79 | 0.10% | 83 | 0.10% | 0.0000 |
| Cancer | 25,627 | 2.62% | 5,597 | 6.75% | 0.1336 | 3,850 | 4.64% | 5,597 | 6.75% | 0.0911 |
| Coexisting comorbidities | ||||||||||
| Diabetes | 83,473 | 8.53% | 14,409 | 17.37% | 0.2656 | 14,283 | 17.22% | 14,409 | 17.37% | 0.0040 |
| Hypertension | 150,101 | 15.34% | 28,407 | 34.25% | 0.4488 | 28,382 | 34.22% | 28,407 | 34.25% | 0.0006 |
| Hyperlipidemia | 129,783 | 13.27% | 18,692 | 22.54% | 0.2436 | 18,752 | 22.61% | 18,692 | 22.54% | 0.0017 |
| Non-alcoholic steatohepatitis (NASH) | 46,862 | 4.79% | 5,918 | 7.14% | 0.0993 | 5,583 | 6.73% | 5,918 | 7.14% | 0.0161 |
| Alcohol-related liver diseases | 21,873 | 2.24% | 3,188 | 3.84% | 0.0933 | 3,029 | 3.65% | 3,188 | 3.84% | 0.0100 |
| CKD | 9,057 | 0.93% | 2,714 | 3.27% | 0.1637 | 2,615 | 3.15% | 2,714 | 3.27% | 0.0068 |
| Cholelithiasis | 32,908 | 3.36% | 4,995 | 6.02% | 0.1261 | 4,691 | 5.66% | 4,995 | 6.02% | 0.0154 |
| COPD | 77,867 | 7.96% | 24,060 | 29.01% | 0.5634 | 23,837 | 28.74% | 24,060 | 29.01% | 0.0060 |
| Pneumonia | 29,231 | 2.99% | 10,531 | 12.70% | 0.3672 | 9,817 | 11.84% | 10,531 | 12.70% | 0.0262 |
| Bronchitis | 311,534 | 31.85% | 41,003 | 49.44% | 0.3640 | 41,085 | 49.54% | 41,003 | 49.44% | 0.0020 |
| Pulmonary Cystic fibrosis | 6 | 0.00% | 3 | 0.00% | 0.2130 | 3 | 0.00% | 3 | 0.00% | 0.0000 |
| Myocardial infarction | 3,317 | 0.34% | 1,155 | 1.39% | 0.1136 | 965 | 1.16% | 1,155 | 1.39% | 0.0205 |
| Congestive heart failure | 10,545 | 1.08% | 4,356 | 5.25% | 0.2399 | 3,898 | 4.70% | 4,356 | 5.25% | 0.0253 |
| Cerebrovascular disease | 29,763 | 3.04% | 9,028 | 10.89% | 0.3121 | 8,611 | 10.38% | 9,028 | 10.89% | 0.0165 |
| Obesity | 8,103 | 0.83% | 1,010 | 1.22% | 0.0387 | 920 | 1.11% | 1,010 | 1.22% | 0.0103 |
| Ascites | 4,440 | 0.45% | 624 | 0.75% | 0.0389 | 523 | 0.63% | 624 | 0.75% | 0.0145 |
| Hepatic coma | 2,255 | 0.23% | 416 | 0.50% | 0.0448 | 348 | 0.42% | 416 | 0.50% | 0.0118 |
| Medication use | ||||||||||
| Anti-HBV treatment | 63,264 | 6.47% | 7,743 | 9.34% | 0.1065 | 7,655 | 9.23% | 7,743 | 9.34% | 0.0038 |
| Statin | 146,016 | 14.93% | 23,042 | 27.78% | 0.3175 | 23,673 | 28.54% | 23,042 | 27.78% | 0.0021 |
| Metformin | 93,003 | 9.51% | 14,753 | 17.79% | 0.2429 | 14,945 | 18.02% | 14,753 | 17.79% | 0.0060 |
| Aspirin | 132,783 | 13.57% | 28,269 | 34.08% | 0.4960 | 28,891 | 34.83% | 28,269 | 34.08% | 0.0158 |
| NAC, cDDD | ||||||||||
| Mean (SD) | 0 | 143.16 ± 283.10 | 0 | 143.16 ± 283.10 | ||||||
| Median (q1, q3) | 0 | 58.82 (38.02, 119.40) | 0 | 58.82 (38.02, 119.40) | ||||||
| NAC, cDDD | ||||||||||
| Never use | 978,235 | 100% | 0 | 0.00% | 82,939 | 100.00% | 0 | 0.00% | ||
| Q1 | 0 | 0.00% | 20,581 | 24.81% | 0 | 0.00% | 20,581 | 24.81% | ||
| Q2 | 0 | 0.00% | 21,023 | 25.35% | 0 | 0.00% | 21,023 | 25.35% | ||
| Q3 | 0 | 0.00% | 20,538 | 24.76% | 0 | 0.00% | 20,538 | 24.76% | ||
| Q4 | 0 | 0.00% | 20,797 | 25.00% | 0 | 0.00% | 20,797 | 25.08% | ||
| DDD (Daily density) | ||||||||||
| Never use | 978,235 | 100% | 0 | 0.00% | 82,939 | 100.00% | 0 | 0.00% | ||
| < 1 | 0 | 0.00% | 38,799 | 46.78% | 0 | 0.00% | 38,799 | 46.78% | ||
| ≥ 1 | 0 | 0.00% | 44,140 | 53.22% | 0 | 0.00% | 44,140 | 53.22% | ||
| Mean (SD) follow-up time, year | 7.57 ± 3.80 | 7.57 ± 3.78 | 6.72 ± 3.72 | 7.57 ± 3.78 | ||||||
| Median (IQR) follow-up time, year | 7.64 (4.34, 10.91) | 7.58 (4.36, 10.81) | 6.47 (3.48, 9.60) | 7.58 (4.36, 10.81) | ||||||
| Primary outcome | P-value | P-value | ||||||||
| HCC | 33,863 | 3.46% | 1,848 | 2.23% | < 0.0001 | 4,043 | 4.87% | 1,848 | 2.23% | < 0.0001 |
Abbreviations: HCC, Hepatocellular Carcinoma; HBV, Hepatitis B Virus; NAC, N-Acetylcysteine; cDDD, Cumulative Defined Daily Doses; DDD, Defined Daily Dose; Q, Quartile; PSM, Propensity Score Matching; CCI, Charlson Comorbidity Index; NTD, New Taiwan Dollar; SD, Standard Deviation; IQR, Interquartile Range; NASH, Non-Alcoholic Steatohepatitis; COPD, Chronic Obstructive Pulmonary Disease; CKD, Chronic Kidney Disease; ASMD, Absolute Standardized Mean Difference; N, Number.
To evaluate the relationship between NAC use and HCC risk, we computed incidence rates (IRs) and incidence rate ratios (IRRs), and estimated adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) using Cox regression models. These adjustments accounted for factors including age, sex, income, urbanization, CCI Scores, existing comorbidities, and medication use, as listed in Table 1. Additionally, we employed time-varying Cox regression to analyze the impact of different cDDD levels of NAC and daily intensity (≥ 1 DDD or < 1 DDD) on HCC risk in HBV patients. The Fine and Gray methodology was utilized to account for ischemic stroke hazards, considering competing mortality risks. The Kaplan-Meier method estimated cumulative HCC incidence, with comparisons drawn using the log-rank test.
All statistical analyses were conducted using SAS for Windows (version 9.4; SAS Institute, Cary, NC). A two-sided P-value of less than 0.05 was deemed indicative of statistical significance.
Results
Demographic and clinical profile of the chronic hepatitis B cohort
In this investigation, we analyzed data from 1,061,174 HBV patients enrolled between 2008 and 2018, as detailed in Table 1. Prior to PSM, the NAC use group showed a higher prevalence of older individuals, lower income, more rural residency, increased CCI scores, and greater frequency of comorbidities such as diabetes, hypertension, hyperlipidemia, chronic kidney diseases, cholelithiasis, chronic obstructive pulmonary disease, pneumonia, bronchitis, pulmonary cystic fibrosis, myocardial infarction, congestive heart failure, and cerebrovascular disease. This group also had higher usage of concurrent medications including anti-HBV treatments, statins, metformin, and aspirin, compared to the never-NAC use group.
For comparative analysis, we conducted 1:1 matching, resulting in 82,939 patients in each group. The post-PSM age distribution was analogous in both groups (Table 1). Following PSM, variables such as age, sex, income levels, urbanization, CCI Scores, coexisting comorbidities, and medication use were statistically similar between the NAC user and never user groups. The crude incidence of HCC post-PSM was 2.23% in the NAC use group and 4.87% in the never-NAC use group (P < 0.0001).
HCC risk in HBV: NAC users vs. non-users
Post-PSM, no covariates listed in Table 1 were significantly linked to HCC risk. The sole significant independent predictor identified was the use of NAC. The aHR for HCC in the NAC use group, relative to the never-NAC use group, was 0.38 (95% CI: 0.36-0.40, P < 0.0001), as shown in Table 2. When accounting for the competing risk of death, the aHR was 0.39 (95% CI: 0.37-0.42) for NAC users versus non-users. Moreover, Kaplan-Meier curves depicting HCC cumulative incidence revealed a significantly higher rate in the never-NAC use group compared to the NAC use group (Figure 1, P < 0.0001).
Table 2.
Risk of hepatocellular carcinoma in propensity score matched chronic hepatitis B carriers: comparative analysis of N-acetylcysteine use, stratified by usage intensity and cumulative dosage
| Hepatocellular Carcinoma stroke risk | ||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Crude HR (95% CI) | P-value | aHR* (95% CI) | P-value | aHR# (95% CI) | P-value | |
| NAC (ref. Never-NAC use) | 1.00 - | - | 1.00 - | - | 1.00 - | - |
| NAC use | 0.41 (0.39, 0.43) | < 0.0001 | 0.38 (0.36, 0.40) | < 0.0001 | 0.39 (0.37, 0.42) | < 0.0001 |
| cDDD of NAC (ref. Never-NAC use) | 1.00 - | - | 1.00 - | - | 1.00 - | - |
| Q1 | 0.42 (0.38, 0.46) | < 0.0001 | 0.51 (0.46, 0.56) | < 0.0001 | 0.51 (0.46, 0.56) | < 0.0001 |
| Q2 | 0.41 (0.37, 0.45) | < 0.0001 | 0.44 (0.40, 0.49) | < 0.0001 | 0.44 (0.4, 0.49) | < 0.0001 |
| Q3 | 0.42 (0.39, 0.47) | < 0.0001 | 0.40 (0.36, 0.44) | < 0.0001 | 0.39 (0.36, 0.43) | < 0.0001 |
| Q4 | 0.38 (0.34, 0.42) | < 0.0001 | 0.26 (0.23, 0.28) | < 0.0001 | 0.25 (0.23, 0.28) | < 0.0001 |
| P for trend | < 0.0001 | < 0.0001 | < 0.0001 | |||
| DDD of NAC (ref. Never-NAC use) | 1.00 - | - | 1.00 - | - | 1.00 - | - |
| < 1 | 0.52 (0.48, 0.55) | < 0.0001 | 0.45 (0.42, 0.48) | < 0.0001 | 0.45 (0.42, 0.48) | < 0.0001 |
| ≥ 1 | 0.31 (0.29, 0.34) | < 0.0001 | 0.31 (0.29, 0.34) | < 0.0001 | 0.31 (0.29, 0.34) | < 0.0001 |
| P for trend | < 0.0001 | < 0.0001 | < 0.0001 | |||
Abbreviations: NAC, N-Acetylcysteine; CI, Confidence interval; aHR, Adjusted hazard ratio; HR, hazard ratio; cDDDs, cumulative defined daily doses; DDD, defined daily doses; ref., reference group; Q, quartiles; N, Number.
The time-varying Cox model, which treats NAC use as a dynamic variable, was adjusted to account for several factors. These include age, sex, income levels, urbanization level, CCI Scores, other coexisting medical conditions, and the use of various medications.
The Fine and Gray method was adapted to estimate the hazard of HCC considering competing risks from death.
Figure 1.
Kaplan-Meier analysis comparing the cumulative incidence of hepatocellular carcinoma in chronic hepatitis B patients with and without N-acetylcysteine use.
Dose-response: NAC and HCC risk in HBV patients
We examined the dose-response relationship between NAC use and HCC risk by dividing cDDDs into four quartiles (Q1, Q2, Q3, and Q4), as shown in Table 2. The aHRs for HCC risk in these quartiles, in comparison to non-NAC users, were as follows: Q1, 0.51 (95% CI, 0.46-0.56); Q2, 0.44 (95% CI, 0.40-0.49); Q3, 0.40 (95% CI, 0.36-0.44); and Q4, 0.26 (95% CI, 0.23-0.28). A significant dose-response trend (P < 0.0001) was observed, indicating a reduction in HCC risk with increasing NAC use among HBV patients. This trend persisted even after adjusting for the competing risk of death. Kaplan-Meier curves also demonstrated significantly lower cumulative incidences of HCC in the NAC use groups, particularly in the higher quartiles (Q4 to Q1), compared to the non-NAC use group (Figure 2, P < 0.0001).
Figure 2.
Kaplan-Meier survival curves depicting the cumulative incidence of hepatocellular carcinoma in chronic hepatitis B patients, categorized by various cDDD of N-acetylcysteine.
Impact of NAC daily dosing intensity on HCC risk in HBV patients
To evaluate the relationship between daily NAC dose intensity and HCC risk in HBV patients, we classified the DDD into two categories: DDD < 1 and DDD ≥ 1, as detailed in Table 2. The aHRs for HCC risk, relative to non-NAC users, were: DDD < 1, 0.45 (95% CI, 0.42-0.48); and DDD ≥ 1, 0.31 (95% CI, 0.29-0.34). A significant trend (P < 0.0001) was observed, indicating that increased daily NAC intensity corresponded with a decreased HCC risk in HBV patients. This trend remained significant even after accounting for the competing risk of death. Kaplan-Meier curves also showed a notably lower cumulative incidence of HCC in the DDD ≥ 1 NAC group, followed by the DDD < 1 group, and then the never-NAC use group (Figure 3, P < 0.0001).
Figure 3.
Kaplan-Meier curves illustrating the cumulative incidence of hepatocellular carcinoma among chronic hepatitis B carriers, segregated by different daily intensity levels (Defined Daily Dose, DDD) of N-acetylcysteine usage.
Additionally, Supplementary Figure 1 illustrates the relationship between NAC daily intensity (DDD) and the HCC hazard ratio in chronic hepatitis B carriers. It suggests that a daily dose of 1.40 DDD might be the minimal recommended dosage, as the HCC risk reduction capacity appears to plateau beyond NAC > 1.40 DDD, though there is still a gradual decrease in HCC risk with higher DDD of NAC use.
HCC incidence comparisons between NAC users and never-users
Table 3 delineates the association between NAC use and HCC development in our HBV cohort. The HCC incidence rate per 10,000 person-years was significantly lower in NAC users (29.45) compared to never-users (72.52). The incidence rate ratio (IRR) of HCC for NAC users, with a 95% CI, was 0.41 (0.38-0.43) relative to never-users.
Table 3.
Incidence rate ratios and hazard ratios for hepatocellular carcinoma in propensity score matched chronic hepatitis B carriers: detailed comparative analysis of N-acetylcysteine use, categorized by intensity of use and cumulative dosage
| Events | Person-years | IR (per 10,000 person-year) | IRR | 95% CI for IRR | P | |
|---|---|---|---|---|---|---|
| NAC use | ||||||
| Never-NAC use | 4,043 | 557,503.9 | 72.52 | Ref. | ||
| NAC use | 1,848 | 627,439.9 | 29.45 | 0.41 | (0.38, 0.43) | < 0.0001 |
| NAC use (cDDD) | ||||||
| Never-NAC use | 4,043 | 557,503.9 | 72.52 | Ref. | ||
| NAC user dose, Q1 | 480 | 158,474.0 | 30.29 | 0.42 | (0.38, 0.46) | < 0.0001 |
| NAC user dose, Q2 | 476 | 161,316.8 | 29.51 | 0.41 | (0.37, 0.45) | < 0.0001 |
| NAC user dose, Q3 | 476 | 155,818.3 | 38.55 | 0.40 | (0.38, 0.46) | < 0.0001 |
| NAC user dose, Q4 | 416 | 151,830.8 | 27.40 | 0.38 | (0.34, 0.42) | < 0.0001 |
| NAC use (daily density, DDD) | ||||||
| Never-NAC use | 4,043 | 557,503.9 | 72.52 | Ref. | ||
| < 1 | 1,092 | 292,080.4 | 37.39 | 0.52 | (0.48, 0.55) | < 0.0001 |
| ≥ 1 | 756 | 335,359.5 | 22.54 | 0.31 | (0.29, 0.34) | < 0.0001 |
Abbreviations: NAC, N-Acetylcysteine; cDDD, cumulative defined daily dos; DDD, defined daily doses; IR, incidence rate; IRR, incidence rate ratio; Ref., reference; CI, confidence interval; Q, Quarter.
A dose-response relationship between NAC use and reduced HCC risk was evident. IRRs for HCC in the quartiles of NAC use (Q1, Q2, Q3, Q4) were 0.42 (0.38-0.46), 0.41 (0.37-0.45), 0.40 (0.38-0.46), and 0.38 (0.34-0.42) respectively, compared to nonuse. Additionally, a correlation between daily dose density and HCC risk was observed. For NAC use at DDD < 1 and DDD ≥ 1, the IRRs were 0.52 (0.48-0.55) and 0.31 (0.29-0.34), respectively, compared to never-users.
Discussion
In our clinical investigation into the use of NAC and its potential to safeguard against HCC in HBV carriers, we unearthed several pivotal insights that could markedly influence both clinical practice and research. The cornerstone of our study is the emergent role of NAC, classically utilized as a mucolytic agent and as an antidote for acetaminophen toxicity, as a viable chemopreventive tool against HCC in individuals with HBV (see Tables 2 and 3; Figures 2 and 3). This reevaluation of NAC’s clinical utility extends its therapeutic potential beyond its conventional use in respiratory conditions and acute liver failure. Critically, our data delineate a dose-response curve, illustrating that increased cumulative doses and intensified daily NAC administration correlate with a substantial diminution in HCC incidence among HBV patients (refer to Tables 2 and 3). This finding is particularly salient for clinicians, presenting a pragmatic chemoprevention strategy to curtail HCC risk in chronic HBV patients - a group notably susceptible to this malignancy. This is especially relevant considering the lifelong, costly anti-HBV therapies that are often not reimbursed under Taiwan’s National Health Insurance, placing a significant burden on patients. Further, our analysis sheds light on the optimal NAC dosing required to maximize its protective effect against HCC (as illustrated in Supplementary Figure 1). This presents clinicians with an empirical basis to customize NAC treatment regimens, potentially heralding more individualized and efficacious preventive strategies against liver cancer in HBV carriers. Employing a comprehensive, decade-spanning dataset from Taiwan’s NHIRD, our large-scale, nationwide population-based study confers substantial validity to these findings. This extensive analysis not only bolsters the reliability of our results but also enhances their applicability to a broader patient population. Our study not only reaffirms the multifaceted application of NAC in clinical settings but also forges new pathways in oncological research, especially in the prophylaxis and management of HCC among HBV carriers. It beckons further exploration into the mechanistic underpinnings of NAC’s protective properties and potentially lays the groundwork for groundbreaking therapeutic approaches to combat liver cancer in high-risk groups.
Chronic HBV infection, a known potent hepato-carcinogen, increases the lifetime risk of developing HCC by 10- to 25-fold compared to non-carriers [3]. The pathogenesis of HBV-related HCC involves both direct and indirect mechanisms, including HBV DNA integration into the host genome leading to genomic instability and insertional mutagenesis of various cancer-related genes [25]. Prolonged expression of viral regulatory proteins, such as HBx and altered versions of the preS/S envelope proteins, has been shown to disrupt cellular transcription and proliferation control, acting as carcinogenic factors [26,27]. Particularly, the HBx protein induces ROS, creating a feedback loop that triggers carcinogenesis. Given this background, antioxidants like NAC, traditionally used as a mucolytic agent in respiratory diseases, could theoretically reduce ROS levels [28]. NAC’s protective mechanisms are multifaceted, including the inhibition of mutagenic agents, reduction of ROS genotoxicity, and protection of DNA and nuclear enzymes [29-31]. Although NAC’s role as a chemopreventive agent in upper airway malignancies has been considered, its efficacy in prolonging survival in primary lung cancer or head and neck cancer was not demonstrated in the EURSCAN randomized control trial (RCT) [32], possibly due to suboptimal dosing. It is hypothesized that NAC’s efficacy in reducing HCC risk may be attributed to its potentiation of hepatic glutathione, enhancing liver detoxification processes, a mechanism possibly more effective in the liver than in lung or head and neck cancers [9,10]. Recent studies have also highlighted NAC’s role in reducing chemotherapy-induced neuropathy [13,33] and its synergistic effects with chemotherapeutic agents in hepatopancreatic malignancies [34]. These findings open new avenues for the use of NAC in oncology, particularly in the prevention and adjunctive treatment of liver cancer, underscoring the need for further research to optimize dosing and application in various cancer types.
In the realm of liver disease management, NAC extends beyond its established role as an antidote in acetaminophen poisoning, demonstrating substantial antioxidant effects through the augmentation of depleted glutathione levels in hepatocytes [15]. Clinically, NAC has shown a tendency to enhance liver function in conditions such as non-alcoholic fatty liver disease and alcoholic liver disease, as evidenced by studies [35,36]. However, its application in the treatment of HBV infection has not been extensively explored. To our knowledge, this study represents the inaugural clinical evidence of NAC’s protective role against malignant transformation in HBV carriers. A critical observation from our data is the importance of both the cumulative dosage and the daily intensity of NAC in diminishing the incidence of HCC and associated mortality risk. Particularly noteworthy is the finding that higher cDDDs of NAC correspond with a significantly lower risk of developing HCC (aHR=0.25, as shown in Table 2 and Figure 2). This suggests that long-term NAC administration may confer enhanced HCC preventive benefits in the HBV carrier population. Additionally, our study identified a significant protective threshold for HCC incidence at a daily NAC dosage of 1.40 defined daily doses (DDDs), with a continued, albeit gradual, decrease in HCC risk at higher NAC dosages. This implicates a minimum effective daily dose of 1.40 DDDs of NAC necessary for achieving a meaningful reduction in HCC risk among HBV patients. The underlying mechanisms driving this protective effect warrant further investigation through comprehensive experimental studies, potentially illuminating new pathways in HBV-associated carcinogenesis and its prevention.
This study boasts several notable strengths, including a large sample size that significantly enhances the statistical power and generalizability of our findings. The inclusion of a substantial validation cohort further underpins the robustness and reliability of our results. The deployment of PSM ensured a homogenous distribution of covariates across study groups, effectively mitigating potential confounding influences and bolstering the legitimacy of our comparative analyses. Furthermore, the longitudinal verification of medication records in our dataset lends credence to our conclusions, facilitating an in-depth examination of the prolonged effects of medication use over time.
However, our study is not without its limitations. A primary concern lies in the demographic composition of our cohort, which consisted solely of Asian HBV carriers. Consequently, the extrapolation of our findings to other ethnicities necessitates further validation through additional studies. Additionally, under Taiwan’s National Health Insurance system, NAC is predominantly prescribed for its mucolytic properties. This specificity in prescription purpose renders it challenging to discern the exact motivations behind NAC prescriptions in our cohort. While all known indications for NAC use were matched and accounted for in our analysis (as shown in Table 1), the direct causal relationship between NAC use and the observed reduction in HCC incidence remains ambiguous, potentially representing a bystander effect rather than a direct therapeutic consequence. Another notable limitation is the lack of data on patient adherence to NAC prescriptions. This gap introduces a potential bias, as non-adherence could confound the observed association between NAC use and HCC risk reduction. However, it is important to note that poor adherence would likely lead to an underestimation of NAC’s effect on reducing HCC risk, rather than invalidating our conclusions. In summation, while our study provides important insights into the potential protective role of NAC against HCC in HBV carriers, these limitations necessitate a cautious interpretation of our results. The findings underscore the need for further research, particularly randomized controlled trials, to more definitively establish the efficacy of NAC in this context.
Conclusion
Our study presents compelling evidence for the potential protective role of NAC against HCC in HBV carriers. Leveraging a large-scale, real-world dataset, our findings reveal a significant association between NAC use and a reduced incidence of HCC in this high-risk group. The dose-response relationship observed underscores the importance of adequate NAC dosing in achieving optimal protective effects.
Acknowledgements
Thank to Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, supports Szu-Yuan Wu’s work (Grant Numbers: 10908, 10909, 11001, 11002, 11003, 11006, and 11013) and Wan Fang Hospital, Taipei Medical University, supports Ruey-Shyang Soong’s work (Grant Number: 112-wf-eva-10).
Disclosure of conflict of interest
None.
Abbreviations
- HCC
Hepatocellular Carcinoma
- HBV
Hepatitis B Virus
- HCV
Hepatitis C Virus
- GSH
Glutathione
- NAC
N-Acetylcysteine
- ROS
Reactive Oxygen Species
- NHI
National Health Insurance
- NHIRD
National Health Insurance Research Database
- cDDD
Cumulative Defined Daily Doses
- DDD
Defined Daily Dose
- Q
Quartile
- IRB
Institutional Review Board
- PSM
Propensity Score Matching
- ICD
International Classification of Diseases
- ICD-9-CM
International Classification of Diseases, Ninth Revision, Clinical Modification
- ICD-10-CM
International Classification of Diseases, Tenth Revision, Clinical Modification
- CCI
Charlson Comorbidity Index
- IR
Incidence Rate
- IRR
Incidence Rate Ratio
- CI
Confidence Interval
- HR
Hazard Ratio
- aHR
Adjusted Hazard Ratio
- DNA
Deoxyribonucleic Acid
- RCT
Randomized Controlled Trial
- HBx
Hepatitis B Virus X protein
- NTD
New Taiwan Dollar
- SD
Standard Deviation
- IQR
Interquartile Range
- NASH
Non-Alcoholic Steatohepatitis
- COPD
Chronic Obstructive Pulmonary Disease
- CKD
Chronic Kidney Disease
Supporting Information
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