Evaluation of Nomenclature of Fatty Liver Disease in Association with Hepatocellular Carcinoma: A 14.5-Year Cohort Study in Korea

Tung Hoang; Jeonghee Lee; Bo Hyun Kim; Yuri Cho; Jeongseon Kim

doi:10.4143/crt.2024.876

Articles

Page Path: HOME > Cancer Res Treat > Volume 57(4); 2025 > Article

Original Article Gastrointestinal cancer Evaluation of Nomenclature of Fatty Liver Disease in Association with Hepatocellular Carcinoma: A 14.5-Year Cohort Study in Korea: Tung Hoang^1,2, Jeonghee Lee¹, Bo Hyun Kim³, Yuri Cho³, Jeongseon Kim¹; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2025;57(4):1144-1155.
DOI: https://doi.org/10.4143/crt.2024.876
Published online: February 11, 2025

¹Department of Cancer AI & Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Korea

²Faculty of Pharmacy, University of Health Sciences, Vietnam National University Ho Chi Minh City, Vietnam

³Department of Internal Medicine, Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang, Korea

Correspondence: Jeongseon Kim, Department of Cancer AI & Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea
Tel: 82-31-920-2579 E-mail: jskim@ncc.re.kr

• Received: September 7, 2024 • Accepted: February 10, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

3,010 Views
119 Download
3 Web of Science
5 Crossref
4 Scopus

prev next

Full Article

Download PDF

Abstract

Purpose
New nomenclature has incorporated metabolic traits and/or alcohol intake history to replace nonalcoholic fatty liver disease (NAFLD). Concerning the performance of different terminologies in Asian population, this study aimed to investigate the risk of developing hepatocellular carcinoma (HCC) in persons meeting the criteria for subclasses of fatty liver disease.
Materials and Methods
Between 2002 and 2021, 28,749 participants from the cancer registry linkage, who had no prior history of HCC, were prospectively included. Fatty liver disease was defined using abdominal sonography and fatty liver index. Participants were classified as having NAFLD, metabolic dysfunction–associated fatty liver disease (MAFLD), metabolic dysfunction–associated steatotic liver disease (MASLD), steatotic liver disease with increased alcohol intake (MetALD), or alcohol-related liver disease (ALD) and their association with HCC risk was investigated using Cox regression models.
Results
During a median follow-up of 14.5 years, 143 HCC cases were newly diagnosed. The prevalences of NAFLD and MASLD were 19.7% and 18.7%, respectively, whereas MAFLD was observed in 32.3% of the study population. Given the low proportion of excessive alcohol consumption, we identified 3.3% MetALD and 3.5% ALD cases. Overall, MAFLD was suggestively associated with HCC risk (hazard ratio, 1.40; 95% confidence interval, 0.99 to 1.98). In contrast, the results for other nomenclature were not significant.
Conclusion
Our results suggest the importance of both fatty liver and the presence of metabolic dysfunction in relation to HCC risk and the need to reconsider alcohol intake thresholds in the diagnostic criteria for NAFLD and MASLD within the Korean population.
Key words: Non-alcoholic fatty liver disease, Metabolic dysfunction–associated liver disease, Steatotic liver disease, Hepatocellular carcinoma

Introduction

According to GLOBOCAN 2022, liver cancer is the sixth incidence cancer rank and the third cancer-related mortality worldwide, with an estimated 865,269 newly diagnosed patients and 757,948 deaths in 2022 [1]. Hepatocellular carcinoma (HCC), the most common type of liver cancer, is attributed to hepatitis B virus in 44% of cases and hepatitis C virus in 21% of cases [2]. Given the great variation in HCC in Eastern versus Western populations, there were also notable differences in risk factors between Asian and non-Asian individuals [3]. In North America and Europe (Western, Central, and Eastern), obesity and alcohol consumption are major contributors to HCC, whereas in Asia and Africa, chronic viral hepatitis, sex, and smoking are the major risk factors [2].

With the global increase in obesity and other risk factors, nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Western countries [4,5]. NAFLD encompasses a range of clinical conditions, from simple steatosis with normal liver function to active inflammation, leading to fibrosis, cirrhosis, and ultimately HCC [6]. The initial “two-hit” hypothesis, which aimed to explain the pathophysiology of hepatic steatosis suggested that steatosis was the first hit, making the liver more susceptible, and oxidative stress was the second hit, leading to necro-inflammation [4]. However, it is now understood that multiple factors may contribute to the pathogenesis of fatty liver disease [4]. NAFLD has recently become a major public health problem in Asia, with an updated population prevalence of 34% [7].

Since Ludwig introduced the acronym NAFLD in 1980 [8], there has been an ongoing debate about the use of this terminology. In 2020, Eslam et al. [9,10] reached a consensus recommending metabolic dysfunction–associated fatty liver disease (MAFLD) as a more accurate term for fatty liver disease linked to metabolic dysfunction. Recently, metabolic dysfunction–associated steatotic liver disease (MASLD) was proposed as an alternative to NAFLD. Moreover, a new subcategory of metabolic dysfunction–associated steatotic liver disease with increased alcohol intake (MetALD) has been suggested along with MASLD, and alcohol-related liver disease (ALD) is another subclassification of fatty liver disease [11]. A previous study of Danish individuals validated the performance of the new nomenclature of steatotic liver diseases in patients with excessive alcohol consumption and observed an increased risk of HCC from MASLD, through MetALD, to ALD in a stepwise manner [12]. However, evidence in the Asian population remains limited.

Using data from a long-term follow-up prospective cohort study, we aimed to examine the performance of various nomenclatures for fatty liver disease by investigating the associations between different subclasses of fatty liver disease and HCC risk in Korean adults.

Materials and Methods

1. Data source and study population

The study used data from the Cancer Screenee Cohort, which was established in 2002 to investigate the risk factors of cancer in South Korea [13]. Detailed descriptions of the study design and protocol have been published elsewhere [13].

In total, 54,029 participants were recruited from June 2002 to December 2023. Data on demographics, lifestyles, and laboratory tests were available for 39,333 participants. We excluded 3,052 participants without information on fatty liver disease, seven participants younger than 20 years, four participants with implausible data (height < 120 or > 200 cm; weight < 30 or > 400 kg; body mass index [BMI] < 14 or > 70 kg/m²) [14], and 53 participants who were previously diagnosed with HCC at enrollment. Among 36,217 participants with no prior history of HCC, cancer incidence data for 7,468 participants were obtained solely from medical records. Consequently, 28,749 participants linked to cancer registry data were included in the final analysis (Fig. 1).

2. Measurements of clinical and biochemical parameters

Demographics such as age, sex, marital status, education, and household income were obtained using a structured questionnaire. Information on cigarette smoking was obtained through questions about whether they smoked at the time of the survey, used to smoke but stopped at the center visit, or never smoked. Physical activity was assessed using questions about whether participants regularly performed the exercise. When data on vigorous or moderate activities were available, regular exercise was defined as performing these activities at least once per week. Alcohol consumption was assessed using self-reported average drinking frequency and quantity over the past year. We harmonized the data on alcohol intake from different versions of the questionnaire and calculated the daily amount of ethanol consumption (S1 Table). Additionally, we collected self-reported data on the use of antihypertensive, lipid-lowering, and antidiabetic drugs.

For physical assessment, BMI was calculated as the ratio of weight (kg) to height squared (m²), and waist circumference (WC) was measured at the midpoint between the lower costal margin and iliac crest. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by well-trained personnel using a mercury sphygmomanometer. Blood samples were collected after overnight fasting and analyzed for total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting glucose, glycated hemoglobin (HbA1c), hepatitis B surface antigen (HBsAg), hepatitis C antibody (anti-HCV), platelet count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyl transferase (GGT). Chronic viral hepatitis was defined as a positive HBsAg or anti-HCV test result. Abnormal liver function, including fatty liver disease, chronic liver disease, and cirrhosis, was evaluated using abdominal ultrasonography. Liver fibrosis was assessed by calculating the fibrosis-4 index (FIB-4) as follows:

FIB-4=Age (years)×AST level (U/L)Platelet count (109/L)×ALT level (U/L)

In a meta-analysis of 10 studies including 2,759 patients, the performance of FIB-4 for the absence of significant fibrosis (F2) with a threshold in the 1.24-1.45 range demonstrated a mean sensitivity of 77.8% and specificity of 71.2% [15,16]. A cutoff of 1.3 was recommended in clinical practice for the exclusion of advanced fibrosis (F3) [15].

3. Definition of fatty liver diseases

Fatty liver disease was defined based on findings from abdominal ultrasonography [17] or the well-validated fatty liver index (FLI) [18], which was calculated as follows:

FLI=e0.95×logetriglyceride+0.139×BMI+0.718×logeGGT+0.053×WC–15.7451+e0.95×logetriglyceride+0.139×BMI+0.718×logeGGT+0.053×WC–15.745×100

Since WC was not available for participants enrolled from August 2002 to June 2007, fatty liver disease diagnosis was based on any levels (mild, moderate, and severe) of fatty liver detected through ultrasound findings only, whereas the diagnosis for other individuals was based on both ultrasound findings and FLI ≥ 60.

The detailed diagnostic criteria for the different subclasses of fatty liver disease are described in Fig. 2. NAFLD was defined as hepatic steatosis in the absence of excessive alcohol consumption (≥ 20 g/day for men and ≥ 30 g/day for women) and other chronic liver diseases (including chronic viral hepatitis and cirrhosis) [19]. MAFLD was defined as hepatic steatosis with the presence of any one of the following three metabolic conditions: diabetes mellitus, overweight/obesity, or metabolic dysfunction as at least two criteria of metabolic abnormalities [20]. MASLD was defined as hepatic steatosis in the presence of at least one of five metabolic risk factors in addition to the exclusion criteria of no excessive alcohol consumption and no other chronic liver diseases [21]. In addition, MetALD was defined as MASLD in addition to the self-report of moderate alcohol consumption (30-60 g/day for men and 20-50 g/day for women), and ALD was defined as hepatic steatosis in the presence of heavy alcohol consumption (> 60 g/day for men and 50 g/day for women) [11].

4. Ascertainment of HCC

HCC was defined using International Classification of Diseases, 10th revision (ICD-10) codes C220 and C229 [22,23]. We followed the participants from baseline until the date of HCC diagnosis, date of death, and data cutoff date, whichever came first. Cancer incidence data were originally linked to cancer registration data by using resident registration numbers. However, due to the Personal Information Protection Act enforced in August 2014, cancer incidence information was then obtained from medical records at the National Cancer Center (NCC). In our study, cancer incidence was determined through linkage to cancer registration data in 2020 and 2021 for 28,749 participants and through medical records in 2023 for 7,468 participants. Mortality data were obtained by linking death certificate records in 2022.

5. Statistical analysis

The baseline characteristics of the study participants were described as mean with standard deviation for continuous variables and as frequency with proportion for categorical variables. The associations of demographics, lifestyle, metabolic factors, and fatty liver diseases with HCC were examined using Cox proportional hazard models. The follow-up period was measured from the date of study enrollment to the earliest of the following events: HCC diagnosis, death, or last recorded date in the cancer registry (end of 2020 or 2021). The hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated based on a crude model and multivariable model, which was adjusted for the main risk factors of HCC, including age, sex, chronic liver disease, chronic viral hepatitis, cirrhosis, and FIB-4.

Given that chronic viral hepatitis and cirrhosis significantly contribute to the risk of developing HCC and that MAFLD, but not NAFLD or MASLD, includes individuals with chronic liver conditions (chronic liver disease, chronic viral hepatitis, and cirrhosis), we additionally performed subgroup analyses of the association between fatty liver diseases and HCC risk in participants without chronic liver conditions. Additionally, we performed subgroup analyses by FIB-4 using thresholds for significant fibrosis (F2, FIB-4 < 1.3) and advanced fibrosis (F3, FIB-4 < 2.67) [24] to investigate the association between fatty liver disease and HCC risk in different fibrosis risk groups.

In sensitivity analyses, we restricted the analysis to individuals with a follow-up duration of at least 2 years to minimize reverse causation. While the primary analyses included ICD-10 code C229 as HCC, given that HCC is the most common type of liver cancer, we conducted further analysis excluding seven cases of unspecified liver cancer (C229) to ensure robust findings.

Furthermore, to explore the performance of different subclassifications of fatty liver disease in detecting HCC, we calculated the cumulative HCC risk, which was defined as the cumulative survival adjusted for the covariates. All statistical analyses were performed using R ver. 4.1.2 (Foundation for Statistical Computing).

Results

1. Participant characteristics

The baseline characteristics of all Cancer Screenee Cohort participants included in this analysis are displayed in Table 1. The mean age was 48.7 years (standard deviation, 9.2 years), and 56.5% of the participants were men. Individuals of age ≥ 55 years and men were related to increased risks of HCC, with HRs of 2.56 (95% CI, 1.50 to 4.38) and 5.64 (95% CI, 3.30 to 9.65), respectively. Of 28,749 participants included in the final analysis, 143 HCC cases were newly diagnosed during a median follow-up of 14.5 years (interquartile range, 12.0 to 16.92 years).

The associations of demographics, lifestyles, and underlying diseases with HCC risks are also shown in Table 1. Participants who were educated to college level or above, had a monthly income of ≥ 3,000 USD, or currently smoked had a decreased risk of HCC; however, not all categories of these factors showed significant results, and we did not include them in the multivariable regression models to avoid overadjustment. In this study, the majority of participants did not have any chronic liver diseases (95.8%), chronic viral hepatitis (90.4%), or cirrhosis (96.1%), and over half of the participants had a low risk of significant fibrosis (F2, 70.0%). The presence of these conditions was strongly associated with the development of HCC and thus was further adjusted in multivariable analyses.

2. Clinical profile and diagnostic components of fatty liver disease

Table 2 presents the clinical profiles of participants with and without fatty liver disease, as well as their subclasses. Among the 28,749 participants, 10,131 (19.7%) were diagnosed with fatty liver disease at baseline. The prevalences of NAFLD, MAFLD, MASLD, MetALD, and ALD were 19.7%, 32.3%, 18.7%, 3.3%, and 3.5%, respectively. Individuals in the fatty liver disease and subclass groups had higher levels of blood pressure (SBP and DBP), blood lipids (total cholesterol, LDL-C, and triglycerides), and liver enzymes (AST, ALT, and GGT) than their counterparts, whereas the HDL-C concentration was lower (p < 0.001). In addition, alcohol consumption and BMI were considered in the diagnostic criteria for fatty liver subclasses. Except for NAFLD, MASLD, MetALD, and ALD, which included the daily amount of alcohol intake in their diagnosis, the proportion of non- and former drinkers was lower in the fatty liver disease and MAFLD groups (p < 0.001). Furthermore, individuals with fatty liver disease and subclasses were observed to have a higher BMI than those who did not (p < 0.001).

Among the diagnostic components of the fatty liver disease subclasses, only prediabetes and diabetes mellitus and hypertension were significantly associated with an increased risk of developing HCC (S2 Table). The HRs in the multivariable regression models were 1.76 (95% CI, 1.17 to 2.66) and 1.52 (95% CI, 1.05 to 2.22), respectively.

3. HCC risks attributed to fatty liver disease

Univariate and multivariable regression models were generated to detect associations of fatty liver disease and subclasses with HCC risks. As shown in Table 3, individuals with fatty liver disease or MAFLD had a suggestive association with the risk of HCC (HR, 1.39; 95% CI, 0.98 to 1.96 and HR, 1.40; 95% CI, 0.99 to 1.98, respectively). No significant findings were observed for other nomenclature, including NAFLD, MASLD, MetALD, and ALD. Significant results for fatty liver disease and MAFLD were found in the subgroup analyses of individuals without chronic liver disease (n=27,545), chronic viral hepatitis (n=25,989), and cirrhosis (n=27,628) (S3 Table). MAFLD was further significantly associated with HCC risk in individuals who were absent of all three chronic liver conditions (HR, 2.09; 95% CI, 1.12 to 3.89). However, findings in those without two chronic liver conditions were suggestive (S4 Table).

S5 Table summarizes the findings from the stratification analyses by FIB-4 categories. The study population consisted of 20,131 participants with FIB-4 < 1.3, 7,401 participants with FIB-4 ranging from 1.3 to < 2.67, and 402 participants with FIB-4 ≥ 2.67. Accordingly, stratification analyses were performed for individuals in the < F2 and F2 to < F3 groups only. Overall, the association between MAFLD and HCC risk was not significant among individuals with either early or advanced fibrosis.

In the sensitivity analysis of 28,650 individuals with at least 2 years of follow-up (S6 Table), as well as in the analysis restricted to 136 HCC cases identified solely by ICD-10 code C220 (S7 Table), we observed suggestive associations between MAFLD and the risk of developing HCC, while no significant associations were found for other nomenclature. The HRs for the association between MAFLD and HCC were 1.40 (95% CI, 0.95 to 2.06) and 1.39 (95% CI, 0.96 to 2.02), respectively.

The probability of individuals without HCC during follow-up after adjusting for covariates is shown in S8A-F Fig. The Kaplan-Meier curves appeared to be more discriminated between the two groups for fatty liver disease and MAFLD than those for NAFLD and MASLD. In addition, Fig. 3A-F display the cumulative incidence of developing HCC after 5, 10, 15, and 20 years from baseline. The proportions of individuals with and without MAFLD were 0.22% vs. 0.16%, 0.41% vs. 0.31%, 0.62% vs. 0.46%, and 0.89% vs. 0.66%, respectively.

Discussion

In this prospective cohort study of 36,200 Korean adults, the prevalence of NAFLD and MASLD were 19.7% and 18.7%, respectively, whereas MAFLD was observed in 32.3% of the study population at baseline. Overall, MAFLD was suggestively associated with an increased risk of HCC; however, no significant associations were observed for other nomenclature. Sensitivity analyses further supported the marginal association between MAFLD and HCC risk.

Since the introduction of NAFLD by Ludwig in 1980, there has been debate regarding the significant drawbacks of this terminology, such as overestimation of the absence of alcohol consumption in HCC development and overlooking the role of metabolic risk factors in HCC etiology [25]. In a modified Delphi consensus of 236 panelists from 56 countries, MASLD was voted to replace NAFLD, which further considers the presence of five cardiometabolic risk factors [21]. Given the same restriction of limited alcohol consumption and no other causes of hepatic steatosis, the prevalence and clinical profile were expected to be similar between NAFLD and MASLD in our study [26,27]. This expectation was based on the low proportion of nondrinkers, formal drinkers, and light alcohol consumers, and the prevalence of chronic liver disease. In contrast, MAFLD, which considers the presence of fatty liver disease with at least one condition of overweight/obesity, diabetes mellitus, or metabolic dysfunction is more inclusive in the etiology of fatty liver disease than NAFLD and MASLD. As the diagnosis of MAFLD is irrelevant to alcohol consumption and concurrent liver diseases, as expected, the prevalence of MAFLD was higher than that of NAFLD [28]. Our findings are in line with prior studies that reported a higher prevalence of MAFLD than NAFLD in East Asia (the Nationwide Health Insurance Service: 37% vs. 28%; the Shanghai Nicheng Cohort Study: 46.7% vs. 40.3% for MAFLD vs. NAFLD) [28-30]. In contrast, the opposite finding was observed in the Third National Health and Nutrition Examination Survey (MAFLD: 31.24% vs. NAFLD: 33.23%) due to the lack of data for viral hepatitis and lower proportions of metabolic abnormalities [28,31]. Overall, MAFLD may accurately capture hepatic and extrahepatic outcomes, whereas MASLD only tends to reproduce NAFLD scenarios [32].

Clinical evidence has supported the role of metabolic traits in the connection between MAFLD and HCC development [33,34]. A meta-analysis of 14 studies found a 76% increased risk of HCC cases with MAFLD (pooled relative risk, 1.76; 95% CI, 1.33 to 2.33) [35]. A nationwide prospective cohort study of Taiwanese individuals highlighted an increased risk of developing HCC among NAFLD patients with coexistence of multiple metabolic risk factors [36]. Data from 73,691 adults recruited at a tertiary hospital in Korea revealed 220 incident cases of HCC after a follow-up period of 9.9 years [37]. There was no difference in HCC risk between those with and without MAFLD in the overall cohort; however, MAFLD was linked to a higher HCC risk in individuals without chronic liver disease [37]. Our current study, which included a longer follow-up period and incorporated FLI in diagnosing fatty liver disease, may explain the different findings compared to the previous study.

Numerous studies have investigated the incidence of HCC in relation to NAFLD. Pooled analyses of 103 studies comprising almost 1 million individuals found that NAFLD was associated with a higher risk of HCC in Western countries (pooled HR, 2.62; 95% CI, 1.79 to 3.87) but not in Asian countries (pooled HR, 1.43; 95% CI, 0.94 to 2.17) [38]. Our study is consistent with the previous literature, showing a nonsignificant association between NAFLD and HCC risk. Excessive alcohol consumption has been reported to be involved in liver carcinogenesis through direct acetaldehyde toxicity and tumor promotion, production of reactive oxygen species, activation of innate immunity, cytokine, and chemokine systems, change to folate metabolism, and modulation of lipid metabolism [39]. Recently, a prospective cohort study of Danish population evaluated the new nomenclature of steatotic liver disease in individuals with a history of excessive alcohol [12]. Accordingly, an increased risk of hepatic decompensation (HR: MASLD, 4.73; MetALD, 7.69, and ALD, 10.2; p < 0.05) and all-cause mortality (HR: MASLD, 2.30; MetALD, 2.94, and ALD, 3.57; p < 0.05) was observed in a stepwise manner. In contrast, our present study identified relatively low proportions of moderate and heavy alcohol consumption, which is in line with data from 2.8 million Korean men (20-50 g/day: 10.2% and ≥ 50 g/day: 3.8%) [40]. Thus, we could not detect any significant findings for MASLD, MetALD, or ALD. Therefore, there is a need to establish clear criteria for including history of alcohol intake in the classification of steatotic liver disease in the Korean population. However, another study of 85,119 Korean adults observed significant associations of MASLD and MetALD with HCC risk [37]. Given that alcohol intake restriction was not incorporated as an exclusion criterion, MASLD was reported to have a relatively high prevalence and resulted different results from those of our present study [37,41]. However, our nonsignificant findings for MASLD were further supported by adjusting for important factors of HCC risk, such as chronic liver disease, fatty liver disease, and cirrhosis.

The Cancer Screenee Cohort Study was initiated by the NCC in Korea in 2002 to explore all potential cancer risk factors and enhance the collection of biological samples for developing effective cancer detection, diagnosis, and prevention methods. This study collected comprehensive and detailed clinical data, and evaluated multiple outcome events. Our study, for the first time, reported the prevalence of different nomenclature of fatty liver disease in a cohort and assessed the effects of fatty liver disease subclasses on incident HCC. Another strength of our study is the long follow-up period, which enabled us to detect sufficient incident HCC cases. Furthermore, the availability of data on chronic viral hepatitis and cirrhosis as well as other chronic liver diseases enabled us to control for important confounding factors that affect HCC risk.

Despite its strengths, the study has several limitations that need to be addressed. First, there are shortcomings related to the fulfillment of variable definition. Although liver biopsy remains the “gold standard” to achieve a diagnosis of fatty liver disease, it is an invasive method, and thus impractical for large-scale epidemiological studies of asymptomatic individuals [17]. Our study used abdominal sonography as a noninvasive imaging-based modality with a sensitivity of 60%-94% and a specificity of 66%-95% [17,42]. Fatty liver disease was further incorporated with the FLI, which had an area under the curve of 0.785-0.844 and has been widely accepted and validated as a noninvasive test for identifying hepatic steatosis in the Asian population [18,43-45]. We also acknowledge the current lack of FLI information for participants recruited before June 2007. In addition, since alcohol consumption data were obtained via a self-reported questionnaire, the amount of alcohol consumed may have been underestimated. There could be also a diagnostic bias of metabolic dysfunctions in MAFLD due to the lack of homeostatic model assessment for insulin resistance and high-sensitivity C-reactive protein data. As an observational study, the possibility of residual confounding, especially due to diet, medications, and genetic predisposition, cannot be ruled out. Furthermore, we studied only Korean participants who underwent health screening check-ups at the National Cancer Center, which may have caused a selection bias and limited the generalizability of our findings to the general population. Of the 54,029 participants recruited for the study, only 28,749 (53.2%) were included in the final analysis. A comparison of baseline characteristics revealed that participants included in the analysis were older and more likely to be men, both of which were associated with a higher risk of HCC than those excluded (S9 Table). While other factors, such as marital status, educational level, monthly income, smoking status, and regular exercise, also differed between the included and excluded participants, these factors were not significantly associated with HCC risk.

Second, there are limitations that may contribute to the uncertainty in the estimates. Among the 36,217 participants had no prior history of HCC at baseline, only 28,749 (79.4%) were linked to the cancer registry data, providing reliable records of HCC history and incidence. Misclassification bias may have occurred for the remaining participants, whose cancer incidence was confirmed through medical records at the NCC. Some participants might have sought care at other hospitals, leading to the underreporting of incident cases in our dataset. Additionally, undiagnosed HCC cases at the NCC transferred to other hospitals could further contribute to misclassification. In addition, we defined HCC using the ICD-10 codes C220 and C229. However, C229 represents an unclassified liver cancer, which may include conditions other than HCC, introducing additional uncertainty. To address misclassification bias, sensitivity analyses were conducted, showing marginal associations between MAFLD and HCC. Moreover, adequate follow-up duration is also crucial to capture the long-term risk of HCC from fatty liver disease and to reduce the likelihood of reverse causation. Our sensitivity analysis of individuals with at least 2 years of follow-up similarly showed marginal associations between MAFLD and HCC but nonsignificant associations for other nomenclature. These findings reinforce the evidence of a marginal association between MAFLD and HCC while highlighting the limitations of our data.

Third, limitations related to the validity and transparency of the study’s findings. Hepatic steatosis, while potentially independent of fibrosis, can also serve as a precursor to hepatic fibrosis. Traditional statistical approaches that adjust for mediators in regression models aim to estimate direct effects but may inadvertently bias the total effect by accounting for a mediator [46,47]. In this study, we were unable to confirm an interaction between fatty liver disease and FIB-4 and therefore could not determine the mediating role of FIB-4 in the relationship between fatty liver disease and HCC. Although we attempted to evaluate whether the direct effects of fatty liver disease vary by FIB-4 levels, the small number of cases in each stratum limited our ability to draw definitive conclusions.

In conclusion, within the subclasses of fatty liver disease, there was a suggestive association between MAFLD, but not NAFLD and MASLD, and HCC risk. This highlights the importance of both fatty liver and the presence of metabolic abnormalities in relation to HCC risk and suggests the modification of alcohol intake thresholds in the diagnostic criteria for NAFLD and MASLD within the Korean population.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2024-876_S1_Table.pdf

crt-2024-876_S2_Table.pdf

crt-2024-876_S3_Table.pdf

crt-2024-876_S4_Table.pdf

crt-2024-876_S5_Table.pdf

crt-2024-876_S6_Table.pdf

crt-2024-876_S7_Table.pdf

crt-2024-876_S8_Fig.pdf

crt-2024-876_S9_Table.pdf

NOTES

Ethical Statement

All participants provided written informed consent, and the study protocol was approved by the Institutional Review Board of the National Cancer Center (NCC2024-0106).

Author Contributions

Conceived and designed the analysis: Hoang T, Lee J, Kim BH, Cho Y, Kim J.

Collected the data: Lee J, Kim J.

Contributed data or analysis tools: Lee J, Kim J.

Performed the analysis: Hoang T.

Wrote the paper: Hoang T.

Critical comments of the manuscript: Lee J, Kim BH, Cho Y, Kim J.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This work was supported by the grant from National Cancer Center, Korea (2210990).

Fig. 1.

Flowchart of participant selection. BMI, body mass index.

Fig. 2.

Diagnostic criteria for fatty liver disease subclasses. WC data were not available from 2002 to 2007; lipid-lowering medication data were not available from 2002 to 2007. ALD, alcohol-related liver disease; BMI, body mass index; BP, blood pressure; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; MAFLD, metabolic dysfunction–associated fatty liver disease; MASLD, metabolic dysfunction–associated steatotic liver disease; MetALD, metabolic dysfunction–associated steatotic liver disease with increased alcohol intake; NAFLD, nonalcoholic fatty liver disease; TG, triglyceride; WC, waist circumference.

Fig. 3.

Adjusted cumulative incidence of hepatocellular carcinoma during 20 years of follow-up for fatty liver disease (A), nonalcoholic fatty liver disease (NAFLD) (B), metabolic dysfunction–associated fatty liver disease (MAFLD) (C), metabolic dysfunction–associated steatotic liver disease (MASLD) (D), metabolic dysfunction–associated steatotic liver disease with increased alcohol intake (MetALD) (E), and alcohol-related liver disease (ALD) (F).

Table 1.

Baseline characteristics and their associations with risk of hepatocellular carcinoma

Factor	Study cohort (n=28,749)	Incident cases (n=143)	Adjusted HR (95% CI)
Age (yr)	48.7±9.2	54.4±9.4
20-44	9,911 (34.5)	22 (15.4)	1.00 (reference)
45-54	11,160 (38.8)	49 (34.3)	1.22 (0.72-2.08)
≥ 55	7,678 (26.7)	72 (50.3)	2.56 (1.50-4.38)^a)
Sex
Women	12,512 (43.5)	15 (10.5)	1.00 (reference)
Men	16,237 (56.5)	128 (89.5)	5.64 (3.30-9.65)^a)
Marital status
Married, cohabitant	25,055 (87.2)	125 (87.4)	1.00 (reference)
Others	2,409 (8.4)	10 (7.0)	1.64 (0.84-3.20)
Missing	1,285 (4.5)	8 (5.6)	-
Educational level
< High school	3,742 (13.0)	29 (20.3)	1.00 (reference)
High school graduate	9,625 (33.5)	48 (33.6)	0.59 (0.36-0.96)^a)
≥ College	13,649 (47.5)	53 (37.1)	0.34 (0.21-0.56)^a)
Missing	1,733 (6.0)	13 (9.1)	-
Monthly income ($)
< 1,500	3,068 (10.7)	26 (18.2)	1.00 (reference)
1,500-3,000	7,394 (25.7)	39 (27.3)	0.69 (0.40-1.13)
≥ 3,000	14,230 (49.5)	55 (38.5)	0.57 (0.35-0.93)^a)
Missing	4,057 (14.1)	23 (16.1)	-
Smoking status
Nonsmokers	13,302 (46.3)	35 (24.5)	1.00 (reference)
Former smokers	6,383 (22.2)	46 (32.2)	1.00 (0.61-1.64)
Current smokers	7,634 (26.6)	56 (39.2)	1.58 (0.98-2.53)
Missing	1,430 (5.0)	6 (4.2)	-
Regular exercise
No	10,672 (37.1)	46 (32.2)	1.00 (reference)
Yes	15,652 (54.4)	79 (55.2)	0.86 (0.59-1.18)
Missing	2,425 (8.4)	18 (12.6)	-
Chronic liver disease
No	27,545 (95.8)	113 (79.0)	1.00 (reference)
Yes	157 (0.5)	28 (19.6)	3.77 (2.34-6.09)^a)
Missing	1,047 (3.6)	2 (1.4)	-
Chronic viral hepatitis
No	25,989 (90.4)	50 (35.0)	1.00 (reference)
Yes	1,483 (5.2)	91 (63.6)	16.9 (11.7-24.5)^a)
Missing	1,277 (4.4)	2 (1.4)	-
Cirrhosis
No	27,628 (96.1)	108 (75.5)	1.00 (reference)
Yes	74 (0.3)	33 (23.1)	7.98 (5.23-12.2)^a)
Missing	1,047 (3.6)	2 (1.4)	-
FIB-4
< 1.3	20,131 (70.0)	25 (17.5)	1.00 (reference)
≥ 1.3	7,803 (27.1)	118 (82.5)	4.49 (2.89-6.97)^a)
Missing	815 (2.8)	0	-

Values are presented as mean±standard deviation or number (%) unless otherwise indicated. Multivariable models were adjusted for age, sex, chronic liver disease, chronic viral hepatitis, cirrhosis, and FIB-4. CI, confidence interval; FIB-4, fibrosis-4; HR, hazard ratio.

^a) Significant differences.

Table 2.

Characteristics of alcohol consumption, BMI, and clinical profile of fatty liver diseases

Factor	FLD (n=28,749)		NAFLD (n=28,253)		MAFLD (n=28,727)		MASLD (n=28,056)		MetALD (n=28,321)		ALD (n=28,340)
Factor	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No
Sample size	10,131	18,618	5,555	22,698	9,293	19,434	5,258	22,798	928	27,303	979	27,361
Alcohol consumption
Non- and formal drinkers	2,617 (25.8)	6,319 (33.9)	2,422 (43.6)	6,450 (28.4)	2,270 (43.2)	6,514 (28.6)	-	8,936 (32.6)	-	8,936 (32.7)	2,617 (25.8)	6,319 (33.9)
Light drinkers	3,328 (32.8)	6,231 (33.5)	3,133 (56.4)	6,379 (28.1)	2,988 (56.8)	6,415 (28.1)	-	9,559 (34.9)	-	9,559 (34.9)	3,328 (32.8)	6,231 (33.5)
Moderate drinkers	957 (9.4)	1,419 (7.6)	-	2,376 (10.5)	-	2,376 (10.4)	928 (100)	1,428 (5.2)	-	2,376 (8.7)	957 (9.4)	1,419 (7.6)
Heavy drinkers	979 (9.7)	1,247 (6.7)	-	2,226 (9.8)	-	2,226 (9.8)	-	2,226 (8.1)	979 (100)	1,247 (4.6)	979 (9.7)	1,247 (6.7)
BMI (kg/m²)	25.9±2.7	22.8±2.6	25.8±2.7	23.4±2.9	26.0±2.6	23.4±2.9	26.2±2.5	23.8±3.0	26.2±2.5	23.8±3.0	25.9±2.7	22.8±2.6
Fasting glucose (mg/dL)	97.4±24.4	86.9±16.2	96.4±23.2	89.0±18.8	97.0±23.6	89.0±18.8	97.1±23.4	90.3±19.9	99.7±29.1	90.2±19.5	97.4±24.4	86.9±16.2
SBP (mmHg)	126.8±14.8	119.4±14.9	126.1±14.8	120.9±15.2	126.7±14.8	120.8±15.2	127.8±14.5	121.7±15.3	128.2±13.9	121.7±15.3	126.8±14.8	119.4±14.9
DBP (mmHg)	78.0±10.6	73.0±10.7	77.2±10.5	74.2±11.0	77.5±10.5	74.1±11.0	79.0±10.7	74.6±10.9	80.0±10.0	74.6±10.9	78.0±10.6	73.0±10.7
Total cholesterol (mg/dL)	209.2±37.5	196.1±34.3	209.1±37.6	198.6±35.2	209.3±37.5	198.6±35.2	210.4±37.0	200.3±35.9	211.5±38.0	200.3±35.8	209.2±37.5	196.1±34.3
Triglycerides (mg/dL)	165.3±109.3	91.3±56.9	154.1±100.4	107.4±80.3	158.8±101.6	107.3±80.1	183.4±119.7	114.6±84.5	200.0±130.7	113.9±83.2	165.3±109.3	91.3±56.9
HDL-C (mg/dL)	49.9±11.7	59.1±14.3	49.5±11.8	57.5±14.3	48.9±11.5	57.5±14.3	50.5±11.9	56.1±14.2	51.1±12.0	56.1±14.2	49.9±11.7	59.1±14.3
LDL-C (mg/dL)	134.6±33.1	121.5±30.6	135.8±33.0	123.7±31.4	136.0±32.9	123.7±31.4	133.1±32.4	125.9±32.1	132.3±32.5	125.9±32.1	134.6±33.1	121.5±30.6
AST (IU/L)	30.7±61.4	24.2±13.0	28.6±15.4	26.0±42.1	28.8±15.6	25.9±42.0	30.6±13.4	26.3±38.9	32.7±16.2	26.2±38.8	30.7±61.4	24.2±13.0
ALT (IU/L)	36.0±33.8	21.5±19.0	34.0±23.6	24.7±26.5	34.6±23.8	24.7±26.5	37.1±23.8	26.2±26.2	38.1±25.2	26.1±26.1	36.0±33.8	21.5±19.0
GGT (IU/L)	57.3±69.4	28.9±34.1	46.8±59.3	36.7±48.4	48.1±60.8	36.7±48.3	70.9±55.9	37.7±50.5	82.1±76.6	37.2±49.3	57.3±69.4	28.9±34.1

Values are presented as number (%) or mean±SD. Alcohol consumption: light drinkers (< 30 and < 20 g/day for men and women); moderate drinkers (30-60 and 20-50 g/day for men and women); heavy drinkers (> 60 and > 50 g/day for men and women). ALD, alcohol-related liver disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FLD, fatty liver disease; GGT, gamma glutamyl transferase; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein; MAFLD, metabolic dysfunction–associated fatty liver disease; MASLD, metabolic dysfunction–associated steatotic liver disease; MetALD, metabolic dysfunction–associated steatotic liver disease with increased alcohol intake; NAFLD, nonalcoholic fatty liver disease; SBP, systolic blood pressure; SD, standard deviation.

Table 3.

Associations between fatty liver diseases and hepatocellular carcinoma among participants with cancer incidence linked to cancer registry data (n=28,749)

Factor	Incident cases	Sample size	Person-years	Incidence (per 100,000 person-years)	Crude HR (95% CI)	Adjusted HR (95% CI)
Fatty liver disease
Absence	81	18,618	264,032	30.68	1.00 (reference)	1.00 (reference)
Presence	54	10,131	144,912	37.26	1.06 (0.77-1.46)	1.39 (0.98-1.96)
NAFLD
Absence	120	22,698	321,644	37.31	1.00 (reference)	1.00 (reference)
Presence	12	5,555	80,348	14.94	0.36 (0.21-0.64)	1.09 (0.58-2.05)
MAFLD
Absence	85	19,434	276,039	30.79	1.00 (reference)	1.00 (reference)
Presence	50	9,293	132,596	37.71	1.09 (0.24-1.51)	1.40 (0.99-1.98)
MASLD
Absence	118	22,798	322,756	36.56	1.00 (reference)	1.00 (reference)
Presence	13	5,258	75,854	17.14	0.42 (0.24-0.73)	1.30 (0.70-2.41)
MetALD
Absence	123	27,303	389,142	31.61	1.00 (reference)	1.00 (reference)
Presence	6	928	13,105	45.78	1.45 (0.68-3.09)	1.69 (0.78-3.66)
ALD
Absence	126	27,361	389,658	32.34	1.00 (reference)	1.00 (reference)
Presence	3	979	12,909	23.24	0.63 (0.20-1.97)	0.77 (0.24-2.44)

Multivariable models were adjusted for age, sex, chronic liver disease, chronic viral hepatitis, cirrhosis, and fibrosis-4. ALD, alcohol-related liver disease; CI, confidence interval; HR, hazard ratio; MAFLD, metabolic dysfunction–associated fatty liver disease; MASLD, metabolic dysfunction–associated steatotic liver disease; MetALD, metabolic dysfunction–associated steatotic liver disease with increased alcohol intake; NAFLD, nonalcoholic fatty liver disease.

REFERENCES

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. Article PubMed
2. Baecker A, Liu X, La Vecchia C, Zhang ZF. Worldwide incidence of hepatocellular carcinoma cases attributable to major risk factors. Eur J Cancer Prev. 2018;27:205–12. Article PubMed PMC
3. Choo SP, Tan WL, Goh BKP, Tai WM, Zhu AX. Comparison of hepatocellular carcinoma in Eastern versus Western populations. Cancer. 2016;122:3430–46. Article PubMed
4. Ahmad MI, Khan MU, Kodali S, Shetty A, Bell SM, Victor D. Hepatocellular carcinoma due to nonalcoholic fatty liver disease: current concepts and future challenges. J Hepatocell Carcinoma. 2022;9:477–96. Article PubMed PMC PDF
5. Wong VW, Ekstedt M, Wong GL, Hagstrom H. Changing epidemiology, global trends and implications for outcomes of NAFLD. J Hepatol. 2023;79:842–52. Article PubMed
6. Anstee QM, Reeves HL, Kotsiliti E, Govaere O, Heikenwalder M. From NASH to HCC: current concepts and future challenges. Nat Rev Gastroenterol Hepatol. 2019;16:411–28. Article PubMed PDF
7. Yip TC, Lee HW, Chan WK, Wong GL, Wong VW. Asian perspective on NAFLD-associated HCC. J Hepatol. 2022;76:726–34. Article PubMed
8. Ludwig J, Viggiano TR, McGill DB, Oh BJ. Nonalcoholic steatohepatitis: Mayo Clinic experiences with a hitherto unnamed disease. Mayo Clin Proc. 1980;55:434–8. Article PubMed
9. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73:202–9. PubMed
10. Eslam M, Sanyal AJ, George J; International Consensus Panel. MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158:1999–2014. Article PubMed
11. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966–86. PubMed PMC
12. Israelsen M, Torp N, Johansen S, Hansen CD, Hansen ED, Thorhauge K, et al. Validation of the new nomenclature of steatotic liver disease in patients with a history of excessive alcohol intake: an analysis of data from a prospective cohort study. Lancet Gastroenterol Hepatol. 2024;9:218–28. Article PubMed
13. Kim J. Cancer screenee cohort study of the National Cancer Center in South Korea. Epidemiol Health. 2014;36:e2014013Article PubMed PMC
14. Booth HP, Prevost AT, Gulliford MC. Epidemiology of clinical body mass index recording in an obese population in primary care: a cohort study. J Public Health (Oxf). 2013;35:67–74. Article PubMed
15. Wattacheril JJ, Abdelmalek MF, Lim JK, Sanyal AJ. AGA clinical practice update on the role of noninvasive biomarkers in the evaluation and management of nonalcoholic fatty liver disease: expert review. Gastroenterology. 2023;165:1080–8. Article PubMed
16. Xiao G, Zhu S, Xiao X, Yan L, Yang J, Wu G. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: a meta-analysis. Hepatology. 2017;66:1486–501. Article PubMed PDF
17. Schwenzer NF, Springer F, Schraml C, Stefan N, Machann J, Schick F. Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J Hepatol. 2009;51:433–45. Article PubMed
18. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33.Article PubMed PMC PDF
19. Han SK, Baik SK, Kim MY. Non-alcoholic fatty liver disease: definition and subtypes. Clin Mol Hepatol. 2023;29:S5–16. Article PubMed PMC PDF
20. Huang J, Ou W, Wang M, Singh M, Liu Y, Liu S, et al. MAFLD criteria guide the subtyping of patients with fatty liver disease. Risk Manag Healthc Policy. 2021;14:491–501. Article PubMed PMC PDF
21. Rinella ME, Sookoian S. From NAFLD to MASLD: updated naming and diagnosis criteria for fatty liver disease. J Lipid Res. 2024;65:100485.Article PubMed PMC
22. Kang MG, Lee CH, Shen C, Kim JS, Park JH. Longitudinal changes in fatty liver index are associated with risk of hepatocellular carcinoma: a nationwide cohort study in Korea. J Hepatol. 2024;80:e216–8. Article PubMed
23. Kim YR, Baek JY, Seo SH, Park H, Cho S, Shin A, et al. Operational definition of liver cancer in studies using data from the National Health Insurance Service: a systematic review. J Cancer Prev. 2023;28:47–52. Article PubMed PMC
24. Han S, Choi M, Lee B, Lee HW, Kang SH, Cho Y, et al. Accuracy of noninvasive scoring systems in assessing liver fibrosis in patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis. Gut Liver. 2022;16:952–63. Article PubMed PMC
25. Kaya E, Yilmaz Y. Epidemiology, natural history, and diagnosis of metabolic dysfunction-associated fatty liver disease: a comparative review with nonalcoholic fatty liver disease. Ther Adv Endocrinol Metab. 2022;13:20420188221139650.Article PubMed PMC PDF
26. Canivet CM, Boursier J, Loomba R. New nomenclature for nonalcoholic fatty liver disease: understanding metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction- and alcohol-associated liver disease, and their implications in clinical practice. Semin Liver Dis. 2024;44:35–42. Article PubMed
27. Younossi ZM, Paik JM, Stepanova M, Ong J, Alqahtani S, Henry L. Clinical profiles and mortality rates are similar for metabolic dysfunction-associated steatotic liver disease and non-alcoholic fatty liver disease. J Hepatol. 2024;80:694–701. Article PubMed
28. Liang Y, Chen H, Liu Y, Hou X, Wei L, Bao Y, et al. Association of MAFLD with diabetes, chronic kidney disease, and cardiovascular disease: a 4.6-year cohort study in China. J Clin Endocrinol Metab. 2022;107:88–97. Article PubMed PMC PDF
29. Lee H, Lee YH, Kim SU, Kim HC. Metabolic dysfunction-associated fatty liver disease and incident cardiovascular disease risk: a nationwide cohort study. Clin Gastroenterol Hepatol. 2021;19:2138–47. Article PubMed
30. Lee H, Lee HW, Kim SU, Chang Kim H. Metabolic dysfunction-associated fatty liver disease increases colon cancer risk: a nationwide cohort study. Clin Transl Gastroenterol. 2022;13:e00435Article PubMed PMC
31. Lin S, Huang J, Wang M, Kumar R, Liu Y, Liu S, et al. Comparison of MAFLD and NAFLD diagnostic criteria in real world. Liver Int. 2020;40:2082–9. Article PubMed PDF
32. Colaci C, Gambardella ML, Scarlata GG, Boccuto L, Colica C, Luzza F, et al. Dysmetabolic comorbidities and non-alcoholic fatty liver disease: a stairway to metabolic dysfunction-associated steatotic liver disease. Hepatoma Res. 2024;10:16.Article
33. Ghazanfar H, Javed N, Qasim A, Zacharia GS, Ghazanfar A, Jyala A, et al. Metabolic dysfunction-associated steatohepatitis and progression to hepatocellular carcinoma: a literature review. Cancers (Basel). 2024;16:1214.Article PubMed PMC
34. Chavez-Tapia NC, Murua-Beltran Gall S, Ordonez-Vazquez AL, Nuno-Lambarri N, Vidal-Cevallos P, Uribe M. Understanding the role of metabolic syndrome as a risk factor for hepatocellular carcinoma. J Hepatocell Carcinoma. 2022;9:583–93. Article PubMed PMC PDF
35. Ren H, Wang J, Gao Y, Yang F, Huang W. Metabolic syndrome and liver-related events: a systematic review and meta-analysis. BMC Endocr Disord. 2019;19:40.Article PubMed PMC PDF
36. Chen YG, Yang CW, Chung CH, Ho CL, Chen WL, Chien WC. The association between metabolic risk factors, nonalcoholic fatty liver disease, and the incidence of liver cancer: a nationwide population-based cohort study. Hepatol Int. 2022;16:807–16. Article PubMed PDF
37. Song BG, Choi SC, Goh MJ, Kang W, Sinn DH, Gwak GY, et al. Metabolic dysfunction-associated fatty liver disease and the risk of hepatocellular carcinoma. JHEP Rep. 2023;5:100810.Article PubMed PMC
38. Petrelli F, Manara M, Colombo S, De Santi G, Ghidini M, Mariani M, et al. Hepatocellular carcinoma in patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis: HCC and Steatosis or Steatohepatitis. Neoplasia. 2022;30:100809.Article PubMed PMC
39. Ganne-Carrie N, Nahon P. Hepatocellular carcinoma in the setting of alcohol-related liver disease. J Hepatol. 2019;70:284–93. Article PubMed
40. Bui TT, Han M, Luu NM, Tran TPT, Lim MK, Oh JK. Cancer risk according to alcohol consumption trajectories: a population-based cohort study of 2.8 million Korean men. J Epidemiol. 2023;33:624–32. Article PubMed PMC
41. Moon JH, Jeong S, Jang H, Koo BK, Kim W. Metabolic dysfunction-associated steatotic liver disease increases the risk of incident cardiovascular disease: a nationwide cohort study. EClinicalMedicine. 2023;65:102292.Article PubMed PMC
42. Lapadat AM, Jianu IR, Ungureanu BS, Florescu LM, Gheonea DI, Sovaila S, et al. Non-invasive imaging techniques in assessing non-alcoholic fatty liver disease: a current status of available methods. J Med Life. 2017;10:19–26. PubMed PMC
43. Nomura T, Ono M, Kobayashi K, Akaiwa Y, Ayaki M, Ogi T, et al. Validation of fatty liver index as a predictor of hepatic steatosis in Asian populations: impact of alcohol consumption and sex. Hepatol Res. 2023;53:968–77. Article PubMed
44. Lee YH, Bang H, Park YM, Bae JC, Lee BW, Kang ES, et al. Non-laboratory-based self-assessment screening score for non-alcoholic fatty liver disease: development, validation and comparison with other scores. PLoS One. 2014;9:e107584Article PubMed PMC
45. Kim JH, Kwon SY, Lee SW, Lee CH. Validation of fatty liver index and lipid accumulation product for predicting fatty liver in Korean population. Liver Int. 2011;31:1600–1. Article PubMed
46. Wang T, Li H, Su P, Yu Y, Sun X, Liu Y, et al. Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies. BMJ Open. 2017;7:e015640Article PubMed PMC
47. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42:1511–9. Article PubMed

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

Distinct Laboratory and Clinical Features of Metabolic and Alcohol-Related Liver Disease (MetALD): A Systematic Review and Meta-Analysis
Maria Tampaki, Vasileios Lekakis, Christos Chologkitas, Stergios Α. Polyzos, Evangelos Cholongitas
Current Obesity Reports.2026;[Epub] CrossRef
Dual etiology vs. MetALD: how MAFLD and MASLD address liver diseases coexistence
Shadi Zerehpooshnesfchi, Amedeo Lonardo, Jian-Gao Fan, Reda Elwakil, Tawesak Tanwandee, Munira Y. Altarrah, Necati Örmeci, Mohammed Eslam
Metabolism and Target Organ Damage.2025;[Epub] CrossRef
Comment on " posttreatment FIB-4 score change predicts hepatocellular carcinoma in chronic hepatitis C patients: Findings from the Taiwan hepatitis C registry program"
Junwei Guo, Dongsheng Huang
Journal of the Formosan Medical Association.2025;[Epub] CrossRef
MAFLD vs. MASLD: a year in review
Mingqian Jiang, Amna Subhan Butt, Ian Homer Cua, Ziyan Pan, Said A Al-Busafi, Nahum Méndez-Sánchez, Mohammed Eslam
Expert Review of Endocrinology & Metabolism.2025; 20(4): 267. CrossRef
Risk of hepatic and extrahepatic outcomes associated with metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction and alcohol-associated steatotic liver disease: a systematic review and meta-analysis
Ciro Celsa, Grazia Pennisi, Adele Tulone, Giacinta Ciancimino, Marco Vaccaro, Fabiana Pecorella, Gabriele Di Maria, Marco Enea, Federico Midiri, Alessandro Mantovani, Giovanni Targher, Aleksander Krag, Mary E Rinella, Calogero Cammà, Salvatore Petta
The Lancet Gastroenterology & Hepatology.2025; 10(11): 998. CrossRef

PubReader
ePub Link

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Evaluation of Nomenclature of Fatty Liver Disease in Association with Hepatocellular Carcinoma: A 14.5-Year Cohort Study in Korea

Cancer Res Treat. 2025;57(4):1144-1155. Published online February 11, 2025

DOI: https://doi.org/10.4143/crt.2024.876

XML Download

Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Evaluation of Nomenclature of Fatty Liver Disease in Association with Hepatocellular Carcinoma: A 14.5-Year Cohort Study in Korea

Fig. 1. Flowchart of participant selection. BMI, body mass index.

Fig. 2. Diagnostic criteria for fatty liver disease subclasses. WC data were not available from 2002 to 2007; lipid-lowering medication data were not available from 2002 to 2007. ALD, alcohol-related liver disease; BMI, body mass index; BP, blood pressure; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; MAFLD, metabolic dysfunction–associated fatty liver disease; MASLD, metabolic dysfunction–associated steatotic liver disease; MetALD, metabolic dysfunction–associated steatotic liver disease with increased alcohol intake; NAFLD, nonalcoholic fatty liver disease; TG, triglyceride; WC, waist circumference.

Fig. 3. Adjusted cumulative incidence of hepatocellular carcinoma during 20 years of follow-up for fatty liver disease (A), nonalcoholic fatty liver disease (NAFLD) (B), metabolic dysfunction–associated fatty liver disease (MAFLD) (C), metabolic dysfunction–associated steatotic liver disease (MASLD) (D), metabolic dysfunction–associated steatotic liver disease with increased alcohol intake (MetALD) (E), and alcohol-related liver disease (ALD) (F).

Fig. 1.

Fig. 2.

Fig. 3.

Evaluation of Nomenclature of Fatty Liver Disease in Association with Hepatocellular Carcinoma: A 14.5-Year Cohort Study in Korea

Factor	Study cohort (n=28,749)	Incident cases (n=143)	Adjusted HR (95% CI)
Age (yr)	48.7±9.2	54.4±9.4
20-44	9,911 (34.5)	22 (15.4)	1.00 (reference)
45-54	11,160 (38.8)	49 (34.3)	1.22 (0.72-2.08)
≥ 55	7,678 (26.7)	72 (50.3)	2.56 (1.50-4.38)^a)
Sex
Women	12,512 (43.5)	15 (10.5)	1.00 (reference)
Men	16,237 (56.5)	128 (89.5)	5.64 (3.30-9.65)^a)
Marital status
Married, cohabitant	25,055 (87.2)	125 (87.4)	1.00 (reference)
Others	2,409 (8.4)	10 (7.0)	1.64 (0.84-3.20)
Missing	1,285 (4.5)	8 (5.6)	-
Educational level
< High school	3,742 (13.0)	29 (20.3)	1.00 (reference)
High school graduate	9,625 (33.5)	48 (33.6)	0.59 (0.36-0.96)^a)
≥ College	13,649 (47.5)	53 (37.1)	0.34 (0.21-0.56)^a)
Missing	1,733 (6.0)	13 (9.1)	-
Monthly income ($)
< 1,500	3,068 (10.7)	26 (18.2)	1.00 (reference)
1,500-3,000	7,394 (25.7)	39 (27.3)	0.69 (0.40-1.13)
≥ 3,000	14,230 (49.5)	55 (38.5)	0.57 (0.35-0.93)^a)
Missing	4,057 (14.1)	23 (16.1)	-
Smoking status
Nonsmokers	13,302 (46.3)	35 (24.5)	1.00 (reference)
Former smokers	6,383 (22.2)	46 (32.2)	1.00 (0.61-1.64)
Current smokers	7,634 (26.6)	56 (39.2)	1.58 (0.98-2.53)
Missing	1,430 (5.0)	6 (4.2)	-
Regular exercise
No	10,672 (37.1)	46 (32.2)	1.00 (reference)
Yes	15,652 (54.4)	79 (55.2)	0.86 (0.59-1.18)
Missing	2,425 (8.4)	18 (12.6)	-
Chronic liver disease
No	27,545 (95.8)	113 (79.0)	1.00 (reference)
Yes	157 (0.5)	28 (19.6)	3.77 (2.34-6.09)^a)
Missing	1,047 (3.6)	2 (1.4)	-
Chronic viral hepatitis
No	25,989 (90.4)	50 (35.0)	1.00 (reference)
Yes	1,483 (5.2)	91 (63.6)	16.9 (11.7-24.5)^a)
Missing	1,277 (4.4)	2 (1.4)	-
Cirrhosis
No	27,628 (96.1)	108 (75.5)	1.00 (reference)
Yes	74 (0.3)	33 (23.1)	7.98 (5.23-12.2)^a)
Missing	1,047 (3.6)	2 (1.4)	-
FIB-4
< 1.3	20,131 (70.0)	25 (17.5)	1.00 (reference)
≥ 1.3	7,803 (27.1)	118 (82.5)	4.49 (2.89-6.97)^a)
Missing	815 (2.8)	0	-

Factor	FLD (n=28,749)		NAFLD (n=28,253)		MAFLD (n=28,727)		MASLD (n=28,056)		MetALD (n=28,321)		ALD (n=28,340)
Factor	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No
Sample size	10,131	18,618	5,555	22,698	9,293	19,434	5,258	22,798	928	27,303	979	27,361
Alcohol consumption
Non- and formal drinkers	2,617 (25.8)	6,319 (33.9)	2,422 (43.6)	6,450 (28.4)	2,270 (43.2)	6,514 (28.6)	-	8,936 (32.6)	-	8,936 (32.7)	2,617 (25.8)	6,319 (33.9)
Light drinkers	3,328 (32.8)	6,231 (33.5)	3,133 (56.4)	6,379 (28.1)	2,988 (56.8)	6,415 (28.1)	-	9,559 (34.9)	-	9,559 (34.9)	3,328 (32.8)	6,231 (33.5)
Moderate drinkers	957 (9.4)	1,419 (7.6)	-	2,376 (10.5)	-	2,376 (10.4)	928 (100)	1,428 (5.2)	-	2,376 (8.7)	957 (9.4)	1,419 (7.6)
Heavy drinkers	979 (9.7)	1,247 (6.7)	-	2,226 (9.8)	-	2,226 (9.8)	-	2,226 (8.1)	979 (100)	1,247 (4.6)	979 (9.7)	1,247 (6.7)
BMI (kg/m²)	25.9±2.7	22.8±2.6	25.8±2.7	23.4±2.9	26.0±2.6	23.4±2.9	26.2±2.5	23.8±3.0	26.2±2.5	23.8±3.0	25.9±2.7	22.8±2.6
Fasting glucose (mg/dL)	97.4±24.4	86.9±16.2	96.4±23.2	89.0±18.8	97.0±23.6	89.0±18.8	97.1±23.4	90.3±19.9	99.7±29.1	90.2±19.5	97.4±24.4	86.9±16.2
SBP (mmHg)	126.8±14.8	119.4±14.9	126.1±14.8	120.9±15.2	126.7±14.8	120.8±15.2	127.8±14.5	121.7±15.3	128.2±13.9	121.7±15.3	126.8±14.8	119.4±14.9
DBP (mmHg)	78.0±10.6	73.0±10.7	77.2±10.5	74.2±11.0	77.5±10.5	74.1±11.0	79.0±10.7	74.6±10.9	80.0±10.0	74.6±10.9	78.0±10.6	73.0±10.7
Total cholesterol (mg/dL)	209.2±37.5	196.1±34.3	209.1±37.6	198.6±35.2	209.3±37.5	198.6±35.2	210.4±37.0	200.3±35.9	211.5±38.0	200.3±35.8	209.2±37.5	196.1±34.3
Triglycerides (mg/dL)	165.3±109.3	91.3±56.9	154.1±100.4	107.4±80.3	158.8±101.6	107.3±80.1	183.4±119.7	114.6±84.5	200.0±130.7	113.9±83.2	165.3±109.3	91.3±56.9
HDL-C (mg/dL)	49.9±11.7	59.1±14.3	49.5±11.8	57.5±14.3	48.9±11.5	57.5±14.3	50.5±11.9	56.1±14.2	51.1±12.0	56.1±14.2	49.9±11.7	59.1±14.3
LDL-C (mg/dL)	134.6±33.1	121.5±30.6	135.8±33.0	123.7±31.4	136.0±32.9	123.7±31.4	133.1±32.4	125.9±32.1	132.3±32.5	125.9±32.1	134.6±33.1	121.5±30.6
AST (IU/L)	30.7±61.4	24.2±13.0	28.6±15.4	26.0±42.1	28.8±15.6	25.9±42.0	30.6±13.4	26.3±38.9	32.7±16.2	26.2±38.8	30.7±61.4	24.2±13.0
ALT (IU/L)	36.0±33.8	21.5±19.0	34.0±23.6	24.7±26.5	34.6±23.8	24.7±26.5	37.1±23.8	26.2±26.2	38.1±25.2	26.1±26.1	36.0±33.8	21.5±19.0
GGT (IU/L)	57.3±69.4	28.9±34.1	46.8±59.3	36.7±48.4	48.1±60.8	36.7±48.3	70.9±55.9	37.7±50.5	82.1±76.6	37.2±49.3	57.3±69.4	28.9±34.1

Factor	Incident cases	Sample size	Person-years	Incidence (per 100,000 person-years)	Crude HR (95% CI)	Adjusted HR (95% CI)
Fatty liver disease
Absence	81	18,618	264,032	30.68	1.00 (reference)	1.00 (reference)
Presence	54	10,131	144,912	37.26	1.06 (0.77-1.46)	1.39 (0.98-1.96)
NAFLD
Absence	120	22,698	321,644	37.31	1.00 (reference)	1.00 (reference)
Presence	12	5,555	80,348	14.94	0.36 (0.21-0.64)	1.09 (0.58-2.05)
MAFLD
Absence	85	19,434	276,039	30.79	1.00 (reference)	1.00 (reference)
Presence	50	9,293	132,596	37.71	1.09 (0.24-1.51)	1.40 (0.99-1.98)
MASLD
Absence	118	22,798	322,756	36.56	1.00 (reference)	1.00 (reference)
Presence	13	5,258	75,854	17.14	0.42 (0.24-0.73)	1.30 (0.70-2.41)
MetALD
Absence	123	27,303	389,142	31.61	1.00 (reference)	1.00 (reference)
Presence	6	928	13,105	45.78	1.45 (0.68-3.09)	1.69 (0.78-3.66)
ALD
Absence	126	27,361	389,658	32.34	1.00 (reference)	1.00 (reference)
Presence	3	979	12,909	23.24	0.63 (0.20-1.97)	0.77 (0.24-2.44)

Table 1. Baseline characteristics and their associations with risk of hepatocellular carcinoma

Significant differences.

Table 2. Characteristics of alcohol consumption, BMI, and clinical profile of fatty liver diseases

Table 3. Associations between fatty liver diseases and hepatocellular carcinoma among participants with cancer incidence linked to cancer registry data (n=28,749)