Introduction
Nonalcoholic fatty liver disease (NAFLD) is the most common liver disorder globally with a prevalence of 25%1. The incidence has been rapidly progressing in the past several decades throughout the Asia-Pacific region in parallel with the rapid Westernization of the region2,3. Despite a significantly lower body mass index (BMI) and lower rates of obesity compared to other ethnic groups, Asians have a significant prevalence of NAFLD as well as other metabolic disorders such as hypertension, type 2 diabetes mellitus (T2DM), and metabolic syndrome (MetS)4. Extensive investigation of metabolic liver diseases with complex mechanisms is essential for diagnosis, management, and outcome prediction.
Recently, metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as a new definition for patients with fatty liver disease5. A recent meta-analysis showed that the overall prevalence of MAFLD was 38.8%. It estimated that 5.37% of lean and 29.78% of nonobese individuals had MAFLD6. The prevalence reached nearly half of the population in some regions7. The new definition was designed to avoid stigma and achieve alignment with other liver diseases, focusing on metabolic alterations of the disease and an overarching approach for disease awareness and the management of patients8. The major intent of the new nomenclature was to shift toward a diagnosis of inclusion based on the presence of metabolic dysfunction and hepatic steatosis. Therefore, clarification of the new definition according to disease outcome is essential and informative for early diagnosis and prevention efforts in addition to implementation of a region-based strategy. Nevertheless, disease severity in MAFLD has rarely been investigated in a community-based setting in the Asia-Pacific region.
Liver fibrosis is the major determinant and the significant predictor of long-term outcome in patients with NAFLD. There is a dose-dependent association between the risk of mortality and the stage of fibrosis, in which a higher risk of mortality is associated with a higher stage of fibrosis9,10. Nonalcoholic steatohepatitis patients with advanced fibrosis had a significantly higher risk of all-cause mortality and liver-related mortality compared with NASH patients without fibrosis11. Moreover, the risk of liver-related mortality increased on an exponential rather than linear scale with an increase in fibrosis stage12. Liver biopsy is an expensive invasive procedure with potential complications, a high rate of sampling error, and interobserver variability13. Recently, the noninvasive fibrosis-4 index (FIB-4) was validated to provide an accurate prediction of liver fibrosis and liver-related events in patients with NAFLD11,12. The serum-based algorithm has been adapted by major societies as a clinically useful tool for advanced fibrosis assessment14–16. Recently it has been also validated in patients with MAFLD with different BMIs17–19. Therefore, its application in a community level deserves investigation.
Taiwan is an endemic for hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, and nearly half of the adults have NAFLD. This unique background provides an excellent opportunity for elucidation of the characteristics of MAFLD and the interaction between steatosis per se and viral infections. Consequently, we conducted a community-based study aiming to elucidate the features and characteristics of MAFLD patients. We also aimed to elucidate the disease severity between different MAFLD characteristics and the impact of the prevalent viral hepatitis on the disease severity of MAFLD.
Methods
Study population
The Ethics Committee of the Kaohsiung Medical University Hospital (Kaohsiung City, Taiwan) approved this cross-sectional study before it was initiated. The recruited subjects had participated in a multipurpose integrated health examination that was part of a nonprofit community care program at 10 primary care stations in southern Taiwan between January 2009 and December 2020. Written informed consent was obtained from patients prior to enrollment, the study interview, medical record review, anthropomorphic measurements, and blood testing. Patients who had a weekly ethanol intake of more than 140 g were excluded. Anthropometric data, which included blood pressure, waist circumference, and body weight and height, were measured by standardized techniques. The enrolled subjects fasted overnight for 12 h fast before blood tests, including high-sensitivity C-reactive protein (hs-CRP), fasting plasma glucose (FPG), insulin, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), uric acid (UA), and transaminase (aspartate aminotransferase [AST]/alanine aminotransferase [ALT]) levels.
Fatty liver and fibrosis assessment
Abdominal sonography was performed for each participant by well-experienced and licensed hepatologists at the same institution to ensure interobserver consistency. The precise recruitment of patients with fatty liver was further validated by the fatty liver index (FLI) on diagnosis of fatty liver by sonography.
FLI=e0.953×loge1+e0.953×logetriglycerides+0.139×BMI+0.718×logeGGT+0.053×waist circumference−15.745×100.
FLI≥60 was used to rule in patients with hepatic steatosis20. The blood fibrosis test FIB-4 was calculated as age * AST[IU/L)]/[platelets (109/L) * ALT (IU/L)1/2].21 A cutoff of >2.67 was defined as high risk of advanced fibrosis stage.22 The risk for advanced fibrosis was classified as low (FIB-4≤1.3) and indeterminate (1.3<FIB-4≤2.67).
MAFLD definition
We defined MAFLD as the presence of metabolic risk factors in the setting of hepatic steatosis based on the diagnostic criteria proposed by an international expert panel23. MAFLD was diagnosed as the presence of hepatic steatosis with ≥1 of the followings: T2DM, overweight or obese (BMI>23 kg/m2), and the presence of at least two metabolic risk abnormalities24. The metabolic risk abnormalities included seven items: (1) blood pressure ≥130/85 mmHg or specific drug treatment; (2) waist circumference ≥90 cm for men and ≥80 cm for women; (3) fasting plasma TG≥150 mg/dL or specific drug treatment; (4) plasma HDL-C<40 mg/dL for men and <50 mg/dL for women or specific drug treatment; (5) prediabetes with FPG 100–125 mg/dL or hemoglobin A1c 5.7–6.4%; (6) homeostasis model assessment of insulin resistance (HOMA-IR) ≥2.5; and (7) plasma hs-CRP>2 mg/L. HOMA-IR was calculated as FPG (mg/dL) × fasting insulin level (µU/mL) / 405. We stratified the subjects to group A with T2DM, group B with BMI>23 kg/m2, and group C with BMI≤23 kg/m2 with at least two metabolic factors.
Statistical analysis
Pearson’s chi-square test was used to compare the differences between categorical variables, and Student’s t-test analysis/analysis of variance were performed to test differences between/among continuous variables. Logistic regression analysis was used to test the statistical significance (p<0.05) of age, sex, metabolic factors, BMI, excessive alcohol use, smoking, T2DM, and viral hepatitis markers of MAFLD by univariate or multivariate models based on clinical relevance. Two sensitivity analyses were used to validate the results by different periods and randomized assignment to testing and validation groups. The two analyses included stratification of the recruited patients by different periods and 1:1 randomization into testing and validation groups by triple bootstrap sampling. p<0.05 was considered statistically significant. Quality control procedures, database processing, and statistical analysis were performed with SAS Enterprise Guide (SAS Institute Inc., Cary, NC, USA).
Results
Patient characteristics
A total of 5,180 adults≥20 years of age participated the community screening. We excluded 3,232 adults who had missing laboratory data (hepatitis B surface antigen [HBsAg] in 74 subjects and anti-HCV in 78 subjects) or incomplete sonographic examination (3,080 subjects). Finally, a total of 1,948 community-based MAFLD patients (mean age of 51.5±13.4 years and 52.1% women) were enrolled. The patients had an FLI>60, and fatty liver was diagnosed by abdominal ultrasound. The mean BMI was 29.4±4.5 kg/m2. The presence of past history for T2DM, either diagnosed previously or under antidiabetic treatment, was 258 patients. An additional 137 patients met the diagnostic criteria of T2DM during surveillance, yielding a T2DM prevalence of 20.3% (395/1,948). The study pool included 478 (24.5%) patients with concomitant liver diseases, including 275 (14.1%) HBsAg+ patients, 183 (9.4%) anti-HCV+ patients, and 20 (1%) HBsAg+ and anti-HCV+ patients. Totally there were 395 (20.3%) patients in group A, 1,818 (93.3%) in group B, and 44 (2.3%) in group C, respectively.
Disease severity among groups and the impact of viral hepatitis infections
One hundred twenty-five (6.4%) patients had advanced fibrosis confirmed by the FIB-4 value (>2.67). Those patients were older (62.9±12.1 vs. 50.7±13.1 years of age; p<0.01), and had lower BMIs (28.0±4.4 vs. 29.5±4.5 kg/m2; p=0.001) and higher prevalence of anti-HCV+ (34.4% vs. 7.7%; p<0.01) than their counterparts. Their mean AST and ALT levels were also significantly higher than those without advanced fibrosis. In addition, they had lower TG, TC, LDL-C, and platelet levels compared with patients with FIB-4≤2.67 (Table 1). There was a significantly higher percentage of group C patients (25.0%, 11/44) with advanced fibrosis than group A (7.6%, 30/395) and group B (5.8%, 106/1,818) patients (p<0.01; Fig. 1). To determine the roles of common viral infections in MAFLD disease severity among groups, we further analyzed the results by stratifying for the presence of HBV or HCV infection. Excluding the 478 patients with concomitant viral hepatitis infections, 20.7% (6/29) of group C patients had advanced fibrosis, which was significantly higher than the proportion of group A (4.4%) and group B (3.4%) patients (p<0.01).
Table 1Baseline characteristics of the MAFLD patients
| Characteristics | Total, n=1,948 | FIB-4≤2.67, n=1,823 | FIB-4>2.67, n=125 | p-value |
|---|
| Age (years) | 51.5±13.4 | 50.7±13.1 | 62.9±12.1 | <0.01* |
| Females | 1,015 (52.1) | 949 (52.1) | 66 (52.8) | 0.87 |
| Waist circumference (cm) | 93.9±11.2 | 94.0±11.3 | 93.5±10.5 | 0.63 |
| >90 cm for male, >80 cm for female | 1,502 (77.7) | 1,409 (77.8) | 93 (76.2) | 0.69 |
| BMI (kg/m2) | 29.4±4.5 | 29.5±4.5 | 28.0±4.4 | 0.001* |
| Alcohol (n=1,572) | 406 (20.8) | 383 (21.0) | 23 (18.4) | 0.49 |
| Smoking (n=1,577) | 425 (21.8) | 402 (22.1) | 23 (18.4) | 0.34 |
| Viral infections | | | | |
| HBsAg+ | 275 (14.1) | 253 (13.9) | 22 (17.6) | <0.01* |
| Anti-HCV+ | 183 (9.4) | 140 (7.7) | 43 (34.4) | |
| Both+ | 20 (1.0) | 15 (0.8) | 5 (4.0) | |
| Hypertension | 605 (37.3) | 555 (36.4) | 50 (51.0) | <0.01* |
| Dyslipidemia | 427 (27.1) | 399 (27.0) | 28 (28.9) | 0.69 |
| TG (mg/dL) | 201.0±189.7 | 202.6±192.7 | 177.8±137.5 | 0.03* |
| TC (mg/dL) | 206.2±42.5 | 207.6±42.4 | 186.4±38.4 | <0.01* |
| HDL-C (mg/dL) | 49.6±13.1 | 49.6±12.9 | 49.6±15.5 | 0.97 |
| LDL-C (mg/dL) | 117.9±37.3 | 119.3±37.2 | 96.7±33.2 | <0.01 |
| TC/LDL-C | 1.9±1.0 | 1.9±0.8 | 2.3±2.3 | 0.09 |
| T2DM | 258 (13.2) | 223 (15.5) | 23 (23.7) | 0.03* |
| FPG (mg/dL) | 106.0±45.0 | 105.9±45.5 | 106.8±36.7 | 0.81 |
| HbA1c (%) | 6.3±1.4 | 6.3±1.4 | 6.5±1.6 | 0.42 |
| 5.7–6.4 | 424 (40.3) | 397 (40.0) | 27 (46.6) | 0.56 |
| ≥6.5 | 279 (26.5) | 264 (26.6) | 15 (25.9) | |
| HOMA-IR | 4.1±5.6 | 4.2±5.8 | 3.5±2.8 | 0.30 |
| ≥2.5 | 231 (60.6) | 219 (61.5) | 12 (48.0) | 0.18 |
| AST (U/L) | 35.3±30.0 | 31.6±16.4 | 89.8±83.8 | <0.01* |
| ALT (U/L) | 41.4±31.9 | 39.4±28.4 | 69.8±56.8 | <0.01* |
| Platelet (× 103/µL) | 265.7±77.0 | 273.6±71.9 | 150.4±53.4 | <0.01* |
Associated factors for predicting advanced fibrosis
We performed multivariate logistic regression analysis to elucidate the factors associated with advanced fibrosis in MAFLD patients. The results demonstrated that HBV/HCV coinfection was the leading factor associated with advanced fibrosis (odds ratio [OR]: 12.14, 95% confidence interval [CI]: 4.04–36.52; p<0.01). The other significant factors for predicting advanced fibrosis included HCV infection (OR: 7.87, 95% CI: 4.78–12.97; p<0.01), group C (OR: 6.00, 95% CI: 2.53–14.22; p<0.01), and TC/LDL-C (OR: 1.21, 95% CI: 1.06–1.38; p<0.01; Table 2).
Table 2Multivariate logistic regression analysis of risk factors predicting advanced fibrosis in MAFLD patients
| Factor | FIB-4
| Crude
| Adjusted
|
|---|
| ≤2.67, n=1,823 | >2.67, n=125 | OR (95% CI) | p-value | OR (95% CI) | p-value |
|---|
| Age (years) | 50.7±13.1 | 62.9±12.1 | 1.08 (1.06–1.10) | <0.01* | – | – |
| Female | 949 (93.5) | 66 (6.5) | 1.03 (0.71–1.48) | 0.87 | 1.05 (0.66–1.66) | 0.83 |
| Male | 874 (93.7) | 59 (6.3) | 1 | | 1 | |
| BMI | 29.5±4.5 | 28.0±4.4 | 0.92 (0.88–0.96) | <0.01* | – | – |
| Alcohol, Yes | 383 (94.3) | 23 (5.7) | 0.85 (0.53–1.35) | 0.49 | – | – |
| No | 1,440 (93.4) | 102 (6.6) | 1 | | – | – |
| Smoking, Yes | 402 (94.6) | 23 (5.4) | 0.80 (0.50–1.27) | 0.34 | – | – |
| No | 1,421 (93.3) | 102 (6.7) | 1 | | – | – |
| TC/LDL-C | 1.9±0.8 | 2.3±2.3 | 1.21 (1.06–1.42) | <0.01* | 1.21 (1.06–1.38) | <0.01* |
| HbA1c (%) | 6.3±1.4 | 6.5±1.6 | 1.08 (0.90–1.26) | 0.38 | – | – |
| MAFLD phenotype | | | | | | |
| BMI≤23 kg/m2 and ≥2 metabolic items | 33 (75.0) | 11 (25.0) | 5.38 (2.64–10.95) | <0.01* | 6.00 (2.53–14.22) | <0.01* |
| ≤23 kg/m2 and <2 metabolic items | 73 (91.3) | 7 (8.7) | 1.54 (0.70–4.45) | 0.28 | 0.77 (0.22–2.68) | 0.68 |
| >23 kg/m2 | 1,712 (94.2) | 106 (5.8) | 1 | | 1 | |
| T2DM | | | | | | |
| Yes | 365 (92.4) | 30 (7.6) | 1.26 (0.82–1.93) | 0.29 | 0.99 (0.56–1.72) | 0.96 |
| No | 1,457 (93.9) | 95 (6.1) | 1 | | 1 | |
| HBsAg+/anti-HCV+ | 15 (75.0) | 5 (25.0) | 8.58 (3.01–24.44) | <0.01* | 12.14 (4.04–36.52) | <0.01* |
| Anti-HCV+ | 140 (86.5) | 43 (23.5) | 7.90 (5.11–12.21) | <0.01* | 7.87 (4.78–12.97) | <0.01* |
| HBsAg+ | 253 (92.0) | 22 (8.0) | 2.24 (1.34–3.73) | <0.01* | 1.02 (0.42–2.45) | 0.97 |
| HBsAg–/anti-HCV– | 1,415 (96.3) | 55 (3.7) | 1 | | 1 | |
Validation of the associated factors for advanced fibrosis
We used two sensitivity analyses to validate the results. The first was to stratify the recruited patients by different periods. The results were consistent between the study periods of 2009–2014 and 2015–2020 (Supplementary Table 1). Group C patients, besides HCV infection and age, remained the significant factors associated with advanced fibrosis. The concordant results demonstrated that group C patients, in addition to HBV and HCV, were significant predictors of MAFLD with advanced fibrosis by triple bootstrap sampling for sensitivity analysis. (Supplementary Table 2).
Discussion
The change from NAFLD to MAFLD is more than a simple change to the nomenclature, with many clinical implications. Accordingly, the optimal approach is to elucidate the distribution and characteristics of disease severity within and between the defined components of MAFLD. Our results demonstrated that the diagnostic criteria of being overweight, namely having a BMI>23 kg/m2, was the main cause of MAFLD in a community-based cohort. Patients with advanced fibrosis were older in age and had a lower BMI, higher TC/LDL-C, higher HCV prevalence, and a different metabolic profile than their counterparts. Of note was that there were significantly more group C patients, defined as BMI≤23 kg/m2 with at least two metabolic factors, with advanced fibrosis (25.0%) than the other two groups. The observation remained significant after excluding the factor of viral hepatitis. Multivariate regression analysis showed that group C criteria were the major predictive factor of advanced fibrosis. Thus, our results provide evidence of a link between metabolic alterations and disease severity in MAFLD, and also shed light on the risk stratification and high-risk surveillance of metabolic liver disorders, at least at the community level.
Liver fibrosis is the process of formation and deposition of fibrous connective tissue and extracellular matrix leading to progressive structural tissue remodeling. It is a sequela of necroinflammation and/or cellular insults. A multinational, retrospective analysis of NAFLD patients demonstrated that long-term prognosis and survival after liver transplant depended less on a diagnosis of NASH or non-NASH than on the presence of fibrosis, indicating that fibrosis is the major determinant for long-term outcomes in NAFLD patients9. Generally, fibrosis measurement is essential for determination of the disease course and outcome of viral hepatitis infection, before and after viral eradication or sufficient suppression. However, fibrosis measurement is much more vital in the context of MAFLD as there is no reliable surrogate biomarker for this complex metabolic disorder.
Several noninvasive tests to assess liver fibrosis have been developed. The FIB-4 index is a simple and easy-to-access test index with high predictive performance and reproducibility. A score of >2.67 was defined as advanced-stage fibrosis and allowed avoiding liver biopsy examination25. Recent practice guidelines recommend FIB-4 as the initial noninvasive test for risk stratification in NAFLD patients based on metabolic risk factors owing to its simplicity and ease of use25,26. However, the use of the FIB-4 index for fibrosis assessment has rarely been investigated in MAFLD in a community-based study. Our results demonstrated that a low proportion (6.4%) of MAFLD patients had advanced fibrosis as assessed by the FIB-4 index. There was a significantly higher proportion of patients with advanced fibrosis in group C than their counterpart groups. The high proportion remained significant even after excluding the factor of viral hepatitis infection. The results were validated and confirmed by sensitivity analysis using period stratification and triple bootstrap sampling. The observation was in accord with previous studies showing that metabolic abnormality is the key driver of fatty liver disease, irrespective of BMI27,28. Previous studies have indicated that patients with lean NAFLD had a lower prevalence of T2DM, hypertension, dyslipidemia, and MetS but higher fibrosis scores than their non-lean counterparts29–31. Our results suggest the benefits of high-risk surveillance of advanced fibrosis in MAFLD on group C patients, at least in a community-based approach. However, discordant results from a recent Asian study showed that the prevalence of advanced fibrosis in MAFLD was higher in group B (9.5%) than group C (3.1%) patients based on magnetic resonance elastography32. The discrepancy might be attributed to the differences in patient selection, the diagnostic methods for initial recruitment, fibrosis assessment tools, genetic predispositions, or racial difference31,33,34. Collaborative longitudinal studies across different regions and races with a uniform study design may be informative.
Recent studies consistently demonstrated that MAFLD and NAFLD do not define the same condition and should not be regarded as synonymous despite the many overlaps between the two nomenclatures35. Nonetheless, the new definition of MAFLD opened a wide scope for addressing the mutual impact between steatosis and viral hepatitis infection. As anticipated, age and viral infections were the major risk factors associated with advanced fibrosis. Liver steatosis is a common phenomenon in community health center patients. It is estimated that one-third of those patients have steatosis that differs in extent, possibly because of changes in host metabolism or infection with HCV genotype 3. Our results are consistent with previous studies showing a link between HCV infection and steatosis36,37. By contrast, the link between HBV and steatosis remains unknown. Our recent study showed that steatosis-chronic hepatitis B patients had a lower 10-year cumulative rate of cirrhosis and hepatocellular carcinoma, and a higher HBsAg seroclearance rate than their nonsteatosis counterparts38. HBsAg seropositivity was associated with a lower risk of developing NAFLD in a large-scale Asian study, suggesting a possible effect of HBV infection on the pathogenesis of NAFLD development39. A future longitudinal cohort study is needed to clarify the issue of viral-metabolic interaction from genetic to epigenetic aspects. Although the new definition is a steatosis-centered diagnosis, our study suggests the importance of surveillance for viral hepatitis in MAFLD patients.
Our study had some limitations. First, the cross-sectional design did not provide sufficient information regarding the changes in disease severity assessment in a longitudinal manner. A call-back follow-up study will be informative of differences in long-term outcome. Second, we did not use other noninvasive methods such as imaging-based modalities for measurement validation and the potential discordance between sonography and FLI. Nevertheless, the enrolled MAFLD patients were assessed by both abdominal ultrasound and FLI prior to FIB-4 evaluation for disease severity. FLI is an acceptable alternative for the diagnosis of steatosis whenever imaging tools are not available or feasible40. The stringent diagnosis could have decreased potential bias because MAFLD is a heterogenous and complex disorder. Third, the main clinical utility of FIB-4 in NAFLD patients lies in the ability to exclude, but not identify, advanced fibrosis41. Therefore, patients with indeterminate FIB-4 value might have advanced fibrosis42. Further validation study with histopathological approach will be helpful to improve the limitation. Lastly, we did not analyze the potential therapeutic effects of antidiabetes drugs and lifestyle modifications as this was a cross-sectional study. Currently there is no approved drug for the amelioration of fibrogenesis in MAFLD, which might greatly decrease potential bias in this aspect.
In conclusion, the study described the characteristics of disease severity in a community-based setting. Viral hepatitis infection was the major factor contributing to the occurrence of advanced fibrosis. A significantly higher proportion of lean patients with metabolic abnormalities had advanced fibrosis than their counterparts. This observation remained significant after excluding viral hepatitis infection. Further longitudinal studies are needed for risk stratification and precision prevention.
Supporting information
Supplementary Table 1
Multivariate logistic regression analysis of risk factors predicting advanced fibrosis in MAFLD patients between the study periods of 2009–2014 and 2015–2020.
(DOCX)
Supplementary Table 2
Multivariate logistic regression analysis of risk factors predicting advanced fibrosis in MAFLD patients by 1:1 randomization into testing and validation groups.
(DOCX)
Abbreviations
- ALT:
alanine transaminase
- AST:
aspartate transaminase
- BMI:
body mass index
- CI:
confidence interval
- FIB-4:
fibrosis-4 index
- FLI:
fatty liver index
- FPG:
fasting plasma glucose
- HBV:
hepatitis B virus
- HCV:
hepatitis C virus
- HDL-C:
high-density lipoprotein cholesterol
- hs-CRP:
high-sensitivity C-reactive protein
- LDL-C:
low-density lipoprotein cholesterol
- MAFLD:
metabolic dysfunction-associated fatty liver disease
- MetS:
metabolic syndrome
- NAFLD:
nonalcoholic fatty liver disease
- NASH:
nonalcoholic steatohepatitis
- OR:
odds ratio
- T2DM:
type 2 diabetes mellitus
- TC:
total cholesterol
- TG:
triglycerides
- UA:
uric acid
Declarations
Acknowledgement
The authors are grateful for secretarial help from the Taiwan Liver Research Foundation (TLRF) and Fatty Liver Special Interest Group of Taiwan Association for the Study of the Liver (TASL). Neither the Foundation nor the Association influenced how the study was conducted or the approval of the manuscript. The study was presented in part (FRI118) at the 2022 The Internal Liver Congress of The European Association for the Study of the Liver (EASL), June 22–26, 2022.
Ethical statement
The trial was conducted in compliance with the Declaration of Helsinki and the Good Clinical Practice guidelines of the International Conference on Harmonization. The study has been approved by the Institutional Review Board, Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20210355).
Data sharing statement
All data relevant to the study are included in the article. Inquiries regarding the datasets used and/or analyzed during the current study can be directed to the corresponding authors.
Funding
This study was supported in part by grants from The Ministry of Science and Technology, Taiwan (MOST 110-2314-B-03-073-MY3), The Ministry of Health and Welfare, Taiwan (MOHW, 112-TDU-B-221-124007), National Yang Ming Chiao Tung University-Kaohsiung Medical University Joint Research Project (NYCU-KMU-111-I001, NYCU-KMU-112-I001), and Kaohsiung Medical University Hospital (KMUH SA10907, KMUH110-0R05).
Conflict of interest
Jee-Fu Huang: Consultant of Roche, BMS, Gilead, Merck, Sysmex, Pharmaessential, Polaris, Aligos, and Instylla. Speaker for Abbvie, BMS, Gilead, Merck, Sysmex, and Roche. Editorial board member of Journal of Clinical and Translational Hepatology since 2022. Chia-Yen Dai: Consultant of Abbvie and Roche; Speaker for Abbvie, Gilead, and Roche. Chung-Feng Huang: Speaker for Abbvie, BMS, Bayer, Gilead, Merck, and Roche. Ming-Lung Yu: Research grant from Abbott, BMS, Merck, and Gilead; Consultant of Abbvie, Abbott, Ascletis, BMS, Merck, Gilead, and Roche; Speaker for Abbvie, Abbott, BMS, Merck, Gilead, and IPSEN. Editorial board member of Journal of Clinical and Translational Hepatology since 2023. Wan-Long Chuang: Consultant of Gilead, AbbVie, BMS, PharmaEssentia, and Aligos; Speaker for Gilead, AbbVie, BMS, and PharmaEssentia. Editorial board member of Journal of Clinical and Translational Hepatology since 2022. The other authors have no conflict of interests related to this publication.
Authors’ contributions
Conception and design: JFH, MLY, MLY, CFH, MHL, WLC. Acquisition of data: JFH, CYD, CFH, MLY, CIH, MHH, YHL, JFY, MJB, PYH, CWW, YJW, PCL, YHL, TYJ, ZYL. Data analysis and interpretation: JFH, PCT, CIH, CFH, WLC, MLY. Manuscript drafting and critical revising: JFH, PCT, MLY, MLY, WLC. The corresponding authors attest that all listed authors meet authorship criteria. All authors provided critical review and approved the final version.