Abstract
Background & Aims
Changes in the amount of alcohol consumption among individuals with steatotic liver disease (SLD) result in shifts between the phenotypes of metabolic dysfunction-associated SLD (MASLD), MASLD with increased alcohol intake (MetALD), and alcohol-related liver disease (ALD). Yet, real-world evidence on how these shifts affect the incidence of liver-related events (LREs) remains scarce. Therefore, we aimed to evaluate the impact of these dynamic subtype shifts on the risk of LREs.
Methods
In the nationwide cohort, 2,795,409 Korean adults who completed two consecutive biennial health examinations (2009–2010 and 2011–2012) were classified as MASLD, MetALD, or ALD at both assessments. Subtype transitions were determined based on fatty liver index-defined hepatic steatosis, cardiometabolic risk factors, and alcohol consumption. Participants were retrospectively followed from 2013 to 2022 for the occurrence of LREs, ascertained through diagnostic codes. Adjusted subdistribution hazard ratios (aSHRs) were calculated using the Fine–Gray model, accounting for death as a competing event.
Results
The shift in SLD subtype from baseline to follow-up was closely aligned with corresponding changes in LRE risk. Individuals who remained in MASLD at both time points exhibited the lowest risk of composite LREs during the 9-year follow-up. In contrast, shifts from MASLD or MetALD to ALD were associated with the greatest escalation in LRE risk (MASLD-to-ALD: aSHR, 1.92 [95% CI, 1.83–2.02]; MetALD-to-ALD: aSHR, 1.27 [95% CI, 1.19–1.35]). Conversely, transitions toward MASLD were linked to substantial reductions in risk (MetALD-to-MASLD: aSHR, 0.89 [95% CI, 0.85–0.94]; ALD-to-MASLD: aSHR, 0.78 [95% CI, 0.73–0.83]).
Conclusions
Because dynamic changes in SLD subtypes strongly influence liver-related outcomes, regular reassessment of SLD classification is essential for timely and appropriate clinical management.
Impact and implications
In this large, nationwide cohort of over 2.7 million Korean adults, we demonstrated marked heterogeneity in long-term liver outcomes according to dynamic steatotic liver disease subtype trajectories, providing strong scientific justification for monitoring disease evolution rather than relying on static classifications. These findings underscore the prognostic value of both alcohol intake reduction and metabolic risk management, highlighting the importance of integrated strategies for patients with hepatic steatosis. Clinicians and researchers should recognize the need for comprehensive care models that combine behavioral interventions such as alcohol screening, counseling, and referral to addiction services with pharmacological management of metabolic risk factors. Particularly, patients with MetALD and those with ALD represent highly vulnerable subgroups, underscoring the need for tailored strategies that address both alcohol use and metabolic dysfunction.
Keywords: Steatotic liver disease, Alcohol consumption, Phenotype transition, Liver-related events
Graphical abstract
Highlights
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Shifts in drinking levels change liver disease type and future health outcomes.
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People who remained in the metabolic type with low drinking had the best outcomes.
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Heavy drinking types were linked to much higher risks of cirrhosis and liver cancer.
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Reducing drinking, even partly, lowered the risk of cirrhosis and liver cancer.
Introduction
Steatotic liver disease (SLD), which encompasses a spectrum of hepatic steatosis with varying metabolic and alcohol-related contributions, is a major chronic condition that requires systematic medical attention because of its potential to progress to severe complications, especially liver diseases.1,2 Although fatty liver disease previously was classified as either non-alcoholic fatty liver disease (NAFLD) or alcoholic liver disease, recent updates in nomenclature and diagnostic criteria have emphasized the synergistic contributions of metabolic dysfunction and alcohol use in the pathogenesis of liver disease.[3], [4], [5] The updated classification includes metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol associated liver disease (MetALD, representing cases with both moderate alcohol consumption and metabolic abnormalities), and alcohol-related liver disease (ALD).[6], [7], [8] Revised nomenclature enables more precise assessment and management of patients at risk for liver-related complications. As ongoing concerns of complications, liver-related events (LREs), including hepatocellular carcinoma (HCC), primary liver cancer (PLC), intrahepatic cholangiocarcinoma (IHC), and decompensated liver cirrhosis (DLC) are critical outcomes caused by SLD.[9], [10], [11] To investigate an association between hepatic steatosis and LREs, previous studies proposed that excessive alcohol intake and cardiometabolic risk factors (CMRFs), such as hypertension, hyperglycemia, and dyslipidemia, may independently contribute to hepatic fibrosis and cancer development.12,13 For instance, as modest alcohol consumption is associated with fibrosis progression in MASLD,14 binge drinking habit increases liver-related mortality in populations with fatty liver and comorbidities.15,16 However, most of these studies relied on outdated classifications, without accounting for dynamic transitions in subtype of SLD. Because biological mechanisms showing that alcohol and metabolic dysfunction exacerbate hepatic injury have been found, alcohol exposure accompanied by metabolic abnormalities induces LREs in the absence of intervention.17 Despite these findings, few large-scale studies have examined how transitions across SLD subtypes influence the risk of LREs.
We hypothesize that dynamic shifts across SLD subtypes (MASLD, MetALD, and ALD) are potent, independent drivers of LREs risk, and that simultaneously targeting alcohol reduction and metabolic risk control is crucial to modifying this trajectory. To test this hypothesis, we examined the association between subtype transitions and LREs risk through a nationwide repeated health screening cohort of over 2.7 million Korean adults.
Patients and methods
Data source and study population
The study population in this nationwide representative cohort study were derived from the Korea National Health Insurance Service (NHIS) cohort. The NHIS operates a compulsory health insurance program that provides medical coverage for the entire Korean population. It maintains comprehensive, individual-level data that includes sociodemographic variables, health screening records, lifestyle questionnaires, medical treatment histories, hospital utilization records, and prescription records.18 This database has been widely used in large-scale epidemiological research, and its validity has been documented in previous studies.[18], [19], [20]
A total of 2,811,780 Korean adults with MASLD, MetALD, or ALD who completed two consecutive biennial health examinations conducted by the NHIS between 2009–2010 and 2011–2012 were initially identified (NHIS-2025-01-1-032). Among 9,673,126 Korean adults who underwent two consecutive biennial health examinations between 2009 and 2012, individuals without SLD at both baseline (2009–2010; n = 6,131,894) and the follow-up examination (2011–2012; n = 729,452) were excluded. In addition, individuals with a history of LREs before 1 January 2013 (n = 16,127) or with missing data on covariates (n = 244) were excluded. The final analytic cohort, therefore, consisted of 2,795,409 participants (Fig. 1).
Fig. 1.
Flow diagram for the inclusion of the study population.
Data on 9,673,126 Korean adults who underwent two consecutive biennial health screening examinations between 2009–2010 and 2011–2012 were collected from the Korea National Health Insurance Service. The final analytic cohort was conducted after excluding individuals without SLD at baseline or follow-up examinations, those with a history of liver-related events prior to follow-up, and those with missing covariate data. ALD, alcohol-related liver disease; LRE, liver-related disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake; SLD, steatotic liver disease.
Ethical approval
This study was approved by the Institutional Review Board of Korea University Guro Hospital (Seoul, Republic of Korea; K2024-2299-001), which complies with the principles of the Declaration of Helsinki: (1) the encrypted information and no informed consent, (2) individuals cannot be identified through de-identified data of authorized institutions, and (3) raw data can only be accessible at security centers, and iv. processed results (tables and graphs) can only be taken out with approval.
Definition of SLD and its subtypes
The Fatty Liver Index (FLI) is an algorithm developed to detect fatty liver based on BMI, waist circumference, triglyceride levels, and gamma-glutamyl transferase (GGT).21 It is widely utilized as a non-invasive indicator for diagnosing hepatic steatosis in Asian populations, demonstrating an area under the receiver operating characteristic curve (AUROC) of 0.844.22 Because Asian populations tend to exhibit higher visceral adiposity and metabolic risk at lower BMI levels, the components of FLI may reflect clinically meaningful hepatic steatosis at lower values than in Western populations.23 Accordingly, recent studies have suggested that lower FLI thresholds may be appropriate for Asian populations compared with those in Western countries.[24], [25], [26] Therefore, in the present study, we operationally defined SLD as FLI ≥30, based on the cut-off value identified in a previous study that validated the utility of FLI against abdominal computed tomography in a Korean population.26 To further illustrate the distribution of hepatic steatosis in the study population, the distributions of FLI at both baseline and follow-up health examinations are presented in Fig. S1.
MASLD was defined as the presence of SLD along with at least one of five CMRFs with no or light alcohol use (≤30 g/day for men; ≤20 g/day for women).27 CMRFs were defined as follows: (1) elevated BMI (≥23 kg/m2) or waist circumference (≥90 cm for men; ≥85 cm for women), (2) elevated fasting serum glucose (≥100 mg/dl), (3) elevated blood pressure (≥130/85 mmHg), (4) elevated triglyceride levels (≥150 mg/dl), and (5) reduced high-density lipoprotein (HDL) cholesterol (≤40 mg/dl for men; ≤50 mg/dl for women). MetALD was defined as MASLD with moderate alcohol use (30–60 g/day for men; 20–50 g/day for women).27 ALD was defined in the presence of SLD as follows: (1) for individuals without any CMRFs, alcohol consumption >30 g/day for men and >20 g/day for women; and (2) for those with at least one CMRF and alcohol consumption >60 g/day for men and >50 g/day for women.28
Information on alcohol consumption was collected through two structured questionnaires assessing (1) the frequency of alcohol intake (number of drinking occasions per week, times/week) and (2) the average number of standard drinks consumed per occasion. Average daily alcohol consumption (g of ethanol/day) was calculated as:
([weekly drinking frequency × the number of drinks per occasion] ÷ 7) × 10 g of ethanol.
In accordance with a previously published study, one standard drink was defined as containing 10 g of ethanol.29
The baseline study population comprised individuals diagnosed with MASLD, MetALD, or ALD at the first health screening (2009–2010), and dynamic changes in SLD subtype between the first and second health examinations (2011–2012) were subsequently evaluated.
Definition of LREs
The primary outcomes were LREs, including PLC, HCC, IHC, liver cirrhosis, and DLC. These outcomes were defined according to the International Classification of Diseases, 10th Revision (ICD-10) codes as follows:11 (1) PLC: C22; (2) HCC: C220; (3) IHC: C221; (4) liver cirrhosis: I859, I982, K702, K703, K717, K740, K741, K742, K744, K745, K746, and K766; and (5) DLC: meeting the criteria for liver cirrhosis in addition to having at least one of the following codes: G934, I850, I983, K704, K720, K721, K729, K767, or R18.
Participants were followed from 1 January 2013, until the first occurrence of each outcome, death, or 31 January 2022, whichever came first; thus, all outcomes were analyzed using a time-to-first event framework.
The composite LRE outcome was defined as the first occurrence of any LRE, and only the earliest event was counted when multiple events occurred. Although PLC encompasses HCC and IHC, each outcome, including PLC, HCC, IHC, liver cirrhosis, and DLC, was independently analyzed as separate, outcome-specific endpoint, with no overlap between outcomes.
Key variables and statistical analysis
Categorical variables were expressed as frequencies and percentages, whereas continuous variables were summarized as means with SD or, for non-normally distributed data, as medians with IQRs. To evaluate the association between dynamic changes in SLD status and the risk of incident LREs, we used the Fine and Gray subdistribution hazard model adjusted for multiple covariates. This model calculated subdistribution hazard ratios (SHRs) and accounted for death occurring before LRE onset as the sole competing event, thereby enabling the assessment of the marginal probability of LREs.
The multivariable model was adjusted for the following covariates: age (continuous; years), sex (categorical; men and women), household income (categorical; 1st, 2nd, 3rd, and 4th quartiles), BMI (continuous; kg/m2), systolic blood pressure (continuous; mmHg), fasting serum glucose (continuous; mg/dl), total cholesterol (continuous; mg/dl, cigarette smoking (categorical; never, past, and current), moderate-to-vigorous physical activity (MVPA; categorical; no, 1–2, 3–4, and ≥5 times/week), and the Charlson Comorbidity Index (CCI; categorical; 0, 1, and ≥2). To identify independent baseline determinants of LREs in the entire cohort, we conducted an additional multivariable analysis that included alcohol consumption (continuous; 10 g/day), baseline SLD subtype (categorical; MASLD, MetALD, and ALD), and additional metabolic risk factors, including diastolic blood pressure (continuous; mmHg), triglycerides (continuous; mg/dl), and HDL cholesterol (continuous; mg/dl). In addition, the Cirrhosis Outcome Risk Estimator (CORE) score (continuous; %) was included in the model and was calculated using age, sex, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and GGT.30
Incidence rates were calculated as the number of events (n) per 1,000 person-years (PYs). Restricted cubic spline (RCS) analyses were conducted to visualize the association between alcohol consumption and the risk of LREs among individuals with ALD.
To validate the consistency of the results, multiple subgroup and sensitivity analyses were performed. Participants were stratified by age, sex, BMI, number of CMRFs, cardiometabolic conditions such as hypertension, type 2 diabetes, and dyslipidemia, smoking status, MVPA, and the CCI. Additionally, to precisely estimate the risk associated with changes in CMRF levels, participants were categorized into groups based on whether each CMRF increased or decreased between the first (2009–2010) and second (2011–2012) health examinations, and the association of these changes with the risk of LREs was analyzed. SHRs were calculated among participants whose SLD type remained unchanged between the second (2011–2012) and third (2013–2014) health screening examinations. In addition, liver fibrosis risk was assessed using the BARD (BMI, AST/ALT ratio, and diabetes mellitus) score, a non-invasive index for fibrosis assessment.31 Points were assigned as follows: 1 point for BMI ≥28 kg/m2, 2 points for an AST/ALT ratio ≥0.8, and 1 point for the presence of diabetes mellitus. Advanced fibrosis was defined as a BARD score ≥2,32 and subgroup analyses were conducted by stratifying participants into BARD <2 and BARD ≥2 groups.
In sensitivity analyses, the main analysis was repeated using an alternative definition of hepatic steatosis based on an FLI threshold of ≥60 to evaluate the robustness of the results to the choice of cut-off. LRE cases occurring within the first 1, 2, and 3 years of follow-up were excluded to minimize the inclusion of pre-existing cases. Furthermore, to assess the independent effects of the FLI and components of CMRFs, as well as to verify the robustness of the findings, additional sensitivity analyses were conducted excluding BMI, systolic blood pressure, and fasting serum glucose from the covariates.
Cumulative incidence function was generated using the R Project for Statistical Computing (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria). All data collection, data mining, and statistical analyses were conducted using SAS Enterprise Guide version 7.1 (SAS Institute Inc., Cary, NC, USA).
Results
Baseline characteristics of the study population
The study population comprised 2,795,409 individuals with a mean age of 52.0 years (SD, 12.5), of whom 77.9% (n = 2,178,726) were male (Table 1). At both the first health examination (2009–2010; 83.2%; n = 2,326,678) and the second health examination (2011–2012; 84.3%; n = 2,357,332), the majority of participants were classified as having MASLD (Table 1). Baseline characteristics across groups categorized by dynamic changes in SLD status are detailed in Table S1–3. To visually depict the dynamic transitions in SLD subtypes between baseline and follow-up examinations, a Sankey diagram is presented in Fig. 2.
Table 1.
Baseline characteristics of the study population.
| Characteristic | Participants (N = 2,795,409) |
|---|---|
| SLD status at first health screening examination (2009–10), n (%) | |
| MASLD | 2,326,678 (83.2) |
| MetALD | 325,166 (11.6) |
| ALD | 143,565 (5.1) |
| SLD status at second health screening examination (2011–12), n (%) | |
| MASLD | 2,357,332 (84.3) |
| MetALD | 307,940 (11.0) |
| ALD | 130,137 (4.7) |
| Age, years, mean (SD) | 52.0 (12.5) |
| Sex, n (%) | |
| Male | 2,178,726 (77.9) |
| Female | 616,683 (22.1) |
| Household income, n (%) | |
| 1st quartile (highest) | 1,197,368 (42.8) |
| 2nd quartile | 796,001 (28.5) |
| 3rd quartile | 423,017 (15.1) |
| 4th quartile (lowest) | 379,023 (13.6) |
| BMI, kg/m2, mean (SD) | 26.7 (2.8) |
| Waist circumference, cm, mean (SD) | 89.1 (6.8) |
| Systolic blood pressure, mmHg, mean (SD) | 128.1 (14.0) |
| Diastolic blood pressure, mmHg, mean (SD) | 80.0 (9.6) |
| Fasting serum glucose, mg/dl, mean (SD) | 105.3 (28.2) |
| Total cholesterol, mg/dl, mean (SD) | 204.5 (39.1) |
| Triglycerides, mg/dl, mean (SD) | 204.0 (136.7) |
| Alanine aminotransferase, IU/L, mean (SD) | 35.3 (29.4) |
| Aspartate aminotransferase, IU/L, mean (SD) | 30.1 (23.1) |
| GGT, IU/L, median (IQR) | 45.0 (29.0–73.0) |
| Alcohol consumption, n (%) | |
| No | 1,080,826 (38.7) |
| 1–2 times/week | 1,095,691 (39.2) |
| 3–4 times/week | 441,288 (15.8) |
| ≥5 times/week | 177,604 (6.4) |
| Smoking, n (%) | |
| Never | 1,166,118 (41.7) |
| Past | 689,121 (24.7) |
| Current | 940,170 (33.6) |
| MVPA, time/week, n (%) | |
| 0 | 1,408,218 (50.4) |
| 1–2 | 937,294 (33.5) |
| 3–4 | 322,897 (11.6) |
| ≥5 | 127,000 (4.5) |
| Charlson Comorbidity Index, n (%) | |
| 0 | 1,655,889 (59.2) |
| 1 | 740,088 (26.5) |
| ≥2 | 399,432 (14.3) |
All variables had no missing values because individuals with any missing covariate data were excluded from the study population (Fig. S1). Continuous data are presented as mean (SD) and median (IQR) if normally distributed and not normally distributed, respectively. Categorical data are expressed as the number (%). ALD, alcohol-related liver disease; GGT, gamma-glutamyl transpeptidase; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake; MVPA, moderate-to-vigorous physical activity; SLD, steatotic liver disease.
Fig. 2.
Sankey diagram illustrating transitions in steatotic liver disease subtypes between baseline and follow-up.
The heights of the colored boxes and connecting bands represent the relative numbers of participants undergoing each steatotic liver disease transition between baseline and follow-up. ALD, alcohol-related liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake.
Alcohol consumption as a primary risk factor of LREs in patients with SLD
During a total follow-up of 24,735,392 PYs, 62,949 LREs occurred among 2,795,409 participants (Fig. 3). Among individuals with MASLD at baseline, 42,673 events occurred in those who remained in MASLD, 3,604 in those who transitioned to MetALD, and 1,568 in those who transitioned to ALD. An individual’s alcohol consumption is a key determinant of SLD subtypes and represents a critical factor in their classification. In this context, the cumulative incidence of LREs increased progressively with higher levels of alcohol consumption, with the highest incidence observed in the ALD-to-ALD group (6.1/1,000 PYs) (Fig. 3). Furthermore, compared with individuals who remained in the MASLD group, those who transitioned from MASLD to ALD exhibited the highest adjusted subdistribution hazard ratio (aSHR) for LREs at 1.92 (95% CI, 1.83–2.02) (Fig. 3). Conversely, individuals who transitioned from baseline ALD to MASLD or MetALD demonstrated a reduced risk compared with those who remained in ALD, with an aSHR of 0.78 (Fig. 3). Consistent findings were observed in the subtype-specific aSHRs for LREs; notably, the aSHR for DLC was 2.56 (95% CI, 2.26–2.91) in the MASLD-to-ALD group, whereas it was 0.63 (95% CI, 0.55–0.73) in the ALD to MASLD group, highlighting the impact of changes in alcohol consumption on LRE risk (Table 2). Fig. 4 illustrates the trends in cumulative incidence rates of LREs according to dynamic changes in SLD status across groups. Table S4 presents aSHRs for the risk of LREs according to dynamic changes in SLD status, using the persistent MASLD group as the reference. The highest risk was observed in the ALD-to-ALD group with an aSHR of 2.43 (95% CI, 2.34–2.53). Consistent results were also found across LRE subtypes (Table S4).
Fig. 3.
Association between dynamic changes in SLD status and liver-related events risk.
Adjusted subdistribution hazard ratios (95% CIs) were estimated using the Fine–Gray subdistribution hazard model after adjustments for age, sex, household income, BMI, systolic blood pressure, fasting serum glucose, total cholesterol, smoking, moderate-to-vigorous physical activity, and the Charlson Comorbidity Index. Overall mortality was defined as a competing risk. Values of p were derived from the Fine–Gray model. Statistical significance was defined as p <0.05. ALD, alcohol-related liver disease; aSHR, adjusted subdistribution hazard ratio; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake; PY, person-year; SLD, steatotic liver disease.
Table 2.
SHRs for liver-related events, categorized by subtypes, according to the dynamic changes in SLD status.
| MASLD∗ |
MetALD∗ |
ALD∗ |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| MASLD (n = 2,138,130) | MetALD (n = 149,775) | ALD (n = 38,773) | MASLD (n = 170,504) | MetALD (n = 113,281) | ALD (n = 41,381) | MASLD (n = 48,698) | MetALD (n = 44,884) | ALD (n = 49,983) | ||
| Primary liver cancer | Event | 18,287 | 1,307 | 546 | 1,641 | 1,029 | 549 | 699 | 576 | 817 |
| SHR | 1.00 (ref.) | 1.16 (1.10–1.23)∗∗∗ | 1.49 (1.37–1.62)∗∗∗ | 0.95 (0.88–1.03) | 1.00 (ref.) | 1.21 (1.09–1.34)∗∗∗ | 0.84 (0.75–0.93)∗∗∗ | 0.89 (0.80–0.99)∗ | 1.00 (ref.) | |
| Hepatocellular carcinoma | Event | 12,380 | 942 | 403 | 1,157 | 769 | 408 | 507 | 442 | 623 |
| SHR | 1.00 (ref.) | 1.16 (1.09–1.24)∗∗∗ | 1.54 (1.39–1.70)∗∗∗ | 0.91 (0.83–1.00)∗ | 1.00 (ref.) | 1.20 (1.07–1.36)∗∗ | 0.80 (0.71–0.90)∗∗∗ | 0.90 (0.79–1.01) | 1.00 (ref.) | |
| Intrahepatic cholangiocarcinoma | Event | 4,453 | 297 | 121 | 361 | 207 | 114 | 161 | 97 | 163 |
| SHR | 1.00 (ref.) | 1.29 (1.14–1.45)∗∗∗ | 1.47 (1.22–1.76)∗∗∗ | 0.96 (0.81–1.15) | 1.00 (ref.) | 1.18 (0.94–1.49) | 0.94 (0.75–1.17) | 0.76 (0.60–0.98)∗ | 1.00 (ref.) | |
| Liver cirrhosis | Event | 30,876 | 2,824 | 1,276 | 3,257 | 2,407 | 1,295 | 1,566 | 1,372 | 2,222 |
| SHR | 1.00 (ref.) | 1.42 (1.37–1.48)∗∗∗ | 2.15 (2.03–2.27)∗∗∗ | 0.86 (0.82–0.91)∗∗∗ | 1.00 (ref.) | 1.28 (1.20–1.37)∗∗∗ | 0.74 (0.69–0.79)∗∗∗ | 0.76 (0.71–0.81)∗∗∗ | 1.00 (ref.) | |
| Decompensated liver cirrhosis | Event | 4,716 | 501 | 266 | 573 | 448 | 269 | 284 | 288 | 495 |
| SHR | 1.00 (ref.) | 1.64 (1.49–1.80)∗∗∗ | 2.56 (2.26–2.91)∗∗∗ | 0.84 (0.74–0.95)∗∗ | 1.00 (ref.) | 1.39 (1.20–1.62)∗∗∗ | 0.63 (0.55–0.73)∗∗∗ | 0.73 (0.63–0.85)∗∗∗ | 1.00 (ref.) | |
∗The bolded terms indicate the SLD status at baseline (2009–2010). Adjusted SHRs (95% CIs) were estimated using the Fine–Gray subdistribution hazard model after adjustments for age, sex, household income, BMI, systolic blood pressure, fasting serum glucose, total cholesterol, smoking, moderate-to-vigorous physical activity, and the Charlson Comorbidity Index. Overall mortality was defined as a competing risk. Values of p were derived from the Fine–Gray model. Levels of significance: ∗p <0.05, ∗∗p <0.01, and ∗∗∗p <0.001. ALD, alcohol-related liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake; SHR, subdistribution hazard ratio; SLD, steatotic liver disease.
Fig. 4.
Cumulative incidence function for incident liver-related events according to the dynamic change in SLD status.
(A) Participants with MASLD at baseline, (B) Participants with MetALD at baseline, and (C) Participants with ALD at baseline. Values of p were calculated across the groups with MASLD, MetALD, and ALD as baseline categories, as per the Fine–Gray test. Statistical significance was defined as p <0.05. ALD, alcohol-related liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake.
In the multivariable analysis of baseline predictive variables for subsequent LREs, older age (aSHR, 1.04), alcohol consumption (aSHR, 1.01), male sex (aSHR, 1.26), baseline MetALD (aSHR, 1.14) or ALD (aSHR, 1.30) status, current smoking (aSHR, 1.18), and a higher CCI (CCI = 1: aSHR, 1.19; CCI = ≥2: aSHR, 1.35) were independently associated with an increased risk of LREs (Fig. 5). Based on these findings, the corresponding directed acyclic graph is presented in Fig. S2.
Fig. 5.
Multivariable analysis of baseline determinants of liver-related events.
Adjusted subdistribution hazard ratios (95% CIs) were estimated using the Fine–Gray subdistribution hazard model after adjustments for age, sex, household income, BMI, systolic blood pressure, diastolic blood pressure, fasting serum glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, alcohol consumption, the Cirrhosis Outcome Risk Estimator (CORE) score, baseline steatotic liver disease subtype, smoking, moderate-to-vigorous physical activity, and the Charlson Comorbidity Index. Overall mortality was defined as a competing risk. (A) Continuous variables are presented per 1-unit increase, except for alcohol consumption which is presented per 10 g/day. (B) Categorical variables are presented relative to the respective reference category. Values of p were derived from the Fine–Gray model. Levels of significance: ∗p <0.05; ∗∗p <0.01; ∗∗∗p <0.001. ALD, alcohol-related liver disease; aSHR, adjusted subdistribution hazard ratio; CCI, Charlson Comorbidity Index; CORE, Cirrhosis Outcome Risk Estimator; DBP, diastolic blood pressure; HDL, high-density lipoprotein; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with increased alcohol intake; MVPA, moderate-to-vigorous physical activity; SBP, systolic blood pressure; SLD, steatotic liver disease.
Additionally, at baseline (2009–2010), the risk of LREs increased more significantly with each 10 g/day increment in alcohol consumption among individuals with MASLD (aSHR, 1.06; 95% CI, 1.05–1.06) compared with those with MetALD (aSHR, 1.03; 95% CI, 1.03–1.04) or ALD (aSHR, 1.03; 95% CI, 1.02–1.03) (Table S5). RCS analyses further demonstrated a consistent positive dose–response relationship between alcohol intake and LRE risk among individuals with ALD, regardless of CMRF-based ALD definitions (Fig. S3).
Main determinants of LREs remain consistent in subgroup and sensitivity analyses
Stratified analyses demonstrated consistent results regardless of various covariates (Table S6–8). Notably, compared with the MASLD-to-MASLD group, the MASLD-to-ALD group exhibited a significantly higher risk of LREs among older individuals, females, non-obese participants, those with three or more CMRFs, and individuals with hypertension, type 2 diabetes, dyslipidemia, absence of physical activity, and higher CCI scores (p for interaction <0.05; Table S6). Similar patterns were observed in sex-stratified analyses across individual LRE subtypes (Tables S9 and S10). Among CMRFs, participants with decreased HDL levels had the highest risk, with an aSHR of 2.01 (95% CI, 1.86–2.16) for the MASLD-to-ALD group compared with MASLD-to-MASLD (Table S11).
Furthermore, Table S12 presents SHRs for LRE risk among 2,414,860 individuals who maintained their SLD status between the second (2011–2012) and third (2013–2014) health examinations (Fig. S4). The aSHR was 0.78 (95% CI, 0.73–0.83) for those who transitioned from ALD to MASLD, and decreased further to 0.67 (95% CI, 0.62–0.73) when MASLD status was persistently maintained from ALD to MASLD-to-MASLD.
In the analysis stratified by BARD score, individuals who remained in the ALD category exhibited an aSHR of 1.88 (95% CI, 1.67–2.11) in the BARD <2 group, whereas the corresponding aSHR increased to 2.54 (95% CI, 2.43–2.65) in the BARD ≥2 group (Table S13). This graded increase in risk suggests that a higher baseline fibrosis burden amplifies the risk of LREs. When stratified by CORE score, the amplification of LRE risk associated with transitions to ALD was most pronounced among individuals with a CORE score >0.4% (Table S14).
Consistent findings were observed when hepatic steatosis was alternatively defined using a higher FLI threshold (≥60) (Table S15), as well as after excluding LRE cases occurring within the first 1–3 years of follow-up, and when components of the FLI and CMRFs were excluded from the covariates (Tables S16 and S17).
Discussion
In this large, nationwide cohort of over 2.7 million Korean adults with two consecutive biennial health examinations, we observed striking heterogeneity in long-term liver outcomes according to dynamic SLD subtype trajectories. Patients who remained in MASLD at both time points had the most favorable prognosis. Although transitions to ALD (regardless of whether from MASLD or MetALD) conferred the greatest risk of liver cancer (PLC, HCC, and IHC) and cirrhosis over 9 years, transitions out of ALD were associated with overall risk reductions regardless of whether individuals moved to MASLD or MetALD, underscoring the central benefit of reducing heavy alcohol exposure. Notably, progression from MASLD to MetALD was also linked to a significantly increased risk of LREs compared with persistent MASLD, indicating that even moderate alcohol intake adds prognostic risk beyond metabolic dysfunction alone. Outcome-specific analyses consistently showed lower risks among individuals transitioning from ALD to MASLD than among those transitioning to MetALD, suggesting that residual moderate alcohol consumption may continue to adversely affect certain liver-related outcomes.
The elevated risk observed among individuals transitioning to ALD, as well as the increase in aSHR per 10 g/day increment in ethanol intake, can be explained by several interrelated biological mechanisms. Chronic excessive alcohol consumption induces hepatic oxidative stress and promotes lipid oxidation,33,34 leading to mitochondrial dysfunction,35 hepatocyte apoptosis,36 and activation of hepatic stellate cells,37 thereby accelerating fibrogenesis and carcinogenesis.38,39 Alcohol also disrupts intestinal barrier function and promotes hepatic inflammation.40 In individuals with metabolic dysfunction, such as those with MetALD, these alcohol-related insults may act synergistically with insulin resistance, hyperlipidemia, and adipose tissue-derived inflammatory cytokines, further exacerbating liver injury and cirrhosis.41 There mechanisms likely underlie the markedly increased risk of LREs observed in our study, particularly among individuals transitioning from the MetALD to ALD (aSHR for LREs = 1.27).
Our findings are consistent with prior studies demonstrating that both alcohol use and CMRFs are major, modifiable drivers of liver disease progression.42,43 Even modest alcohol intake has been shown to accelerate fibrosis progression in patients with NAFLD,14 whereas heavier consumption is strongly associated with cirrhosis and HCC.44 Similar evidence indicates that occasional to moderate drinking substantially increases liver-related mortality in NAFLD populations.45 Although most prior studies were conducted in Western cohorts, our results from a large Asian population support the generalizability of alcohol-related risk across diverse ethnic and healthcare settings. Moreover, the results in our study, which identified the increased risk of LREs (aSHR = 1.35), blood pressure (+1.1 mmHg of systolic), and fasting serum glucose (+1.0 mg/dl) in MASLD-to-MetALD compared with those MASLD-to-MASLD, demonstrate that metabolic abnormalities significantly contribute to the development and progression of liver disease in the absence of excessive alcohol intake. Several biological, observational, and clinical trial studies have confirmed that metabolic abnormalities, including elevated blood pressure and serum glucose, can significantly impact the progression and severity of liver disease.46 However, previous studies did not account for dynamic changes in the subtype classifications, limiting the comparison of subtypes with changes in health behavior and comorbidities such as hyperglycemia and hypertension. Our study addresses this gap by leveraging the recently updated classification of fatty liver diseases, encompassing MASLD, MetALD, and ALD, which explicitly integrates both metabolic and alcohol-related contributions to liver disease pathogenesis. Thus, we provide clinically meaningful evidence that dynamic transitions across subtypes of SLD are associated with the incidence of LREs.
From a clinical perspective, key findings underscore the prognostic importance of dynamic SLD subtype transitions and highlight the clinical value of both alcohol reduction and metabolic risk management in patients with hepatic steatosis. Accordingly, clinical care should emphasize sustained behavioral support alongside pharmacological optimization, with a particular focus on reducing alcohol consumption. Routine alcohol screening and counseling, including motivational interviewing and referral to addiction services, may be especially beneficial for patients with ALD. Although complete abstinence may be challenging for some individuals, even modest reductions in alcohol intake were associated with meaningful risk reductions, as evidence by the lower LRE risk observed among individuals transitioning from ALD (heavy drinker) to MetALD (usually moderate drinker). In addition to behavioral interventions, pharmacologic management remains essential. Given the combined burden of alcohol exposure and metabolic dysfunction, patients with MetALD represent a key target for tailored strategies prioritizing glycemic control, lipid-lowering therapy, and blood pressure management. Ensuring adherence to antihypertensive or antidiabetic medications is particularly important in patients with hepatic steatosis, as poor adherence has been linked to greater liver disease severity.47 Sustained efforts to improve medication adherence through patient education, digital health tools, and coordinated care models should therefore be considered integral components of management.
Limitations
Several limitations should be noted. First, SLD was defined using FLI (≥30), which is derived from serum triglycerides, BMI, GGT, and waist circumference, while SLD subtypes were further classified using alcohol intake, blood pressure, and lipid profiles. Accordingly, misclassification because of measurement variability or temporal changes in these parameters cannot be entirely excluded. The use of a lower FLI threshold may also have influenced absolute estimates of SLD prevalence and limited direct comparability with Western studies that commonly apply higher cut-offs. However, lower FLI thresholds have been validated in Asian populations and are considered appropriate in population-based studies to improve sensitivity for detecting hepatic steatosis. Importantly, sensitivity analyses using a higher FLI cut-off (≥60) yielded consistent associations between dynamic SLD subtype transitions and LREs risk, indicating that our conclusions are not driven by threshold selection. Moreover, the NHIS database does not provide imaging data, such as ultrasonography or computed tomography, or liver biopsy results, precluding direct assessment of hepatic steatosis severity. As with all longitudinal cohort studies, the potential for survivorship bias should be considered. Because this cohort was constructed among individuals who survived until the index year (2013), individuals who died before baseline were not included. In addition, participants with LREs before follow-up were excluded. Notably, the proportion of individuals excluded because of pre-index LREs was higher in the ALD group (0.97%) compared with MASLD (0.56%) and MetALD (0.52%). This differential exclusion suggests that individuals with more advanced disease, particularly in the ALD group, may have been underrepresented at baseline, potentially leading to an underestimation of the true disease burden in this group. In addition, alcohol consumption and other health behaviors were assessed using self-reported questionnaires and are therefore subject to recall and social desirability bias. To mitigate this limitation, alcohol exposure was evaluated using both continuous measures and dynamic transitions across SLD subtypes, and the observed associations remained stable across multiple sensitivity analyses, supporting the robustness of our findings. Additionally, although we use fully adjusted models with many confounders (sociodemographic, comorbidities, health behaviors, and laboratory tests from the health data), residual confounding effects from unmeasured factors such as dietary intake, sleep pattern, or infection with hepatitis B/C virus may persist. Finally, as SLD subtypes and metabolic profiles may have changed continuously throughout the follow-up period (2013–2022), interim transitions or fluctuations in FLI and alcohol use could not be fully captured. Future studies should consider reassessment of drinking behaviors, changes in FLI, and additional covariates to dynamically refine risk stratification in patients with SLD.
Conclusions
In conclusion, changes in alcohol consumption habit-derived dynamic SLD trajectories are decisive predictors of LREs. Remaining in MASLD confers the most favorable prognosis, whereas transitions to MetALD or ALD markedly accelerate the progression to cirrhosis and liver cancer. These findings mandate urgent, sustained efforts to eliminate harmful alcohol use and aggressively control metabolic risk factors, with particular priority given to the highly vulnerable SLD population. Multidisciplinary, precision-based interventions are essential, not only to reverse steatotic liver changes and restore hepatic function, but also to curb the looming global epidemic of progressive fatty liver disease fueled by the dual burdens of alcohol and metabolic dysfunction.
Abbreviations
ALD, alcohol-related liver disease; ALT, alanine aminotransferase; aSHR, adjusted subdistribution hazard ratio; AST, aspartate aminotransferase; AUROC, area under the receiver operating characteristic curve; BARD, BMI, AST/ALT ratio, and diabetes mellitus; CCI, Charlson Comorbidity Index; CMRF, cardiometabolic risk factor; CORE, Cirrhosis Outcome Risk Estimator; DLC, decompensated liver cirrhosis; FLI, Fatty Liver Index; GGT, gamma-glutamyl transferase; HCC, hepatocellular carcinoma; ICD-10, International Classification of Diseases, 10th Revision; IHC, intrahepatic cholangiocarcinoma; LRE, liver-related event; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction and alcohol associated liver disease; MVPA, moderate-to-vigorous physical activity; NAFLD, non-alcoholic fatty liver disease; NHIS, National Health Insurance Service; PLC, primary liver cancer; PY, person-year; RCS, restricted cubic spline; SHR, subdistribution hazard ratio; SLD, steatotic liver disease.
Authors’ contributions
Contributed equally to this work as first authors: S. Jang, J. Song. Had full access to all of the data: S. Jang, E.S. Kang, Y. Kim, S. Jeong. Conceptualized the study and prepared the study protocol: S. Jang, J. Song, E.S. Kang, S. Jeong. Data analysis: S. Jang, J. Song, S. Jeong. Drafted the manuscript: S. Jang, J. Song, E.S. Kang, S. Jeong. Manuscript editing and approval: all authors.
Guarantor: S. Jeong. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Data availability
The original and processed data in this cohort study (NHIS-2025-01-1-032) are only accessible to qualified researchers in permitted security facilities for a certain period (from January 2025 to April 2026) since the database used was based on records of the national insurance in Korea. Thus, while the raw data cannot be shared openly, the code used in this study can be shared for non-commercial and academic purposes.
Declaration of generative AI and AI-assisted technologies in the manuscript preparation process
Generative AI (ChatGPT) was used exclusively to clarify sentences and to check grammar. All research processes, including data analysis, presentation of results (tables, figures, and supplementary materials), and manuscript writing, were entirely conducted by the authors and authored under the full responsibility of the authors.
Financial support
This work was supported by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ICAN (ICT Challenge and Advanced Network of HRD; IITP-2026-RS-2024-00438263) and -Digital Columbus Project (IITP-2026-RS-2026-25518787) grants funded by the Korea government (Ministry of Science and ICT) (IITP-2026-RS-2024-00438263) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00441029, RS-2025-00523629).
Conflicts of interest
All authors declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.
Please refer to the accompanying ICMJE disclosure forms for further details.
Acknowledgements
We sincerely appreciate the efforts of all investigators, and research staff involved in this study.
Footnotes
Author names in bold designate shared co-first authorship
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhepr.2026.101866.
Supplementary data
The following are the Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original and processed data in this cohort study (NHIS-2025-01-1-032) are only accessible to qualified researchers in permitted security facilities for a certain period (from January 2025 to April 2026) since the database used was based on records of the national insurance in Korea. Thus, while the raw data cannot be shared openly, the code used in this study can be shared for non-commercial and academic purposes.