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Original Article
Trends in Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease: A Nationwide Survey in Korea
Junhyun Kwon1orcid, Hanbit Shin2orcid, Dae Ho Lee3orcid, Eunji Kim1,2orcid

DOI: https://doi.org/10.3803/EnM.2025.2578
Published online: December 24, 2025

1Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea

2Artificial Intelligence and Big-Data Convergence Center, Gachon University Gil Medical Center, Incheon, Korea

3Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea

Corresponding authors: Eunji Kim. Department of Preventive Medicine, Gachon University College of Medicine, 21 Namdong-daero 774beon-gil, Namdong-gu, Incheon 21565, Korea Tel: +82-32-458-2609, Fax: +82-508-9606-3825, E-mail: eunjikim@gachon.ac.kr
Dae Ho Lee. Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, 21 Namdong-daero 774beon-gil, Namdong-gu, Incheon 21565, Korea Tel: +82-32-458-2733, Fax: +82-32-468-5836, E-mail: drhormone@naver.com
• Received: July 24, 2025   • Revised: September 9, 2025   • Accepted: October 15, 2025

Copyright © 2025 Korean Endocrine Society

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.

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  • Background
    The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD), which is associated with higher risks of obesity, type 2 diabetes mellitus, cardiovascular disease, chronic kidney disease, and extrahepatic cancer, is increasing. This study aimed to examine the temporal trends in MASLD prevalence among Korean adults, with a focus on age-stratified patterns and related health conditions.
  • Methods
    This secondary analysis of Korea National Health and Nutrition Examination Survey data included data of 90,912 adults aged ≥19 years from 2007 to 2023. MASLD was defined as having the presence of at least one cardiometabolic risk factor and no heavy alcohol consumption. Temporal trends in MASLD prevalence and associated cardiometabolic risk factors were examined across age groups using weighted prevalence estimates and log-linear regression models.
  • Results
    The overall prevalence of MASLD increased from 25.0% in 2007–2009 to 31.0% in 2022–2023 (annual percent change [APC], 1.36%). The most substantial increase was observed in young adults aged 19–44 years (22.3% to 30.5%; APC=2.09%), particularly among men. Middle-aged (45–64 years) and older (≥65 years) adults showed relatively stable or modest increases over time. Among young adults with MASLD, chronic kidney disease prevalence increased from 2.1% to 5.7%.
  • Conclusion
    This nationwide study revealed a significant and continuous increase in MASLD prevalence among Korean adults, particularly in younger age groups. The disproportionate burden in these populations, along with high rates of metabolic comorbidities, underscores an emerging public health concern that may place considerable strain on future healthcare systems.
  • Keywords: Metabolic dysfunction-associated steatotic liver disease; Non-alcoholic fatty liver disease; Epidemiology; Liver diseases; Hepatic steatosis index
Metabolic dysfunction-associated steatotic liver disease (MASLD) has replaced the traditional term non-alcoholic fatty liver disease (NAFLD) to reflect the central role of metabolic dysfunction in its pathogenesis more accurately. Although NAFLD is defined by the absence of significant alcohol consumption [1], MASLD is defined by the presence of hepatic steatosis in conjunction with at least one cardiometabolic risk factor, thereby emphasizing the link between metabolic syndrome and fatty liver disease [2–4].
The global prevalence of MASLD among adults is approximately 30% [5], with a higher incidence observed in men than in women [6]. Notably, the prevalence of MASLD continues to increase among patients with type 2 diabetes mellitus (T2DM), and a significant proportion of these patients also present with hepatic fibrosis [7]. Recent Korean studies have reported prevalence rates ranging from approximately 19% to over 40% [8–11], based on variations in the study population and diagnostic criteria.
MASLD begins with simple steatosis but can progress to more severe liver diseases, including cirrhosis, liver failure, and hepatocellular carcinoma. Additionally, it is also associated with higher risks of obesity, T2DM, cardiovascular disease (CVD), chronic kidney disease (CKD), and extrahepatic cancer, contributing to its growing global burden [12–15]. As metabolic dysfunction becomes increasingly prevalent among younger individuals [12,16], understanding how MASLD prevalence differs across age groups is essential for predicting the future disease burden and developing targeted prevention strategies. Accordingly, clarifying age-stratified patterns and their relationship with cardiometabolic profiles is becoming increasingly important in clinical and public health settings.
Therefore, this study aimed to investigate the epidemiological trends of MASLD by analyzing the temporal changes in its prevalence across different age groups using nationally representative data. In addition, by evaluating the concurrent trends in cardiometabolic risk factors and related diseases, this study sought to provide a broader understanding of the evolving burden of MASLD in Korea.
Data source and study population
This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES), which provides repeated cross-sectional, nationally representative health examination data for South Korea and is conducted annually by the Korea Disease Control and Prevention Agency. It assesses the health and nutritional status of the Korean population using a comprehensive approach combining structured interviews, physical examinations, and laboratory tests.
We used the KNHANES data from 2007 to 2023, grouped by survey waves to ensure an adequate sample size per period: wave 4 (2007–2009), wave 5 (2010–2012), wave 6 (2013–2015), wave 7 (2016–2018), wave 8 (2019–2021), and wave 9 (2022–2023). The data from each wave were analyzed with appropriate sampling weights to produce weighted prevalence estimates that reflected the characteristics of the Korean population during that period. Grouping by 3-year waves (2 years for wave 9, due to data availability only through 2023) improved the stability of the estimates and aligned them with the reporting practice of KNHANES.
Among 133,375 adults aged ≥19 years who participated in the KNHANES across six survey waves (22,229 participants per wave on an average), we included individuals with complete and valid data required for MASLD assessment. After excluding individuals aged <19 years and those with missing or incomplete values on educational level, MASLD status, or hepatic steatosis index (HSI), a total of 90,912 adults were included in the final study population (Supplemental Fig. S1). The KNHANES was approved by the Institutional Review Board (IRB) of the KDCA for each survey cycle as follows: wave 4 (2007–2009) approval IRB no. 2007-02CON-04-P, 2008-04EXP-01-C, and 2009-01CON-03-2C; wave 5 (2010–2012) approval IRB no. 2010-02CON-21-C, 2011-02CON-06-C, and 2012-01EXP-01-2C; wave 6 (2013–2015) approval IRB no. 2013-07CON-03-4C, and 2013-12EXP-03-5C; wave 7 (2016–2018) approval IRB no. 2018-01-03-P-A; wave 8 (2019–2021) approval IRB no. 2018-01-03-C-A, 2018-01-03-2C-A, and 2018-01-03-5C-A; wave 9 (2022–2023) approval IRB no. 2018-01-03-4C-A, and 2022-11-16-R-A. All participants provided written informed consent prior to their participation in the survey.
Definition of MASLD
We operationally defined MASLD in this epidemiologic context based on surrogate markers: (1) HSI ≥33.9 to indicate the presence of fatty liver; (2) the presence of ≥1 out of five specified cardiometabolic risk factors; and (3) no heavy alcohol consumption above standard cutoffs (men drinking ≤30 g/day and women drinking ≤20 g/day of alcohol) (Supplemental Fig. S2). This approach mirrors the MASLD criteria by including both steatosis and metabolic dysfunction, as described in Appendix 1 (SAS code for HSI and cardiometabolic risk factors definition).
The HSI is a validated noninvasive index calculated from alanine aminotransferase (ALT) level, aspartate aminotransferase (AST) level, body mass index (BMI), sex, and diabetes status. An HSI value of approximately ≥36 is commonly used as a cutoff for NAFLD, offering a high specificity (approximately 92%) for detecting fatty liver [17]. We chose 33.9 as the threshold for this analysis, which is equivalent to flagging steatosis [18]. We also observed consistent prevalence trends when applying alternative cutoff points (≥30 and ≥36) (Supplemental Table S1). Individuals meeting the HSI criteria were classified as having MASLD if they had at least one of the following five metabolic risk factors: overweight or obesity, high plasma triglyceride (TG), low high-density lipoprotein cholesterol (HDL-C), high blood pressure (BP), and dysglycemia or T2DM. Additionally, we applied the standard alcohol use exclusion criteria used in the NAFLD definitions.
Definition of cardiometabolic risk factors and other indicators
Cardiometabolic risk factors were defined based on established clinical guidelines and previous epidemiological studies that utilized the KNHANES data. Overweight or obesity was defined as a BMI of ≥23.0 kg/m2, according to the Asia-Pacific classification proposed by the World Health Organization [19]. A high TG level was identified as a serum TG level of ≥150 mg/dL or the current use of lipid-lowering medications. A low HDL-C level was defined as a level <40 mg/dL in men or <50 mg/dL in women or the use of lipid-modifying treatment. High BP was determined by a systolic BP ≥130 mm Hg, diastolic BP ≥85 mm Hg, or use of antihypertensive medication. Dysglycemia or T2DM was defined as a fasting plasma glucose (FPG) level ≥100 mg/dL or a self-reported physician diagnosis of T2DM. Participants were classified as having cardiometabolic dysfunction if they met one or more of the five criteria from among overweight/obesity, high TG, low HDL-C, high BP, or dysglycemia/T2DM. CKD was defined as the presence of kidney failure, a urine protein level of 1+ or higher, or an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. eGFR was calculated using Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation that was calibrated to isotope dilution mass spectrometry standards (Supplemental Table S2).
General demographic variables such as age, sex, and education level were included as other indicators. Age was treated as a continuous variable and categorized into three groups for the subgroup analysis: 19–44 years (young adults), 45–64 years (middle-aged), and ≥65 years (older adults). Sex was recorded as male or female. Educational level was self-reported and was classified into three groups: middle school or lower, high school graduate, and college or higher.
Statistical analysis
Descriptive statistics were used to summarize the general characteristics of the study population. For continuous variables, weighted means and 95% confidence intervals were presented, whereas categorical variables were reported as weighted percentages.
Each variable was evaluated using a survey wave to examine changes in sociodemographic and metabolic characteristics over time. P values for the linear trends were reported to determine whether each characteristic exhibited a statistically significant temporal shift across the KNHANES waves.
The prevalence of MASLD was estimated for each survey cycle, and trends over time were assessed using weighted prevalence rates, with survey waves treated as ordinal variables. To quantify the average rate of change in MASLD prevalence over time, the annual percent change (APC) and corresponding P values were estimated using a log-linear regression model in which the survey year was treated as a continuous variable. Subgroup analyses were also conducted to evaluate disparities in MASLD prevalence and associated cardiometabolic risk factors. Additionally, logistic regression was used to assess the association between MASLD and cardiometabolic risk factors or CKD by comparing individuals with MASLD to those without steatotic liver disease.
All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria). The complex sampling design of the KNHANES, including sampling weights, stratification, and clustering, was accounted for in all the analyses using appropriate survey procedures.
General characteristics of the study population
Table 1 presents the general characteristics of the total study population from 2007 to 2023 across the KNHANES waves. The mean age of the participants increased over time, from 44.2 years in wave 4 (2007–2009) to 48.6 years in wave 9 (2022–2023). The sex distribution remained stable across waves, with approximately equal proportions of male and female participants. The proportion of individuals with lower educational levels (middle school or lower) decreased from 30.2% in wave 4 to 18.1% in wave 9, indicating an overall increase in educational attainment over time. In addition, the proportion of those with a college education or higher increased from 29.7% to 46.7% during the same period.
The metabolic health indicators showed notable temporal trends. The mean BMI increased slightly from 23.6 kg/m2 in wave 4 to 24.2 kg/m2 in wave 9, whereas the mean FPG increased from 96.2 to 100.0 mg/dL, respectively. In contrast, the mean HDL-C levels improved modestly from 48.2 to 57.0 mg/dL over the study period. All trends showed statistically significant linear changes (P for-trend <0.0001).
Compared with individuals without steatotic liver disease, participants with MASLD tended to be older, were more likely to be male, and more frequently had lower educational attainment. Moreover, individuals with MASLD consistently exhibited higher mean BMI, FPG, and BP, along with lower HDL-C levels across all survey periods. These differences were statistically significant across waves, underscoring the distinct cardiometabolic profiles of the MASLD population (Supplemental Tables S3, S4).
Prevalence of MASLD over time
Fig. 1 illustrates the temporal trends in the weighted prevalence of MASLD, defined by HSI ≥33.9 in the presence of at least one cardiometabolic risk factor and below-threshold alcohol consumption. The overall prevalence increased from 25.0% in 2007–2009 to 31.0% in 2022–2023, showing a significant upward trend over the 16-year study period (APC=1.36%, P for-trend=0.009). Additional analyses examining age-standardized prevalence and alcohol consumption–stratified prevalence are provided in Supplemental Tables S5 and S6.
The prevalence of MASLD showed distinct temporal patterns across age groups over the 16-year period (Fig. 2). Among individuals aged 19–44 years, the overall prevalence increased markedly from 22.3% in 2007–2009 to 30.5% in 2022–2023, with a significant APC of 2.09% (P for-trend=0.004). This upward trend was particularly pronounced in men (APC=2.51%, P for-trend <0.001), whereas the increase in women (APC= 1.22%, P for-trend=0.295) was not statistically significant. In the 45–64 year group, the prevalence remained consistently higher than that in younger adults, ranging from 29.4% to 32.2%. However, the overall trend was not statistically significant (APC=0.44%, P for-trend=0.237), with men exhibiting a slight increase (APC=1.54%, P for-trend=0.006) and women showing a small decrease (APC=–0.49%, P for-trend=0.513). In contrast, among adults aged ≥65 years, the overall prevalence remained relatively stable over the study period, ranging from 25.3% to 29.5%, with no statistically significant trend observed (APC=0.95%, P for-trend=0.143). Notably, although the prevalence in men increased steadily (APC=2.76%, P for-trend= 0.020), the prevalence in women remained mostly unchanged (APC=0.32%, P for-trend=0.69) and declined during certain intervals.
Cardiometabolic and renal profiles by MASLD status
Fig. 3 compares the prevalence of the five cardiometabolic risk factors and CKD across the total population, MASLD group, and non-steatotic group. Individuals with MASLD consistently showed markedly higher odds and prevalence of all cardiometabolic risk factors and CKD compared with those without steatosis (Supplemental Table S7). In wave 9 (2022–2023), 97.5% of the MASLD group was overweight or obese, 56.3% had dysglycemia or T2DM, 55.3% had high TG levels, 49.2% had low HDL-C levels, and 40.4% had high BP. In contrast, the non-steatotic group had a substantially lower prevalence of each risk factor throughout the study period.
Age-specific patterns of cardiometabolic risk and CKD in MASLD
Fig. 4 shows the age-specific prevalence rates of cardiometabolic risk factors and CKD between 2007 and 2023 among the MASLD cases. Among young adults (19–44 years), the prevalence of CKD increased steadily from 2.0% in 2007–2009 to 5.7% in 2022–2023. Dysglycemia or T2DM in this group also showed a transient increase followed by a decline, whereas high BP remained consistently low (<20%) throughout the study period. Dysglycemia or T2DM increased sharply among middle-aged and older adults, peaking at 80.9% in the 45–64 years age group and 89.6% in the age ≥65 years group during 2019–2021, before a slight decline in the final wave.
High TG and low HDL-C levels showed age-dependent divergence; both indicators remained the highest and steadily increased in older age groups, whereas they showing mild fluctuations or declining trends in younger adults. High BP was consistently more common in the older age groups (65%–77%), with a relatively stable prevalence over time, whereas it remained <20% in adults aged 19–44 years. Overweight or obesity remained consistently high across all age groups with minimal variation over time.
This study provides a comprehensive assessment of the temporal trends in MASLD prevalence across age groups in the Korean adult population using nationally representative data from 2007 to 2023. The overall prevalence of MASLD showed a significant upward trajectory over the 16-year study period, increasing from 25.0% to 31.0%. This trend parallels the concurrent increase in key metabolic indicators including BMI and FPG levels, reflecting a broader deterioration in metabolic health at the population level. These findings are consistent with global epidemiological patterns, particularly in countries undergoing rapid lifestyle changes and showing increased rates of obesity and T2DM [20–22]. In Korea, the upward trajectory of MASLD prevalence aligns with the mounting evidence of increasing metabolic dysfunction, particularly among younger adults [16,23,24].
The age-specific analysis revealed a noteworthy divergence in temporal trends, with a significant increase observed among young adults aged 19–44 years. The prevalence in this group increased from 22.3% to 30.5%, with an APC of 2.09%. This increase was particularly marked among young men, highlighting the potential shift in the metabolic disease burden in younger male populations. This may increase the risk of developing major chronic diseases, including CVD, CKD, and other related complications, through long-term cumulative exposure to metabolic stress [25,26].
A notable finding was the concurrent increase in CKD among young adults with MASLD, with the CKD prevalence in this group increasing from 2.1% to 5.7% over the study period. This highlights the silent yet progressive burden of renal impairment that may accompany subclinical metabolic dysfunction in younger populations. Patients with both MASLD and CKD are known to have CVD risk, which may increase risks for fatal outcomes [25,27,28].
Given this increasing comorbidity burden among younger individuals, it highlights the need for age-specific management strategies and early detection that prioritize timely intervention and comprehensive metabolic risk assessment. In contrast to traditional approaches that have focused primarily on older adults, future clinical practice guidelines may need to incorporate more proactive screening and preventive measures for younger populations, particularly for young men who demonstrated the steepest increase in MASLD prevalence. Accordingly, population-based screening initiatives, workplace health promotion programs, and youth-targeted metabolic health assessments represent promising strategies to mitigate the long-term burden of MASLD and CKD. Future research should prioritize evaluating the effectiveness of these policy-driven interventions and continuously monitoring their impact on reducing the chronic disease burden across different population groups.
Furthermore, individuals diagnosed with MASLD consistently demonstrated a substantial burden of cardiometabolic risk factors, including overweight/obesity (97.5%), high TG levels (55.3%), low HDL-C levels (49.2%), and dysglycemia/T2DM (56.3%), in the most recent survey wave. Although the MASLD diagnostic criteria inherently require the presence of at least one cardiometabolic abnormality, thereby contributing to higher prevalence rates of these factors, the ability to delineate which specific risk components are present among patients with MASLD provides valuable clinical insight. This information can inform more tailored management strategies, enabling targeted interventions based on individual risk profiles rather than a uniform approach.
In this study, MASLD was defined based on an HSI cutoff of ≥33.9, the presence of at least one cardiometabolic risk factor, and low alcohol intake (≤30 g/day for men and ≤20 g/day for women). However, because BMI is included as a component of the HSI and is also used as the criterion for obesity among the cardiometabolic risk factors, an inherent overlap was unavoidable in this study. This overlap contributed to the high prevalence of obesity or overweight observed in the MASLD group. Recognizing this limitation in the interpretation of our results, we conducted sensitivity analyses in which obesity/overweight was excluded from the risk factor set and alternative HSI cutoff thresholds were applied, as shown in Supplemental Table S1. These analyses consistently yielded similar overall patterns, suggesting that the findings of this study are not solely attributable to the definitional overlap.
This study leveraged 16 years of nationally representative data to examine long-term trends in MASLD prevalence and associated cardiometabolic risk factors. The large sample size enhanced the statistical power and improved the generalizability of the findings to the Korean adult population. In addition, the use of a consistently applied proxy definition for MASLD, which reflects the updated diagnostic framework, ensures that this study remains relevant to current clinical and epidemiological discussions. The age-stratified analytical approach further strengthened the level of detail of the findings and enabled the identification of emerging risk patterns among younger adults, a group often underrepresented in chronic liver disease research.
While this study provides nationally representative evidence, several limitations should be considered. First, the diagnosis of MASLD was based on surrogate markers rather than on imaging or histological confirmation, which may have led to misclassification. However, the use of HSI and metabolic criteria has been validated and widely accepted in large-scale epidemiological studies. Second, variations in measurement methods and analytic techniques across survey waves may have affected liver enzyme results. In 2022, the analytic laboratory, instruments and reagents were changed. Although cross-laboratory evaluation indicated that the differences were within the National Cholesterol Education Program recommended accuracy limits and that no clinically meaningful discrepancies were observed at conventional decision thresholds (ALT <40 mg/dL), temporal comparisons of ALT and AST levels should nonetheless be interpreted with caution. Third, the cross-sectional nature of the KNHANES limits its ability to establish causality or temporal sequencing of MASLD and associated metabolic outcomes. Third, some relevant confounders, such as detailed dietary intake, physical activity levels, and medication use, were not fully accounted for, which may have influenced the observed associations. Finally, reliance on self-reported alcohol consumption may have introduced an underreporting bias, although standard exclusion thresholds were applied.
In conclusion, the increasing prevalence of MASLD among young Korean adults indicates a shift in the epidemiological landscape. The early clustering of cardiometabolic risk factors in this population calls for prompt and proactive strategies to address metabolic health throughout the lifespan. Future research should further explore the longitudinal outcomes of young-onset MASLD and evaluate the effectiveness of early intervention models in reducing disease progression and comorbidity risks.

Supplemental Fig. S1.

Flowchart of the study population. KNHANES, Korea National Health and Nutrition Examination Survey; MASLD, metabolic dysfunction-associated steatotic liver disease.
enm-2025-2578-Supplemental-Fig-S1.pdf

Supplemental Fig. S2.

Diagnostic flowchart for metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated subtypes. HSI, hepatic steatosis index; MetALD, metabolic dysfunction-associated alcohol-related liver disease; ALD, alcohol-related liver disease; DILI, drug-induced liver injury; SLD, steatotic liver disease.
enm-2025-2578-Supplemental-Fig-S2.pdf

Supplemental Table S1.

Distribution of MASLD Prevalence Based on Different HSI Cutoffs from 2007 to 2023
enm-2025-2578-Supplemental-Table-S1.pdf

Supplemental Table S2.

Definitions and Measurement of Study Variables
enm-2025-2578-Supplemental-Table-S2.pdf

Supplemental Table S3.

General Characteristics among MASLD Cases from 2007 to 2023
enm-2025-2578-Supplemental-Table-S3.pdf

Supplemental Table S4.

General Characteristics among Non-Steatosis Cases from 2007 to 2023
enm-2025-2578-Supplemental-Table-S4.pdf

Supplemental Table S5.

Age-Standardized and Crude Prevalence of MASLD from 2007 to 2023
enm-2025-2578-Supplemental-Table-S5.pdf

Supplemental Table S6.

Prevalence of MASLD by Alcohol Consumption Strata and Survey Wave
enm-2025-2578-Supplemental-Table-S6,7.pdf

Supplemental Table S7.

Prevalence of MASLD by Alcohol Consumption Strata and Survey Wave
enm-2025-2578-Supplemental-Table-S6,7.pdf

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENTS

This work was supported by the Gachon University Gil Medical Center (grant number: FRD2023-15).

The Korea National Health and Nutrition Examination Survey (KNHANES) datasets analyzed in this study are publicly available from the Korea Disease Control and Prevention Agency (https://knhanes.kdca.go.kr/).

AUTHOR CONTRIBUTIONS

Conception or design: H.S., D.H.L., E.K. Acquisition, analysis, or interpretation of data: J.K., H.S., D.H.L., E.K. Drafting the work or revising: J.K., H.S., E.K. Final approval of the manuscript: D.H.L., E.K.

Fig. 1
Prevalence of metabolic dysfunction-associated steatotic liver disease from 2007 to 2023: (A) total population, (B) age, (C) sex, (D) education level. APC, annual percent change.
enm-2025-2578f1.jpg
Fig. 2
The prevalence of metabolic dysfunction-associated steatotic liver disease from 2007 to 2023 by age group: (A) 19–44 years, (B) 45–64 years, (C) ≥65 years. APC, annual percent change.
aP<0.05.
enm-2025-2578f2.jpg
Fig. 3
Prevalence patterns of cardiometabolic risk factors and chronic kidney disease in general and stratified populations by steatotic liver disease status: (A) total population, (B) metabolic dysfunction-associated steatotic liver disease, (C) non-steatosis. T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol.
enm-2025-2578f3.jpg
Fig. 4
Trends in the prevalence of cardiometabolic risk factors and chronic kidney disease among individuals with metabolic dysfunction-associated steatotic liver disease. T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol.
enm-2025-2578f4.jpg
enm-2025-2578f5.jpg
Table 1
General Characteristics of the Total Study Population from 2007 to 2023
Variable Wave 4 (2007–2009) Wave 5 (2010–2012) Wave 6 (2013–2015) Wave 7 (2016–2018) Wave 8 (2019–2021) Wave 9 (2022–2023) P for-trend
Age, yr 44.2 (43.74–44.68) 44.9 (44.47–45.39) 45.5 (45.04–45.93) 47.0 (46.49–47.42) 47.5 (47.06–48.01) 48.6 (48.01–49.20) <0.0001
Sex <0.0001
 Male 49.9 (49.09–50.65) 49.8 (49.01–50.59) 49.8 (48.97–50.60) 50.0 (49.20–50.71) 50.1 (49.36–50.79) 50.2 (49.21–51.11)
 Female 50.1 (49.35–50.91) 50.2 (49.41–50.99) 50.2 (49.40–51.03) 50.0 (49.29–50.80) 49.9 (49.21–50.64) 49.8 (48.89–50.79)
Education level <0.0001
 Middle school or less 30.2 (28.73–31.66) 28.1 (26.85–29.42) 24.6 (23.41–25.82) 23.1 (21.81–24.30) 19.4 (18.18–20.55) 18.1 (16.78–19.51)
 High school graduate 40.2 (38.82–41.48) 39.3 (38.06–40.48) 38.8 (37.66–40.03) 35.3 (34.12–36.44) 37.2 (36.05–38.39) 35.1 (33.81–36.44)
 College or higher 29.7 (28.17–31.13) 32.6 (31.26–33.93) 36.5 (35.12–37.96) 41.7 (40.10–43.22) 43.4 (41.81–45.01) 46.7 (44.90–48.56)
BMI, kg/m2 23.6 (23.56–23.72) 23.7 (23.63–23.79) 23.8 (23.68–23.83) 24.0 (23.89–24.03) 24.1 (24.07–24.23) 24.2 (24.10–24.30) <0.0001
Waist circumference, cm 81.3 (81.05–81.61) 81.1 (80.81–81.33) 81.2 (80.97–81.47) 82.2 (82.01–82.48) 84.1 (83.85–84.30) 84.0 (83.70–84.27) <0.0001
Fasting glucose, mg/dL 96.2 (95.66–96.75) 96.7 (96.25–97.13) 98.7 (98.27–99.18) 100.0 (99.55–100.48) 100.7 (100.22–101.14) 100.0 (99.41–100.54) <0.0001
HbA1c, % 7.2 (7.11–7.31) 5.7 (5.72–5.77) 5.7 (5.70–5.74) 5.6 (5.62–5.66) 5.8 (5.73–5.77) 5.6 (5.54–5.58) <0.0001
Triglyceride, mg/dL 134.2 (132.06–136.32) 134.1 (131.65–136.63) 136.7 (134.33–139.08) 138.6 (135.99–141.12) 131.8 (129.55–134.10) 128.9 (126.56–131.24) <0.0001
HDL-C, mg/dLa 48.2 (47.91–48.42) 49.7 (49.43–49.98) 51.2 (50.92–51.39) 51.1 (50.87–51.42) 52.3 (51.99–52.56) 57.0 (56.63–57.39) <0.0001
SBP, mm Hg 116.2 (115.67–116.79) 118.2 (117.73–118.59) 116.4 (115.97–116.78) 117.5 (117.11–117.88) 118.2 (117.81–118.57) 118.1 (117.60–118.57) <0.0001
DBP, mm Hg 76.5 (76.13–76.84) 76.5 (76.26–76.83) 75.1 (74.82–75.38) 75.9 (75.68–76.15) 75.5 (75.24–75.72) 74.0 (73.70–74.27) <0.0001
ALT, U/L 23.1 (22.71–23.59) 22.0 (21.60–22.45) 22.0 (21.58–22.35) 22.9 (22.55–23.21) 24.5 (24.05–24.87) 22.7 (22.20–23.15) <0.0001
AST, U/L 23.1 (22.71–23.39) 22.3 (22.00–22.51) 22.2 (21.98–22.47) 23.0 (22.74–23.23) 24.8 (24.50–25.10) 22.6 (22.28–22.90) <0.0001
Creatinine, mg/dL 0.9 (0.89–0.91) 0.8 (0.84–0.84) 0.8 (0.84–0.86) 0.8 (0.82–0.83) 0.8 (0.81–0.82) 0.8 (0.80–0.81) <0.0001

Values are expressed as weighted mean or percentages (95% confidence interval).

BMI, body mass index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase.

a HbA1c was measured only among participants with diagnosed diabetes under treatment or with fasting plasma glucose ≥126 mg/dL in wave 4 (2007–2009).

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Appendix 1
SAS Code for Variable Definition and Survey-Weighted Analysis in the Korea National Health and Nutrition Examination Survey Dataset
/* HSI design */
proc sql;
create table KNHANES_data as
select distinct *, case when 126<= HE_GLU or DE1_31=1 or DE1_32=1 or DE1_dg=1 or 6.5<=HE_HbA1c or sex=2 then
8*HE_alt/HE_ast+HE_BMI+2 else 8*HE_alt/HE_ast+HE_BMI end as HSI
from KNHANES_data;
quit;

/* CRF design*/
data KNHANES_data; set KNHANES_data;
IF he_bmi>=23 or (he_wc>=90 and sex=1) or (he_wc>=85 and sex=2) then obe=1;
else if 0<he_bmi<23 or (0<he_wc<90 and sex=1) or (0<he_wc<85 and sex=2) then obe=0;

IF he_hba1c>=6.5 or he_glu>=126 then dysg=2;
else if 5.7<=he_hba1c<6.5 or 100<=he_glu<126 then dysg=1;
else if 0<he_hba1c<5.7 or 0<he_glu<100 then dysg=0;

IF he_tg>=150 or di2_pt=1 then plasma=1;
else if 0<he_tg<150 or di2_pt=0 then plasma=0;

IF (0<he_hdl_st2<40 and sex=1) or (0<he_hdl_st2<50 and sex=2) or di2_pt=1 then hdl=1;
else if (he_hdl_st2>=40 and sex=1) or (he_hdl_st2>=50 and sex=2) or di2_pt=0 then hdl=0;

if he_sbp>=130 and he_dbp>=85 or di1_pt=1 then bp=1;
else if 0<he_sbp<130 and 0<he_dbp<85 or di1_pt=0 then bp=0;
run;

data KNHANES_data; set KNHANES_data;
if obe=1 or dysg in (1,2) or plasma=1 or hdl=1 or bp=1 then crf=1;
else if obe=0 and dysg=0 and plasma=0 and hdl=0 and bp=0 then crf=0;

/* Survey design */
proc surveyfreq data=KNHANES_data nomcar;
   strata kstrata;
   cluster psu;
   weight wt_itvex;
   table wave*masld/cl;
run;

Figure & Data

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      Trends in Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease: A Nationwide Survey in Korea
      Image Image Image Image Image
      Fig. 1 Prevalence of metabolic dysfunction-associated steatotic liver disease from 2007 to 2023: (A) total population, (B) age, (C) sex, (D) education level. APC, annual percent change.
      Fig. 2 The prevalence of metabolic dysfunction-associated steatotic liver disease from 2007 to 2023 by age group: (A) 19–44 years, (B) 45–64 years, (C) ≥65 years. APC, annual percent change. aP<0.05.
      Fig. 3 Prevalence patterns of cardiometabolic risk factors and chronic kidney disease in general and stratified populations by steatotic liver disease status: (A) total population, (B) metabolic dysfunction-associated steatotic liver disease, (C) non-steatosis. T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol.
      Fig. 4 Trends in the prevalence of cardiometabolic risk factors and chronic kidney disease among individuals with metabolic dysfunction-associated steatotic liver disease. T2DM, type 2 diabetes mellitus; HDL-C, high-density lipoprotein cholesterol.
      Graphical abstract

      Fig. 1

      Fig. 2

      Fig. 3

      Fig. 4

      Graphical abstract

      Trends in Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease: A Nationwide Survey in Korea
      Variable Wave 4 (2007–2009) Wave 5 (2010–2012) Wave 6 (2013–2015) Wave 7 (2016–2018) Wave 8 (2019–2021) Wave 9 (2022–2023) P for-trend
      Age, yr 44.2 (43.74–44.68) 44.9 (44.47–45.39) 45.5 (45.04–45.93) 47.0 (46.49–47.42) 47.5 (47.06–48.01) 48.6 (48.01–49.20) <0.0001
      Sex <0.0001
       Male 49.9 (49.09–50.65) 49.8 (49.01–50.59) 49.8 (48.97–50.60) 50.0 (49.20–50.71) 50.1 (49.36–50.79) 50.2 (49.21–51.11)
       Female 50.1 (49.35–50.91) 50.2 (49.41–50.99) 50.2 (49.40–51.03) 50.0 (49.29–50.80) 49.9 (49.21–50.64) 49.8 (48.89–50.79)
      Education level <0.0001
       Middle school or less 30.2 (28.73–31.66) 28.1 (26.85–29.42) 24.6 (23.41–25.82) 23.1 (21.81–24.30) 19.4 (18.18–20.55) 18.1 (16.78–19.51)
       High school graduate 40.2 (38.82–41.48) 39.3 (38.06–40.48) 38.8 (37.66–40.03) 35.3 (34.12–36.44) 37.2 (36.05–38.39) 35.1 (33.81–36.44)
       College or higher 29.7 (28.17–31.13) 32.6 (31.26–33.93) 36.5 (35.12–37.96) 41.7 (40.10–43.22) 43.4 (41.81–45.01) 46.7 (44.90–48.56)
      BMI, kg/m2 23.6 (23.56–23.72) 23.7 (23.63–23.79) 23.8 (23.68–23.83) 24.0 (23.89–24.03) 24.1 (24.07–24.23) 24.2 (24.10–24.30) <0.0001
      Waist circumference, cm 81.3 (81.05–81.61) 81.1 (80.81–81.33) 81.2 (80.97–81.47) 82.2 (82.01–82.48) 84.1 (83.85–84.30) 84.0 (83.70–84.27) <0.0001
      Fasting glucose, mg/dL 96.2 (95.66–96.75) 96.7 (96.25–97.13) 98.7 (98.27–99.18) 100.0 (99.55–100.48) 100.7 (100.22–101.14) 100.0 (99.41–100.54) <0.0001
      HbA1c, % 7.2 (7.11–7.31) 5.7 (5.72–5.77) 5.7 (5.70–5.74) 5.6 (5.62–5.66) 5.8 (5.73–5.77) 5.6 (5.54–5.58) <0.0001
      Triglyceride, mg/dL 134.2 (132.06–136.32) 134.1 (131.65–136.63) 136.7 (134.33–139.08) 138.6 (135.99–141.12) 131.8 (129.55–134.10) 128.9 (126.56–131.24) <0.0001
      HDL-C, mg/dLa 48.2 (47.91–48.42) 49.7 (49.43–49.98) 51.2 (50.92–51.39) 51.1 (50.87–51.42) 52.3 (51.99–52.56) 57.0 (56.63–57.39) <0.0001
      SBP, mm Hg 116.2 (115.67–116.79) 118.2 (117.73–118.59) 116.4 (115.97–116.78) 117.5 (117.11–117.88) 118.2 (117.81–118.57) 118.1 (117.60–118.57) <0.0001
      DBP, mm Hg 76.5 (76.13–76.84) 76.5 (76.26–76.83) 75.1 (74.82–75.38) 75.9 (75.68–76.15) 75.5 (75.24–75.72) 74.0 (73.70–74.27) <0.0001
      ALT, U/L 23.1 (22.71–23.59) 22.0 (21.60–22.45) 22.0 (21.58–22.35) 22.9 (22.55–23.21) 24.5 (24.05–24.87) 22.7 (22.20–23.15) <0.0001
      AST, U/L 23.1 (22.71–23.39) 22.3 (22.00–22.51) 22.2 (21.98–22.47) 23.0 (22.74–23.23) 24.8 (24.50–25.10) 22.6 (22.28–22.90) <0.0001
      Creatinine, mg/dL 0.9 (0.89–0.91) 0.8 (0.84–0.84) 0.8 (0.84–0.86) 0.8 (0.82–0.83) 0.8 (0.81–0.82) 0.8 (0.80–0.81) <0.0001
      Table 1 General Characteristics of the Total Study Population from 2007 to 2023

      Values are expressed as weighted mean or percentages (95% confidence interval).

      BMI, body mass index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase.

      HbA1c was measured only among participants with diagnosed diabetes under treatment or with fasting plasma glucose ≥126 mg/dL in wave 4 (2007–2009).

      Table 1

      Endocrinol Metab : Endocrinology and Metabolism
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