ABSTRACT
Background
Metabolic dysfunction‐associated steatotic liver disease is a significant driver of the increasing global burden of chronic liver disease. This study aimed to describe the temporal trends and inequalities of liver complications related to metabolic dysfunction‐associated steatotic liver disease (LC‐MASLD) by geographical region, age and sex during 1990–2021.
Methods
Global Burden of Diseases Study 2021 data were analysed to assess LC‐MASLD incidence, prevalence, mortality and disability‐adjusted life years (DALYs). Temporal trends during 1990–2021 were measured by ‘estimated annual percentage change’ (EAPC). Inequalities of LC‐MASLD burden across countries were evaluated by the slope index of inequality (SII) and the relative concentration index (RCI).
Results
During 1990–2021, LC‐MASLD rose annually by 0.73% in incidence and prevalence, 0.19% in mortality and 0.16% in DALYs. In 2021, the Middle East and North Africa had the highest incidence and prevalence and Andean and Central Latin America had the highest mortality and DALY rates. While LC‐MASLD incidence was earliest in the 15–19 age group, both prevalence and DALY rates peaked at 75–79 years for both sexes. Inequalities in mortality and DALYs by countries' socioeconomic development index increased during 1990–2021, demonstrated by a decline in SII from −0.09 to −0.56 per 100 000 for mortality and from 1.41 to −7.74 per 100 000 for DALYs. RCI demonstrated similar findings.
Conclusion
The LC‐MASLD burden is increasing globally, especially in economically disadvantaged countries, with widening disease inequalities during 1990–2021. Effective prevention and subregional interventions are crucial, with a specific focus on resource optimisation for disadvantaged populations.
Keywords: disease burden, Global Burden of Disease Study 2021, inequalities, metabolic dysfunction‐associated steatotic liver disease, non‐alcoholic fatty liver disease, temporal trend
Abbreviations
- ASDR
age‐standardised DALY rate
- ASIR
age‐standardised incidence rate
- ASMR
age‐standardised mortality rate
- ASPR
age‐standardised prevalence rate
- ASR
age‐standardised rate
- CI
certainty interval
- DALYs
Disability‐adjusted life‐years
- EAPC
estimated annual percentage change
- GBD 2021
Global Burden of Disease 2021
- HAQ
Healthcare Access and Quality
- LC‐MASLD
liver complications related to MASLD
- MALC
liver cancer due to MASH
- MASH
metabolic‐associated steatohepatitis
- MASLD
metabolic dysfunction‐associated steatotic liver disease
- RCI
relative concentration index
- SDI
socioeconomic development index
- SII
slope index of inequality
- the MENA
the Middle East and North Africa
- the UAE
the United Arab Emirates
- UI
uncertainty interval
Summary.
The global burden of liver complications related to metabolic dysfunction‐associated steatotic liver disease (LC‐MASLD) has increased significantly during 1990–2021, highlighting its role as a critical driver of the rising burden of chronic liver disease.
Countries with higher SDI experienced more substantial increases in LC‐MASLD incidence and prevalence rates, with metabolic risks increasingly contributing to the global LC‐MASLD burden over the past two decades.
The LC‐MASLD burden is increasing globally, especially in economically disadvantaged countries, with widening disease inequalities during 1990–2021.
1. Introduction
Metabolic dysfunction‐associated steatotic liver disease (MASLD) is characterised by the accumulation of fat in more than 5% of hepatocytes, with a spectrum of conditions including metabolic‐associated fatty liver (MAFL), metabolic‐associated steatohepatitis (MASH) and its associated cirrhosis and hepatocellular carcinoma [1, 2, 3, 4]. Affecting approximately over 30% of adults, MASLD has become a significant driver of the increasing global burden of chronic liver disease (CLD) [5, 6, 7, 8]. A modelling study demonstrated an exponential increase in the disease burden of MASLD, with a projected prevalence of MASLD in the adult population (≥ 15 years) of 33.5% by 2030 [6]. Environmental shifts, including socioeconomic level, dietary patterns and lifestyle changes all contribute to the growth of MASLD [9, 10, 11]. Given that there are limited effective therapies for MASLD, such as lifestyle and dietary modifications and the use of resmetirom for MASH, the prevention of MASLD has become a key focus for clinicians and health policymakers [4, 5, 12, 13, 14].
MASLD imposes substantial burdens on healthcare systems and the global economy, compounded by its complex pathophysiology, comorbidities such as obesity and type 2 diabetes mellitus (T2DM) and combination treatment [15, 16, 17]. High‐income countries, notably those in Europe, have spearheaded MASLD research, offering valuable insights for global management strategies [18]. Conversely, low‐income and low‐middle‐income countries face challenges in prevention and treatment due to healthcare resource shortages and inadequate public health education [1]. The diversity in socioeconomic status among countries may result in health disparities in MASLD, a topic that was understudied so far.
Despite MASLD represents a significant public health challenge, comprehensive public health responses are absent in most countries [18, 19]. International health policy organisations, including the World Health Organisation's Universal Health Coverage (UHC) programme and the United Nations' Sustainable Development Goals (SDGs), have not directly include MASLD in its prevention and treatment guidelines [19, 20]. Advocating for orchestrated international collaboration in MASLD prevention and management, particularly in low‐income countries, is imperative. Urgent action is also necessary to formulate comprehensive strategies at both national and global levels to confront the challenge of MASLD. Therefore, we utilised data from the Global Burden of Disease Study 2021 (GBD2021) to assess the total burden of liver complications related to MASLD (LC‐MASLD) and disease inequalities across countries during 1990–2021. These estimates will help policymakers allocate healthcare resources effectively and develop targeted interventions.
2. Methods
2.1. Data Acquisition and Definition
This study is a part of the Global Burden of Disease Study 2021 (GBD2021). GBD2021 estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs) and disability‐adjusted life‐years (DALYs) due to 371 diseases and injuries, for 25 distinct age groups, males, females and both sexes and the combined data across 204 countries and territories based on the newest available data advanced statistical models [21]. The estimates for each metric contain 95% uncertainty intervals (UIs). The GBD algorithm, central to the derivation of these intervals, takes into account the differences between calculation approaches among different countries, as well as the uncertainty of multiple imputations of missing data from countries and is calculated by multiple repeated sampling through the correlation matrix. All rates are reported per 100 000 population, following epidemiological conventions.
For our study, we obtained the GBD estimates of incident cases, prevalent cases, deaths and DALYs, as well as the age‐standardised incidence, prevalence, mortality and DALY rates, for liver complications related to metabolic dysfunction‐associated steatotic liver disease (LC‐MASLD) from 1990 to 2021 by sex, age group and Socio‐Demographic Index (SDI) at regional and national levels, to provide a complete assessment of the burden of LC‐MASLD. Data extraction was performed using the Global Health Data Exchange (GHDx) query tool, accessible at https://ghdx.healthdata.org/gbd‐2021.
In this study, the total disease burden of LC‐MASLD included metabolic dysfunction‐associated steatotic liver disease including cirrhosis (MASLD including cirrhosis) and liver cancer caused by MASH (MALC) (Table S1). MASLD was identified using the International Classification of Disease (ICD) 10 code K76.0, whereas MASH was identified by the ICD‐10 code K75.8. Ultrasound or other imaging studies were adopted as reference diagnostic methods for MASLD [21, 22, 23].
2.2. Temporal Trends Analysis Stratified by Geographical Regions
We measured the disease burden of LC‐MASLD by describing the age‐standardised rates (ASRs) of incidence, prevalence, mortality and DALYs from 1990 to 2021, along with the corresponding number of cases. Data were available for 204 countries and territories, categorised into 5 SDI regions and 21 GBD regions [21]. Standardisation is necessary for comparing cohorts with distinct age structures or for tracking the same population with changing age distributions over time [24]. ASR data can be accessed to GHDx and detailed calculations are available in the literature [25, 26].
To reflect temporal trends, we calculated the estimated annual percentage changes (EAPCs) of the ASRs.
The EAPC was based on a log‐linear model (i.e., y = α + βx + ε, where y = ln (age‐standardised rate) and x = calendar year). The slope of the fitted line was calculated and EAPC was determined as 100 × (e β − 1), with its 95% confidence interval (CI) also derived from a linear regression model. An increasing trend was identified if both the EAPC estimate and its lower 95% CI boundary were > 0, a decreasing trend if both were < 0 and a stable trend otherwise [27, 28].
Additionally, we calculated and reported the counts, crude rates and ASRs percentage changes over the past two decades in deaths and DALYs for LC‐MASLD due to two level 1 risk factors, metabolic and behavioural risks (established LC‐MASLD risk factors with available data in GBD). Details of these risk factors and their associated risk with LC‐MASLD are disclosed elsewhere [29].
2.3. Correlation Analysis of LC‐MASLD Disease Burden
This study used the SDI to determine the relationship between a country's socio‐economic development status and the ASR of LC‐MASLD. The SDI, ranging from 0 (worst) to 1 (best), includes total fertility rate under 25, mean education for those 15 and older and lag‐distributed income per capita [21, 22, 30]. We used Pearson correlation analyses to assess the association between ASRs, EAPCs and country‐level SDI, identifying potential factors affecting disease burden trends. The same method was applied to the Healthcare Access and Quality (HAQ) Index analysis. The HAQ Index ranges from 0 (worst) to 100 (best) and measures healthcare access and quality based on treatable deaths or preventable deaths under effective healthcare conditions [31].
2.4. Cross‐Country Health Inequalities Analysis
The slope index of inequality (SII) and the relative concentration index (RCI) were used to assess the distributive inequality of the LC‐MASLD disease burden across different countries [32, 33, 34]. These two metrics represented absolute and relative gradients of inequality and were calculated by the Health Equity Assessment Toolkit developed by the World Health Organisation (WHO) [35, 36].
The SII was computed by regressing mortalities and DALY rates for LC‐MASLD in each country based on an income‐related relative social position scale, defined by the midpoint of the cumulative class interval of the population ranked by SDI. We used a weighted regression model to address heteroskedasticity and log‐transformed the relative social status values to accommodate nonlinearities arising from marginal utility.
Health inequality concentration index was computed by fitting a Lorenz concentration curve to the observed cumulative relative distributions of the population ranked by SDI and the DALYs and death burden and numerically integrating the area under the curve. When the Lorenz curve is above the equality line, the health burden is concentrated in low‐SDI countries, yielding a negative concentration index. Conversely, if the curve is below the equality line, the health burden is concentrated in high‐SDI countries, resulting in a positive concentration index [37, 38].
2.5. Statistical Analysis
All statistical analyses were performed using the RStudio software (V.4.3.3) (https://www.r‐project.org/). A probability value of p < 0.05 was considered statistically significant. This study complies with the guidelines for Accurate and Transparent Health Estimates Reporting statement [39].
3. Results
3.1. Temporal Trends of LC‐MASLD Disease Burden
3.1.1. Global
From 1990 to 2021, the global age‐standardised incidence rate (ASIR) of LC‐MASLD increased from 475.5 (95% UI [432.6–518.2]) to 593.3 per 100 000 (95% UI [542.7–643.7]), with an EAPC of 0.73 (95% CI [0.69–0.77]). Similarly, the age‐standardised prevalence rate (ASPR) rose from 12 085.1 (95% UI [11 058.4–13 184.3]) to 15 018.1 per 100 000 (95% UI [13 756.5–16 147.6]), with an EAPC of 0.73 (95% CI [0.67–0.79]). Concurrently, the age‐standardised mortality (ASMR) and DALY rate (ASDR) increased by an average of 0.19% (EAPC = 0.19 [0.15–0.24]) and 0.16% (EAPC = 0.16 [0.10–0.23]) per year, respectively, from 1990 to 2021. Table 1 presents ASR percentage changes over 1990–2021, including the impact of the COVID‐19 pandemic. Despite the pandemic, the incidence and prevalence of LC‐MASLD maintained long‐term upward trends, with only slight decreases in mortality and DALYs. The trends for MASLD including cirrhosis were generally consistent with LC‐MASLD incidence and prevalence, though global growth trends for mortality and DALYs were not significant (Table S2). For MALC, the global trends aligned with LC‐MASLD, indicating a 0.99% (EAPC = 0.99 [0.91–1.07]) annual increase in ASIR from 1990 to 2021 (Table S3).
TABLE 1.
The disease burden of incidence, prevalence, mortality and DALYs of LC‐MASLD and their EAPCs during 1990–2021 at the global different levels.
| Characteristics | ASR per 100 000 No. (95% UI) | Percentage changes, % No. (95% UI) | EAPC, %, 1990–2021 No. (95% CI) a | |||
|---|---|---|---|---|---|---|
| 1990 | 2019 | 2021 | 2019–2021 | 1990–2021 | ||
| Incidence | ||||||
| Global | 475.5 (432.6–518.2) | 585.3 (534.6–636) | 593.3 (542.7–643.7) | 0.8 (0.5–1.1) b | 17.9 (16.8–19.2) b | 0.73 (0.69–0.77) b |
| Sex | ||||||
| Male | 501.2 (456.7–547) | 623.1 (571–676.8) | 621.2 (569.6–675.1) | −0.7 (−1.2 to −0.2) | 15.8 (14.4–17.2) | 0.75 (0.69–0.8) |
| Female | 448.9 (408.2–490.4) | 545.2 (496.8–593) | 563.2 (513.3–612.4) | 2.4 (1.9–2.9) | 20.1 (19–21.2) | 0.71 (0.68–0.74) |
| Type | ||||||
| MASLD including cirrhosis | 475.2 (432.3–517.8) | 584.8 (534.1–635.5) | 592.8 (542.2–643.2) | 0.8 (0.5–1.1) b | 17.9 (16.8–19.2) b | 0.73 (0.69–0.77) b |
| MALC | 0.4 (0.3–0.4) | 0.5 (0.4–0.6) | 0.5 (0.4–0.6) | ‐0.2 (−7 to 7.2) | 36.1 (19.1–53.4) | 0.99 (0.91–1.07) |
| Socio‐demographic index (SDI) | ||||||
| Low SDI | 483.4 (440.2–527.6) | 549.3 (500.9–598.4) | 553.7 (503.5–605.1) | 0.6 (0–1.2) | 9.3 (8.4–10.3) | 0.44 (0.41–0.47) |
| Low‐middle SDI | 520.7 (474.2–566.6) | 617.6 (563.8–669.9) | 623.2 (569.6–676.2) | 0.3 (−0.3 to 0.8) | 10.6 (9.6–11.8) | 0.59 (0.55–0.62) |
| Middle SDI | 533.7 (485.4–580.7) | 647.7 (592.5–703.3) | 657 (602.1–712.9) | 0.6 (0.1–1) | 13 (11.7–14.4) | 0.68 (0.65–0.72) |
| Middle‐high SDI | 478.4 (435.3–522.1) | 601.4 (549–654.9) | 611.3 (558–665.6) | 1.3 (0.6–2.1) | 18.8 (17.1–20.4) | 0.80 (0.71–0.89) |
| High SDI | 342.7 (312.2–373.5) | 447.3 (409.3–485.6) | 450 (412.2–488.4) | 0.5 (0–1) | 25.7 (24–27.3) | 1.00 (0.95–1.05) |
| Country income tiers | ||||||
| Low‐income countries (LICs) | 474.4 (432.8–518.8) | 534.3 (486.7–582.8) | 539.3 (492–589.4) | 0.8 (0.2–1.5) | 10.6 (9.4–11.8) | 0.42 (0.41–0.44) |
| Lower middle‐income countries (LMICs) | 542 (493.6–590.8) | 640.8 (585.3–694.5) | 645.4 (590.3–700.4) | 0.2 (−0.2 to 0.6) | 11.1 (10.1–12.1) | 0.57 (0.55–0.59) |
| Upper middle‐income countries (UMICs) | 494.3 (449.6–538.3) | 610.2 (557.5–663.9) | 621.1 (567.5–675.6) | 1 (0.3–1.7) | 14.6 (13–16.3) | 0.75 (0.65–0.84) |
| High‐income countries (HICs) | 352.6 (321.7–384) | 449.9 (412.1–487.3) | 452.4 (414.9–490.3) | 0.5 (0–1) | 24.5 (23–26.1) | 0.90 (0.87–0.93) |
| Prevalence | ||||||
| Global | 12 085.1 (11 058.4–13 184.3) | 14 848.1 (13 608.4–16 147.6) | 15 018.1 (13 756.5–16 147.6) | 1.1 (0.8–1.5) b | 24.3 (23.2–25.4) b | 0.73 (0.67–0.79) b |
| Sex | ||||||
| Male | 12 843.4 (11 772.3–14 034.1) | 15 925.8 (14 583.8–17 322.8) | 15 731.4 (14 392.7–17 167.4) | −1.2 (−1.7 to −0.8) | 22.5 (21.2–23.9) | 0.74 (0.67–0.82) |
| Female | 11 350.7 (10 364.1–12 388.2) | 13 792.2 (12 658.8–15 006.7) | 14 310.6 (13 114.9–15 573.6) | 3.8 (3.3–4.2) | 26.1 (24.9–27.3) | 0.71 (0.66–0.77) |
| Type | ||||||
| MASLD including cirrhosis | 12 084.7 (11 058–13 183.9) | 14 847.5 (13 607.8–16 147) | 15 017.5 (13 755.8–16 360.8) | 1.1 (0.8–1.5) b | 24.3 (23.2–25.4) b | 0.73 (0.67–0.79) b |
| MALC | 0.4 (0.3–0.5) | 0.6 (0.5–0.7) | 0.6 (0.5–0.7) | −0.1 (−6.9 to 7.2) | 52.3 (33.8–70.7) | 1.39 (1.27–1.5) |
| Socio‐demographic index (SDI) | ||||||
| Low SDI | 12 421.5 (11 351.3–13 593.5) | 13 761.3 (12 583.2–15 029.2) | 13 889.2 (12 685.4–15 029.2) | 0.9 (0.3–1.5) | 11.8 (10.8–12.9) | 0.35 (0.32–0.39) |
| Low‐middle SDI | 13 579 (12 385.8–14 820.3) | 15 668.8 (14 342.1–17 060.8) | 15 748.7 (14 418.6–17 060.8) | 0.5 (−0.1 to 1) | 16 (15–17) | 0.49 (0.44–0.53) |
| Middle SDI | 13 847 (12 679.9–15 124.7) | 16 427.7 (15 055.5–17 891.7) | 16 589.9 (15 168.3–17 891.7) | 1 (0.5–1.5) | 19.8 (18.5–21.2) | 0.61 (0.54–0.68) |
| Middle‐high SDI | 12 230.4 (11 204.1–13 334.1) | 15 215.6 (13 969.5–16 538.5) | 15 471.1 (14 155.4–16 538.5) | 1.7 (1–2.4) | 26.5 (24.9–28.1) | 0.78 (0.67–0.88) |
| High SDI | 8520.1 (7809.2–9274.2) | 11 439.9 (10 499.9–12 419) | 11 543.7 (10 585.6–12 419) | 0.9 (0.4–1.5) | 35.5 (33.9–37.4) | 1.09 (1.05–1.14) |
| Country income tiers | ||||||
| Low‐income countries (LICs) | 12 936.2 (11 815.2–14 153.2) | 14 604.5 (13 392–15 936.4) | 14 747 (13 511.3–15 936.4) | 1 (0.3–1.6) | 14 (12.9–15.2) | 0.44 (0.42–0.45) |
| Lower middle‐income countries (LMICs) | 13 648.9 (12 461.9–14 912.5) | 15 830.9 (14 492–17 230.7) | 15 900.2 (14 579.2–17 230.7) | 0.4 (0–0.8) | 16.5 (15.7–17.5) | 0.5 (0.47–0.53) |
| Upper middle‐income countries (UMICs) | 13 216.3 (12 112.9–14 438.2) | 15 887.9 (14 557.7–17 265.8) | 16 126 (14 743.3–17 265.8) | 1.5 (0.8–2.2) | 22 (20.5–23.6) | 0.67 (0.54–0.8) |
| High‐income countries (HICs) | 8592.6 (7874.4–9348.5) | 11 406.9 (10 457.6–12 395.7) | 11 516.1 (10 564.7–12 395.7) | 1 (0.4–1.5) | 34 (32.5–35.7) | 1.04 (1.01–1.08) |
| Mortality | ||||||
| Global | 1.5 (1.2–2) | 1.6 (1.3–2) | 1.6 (1.3–2) | −1.5 (−5.3 to 2.7) | 5.5 (−6.9 to 17.8) | 0.19 (0.15–0.24) |
| Sex | ||||||
| Male | 1.5 (1.1–1.9) | 1.7 (1.3–2.1) | 1.7 (1.3–2.1) | −1.1 (−6.4 to 4.3) | 10.7 (−2.6 to 22.2) | 0.32 (0.26–0.39) |
| Female | 1.5 (1.2–2) | 1.6 (1.2–2.0) | 1.6 (1.2–1.9) | −1.8 (−6.1 to 3) | 1.9 (−10.9 to 17.3) | 0.11 (0.06–0.15) |
| Type | ||||||
| MASLD including cirrhosis | 1.2 (0.8–1.6) | 1.2 (0.8–1.5) | 1.1 (0.8–1.5) | −2 (−5.4 to 1.2) | −1.7 (−13.5 to 10.1) | −0.03 (−0.09 to 0.04) |
| MALC | 0.4 (0.3–0.5) | 0.5 (0.4–0.6) | 0.5 (0.4–0.6) | −0.2 (−6.6 to 6.6) | 27.8 (11.6–44.5) | 0.79 (0.71–0.87) |
| Socio‐demographic index (SDI) | ||||||
| Low SDI | 1.9 (1.3–2.7) | 1.7 (1.3–2.2) | 1.7 (1.3–2.2) | −0.3 (−4.4 to 3.8) | −7.7 (−25 to 11.8) | −0.32 (−0.36 to −0.28) |
| Low‐middle SDI | 1.7 (1.2–2.4) | 1.9 (1.4–2.4) | 1.9 (1.4–2.4) | ‐0.2 (−5.6 to 5.5) | 10.6 (−17.2 to 38.4) | 0.37 (0.35–0.39) |
| Middle SDI | 1.6 (1.2–2.1) | 1.8 (1.4–2.2) | 1.8 (1.4–2.2) | −0.8 (−6.8 to 5.5) | 9.3 (−6.7 to 26.5) | 0.36 (0.33–0.4) |
| Middle‐high SDI | 1.5 (1.1–1.9) | 1.4 (1.1–1.8) | 1.4 (1.1–1.8) | −3.4 (−9.4 to 3.9) | −4.7 (−12.8 to 5) | −0.14 (−0.29 to 0) |
| High SDI | 1.4 (1.1–1.8) | 1.5 (1.1–1.8) | 1.5 (1.1–1.8) | −2.7 (−4.1 to −1) | 3.7 (−1.8 to 10.7) | 0.13 (0.05–0.21) |
| Country income tiers | ||||||
| Low‐income countries (LICs) | 2 (1.4–2.8) | 1.8 (1.4–2.5) | 1.8 (1.4–2.5) | 0.2 (−3.3 to 4) | −9 (−25.1 to 9.9) | −0.44 (−0.5 to −0.38) |
| Lower middle‐income countries (LMICs) | 1.6 (1.2–2.4) | 1.8 (1.4–2.4) | 1.8 (1.4–2.4) | −0.1 (−4.7 to 5.1) | 13 (−14.6 to 41.5) | 0.4 (0.35–0.44) |
| Upper middle‐income countries (UMICs) | 1.3 (1–1.6) | 1.5 (1.2–1.9) | 1.5 (1.2–1.9) | −1.7 (−9.8 to 6.6) | 14.9 (1.6–30.3) | 0.58 (0.51–0.65) |
| High‐income countries (HICs) | 1.7 (1.3–2.2) | 1.6 (1.2–2) | 1.6 (1.2–2) | −2.9 (−4.3 to −1.4) | −6.8 (−11.6 to −0.5) | −0.26 (−0.34 to −0.19) |
| DALYs | ||||||
| Global | 40.2 (30.73–52.23) | 42.91 (33.96–53.64) | 42.4 (33.6–78.4) | −1.2 (−5.2 to 3.1) | 5.5 (−6.2 to 16.9) | 0.16 (0.1–0.23) |
| Sex | ||||||
| Male | 40.7 (31.2–54) | 45.3 (35.4–57.5) | 44.9 (35–57.4) | −1 (−6.5 to 4.7) | 10.2 (−2.4 to 21.1) | 0.28 (0.2–0.37) |
| Female | 39.3 (30–50.7) | 40.4 (32.3–50.5) | 39.8 (31.4–50) | −1.4 (−5.9 to 3.5) | 1.4 (−10.8 to 15.8) | 0.06 (0.01–0.1) |
| Type | ||||||
| MASLD including cirrhosis | 30.6 (21.2–42) | 31.4 (22.4–41.9) | 30.9 (22.2–41.5) | −1.5 (−5 to 1.8) | 1.1 (−10.6 to 12.3) | 0.04 (−0.05 to 0.13) |
| MALC | 9.6 (7.7–11.9) | 11.5 (9.4–13.9) | 11.5 (9.4–13.8) | −0.3 (−7.2 to 7.3) | 19.5 (4.7–35.3) | 0.53 (0.45–0.62) |
| Socio‐demographic index (SDI) | ||||||
| Low SDI | 47.45 (34.47–64.59) | 42.97 (33.62–55.6) | 42.9 (33.6–86.2) | −0.2 (−4.1 to 4) | −9.6 (−24 to 6.6) | −0.42 (−0.46 to −0.38) |
| Low‐middle SDI | 41.94 (30.32–58.47) | 47.66 (36.47–61.55) | 47.6 (35.8–61.5) | 0 (−5.6 to 6) | 13.6 (−13.1 to 39.6) | 0.46 (0.43–0.49) |
| Middle SDI | 42.14 (32.23–54.23) | 45.51 (36.2–55.82) | 45.5 (36–88.3) | 0 (−6.2 to 6.6) | 8 (−6.5 to 22.8) | 0.25 (0.22–0.29) |
| Middle‐high SDI | 37.79 (28.89–48.32) | 39.32 (30.79–50.01) | 38 (29.7–50.7) | −3.4 (−9.6 to 4.1) | 0.6 (−8.9 to 11) | 0.02 (−0.19 to 0.23) |
| High SDI | 38.39 (29.45–49.78) | 39.89 (31.35–50.68) | 38.7 (30.5–31.1) | −2.9 (−4.3 to −1.3) | 0.9 (−3.8 to 6.4) | 0.02 (−0.07 to 0.11) |
| Country income tiers | ||||||
| Low‐income countries (LICs) | 48.82 (36.11–66.35) | 44.22 (33.11–58.93) | 44.4 (33.3–112) | 0.3 (−3.2 to 4.2) | −9.1 (−23 to 6.7) | −0.46 (−0.53 to −0.39) |
| Lower middle‐income countries (LMICs) | 41.31 (29.94–57.58) | 46.39 (35.95–59.62) | 46.3 (35.4–61.9) | −0.2 (−5 to 5.1) | 12.1 (−12.7 to 36.6) | 0.35 (0.28–0.42) |
| Upper middle‐income countries (UMICs) | 34.58 (26.46–43.8) | 40.53 (32.27–50.27) | 40.1 (31.5–44.5) | −1 (−9.2 to 7.3) | 16.1 (2.1–30.4) | 0.55 (0.47–0.63) |
| High‐income countries (HICs) | 45.86 (34.6–59.68) | 43.06 (33.51–54.92) | 41.7 (32.6–86.2) | −3.1 (−4.5 to −1.6) | −9 (−13.1 to −4.2) | −0.36 (−0.44 to −0.28) |
Note: EAPC values are presented with two decimal places for greater clarity due to their relatively small magnitude.
Abbreviations: CI, confidence interval; EAPC, estimated annual percentage change; LC‐MASLD, liver complications related to metabolic‐associated steatotic liver disease; MALC, liver cancer due to MASH; UI, uncertainty interval.
The ASR was deemed to be in an increasing trend if the EAPC and the lower boundary of its 95% CI were both > 0%; the ASR was in a decreasing trend if the EAPC estimation and the upper boundary of its 95% CI were both < 0%; otherwise, the ASR was deemed to be uncertain over time.
The identical values for percentage changes and EAPC result from the overwhelming dominance of ‘MASLD including cirrhosis,’ which accounts for more than 99.99% of both incidence and prevalence case numbers.
3.1.2. Regional
From 1990 to 2021, variations in the age‐standardised incidence, prevalence, mortality and DALY rates for LC‐MASLD showed heterogeneity across the 21 GBD regions (Figure 1). The ASPR of LC‐MASLD peaked in the middle SDI region in 2021, while the highest annual rise of 1.09% (EAPC = 1.09 [1.05–1.14]) was observed in the high SDI region. The MENA region had the highest incidence and prevalence in 2021, while South Asia and high‐income North America experienced the most significant increases since 1990. The ASMR and ASDR for LC‐MASLD notably increased in middle and low‐middle SDI countries. The highest mortality and DALY rates for LC‐MASLD were recorded in Andean Latin America and Central Latin America, with Eastern Europe and Australasia showing the most significant increases during 1990–2021 (Table S4). The temporal trends of MASLD including cirrhosis were consistent with the overall burden (Tables S2 and S5; Figure S7). The incidence, mortality and DALYs of MALC steadily increased globally from 1990 to 2021, particularly in high SDI regions like Australasia, which encountered significant increases in MALC disease burden (Tables S3 and S6; Figure S8).
FIGURE 1.
The EAPCs of liver complications related to metabolic dysfunction‐associated steatotic liver disease (LC‐MASLD) ASR from 1990 to 2019 at the global and regional level. ASIR, age‐standardised incidence rate; ASPR, age‐standardised prevalence rate; ASMR, age‐standardised mortality rate; ASDR, age‐standardised disability‐adjusted life‐years (DALYs) rate; EAPC, estimated annual percentage change; LC‐MASLD, liver complications related to metabolic dysfunction‐associated steatotic liver disease.
3.1.3. National
Qatar demonstrated the highest percentage increase in incident (761.8%) and prevalent cases (972.2%), while the United Arab Emirates (UAE) experienced the largest percentage increases in mortalities (1028.3%) and DALYs (1076.5%) during 1990–2021. Countries with extreme case numbers of were annotated in detail (Figure S1). In 2021, the highest incidence, prevalence, mortality and DALY rates for LC‐MASLD were in Egypt (1188.6/100 000), Kuwait (32 312.2/100 000), Egypt (9.4/100 000) and Mexico (201.9/100 000). Conversely, the lowest rates were recorded in Finland (310.2/100 000), Japan (8133.5/100 000), Papua New Guinea (0.4/100 000) and Singapore (10.6/100 000). The Russian Federation demonstrated the most significant upward trends in mortality and DALYs, while the Republic of Korea experienced notable declines (Figure 2). The national disease burden of MASLD including cirrhosis and MALC was similarly analysed and the results were shown in detail (Figures S9 and S10).
FIGURE 2.
The global disease burden of LC‐MASLD for both sexes in 204 countries and territories. The ASR (A = Incidence, B = Prevalence, C = Mortality, D = DALYs) (per 100 000 persons) of LC‐MASLD in 2021 and the EAPC of LC‐MASLD ASR (E = Incidence, F = Prevalence, G = Mortality, H = DALYs) from 1990 to 2021. Countries with an extreme number of cases/evolutions were annotated. ASR, age‐standardised rate; EAPC, estimated annual percentage Change; LC‐MASLD, liver complications related to metabolic dysfunction‐associated steatotic liver disease (The maps were drawn by authors according to the corresponding data).
3.1.4. Age‐ and Sex‐Specific Distribution
Globally, the LC‐MASLD disease burden displayed an age‐specific pattern across both genders (Figure S2). The youngest age group affected by LC‐MASLD was 15–19 years, with incidence peaking earlier in males compared to females, while mortality exhibited an ascending trajectory with advancing age across both sexes. The prevalence and DALY rates escalated with age, reaching a peak at 75–79 years in both sexes, after which there was a slight decline. We supplemented the distribution characteristics of the two subtypes of incidence, mortality and DALY rate across age groups, where MASLD including cirrhosis was similar to the total burden (Figure S11). The age‐group characteristics of the MALC distribution were consistent with the general cancer presentation, that is, incidence, mortality and DALY rate all exhibited an increasing trend with age (Figure S12).
3.1.5. Risk‐Attributable LC‐MASLD Disease Burden
Globally, the proportion of deaths and DALYs due to LC‐MASLD attributable to metabolic risks has increased over the past 20 years. From 2000 to 2010, the number of deaths, crude rate and age‐standardised rate attributable to metabolic risks increased by 51.84%, 33.23% and 16.63%, respectively. These rates further increased to 61.88%, 42.58% and 17.27% in the period 2010–2021. DALYs due to metabolic risks followed a similar upward trends. High and high‐middle SDI countries showed smaller increases, while the most pronounced increases were in low‐middle and middle SDI countries (Figure S3).
3.2. Correlation of LC‐MASLD Disease Burden
The regional and national ASRs of incidence, prevalence, mortality and DALYs, versus the expected levels for each location based on SDI were displayed in Figure 3. High SDI regions like the high‐income Asia Pacific and Central Europe aligned closely with expected trends, showing lower ASRs over time. However, the observed patterns varied in medium SDI regions, with some remaining below expected levels throughout the study period and with little change in ASRs, while others were above expected levels and with more pronounced fluctuations in ASRs. Significant negative correlations were observed between SDI and the following: ASIR (ρ = −0.11, p = 0.127), ASMR (ρ = −0.21, p = 0.003) and ASDR (ρ = −0.19, p = 0.006) (Figure S4). Conversely, significant positive correlations were found between SDI and the EAPCs of both ASIR (ρ = 0.45, p < 0.001) and ASPR (ρ = 0.54, p < 0.001). This indicates that countries with higher SDI experienced more substantial increases in age‐standardised incidence and prevalence rates of LC‐MASLD. These findings were consistent with the analysis between the Healthcare Access and Quality (HAQ) Index and EAPCs (Figure S5).
FIGURE 3.
Age‐standardised rates for LC‐MASLD for 21 GBD regions and 204 countries and territories by Socio‐demographic Index, 1990–2021. (A/E = Incidence, B/F = Prevalence, C/G = Mortality, D/H = DALYs) Expected values based on Socio‐demographic Index and disease rates are shown as the black line. GBD, Global Burden of Diseases, Injuries and Risk Factors Study. LC‐MASLD, liver complications related to metabolic dysfunction‐associated steatotic liver disease; DALYs, disability‐adjusted life‐years.
3.3. Cross‐Country Health Inequalities of LC‐MASLD Disease Burden
Significant variations were evident in LC‐MASLD‐associated mortality and DALYs and by absolute and relative SDI, with the burden disproportionately concentrated in countries with low SDI. The slope index of inequality (SII) for global mortality decreased from −0.09 (−0.13 to −0.05) per 100 000 in 1990 to −0.56 (−0.60 to −0.53) per 100 000 in 2021. Similarly, the relative concentration index (RCI) decreased from −0.92 (−0.97 to −0.86) in 1990 to −4.98 (−5.28 to −4.67) in 2021, indicating that over time, mortality related to LC‐MASLD has become more concentrated in countries with lower SDI, reflecting a widening inequality in mortality (Figure 4A,C).
FIGURE 4.
SDI‐related health inequality regression and concentration curves for the burden of LCMASLD across global, in 1990 and 2021. Health inequality regression curves for morality (A) and DALYs rate (B); Health inequality concentration curves for mortality (C) and DALYs rate (D). DALYs, disability‐adjusted life‐years; LC‐MASLD, liver complications related to metabolic dysfunction‐associated steatotic liver disease; SDI, Socio‐demographic Index.
From 1990 to 2021, the SII for global DALYs changed from 1.41 (1.21 to 1.60) per 100 000 to −7.74 (−7.91 to −7.57) per 100 000. Similarly, the RCI shifted from 0.55 (0.49 to 0.61) to −2.73 (−2.90 to −2.56). These changes indicate a significant shift in the burden of DALYs towards more deprived populations, reflecting a widening inequality over time (Figure 4B,D).
We repeated the inequality analysis using the country's GDP per capita rather than SDI. We observed that the burden of mortality and DALYs was disproportionately concentrated in economically backward countries, consistent with the results of using SDI (Figure S6).
4. Discussion
This study comprehensively analysed the global burden and inequalities of LC‐MASLD during 1990–2021. We reported a significant increase in the incidence, prevalence, mortality and DALYs of LC‐MASLD, consistent with previous findings [2, 11, 40]. The drivers of the increasing trend in LC‐MASLD disease burden are multifactorial [9, 10, 12]. With increasing urbanisation, modifiable risk factors such as adherence to a diet characterised by high fat, sugar and salt content and sedentary lifestyles are often major contributors for the development of MASLD [9, 10]. The prevalence of MASLD increases with age and thus the contribution of population ageing cannot be neglected as severe NASH and cirrhosis are more common in the elderly population [41, 42]. Changes in the disease spectrum also influence the LC‐MASLD disease burden. Associations between obesity and T2DM, with heightened MASLD risk, have been reported, portending a further rise in MASLD prevalence worldwide concomitant with rising rates of obesity and T2DM [11, 43]. We observed a brief decline in age‐standardised mortality and DALY rates for LC‐MASLD during the COVID‐19 pandemic, likely due to resource reallocation and delayed data collection [21, 44, 45]. Further, patients infected with COVID‐19, especially those with pre‐existing liver disease, may have faced higher health risks [46, 47]. However, due to the high morbidity and mortality rates associated with COVID‐19, deaths in these patients may have been more frequently attributed to COVID‐19 infection, potentially contributing to the observed decrease in LC‐MASLD‐related mortality and DALY rates.
High‐risk areas for LC‐MASLD burden were concentrated in Central Latin America, Andean Latin America and the MENA region. A recent systematic evaluation reported that Latin America has the highest prevalence of MASLD (44.4%), with findings from other Latin American countries highlighting a disproportionate burden of MASLD in this region [48]. The rapid increase in obesity rates in the MENA region is a key driver of its high MASLD prevalence [1]. A recent meta‐analysis showed that the prevalence of MASLD in the MENA region was as high as 36.5%. While high‐risk regions for growth rates in LC‐MASLD incidence and prevalence were clustered in Central Asia, the MENA region, warrants in‐depth study to elucidate precise causes. Environmental factors, rather than genetic variations, likely drive these trends, with socio‐economic status, population base, obesity prevalence and dietary preferences deserving attention.
Recent predictive studies offer critical insights into the future burden of MASLD. Projections from 2016 to 2030 in countries such as China, the United States and European nations suggest a modest increase (0%–30%) in MASLD cases, while MASH prevalence is expected to rise significantly, accompanied by a doubling of liver‐related mortality and advanced liver disease [49]. In the MENA region, particularly in Saudi Arabia and the UAE, MASLD cases are projected to rise to 12.53 million and 0.37 million, respectively, by 2030, with MASH placing a considerable burden on healthcare systems [50, 51]. These projections indicate a substantial increase in the disease burden of MASLD over the coming decades and the urgency of targeted interventions for MASLD in the near future.
Globally, the proportion of LC‐MASLD deaths and DALYs attributable to metabolic risks has increased over the past two decades, underscoring their critical role in the disease burden. A recent study, such as the global analysis of metabolic diseases from 1990 to 2021, highlights the significant global health challenges posed by common metabolic diseases in the 21st century [52]. The official renaming of NAFLD to MASLD underscores the importance of metabolic dysregulation in disease progression [53]. Future efforts should prioritise addressing metabolic risk factors, especially in regions with limited health resources, through targeted public health policies and further research into the relationship between metabolic factors and MASLD progression to develop effective interventions.
Our study assessed the inequalities in LC‐MASLD mortality and DALYs across SDI levels, revealing a greater burden in lower SDI countries and a widening inequality gap over time. Lower SDI countries face higher burdens of infectious diseases, often neglecting the health hazards of chronic diseases such as MASLD. Economic disparities contribute to these inequalities, with higher SDI countries benefiting from better healthcare infrastructure and services, while lower SDI countries struggle with inadequate healthcare resources, delaying MASLD diagnosis and treatment, consequently magnifying the mortality and DALYs. Multisectoral efforts, including improving medical infrastructure, rationalising resource allocation, promoting international collaboration in health and advocating for healthy diets and lifestyles, are essential to reduce inequality gaps [54].
We advocate for a comprehensive approach to address the health challenges posed by MASLD. Firstly, it is crucial to establish clear diagnostic and care standards for MASLD, serving as a foundation for ensuring consistent and high‐quality care across borders. Secondly, we suggested strengthening research efforts focused on MASLD, including exploring the pathogenesis of MASLD and promoting new diagnostic tools and treatments. Thirdly, we emphasise the improvement of healthcare and services to enhance the prevention, diagnosis and management of MASLD, ensuring the efficacy and sustainability of efforts. This approach aims to reduce disparities in healthcare access, regardless of geographic or socioeconomic status. The above strategies serve as references for addressing the global challenges engendered by MASLD while aligning with the health equity advocated by the WHO.
Limitations of this study are mainly related to the restrictions of the data set. The inherent limitations in GBD estimates of LC‐MASLD incidence, prevalence, mortality and DALYs are equally applicable to our study. The quality of GBD data is limited by the quality and accessibility of vital registration systems within each country. Potential data gaps, particularly from economically disadvantaged countries, could bias our findings. Due to the lack of data on other relevant parameters, other possible confounding factors were not included in the correlation analysis. The concentration index can be influenced by countries with large populations, warranting future analyses should delve into subnational data to more equitably account for population size disparities across countries. Finally, the lack of MASLD data in GBD for individuals under the age of 15 years may impact our findings' comprehensiveness. Therefore, current epidemiological findings on MASLD should be approached with caution and require further confirmation in future studies.
In conclusion, the global disease burden of LC‐MASLD has steadily increased over the past three decades, especially in economically disadvantaged countries. Due to the lack of effective therapies for MASLD, prevention and therapeutic strategy development are crucial. Public health efforts must focus on preventing LC‐MASLD and optimising healthcare resource distribution. Further research should target the epidemiology of LC‐MASLD in high‐risk populations, with differentiated management and precise prevention strategies for different regions, genders and age groups to address these growing disparities.
Author Contributions
F.L. and L.Z. conceived the ideas for this research and provided overall guidance. F.L., F.J. and L.Z. prepared the first draft and finalised the manuscript based on comments from all other authors. All other authors contributed to the analysis and approved the manuscript.
Ethics Statement
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Appendix S1.
Acknowledgements
This manuscript uses publicly available results from the Global Burden of Disease (GBD) Results Tool. The Institute for Health Metrics and Evaluation (IHME), the University of Washington (UW) and the GBD Collaborator Network were not involved in the preparation of this manuscript; the contents and views in this manuscript are those of the authors and should not be construed to represent the views or interpretation of results of IHME, UW or the GBD Collaborator Network. This study was supported by National Key R&D Programme of China (2022YFC2505100, 2022YFC2505103); Outstanding Young Scholars Support Programme (grant number: 3111500001) and Xi'an Jiaotong University Young Scholar Support Grant (grant number: YX6J004). Open access publishing facilitated by Monash University, as part of the Wiley ‐ Monash University agreement via the Council of Australian University Librarians.
Funding: This study was supported by National Key R&D Programme of China (Grants 2022YFC2505100, 2022YFC2505103); Outstanding Young Scholars Support Program (Grant 3111500001) and, Xi'an Jiaotong University Young Scholar Support Grant (Grant YX6J004).
Handling Editor: Luca Valenti.
Contributor Information
Fanpu Ji, Email: jifanpu1979@163.com.
Lei Zhang, Email: lei.zhang1@monash.edu.
Data Availability Statement
To download the data used in the present study, please visit the Global Health Data Exchange GBD 2021 website.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1.
Data Availability Statement
To download the data used in the present study, please visit the Global Health Data Exchange GBD 2021 website.