Abstract
Background
We examined the incidence and predictors of clinical outcomes in metabolic dysfunction‐associated fatty liver disease (MAFLD), focusing on anthropometric parameters.
Methods
Adult patients with MAFLD were identified in nationwide databases and a hospital cohort. Primary endpoints were atherosclerotic cardiovascular disease (ASCVD) and advanced fibrosis. Logistic and Cox regression analyses were used to analyse the association between anthropometric parameters and endpoints.
Results
In total, 4407 of 15 256 (28.9%) and 6274 of 25 784 subjects (24.3%) had MAFLD in the nationwide database; of these, 403 (9.2%) and 437 (7.0%) subjects were of lean/normal weight, respectively. Compared to the overweight/obese group, the lean/normal weight group had a significantly lower muscle mass (15.0 vs. 18.9 kg) and handgrip strength (31.9 vs. 35.1 kg) and had a higher ASCVD risk (9.0% vs. 6.3% and 15.9% vs. 8.5%; Ps < 0.001). Sarcopenia (odds ratio [OR], 6.66; 95% confidence interval [CI], 1.79–24.80) and handgrip strength (OR, 0.92; 95% CI, 0.86–0.97; Ps = 0.005) were associated with the ASCVD risk in the lean/normal weight group. In a hospital cohort (n = 1363), the ASCVD risk was significantly higher in the lean/normal weight group than in the overweight/obese group (median follow‐up, 39.1 months). Muscle mass was inversely correlated with the ASCVD risk (hazard ratio [HR], 0.72; 95% CI, 0.56–0.94), while visceral adiposity was associated with advanced fibrosis (HR, 1.36; 95% CI, 1.10–1.69; Ps < 0.05).
Conclusions
Muscle mass/strength was significantly associated with the ASCVD risk in patients with MAFLD. Visceral adiposity was an independent predictor of advanced fibrosis.
Keywords: cardiovascular disease, fatty liver, fibrosis, metabolic dysfunction, sarcopenia, visceral adiposity
Introduction
Metabolic dysfunction‐associated fatty liver disease (MAFLD) is an inclusionary term that involves all patients with hepatic steatosis if they are diabetic, overweight/obese or metabolically unhealthy. 1 Compared with the traditional definition of non‐alcoholic fatty liver disease (NAFLD), which excluded patients with excessive alcohol consumption or those with chronic liver diseases, 2 , 3 such mitigation of diagnostic stringency focuses more on metabolic risk factors. As metabolic perturbation is a key mechanism in the progression of fatty liver disease and development of clinical sequelae, 2 , 3 , 4 MAFLD, rather than NAFLD, might better identify patients at higher risk of hepatic and extrahepatic outcomes. 5 The use of MAFLD could be particularly suitable for patients in the Asia‐Pacific region, where lower cut‐off points of body mass index (BMI) are used for determining overweight/obesity and the prevalence of viral hepatitis is high. 6
In a recent meta‐analysis and systematic review, the estimated overall prevalence of MAFLD was 38.8%, which was slightly higher than that of NAFLD. 7 Of note, components of metabolic syndrome, such as hypertension or diabetes, were associated with hepatic steatosis in lean or non‐obese subjects, highlighting the importance of metabolic health in the pathogenesis of fatty liver. 7
There are three subgroups of MAFLD patients according to metabolic abnormalities and BMI: (1) those with type 2 diabetes, (2) those with overweight/obesity and (3) those with normal body weight and with ≥2 abnormalities in metabolic risk parameters. 1 Previous studies that compared the clinical outcomes of patients with lean/normal weight versus overweight/obese NAFLD showed inconsistent results. 8 , 9 , 10 , 11 Of note, atherosclerotic cardiovascular disease (ASCVD), a major cause of morbidity and mortality in patients with NAFLD, occurred more frequently in those with a lean/normal weight MAFLD than in those who had an overweight/obese MAFLD. 12
One of the potential mechanisms that underlie the discrepancies in the clinical outcomes between lean/normal weight and overweight/obese MAFLD is metabolic flexibility; lean/normal weight MAFLD patients are more metabolically flexible and thus have higher metabolic adaptability. 13 Loss of this adaptive response can lead to adverse clinical outcomes, and a recent experimental study showed that epigenetic changes induced by endotoxemia were involved in the loss of adaptation in patients with lean/normal weight MAFLD. 14 From the clinical perspective, changes in body composition could be an important factor in the deterioration of adaptive response, as shown in previous studies that demonstrated that anthropometric parameters, rather than BMI itself, were associated with cardiometabolic outcomes in various patient populations, including those with NAFLD. 15 , 16 , 17 , 18 , 19
Therefore, in the present study, we aimed to compare the prevalence and incidence of the risk of ASCVD and advanced fibrosis in patients with MAFLD according to BMI criteria (lean/normal weight vs. overweight/obesity) and then to analyse the impact of changes in body composition on clinical outcomes in the cross‐sectional nationwide database (Korea National Health and Nutrition Examination Survey [KNHANES]) and in the longitudinal hospital cohort. We focused particularly on muscle mass, muscle strength and visceral adiposity in our analysis.
Methods
Study population
The KNHANES is a nationally representative cross‐sectional estimate of the health and nutritional status of non‐institutionalized Koreans. Details of the KNHANES have been previously described in detail. 20 For the present study, we used data collected during the 2008–2011 and 2013–2018 periods, in which dual‐energy X‐ray absorptiometry (DXA) and handgrip strength (HGS) were measured.
The hospital cohort was accompanied by data on demographic details, medical history, diagnosis codes and laboratory tests of patients who underwent vibration‐controlled transient elastography (VCTE) (FibroScan®, Echosens, Paris, France) and bioelectrical impedance analysis (BIA) at our gastroenterology clinic between January 2018 and December 2019. Patients who had a history of ischaemic stroke, myocardial infarction, liver cirrhosis, advanced fibrosis, liver cancer or organ transplant were excluded from analysis.
The study complied with the Declaration of Helsinki. Written informed consent was obtained from all participants, and the research project was approved by the Korea Disease Control and Prevention Agency for the KNHANES database. The study protocol of the hospital cohort was approved by the Institutional Review Board of CHA Bundang Medical Center, CHA University, Seongnam, South Korea, and written informed consent was waived (No. 2022‐09‐019).
Key variables
To define hepatic steatosis, the liver fat score (LFS) 21 and the Framingham steatosis index (FSI) 22 were calculated by using blood parameters included in the KNHANES 2008–2011 and 2013–2018 data, respectively. Participants were considered to have hepatic steatosis if LFS > −0.640 or FSI ≥ 23.
Information on sociodemographic features and comorbidities was collected with the use of standardized questionnaires. The anthropometric assessments included height, weight, BMI and waist circumference. Body composition was measured via DXA, using a Discovery fan‐beam densitometer (QDR 4500A, Hologic, Inc., Marlborough, MA, USA). The appendicular skeletal muscle mass (ASM, kg) was calculated from the sum of the skeletal muscles in the arms and legs from the KNHANES data for 2008–2011. The sarcopenia index was calculated as follows: total ASM (kg) / BMI (kg/m2), and sarcopenia was defined if the sarcopenia index was <0.789 in males and <0.521 in females, according to the criteria by the Foundation for the National Institutes of Health Sarcopenia Project. 23 HGS was measured using a digital grip strength dynamometer (TKK 5401, Takei Scientific Instruments Co., Ltd., Tokyo, Japan). Participants were asked to apply their maximum grip strength in the standing position three times with both hands, with a resting interval of at least 30 s after each measurement. The highest of the six measured values was collected as the absolute HGS.
The diagnosis of metabolic syndrome was made according to the definition of the International Diabetes Federation (IDF). 24 Significant alcohol consumption was defined as >210 g/week for males and >140 g/week for females. Diagnosis of hepatitis B virus (HBV) and hepatitis C virus (HCV) infections was based on the seropositivity of laboratory tests.
In the hospital cohort, clinical and biochemical measurements were collected from the electronic medical records of each patient. Hepatic steatosis was diagnosed by abdominal ultrasound, which was performed by gastroenterologists or radiologists of >5 years' experience. TE was used to assess hepatic steatosis and fibrosis by measuring the controlled attenuation parameter (CAP, dB/m) and liver stiffness measurement (LSM, kPa), respectively. 25
Baseline anthropometric characteristics included height, weight, waist circumference, waist‐to‐hip ratio and body composition. BIA was performed per protocol using the direct segmental multi‐frequency analyser (InBody 770, InBody Co., Ltd., Seoul, South Korea). Patients were asked to stand barefoot on the floor electrodes and hold both hand electrodes, with the shoulder abducted and arms straightened to ensure no contact between the arms and torso. The analyser used six measurement frequencies, that is, 1, 5, 50, 250, 500 and 1000 kHz, with an applied current of 80 μA (±10 μA).
ASM was calculated by summing the skeletal muscle mass of four limbs. The ASM was then adjusted to the square of height to calculate the ASM index (kg/m2). Additionally, the visceral fat area (VFA, cm2) was calculated on the basis of the measurements and software program equations. VFA adjusted by BMI (VFA‐to‐BMI ratio) was used to define visceral adiposity.
The diagnosis of MAFLD was made in subjects with hepatic steatosis defined by LFS or FSI in the KNHANES database or by ultrasound in the hospital cohort, in accordance with current consensus criteria. 1
Clinical outcomes
The primary outcomes were ASCVD and advanced fibrosis. The 10‐year ASCVD risk score was calculated in the KNHANES database at the time of the survey and in the hospital cohort at the last follow‐up date or 31 December 2021. ASCVD was defined if the calculated 10‐year ASCVD risk score was 10% or higher. Advanced fibrosis was assessed by the NAFLD fibrosis score (NFS). NFS was computed using the following formula: −1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m2) + 1.13 × impaired fasting glucose or diabetes (yes = 1, no = 0) + 0.99 × aspartate aminotransferase (AST) (U/L)/alanine aminotransferase (ALT) (U/L) − 0.013 × platelet (109/L). The highest quartile value of NFS was used to define advanced fibrosis in the KNHANES database due to lack of data. In the hospital cohort, NFS was calculated in all patients at the last date of the clinic visit or 31 December 2021.
Subjects who did not have any event were censored at their last follow‐up or 31 December 2021, whichever occurred first.
Statistical analysis
Baseline characteristics were reported in terms of mean ± standard deviation (SD) or the median (inter‐quartile range [IQR]), or n (%). The clinical outcomes of ASCVD and advanced fibrosis were compared between the lean/normal weight and overweight/obese MAFLD groups.
In the cross‐sectional KNHANES database, logistic regression analysis was performed to determine whether sarcopenia, defined by DXA or HGS, was associated with ASCVD and advanced fibrosis. Odds ratios (ORs) were adjusted for age, sex, significant alcohol consumption, current smoking, physical activity, household income, chronic kidney disease, diabetes and malignancy. Covariates were selected a priori on the basis of their possible associations with outcomes.
In the hospital cohort, the Cox proportional hazards model was used to identify the predictors of clinical outcomes. To examine the impact of predictors of advanced fibrosis, propensity score matching (PSM) was implemented using a 1:1 nearest neighbour matching algorithm without replacement, with distance determined by logistic regression. The PSM was performed based on the following variables: age, sex, LSM, ASM index, comorbidities (hypertension, diabetes, HBV infection, HCV infection and malignancy), albumin, fasting glucose, creatinine, total cholesterol, AST, ALT, gamma‐glutamyl transpeptidase (GGT) and platelet counts. After matching, covariate distributions were evaluated with stratification by pairs, and weighted statistics were reported.
All statistical analyses were performed with the use of R Version 4.2.1 (R Foundation for Statistical Computing), RStudio Version 2022.07.1+554 (Integrated Development for R, RStudio, Boston, MA, USA) and SPSS Version 27.0 for Windows (IBM Corp., Chicago, IL, USA). P values <0.05 were used to denote statistical significance.
Results
Patient characteristics and clinical outcomes of the Korea National Health and Nutrition Examination Survey database
A total of 18 795 adults aged >18 years were identified in the KNHANES 2008–2011 database (Figure S1 ). After excluding 539 subjects who met exclusion criteria (344 with ischaemic stroke, 135 with myocardial infarction, 41 with liver cirrhosis and 19 with liver cancer), LFS was calculated in 18 256 subjects. A total of 3000 subjects were further excluded due to a lack of data for calculating liver fat, and 10 849 patients who did not fulfil diagnostic criteria for MAFLD were also removed. A final analytical sample included 4407 participants with MAFLD; of them, 403 (9.2%) were of lean/normal weight and 3999 (90.8%) were overweight/obese (Table S1 ). Compared to the overweight/obese group, the lean/normal weight group was significantly older (mean age, 57 vs. 53 years; P < 0.001) and more likely to be female (56.1% vs. 50.8%; P = 0.047). ASM was significantly lower in the lean/normal weight group. The sarcopenia index (ASM/BMI) was, however, comparable between the two groups. The proportion of patients with metabolic syndrome was significantly higher in the overweight/obese group than in the lean/normal weight group (70.9% vs. 28.0%; P < 0.001), and the household income was relatively higher in the overweight/obese group (highest quartile, 25.8% vs. 22.0%; P < 0.001). The lean/normal weight group showed a significantly higher fasting glucose and lower fasting insulin than the overweight/obese group, whereas lipid profiles including triglycerides, total cholesterol and high‐density lipoprotein (HDL) cholesterol showed more favourable features in the lean/normal weight group. Median values of liver biochemistry and platelet counts were within normal limits in both groups.
Based on the 10‐year risk score, the lean/normal weight group exhibited a higher risk for ASCVD than the overweight/obese group. However, the NFS was comparable between the two groups (Table S1 ). The presence of sarcopenia was associated with a higher ASCVD risk in the univariable analysis (OR, 4.68; 95% confidence interval [CI], 2.38–9.17; P < 0.001; Table 1 ). The OR (95% CI) for ASCVD of sarcopenic subjects was 6.66 (1.79–24.80; P = 0.005), after adjusting for age, sex, alcohol consumption, current smoking, physical activity, household income, chronic kidney disease, diabetes and malignancy. Sarcopenia did not show a statistically significant association with advanced fibrosis after multivariable adjustment (OR, 1.94; 95% CI, 0.98–3.84; P = 0.06; Table 1 ).
Table 1.
Univariable and multivariable logistic regression analysis for clinical outcomes in the KNHANES 2008–2011 database
| Atherosclerotic cardiovascular disease | ||||||||
|---|---|---|---|---|---|---|---|---|
| Group | Model 1 | Model 2 | Model 3 | Model 4 | ||||
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| No sarcopenia | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | ||||
| Sarcopenia | 4.68 (2.38–9.17) | <0.001 | 6.18 (2.04–18.75) | 0.001 | 7.30 (2.10–25.38) | 0.002 | 6.66 (1.79–24.80) | 0.005 |
| Advanced fibrosis | ||||||||
|---|---|---|---|---|---|---|---|---|
| Group | Model 1 | Model 2 | Model 3 | Model 4 | ||||
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| No sarcopenia | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | ||||
| Sarcopenia | 2.74 (1.52–4.95) | 0.001 | 1.78 (0.91–3.49) | 0.09 | 1.94 (0.98–3.84) | 0.06 | 1.94 (0.98–3.84) | 0.06 |
Note: Model 1 was unadjusted. Model 2 was adjusted for age (categorical variable with a median cut‐off value of 61) and sex. Model 3 was further adjusted for significant alcohol consumption, current smoking, physical activity and household income. Model 4 was further adjusted for chronic kidney disease, diabetes and malignancy. Abbreviations: CI, confidence interval; KNHANES, Korea National Health and Nutritional Examination Survey; OR, odds ratio.
Second, HGS was measured in 28 156 adults aged >18 years who participated in the KNHANES 2014–2018. A total of 964 subjects were excluded due to a previous history of ischaemic stroke (n = 572), myocardial infarction (n = 276), liver cirrhosis (n = 95) and liver cancer (n = 21). Among the remaining 27 192 subjects, we identified 25 784 subjects in whom FSI was computable. After excluding 19 510 subjects who did not have MAFLD, a total of 6274 subjects with MAFLD were analysed; 437 (7.0%) of whom were of lean/normal weight and the remaining 5837 (93.0%) were overweight/obese (Figure S2 ). HGS (31.9 vs. 35.1 kg; P < 0.001) was significantly lower in subjects with lean/normal weight MAFLD than those with overweight/obese MAFLD (Table S2 ). The lean/normal weight group had more diabetes than the overweight/obese group (57.0% vs. 36.8%; P < 0.001), whereas metabolic syndrome was significantly more frequent in the overweight/obese group (67.8% vs. 17.8%; P < 0.001). Similar to the KNHANES 2008–2011 cohort, household income was higher for overweight/obese MAFLD than for lean/normal weight MAFLD (highest quartile, 25.7% vs. 19.2%; P < 0.001).
The proportion of patients who had 10‐year ASCVD risk of ≥10% was significantly higher in the lean/normal weight group than in overweight/obese group (64.0% vs. 45.7%; P < 0.001; Table S2 ). NFS did not, however, differ between the two groups. In the univariable logistic regression analysis, a higher HGS was associated with a lower ASCVD risk (OR, 0.96; 95% CI, 0.94–0.98; P < 0.001), and the results remained consistent after multivariable adjustment (Table 2 ). There was no significant association between HGS and advanced fibrosis after adjusting for multiple variables (Table 2 ).
Table 2.
Univariable and multivariable logistic regression analysis for clinical outcomes in the KNHANES 2013–2018 database
| Atherosclerotic cardiovascular disease | ||||||||
|---|---|---|---|---|---|---|---|---|
| Group | Model 1 | Model 2 | Model 3 | Model 4 | ||||
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| HGS (per 1 kg) | 0.96 (0.94–0.98) | <0.001 | 0.91 (0.87–0.96) | <0.001 | 0.89 (0.84–0.95) | <0.001 | 0.92 (0.86–0.97) | 0.005 |
| Advanced fibrosis | ||||||||
|---|---|---|---|---|---|---|---|---|
| Group | Model 1 | Model 2 | Model 3 | Model 4 | ||||
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| HGS (per 1 kg) | 0.96 (0.94–0.98) | <0.001 | 0.94 (0.91–0.98) | 0.001 | 0.96 (0.92–1.00) | 0.05 | 0.98 (0.94–1.03) | 0.41 |
Note: Model 1 was unadjusted. Model 2 was adjusted for age (categorical variable with a median cut‐off value of 62) and sex. Model 3 was further adjusted for significant alcohol consumption, current smoking, physical activity and household income. Model 4 was further adjusted for chronic kidney disease, diabetes and malignancy. Abbreviations: CI, confidence interval; HGS, handgrip strength; KNHANES, Korea National Health and Nutrition Examination Survey; OR, odds ratio.
Patient characteristics and clinical outcomes of the hospital cohort
The hospital cohort consisted of 165 (12.1%) patients with lean/normal weight and 1198 (87.9%) patients with overweight/obese MAFLD (Figure S3 ). The lean/normal weight group (n = 165; 12.1%) was significantly older than overweight/obese group (mean age, 53 vs. 51 years; P = 0.006) and predominantly female (50.9% vs. 38.2%; P = 0.002; Table S3 ). BIA‐measured ASM (18.4 vs. 22.7 kg), ASM adjusted by height (ASM index, 6.9 vs. 8.0 kg/m2) and VFA (66.9 vs. 100.8 cm2) were significantly lower in patients with lean/normal weight MAFLD than in those with overweight/obese MAFLD (Ps < 0.001). The proportion of patients with diabetes was comparable between the two groups. Baseline AST and ALT were higher in the overweight/obese group than in the lean/normal weight group; however, the median values were within normal range in both groups. Other laboratory parameters did not differ according to group.
During a median follow‐up of 39.1 months (95% CI, 37.4–40.8), the lean/normal weight group exhibited a higher risk for ASCVD events based on the 10‐year risk score than the overweight/obese group (Table 3 and Figure 1 A ). The cumulative incidence of advanced fibrosis defined by NFS was comparable between the two groups during a median follow‐up of 36.4 months (35.3–37.5; Table 3 and Figure 1 B ).
Table 3.
Incidence of clinical outcomes between lean/normal weight and overweight/obese MAFLD
| Lean/normal weight MAFLD | Overweight/obese MAFLD | Incidence rate ratio (95% CI) | P value | |
|---|---|---|---|---|
| Cardiovascular disease by 10‐year atherosclerotic cardiovascular disease risk estimation score | ||||
| Cumulative incidence | 100 | 516 | ||
| CIR (95% CI), per 1000 PY | 21.80 (17.80–26.36) | 15.30 (14.02–16.66) | 1.42 (1.15–1.76) | 0.001 |
| Advanced fibrosis by non‐alcoholic fatty liver disease fibrosis score | ||||
| Cumulative incidence | 6 | 52 | ||
| CIR (95% CI), per 1000 PY | 1.30 (0.52–2.63) | 1.53 (1.15–1.99) | 0.85 (0.36–1.97) | 0.70 |
Abbreviations: CI, confidence interval; CIR, cumulative incidence rate; MAFLD, metabolic dysfunction‐associated fatty liver disease; PY, person‐years.
Figure 1.
Cumulative incidence of (A) atherosclerotic cardiovascular disease and (B) advanced fibrosis between lean/normal weight and overweight/obese metabolic dysfunction‐associated fatty liver disease (MAFLD) patients in the hospital cohort.
Predictors of clinical outcomes in the hospital cohort
In the univariable Cox proportional hazards model, age, male sex, baseline CAP values, BMI, ASM, ASM index, VFA, hypertension, diabetes, malignancy, albumin, creatinine, HDL cholesterol and platelet counts were identified as predictors of ASCVD (Table 4 ). Subsequent multivariable analysis demonstrated that a higher ASM index was significantly associated with a lower incidence of ASCVD (hazard ratio [HR], 0.72; 95% CI, 0.56–0.94; P = 0.014), together with age (HR, 1.05; 95% CI, 1.04–1.06; P < 0.001), male sex (HR, 2.08; 95% CI, 1.43–3.01; P < 0.001) and HDL cholesterol levels (HR, 0.93; 95% CI, 0.89–0.96; P < 0.001).
Table 4.
Univariable and multivariable Cox proportional hazards model for clinical outcomes in the hospital cohort
| Variable | Atherosclerotic cardiovascular disease | Advanced fibrosis | ||||||
|---|---|---|---|---|---|---|---|---|
| Univariable | Multivariable | Univariable | Multivariable | |||||
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
| Age (per 1 year) | 1.05 (1.04–1.05) | <0.001 | 1.05 (1.04–1.06) | <0.001 | 1.11 (1.08–1.14) | <0.001 | 1.10 (1.07–1.13) | <0.001 |
| Sex (male) | 1.18 (1.00–1.40) | 0.047 | 2.08 (1.43–3.01) | <0.001 | 0.64 (0.38–1.07) | 0.09 | ||
| CAP (per 10 dB/m) | 0.97 (0.95–0.99) | 0.001 | 0.98 (0.96–1.01) | 0.17 | 0.98 (0.92–1.04) | 0.43 | ||
| LSM (per 1 kPa) | 1.02 (0.99–1.05) | 0.18 | 1.10 (1.07–1.12) | <0.001 | 1.09 (1.06–1.13) | <0.001 | ||
| BMI (per 1 kg/m2) | 0.93 (0.90–0.96) | <0.001 | 0.98 (0.90–1.07) | 0.70 | 1.06 (0.99–1.14) | 0.11 | ||
| WC (per 1 cm) | 0.97 (0.96–0.98) | <0.001 | 1.00 (0.97–1.02) | 0.79 | ||||
| WHR (per 1 unit) | 0.10 (0.02–0.47) | 0.003 | 3.97 (0.05–352.96) | 0.55 | ||||
| ASM (per 1 kg) | 0.97 (0.95–0.98) | <0.001 | 1.02 (0.99–1.05) | 0.25 | ||||
| ASM index (per 1 kg/m2) | 0.85 (0.79–0.92) | <0.001 | 0.72 (0.56–0.94) | 0.014 | 1.06 (0.98–1.14) | 0.14 | ||
| VFA (per 10 cm2) | 0.96 (0.93–0.98) | <0.001 | 1.01 (0.95–1.08) | 0.68 | 1.09 (1.03–1.16) | 0.002 | ||
| Visceral adiposity a (per 1 unit) | 0.90 (0.84–0.98) | 0.009 | 1.36 (1.11–1.68) | 0.003 | 1.36 (1.10–1.69) | 0.005 | ||
| Hypertension | 1.47 (1.25–1.73) | <0.001 | 1.20 (0.96–1.50) | 0.10 | 2.45 (1.46–4.13) | 0.001 | 1.04 (0.59–1.85) | 0.88 |
| Diabetes | 1.32 (1.09–1.61) | 0.005 | 0.99 (0.77–1.27) | 0.92 | 2.95 (1.71–5.08) | <0.001 | 2.14 (1.21–3.77) | 0.009 |
| Metabolic syndrome | 0.89 (0.76–1.05) | 0.17 | 1.17 (0.69–1.99) | 0.56 | ||||
| Significant alcohol consumption | 1.04 (0.85–1.27) | 0.70 | 1.02 (0.53–1.97) | 0.95 | ||||
| HBV infection | 0.95 (0.80–1.13) | 0.55 | 0.58 (0.32–1.05) | 0.07 | ||||
| HCV infection | 1.07 (0.69–1.64) | 0.77 | 2.35 (0.93–5.95) | 0.07 | ||||
| Malignancy | 1.48 (1.09–2.00) | 0.012 | 1.27 (0.86–1.89) | 0.23 | 2.17 (0.98–4.81) | 0.06 | ||
| Albumin (per 1 g/dL) | 0.70 (0.59–0.84) | <0.001 | 0.90 (0.72–1.13) | 0.36 | 0.31 (0.17–0.56) | <0.001 | ||
| Fasting glucose (per 10 mg/dL) | 1.02 (0.99–1.04) | 0.17 | 1.05 (1.00–1.10) | 0.032 | ||||
| Creatinine (per 1 mg/dL) | 1.25 (1.02–1.53) | 0.031 | 1.10 (0.87–1.41) | 0.43 | 1.56 (1.25–1.94) | <0.001 | ||
| Triglyceride (per 10 mg/dL) | 1.01 (1.00–1.02) | 0.07 | 1.01 (0.99–1.03) | 0.38 | ||||
| Total cholesterol (per 10 mg/dL) | 0.99 (0.97–1.01) | 0.32 | 0.92 (0.86–0.99) | 0.026 | ||||
| HDL cholesterol (per 5 mg/dL) | 0.96 (0.92–0.99) | 0.017 | 0.93 (0.89–0.96) | <0.001 | 0.99 (0.88–1.11) | 0.86 | ||
| AST (per 1 IU/L) | 1.00 (1.00–1.00) | 0.66 | 1.01 (1.00–1.01) | <0.001 | ||||
| ALT (per 1 IU/L) | 1.00 (1.00–1.00) | 0.34 | 0.99 (0.97–1.00) | 0.07 | ||||
| GGT (per 1 IU/L) | 1.00 (1.00–1.00) | 0.16 | 1.00 (1.00–1.00) | <0.001 | ||||
| Platelet (per 10 × 1000/μL) | 0.97 (0.96–0.99) | <0.001 | 1.27 (0.86–1.89) | 0.19 | 0.82 (0.78–0.87) | <0.001 | ||
Abbreviations: ALT, alanine aminotransferase; ASM, appendicular skeletal muscle mass; AST, aspartate aminotransferase; BMI, body mass index; CAP, controlled attenuation parameter; CI, confidence interval; GGT, gamma‐glutamyl transpeptidase; HBV, hepatitis B virus; HCV, hepatitis C virus; HDL, high‐density lipoprotein; HR, hazard ratio; LSM, liver stiffness measurement; VFA, visceral fat area; WC, waist circumference; WHR, waist‐to‐hip ratio.
Visceral adiposity was calculated from dividing VFA by BMI.
With regard to advanced fibrosis, age, baseline LSM values, VFA, hypertension and diabetes were associated with an increased risk of advanced fibrosis. Laboratory parameters including albumin, fasting glucose, creatinine, total cholesterol, AST, GGT and platelet counts were also identified as predictors of advanced fibrosis in the univariable analysis (all Ps < 0.05; Table 4 ). Considering the number of events of advanced fibrosis (n = 52), clinically and statistically relevant variables, that is, age, LSM values, hypertension, and diabetes, were selected for multivariable analysis. Additionally, ‘visceral adiposity’ was calculated by dividing VFA by BMI and was put into the multivariable model instead of VFA, due to the significant correlation between VFA and BMI (Pearson's r = 0.74; P < 0.001). As a result, age (HR, 1.10; 95% CI, 1.07–1.13; P < 0.001), baseline LSM value (HR, 1.09; 95% CI, 1.06–1.13; P < 0.001), diabetes (HR, 2.14; 95% CI, 1.21–3.77; P = 0.009) and visceral adiposity (HR, 1.36; 95% CI, 1.10–1.69; P = 0.005) were associated with advanced fibrosis. When we divided the patients into high and low visceral adiposity groups based upon the optimal cut‐off value of 5.03 (Figure S4 ), patients in the high visceral adiposity group were shown to have a significantly increased risk of advanced fibrosis than those in the low visceral adiposity group (HR, 2.88; 95% CI, 1.68–4.93; P < 0.001; Figure S5A ).
Because laboratory parameters, including albumin, fasting glucose, creatinine, total cholesterol, AST, GGT and platelet counts, were not adjusted in the multivariable analysis due to the limited number of events, we implemented a PSM to examine the impact of visceral adiposity while minimizing the effect of unadjusted covariates. Through PSM, a total of 157 pairs were extracted, with no remarkable intergroup differences in the abovementioned laboratory values (Table S4 ). Consistent with the results obtained from the entire hospital cohort, the advanced fibrosis events were significantly more common in the high visceral adiposity group than in the low visceral adiposity group (HR, 4.00; 95% CI, 1.13–14.17; P = 0.032; Figure S5B ).
Sensitivity analyses
In stratified analyses, patients with a higher ASM index showed a significantly lower ASCVD risk, regardless of sex and HBV infection (Table S5 ). A significant inverse correlation between ASM index and ASCVD risk was demonstrated in patients with obesity or metabolic syndrome, as well as in those without hypertension, diabetes, a history of significant alcohol consumption, HCV infection or malignancy. Although statistical significance was not reached, the results tended to be consistent in patients without obesity or metabolic syndrome and in those with hypertension, diabetes, history of significant alcohol consumption, HCV infection or malignancy (all HRs < 1.00). The association between visceral adiposity and the risk of advanced fibrosis was also similar to those from the main analyses across the subgroups.
Discussion
In this analysis of cross‐sectional nationwide databases and longitudinal hospital data, lean/normal weight MAFLD consisted of <15% of all MAFLD cases. The lean/normal weight group exhibited a significantly higher risk of ASCVD than the overweight/obese group; however, the risk of advanced fibrosis did not differ between the two groups. Sarcopenia, defined by a decrease in muscle mass or strength, was independently associated with the risk of ASCVD in all the analyses; however, this association was not observed for advanced fibrosis. Instead, visceral adiposity predicted the risk of advanced fibrosis in the entire hospital cohort, as well as in the matched population.
Given the importance of metabolic dysfunction in the pathogenesis and progression of fatty liver disease, MAFLD was recently proposed as a new nomenclature replacing NAFLD in the consensus statement by international experts. 1 As the name suggests, a diagnosis of NAFLD cannot be made if hepatic steatosis was attributable to excessive alcohol consumption or to other chronic liver diseases, such as viral hepatitis. In contrast, MAFLD is a comprehensive term, which includes all patients with hepatic steatosis if they are overweight/obese or diabetic. In the case of lean/normal weight patients with hepatic steatosis, the presence of two or more metabolic derangement is required for diagnosis of MAFLD. Compared with NAFLD, MAFLD is simpler and more inclusive and has superior ability to identify patients with advanced fibrosis and to predict clinical outcomes, including ASCVD‐related mortality. 5 , 26 MAFLD criteria can be particularly useful for patients in the Asia‐Pacific region who have a risk of cardiometabolic complications of overweight/obesity even at lower BMI cut‐off points. 6 , 27
Of note, a recent meta‐analysis reported that lean NAFLD consisted of 19.2% of all NAFLD patients, 8 a figure that is similar to that of our hospital cohort. In that study, the incidence of overall, cardiovascular and liver‐specific mortality was higher in the non‐obese patients than in the obese patients. Similarly, a study that analysed the National Health and Nutrition Examination Survey (NHANES) 1999–2016 database reported that cardiovascular disease (CVD)‐related and 15‐year overall mortality were significantly higher in patients with non‐obese NAFLD than in those with obese NAFLD. 28 On the other hand, a recent study that analysed Caucasian NAFLD patients, all of whom had delivered liver biopsy samples, demonstrated that the incidence of clinical outcomes, including hepatic decompensation and cardiovascular events, tended to be lower in the lean group than in the non‐lean group, although statistical significance was not reached. 11 Other studies also did not show consistent results with regard to the clinical outcomes of lean/normal weight (or non‐obese) NAFLD patients compared to overweight (or obese) patients. 9 , 10 , 29 , 30 , 31 , 32 These discrepancies could be due to the heterogeneity of the patient population (lean vs. non‐lean group or non‐obese vs. obese group) and the use of different criteria defining clinical outcome, such as CVD. Furthermore, most studies included patients who were diagnosed with NAFLD, not MAFLD. Therefore, in this study, we identified a homogeneous population of patients who were diagnosed with MAFLD according to the current consensus statement and categorized the patients into two groups, the lean/normal weight group and the overweight/obese group, according to a BMI cut‐off of 23. Also, well‐validated clinical scores, 10‐year ASCVD risk scores 33 and NFS 34 were used to define ASCVD and advanced fibrosis.
Skeletal muscle is a major regulator involved in glucose metabolism and homeostasis, and therefore, decrease in skeletal muscle mass and strength is associated with insulin resistance and metabolic dysregulation. 35 Additionally, a significant correlation between excessive visceral fat and many facets of metabolic syndrome, such as impairment in glucose tolerance, insulin signalling and lipid metabolism, has been documented in previous studies. 36 Given that metabolic alterations are implicated in the pathogenesis of hepatic steatosis and are associated with CVD, 2 , 3 we particularly focused on the impact of anthropometric measurements on the incidence of clinical outcomes in patients with MAFLD.
Our findings revealed that essential components of sarcopenia, muscle mass and strength were significantly lower in the lean/normal weight group than in the overweight/obese group, when assessed by DXA and HGS, respectively. Additionally, sarcopenia (defined by sarcopenia index) was associated with an approximately seven times higher risk of ASCVD (OR, 6.66; 95% CI, 1.79–24.80), after adjusting for multiple covariates. Similarly, a 1‐kg increase in muscle strength was independently associated with an 8% decrease in the ASCVD risk. This finding was reproduced in the hospital cohort, which showed a significant decrease in the risk of ASCVD in those with a high ASM index. Meanwhile, skeletal muscle‐related parameters did not show a statistically significant correlation with advanced fibrosis in all the analyses. Instead, visceral adiposity predicted advanced fibrosis in the hospital cohort, both in the entire and in the propensity score‐matched analyses.
Apart from anthropometric parameters, age was significantly associated with both ASCVD and advanced fibrosis in our hospital cohort. It is consistent with previous reports that showed a significant association between age and clinical consequences in patients with NAFLD, regardless of fibrotic burden. 37 , 38 Considering the rapidly ageing population, global obesity epidemic and its close correlation with various cardiometabolic diseases, incorporating MAFLD in the multidisciplinary care of patients with metabolic dysregulation is of utmost importance to prevent complications of metabolic diseases. 39 Furthermore, considering the lack of pharmacological therapies for MAFLD, clinical trials with adaptive and flexible design are needed, and anthropometric parameters could be potential targets in these trials.
This study has several strengths. We identified the association of various anthropometric measurements, including muscle mass/strength and visceral adiposity, with the risk of ASCVD and advanced fibrosis in patients with MAFLD. The use of a nationwide database covering the entire Korean population allowed a large sample of patients with MAFLD. These, together with subsequent analyses of patients in a longitudinal hospital cohort, all of whom had body composition data at baseline, added the robustness to our findings. Furthermore, all the patients in the hospital cohort had imaging data for assessment of the presence and degree of hepatic steatosis.
Our findings have several limitations. First, despite a large number of patients, we did not have enough clinical events in terms of ASCVD and advanced fibrosis in the hospital cohort. This is understandable considering the epidemiology and clinical course of ASCVD and advanced fibrosis. Therefore, use of well‐validated clinical scores is a reasonable option to define clinical outcomes. Second, BIA was used to assess body composition in our hospital cohort, not a computed tomography scan that is considered the gold standard for measuring muscle mass and visceral fat. However, BIA is a guideline‐accepted method for sarcopenia screening, 40 which is affordable, available and safe. Third, our study was limited to Asian patients; therefore, the results should be interpreted with caution when applied to different race groups.
In conclusion, our results demonstrated that lean/normal weight MAFLD consisted of <15% of all MAFLD patients. The lean/normal weight group was at a significantly higher risk of ASCVD than the overweight/obese group; however, the risk of advanced fibrosis was comparable. A decrease in muscle mass/strength was associated with the risk of ASCVD, whereas visceral adiposity predicted advanced fibrosis. Anthropometric assessments may confer additional benefits in predicting cardiovascular and liver‐specific risks in patients with MAFLD.
Conflict of interest statement
The authors have no relevant financial or non‐financial interests to disclose.
Supporting information
Table S1. Baseline Characteristics of Participants in the KNHANES 2008–2011 Database.
Table S2. Baseline Characteristics of Participants in the KNHANES 2013–2018 Database.
Table S3. Baseline Characteristics of Patients in the Hospital Cohort.
Table S4. Baseline Characteristics of Patients in the Hospital Cohort according to Visceral Adiposity after Propensity Score Matching.
Table S5. Stratified Analyses for Risk of Atherosclerotic Cardiovascular Disease and Advanced Fibrosis in the Hospital Cohort according to Anthropometric Parameters.
Figure S1. Inclusion and exclusion criteria for the KNHANES 2008–2011 analysis.
Figure S2. Inclusion and exclusion criteria for the KNHANES 2014–2018 analysis.
Figure S3. Inclusion and exclusion criteria for the hospital cohort analysis.
Figure S4. Cut‐off value of visceral fat area to body mass index ratio.
Figure S5. Cumulative incidence of advanced fibrosis according to visceral adiposity (A) in the entire hospital cohort and (B) in the propensity score‐matched hospital cohort.
Choi K. Y., Kim T. Y., Chon Y. E., Kim M. N., Lee J. H., Hwang S. G., et al (2023) Impact of anthropometric parameters on outcomes in Asians with metabolic dysfunction‐associated fatty liver disease, Journal of Cachexia, Sarcopenia and Muscle, 14, 2747–2756, doi: 10.1002/jcsm.13351
Contributor Information
Yun Mi Choi, Email: ymchoi@hallym.or.kr.
Yeonjung Ha, Email: yeonjung.ha@gmail.com.
Data availability statement
The datasets used and/or analysed during this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Baseline Characteristics of Participants in the KNHANES 2008–2011 Database.
Table S2. Baseline Characteristics of Participants in the KNHANES 2013–2018 Database.
Table S3. Baseline Characteristics of Patients in the Hospital Cohort.
Table S4. Baseline Characteristics of Patients in the Hospital Cohort according to Visceral Adiposity after Propensity Score Matching.
Table S5. Stratified Analyses for Risk of Atherosclerotic Cardiovascular Disease and Advanced Fibrosis in the Hospital Cohort according to Anthropometric Parameters.
Figure S1. Inclusion and exclusion criteria for the KNHANES 2008–2011 analysis.
Figure S2. Inclusion and exclusion criteria for the KNHANES 2014–2018 analysis.
Figure S3. Inclusion and exclusion criteria for the hospital cohort analysis.
Figure S4. Cut‐off value of visceral fat area to body mass index ratio.
Figure S5. Cumulative incidence of advanced fibrosis according to visceral adiposity (A) in the entire hospital cohort and (B) in the propensity score‐matched hospital cohort.
Data Availability Statement
The datasets used and/or analysed during this study are available from the corresponding author upon reasonable request.