Low skeletal muscle mass is associated with non-alcoholic fatty liver disease in Korean adults: the Fifth Korea National Health and Nutrition Examination Survey

Hee Yeon Kim, Chang Wook Kim, Chung-Hwa Park, Jong Young Choi, Kyungdo Han, Anwar T Merchant and Yong-Moon ParkSeoul, Korea and Research Triangle Park, USA

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Low skeletal muscle mass is associated with non-alcoholic fatty liver disease in Korean adults: the Fifth Korea National Health and Nutrition Examination Survey

Hee Yeon Kim, Chang Wook Kim, Chung-Hwa Park, Jong Young Choi, Kyungdo Han, Anwar T Merchant and Yong-Moon Park
Seoul, Korea and Research Triangle Park, USA

Author Affiliations: Division of Hepatology, Department of Internal Medicine (Kim HY, Kim CW, Park CH and Choi JY) and Department of Biostatistics (Han K), College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA (Merchant AT); Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC 27709, USA (Park YM)

? 2016, Hepatobiliary Pancreat Dis Int. All rights reserved.

Published online November 9, 2015.

BACKGROUND: Sarcopenia and non-alcoholic fatty liver disease (NAFLD) share similar pathophysiological mechanisms, and the relationship between sarcopenia and NAFLD has been recently investigated. The study investigated whether low skeletal muscle mass is differentially associated with NAFLD by gender in Korean adults.

METHODS: We conducted a cross-sectional analysis of the data from the Fifth Korea National Health and Nutrition Examination Survey. The skeletal muscle index (SMI) was obtained by the appendicular skeletal muscle mass divided by the weight. NAFLD was defined as a fatty liver index (FLI) ≥60 in the absence of other chronic liver disease.

RESULTS: Among the included subjects, 18.3% (SE: 1.4%) in men and 7.0% (SE: 0.7%) in women were classified as having FLI-defined NAFLD. Most of the risk factors for FLI-defined NAFLD showed a significant negative correlation with the SMI in both genders. Multiple logistic regression analysis showed

that low SMI was associated with FLI-defined NAFLD, independent of other metabolic and lifestyle parameters in both genders [males: odds ratio (OR)=1.35; 95% confidence interval (CI): 1.17-1.54; females: OR=1.36; 95% CI: 1.18-1.55]. The magnitude of the association between FLI-defined NAFLD and low SMI was higher in middle aged to elderly males (OR=1.50; 95% CI: 1.22-1.84) than in males less than 45 years of age (OR=1.25; 95% CI: 1.02-1.52) and in premenopausal females (OR=1.50; 95% CI: 1.12-2.03) than in postmenopausal females (OR=1.36; 95% CI: 1.20-1.54).

CONCLUSIONS: Low SMI is associated with the risk of FLI-defined NAFLD independent of other well-known metabolic risk factors in both genders. This association may differ according to age group or menopausal status. Further studies are warranted to confirm this relationship.

(Hepatobiliary Pancreat Dis Int 2016;15:39-47)

KEY WORDS:Korea National Health and Nutrition Examination Survey; non-alcoholic fatty liver disease; sarcopenia; skeletal muscle

Introduction

Sarcopenia, defined as a loss of muscle mass and strength, was originally regarded as an age-related change.[1]Growing evidence demonstrates that chronic inflammation plays an important role in the development and progression of sarcopenia.[2, 3]Moreover, insulin resistance may accelerate muscle protein loss, and thereby decrease muscle mass and strength.[4, 5]In contrast, sarcopenia may promote insulin resistance because skeletal muscle is a primary insulin-responsive target tissue.[6]Several reports[7-9]have revealed that low skeletalmuscle mass is associated with metabolic syndrome or type 2 diabetes.

Non-alcoholic fatty liver disease (NAFLD), which is characterized by abnormal fat accumulation in the liver, is the most prevalent chronic liver disease in Western countries;[10]its prevalence is also increasing rapidly in Asian countries, including Korea.[11]Insulin resistance plays a key role in the development of NAFLD, and studies[12, 13]have revealed a close relationship between NAFLD and each component of metabolic syndrome. Therefore, NAFLD is regarded as the hepatic manifestation of metabolic syndrome.[14]Moreover, NAFLD is associated with chronic oxidative stress and inflammation of the liver secondary to hepatic triglyceride accumulation.[15]

Accordingly, NAFLD and sarcopenia share similar pathophysiological mechanisms of insulin resistance and chronic inflammation. Two recent studies investigated the association between sarcopenia and NAFLD.[16, 17]However, neither studies analyzed the gender-specific impact of sarcopenia on the development of NAFLD despite the fact that gender and age differentially influence muscle mass.[18]

The aim of the present study was to investigate whether low skeletal muscle mass is differentially associated with NAFLD by gender, independent of other metabolic factors, in a representative Korean adult population based on the data from the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V), conducted from 2010 to 2011.

Methods

Study population

This study used the data from the KNHANES V, conducted from 2010 to 2011. The KNHANES is a series of nationally representative, cross-sectional surveys administered since 1998. It uses a complex, stratified, multistage, probability sampling design to assess the health and nutritional status of the non-institutionalized civilian Korean population.[19, 20]The survey was approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention. All participants provided written informed consent. Moreover, we used deidentified data in the study.

A total of 6093 subjects aged ≥19 years participated in the health examination, which included whole-body dual-energy X-ray absorptiometry (DXA) and interview. Subjects were excluded for the following reasons: a history of malignancy (n=83), a physician’s diagnosis of chronic hepatitis or cirrhosis (n=35), chronic renal failure (n=289), pregnancy (n=24), excessive alcohol use (>20 g/day in male subjects, >10 g/day in female subjects) (n=991), and absence of data (n=932). These exclusion criteria eliminated many participants. However, this conservative approach was important to minimize potential bias due to inclusion of chronic liver disease other than NAFLD or other factors influencing skeletal muscle mass. After exclusion of ineligible subjects, 3739 subjects (1184 males and 2555 females) were included in the analysis.

Dual-energy X-ray absorptiometry

In the KNHANES V, a whole-body DXA scan (Discovery-W; Hologic, Waltham, MA, USA) was indicated for each participant aged ≥10 years to measure the whole-body skeletal muscle and fat mass. The appendicular skeletal muscle (ASM), defined as the sum of the lean soft tissue masses of the arms and legs,[21]has been known to correlate with total body skeletal muscle.[22]The skeletal muscle index [SMI (%)=ASM (kg)/weight (kg)×100] was calculated as described by Janssen et al.[23]

Surrogate measure of fatty liver

To identify fatty liver, the fatty liver index (FLI) was calculated according to an algorithm based on triglycerides, body mass index (BMI), gamma-glutamyl transferase (GGT), and waist circumference:[24]

The FLI score ranges from 0 to 100. It has been validated against fatty liver diagnosed by ultrasonography with a proven accuracy of 0.84 [95% confidence interval (CI): 0.81-0.87].[24]When the FLI is ≥60, the likelihood of having fatty liver disease is >78%.[25]In this study, subjects were classified as having NAFLD if the FLI was ≥?60 in the absence of other causes of chronic liver disease (history of hepatitis or cirrhosis, hepatitis B surface antigen negative, excessive alcohol consumption, as defined above).

Anthropometric and laboratory measurements

Height (m) and weight (kg) were measured with the subject wearing light clothing and barefoot. Height was measured using a stadiometer (Seca 225; Seca, Hamburg, Germany), and weight was measured using an electronic scale (GL-6000-20; Caskorea, Seoul, Korea). BMI was calculated as weight (kg)/height2(m2). Waist circumference (cm) was measured at the midpoint between the costal margin and the iliac crest at the end of a normal expiration. Blood pressure (BP) was measured three times using a mercury sphygmomanometer (Baumanometer; Baum, Copiague, NY, USA) on the right arm after a5-minute rest period. The average of the last two measurements was used for the analysis.

Venous blood samples were collected from each participant after >8 hours of fasting. Serum samples were processed, immediately refrigerated, and transported to the central laboratory (NeoDIN Medical Institute, Seoul, South Korea). All blood samples were analyzed within 24 hours after transportation. Serum levels of glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), GGT, triglycerides, and cholesterol were measured using a Hitachi Automatic Analyzer 7600 (Hitachi, Tokyo, Japan). The white blood cell count (WBC) was measured with an XE-2100D system (Sysmex, Japan). Hepatitis B surface antigen was analyzed using an electrochemiluminescence immunoassay (Modular E-170; Roche Diagnostics, Mannheim, Germany). Serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured by radioimmunoassay (DiaSorin Inc., Stillwater, MN, USA) using a 1470 WIZARD γ-counter (Perkin-Elmer, Turku, Finland). Serum insulin levels were obtained by an immunoradiometric assay (BioSource, Nivelles, Belgium) using a 1470 WIZARD γ-counter (Perkin-Elmer). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as follows: HOMA-IR=fasting serum glucose (mg/dL)×fasting insulin (μU/mL)/405.[26]

Covariates

Demographic variables, history of medical illness, smoking, alcohol consumption, physical activities and menopausal status were determined by self-reported questionnaires. Participants were categorized as current smokers, past smokers, and nonsmokers. The amount of alcohol consumed per day was calculated from the frequency and amount of reported alcohol consumption. Regular exercise was defined as performing moderate or vigorous physical activity for at least 20 minutes more than three times per week. Dietary intake was collected through food-frequency questionnaires comprising 63 food items and food intake questionnaire using the 24-hour recall method.[20]Daily energy and nutrient intake, including protein and fat, was calculated using the CAN-Pro 3.0 nutrient intake assessment software developed by the Korean Nutrition Society.

Definitions

Subjects with diabetes included those with a fasting glucose level of ≥126 mg/dL, a previous diagnosis of diabetes by a health care professional, or the use of hypoglycemic medications. Subjects with hypertension were defined as those with a systolic BP of ≥140 mmHg, a diastolic BP of ≥90 mmHg, a previous diagnosis of hypertension, or the use of antihypertensive medication. According to the criteria proposed by the American Heart Association and the National Heart, Lung, and Blood Institute together with the International Diabetes Federation in 2009 using the adjusted waist circumference for Asians,[27]metabolic syndrome was determined to be present if the participant had any three of the following five criteria: (1) an abdominal waist circumference of ≥90 cm in males or ≥80 cm in females; (2) triglycerides of ≥150 mg/dL or use of relevant medication; (3) highdensity-lipoprotein cholesterol of <40 mg/dL in males or <50 mg/dL in females or use of relevant medication; (4) systolic BP of ≥130 mmHg or diastolic BP of ≥85 mmHg, or use of antihypertensive medication; and (5) fasting plasma glucose level of ≥100 mg/dL or use of antihyperglycemic medication.

Statistical analysis

The SAS survey procedure (ver. 9.3; SAS Institute, Inc., Cary, NC, USA) was used for statistical analyses to reflect the complex sampling design and sampling weights of the KNHANES and to provide nationally representative prevalence estimates. For the subgroup analysis, domain analysis was applied to preserve the complex sampling design in which the entire sample was used to estimate the variance of subpopulations. A P value of <0.05 indicated statistical significance in all analyses.

The levels of triglycerides, AST, ALT, GGT, WBC and HOMA-IR were log-transformed because of a positively skewed distribution. Data were expressed as mean±standard error (SE) for continuous variables with normal distributions, and as geometric means (95% CIs) for continuous variables with skewed distributions. Categorical variables were expressed as proportions (SE). Differences between the two groups were compared using a linear regression analysis for continuous variables and a Rao-Scott Chi-square test for categorical variables. Correlations of FLI and SMI with other metabolic variables were evaluated by linear regression analyses, since the correlation analysis is not available in the SAS survey procedure. For this correlation analysis, FLI was logtransformed because of a positively skewed distribution.

Multiple logistic regression analyses were performed to estimate the magnitude of the association between the decline in skeletal muscle mass and the presence of NAFLD (FLI ≥60) by gender. In addition to the age-adjusted model, the following covariates were included in the model: smoking status (nonsmoker/past smoker/current smoker), alcohol drinking status (nondrinker/<20 g/ day for males or <10 g/day for females), regular exercise (no/yes) in both genders, with total energy intake, carbohydrate intake (energy %), and fat intake (energy%) in women. In the final model, WBC, HOMA-IR, 25[OH]D levels, number of metabolic syndrome, diabetes, and hypertension were further adjusted. In addition, subgroup analyses were conducted to assess the effect modification by age or menopause status on the association between SMI and FLI-defined NAFLD.

Results

Baseline characteristics

Geometric means of FLI were 22.0 (95% CI: 20.6-23.5) and 10.0 (95% CI: 9.3-10.7) in males and females, respectively. Minimum and maximum values for FLI of men were 0.6 and 99.5, respectively while the corresponding values for females were 0.5 and 97.3 respectively. The weighted prevalence estimates of FLI-defined NAFLD were 18.3% (SE: 1.4%) of the males and 7.0% (SE: 0.7%) of the females in our KNHANES cohort. The weighted proportions of FLI-defined NAFLD were 18.1% (SE: 2.1%) of the males aged <45 years and 18.5% (SE: 1.8%) of those aged ≥45 years, respectively. Among the females, the weighted proportions of FLI-defined NAFLD were 4.1% (SE: 0.8%) in the premenopause group and 11.6% (SE: 1.3%) in the postmenopause group.

Table 1. Baseline characteristics of the study subjects

The baseline characteristics of the study participants are shown in Table 1. Clinical, anthropometric, and metabolic variables were analyzed according to the FLIgrouping (FLI ≥60, NAFLD group; FLI <60, non-NAFLD group) stratified by gender. Subjects of both genders in the FLI-defined NAFLD group had a higher BMI, waist circumference, and total body fat mass than did those in the non-NAFLD group. In addition, systolic BP, diastolic BP, fasting blood glucose, AST, ALT, GGT, WBC, HOMA-IR, total cholesterol, triglyceride, and low-density-lipoprotein cholesterol were higher in the FLI-defined NAFLD group than in the non-NAFLD group. The SMI was lower in the FLI-defined NAFLD group than in the non-NAFLD group.

Correlation of SMI and FLI with metabolic factors

Table 2 shows the correlation of the SMI and FLI with other metabolic parameters in the study subjects. The SMI was negatively correlated with most of the metabolic parameters, including BMI, waist circumference, systolic BP, diastolic BP, fasting blood glucose, total cholesterol, triglyceride, low-density-lipoprotein cholesterol, AST, ALT, GGT, WBC, and total body fat mass in both males and females. In contrast, the FLI was positively correlated with these variables. Interestingly, HOMAIR levels were negatively correlated with the SMI and positively correlated with the FLI in both males and females. The SMI was lower in subjects with diabetes than in those without diabetes. However, the SMI was higher in subjects with hypertension than in those without hypertension (Table 3). The scatter plots of the correlations between the SMI and FLI are shown in Fig..

Multiple logistic regression analysis for NAFLD

Table 2. Correlation of SMI and FLI with clinical and metabolic variables

Table 3. Distributions of SMI and FLI with categorical variables

Table 4. Unadjusted and adjusted odds ratios (95% confidence interval) for non-alcoholic fatty liver disease (fatty liver index of ≥60) by decreasing skeletal muscle index after adjusting for covariates

Fig. Scatter plots of log fatty liver index (FLI) and skeletal muscle index (SMI). The variables showed a significant inverse correlation in both (A) males (r=-0.54, P<0.001) and (B) females (r=-0.41, P<0.001).

To determine whether a decline in skeletal muscle mass independently impacts the presence of fatty liver, multiple logistic regression analyses were performed using the presence of NAFLD (FLI ≥60) as a dependent variable, the SMI (as a continuous variable) as an independent variable, and potential confounders as covariates (Table 4). In the unadjusted model, subjects of both genders with a lower SMI [males: odds ratio (OR)=1.49; 95% CI: 1.38-1.61; females: OR=1.47; 95% CI: 1.35-1.60] had a significantly higher odds of FLI-defined NAFLD. This association remained statistically significant even after adjusting for potential confounders including age, smoking, alcohol drinking status, physical activity, total energy intake, carbohydrate intake, fat intake, WBC, HOMA-IR, 25[OH]D level, number of metabolic syndrome components, diabetes, and hypertension in both males (OR=1.35; 95% CI: 1.17-1.54) and females (OR=1.36; 95% CI: 1.18-1.55).

For subgroup analysis, age was categorized as either <45 years or ≥45 years in males, and as either premenopause or postmenopause in females. Among male participants, the odds of FLI-defined NAFLD increased as the SMI decreased in both age groups. However, the adjusted OR was higher in middle-aged to elderly males (males ≥45 years: adjusted OR=1.50; 95% CI: 1.22-1.84; males <45 years: adjusted OR=1.25; 95% CI: 1.02-1.52). In females, a negative association between the SMI and FLI-defined NAFLD was evident in both subgroups (premenopause and postmenopause). However, the postmenopause group (adjusted OR=1.36; 95% CI: 1.20-1.54) showed a smaller effect size than did the premenopause group (adjusted OR=1.50; 95% CI: 1.12-2.03).

Discussion

In this cross-sectional study, we investigated the implication of sarcopenia in NAFLD defined by FLI using a representative sample of the general Korean population. Multiple logistic regression analysis revealed that subjects of both genders with low skeletal muscle mass had a significantly higher prevalence of FLI-defined NAFLD independent of potential confounding factors, such as age, smoking, vigorous physical activity, alcohol drinking, WBC, HOMA-IR, 25[OH]D, total energy intake, carbohydrate intake, fat intake, number of metabolic syndrome components, diabetes, and hypertension. In the subgroup analysis according to age group or menopausal status, the odds for the association of FLI-defined NAFLD with sarcopenia were higher in middle-aged to elderly males and premenopausal females.

Sarcopenia indicates age-related loss of skeletal muscle mass and strength. It contributes to a higher risk of functional limitations in the elderly population.[1]In addition, several studies[7, 9, 28, 29]have reported that subjects with low muscle mass have an increased risk of chronic metabolic disorders. Several mechanisms have been proposed to explain the pathophysiology of sarcopenia. Among these, insulin resistance is regarded as a crucial causative factor of sarcopenia.[30]In line with this finding,[30]the present study showed a significant negative correlation between insulin resistance (HOMA-IR) and SMI (an index of muscle mass). Additionally, chronic low-grade inflammation is known to play an important role in the loss of skeletal muscle mass. Proinflammatory cytokines, tumor necrosis factor-α, interleukin-6, and C-reactive protein have been shown to mediate skeletal muscle catabolism, leading to sarcopenia.[2]Vitamin D deficiency was recently reported to be related to sarcopenia.[31]Lower 25[OH]D levels are associated with lower muscle mass in the elderly. In the current study, correlation analysis revealed that the SMI was positively correlated with 25[OH]D levels in males.

NAFLD, which has the characteristic feature of abnormal fat accumulation in the liver, is recognized as the hepatic manifestation of metabolic syndrome.[14]Insulin resistance, inflammation, and vitamin D deficiency are reportedly involved in the pathogenesis of NAFLD.[13, 15, 32]Our results also revealed a significant positive correlation between insulin resistance and the FLI (an index of liver fat accumulation).

Together, 25[OH]D deficiency, chronic inflammation, and insulin resistance contribute to the development of sarcopenia and NAFLD. Considering the similarities in their underlying pathophysiological mechanisms, we considered the possibility of a link between sarcopenia and NAFLD independent of metabolic variables. Recent findings lend support to this idea by demonstrating the association between fat accumulation in the skeletal muscle and the liver.[33, 34]Moreover, it has been suggested that NAFLD, which is common in the elderly has a link with other clinical syndromes associated with aging such as sarcopenia.[35]In our correlation analysis, the SMI was negatively correlated with well-known risk factors for metabolic disorders, whereas the FLI had a positive relationship with those factors. Our findings are consistent with the result of previous studies of the relationship between metabolic disorders and sarcopenia or NAFLD.[7, 9, 14, 25, 29, 36]Furthermore, the current study revealed that the SMI was negatively associated with the FLI.

To evaluate the role of low skeletal muscle mass in increasing NAFLD independent of metabolic variables, we calculated the gender-stratified ORs for the presence of NAFLD (FLI ≥60) after adjusting for confounding factors associated with development of NAFLD, stratified by gender. We found that low skeletal muscle mass independently predicted the presence of NAFLD as identified by the FLI in a large number of Korean males (OR=1.35; 95% CI: 1.17-1.54) and females (OR=1.36; 95% CI: 1.18-1.55). This finding was also demonstrated in recent reports that showed an independent association between sarcopenia and NAFLD.[16, 17]However, neither of these previous two studies analyzed the inverse correlation of sarcopenia with NAFLD in a gender-specific manner.

In our subgroup analyses performed to assess the effect modification of age or menopause, the negative association remained significant after stratifying by age group or menopause status. The adjusted OR was higher in middle-aged to elderly males than in those aged <45 years. However, the adjusted OR was lower in postmenopausal than in premenopausal females. One possible explanation is that estrogen-dependent factors promote the development of NAFLD in postmenopausal females.[37]Therefore, sarcopenia may have less influence on the development of NAFLD in postmenopausal females.

This study had several limitations. First, its crosssectional design made it difficult to examine a causal relationship between skeletal muscle and NAFLD. Second, NAFLD was diagnosed based on predictive equations that utilized the FLI instead of liver biopsy, the gold standard for assessing hepatic steatosis. However, liver biopsy also has limitations, including its invasiveness, high cost, and potential complications. Liver ultrasonography is a non-invasive, inexpensive and widely accepted method to assess liver fat accumulation.[38]Because liver ultrasonography was not included in the KNHANES, we used the FLI as the surrogate marker of fatty liver. The FLI was recently proposed and validated in the general population for objective estimation of fatty liver.[24, 38]The FLI also accurately predicted ultrasonographically detected NAFLD in a large Western elderly population [area under the receiver operating characteristic curve: 0.813 (95% CI: 0.797-0.830)].[39]Finally, we did not use muscle strength in the determination of sarcopenia. It was recently recommended that both low muscle strength and function in addition to low muscle mass should be considered when diagnosing sarcopenia.[40]

Despite these limitations, however, this study also had a number of strengths. First, our findings were based on a representative sample of the general Korean population, reducing the possibility of selection bias. A recent report by Hong et al[16]was not based on a large population, limiting its generalizability. Second, we used DXA which is the gold standard technique for muscle mass measurement. A recent report by Moon et al,[17]which revealed an independent association between sarcopenia and NAFLD, used the bioelectric impedance method instead of DXA to measure muscle mass. Third, a wide range of lifestyle variables was adjusted to minimize the impact of residual confounding. Finally, it is important to analyze the relationship between NAFLD and sarcopenia stratified by gender because of its potential influence on sarcopenia. In this context, it is notable that this study investigated the independent role of the SMI in predicting the presence of FLI-defined NAFLD stratified by gender.

In conclusion, this nationwide study revealed that low skeletal muscle mass was closely associated with the risk of NAFLD as diagnosed by the FLI, independent of other well-known metabolic factors, although the magnitude of the association between sarcopenia and NAFLD was dependent upon age group and menopausal status. Further prospective studies are needed to identify whether reducing or slowing the onset of sarcopenia would be effective for prevention of NAFLD.

Contributors: KHY and KCW were responsible for the study design, analysis and interpretation of the data, drafting of the manuscript, and contributed equally to this study. HK performed the statistical analysis. PCH and CJY interpreted the data. MAT revised the manuscript for important intellectual content. PYM was responsible for the study supervision. PYM is the guarantor. Funding: None.

Ethical approval: The survey was approved by the Institutional Review Board of the Korea Centers for Disease Control and Prevention. All participants provided written informed consent.

Competing interest: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

References

1 Karakelides H, Nair KS. Sarcopenia of aging and its metabolic impact. Curr Top Dev Biol 2005;68:123-148.

2 Beyer I, Mets T, Bautmans I. Chronic low-grade inflammation and age-related sarcopenia. Curr Opin Clin Nutr Metab Care 2012;15:12-22.

3 Schrager MA, Metter EJ, Simonsick E, Ble A, Bandinelli S, Lauretani F, et al. Sarcopenic obesity and inflammation in the InCHIANTI study. J Appl Physiol (1985) 2007;102:919-925.

4 Nomura T, Ikeda Y, Nakao S, Ito K, Ishida K, Suehiro T, et al. Muscle strength is a marker of insulin resistance in patients with type 2 diabetes: a pilot study. Endocr J 2007;54:791-796.

5 Guillet C, Boirie Y. Insulin resistance: a contributing factor to age-related muscle mass loss? Diabetes Metab 2005;31:5S20-5S26.

6 Klip A, Paquet MR. Glucose transport and glucose transporters in muscle and their metabolic regulation. Diabetes Care 1990;13:228-243.

7 Moon SS. Low skeletal muscle mass is associated with insulin resistance, diabetes, and metabolic syndrome in the Korean population: the Korea National Health and Nutrition Examination Survey (KNHANES) 2009-2010. Endocr J 2014;61:61-70.

8 Park BS, Yoon JS. Relative skeletal muscle mass is associated with development of metabolic syndrome. Diabetes Metab J 2013;37:458-464.

9 Park SH, Park JH, Park HY, Jang HJ, Kim HK, Park J, et al. Additional role of sarcopenia to waist circumference in predicting the odds of metabolic syndrome. Clin Nutr 2014;33:668-672.

10 Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 2004;40:1387-1395.

11 Park SH. Current status of liver disease in Korea: nonalcoholicfatty liver disease. Korean J Hepatol 2009;15:S34-39.

12 Kotronen A, Yki-J?rvinen H. Fatty liver: a novel component of the metabolic syndrome. Arterioscler Thromb Vasc Biol 2008;28:27-38.

13 Choudhury J, Sanyal AJ. Insulin resistance and the pathogenesis of nonalcoholic fatty liver disease. Clin Liver Dis 2004;8: 575-594, ix.

14 Boppidi H, Daram SR. Nonalcoholic fatty liver disease: hepatic manifestation of obesity and the metabolic syndrome. Postgrad Med 2008;120:E01-E07.

15 Wieckowska A, Papouchado BG, Li Z, Lopez R, Zein NN, Feldstein AE. Increased hepatic and circulating interleukin-6 levels in human nonalcoholic steatohepatitis. Am J Gastroenterol 2008;103:1372-1379.

16 Hong HC, Hwang SY, Choi HY, Yoo HJ, Seo JA, Kim SG, et al. Relationship between sarcopenia and nonalcoholic fatty liver disease: the Korean Sarcopenic Obesity Study. Hepatology 2014;59:1772-1778.

17 Moon JS, Yoon JS, Won KC, Lee HW. The role of skeletal muscle in development of nonalcoholic fatty liver disease. Diabetes Metab J 2013;37:278-285.

18 Kirchengast S, Huber J. Gender and age differences in lean soft tissue mass and sarcopenia among healthy elderly. Anthropol Anz 2009;67:139-151.

19 Ko SH, Kwon HS, Kim DJ, Kim JH, Kim NH, Kim CS, et al. Higher prevalence and awareness, but lower control rate of hypertension in patients with diabetes than general population: the fifth korean national health and nutrition examination survey in 2011. Diabetes Metab J 2014;38:51-57.

20 Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, et al. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol 2014;43:69-77. 21 Heymsfield SB, Smith R, Aulet M, Bensen B, Lichtman S, Wang J, et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry. Am J Clin Nutr 1990;52:214-218. 22 Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 2002;76:378-383.

23 Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889-896.

24 Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol 2006;6:33.

25 Gastaldelli A, Kozakova M, H?jlund K, Flyvbjerg A, Favuzzi A, Mitrakou A, et al. Fatty liver is associated with insulin resistance, risk of coronary heart disease, and early atherosclerosis in a large European population. Hepatology 2009;49:1537-1544.

26 Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-419.

27 Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-1645.

28 Lee SW, Youm Y, Lee WJ, Choi W, Chu SH, Park YR, et al. Appendicular skeletal muscle mass and insulin resistance in an elderly korean population: the korean social life, health and aging project-health examination cohort. Diabetes Metab J 2015;39:37-45.

29 Srikanthan P, Hevener AL, Karlamangla AS. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: findings from the National Health and Nutrition Examination Survey III. PLoS One 2010;5:e10805.

30 Abbatecola AM, Paolisso G, Fattoretti P, Evans WJ, Fiore V, Dicioccio L, et al. Discovering pathways of sarcopenia in older adults: a role for insulin resistance on mitochondria dysfunction. J Nutr Health Aging 2011;15:890-895.

31 Visser M, Deeg DJ, Lips P; Longitudinal Aging Study Amsterdam. Low vitamin D and high parathyroid hormone levels as determinants of loss of muscle strength and muscle mass (sarcopenia): the Longitudinal Aging Study Amsterdam. J Clin Endocrinol Metab 2003;88:5766-5772.

32 Barchetta I, Angelico F, Del Ben M, Baroni MG, Pozzilli P, Morini S, et al. Strong association between non alcoholic fatty liver disease (NAFLD) and low 25(OH) vitamin D levels in an adult population with normal serum liver enzymes. BMC Med 2011;9:85.

33 Kitajima Y, Eguchi Y, Ishibashi E, Nakashita S, Aoki S, Toda S, et al. Age-related fat deposition in multifidus muscle could be a marker for nonalcoholic fatty liver disease. J Gastroenterol 2010;45:218-224.

34 Kitajima Y, Hyogo H, Sumida Y, Eguchi Y, Ono N, Kuwashiro T, et al. Severity of non-alcoholic steatohepatitis is associated with substitution of adipose tissue in skeletal muscle. J Gastroenterol Hepatol 2013;28:1507-1514.

35 Bertolotti M, Lonardo A, Mussi C, Baldelli E, Pellegrini E, Ballestri S, et al. Nonalcoholic fatty liver disease and aging: epidemiology to management. World J Gastroenterol 2014;20:14185-14204.

36 Rogulj D, Konjevoda P, Mili? M, Mladini? M, Domijan AM. Fatty liver index as an indicator of metabolic syndrome. Clin Biochem 2012;45:68-71.

37 Parkosadze G, Sulakvelidze M, Mizandari M, Ratiani L, Sanikidze T. Some aspects of pathogenesis of nonalcoholic fatty liver disease in postmenopausal women. Georgian Med News 2012:46-51.

38 Festi D, Schiumerini R, Marzi L, Di Biase AR, Mandolesi D, Montrone L, et al. Review article: the diagnosis of nonalcoholic fatty liver disease -- availability and accuracy of noninvasive methods. Aliment Pharmacol Ther 2013;37:392-400.

39 Koehler EM, Schouten JN, Hansen BE, Hofman A, Stricker BH, Janssen HL. External validation of the fatty liver index for identifying nonalcoholic fatty liver disease in a populationbased study. Clin Gastroenterol Hepatol 2013;11:1201-1204.

40 Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-423.

Received March 11, 2015

Accepted after revision September 25, 2015

Original Article / Liver

doi:10.1016/S1499-3872(15)60030-3

Corresponding Author:Yong-Moon Park, MD, MS, PhD, Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, 111 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA (Tel: +1-919-541-3630; Fax: +1-301-480-3605; Email: mark. park@nih.gov)