Relationship Between Handgrip Strength and Nonalcoholic Fatty Liver Disease: Nationwide Surveys

Abstract

Background:

The association of handgrip strength with nonalcoholic fatty liver disease (NAFLD) has not been studied yet. This study investigated the relationship between handgrip strength and NAFLD in South Korean adults.

Methods:

Referring to the Korea National Health and Nutrition Examination Survey 2014 and 2015 database, South Korean adults (n = 8001, women: 55.5%) aged 19–80 years having complete data were considered for this study. Relative handgrip strength (RHGS) [average handgrip strength of both hands divided by body mass index (BMI)], hepatic steatosis index (HSI), BMI, and components of metabolic syndrome were measured. Demographics, treatment of concurrent illnesses, and health-related behaviors were assessed by using standardized questionnaires. NAFLD was defined by the HSI >36.0, alcohol consumption <20 grams/day, and negative biomarkers for chronic hepatitis B and C.

Results:

The prevalence of NAFLD was 30.3% ± 0.7%. Complex sample logistic regression analysis revealed that individuals with lower RHGS [per 1 standard deviation (SD) decrease] manifested higher odds of suffering from NAFLD (adjusted odds ratio: 1.47, 95% confidence interval: 1.35–1.60). Furthermore, lower RHGS was associated with higher odds for NAFLD throughout the strata of sex, age group, education, BMI category, metabolic syndrome, treatment history of illnesses, smoking status, alcohol consumption, or physical activity. The odds for NAFLD increased in the range of 1.40–1.63 with 1 SD decrease in RHGS according to the strata.

Conclusions:

This study of South Korean adults suggests that lower handgrip strength is associated with NAFLD regardless of sociodemographic characteristics, weight status, metabolic syndrome, concurrent illnesses, and lifestyle.

Introduction

Hepatic steatosis, an accumulation of intrahepatic triglycerides (TG), has become an important public health problem because of its high prevalence and possible progression to severe hepatic condition.¹ Nonalcoholic fatty liver disease (NAFLD) is a major cause of hepatic steatosis. The global prevalence of NAFLD has been estimated as 25%,² and its prevalence in South Korean populations is nearly 18%.³ Evidence show that NAFLD is not only confined to liver-related complications but also contributes to extra-hepatic systems,⁴ including increased risk of type 2 diabetes, metabolic syndrome,⁵ and cardiovascular diseases.⁶

Handgrip strength is a simple indicator to estimate muscle function and overall nutritional status⁷ and to predict the incidence of all-cause mortality, cardiovascular death, and cardiovascular disease.^8,9 Moreover, several population-based studies have suggested inverse associations between the handgrip strength and cardiometabolic risk factors. One study has reported that the handgrip strength adjusted by the body weight is inversely associated with the odds for metabolic syndrome.¹⁰ Another study has reported that low normalized handgrip strength is associated with higher odds for diabetes and cardiometabolic biomarkers.¹¹ Studies also have reported that relative handgrip strength [RHGS, calculated as absolute handgrip strength divided by body mass index (BMI)] is positively associated with a better profile of components of cardiometabolic risk factors.^12,13 These studies implicate that there may be common pathophysiological mechanisms between handgrip strength (an indicator of muscle function) and the cardiometabolic conditions.

Given an evidence for the association between these cardiometabolic conditions and NAFLD, it can be postulated that the handgrip strength may be associated with NAFLD. In addition, previous studies have also suggested associations between sarcopenia (defined as a low appendicular muscle mass) and NAFLD.^14,15 As handgrip strength has been proposed to be included in the diagnosis of sarcopenia as a parameter for representing the muscle function,¹⁶ it is intuitive that handgrip strength may be associated with NAFLD. Nevertheless, the relationship between muscle function assessed by handgrip strength and NAFLD has not yet been clarified.

To expand the relationship between sarcopenia and NAFLD, this study aimed to examine the association of handgrip strength with NAFLD, assessed by the hepatic steatosis index (HSI) in the representative South Korean populations, by referring the database of the Korea National Health and Nutrition Examination Survey 2014 and 2015 (KNHANES).

Methods

Study population

The KNHANES 2014 and 2015 is a nationally represented cross-sectional health survey and health examination of the noninstitutionalized South Koreans, applying a rolling sampling design that involves a complex, stratified multistage probability cluster survey.¹⁷ This survey was performed at the mobile examination centers by trained medical staff and interviewers.¹⁸

Of the 11,921 adults aged 19–80 years, this study included the subjects having complete data for demographic characteristics (age, sex, and educational levels), handgrip strength and anthropometric measurements [weight, height, and waist circumference (WC)], components of metabolic syndrome, components of HSI, serologic markers of chronic hepatitis B and hepatitis C virus infection, health-related behaviors (alcohol consumption, physical activity, and smoking status), and treatment/nontreatment status of concurrent illnesses (cardiovascular diseases, osteoarthritis, hypertension, dyslipidemia, diabetes, and liver cirrhosis).

Therefore, individuals without the data for the HSI (n = 2674), handgrip strength (n = 2121), metabolic syndrome (n = 1764), alcohol consumption (n = 1309), BMI (n = 640), serologic markers of hepatitis B or hepatitis C (n = 1649), smoking status (n = 1324), physical activity (n = 1883), and educational levels (n = 1854) were excluded from the study. Written informed consent was obtained from all the participants. The study as well as original KNHANES was classified into an exemption category for an ethical review by the Law of Bioethics and Safety.

Clinical and laboratory measurements

Handgrip strength was examined thrice in each hand by using a digital grip strength dynamometer (TKK 5401; Takei Scientific Instruments Co., Ltd., Tokyo, Japan). All the subjects were instructed to hold the dynamometer in an upright standing position to keep their arms on their sides. The subjects squeezed the dynamometer with the maximum effort, which was maintained for about 3 seconds. The mean of three trials was considered to assess the grip strength in each hand.¹⁹ Weight and height were measured, with the subjects wearing light clothing without shoes; and the BMI was calculated by dividing the weight (kg) by the squared height (m). RHGS (defined as the summation of the grip strengths of both hands divided by the BMI) was used to represent the muscle function. RHGS, a joint measure of strength and weight status,¹² has been recommended as a tool to measure the muscle weakness and low lean mass²⁰ and to evaluate the accuracy of a cardiovascular risk estimation model in predicting cardiovascular events and all-cause mortality.²¹

Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) (both were assessed by using the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) method without pyridoxal phosphate), glucose (assessed by using hexokinase UV method), TG (assessed by using enzymatic method), and high-density lipoprotein cholesterol (HDL-C) (assessed by using homogeneous enzymatic colorimetric method) were measured in the subjects after fasting for at least 8 hr by using an automatic analyzer (Automatic Chemistry Analyzer 7600-210; Hitachi, Tokyo, Japan) in a central certified laboratory. Blood pressure (BP) was measured by using a sphygmomanometer according to the standard manual. WC was measured at the midpoint between the bottom of the rib cage and the top of the iliac crest.

The metabolic syndrome was defined based on the harmonized criteria,²² in which metabolic syndrome was identified when the individuals manifested any three of five metabolic syndrome components. The five-component criteria were as follows: WC ≥90 cm in males and ≥80 cm in females; BP ≥130/85 mmHg or a history of hypertension; fasting plasma glucose ≥5.6 mmol/L or a history of diabetes mellitus; TG ≥1.7 mmol/L; and HDL-C <1.03 mmol/L in males and <1.29 mmol/L in females. Medical history of hypertension and diabetes was assessed by using a standardized questionnaire.

Self-reported questionnaires were used to determine the treatment history of concurrent illnesses, health-related behaviors, and sociodemographic factors. Treatment history of concurrent illnesses was considered when there was any history of treatment for cardiovascular diseases, osteoarthritis, hypertension, diabetes, dyslipidemia, or liver cirrhosis. The data were collected for age group (<60 years vs. ≥60 years), education level (<high school graduation vs. ≥high school graduation), aerobic physical activity [yes vs. no for engaging in high-intensity activity for >75 min/week or moderate-intensity activity for >150 min/week or a combination of both (1 min of high-intensity activity equaled 2 min of moderate-intensity activity)], frequency of consuming alcohol for ≥1 month in the past year (yes vs. no), and current smoking status (smoker vs. nonsmoker).

Self-reported alcohol intake was estimated as follows: amount of consumption/occasion × frequency of consumption/week. The amount of consumption/occasion was categorized into 0 drink, 1.5 drinks (for 1–2 drinks), 3.5 drinks (for 3–4 drinks), 5.5 drinks (for 5–6 drinks), 8 drinks (for 7–9 drinks), and 10 drinks (for ≥10 drinks) considering a median value of each answer option for this question. The frequency of alcohol consumption/week was categorized into 0, 0.2 (for <1 time/month), 0.25 (for 1 time/month), 0.5 (for 2 times/month), 0.75 (for 2–4 times/month), 2.5 (for 2–3 times/week), and 4 (for ≥4 times/week) considering a median value of each answer for this question. Since alcohol amount per one drink of Korean beverages ranges between 8 and 10 grams, 18 or higher drinks/week [alcohol content: ∼144 grams/week (i.e., about 20 grams/day)] was considered as excessive alcohol consumption.

Definition of NAFLD

NAFLD was assessed by using the HSI, forming a validated fatty liver prediction model. The formula was as follows: HSI = 8 × (ALT/AST ratio) + BMI (+2, in case of a female participant; +2, in case of diabetes mellitus).²³ In this study, NAFLD was defined when the HSI was >36.0, self-reported alcohol intake was ≤20 grams/day, and the serologic markers of hepatitis B virus surface antigen and hepatitis C virus antibody were negative.²³ The HSI was used in a previous study regarding sarcopenia and NAFLD.²⁴

Statistical analyses

All the analyses were conducted as complex sample analyses, considering the sample weights of the survey used to produce estimates that were representative of the noninstitutionalized civilian South Korean population. The excluded subjects were considered as a subpopulation in the analyses to avoid biased results. Complex sample cross-tabs analysis was used to assess the relationships between RHGS (the lowest sex-specific quartile vs. the second, third, and fourth higher sex-specific quartiles) and NAFLD, demographic characteristics, health-related behaviors, presence/absence of metabolic syndrome, BMI category (BMI <25 kg/m² vs. ≥25 kg/m²), and treatment/nontreatment status of concurrent illnesses.

Complex sample logistic regression model was applied to determine the association of NAFLD with RHGS [with the rate of 1 standard deviation (SD) decrease and sex-specific quartiles] after adjustment for confounding factors (demographic characteristics, health behaviors, treatment of illnesses, BMI category, and metabolic syndrome). To find the associations of NAFLD with RHGS according to the strata of each confounding factor, complex sample logistic regression model was applied after adjusting for other confounding factors, except for the stratification factor. The associations of NAFLD with RHGS were further assessed according to the categories of BMI (<19.5 kg/m², 19.5 to <23.0 kg/m², 23.0 to <25.0 kg/m², and ≥25.0 kg/m²) and combination of BMI and metabolic syndrome categories using complex sample logistic regression model after adjusting for confounding factors. Data were analyzed by using an IBM Statistical Package for the Social Sciences, software version 24 (IBM Corp., Armonk, NY).

Results

Of the 11,921 adults aged 19–80 years, complete data were available for 8001 subjects (3562 males; 4439 females; mean age: 49.9 ± 16.4 years). Compared with the excluded subjects, the included subjects were more likely to present with higher RHGS (1.28 ± 0.41 kg/BMI vs. 1.18 ± 0.45 kg/BMI, P < 0.001). However, there was no significant difference in the HSI between them (P = 0.083). The prevalence of NAFLD was 30.3% ± 0.7% in this Korean population aged 19 years or older. Individuals with low RHGS were more likely to suffer from NAFLD, while being older, being less educated, suffering from metabolic syndrome, having higher BMI, being treated for the concurrent illnesses, consuming less alcohol, being nonsmokers currently, and having less regular aerobic physical activity (Table 1).

Table 1.

Comparison of Characteristics Between Individuals with Lowest Sex-Specific Relative Handgrip Strength Quartile and Those with Higher Sex-Specific Relative Handgrip Strength Quartiles ( n = 8001)

	Lowest sex-specific RHGS quartile (n = 1999)	Second to fourth sex-specific RHGS quartiles (n = 6002)	P^a
NAFLD^b
No	941 (50.2 ± 1.3)	4369 (75.7 ± 0.7)	<0.001
Yes	1058 (49.8 ± 1.3)	1633 (24.3 ± 0.7)	<0.001
Age, years
<60	804 (56.5 ± 2.0)	4679 (87.2 ± 0.8)	<0.001
≥60	1195 (43.4 ± 2.0)	1323 (12.8 ± 0.8)	<0.001
Sex
Men	890 (49.0 ± 1.3)	2672 (51.6 ± 0.7)	0.105
Women	1109 (51.0 ± 1.3)	3330 (48.4 ± 0.7)	0.105
Education
<High school	1061 (42.7 ± 2.5)	1379 (16.7 ± 1.2)	<0.001
≥High school	938 (57.3 ± 2.5)	4623 (83.3 ± 1.2)	<0.001
Metabolic syndrome
No	1056 (56.4 ± 1.3)	4586 (81.0 ± 0.8)	<0.001
Yes	873 (43.6 ± 1.3)	1213 (19.0 ± 0.8)	<0.001
BMI
<25 kg/m²	842 (39.6 ± 1.4)	4538 (74.8 ± 0.8)	<0.001
≥25 kg/m²	1157 (60.4 ± 1.4)	1461 (25.2 ± 0.8)	<0.001
Treatment of concurrent illness
No	794 (50.1 ± 1.5)	4368 (78.9 ± 0.8)	<0.001
Yes	1205 (49.9 ± 1.5)	1634 (21.1 ± 0.8)	<0.001
Frequency of alcohol
<Once/month	1090 (50.4 ± 1.4)	2475 (37.0 ± 0.7)	<0.001
≥Once/month	909 (49.6 ± 1.4)	3527 (63.0 ± 0.7)	<0.001
Current smoker
No	1736 (84.2 ± 1.2)	4799 (76.3 ± 0.8)	<0.001
Yes	263 (15.8 ± 1.2)	1203 (23.7 ± 0.8)	<0.001
Regular physical activity
No	1151 (53.9 ± 1.7)	2768 (42.8 ± 1.2)	<0.001
Yes	848 (46.1 ± 1.7)	3234 (57.2 ± 1.2)	<0.001

Values are represented as number (% ± SE). Illness was cardiovascular diseases, diabetes, hypertension, dyslipidemia, liver cirrhosis, or arthritis.

Using complex sample cross-tabs analysis.

Defined by the hepatic steatosis index >36.0, alcohol consumption <20 grams/day, and negative results for hepatitis B virus surface antigen and hepatitis C virus antibody.

NAFLD, nonalcoholic fatty liver disease; BMI, body mass index; RHGS, relative handgrip strength.

NAFLD was inversely associated with the RHGS after adjusting for confounding factors. After adjustment for the confounding factors, except for BMI and metabolic syndrome, the odds for NAFLD increased by 1.99 times with 1 SD decrease in RHGS. After further adjustment for the BMI and metabolic syndrome, the odds for the NAFLD increased by 47% with 1 SD decrease in RHGS. These inverse associations and attenuation of association strengths after adjusting the BMI and metabolic syndrome were replicated for the associations of NAFLD with sex-specific RHGS quartiles. Compared with individuals belonging to the highest sex-specific RHGS quartile, those in lower quartiles were 1.33–2.43 times more likely to experience NAFLD, after adjustment for the confounding factors (Table 2).

Table 2.

Odds Ratio for Nonalcoholic Fatty Liver Disease with Change in Relative Handgrip Strength and Sex-Specific Relative Handgrip Strength Quartiles

		Sex-specific RHGS quartiles
	1 SD decrease of RHGS	Lowest	Second	Third	Highest
Age and sex adjusted	2.03 (1.89–2.18)	5.55 (4.72–6.53)	2.74 (2.28–3.30)	1.74 (1.46–2.09)	1.0
Model I	1.99 (1.85–2.15)	5.26 (4.48–6.18)	2.67 (2.22–3.20)	1.72 (1.44–2.05)	1.0
Model II	1.47 (1.35–1.60)	2.45 (2.07–2.89)	1.61 (1.32–1.96)	1.34 (1.10–1.62)	1.0
Model III	1.47 (1.35–1.60)	2.43 (2.05–2.88)	1.57 (1.28–1.92)	1.33 (1.09–1.61)	1.0

NAFLD was defined by the hepatic steatosis index >36.0, alcohol consumption <20 grams/day, and negative results for hepatitis B virus surface antigen and hepatitis C virus antibody. 1 SD equals to 0.41 kg/BMI. Values are represented as odds ratio (95% confidence interval) using complex sample logistic regression model after adjusting for age, sex, education, physical activity, alcohol use, smoking status, treatment of illness (cardiovascular diseases, diabetes, hypertension, dyslipidemia, liver cirrhosis, or arthritis) in Model I; confounding factors of Model I and BMI in Model II; confounding factors of Model II and metabolic syndrome in Model III.

The odds for NAFLD tended to increase with lower RHGS across the strata of the confounding factors, but with different magnitude of association strength. Considering the metabolic syndrome, the subjects in the lowest sex-specific RHGS quartile were 2.17 times more likely to manifest NAFLD compared with those in the highest quartile. However, considering the age group ≥60 years, the subjects in the lowest quartile were 3.57 times more likely to manifest NAFLD than those in the highest quartile (Table 3). Lower RHGS was associated with higher odds for NAFLD in individuals with BMI <23.0 kg/m² and those with BMI ≥25.0 kg/m² regardless of adjusting for confounding factors including metabolic syndrome. These associations were replicated for individuals with those BMI categories in the subgroup without metabolic syndrome, while found for individuals with BMI ≥25.0 kg/m² in the subgroup with metabolic syndrome (Table 4).

Table 3.

Associations of Nonalcoholic Fatty Liver Disease with Relative Handgrip Strength According to Demographic Characteristics, Health Status, and Health Behaviors

		Sex-specific RHGS quartiles
	1 SD decrease of RHGS	Lowest	Second	Third	Highest
Men (n = 3562)^a	1.54 (1.31–1.82)	2.50 (1.68–3.72)	1.62 (1.05–2.51)	1.18 (0.76–1.85)	1.0
Women (n = 4439)^a	1.42 (1.29–1.56)	2.28 (1.83–2.83)	1.55 (1.25–1.91)	1.41 (1.18–1.70)	1.0
Age <60 years (n = 5483)^b	1.43 (1.29–1.60)	2.32 (1.89–2.84)	1.54 (1.23–1.92)	1.32 (1.08–1.62)	1.0
Age ≥60 years (n = 2518)^b	1.59 (1.37–1.85)	3.57 (2.16–5.89)	2.38 (1.58–3.60)	1.91 (1.10–3.32)	1.0
Education <high school (n = 2440)	1.63 (1.44–1.85)	3.30 (2.29–4.74)	1.95 (1.38–2.77)	1.67 (1.08–2.59)	1.0
Education ≥high school (n = 5561)	1.42 (1.29–1.56)	2.23 (1.81–2.73)	1.54 (1.24–1.91)	1.30 (1.03–1.64)	1.0
BMI <25 kg/m² (n = 5380)^c	1.40 (1.27–1.53)	2.32 (1.85–2.90)	1.60 (1.28–2.01)	1.42 (1.18–1.70)	1.0
BMI ≥25 kg/m² (n = 2618)^c	1.64 (1.40–1.93)	2.29 (1.54–3.40)	1.45 (0.94–2.25)	1.09 (0.69–1.71)	1.0
Without MetS (n = 5642)^d	1.44 (1.32–1.58)	2.36 (1.93–2.89)	1.73 (1.40–2.14)	1.39 (1.15–1.69)	1.0
With MetS (n = 2086)^d	1.58 (1.35–1.84)	2.17 (1.48–3.18)	1.15 (0.75–1.77)	1.07 (0.67–1.71)	1.0
Nontreatment of illness (n = 5162)^e	1.46 (1.31–1.63)	2.37 (1.89–2.99)	1.62 (1.29–2.03)	1.31 (1.05–1.64)	1.0
Treatment of illness (n = 2839)^e	1.49 (1.32–1.69)	2.70 (1.94–3.77)	1.74 (1.18–2.55)	1.58 (1.04–2.41)	1.0
Frequency of alcohol drink <once/month (n = 3565)^f	1.53 (1.35–1.74)	2.96 (2.22–3.95)	1.88 (1.38–2.56)	1.65 (1.23–2.21)	1.0
Frequency of alcohol drink ≥once/month (n = 4436)^f	1.41 (1.27–1.57)	2.06 (1.59–2.68)	1.40 (1.11–1.77)	1.14 (0.90–1.46)	1.0
Noncurrent smoker (n = 6535)^g	1.47 (1.36–1.59)	2.41 (2.02–2.88)	1.57 (1.28–1.93)	1.41 (1.17–1.69)	1.0
Current smoker (n = 1466)^g	1.51 (1.20–1.89)	2.50 (1.44–4.33)	1.59 (0.91–2.80)	0.97 (0.53–1.38)	1.0
Irregular physical activity (n = 3919)^h	1.61 (1.45–1.78)	2.75 (2.09–3.62)	1.76 (1.35–2.29)	1.41 (1.03–1.95)	1.0
Regular physical activity (n = 4082)^h	1.36 (1.21–1.53)	2.20 (1.71–2.83)	1.45 (1.14–1.86)	1.29 (1.05–1.59)	1.0

NAFLD was defined by the hepatic steatosis index >36.0, alcohol consumption <20 grams/day, and negative results for hepatitis B virus surface antigen and hepatitis C virus antibody. Illness was cardiovascular diseases, diabetes, hypertension, dyslipidemia, liver cirrhosis, or arthritis. 1 SD equals to 0.41 kg/BMI. Values are represented as odds ratio (95% confidence interval) after adjusting for confounding factors (age, sex, education, physical activity, alcohol use, smoking status, treatment of illness, BMI, and MetS) except for sex^a; all confounding factors except for age^b; all confounding factors except for BMI^c; all confounding factors except for MetS^d; all confounding factors except for treatment of illness^e; all confounding factors except for alcohol use^f; all confounding factors except for smoking status^g; all confounding factors except for physical activity^h using complex sample logistic regression model.

MetS, metabolic syndrome.

Table 4.

Associations of Nonalcoholic Fatty Liver Disease with Relative Handgrip Strength According to Body Mass Index and Metabolic Syndrome Categories

	1 SD decrease of RHGS
	Noncategorized by MetS		Categorized by MetS
BMI category (kg/m²)	Before adjusting for MetS	After adjusting for MetS	Without MetS	With MetS
<19.5	1.36 (1.09–1.70)	1.37 (1.09–1.71)	1.37 (1.09–1.71)	NA
19.5 to <23.0	1.28 (1.11–1.48)	1.29 (1.11–1.49)	1.29 (1.10–1.50)	1.26 (0.81–1.97)
23.0 to <25.0	1.07 (0.87–1.31)	1.06 (0.85–1.32)	1.05 (0.84–1.31)	1.08 (0.75–1.56)
≥25.0	1.66 (1.42–1.94)	1.64 (1.40–1.93)	1.57 (1.22–2.00)	1.70 (1.40–2.07)

NAFLD was defined by the hepatic steatosis index >36.0, alcohol consumption <20 grams/day, and negative results for hepatitis B virus surface antigen and hepatitis C virus antibody. 1 SD equals to 0.41 kg/BMI. Values are represented as odds ratio (95% confidence interval) after adjusting for confounding factors (age, sex, education, physical activity, alcohol use, smoking status, and treatment of illness) using complex sample logistic regression model.

NA, not applicable.

Discussion

In this nationally representative South Korean population, lower RHGS was independently associated with a higher prevalence of NAFLD, after adjustment for age, sex, educational level, BMI, metabolic syndrome, treatment status of concurrent illnesses, alcohol intake, smoking status, and physical activity. Furthermore, those belonging to lower sex-specific RHGS quartiles presented 1.33–2.43-folds higher prevalence of NAFLD compared with those in the highest quartile. Likewise, 1 SD decrease in RHGS was associated with 47% higher odds for NAFLD. Furthermore, this association was consistent throughout the subgroups, namely age group, sex, educational level, presence of metabolic syndrome, treatment history of illnesses, alcohol intake, smoking status, or physical activity. In addition, these associations were found in individuals with underweight, normal weight, and obesity regardless of adjusting for metabolic syndrome and those without metabolic syndrome.

These key findings suggest that low RHGS (which reflects low muscle function) may be an independent indicator of NAFLD, and this relationship may not be influenced by sex, age group, education level, weight status, presence of metabolic syndrome, health-related behaviors, and treatment status of concurrent illnesses. Current findings are consistent with the previous findings for the associations between low muscle mass and higher prevalence of NAFLD assessed by using diagnostic imaging techniques or prediction models.¹⁴ Current findings of an inverse association of RHGS with NAFLD, regardless of BMI or metabolic syndrome categories, are also similar to the KNHANES findings²⁴ and the Korean Sarcopenic Obesity Study results,¹⁵ in which low muscle mass was associated with increased odds for NAFLD defined by the NAFLD prediction models²⁴ and the liver attenuation index using abdominal computed tomography¹⁵ regardless of weight status, presence of metabolic syndrome,²⁴ or insulin resistance.¹⁵

Although consensus opinions on operative definitions of sarcopenia have generally included low muscle mass, low strength, and low performance,^25,26 research on the relationship of sarcopenia with NAFLD has been mainly conducted including low muscle mass. Therefore, current findings for the association of NAFLD with low muscle strength expand the results of the research on the association between sarcopenia and NAFLD by suggesting the importance of muscle function. However, given an evidence that muscular strength does not always correlate with muscle mass,^25,27 it is unclear whether there are interactions between muscle mass and muscular function for the association with NAFLD. In addition, measurement method of handgrip strength may be important to evaluate types of muscle groups. Grip strength assessed in a standing position than in a sitting position is more likely to measure the lower body and core muscle strength, used for maintaining balance and exertion of force.¹² Later, further studies investigating interaction between muscular strength and muscle mass considering the measurement position of grip strength may confer more knowledge for determining the associations of the sarcopenia with NAFLD.

Based on the previous findings about unfavorable associations of low handgrip strength and NAFLD with cardiometabolic profiles, current observation implicates interconnecting pathogenic mechanisms between low RHGS and NAFLD. Evidence has reported that high levels of ectopic fat deposition in the skeletal muscle and liver are associated with insulin resistance and lesser muscle strength through complicated mechanisms and pathways.²⁵

Ectopic fat deposition and consequently insulin resistance have been explained by the excess energy-induced spillover of energy storage from adipose tissue to the liver and skeletal muscle²⁸; chronic inflammation of the adipose tissue, liver, and skeletal muscle,²⁹ and age-induced changes in the preadipocyte differentiation.²⁵ It has been suggested that muscle dysfunction may be related to the hepatic insulin resistance through impaired secretion and action of the muscle-derived cytokines.^25,30 The associations of RHGS with NAFLD in the individuals with under BMI and normal BMI categories or those without metabolic syndrome may be explained by these muscle-derived cytokines.²⁴ Moreover, metabolic syndrome-independent association across BMI categories could be explained by insulin resistance-independent pathways for these associations, such as catabolic effects of cytokines and deficiency of growth hormone.³¹

The HSI has been used to indicate the incidence of NAFLD in several population-based Korean studies.^23,24,32 Since the HSI has low sensitivity (46%–52%) and high specificity (92%) of the cutoff values (>36.0) for NAFLD, this model may be suitable for the diagnostic rather than the screening purposes. In this population cohort, the prevalence of NAFLD was 30.1% ± 0.7%, and it is in the range of the prevalence of NAFLD in the Asian countries (27.37%, 95% confidence interval, 23.29–31.88), assessed by using the diagnostic imaging technique.²

Although current findings have a strength with respect to the representative population-based results, there are some notable limitations. First, the classification bias for NAFLD based on the prediction model and self-reported alcohol intake should be accounted, although the prevalence was in the range of the meta-analysis findings for Asian population.² Nevertheless, the associations of NAFLD with RHGS may be valid considering the significant inverse association between the HSI and RHGS (as continuous variables), after adjusting for the confounding factors (estimated B = −3.51, 95% confidence interval −6.18 to −0.85). Second limitation is impossible delineation of low muscle strength effect from low muscle mass effect for the associations with NAFLD due to unavailable body composition data. Although consistent associations throughout underweight, normal weight, and obesity categories suggest weight status-independent associations, muscle mass-independent associations need to be clarified. The other limitation is uncertain causality due to the cross-sectional study design.

In conclusion, the current study in a nationally representative sample of South Korean adults suggests an association between lower muscular strength and the higher prevalence of NAFLD assessed by using handgrip strength and a prediction model. This association remains consistent in the subgroups, namely age group, sex, educational level, BMI category, presence of metabolic syndrome, treatment status of concurrent illnesses, alcohol consumption, smoking status, or physical activity. Further prospective studies considering an interaction between muscle function and muscle mass are necessary to understand the causal mechanisms involved in the association of NAFLD with muscular function and muscle mass.

Footnotes

Acknowledgment

This work was supported by the 2018 Inje University research grant.

Author Disclosure Statement

No conflicting financial interests exist.

References

Fabbrini

, Magkos

. Hepatic steatosis as a marker of metabolic dysfunction. Nutrients, 2015; 7:4995–5019.

Younossi

, Koenig

, Abdelatif

, et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology, 2016; 64:73–84.

Loomba

, Sanyal

. The global NAFLD epidemic. Nat Rev Gastroenterol Hepatol, 2013; 10:686–690.

Byrne

, Targher

. NAFLD: A multisystem disease. J Hepatol, 2015; 62(1 Suppl):S47–S64.

Ballestri

, Zona

, Targher

, et al. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta-analysis. J Gastroenterol Hepatol, 2016; 31:936–944.

Wojcik-Cichy

, Koslinska-Berkan

, Piekarska

. The influence of NAFLD on the risk of atherosclerosis and cardiovascular diseases. Clin Exp Hepatol, 2018; 4:1–6.

Norman

, Stobaus

, Gonzalez

, et al. Hand grip strength: Outcome predictor and marker of nutritional status. Clin Nutr, 2011; 30:135–142.

Leong

, Teo

, Rangarajan

, et al. Prognostic value of grip strength: Findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet, 2015; 386:266–273.

Rijk

, Roos

, Deckx

, et al. Prognostic value of handgrip strength in people aged 60 years and older: A systematic review and meta-analysis. Geriatr Gerontol Int, 2016; 16:5–20.

10.

Kawamoto

, Ninomiya

, Kasai

, et al. Handgrip strength is associated with metabolic syndrome among middle-aged and elderly community-dwelling persons. Clin Exp Hypertens, 2016; 38:245–251.

11.

Peterson

, Duchowny

, Meng

, et al. Low normalized grip strength is a biomarker for cardiometabolic disease and physical disabilities among U.S. and Chinese adults. J Gerontol A Biol Sci Med Sci, 2017; 72:1525–1531.

12.

Lawman

, Troiano

, Perna

, et al. Associations of relative handgrip strength and cardiovascular disease biomarkers in U.S. adults, 2011–2012. Am J Prev Med, 2016; 50:677–683.

13.

Lee

, Peng

, Chiou

, et al. Relative handgrip strength is a simple indicator of cardiometabolic risk among middle-aged and older people: A Nationwide Population-Based Study in Taiwan. PLoS One, 2016; 11:e0160876.

14.

Wijarnpreecha

, Panjawatanan

, Thongprayoon

, et al. Sarcopenia and risk of nonalcoholic fatty liver disease: A meta-analysis. Saudi J Gastroenterol, 2018; 24:12–17.

15.

Hong

, Hwang

, Choi

, et al. Relationship between sarcopenia and nonalcoholic fatty liver disease: The Korean Sarcopenic Obesity Study. Hepatology, 2014; 59:1772–1778.

16.

Sousa-Santos

, Amaral

. Differences in handgrip strength protocols to identify sarcopenia and frailty—A systematic review. BMC Geriatr, 2017; 17:238.

17.

Korea Centers for Disease Control and Prevention. Guidelines for health survey. The Sixth Korea National Health and Nutrition Examination Survey (KNHANES VI-1), 2013.

18.

Kweon

, Kim

, Jang

, et al. Data resource profile: The Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol, 2014; 43:69–77.

19.

Procedural Manual. The Sixth Korea National Health and Nutrition Examination Survey (KNHANES VI-1). Osong, South Korea: Korean Centers for Disease Control and Prevention; 2013.

20.

Studenski

, Peters

, Alley

, et al. The FNIH sarcopenia project: Rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci, 2014; 69:547–558.

21.

Lee

, Peng

, Chiou

, et al. Physical health indicators improve prediction of cardiovascular and all-cause mortality among middle-aged and older people: A National Population-based Study. Sci Rep, 2017; 7:40427.

22.

Alberti

, Eckel

, Grundy

, 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.

23.

Lee

, Kim

, et al. Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis, 2010; 42:503–508.

24.

Lee

, Jung

, Kim

, et al. Sarcopaenia is associated with NAFLD independently of obesity and insulin resistance: Nationwide surveys (KNHANES 2008–2011). J Hepatol, 2015; 63:486–493.

25.

Buch

, Carmeli

, Boker

, et al. Muscle function and fat content in relation to sarcopenia, obesity and frailty of old age—An overview. Exp Gerontol, 2016; 76:25–32.

26.

Cruz-Jentoft

, Baeyens

, Bauer

, 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.

27.

Clark

, Manini

. Sarcopenia = / = dynapenia. J Gerontol A Biol Sci Med Sci, 2008; 63:829–834.

28.

Shulman

. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med, 2014; 371:1131–1141.

29.

Liu

, Mei

, Yang

, et al. Roles of chronic low-grade inflammation in the development of ectopic fat deposition. Mediators Inflamm, 2014; 2014:418185.

30.

Gamas

, Matafome

, Seica

. Irisin and myonectin regulation in the insulin resistant muscle: Implications to adipose tissue: Muscle crosstalk. J Diabetes Res, 2015; 2015:359159.

31.

Guichelaar

, Charlton

. Decreased muscle mass in nonalcoholic fatty liver disease: New evidence of a link between growth hormone and fatty liver disease?. Hepatology, 2014; 59:1668–1670.

32.

Lee

, Bang

, Park

, et al. Non-laboratory-based self-assessment screening score for non-alcoholic fatty liver disease: Development, validation and comparison with other scores. PLoS One, 2014; 9:e107584.