Next Article in Journal
Biomechanical Insights for Developing Evidence-Based Training Programs: Unveiling the Kinematic Secrets of the Overhead Forehand Smash in Badminton through Novice-Skilled Player Comparison
Next Article in Special Issue
Supplementation of Nutraceuticals from Dwarf Kiwi and Apple Improves Lipid Profile in Overweight Adults
Previous Article in Journal
Alteration Mechanism of Variscan Granite in a Project Area at the Northern Foot of the Tianshan Mountains, China
Previous Article in Special Issue
Patients with Chronic Coronary Syndrome Can Benefit from Consumption of Enriched Chicken Eggs: The Effects on Microvascular Function, Inflammatory Biomarkers, and Oxidative Status—Randomized Clinical Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 22
10.3390/app132212489
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Low Relative Handgrip Strength Is Associated with a High Risk of Non-Alcoholic Fatty Liver Disease in Italian Adults: A Retrospective Cohort Study

by
Samantha Maurotti
1,†,
Roberta Pujia
2,†,
Elisa Mazza
2,*,
Maria Francesca Pileggi
2,
Franco Arturi
2,3,
Maria Grazia Tarsitano
2,
Tiziana Montalcini
1,3,
Arturo Pujia
2,3 and
Yvelise Ferro
2
1
Department of Clinical and Experimental Medicine, University Magna Græcia, 88100 Catanzaro, Italy
2
Department of Medical and Surgical Science, University Magna Græcia, 88100 Catanzaro, Italy
3
Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Græcia, 88100 Catanzaro, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(22), 12489; https://doi.org/10.3390/app132212489
Submission received: 6 October 2023 / Revised: 10 November 2023 / Accepted: 17 November 2023 / Published: 19 November 2023
(This article belongs to the Special Issue Diet-Related Diseases: Pathophysiology and Novel Evaluation Methods)
Browse Figures
Versions Notes

Abstract

:
Background: Non-alcoholic fatty liver disease (NAFLD) and the presence of low muscle mass (sarcopenia) represent noteworthy health issues. Handgrip strength, a muscle function indicator, is vital for sarcopenia diagnosis. We investigated the link between handgrip strength and hepatic steatosis in Italian adults. Methods: We retrospectively assessed 388 adults (≥50 years), measuring muscle function and hepatic steatosis using a dynamometer and transient elastography. We divided participants into handgrip strength tertiles. Results: 207 had NAFLD. The lowest handgrip strength tertile had a higher NAFLD prevalence (64% vs. 46%, p = 0.02). Tertiles I and II exhibited increased odds of NAFLD in comparison to tertile III, with an odds ratio of 5.30 (95% confidence interval: 2.24–12.57, p < 0.001) and 2.56 (95% confidence interval: 1.17–5.59, p = 0.01), respectively. rHGS predicted NAFLD with an AUC of 0.41 (SE = 0.029, p = 0.003). An rHGS of 1.22 achieved 18% sensitivity and 80% specificity for hepatic steatosis prediction. Conclusion: Low handgrip strength is linked to an increased susceptibility to NAFLD among the Italian population, implying its potential utility in the identification of risk for hepatic steatosis.

1. Introduction

Handgrip strength (HGS) is an indicator of muscle function [1], and current guidelines have emphasized the significance of HGS as the principal metric for sarcopenia diagnosis [2]. Among adult individuals, HGS is a recognized marker of frailty as well as a reflection of their present nutritional status [3,4,5,6,7]. Furthermore, the loss of muscle function predisposes older adults to an increased risk of several clinical conditions [8] and mortality [5,9,10,11]. It is established that muscle strength surpasses skeletal muscle mass in its predictive capacity for adverse outcomes [9,12]. For these reasons, evaluating muscle strength is gaining greater significance in both research and clinical environments, with the aim of enhancing the well-being of aging populations. Non-alcoholic fatty liver disease (NAFLD) represents the most common chronic liver disorder [13,14]. NAFLD presents a significant public health challenge due to its global surge, attributable to the obesity epidemic [13,15], and its growing role as a primary contributor to hepatocellular carcinoma (HCC) [16]. NAFLD is also a predictor of cardiovascular diseases and a greater mortality rate [17,18,19]. Interestingly, hepatic steatosis and sarcopenia, both linked to the subject’s dietary habits, may share common pathogenic mechanisms such as insulin resistance (IR), chronic low-grade inflammation, and decreased physical activity [20,21,22]. Additionally, molecules such as myostatin, insulin-like growth factor-1 (IGF-1), and sirtuin 1 (SIRT 1) are implicated in this relationship. Myostatin, a myokine expressed in skeletal muscle that is a negative regulator of skeletal muscle growth in animals and human is significantly higher in adults with sarcopenia than in those without sarcopenia [23]. Preclinical studies have demonstrated that inhibition of myostatin increases muscle mass, improves insulin sensitivity, and protects against liver steatosis [24,25].
A low level of IGF-1, a regulator of both hepatic glucose production and skeletal muscle cells, has been demonstrated in individuals with NAFLD [26] as well as sarcopenia [27].
SIRT1 is also linked to the spectrum of liver damage and muscle atrophy [28,29]. SIRT1 plays important roles in regulating lipid metabolism, inflammation, and oxidative stress in the liver through deacetylating some transcriptional regulators against the progression of fatty liver diseases [28]. Hepatic oxidative stress aggravates liver damage and muscle wasting progression. Indeed, in subjects with NAFLD, muscular oxidative stress promotes protein degradation, cell apoptosis, and the development of muscle atrophy [30]. Activation of SIRT-1 attenuates hepatic steatosis and improves muscle atrophy in an animal model of non-alcoholic steatohepatitis [30].
Furthermore, recent findings have shown that sarcopenia and reduced appendicular muscle mass serve as indicators for the development of impaired fasting glucose and type 2 diabetes in older women [31], and that IR and inflammation may modulate the connection between HGS and the risk of NAFLD [32]. To date, some studies have evaluated the relationship between muscle function and NAFLD, but especially in Asian populations [32,33,34,35,36,37,38,39]. However, only two Asian studies have investigated this relationship exclusively in adults aged ≥50 years [35,37], who represent the most vulnerable population to sarcopenia and its consequences. Hence, extrapolating these results to non-Asian groups may be problematic due to differences in muscle strength among ethnicities [9]. To date, this relationship has only been evaluated in two studies involving Caucasians [40,41]. In particular, the first study supports the relationship between sarcopenia (as defined in the skeletal muscle index) and NAFLD, assessed with ultrasound, in American Caucasians [40]. The second study, utilizing information from the UK Biobank, revealed that reduced handgrip strength was linked to an elevated risk of developing severe NAFLD (defined as hospitalization or mortality) following an approximately 10-year follow-up period in adults aged between 37 and 73 years [41]. In addition, in most of these Asian studies [32,34,35,37], NAFLD has been identified using steatosis scores calculated with anthropometric and biochemical parameters and not with specific techniques for assessing liver steatosis and fibrosis [42]. Transient elastography (TE) was created to identify and measure liver steatosis through a non-invasive parameter, specifically the CAP (Controlled Attenuation Parameter), which demonstrates a correlation with the histopathological assessment of hepatic steatosis [43,44]. This method is widely employed in population-based investigations owing to its non-invasive nature and ready availability [44]. Consequently, the primary objective of this study was to assess the association between HGS and NAFLD in the Italian adult population. In particular, the primary outcome was to verify whether there is a significant difference in the prevalence of hepatic steatosis, quantified using the CAP score, among HGS tertiles.

2. Materials and Methods

2.1. Study Design

This retrospective cohort study involved the inclusion of 5708 assessments of consecutive outpatient individuals who underwent screening examinations for potential fatty liver disease using transient elastography (TE). The examinations took place at the Nutrition Clinic of the “Mater Domini” University Hospital in Catanzaro, Italy, spanning the period from April 2014 to March 2021. For this study, data available in existing databases was used. We included adults of both genders, those ≥50 years old, with a wide range of body mass index (BMI), whose HGS data were available. We excluded individuals from the study who displayed laboratory and clinical indications of chronic hepatitis B and/or C virus infections. Additionally, we excluded those with a history of or current alcohol misuse, defined as consuming more than 20 g of alcohol per day, where hard liquor equals 45 mL (1.5 oz), wine equals 120 mL (4 oz), and beer equals 350 mL (12 oz), each containing 10 g of alcohol, as confirmed by their medical records. Furthermore, we excluded individuals with ascites and liver cirrhosis, autoimmune liver disease, cholestatic liver disease, chronic kidney disease, nephrotic syndrome, pregnancy, and those taking methotrexate, corticosteroids, antiretroviral agents, valproate, and amiodarone. For the purpose of this analysis, we also removed all patients with a liver stiffness value of ≥7 kPa [45] and those for whom it was technically infeasible to evaluate the CAP score. To detect a difference in the prevalence of hepatic steatosis of at least 18% between HGS tertiles, with a power of 80% and a two-sided significance level of 0.05, approximately 243 participants are needed. Among the adults initially selected, 388 patients meet all eligibility criteria (Figure 1). The protocol was approved by the “Mater Domini” Azienda University Hospital’s local ethical committee (protocol n.121 on 21 April 2023). All participants provided written informed consent, and this study adhered to the ethical guidelines specified in the Declaration of Helsinki. All participants underwent a medical interview, during which anthropometric measurements, HGS assessments, and TE evaluations were conducted. The baseline clinical characteristics of the population, as well as their biochemical parameters, were retrieved from their medical records. Various cardiovascular risk factors were defined according to the following criteria: Hypertension: systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or the use of antihypertensive medications.
Dyslipidemia: total cholesterol levels > 200 mg/dL and/or triglycerides > 200 mg/dL or the use of lipid-lowering drugs.
Type 2 diabetes: fasting blood glucose levels ≥ 126 mg/dL or the use of antidiabetic treatments.
General obesity: a body mass index (BMI) of ≥30 kg/m2.
Smoking: individuals who were current or past smokers (at least in the preceding four months).

2.2. Anthropometric Measurements

Body weight was determined with subjects lightly clothed after a night of fasting before breakfast, and the weight of clothing was subtracted. Height and body weight measurements were taken with a wall-mounted stadiometer and a calibrated scale, respectively, with an accuracy of 1 cm and 0.1 kg. Waist and hip circumferences (WC and HC), along with the waist-to-hip ratio (WHR), were assessed according to established methods [46]. Android obesity was defined as a WHR exceeding 0.90 for males and 0.85 for females [47].

2.3. HGS Measurement

HGS was determined by a digital hand dynamometer (DynX®™ Akern srl, Florence, Italy) with less than 10% variation in results for varied grip postures by an experienced dietitian. At the time of the measurement, the participants were seated with their elbows supported and flexed at 90° [48]. Three measurements of maximal strength from the dominant hand were performed, and the mean value of the three values was recorded [36,37]. In alignment with prior research, the relative HG (rHGS) was computed by adjusting the mean HG of the dominant arm based on BMI using the formula HGS/BMI [33,34,36,38]. This adjustment takes into account the robust correlation between muscle strength and BMI [49].

2.4. Liver Transient Elastography (TE)

TE offers a non-invasive means of quantifying hepatic steatosis through the assessment of the Controlled Attenuation Parameter (CAP) and measuring liver stiffness using Fibroscan® 502 by Echosense SASU, Paris, France [44,45]. The CAP and stiffness scores were obtained from the same volume of liver parenchyma simultaneously. All individuals underwent examination with the standard 3.5 MHz M probe applied to the right lobe of the liver, accessing intercostal spaces while the subjects were in a supine position. The right arm was placed behind the head to facilitate access to the upper right quadrant of the abdomen. The probe transducer’s tip was positioned on the skin between the ribs, at the level of the liver’s right lobe. The same investigator conducted all of the scans. The hepatic stiffness was determined by using the median value (in kPa) derived from 10 measurements taken within a depth range of 25 to 65 mm. Only data with ten valid images and a liver stiffness ratio represented by a 30% interquartile range (IQR) over the median were considered for inclusion. Given the device’s unique CAP algorithm, we exclusively employed the M probe to calculate the CAP score. Each patient underwent 10 successful measurements, and only individuals with all 10 measurements deemed successful were included in this study. The success rate was determined by dividing the total count of successful measurements by the overall number of measurements. We calculated the IQR-to-median ratio of liver stiffness (IQR/MLSM) as an indicator of variability. The ultimate CAP score, within a range of 100 to 400 decibels per meter (dBm–), was computed as the median of individual measurements. A CAP score equal to or greater than 248 dB/m was utilized to diagnose NAFLD. Distinct cutoff values were applied to determine the grade of steatosis: a CAP score of 248 dB/m or higher for the diagnosis of S1 grade, a CAP score of 269 dB/m or higher for the diagnosis of S2 grade, and a CAP score of 281 dB/m or higher for the diagnosis of S3 grade, indicating severe steatosis [50].

2.5. Biochemical Evaluation

Following an overnight fasting period, venous blood samples were collected into vacutainer tubes (Becton & Dickinson, Plymouth, UK) and then promptly centrifuged within 4 h. Serum levels of glucose, creatinine, high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), triglycerides (TG), ALT, AST, and gamma-glutamyl transferase (GT) were determined using a chemiluminescent immunoassay, following the manufacturer’s instructions, and conducted on a COBAS 8000 analyzer (Roche, Switzerland). The Friedewald formula was applied to compute low-density lipoprotein cholesterol (LDL-C).

2.6. Statistical Analysis

Data are reported as mean ± SD. The prevalence of NAFLD in adults was 21.8% [29].
Participants were divided into tertiles based on rHGS, and an ANOVA test was conducted to compare the means among the tertiles. A χ2 test was employed to analyze the prevalence across rHGS tertiles. A general linear model (GLM) was used to adjust the CAP score values, the prevalence of NAFLD, and its severity (S3 grade) for confounding factors (as age, gender, smoking habit, physical activity, hyperlipidemia, general and android obesity, HDL-C, TG, creatinine, and AST) in analyzing rHGS groups. We also repeated the same tests on the subgroup of women because the population of this study is mainly made up of women (n = 271). In the population divided into rHGS tertiles, we also examined the prevalence of NAFLD in two groups: those with a normal weight (BMI ≤ 25 kg/m2) and those who are overweight/obese (BMI > 25 kg/m2). We finally assessed muscle strength (rHGS) in subjects both with and without NAFLD. To assess the risk of hepatic steatosis among individuals with varying rHGS, we conducted a multinomial logistic regression analysis to estimate the adjusted odds ratios (ORs). The ORs were adjusted for confounding variables, i.e., those that were statistically different in the ANOVA and χ2 test. In particular, in Model 1, the ORs of NAFLD were adjusted only for age and gender; and in Model 2, the ORs were adjusted, in addition to age and gender, for smoking habit, physical activity, hyperlipidemia, general obesity, HDL-cholesterol, triglycerides, creatinine, and AST. In Model 3, the ORs were adjusted for the same variables as in Model 2, but the variable “general obesity” was replaced by “android obesity” in the analysis. Subsequently, we conducted a ROC curve analysis to determine the optimal rHGS cutoff point for detecting NAFLD in adult individuals. Statistical significance was considered at p < 0.05 (two-tailed). All statistical comparisons were carried out using SPSS 25.0 for Windows, provided by IBM Corporation, New York, NY, USA.

3. Results

The mean age of the study population was 60.8 ± 7 years, with 70% of the sample being female. The prevalence of NAFLD was 53%, and general and android obesity were 34% and 73%, respectively. The mean HGS was 17.9 ± 3 kg, 22.4 ± 4 kg, and 36.1 ± 7 kg, and the rHGS was 0.59 ± 0.09, 0.80 ± 0.08, and 1.33 ± 0.25 in tertile I, tertile II, and tertile III, respectively (Table 1). An increase in muscle strength was associated with a decrease in the mean age and BMI (p < 0.001 for both factors; Table 1). In the rHGS tertiles, there was also a statistically significant difference in TG, HDL-C, AST, creatinine level, and the prevalence of gender, smoking habit, physical activity, hyperlipidemia, and general and android obesity (Table 1). Furthermore, the CAP score was significantly higher in the individuals in the first tertile of rHGS than those in the second and third tertiles (266 ± 48 dB/m, 252 ± 49 dB/m, and 249 ± 47 dB/m, p = 0.012, respectively) (Table 1).
GLM shows that the CAP score was significantly different among tertiles even after adjustment for confounding variables (p = 0.001; Table 1). The prevalence of hepatic steatosis was 64%, 51%, and 46% in the tertile I, tertile II, and tertile III of rHGS, respectively (p = 0.004) (Figure 2a). The prevalence of the diagnosis S3 grade of steatosis was 37% in the first tertile, 31% in the second tertile, and 28% in the third tertile of rHGS (p = 0.024) (Figure 2b). After adjustment for confounding variables, the statistically significant differences in the prevalence of NAFLD and of steatosis of S3 grade persisted among rHGS tertiles (p = 0.023 and p = 0.005, respectively) (Figure 2a,b).
Demographic, clinical, and instrumental characteristics of the women divided into rHGS tertiles are shown in Table S1. In the female population, we found that the CAP score, prevalence of NAFLD, and hepatic steatosis S3 grade were significantly higher in the first and second tertiles of rHGS than those in the third tertile (Table S1). In women, these results remained significantly different among rHGS tertiles even after adjustment for confounding variables (Supplementary Table S1). We also investigated the prevalence of NAFLD in the population stratified by BMI. Within the normal-weight population divided into rHGS tertiles, we observed a higher prevalence of NAFLD in the first and second tertiles compared to the third (39%, 23%, and 10%, p = 0.035, respectively) (Supplementary Figure S1A). The same result is also confirmed in the overweight/obese population categorized by rHGS tertiles (77%, 63%, and 48%, p = 0.013, respectively) (Supplementary Figure S1B). Additionally, our analysis of participants with or without NAFLD revealed that those with hepatic steatosis exhibited significantly lower rHGS compared to those without NAFLD (0.86 ± 0.3 vs. 0.96 ± 0.4, p = 0.008, respectively). Notably, this result retained statistical significance even after adjusting for confounding variables such as age, gender, smoking habits, physical activity, hyperlipidemia, and general and android obesity (p = 0.004).
After adjusted for age and gender (Model 1), the multinomial logistic regression analysis showed that adults in tertile I and tertile II have significantly higher ORs of having NAFLD (Tertile I: OR = 4.85, 95% CI = 2.30–10.20, p < 0.001; Tertile II: OR = 2.48, 95% CI = 1.26–4.87, p = 0.008, respectively) compared to those in rHGS tertile III (Table 2). In Model 2, only the subjects in the first tertile exhibited a significantly higher odds ratio (OR) compared to those in the second and third tertiles of rHGS (OR = 3.63, 95% CI = 1.49–8.82, p = 0.004; Table 2).
Finally, when the values were adjusted for android obesity rather than general obesity, the risk of NAFLD was 5.30 and 2.56 times higher for the elderly in the first and second tertiles of rHGS, respectively, than for those in the third tertile (Model 3; Table 2). Additionally, the area under the ROC curve for rHGS in detecting the presence of NAFLD was 0.412 (SE = 0.029, p = 0.003, with a lower limit of 0.35 and an upper limit of 0.46) (figure not displayed). An rHGS value of 1.22 exhibited low sensitivity (18%) but high specificity (80%) in identifying hepatic steatosis. On the other hand, an rHGS value of 0.55 demonstrated excellent sensitivity (88%) but low specificity (5%) in predicting NAFLD among the elderly.

4. Discussion

To our knowledge, this is the first report involving Italian adults that highlights a potential association between hepatic steatosis, as evaluated through TE, and a decline in muscle function, as indicated by reduced muscle strength.
The aging of societies and the increase in life expectancy in Italy are unfortunately also associated with a demographic and epidemiological transition towards a frail population. Although at an international level, this increase is seen as a public health success story, in approximately the same period of time, there has been an increase in several non-communicable diseases (NCDs), which include liver disease and sarcopenia [51]. In Italy, the estimated prevalence of NAFLD in the general population is approximately 25%, with higher prevalence rates in the southern regions compared to the northern regions (Campania region: 12.1% vs. Trentino Alto Adige region: 3.1%) [52]. In at-risk populations, such as obese and/or diabetic individuals, the prevalence increases to levels exceeding 50%. The prevalence of NAFLD found in our study is in line with that reported in other similar studies [53,54,55]. Approximately 30% of the Italian population will develop NAFLD by 2030 [56] with likely associated comorbidities, with an increase in the economic and healthcare burden associated with this disease. This treatable clinical condition has huge costs, precedes the appearance of diabetes, and predicts mortality. Therefore, our work suggests how to set up measures for the early identification of those individuals affected by NAFLD and how to treat them.
In particular, our retrospective study reveals that adults, especially females in apparently good health belonging to the first rHGS tertile (lowest rHGS value), had a very high CAP score and a higher prevalence of NALFD than those in the third tertile of rHGS (Figure 2a and Table 1 and Table S1), even after adjusting for confounding factors. Furthermore, individuals in tertile I also had a higher prevalence of severe hepatic steatosis than those in rHGS tertile III (Figure 2b). A key finding was that adults with lower rHGS had a greater risk of having NAFLD than those with higher rHGS values, and that this risk is independent of age (Table 2). These results are in line with those demonstrated for Asian adults regarding the high risk of fatty liver disease in individuals with low muscle function [33,34,35,36,37]. Therefore, our findings suggest that differences in muscle strength values between different ethnicities do not affect this strong relationship [9]. This evidence is strengthened by the prospective study conducted on a European cohort [41]. Indeed, this study showed that individuals in the highest HGS tertile had a lower risk (95% CI 0.64–0.77) of severe NAFLD than those in the lowest HGS tertile [41]. In addition, in a recent meta-analysis, Han et al. also demonstrated that individuals with low HGS had a higher risk of NAFLD compared to those with normal HGS (OR = 3.32, 95% CI: 1.91–5.75). Furthermore, the authors reported that the risk among patients with low HGS affected by NAFLD was higher than that in normal people (OR = 3.31, 95% CI: 2.45–4.47) [57].
We observed, in our study, a higher prevalence of NAFLD among subjects of both genders and age ≥50 years than that found in a similar Asian population (53% vs. 22%, respectively) [35]. The observed dissimilarity may, in part, be attributed to the higher average BMI in our study population and the distinctive methodology employed for NAFLD diagnosis [35]. Notably, the CAP score might prove to be more precise in the detection of hepatic steatosis compared to the hepatic steatosis index [58].
We also found that low HGS had a greater and independent effect in the presence of obesity and, particularly, android obesity, which is already associated with a higher risk of developing NAFLD [59]. In light of the above, our results could have important implications because they confirm, in line with studies on adults in Asia and Caucasians in the USA and UK, that fatty liver disease could be directly associated with muscle strength during aging.
Studies focusing on muscle mass [60,61] have highlighted the link between muscle tissue and the liver; however, they may not be enough to fully explain this cross-talk. Studies have demonstrated that preserving or increasing muscle mass in isolation may not be sufficient to halt the decline in muscle function related to aging [62] and that muscle strength holds greater significance than muscle quantity when assessing mortality risk among the elderly [63].
Several studies have underscored the significance of evaluating muscle strength in individuals with liver disorders. These studies have revealed associations between handgrip strength (HGS) and health-related quality of life [64], as well as the occurrence of hepatic decompensation and HCC in adults with chronic liver conditions [65]. Furthermore, it has been established that a low HGS value serves as a negative prognostic factor for survival in individuals with liver cirrhosis [66] and HCC [67]. Notably, a study [67] involving HCC patients demonstrated that the low HGS group exhibited significantly shorter survival compared to the high HGS group, with low HGS emerging as an independent predictor for overall survival in multivariate analysis, while skeletal muscle depletion did not exhibit the same impact. Additionally, patients with low HGS tended to discontinue therapy more frequently than those with high HGS [67]. HGS is additionally linked to various unfavorable clinical consequences in cirrhosis, such as reduced physical activity and the development of hepatic encephalopathy [68]. Moreover, a study found that reduced handgrip strength (HGS) is linked to a twofold increase in the risk of all-cause and liver-related mortality [66]. Another investigation demonstrated that grip strength is associated with an elevated risk of mortality among patients on liver transplantation waiting lists [69]. Additionally, impaired preoperative HGS is correlated with increased postoperative complications, mortality, and extended hospitalization after surgery [70]. The HGS test stands out as an affordable, widespread screening tool with notable sensitivity and specificity for identifying morbidity and mortality across various healthcare settings. It can be easily administered by nurses, physicians, and other healthcare professionals. When considering the cost and expertise required by alternative tests and their influence on long-term survival, HGS assessment emerges as a superior option. HGS serves as a simple, non-invasive, cost-effective, and real-time predictor of long-term all-cause mortality [69]. Despite this accumulating evidence, the precise relationship between muscle health and metabolic disorders like NAFLD remains incompletely understood. Although our study did not directly assess the mechanisms underlying this relationship, we can nevertheless make some hypotheses.
The role of IR, chronic inflammation, and decreased physical activity has already been demonstrated [20,21,22]. In fact, as IR and inflammation increase, the prevalence of NAFLD increases and muscle strength decreases [20,21,33,36]. Therefore, sarcopenia itself may aggravate IR in non-obese and obese individuals [71]. Furthermore, Lee et al. demonstrated that both non-obese and obese individuals exhibited a markedly higher prevalence of NAFLD in the presence of sarcopenia [34]. In particular, the prevalence of NAFLD was 9–30% in non-obese sarcopenic individuals and 4–14% in non-obese and non-sarcopenic patients, while the prevalence of NAFLD was 61–83% in obese sarcopenic and 50–72% in obese non-sarcopenic patients [34]. Hence, factors beyond insulin resistance and obesity may play a role in the connection between muscle tissue and the liver, including IGF-1, myostatin, and SIRT 1.
In particular, a systematic review and meta-analysis revealed that individuals with NAFLD exhibit lower levels of IGF-1 when compared to healthy control subjects [26]. Nevertheless, IGF-1 is also present in skeletal muscles, where it promotes muscle growth and strength, mitigates degeneration, hinders inflammatory processes, and stimulates the regenerative capacity of satellite cells [72]. In addition, a recent study demonstrated that elderly individuals with sarcopenia exhibit lower IGF-1 levels compared to those without sarcopenia (98.53 ± 28.45 vs. 136.41 ± 48.95 ng/mL, p < 0.001) [27].
Information on serum myostatin levels in individuals with sarcopenia is somewhat limited. However, a connection exists between IGF-1 and myostatin under usual circumstances. IGF-1 acts to obstruct the myostatin pathway; indeed, when IGF-1 is suppressed, myostatin becomes overexpressed [73]. SIRT1 is linked to liver damage and muscle atrophy, regulating lipid metabolism and countering oxidative stress in the liver. It plays a crucial role in fatty liver disease and muscle health [28,29].
All these mechanisms suggest a strong link between skeletal muscle and the liver and, consequently, between sarcopenia and NAFLD. However, it remains uncertain whether impaired muscle function and sarcopenia lead to NAFLD, if it’s the other way around, or whether the relationship does not involve a causal link but rather simultaneous effects on both the liver and muscles.
In our study, we also found in tertile I a serum level of AST lower than tertile III (tertile I with a lower rHGS and higher CAP score than participants in tertile III). This is not surprising because it has already been shown that some patients with NAFLD may have laboratory abnormalities such as high ALT and AST, whereas others may have normal transaminase levels [74]. There is consensus on the fact that NAFLD is diagnosed in clinical settings using ultrasound imaging (as a liver biopsy is not practically feasible). Nice guidelines recommend not using routine liver blood tests to rule out NAFLD but rather offering a liver ultrasound to test people for NAFLD (National Guideline Centre (UK)) [75]. Risk groups recommended for NAFLD screening are the American Association for the Study of Liver Diseases (AASLD) and Asian Pacific Association for the Study of the Liver (APASL), diabetics, and patients who are overweight/obesity with a family history of cirrhosis, moderate-to-high alcohol consumption, or metabolic syndrome [76,77]. Despite having the disease, up to 50% of NAFLD patients can have normal ALT and AST levels [76,78,79]. Elastography can accurately diagnose liver steatosis, especially if the IQR is less than 30% of the median [80].
Our study does have certain limitations. It was conducted at a single center, which means the number of adult participants was restricted, and the study included both males and females, as well as obese and non-obese individuals. We did not find an association between HGS and NAFLD in males. It is essential to publish not only positive research results but also negative ones. Our population differs from that referenced in Kim et al. [35]. The study evaluated an Asiatic population. Additionally, the authors used the hepatic steatosis index as a surrogate marker for NAFLD, while we used TE assessment. There are no studies on the association between HGS and NAFLD in European males. Despite a small sample size, we believe that our findings could be valuable in designing future research for both genders. Furthermore, we did not evaluate IR and inflammation status, serum levels of IGF-1, or myostatin. Due to the lack of muscle mass measurements, we were unable to verify the presence of sarcopenia. We did not perform any liver biopsies, mainly due to the apparently healthy study population, the high cost, and the associated risk. Due to the study design, we were unable to identify a causal relationship. Additional data from across EU countries is needed to confirm our findings. In addition, it is imperative to conduct prospective studies and intervention trials to ascertain a causal connection between impaired muscle function and fatty liver disease. The major strength of this study is that it was a cohort of adults, who are the subjects at greatest risk of metabolic disorders and sarcopenia, and that we used TE to detect NAFLD instead of steatosis scores [33]. Moreover, TE exhibits a high level of sensitivity and specificity in evaluating liver steatosis and fibrosis [34] and is commonly employed in population-based research due to its non-invasive nature and accessibility [35]. Finally, AUROC are 81–84% for ≥S1 (steatosis in at least 5–10% of hepatocytes), 85–88% for ≥S2 (33%), and 86–91% for S3 (66%) steatosis. The reported sensitivities for ≥S1, ≥S2, and ≥S3 are 60–75%, 69–84%, and 77–96%, respectively [81]. Some studies suggest that skin capsular distance (SCD) can affect the accuracy of the CAP score by overestimating this measurement, especially in those with a large SCD (≥25 mm) [82,83]. However, the FibroScan Model 502 and later models have an automated probe selection tool in their software that suggests the choice of the appropriate probe (M and XL) for each patient according to the real-time evaluation of the SCD [84]. Additionally, as in prior studies [32,33,35,36], we utilized HGS to assess muscle function, which bolsters the statistical robustness of our findings. Consequently, the primary outcome of this study highlights that diminished muscle function may represent a risk factor for NAFLD among aging adults who appear to be in good health.

5. Conclusions

We observed a greater risk of fatty liver disease in Italian adults with lower handgrip strength. Although decreased function mass has long been considered an inevitable consequence of aging, it is now regarded as a disease that should be diagnosed and treated to prevent sarcopenia and metabolic diseases such as NAFLD. Therefore, we suggest that a muscle strength assessment be performed for all subjects with NAFLD, and for adults with low appendicular muscle strength, the measurement of intrahepatic fat content should be performed. Although TE is a non-invasive test used for the diagnosis and staging of NAFLD in clinical practice, our data should be taken with caution. Further studies are needed to confirm our findings and identify interventions that increase muscle quality and reduce fatty liver content in order to improve the health of aging populations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app132212489/s1, Table S1: Demographic, clinical, and instrumental characteristics of the females’ population according to relative handgrip strength tertiles. Figure S1: Prevalence of NAFLD in the population according to relative handgrip strength tertiles and stratified by BMI.

Author Contributions

Conceptualization, T.M., A.P. and Y.F.; Data curation, E.M. and M.F.P.; Formal analysis, A.P. and Y.F.; Investigation, S.M., R.P., E.M., M.F.P. and M.G.T.; Methodology, E.M. and M.G.T.; Writing—original draft, S.M., R.P. and Y.F.; Writing—review and editing, F.A., T.M. and A.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by by the Local Ethics Committee in the Calabria Region—Central Area (protocol code n.121 on 21 April 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lee, S.H.; Gong, H.S. Measurement and interpretation of handgrip strength for research on sarcopenia and osteoporosis. J. Bone Metab. 2020, 27, 85. [Google Scholar] [CrossRef]
  2. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A. Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef]
  3. Auyeung, T.W.; Lee, J.; Leung, J.; Kwok, T.; Woo, J. The selection of a screening test for frailty identification in community-dwelling older adults. J. Nutr. Health Aging 2014, 18, 199–203. [Google Scholar] [CrossRef] [PubMed]
  4. Fielding, R.A.; Vellas, B.; Evans, W.J.; Bhasin, S.; Morley, J.E.; Newman, A.B.; van Kan, G.A.; Andrieu, S.; Bauer, J.; Breuille, D. Sarcopenia: An undiagnosed condition in older adults. Current consensus definition: Prevalence, etiology, and consequences. International working group on sarcopenia. J. Am. Med. Dir. Assoc. 2011, 12, 249–256. [Google Scholar] [CrossRef]
  5. Alexandre, T.; Duarte, Y.; Santos, J.; Wong, R.; Lebrao, M. Sarcopenia according to the European Working Group on Sarcopenia in Older People (EWGSOP) versus dynapenia as a risk factor for mortality in the elderly. J. Nutr. Health Aging 2014, 18, 751–756. [Google Scholar] [CrossRef] [PubMed]
  6. Valentini, L.; Schaper, L.; Buning, C.; Hengstermann, S.; Koernicke, T.; Tillinger, W.; Guglielmi, F.W.; Norman, K.; Buhner, S.; Ockenga, J. Malnutrition and impaired muscle strength in patients with Crohn’s disease and ulcerative colitis in remission. Nutrition 2008, 24, 694–702. [Google Scholar] [CrossRef]
  7. Tak, Y.J.; Lee, J.G.; Yi, Y.H.; Kim, Y.J.; Lee, S.; Cho, B.M.; Cho, Y.H. Association of handgrip strength with dietary intake in the Korean population: Findings based on the seventh Korea National Health and Nutrition Examination Survey (KNHANES VII-1), 2016. Nutrients 2018, 10, 1180. [Google Scholar] [CrossRef]
  8. Bohannon, R.W. Dynamometer measurements of hand-grip strength predict multiple outcomes. Percept. Mot. Ski. 2001, 93, 323–328. [Google Scholar] [CrossRef] [PubMed]
  9. Leong, D.P.; Teo, K.K.; Rangarajan, S.; Lopez-Jaramillo, P.; Avezum Jr, A.; Orlandini, A.; Seron, P.; Ahmed, S.H.; Rosengren, A.; Kelishadi, R. Prognostic value of grip strength: Findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 2015, 386, 266–273. [Google Scholar] [CrossRef]
  10. Kim, K.; Ho, J.-H. Handgrip Strength and Mortality in Elderly Koreans: Evidence from the Korea Longitudinal Study of Ageing. Asia Pac. J. Public. Health 2020, 32, 302–309. [Google Scholar] [CrossRef]
  11. Bae, E.-J.; Park, N.-J.; Sohn, H.-S.; Kim, Y.-H. Handgrip strength and all-cause mortality in middle-aged and older Koreans. Int. J. Environ. Res. Public. Health 2019, 16, 740. [Google Scholar] [CrossRef]
  12. Schaap, L.A.; Van Schoor, N.M.; Lips, P.; Visser, M. Associations of sarcopenia definitions, and their components, with the incidence of recurrent falling and fractures: The longitudinal aging study Amsterdam. J. Gerontol. Ser. A 2018, 73, 1199–1204. [Google Scholar] [CrossRef] [PubMed]
  13. Younossi, Z.M.; Koenig, A.B.; Abdelatif, D.; Fazel, Y.; Henry, L.; Wymer, M. Global epidemiology of nonalcoholic fatty liver disease—Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016, 64, 73–84. [Google Scholar] [CrossRef]
  14. Sayiner, M.; Koenig, A.; Henry, L.; Younossi, Z.M. Epidemiology of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis in the United States and the Rest of the World. Clin. Liver Dis. 2016, 20, 205–214. [Google Scholar] [CrossRef] [PubMed]
  15. Younossi, Z.M. Non-alcoholic fatty liver disease–A global public health perspective. J. Hepatol. 2019, 70, 531–544. [Google Scholar] [CrossRef] [PubMed]
  16. Farrell, A.; Ryan, M.; Howell, J. Epidemiology of non-alcoholic fatty liver disease-related hepatocellular carcinoma: A western perspective. Hepatoma Res. 2020, 6, 18. [Google Scholar] [CrossRef]
  17. Dunn, W.; Xu, R.; Wingard, D.L.; Rogers, C.; Angulo, P.; Younossi, Z.M.; Schwimmer, J.B. Suspected nonalcoholic fatty liver disease and mortality risk in a population-based cohort study. Am. J. Gastroenterol. 2008, 103, 2263. [Google Scholar] [CrossRef]
  18. Lee, J.I.; Kim, M.C.; Moon, B.S.; Song, Y.S.; Han, E.N.; Lee, H.S.; Son, Y.; Kim, J.; Han, E.J.; Park, H.-J. The relationship between 10-year cardiovascular risk calculated using the pooled cohort equation and the severity of non-alcoholic fatty liver disease. Endocrinol. Metab. 2016, 31, 86–92. [Google Scholar] [CrossRef]
  19. Dulai, P.S.; Singh, S.; Patel, J.; Soni, M.; Prokop, L.J.; Younossi, Z.; Sebastiani, G.; Ekstedt, M.; Hagstrom, H.; Nasr, P. Increased risk of mortality by fibrosis stage in nonalcoholic fatty liver disease: Systematic review and meta-analysis. Hepatology 2017, 65, 1557–1565. [Google Scholar] [CrossRef]
  20. Guillet, C.; Boirie, Y. Insulin resistance: A contributing factor to age-related muscle mass loss? Diabetes Metab. 2005, 31, S20–S25. [Google Scholar] [CrossRef] [PubMed]
  21. Beyer, I.; Mets, T.; Bautmans, I. Chronic low-grade inflammation and age-related sarcopenia. Curr. Opin. Clin. Nutr. Metab. Care 2012, 15, 12–22. [Google Scholar] [CrossRef] [PubMed]
  22. Merli, M.; Dasarathy, S. Sarcopenia in non-alcoholic fatty liver disease: Targeting the real culprit? J. Hepatol. 2015, 63, 309–311. [Google Scholar] [CrossRef] [PubMed]
  23. Ryan, A.S.; Li, G. Skeletal muscle myostatin gene expression and sarcopenia in overweight and obese middle-aged and older adults. JCSM Clin. Rep. 2021, 6, 137–142. [Google Scholar] [CrossRef]
  24. Wilkes, J.J.; Lloyd, D.J.; Gekakis, N. Loss-of-function mutation in myostatin reduces tumor necrosis factor alpha production and protects liver against obesity-induced insulin resistance. Diabetes 2009, 58, 1133–1143. [Google Scholar] [CrossRef]
  25. Zhang, C.; McFarlane, C.; Lokireddy, S.; Bonala, S.; Ge, X.; Masuda, S.; Gluckman, P.D.; Sharma, M.; Kambadur, R. Myostatin-deficient mice exhibit reduced insulin resistance through activating the AMP-activated protein kinase signalling pathway. Diabetologia 2011, 54, 1491–1501. [Google Scholar] [CrossRef]
  26. Yao, Y.; Miao, X.; Zhu, D.; Li, D.; Zhang, Y.; Song, C.; Liu, K. Insulin-like growth factor-1 and non-alcoholic fatty liver disease: A systemic review and meta-analysis. Endocrine 2019, 65, 227–237. [Google Scholar] [CrossRef] [PubMed]
  27. Bian, A.; Ma, Y.; Zhou, X.; Guo, Y.; Wang, W.; Zhang, Y.; Wang, X. Association between sarcopenia and levels of growth hormone and insulin-like growth factor-1 in the elderly. BMC Musculoskelet. Disord. 2020, 21, 214. [Google Scholar] [CrossRef]
  28. Ding, R.B.; Bao, J.; Deng, C.X. Emerging roles of SIRT1 in fatty liver diseases. Int. J. Biol. Sci. 2017, 13, 852–867. [Google Scholar] [CrossRef]
  29. Lee, D.; Goldberg, A.L. SIRT1 protein, by blocking the activities of transcription factors FoxO1 and FoxO3, inhibits muscle atrophy and promotes muscle growth. J. Biol. Chem. 2013, 288, 30515–30526. [Google Scholar] [CrossRef]
  30. Liu, C.-W.; Huang, C.-C.; Hsu, C.-F.; Li, T.-H.; Tsai, Y.-L.; Lin, M.-W.; Tsai, H.-C.; Huang, S.-F.; Yang, Y.-Y.; Hsieh, Y.-C.; et al. SIRT1-dependent mechanisms and effects of resveratrol for amelioration of muscle wasting in NASH mice. BMJ Open Gastroenterol. 2020, 7, e000381. [Google Scholar] [CrossRef]
  31. Buscemi, C.; Ferro, Y.; Pujia, R.; Mazza, E.; Boragina, G.; Sciacqua, A.; Piro, S.; Pujia, A.; Sesti, G.; Buscemi, S. Sarcopenia and Appendicular Muscle Mass as Predictors of Impaired Fasting Glucose/Type 2 Diabetes in Elderly Women. Nutrients 2021, 13, 1909. [Google Scholar] [CrossRef] [PubMed]
  32. Park, S.H.; Kim, D.J.; Plank, L.D. Association of grip strength with non-alcoholic fatty liver disease: Investigation of the roles of insulin resistance and inflammation as mediators. Eur. J. Clin. Nutr. 2020, 74, 1401–1409. [Google Scholar] [CrossRef] [PubMed]
  33. Meng, G.; Wu, H.; Fang, L.; Li, C.; Yu, F.; Zhang, Q.; Liu, L.; Du, H.; Shi, H.; Xia, Y. Relationship between grip strength and newly diagnosed nonalcoholic fatty liver disease in a large-scale adult population. Sci. Rep. 2016, 6, 33255. [Google Scholar] [CrossRef] [PubMed]
  34. Lee, K. Relationship between handgrip strength and nonalcoholic fatty liver disease: Nationwide surveys. Metab. Syndr. Relat. Disord. 2018, 16, 497–503. [Google Scholar] [CrossRef]
  35. Kim, B.-J.; Ahn, S.H.; Lee, S.H.; Hong, S.; Hamrick, M.W.; Isales, C.M.; Koh, J.-M. Lower hand grip strength in older adults with non-alcoholic fatty liver disease: A nationwide population-based study. Aging 2019, 11, 4547. [Google Scholar] [CrossRef]
  36. Kang, S.; Moon, M.K.; Kim, W.; Koo, B.K. Association between muscle strength and advanced fibrosis in non-alcoholic fatty liver disease: A Korean nationwide survey. J. Cachexia Sarcopenia Muscle 2020, 11, 1232–1241. [Google Scholar] [CrossRef]
  37. Cho, J.; Lee, I.; Park, D.-H.; Kwak, H.-B.; Min, K. Relationships between Socioeconomic Status, Handgrip Strength, and Non-Alcoholic Fatty Liver Disease in Middle-Aged Adults. Int. J. Environ. Res. Public. Health 2021, 18, 1892. [Google Scholar] [CrossRef]
  38. Lee, S.B.; Kwon, Y.J.; Jung, D.H.; Kim, J.K. Association of Muscle Strength with Non-Alcoholic Fatty Liver Disease in Korean Adults. Int. J. Env. Res. Public. Health 2022, 19, 1675. [Google Scholar] [CrossRef]
  39. Gan, D.; Wang, L.; Jia, M.; Ru, Y.; Ma, Y.; Zheng, W.; Zhao, X.; Yang, F.; Wang, T.; Mu, Y.; et al. Low muscle mass and low muscle strength associate with nonalcoholic fatty liver disease. Clin. Nutr. 2020, 39, 1124–1130. [Google Scholar] [CrossRef]
  40. Wijarnpreecha, K.; Kim, D.; Raymond, P.; Scribani, M.; Ahmed, A. Associations between sarcopenia and nonalcoholic fatty liver disease and advanced fibrosis in the USA. Eur. J. Gastroenterol. Hepatol. 2019, 31, 1121–1128. [Google Scholar] [CrossRef]
  41. Petermann-Rocha, F.; Gray, S.R.; Forrest, E.; Welsh, P.; Sattar, N.; Celis-Morales, C.; Ho, F.K.; Pell, J.P. Associations of muscle mass and grip strength with severe NAFLD: A prospective study of 333,295 UK Biobank participants. J. Hepatol. 2022, 76, 1021–1029. [Google Scholar] [CrossRef] [PubMed]
  42. Zhou, J.-H.; Cai, J.-J.; She, Z.-G.; Li, H.-L. Noninvasive evaluation of nonalcoholic fatty liver disease: Current evidence and practice. World J. Gastroenterol. 2019, 25, 1307. [Google Scholar] [CrossRef]
  43. Eddowes, P.J.; Sasso, M.; Allison, M.; Tsochatzis, E.; Anstee, Q.M.; Sheridan, D.; Guha, I.N.; Cobbold, J.F.; Deeks, J.J.; Paradis, V. Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology 2019, 156, 1717–1730. [Google Scholar] [CrossRef]
  44. Mikolasevic, I.; Orlic, L.; Franjic, N.; Hauser, G.; Stimac, D.; Milic, S. Transient elastography (FibroScan®) with controlled attenuation parameter in the assessment of liver steatosis and fibrosis in patients with nonalcoholic fatty liver disease-Where do we stand? World J. Gastroenterol. 2016, 22, 7236. [Google Scholar] [CrossRef] [PubMed]
  45. Bonder, A.; Afdhal, N. Utilization of FibroScan in clinical practice. Curr. Gastroenterol. Rep. 2014, 16, 372. [Google Scholar] [CrossRef] [PubMed]
  46. Pujia, A.; Mazza, E.; Ferro, Y.; Gazzaruso, C.; Coppola, A.; Doldo, P.; Grembiale, R.D.; Pujia, R.; Romeo, S.; Montalcini, T. Lipid oxidation assessed by indirect calorimetry predicts metabolic syndrome and type 2 diabetes. Front. Endocrinol. 2019, 9, 806. [Google Scholar] [CrossRef] [PubMed]
  47. World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8-11 December 2008; World Health Organization: Geneva, Switzerland, 2011. [Google Scholar]
  48. Montalcini, T.; Ferro, Y.; Salvati, M.A.; Romeo, S.; Miniero, R.; Pujia, A. Gender difference in handgrip strength of Italian children aged 9 to 10 years. Ital. J. Pediatr. 2016, 42, 16. [Google Scholar] [CrossRef]
  49. Hardy, R.; Cooper, R.; Aihie Sayer, A.; Ben-Shlomo, Y.; Cooper, C.; Deary, I.J.; Demakakos, P.; Gallacher, J.; Martin, R.M.; McNeill, G. Body mass index, muscle strength and physical performance in older adults from eight cohort studies: The HALCyon programme. PLoS ONE 2013, 8, e56483. [Google Scholar] [CrossRef] [PubMed]
  50. Karlas, T.; Petroff, D.; Sasso, M.; Fan, J.-G.; Mi, Y.-Q.; de Lédinghen, V.; Kumar, M.; Lupsor-Platon, M.; Han, K.-H.; Cardoso, A.C. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J. Hepatol. 2017, 66, 1022–1030. [Google Scholar] [CrossRef] [PubMed]
  51. Montalcini, T.; Pujia, A.; Donini, L.M.; Frittitta, L.; Galvano, F.; Natali, A.; Pironi, L.; Porrini, M.; Riso, P.; Rivellese, A.A.; et al. A Call to Action: Now Is the Time to Screen Elderly and Treat Osteosarcopenia, a Position Paper of the Italian College of Academic Nutritionists MED/49 (ICAN-49). Nutrients 2020, 12, 2662. [Google Scholar] [CrossRef]
  52. Miele, L.e.a. General Practitioner databaSe NAFLD (GPS-NAFLD) study: Italian regional variability of the NAFLD prevalence rate. Dig. Liver Dis. 2021, 53, S30–S31. [Google Scholar] [CrossRef]
  53. Petta, S.; Di Marco, V.; Pipitone, R.M.; Grimaudo, S.; Buscemi, C.; Craxì, A.; Buscemi, S. Prevalence and severity of nonalcoholic fatty liver disease by transient elastography: Genetic and metabolic risk factors in a general population. Liver Int. Off. J. Int. Assoc. Study Liver 2018, 38, 2060–2068. [Google Scholar] [CrossRef]
  54. Colicchio, P.; Tarantino, G.; del Genio, F.; Sorrentino, P.; Saldalamacchia, G.; Finelli, C.; Conca, P.; Contaldo, F.; Pasanisi, F. Non-alcoholic fatty liver disease in young adult severely obese non-diabetic patients in South Italy. Ann. Nutr. Metab. 2005, 49, 289–295. [Google Scholar] [CrossRef]
  55. Bellentani, S.; Saccoccio, G.; Masutti, F.; Crocè, L.S.; Brandi, G.; Sasso, F.; Cristanini, G.; Tiribelli, C. Prevalence of and risk factors for hepatic steatosis in Northern Italy. Ann. Intern. Med. 2000, 132, 112–117. [Google Scholar] [CrossRef]
  56. Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J. Hepatol. 2018, 69, 896–904. [Google Scholar] [CrossRef] [PubMed]
  57. Han, L.; Fu, S.; Li, J.; Liu, D.; Tan, Y. Association between grip strength and non-alcoholic fatty liver disease: A systematic review and meta-analysis. Front. Med. 2022, 9, 988566. [Google Scholar] [CrossRef]
  58. Xu, L.; Lu, W.; Li, P.; Shen, F.; Mi, Y.-Q.; Fan, J.-G. A comparison of hepatic steatosis index, controlled attenuation parameter and ultrasound as noninvasive diagnostic tools for steatosis in chronic hepatitis B. Dig. Liver Dis. 2017, 49, 910–917. [Google Scholar] [CrossRef]
  59. Pang, Q.; Zhang, J.-Y.; Song, S.-D.; Qu, K.; Xu, X.-S.; Liu, S.-S.; Liu, C. Central obesity and nonalcoholic fatty liver disease risk after adjusting for body mass index. World J. Gastroenterol. WJG 2015, 21, 1650. [Google Scholar] [CrossRef] [PubMed]
  60. Linge, J.; Ekstedt, M.; Leinhard, O.D. Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD. JHEP Rep. 2021, 3, 100197. [Google Scholar] [CrossRef] [PubMed]
  61. Oshida, N.; Shida, T.; Oh, S.; Kim, T.; Isobe, T.; Okamoto, Y.; Kamimaki, T.; Okada, K.; Suzuki, H.; Ariizumi, S.-i.; et al. Urinary levels of titin-N fragment, a skeletal muscle damage marker, are increased in subjects with nonalcoholic fatty liver disease. Sci. Rep. 2019, 9, 19498. [Google Scholar] [CrossRef] [PubMed]
  62. Goodpaster, B.H.; Park, S.W.; Harris, T.B.; Kritchevsky, S.B.; Nevitt, M.; Schwartz, A.V.; Simonsick, E.M.; Tylavsky, F.A.; Visser, M.; Newman, A.B. The loss of skeletal muscle strength, mass, and quality in older adults: The health, aging and body composition study. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2006, 61, 1059–1064. [Google Scholar] [CrossRef] [PubMed]
  63. Newman, A.B.; Kupelian, V.; Visser, M.; Simonsick, E.M.; Goodpaster, B.H.; Kritchevsky, S.B.; Tylavsky, F.A.; Rubin, S.M.; Harris, T.B. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2006, 61, 72–77. [Google Scholar] [CrossRef] [PubMed]
  64. Nishikawa, H.; Enomoto, H.; Yoh, K.; Iwata, Y.; Sakai, Y.; Kishino, K.; Ikeda, N.; Takashima, T.; Aizawa, N.; Takata, R. Health-related quality of life in chronic liver diseases: A strong impact of hand grip strength. J. Clin. Med. 2018, 7, 553. [Google Scholar] [CrossRef] [PubMed]
  65. Yoh, K.; Nishikawa, H.; Enomoto, H.; Iwata, Y.; Ikeda, N.; Aizawa, N.; Nishimura, T.; Iijima, H.; Nishiguchi, S. Grip strength: A useful marker for composite hepatic events in patients with chronic liver diseases. Diagnostics 2020, 10, 238. [Google Scholar] [CrossRef] [PubMed]
  66. Hanai, T.; Shiraki, M.; Imai, K.; Suetsugu, A.; Takai, K.; Moriwaki, H.; Shimizu, M. Reduced handgrip strength is predictive of poor survival among patients with liver cirrhosis: A sex-stratified analysis. Hepatol. Res. 2019, 49, 1414–1426. [Google Scholar] [CrossRef] [PubMed]
  67. Kotoh, Y.; Saeki, I.; Yamasaki, T.; Sasaki, R.; Tanabe, N.; Oono, T.; Matsuda, T.; Hisanaga, T.; Matsumoto, T.; Hidaka, I. Effect of Handgrip Strength on Clinical Outcomes of Patients with Hepatocellular Carcinoma Treated with Lenvatinib. Appl. Sci. 2020, 10, 5403. [Google Scholar] [CrossRef]
  68. Tapper, E.B.; Konerman, M.; Murphy, S.; Sonnenday, C.J. Hepatic encephalopathy impacts the predictive value of the Fried Frailty Index. Am. J. Transplant. 2018, 18, 2566–2570. [Google Scholar] [CrossRef]
  69. Wang, C.W.; Feng, S.; Covinsky, K.E.; Hayssen, H.; Zhou, L.-Q.; Yeh, B.M.; Lai, J.C. A comparison of muscle function, mass, and quality in liver transplant candidates: Results from the functional assessment in liver transplantation (FrAILT) study. Transplantation 2016, 100, 1692. [Google Scholar] [CrossRef]
  70. Sultan, P.; Hamilton, M.A.; Ackland, G.L. Preoperative muscle weakness as defined by handgrip strength and postoperative outcomes: A systematic review. BMC Anesthesiol. 2012, 12, 1. [Google Scholar] [CrossRef]
  71. Srikanthan, P.; Hevener, A.L.; Karlamangla, A.S. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: Findings from the National Health and Nutrition Examination Survey III. PLoS ONE 2010, 5, e10805. [Google Scholar] [CrossRef]
  72. Serra, C.; Bhasin, S.; Tangherlini, F.; Barton, E.R.; Ganno, M.; Zhang, A.; Shansky, J.; Vandenburgh, H.H.; Travison, T.G.; Jasuja, R. The role of GH and IGF-I in mediating anabolic effects of testosterone on androgen-responsive muscle. Endocrinology 2011, 152, 193–206. [Google Scholar] [CrossRef]
  73. Elkina, Y.; Von Haehling, S.; Anker, S.D.; Springer, J. The role of myostatin in muscle wasting: An overview. J. Cachexia Sarcopenia Muscle 2011, 2, 143. [Google Scholar] [CrossRef] [PubMed]
  74. Sanyal, A.J. AGA technical review on nonalcoholic fatty liver disease. Gastroenterology 2002, 123, 1705–1725. [Google Scholar] [CrossRef] [PubMed]
  75. National Institute for Health and Clinical. National Institute for Health and Clinical. National Institute for Health and Care Excellence: Guidelines. In Non-Alcoholic Fatty Liver Disease: Assessment and Management; National Institute for Health and Care Excellence (NICE): London, UK, 2003. [Google Scholar]
  76. Rinella, M.E.; Neuschwander-Tetri, B.A.; Siddiqui, M.S.; Abdelmalek, M.F.; Caldwell, S.; Barb, D.; Kleiner, D.E.; Loomba, R. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology 2023, 77, 1797–1835. [Google Scholar] [CrossRef]
  77. Lauschke, V.M. Practice guidance documents for the diagnosis and management of non-alcoholic fatty liver disease-recent updates and open questions. Hepatobiliary Surg. Nutr. 2023, 12, 780–784. [Google Scholar] [CrossRef] [PubMed]
  78. Neuschwander-Tetri, B.A.; Caldwell, S.H. Nonalcoholic steatohepatitis: Summary of an AASLD Single Topic Conference. Hepatology 2003, 37, 1202–1219. [Google Scholar] [CrossRef]
  79. Sanyal, D.; Mukherjee, P.; Raychaudhuri, M.; Ghosh, S.; Mukherjee, S.; Chowdhury, S. Profile of liver enzymes in non-alcoholic fatty liver disease in patients with impaired glucose tolerance and newly detected untreated type 2 diabetes. Indian. J. Endocrinol. Metab. 2015, 19, 597–601. [Google Scholar] [CrossRef] [PubMed]
  80. Wong, V.W.; Petta, S.; Hiriart, J.B.; Cammà, C.; Wong, G.L.; Marra, F.; Vergniol, J.; Chan, A.W.; Tuttolomondo, A.; Merrouche, W.; et al. Validity criteria for the diagnosis of fatty liver by M probe-based controlled attenuation parameter. J. Hepatol. 2017, 67, 577–584. [Google Scholar] [CrossRef]
  81. Zhang, X.; Wong, G.L.; Wong, V.W. Application of transient elastography in nonalcoholic fatty liver disease. Clin. Mol. Hepatol. 2020, 26, 128–141. [Google Scholar] [CrossRef]
  82. Shen, F.; Zheng, R.D.; Shi, J.P.; Mi, Y.Q.; Chen, G.F.; Hu, X.; Liu, Y.G.; Wang, X.Y.; Pan, Q.; Chen, G.Y.; et al. Impact of skin capsular distance on the performance of controlled attenuation parameter in patients with chronic liver disease. Liver Int. Off. J. Int. Assoc. Study Liver 2015, 35, 2392–2400. [Google Scholar] [CrossRef]
  83. Kimura, S.; Tanaka, K.; Oeda, S.; Inoue, K.; Inadomi, C.; Kubotsu, Y.; Yoshioka, W.; Okada, M.; Isoda, H.; Kuwashiro, T.; et al. Effect of skin-capsular distance on controlled attenuation parameter for diagnosing liver steatosis in patients with nonalcoholic fatty liver disease. Sci. Rep. 2021, 11, 15641. [Google Scholar] [CrossRef] [PubMed]
  84. Oeda, S.; Tanaka, K.; Oshima, A.; Matsumoto, Y.; Sueoka, E.; Takahashi, H. Diagnostic Accuracy of FibroScan and Factors Affecting Measurements. Diagnostics 2020, 10, 940. [Google Scholar] [CrossRef] [PubMed]
Applsci 13 12489 g001
Figure 1. Flow diagram of participants for the study.
Figure 1. Flow diagram of participants for the study.
Applsci 13 12489 g001
Applsci 13 12489 g002
Figure 2. Prevalence (a) and severity (b) of NAFLD in the population according to relative handgrip strength tertiles. * Adjusted for age, gender, smoking habit, physical activity, hyperlipidemia, general and android obesity, HDL-C, TG, creatinine, and AST.
Figure 2. Prevalence (a) and severity (b) of NAFLD in the population according to relative handgrip strength tertiles. * Adjusted for age, gender, smoking habit, physical activity, hyperlipidemia, general and android obesity, HDL-C, TG, creatinine, and AST.
Applsci 13 12489 g002
Table 1. Demographic, clinical, and instrumental characteristics of the population according to relative handgrip strength tertiles.
Table 1. Demographic, clinical, and instrumental characteristics of the population according to relative handgrip strength tertiles.
VariablesTotal (n = 388)T1 (n = 129)T2 (n = 130)T3 (n = 129)p for Trendp for Post Hoc Analysis
rHGS (Range)0.26–1.970.26–0.690.70–0.980.99–1.97
Age (years)60.8 ± 7.162.4 ± 761.8 ± 758.1 ± 6<0.001T1 vs. T3 < 0.001
T2 vs. T3 < 0.001
BMI (kg/m2)28.7 ± 4.430.8 ± 527.9 ± 427.3 ± 4<0.001T1 vs. T2 < 0.001
T1 vs. T3 < 0.001
HGS (kg)25.5 ± 9.317.9 ± 322.4 ± 436.1 ± 7<0.001T1 vs. T2 < 0.001
T1 vs. T3 < 0.001
T2 vs. T3 < 0.001
WHR0.91 ± 0.080.89 ± 0.070.89 ± 0.090.94 ± 0.09<0.001T1 vs. T3 < 0.001
T2 vs. T3 < 0.001
SBP (mmHg)125 ± 16125 ± 16126 ± 18125 ± 130.85/
DBP (mmHg)76 ± 1076 ± 1176 ± 977 ± 90.56/
Glucose (mg/dL)99 ± 2199 ± 1698 ± 2499 ± 220.92/
TC (mg/dL)208 ± 42210 ± 39208 ± 42206 ± 460.68/
HDL-C (mg/dL)57 ± 1661 ± 1759 ± 1652 ± 14<0.001T1 vs. T3 < 0.001
T2 vs. T3 0.001
TG (mg/dL)129 ± 70123 ± 62119 ± 62144 ± 810.010T2 vs. T3 0.019
LDL-C (mg/dL)125 ± 38125 ± 36124 ± 37125 ± 400.99/
Creatinine (mg/dL)0.81 ± 0.170.77 ± 0.20.75 ± 0.10.91 ± 0.2<0.001T1 vs. T3 < 0.001
T2 vs. T3 < 0.001
AST (IU/L)22 ± 721 ± 721 ± 723 ± 80.018T1 vs. T3 0.039
ALT (IU/L)23 ± 1421 ± 1223 ± 1725 ± 130.09/
γGT (IU/L)29 ± 2636 ± 4123 ± 1529 ± 240.15/
CAP (Db/m)256 ± 48266 ± 48252 ± 49249 ± 470.012T1 vs. T3 0.021
IQR (%)20 ± 620 ± 621 ± 620 ± 60.90
aCAP § (Db/m)/269 ± 5253 ± 4237 ± 60.001T1 vs. T2 0.023
T1 vs. T3 0.001
Stiffness (KPa)4.8 ± 14.9 ± 14.7 ± 14.7 ± 10.49/
Prevalence (%)
Gender, female70988526<0.001/
Smokers271925380.001/
Physical activity35183750<0.001/
T2DM912970.22/
Dyslipidaemia696562810.007/
Hypertension475445420.06/
General Obesity34503220<0.001/
Android Obesity737266800.034/
§ aCAP score adjusted for gender, age, smoking habit, physical activity, hyperlipidemia, general and android obesity, HDL-C, TG, creatinine, and AST. rHGS = relative handgrip strength; BMI = body mass index; WHR = waist to hip ratio; SBP = systolic blood pressure; DBP = diastolic blood pressure; CAP = controlled attenuation parameter; IQR = interquartile range; TC = total cholesterol; TG = triglycerides; LDL-C = low density lipoprotein cholesterol; AST = aspartate aminotransferase; ALT = alanine aminotransferase; γGT = gamma glutamyl transferase; T2DM = Type 2 diabetes mellitus; general obesity = obesity classified on BMI; and android obesity = obesity classified on WHR.
Table 2. Odds ratio for NAFLD risk by relative handgrip strength tertiles and other factors.
Table 2. Odds ratio for NAFLD risk by relative handgrip strength tertiles and other factors.
OR (95% CI)p
Model 1
High rHGS1 (ref)
Mild rHGS2.48 (1.26–4.87)0.008
Low rHGS4.85 (2.30–10.20)<0.001
Gender2.70 (1.40–5.21)0.003
Model 2
High rHGS1 (ref)
Mild rHGS2.02 (0.91–4.48)0.08
Low rHGS3.63 (1.49–8.82)0.004
General Obesity2.44 (1.41–4.28)0.002
Model 3
High rHGS1 (ref)
Mild rHGS2.56 (1.17–5.59)0.018
Low rHGS5.30 (2.24–12.57)<0.001
Android Obesity2.39 (1.36–4.18)0.002
Model 1 was adjusted for age and gender. Model 2 was adjusted for age, gender, smoking habit, physical activity, hyperlipidemia, HDL-C, TG, creatinine, and AST. Model 3 was adjusted for age, gender, smoking habit, physical activity, hyperlipidemia, android obesity, HDL-C, TG, creatinine, and AST. NAFLD, non-alcoholic fatty liver disease; rHGS, relative handgrip strength; OR, odds ratio; CI, confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Maurotti, S.; Pujia, R.; Mazza, E.; Pileggi, M.F.; Arturi, F.; Tarsitano, M.G.; Montalcini, T.; Pujia, A.; Ferro, Y. Low Relative Handgrip Strength Is Associated with a High Risk of Non-Alcoholic Fatty Liver Disease in Italian Adults: A Retrospective Cohort Study. Appl. Sci. 2023, 13, 12489. https://doi.org/10.3390/app132212489

AMA Style

Maurotti S, Pujia R, Mazza E, Pileggi MF, Arturi F, Tarsitano MG, Montalcini T, Pujia A, Ferro Y. Low Relative Handgrip Strength Is Associated with a High Risk of Non-Alcoholic Fatty Liver Disease in Italian Adults: A Retrospective Cohort Study. Applied Sciences. 2023; 13(22):12489. https://doi.org/10.3390/app132212489

Chicago/Turabian Style

Maurotti, Samantha, Roberta Pujia, Elisa Mazza, Maria Francesca Pileggi, Franco Arturi, Maria Grazia Tarsitano, Tiziana Montalcini, Arturo Pujia, and Yvelise Ferro. 2023. "Low Relative Handgrip Strength Is Associated with a High Risk of Non-Alcoholic Fatty Liver Disease in Italian Adults: A Retrospective Cohort Study" Applied Sciences 13, no. 22: 12489. https://doi.org/10.3390/app132212489

APA Style

Maurotti, S., Pujia, R., Mazza, E., Pileggi, M. F., Arturi, F., Tarsitano, M. G., Montalcini, T., Pujia, A., & Ferro, Y. (2023). Low Relative Handgrip Strength Is Associated with a High Risk of Non-Alcoholic Fatty Liver Disease in Italian Adults: A Retrospective Cohort Study. Applied Sciences, 13(22), 12489. https://doi.org/10.3390/app132212489

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop