Evaluation of muscle strength and quality in North African patients with chronic hepatitis B: A pilot case control study

ABSTRACT

Early detection of alteration of muscle strength, quantity, and quality, and sarcopenia is useful in non-cirrhotic chronic hepatitis B (NC-CHB) patients. Studies, which explored the handgrip strength (HGS) are scarce with questionable results, and no previous case-control study explored the presence of sarcopenia.

The aim of this study was to assess the muscle strength [i.e.; HGS absolute (HGS_A), HGS_A/body mass index (BMI)], muscle quantity [i.e.; appendicular skeletal muscle (ASM), ASM/height², ASM/total body weight (TBW), ASM/BMI], and muscle quality [i.e.; HGS_A/total muscle mass (TMM), HGS_A/ASM] of NC-CHB patients.

This was a case-control study. Cases (n = 26) were untreated NC-CHB patients, and controls (n = 28) were ‘apparently’ healthy participants. Muscle mass was estimated via the TMM (kg) and ASM (kg). Muscle strength was evaluated via the HGS data [i.e.; HGS_A (kg), HGS_A/BMI (m²)]. Six variants of HGS_A were determined: highest values for the dominant and non-dominant hands, highest value between the two hands, averages of the three measurements for the two hands, and the average of the highest values of the two hands. Muscle quantity was expressed in three relative variants (ASM/height², ASM/TBW, and ASM/BMI). Muscle quality was evaluated via relative HGS data adjusted by muscle mass (i.e.; HGS_A/TMM, HGS_A/ASM). Probable and confirmed sarcopenia were retained in front of low muscle strength, and low muscle strength and muscle quantity or quality, respectively.

There were no significant differences between controls and NC-CHB patients in values of muscle i) Strength whatever the HGS’ mode of expression (e.g.; HGS_A/BMI: 1.59 ± 0.54 vs. 1.53 ± 0.54 m², p = 0.622, respectively), ii) Quantity (e.g.; ASM/BMI: 0.79 ± 0.24 vs. 0.77 ± 0.23 m², p = 0.883), and iii) Quality (e.g.; HGS_A/ASM: 2.00 ± 0.25 vs. 2.01 ± 0.41, p = 0.952, respectively). One NC-CHB participant had a confirmed sarcopenia.

To conclude, both controls and NC-CHB patients had similar HGS values. Only one NC-CHB patient had a confirmed sarcopenia.

KEYWORDS:

• Chronic liver diseases
• maximal isometric strength
• muscle mass
• sarcopenia
• Tunisia

1. Introduction

Hepatitis B is a common serious liver infection in the world [1]. It is caused by the hepatitis B virus (HBV) that attacks and injures the liver [1]. Chronic hepatitis B (CHB) affects between 5 and 6% of the world’s population (i.e.; over 250 million patients worldwide), with the increased risk of progression to liver cirrhosis or hepatocellular carcinoma responsible for more than 1.5 million deaths per year worldwide [2]. In addition to the hepatic manifestations, such as liver cirrhosis or hepatocellular carcinoma, a chronic liver infection generates extrahepatic manifestations like immune, renal, articular, cardiovascular, and muscular manifestations, which can increase the morbidity and mortality associated to CHB [1–4]. Multiple studies noticed adverse effects of CHB on the functional state of the body at diverse levels, for example weakness of respiratory muscles, myocardial damage, and sarcopenia [4–6]. HBV can infect muscle fibers and a mediated immune response to viral antigens may cause muscle injuries [7]. It is advocated that patients with liver cirrhosis have the peril of losing muscle mass due to reduced protein synthesis and myofibrillar degradation that results from motor damage [8]. Motor dysfunction is a vital clinical result in patients with liver cirrhosis, and its pathogenesis is unclear [9]. Contemporary studies reported that motor damage might arise in liver cirrhosis patients, even without hepatic encephalopathy [10].

The handgrip strength (HGS) is an interesting and reliable tool for assessing functional hand strength predicting sports performance and general health [11]. It is an indicator of global muscle strength; muscle mass, nutritional status, physical activity status/level, and 6-min walk distance [12–15]. Moreover, HGS is a reliable predictor of mortality, frailty, disability clinical outcome, metabolic syndrome, diabetes mellitus, and surgical complications [16–22]. In chronic liver conditions, quantifying HGS has numerous benefits [23–28]. HGS evaluates the nutritional status and the menace of falling [24,28]; is associated with the muscle mass [23,25]; is an indicator of walking speed and sarcopenia [23,26]; and is associated with the quality of life [27]. Even though HGS was largely evaluated in patients with chronic liver diseases, to the best of the authors’ knowledge, only two previous studies explored the HGS in non-cirrhotic CHB (NC-CHB) patients compared to healthy participants [28,29]. The first study reported that the healthy participants (n = 30) and the patients with CHB (n = 32) had comparable values of HGS in both dominant and non-dominant hands [29]. The second study noted that healthy participants (n = 159) and patients with chronic hepatitis (n = 189) including some patients with CHB (n = 80) had comparable values of HGS in the non-dominant hand [28]. The results of the aforementioned two studies [28,29] are questionable for some reasons making the comparison between studies difficult. The main reasons were related to the i) Expression modes of the HGS like absolute values (i.e.; HGS_A) with no standardization considering some body mass markers [28,29]; ii) Retained HGS’ values for statistical analysis such as average of three HGS values for each hand [29] vs. the non-dominant hand’s highest value among three values [28], and iii) Choice of the ‘statistical’ significance approach (i.e.; a ‘p-value’ <0.05 being considered significant) rather than the ‘clinical’ significance approach [30] like comparing the percentage of participants having abnormal HGS values between the two groups [31].

Sarcopenia, defined as a progressive loss of muscle mass and muscle function (muscle strength and/or physical performance) [32,33], is occurring in 30–70% of liver cirrhosis patients [34], and is associated with metabolic syndrome and advanced fibrosis in chronic liver disease out of cirrhosis [35,36]. The presence of sarcopenia is an important prognostic factor in patients with liver cirrhosis [37], and its early prevention improves survival and reduces the demand for long-term care [38]. To the best of the authors’ knowledge, no previous case-control study explored the presence of sarcopenia in NC-CHB patients.

Taking into account the aforementioned points, the objective of the present North-African case-control study (cases: NC-CHB patients; controls: ‘apparently’ healthy participants) was to compare the muscle strength [i.e.; HGS_A, HGS_A/body mass index(BMI)], muscle quantity [i.e.; appendicular skeletal muscle (ASM), ASM/height², ASM/total body weight (TBW), ASM/BMI], and muscle quality [i.e.; HGS_A/total muscle mass (TMM), (HGS_A/ASM) between the cases and controls. The null hypothesis was that the two groups have comparable HGS whatever its mode of expression [i.e.; HGS_A or relative HGS (rHGS)] [28,29]. Both ‘statistical’ and ‘clinical’ significant approaches will be applied.

2. Population and methods

The present study is part of a project including two groups (NC-CHB patients and ‘apparently’ healthy controls). The project, whose methodology was published as a ‘protocol in progress’ [39], involves three parts. The first part is the objective of this study. The second part will explore the submaximal aerobic capacity and the quality-of-life data of NC-CHB. The third part will analyze the oxidant/antioxidant stress biomarkers and heart rate variability of NC-CHB.

2.1. Study design

This study was a case-control study conducted during September 2020. The study was conducted in collaboration with four departments (i.e.; physiology and functional explorations, infectious diseases, biochemistry, and haematology laboratories) from the Farhat HACHED Hospital, Sousse, Tunisia. All the study procedures were in accordance with the Helsinki Declaration. Approval of the local hospital ethics committee (approval N° 3010/2020) was obtained. All participants signed a written consent. All explorations were free of charge, and participants received a report of their explorations. The study was performed during the pandemic of the coronavirus disease (COVID-19). However, during September 2020, COVID-19 rate was low in Tunisia (average cases per day = 200, average death per day = 5) (https://www.worldometers.info/coronavirus/country/tunisia/#graph-cases-daily). All recommended preventive measures to fight against COVID-19 transmission were applied as physical distance of at least 1 m from others, wearing a fitted face mask properly, cleaning hands frequently with alcohol-based hand rub or soap, and water freezing friction.

2.2. Study population

Two groups of participants were recruited (Figure 1).

Figure 1. Study flow chart.

Note: CHB: chronic hepatitis B. F: females. M: males.

Cases were selected from patients followed for CHB who underwent histological evaluation by liver biopsy or fibroscan from September 2017 to September 2020 at the aforementioned department of infectious diseases. The inclusion criteria were: both male and female sexes, age 25 to 55 years, confirmed diagnosis of CHB, surface antigen persisting for at least six months before inclusion in the study, viral load charge >2000 IU/ml determined at least one year prior inclusion in the study, alanine-aminotransferase testing every three months showing normal levels for at least one year prior to the histological evaluation and had remained normal until the monitoring before the histological evaluation, absence of significant pathological fibrosis’ lesions (i.e.; fibroscan score <6) [40], or a ‘meta-analysis of histological data in viral hepatitis’ (METAVIR) score < A2F2 (i.e.; activity and fibrosis <2) [41]. The non-inclusion criteria were: medication use such as antiviral therapy for CHB, alcohol intake, prior surgical intervention of the hands, contractures/amputations of the upper limbs, physical/mechanical problem (e.g.; history of orthopaedics/rheumatologic/neurologic conditions, such as polyneuropathy and radiculopathy, which may interfere with the HGS test), co-morbidity like respiratory and/or cardiovascular diseases, systemic impairment as diabetes mellitus or renal failure, which may influence blood test results, co-infection with hepatitis virus C or D, human immunodeficiency virus, and antecedents of COVID-19. In clinical practice, the indication of antiviral treatment in NC-CHB patients is the presence of both a high viral load charge (i.e.; > 2000 IU/ml) and cytolysis (i.e.; high transaminases) [42].

Controls were recruited from caregivers of patients with CHB, relatives of persons involved in the study, and via advertisement launched through the personal Facebook account of the first author. Controls were ‘apparently’ healthy participants aged between 25 and 55 years. The non-inclusion criteria were known clinical chronic diseases, alcohol intake, and antecedent of COVID-19. Files with missing biological data were excluded from the final statistical analysis.

2.3. Sample size

The sample size was estimated using the following equation [43]: N=((r + 1)(Z_α+Z_1-β)²σ²)/(rd²); where

• ‘Z_α’ is the one-tailed normal deviate at a level of significance = 1.64 (5% level of significance);

• ‘Z_1-β’ is the normal deviate at 1-β% power with β% of type II error (0.84 at 80% statistical power);

• ‘r’ (= n₁/n₂, n,_1, and n₂ are the sample sizes for the cases and controls, defined as N = n₁ + n₂) is the ratio of the sample size required for the two groups (r = 1 gives the sample size distribution as 1:1 for the two groups);

• ‘σ’ and ‘d’ are the pooled standard deviation (SD) and difference of HGS_A means of the two groups. These two values were obtained from a Turkish study aiming to compare the HGS_A values of cases (29 patients with CHB) and controls (30 healthy participants) [29]. The means ± SD of HGS_A of controls and cases were 29.78 ± 5.47 and 27.06 ± 6.36 kg, respectively. Therefore, ‘σ’ is equal to 5.915 (= (5.47 + 6.36)/2) and ‘d’ is equal to 2.72 (= 29.78-27.06).

The insertion of the aforementioned data into the above-cited equation results in a total sample of 58 participants (29 in each group). The assumption of a 10% loss of some biological data gives a revised sample of 64 participants (= 58/(1–0.90)).

2.4. Serological markers and CHB diagnosis

Serological markers of hepatitis B (i.e.; surface antigen, surface antibody, core antibody, viral protein (HBeAg), antibody against the HBeAg), and antibodies of hepatitis D and C, and human immunodeficiency were evaluated with commercially available enzyme immunoassay. Serum HBV-DNA levels were quantified by a commercial real-time polymerase-chain reaction (COBAS AmpliPrep/COBAS TaqMan, Roche Diagnostics). The virological exam was done three to four months before the patient’s inclusion in the study to determine the viral load [44]. The detection limits varied from 20 IU/ml at the lower level and 110 × 106 IU/ml at the upper level. All patients had NC-CHB (HBeAg positive or negative) with an active viremia (i.e.; HBV-DNA between 2000 and 20,000 IU/ml) [44].

2.5. Liver damages evaluation

A liver fibroscan and/or a liver biopsy were performed to evaluate the liver stiffness and severity of the CHB, respectively [40]. According to national guidelines, one of both aforementioned exams should be performed once every three years [42]. In the present study, data of the latest liver fibroscan and/or liver biopsy were retained.

A liver stiffness <5–6 kPa indicates absent or minimal liver stiffness, and a liver stiffness >12–14 kPa indicates liver cirrhosis [45]. In clinical practice, it is recommended to realize a liver fibroscan first, and if its score is between 6 and 12 kPa (i.e.; grey zone), a liver biopsy should be explored to confirm liver fibrosis [40].

Fibrosis staging (F) and inflammatory activity (A) were decided according to the METAVIR system [41]. Fibrosis staging was divided into F0-F4 (F0 = no fibrosis, F1 = portal fibrosis without septa, F2 = periportal fibrosis with few septa, F3 = septal fibrosis with many septa, and F4 = cirrhosis) [41]. Inflammatory activity was divided into A0-A3 (A0 = no histologic necroinflammatory activity, A1 = minimal activity, A2 = moderate activity, A3 = severe activity) [41]. Significant fibrosis was defined as METAVIR fibrosis with a score ≥ 2 (F2, 3, 4) [46].

2.6. Data collection and applied definitions

Figure 2 exposes the study’s protocol.

Figure 2. Study protocol.

2.6.1. Clinical, sociodemographic, socioeconomic, sex and anthropometric data

Clinical, sociodemographic, and socioeconomic data were collected using a standard medical questionnaire, which is widely used in the above-cited physiological department, and the questions were asked in the Arabic language. The socioeconomic level was determined according to the participant’s profession, and two levels were defined [unfavourable/favourable] [47]. Two schooling levels [low/high] were arbitrarily defined. Cigarette smoking was evaluated in pack-years and participants were classified into two groups (non-smoker/smoker). The chronic liver diseases and the Voorrips questionnaires were also performed, and their data will be analysed in the second study. The Voorrips questionnaire, which estimated the level of physical activity, was largely described elsewhere [39]. It evaluates three types of physical activity: daily, sports and leisure activities. The sum of the three scores represents the total physical activity score.

Sex and anthropometric data [i.e.; age (year, range of 5 years), height (m), TBW (kg), and BMI (kg/m²)] were collected. The corpulence status was determined: underweight (BMI ≤ 18.5 kg/m²), normal weight (BMI: 18.5–24.9 kg/m²), overweight (BMI: 25.0–29.9 kg/m²), and obesity (BMI ≥ 30.0 kg/m²) [48].

2.6.2. Body composition data: bioelectrical impedance analyses (BIA)

The BIA [Beurer (BF-600, Beurer GmbH, Germany)] was realized in the morning (after an overnight fast) in a standing position [49]. The following body composition data were measured: muscle mass rate (%), body fat rate (%), body water (%), bone mass (kg). TMM, (i.e.; muscle mass (%) x TBW/100, kg) and ASM (i.e.; sum of the muscle mass for the arms and legs, kg) were calculated [50]. The ASM was estimated by dividing TMM by 1.33 [51], and the relative ASM was calculated and expressed in the following three ways: ASM/Height² (kg/m²), ASM/TBW (no unit), ASM/BMI (m²) [52,53].

Low muscle mass was proposed as a phenotypic criteria of malnutrition [54]. The following definitions, which retain the diagnosis of low muscle quantity [33], were applied:

1. Low ASM (kg): value <20 (males) or <15 (females);

2. Low ASM/Height² (kg/m²): value <7.0 (males) or <5.5 (females) [55,56],

3. Low ASM/TBW (no unit): value < two SD below the mean of the same-sex control group [57]; and

4. Low ASM/BMI (m²): value <0.789 (males) and <0.512 (females) [58].

2.6.3. Blood data

Blood data (complete blood count, erythrocyte sedimentation rate, prothrombin level, glycaemia, uraemia, creatinaemia, gamma-glutamyl transpeptidase, alkaline phosphatase, bilirubin, transaminase, total cholesterol, high and low-density lipoprotein cholesterol, triglycerides and oxidant/antioxidant stress biomarkers) were determined. The usual techniques were used. Normal ranges of blood data were previously described [39]. Oxidant/antioxidant stress biomarkers data will be analysed in the third study.

2.6.4. HGS measurements, muscle strength, muscle quality, and sarcopenia

The HGS was measured using a digital adjustable dynamometer handle (TKK5401®, Takei Scientific Instruments Co., Ltd., Niigata, Japan) in a standing position with both arms falling naturally [59]. Before the test, the dominant hand was noted, and participants were asked to adjust the grip distance to the appropriate scale based on the size of their own hands. After completing a practice test with the left and right hands used individually, participants were instructed to squeeze for a few seconds the dynamometer with the maximum isometric force during the test with the dominant hand, then with the non-dominant hand alternately, while exhaling during the squeeze. A one-minute rest period was applied as an interval between tests. The test was repeated three times for each hand, and the three HGS values were noted. In this study, the HGS values were expressed in absolute value (HGS_A), and six variants were determined for each participant: the highest values for the dominant and the non-dominant hands, the highest value between the two hands, the mean of the three measurements for the dominant and the non-dominant hands, and the mean of the highest values of the two hands. HGS was also expressed as a relative value, by dividing the highest HGS_A value between the two hands by BMI (HGS_A/BMI, m²) [60]. The diagnosis of a low muscle strength was retained in front of low: i) HGS_A value (whatever the variant, kg): <27 (males) or <16 (females) [60]; or ii) HGS_A/BMI (m²) value <1.0 (males) or <0.56 (females) [58].

Muscle quality was evaluated through the HGS_A highest value between the two hands divided by ASM or TMM [60]. The diagnosis of a low muscle quality was retained in front of low: i) HGS_A/ASM value < two SD below the mean of the same-sex control group, or ii) HGS_A/TMM value < two SD below the mean of the same-sex control group [60].

The following two operational definitions of sarcopenia were applied: i) Probable sarcopenia: low muscle strength (i.e.; low HGS_A (whatever the variant) [33]; and ii) Confirmed sarcopenia: low muscle strength, and low muscle quantity or quality [33].

2.7. Statistical analysis

The analysis of the quantitative data distribution was performed using the Shapiro-Wilk W test. The results were expressed by their means ± SD (and 95% confidence interval, minimum-maximum for age) when the normality test was applied. Otherwise, the results were expressed by their medians (interquartile). Categorical data was expressed as numbers (percentage). The Mann-Whitney U-test was used to compare quantitative data of the two groups. The chi-square test was used to compare the percentages of participants in the two groups. Two significant approaches were applied: i) the ”Statistical” approach (i.e.; p < 0.05), and ii) the ”Clinical” approach (i.e.; comparing the percentage of participants with abnormal biological and HGS values). Hedge’s HGS_A value (i.e.; the highest value of the two hands) was used for measuring the effect size [61]. The effect size was described as small, medium, large, and very large if it was ≤ 0.2, around 0.5, around 0.8, and more than 1.30, respectively [61].

3. Results

Out of the 128 assessed participants (73 NC-CHB, 55 controls), 57 (28 NC-CHB, 29 controls) were willing to participate in the study. Among the 57 participants, three (2 NC-CHB, 1 control) were lost or excluded from the final analysis, leaving a total number of 54 participants forming the final data set [26 NC-CHB (15 males and 11 females), 28 controls (15 males and 13 females)] (Figure 1). The 28 controls were caregivers of patients with CHB (n = 6), relatives of persons involved in the study (n = 12), and the personal Facebook account of the first author (n = 10).

Figure S1 (Appendix) exposes the distribution of NC-CHB patients with respect to the practice of liver fibroscan and liver biopsy. The liver fibroscan was performed in 21 (80%) patients (Cell A1, figure S1), and the mean ± SD of the fibroscan score was 4.67 ± 1.15. The liver biopsy was performed in 13 (50%) patients (Cell B1 and cell B3, figure S1). For the NC-CHB group, the median (interquartile) viral charge was 5230 (3180–12786). The percentage of patients with A0F0, A0F1, A1F0, and A1F1 stages, were respectively, 30.77, 23.07, 53.85, and 38.46%

4. Characteristics of the participants

Table 1 exposes the participants’ characteristics. Compared to the control group, the NC-CHB group was older by ≈5 years, and included higher percentages of participants with low schooling level and unfavorable socioeconomic levels. The two groups had comparable BMI and corpulence status. Figure S2 (Appendix) exposes the repartition of participants according to age ranges. For each age range, the two groups included comparable percentages of participants.

Table 1. Characteristics of the non-cirrhotic chronic hepatitis B (NC-CHB, n = 26) group and the control group (n = 28).

Data	Unit/category	NC-CHB	Control	p-value
Sex	(male)	15 (57.69)	15 (53.57)	0.761
	(female)	11 (42.31)	13 (46.43)
Age	(year)	42 ± 6 (40 to 45) [29–52]	37 ± 7 (34 to 39) [27–51]	0.006*
Height	(cm)	171 ± 10	173 ± 10	0.822
Total body weight	(kg)	82 ± 18	82 ± 14	0.634
Body mass index	(kg/m^2)	27.8 ± 5.8	27.5 ± 4.1	0.979
Smoking	(pack-year)	20 ± 15	12 ± 10	0.159
Corpulence status	(normal weight)	9 (34.61)	7 (25.00)	0.440
	(overweight)	10 (38.46)	13 (46.42)	0.554
	(obese)	7 (26.92)	8 (28.57)	0.892
Smoker	(yes)	10 (38.46)	5 (17.85)	0.091
Schooling level	(low)	8 (30.76)	1 (3.57)	0.007*
Socioeconomic level	(unfavorable)	9 (34.61)	3 (10.71)	0.035*
Dominant hand	(right)	26 (100%)	28 (100%)	1.000

Note: Data were mean±SD and number (%) for quantitative and categorical data of both males and females, respectively.

Age was mean±SD (95% confidence interval) [minimum-maximum].

*p-value (Mann-Whitney U test or 2-sided Chi-2) < 0.05 between the 2 groups.

The two groups had comparable hematological and erythrocyte-sedimentation-rate data and profiles (Table 2). Compared to the control group, the NC-CHB-group had statistically higher significant values of urea and alkaline phosphatase (Table 3). The two groups had comparable biochemical profiles (Table 3).

Table 2. Hematological and erythrocyte-sedimentation-rate (ESR) data of the non-cirrhotic chronic hepatitis B (NC-CHB, n = 26) group and the control group (n = 28).

Data	Unit	Normal range/Definition	NC-CHB	Control	P-value
Hemoglobin	(g/dL)	M: 14–17; F: 12–16	14.51 ± 1.92	14.54 ± 1.79	0.979
Erythrocytes	(10^6/mm^3)	4.2–5.9	4.705 ± 0.474	4.869 ± 0.347	0.246
Leukocytes	(10^3/mm^3)	4.5–11	6.196 ± 2.029	6.586 ± 1.713	0.377
Basophils	(10^3/mm^3)	0–0.22	0	0	-
Lymphocytes	(10^3/mm^3)	0.77–4.5	2.062 ± 0.647	2.136 ± 0.559	0.769
Monocytes	(10^3/mm^3)	0.14–1.3	0.527 ± 0.201	0.554 ± 0.171	0.580
Thrombocytes	(10^3/mm^3)	150–350	216.692 ± 41.669	207.607 ± 50.485	0.268
Neutrophils	(10^3/mm^3)	2.6–8.5	3.431 ± 1.563	3.739 ± 1.447	0.457
Eosinophils	(10^3/mm^3)	0–0.55	0.169 ± 0.126	0.150 ± 0.100	0.646
Prothrombin-level	(%)	70–100	86 ± 9	86 ± 8	0.749
ESR-1^st hour	(mm)	Age<50 years (M: 0–15; F: 0–20) Age≥50 years (M: 0–20; F: 0–30)	6.96 ± 7.13	7.36 ± 6.95	0.986
Hematological and ESR profile
Anemia		Hemoglobin<12 (F) or < 14 (M)	2 (7.69)	2 (7.14)	0.936
Polycythemia		Hemoglobin>17	0	0	-
Leukopenia		Leukocytes<4.5	4 (15.38)	4 (14.28)	0.909
Leukocytosis		Leukocytes>11	0	0	-
Basocythemia		Basophils>0.22	0	0	-
Lymphopenia		Lymphocytes<0.77	0	0	-
Monocytosis		Monocytes>1.3	0	0	-
Thrombocytopenia		Thrombocytes<150	1 (3.85)	2 (7.14)	0.598
Thrombocytosis		Thrombocytes>350	0	1(3.57)	0.330
Low prothrombin-level		Prothrombin-level<70%	0	0	-
Biological inflammatory syndrome		Age<50 years (M:ESR>15; F:ESR>20) Age≥50 years(M:ESR>20; F: ESR>30)	2 (7.69)	3 (10.71)	0.702

Note 1: F: Female. M: Male. Ref: Reference.

Data were mean±SD and number (%) for quantitative and categorical data of both males and females, respectively.

p-value (Mann-Whitney U test or two-sided Chi-2) between the 2 groups.

Note 2: The 2 ‘apparently’ healthy participants (i.e.; control group) with anemia were females and their hemoglobin levels were 10.3 and 11.2 g/dL.

Table 3. Biochemical data of the non-cirrhotic chronic hepatitis B (NC-CHB, n = 26) group and the control group (n = 28).

Data	Unit	Normal range/Definition	NC-CHB	Control	p-value
Fasting-glycaemia	(mmol/L)	3.9–5.8	4.14 ± 0.82	4.08 ± 0.79	0.979
Urea	(g/L)	0.15–0.50	0.20 ± 0.05	0.17 ± 0.06	0.037*
Creatinine	(µmol/L)	60–115	61.50 ± 19.52	55.75 ± 14.99	0.161
Alkaline phosphatase (AP)	(UI/L)	36–150	50.61 ± 16.60	41.64 ± 16.07	0.044*
Alanine-aminotransferase (ALAT)	(UI/L)	0–35	16.26 ± 6.58	16.89 ± 11.54	0.268
Aspartate-aminotransferase (ASAT)	(UI/L)	0–35	21.77 ± 5.09	20.89 ± 13.02	0.069
Gamma-glutamyl-transpeptidase (GGT)	(UI/L)	8–78	15.54 ± 9.55	15.46 ± 10.76	0.640
Total-cholesterol	(mmol/L)	3.88–5.15	3.87 ± 0.91	4.04 ± 1.375	0.808
Triglycerides	(mmol/L)	0–2.82	0.94 ± 0.64	1.07 ± 0.74	0.387
High-density-lipoprotein-cholesterol (HDL-C)	(mmol/L)	≥1.04	2.69 ± 0.82	1.13 ± 0.44	0.580
Low-density-lipoprotein-cholesterol (LDL-C)α	(mmol/L)	≤3.36	2.69 ± 0.82	2.70 ± 1.02	0.897
Creatine-phosphokinase (CPK)	(U/L)	30–170	81.77 ± 51.33	149.96 ± 241.53	0.856
C reactive protein (CRP)	(mg/L)	≤12	5.39 ± 0.98	5.93 ± 1.54	0.180
Biochemical profile
Hyperglycemia		Glycaemia>5.8	0	0	-
Hypoglycemia		Glycaemia<3.9	9 (34.61)	9 (32.14)	0.847
Hyperuremia		Uremia>0.50	0	0	-
Hypercreatininemia		Creatinine>115	0	0	-
Hypocreatininemia		Creatinine<60	12 (46.15)	20 (71.43)	0.059
Hyper APemia		AP>150	0	0	-
Cytolysis		ALAT>35 or ASAT>35	0 (0.00)	3 (10.71)	0.086
Hyper-GGT		GGT>78	0	0	-
Hypercholesterolemia		Total-cholesterol>5.15	2 (7.69)	5 (17.85)	0.267
Hypertriglyceridemia		Triglycerides>2.82	0 (0.00)	1 (3.57)	0.330
Low HDL-C		HDL-C<1.04	16 (61.54)	14 (50)	0.394
High LDL-C		LDL-C>3.36	9 (34.61)	7 (25)	0.440
Myolysis		CPK>170	1 (3.84)	3 (10.71)	0.335
Biological inflammatory syndrome		CRP>12	2 (7.69)	3 (10.71)	0.702

Note: Ref: Reference. αLDL-C (= total-cholesterol -HDL-C – Triglycerides/5).

Quantitative and categorical data were mean±SD and number (%) for both sexes, respectively.

*p-value (Mann-Whitney U test or 2-sided Chi-2) < 0.05 between the 2 groups.

5. BIA and muscle quantity

Table 4 exposes the BIA data and muscle quantity of the participants. The two groups had comparable BIA data and included comparable percentages of participants with low ASM, ASM/BMI, and ASM/Height². Only one NC-CHB patient had a low ASM/TBW.

Table 4. Bioelectrical impedance meter analysis data and muscle quantity of the non-cirrhotic chronic hepatitis B (NC-CHB, n = 26) group and the control group (n = 28).

Data	Category	Unit	NC-CHB	Control	p-value
Body fat	-	(%)	33 ± 15	31 ± 13	0.863
Body water	-	(%)	46 ± 10	48 ± 8	0.822
Bone mass	-	(kg)	3.09 ± 1	2.98 ± 1	0.499
Muscle mass	-	(%)	34.4 ± 7.6	34.7 ± 7.1	0.883
Total muscle mass	-	(kg)	27.7 ± 7.5	28.3 ± 7.7	0.674
Muscle quantity	ASM	(kg)	20.8 ± 5.6	21.3 ± 5.8	0.671
	ASM/Height^2	(kg/m^2)	7.01 ± 1.44	7.04 ± 1.29	0.574
	ASM/TBW	(no unit)	0.26 ± 0.06	0.26 ± 0.05	0.876
	ASM/BMI	(m^2)	0.77 ± 0.23	0.79 ± 0.24	0.883
Low muscle quantity	Low ASM	(yes)	4 (15.4%)	4 (14.3%)	0.910
	Low ASM/Height^2	(yes)	6 (23.1%)	4 (14.3%)	0.406
	Low ASM/TBW	(yes)	1 (3.9%)	-	-
	Low ASM/BMI	(yes)	7 (26.9%)	5 (17.9%)	0.424

Note: ASM: Appendicular skeletal muscle. BMI: Body mass index. TBW: Total body weight.

Quantitative and categorical data were mean±SD and number (%) for both sexes, respectively.

p-value (Mann-Whitney U test or 2-sided Chi-2) between the 2 groups.

6. HGS data, muscle strength, and muscle quality

Table 5 illustrates the HGS data, muscle strength, and muscle quality of the two groups. Whatever the applied significant approach (i.e.; statistical or clinical) and the expression mode of muscle strength/quality, there was no significant difference between the two groups. The HGS_A effect size was small (Hedges unbiased d = −0.184).

Table 5. Handgrip strength (HGS) data, muscle strength, and muscle quality of the non-cirrhotic chronic hepatitis B (NC-CHB, n = 26) group and the control group (n = 28).

Data	Subscale	Expression mode (unit)	Hand	NC-CHB	Control	p-value
Statistical significance approach
Muscle strength	HGSA	Highest values (kg)	Dominant hand	40 ± 10	42 ± 14	0.653
		Highest values (kg)	Non dominant hand	38 ± 10	39 ± 12	0.616
		Highest values (kg)	Between 2 hands	41 ± 10	43 ± 13	0.697
		Mean of 3 measurements (kg)	Dominant hand	38 ± 10	40 ± 13	0.684
		Mean of 3 measurements (kg)	Non dominant hand	36 ± 10	38 ± 11	0.697
		Mean of the highest value of each hand		39 ± 10	41 ± 12	0.653
	HGSA/BMI	(m^2)	Highest value between 2 hands	1.53 ± 0.54	1.59 ± 0.54	0.622
Muscle quality	HGSA/TMM	(no unit)	Highest value between 2 hands	1.51 ± 0.31	1.51 ± 0.18	0.952
	HGSA/ASM	(no unit)	Highest value between 2 hands	2.01 ± 0.41	2.00 ± 0.25	0.952
Clinical significance approach
Low muscle strength	Low HGSA (whatever the variant)		(yes)	1 (3.9%)	-	-
	Low HGSA/BMI		(yes)	1 (3.9%)	-	-
Low muscle quality	Low HGSA/TMM		(yes)	3 (11.5%)	-	-
	Low HGSA/ASM		(yes)	3 (11.5%)	-	-

Note: ASM: Appendicular skeletal muscle. BMI: Body mass index. HGS_A: HGS expressed as absolute value. TMM: Total muscle mass.

Data were mean±SD and number (%) for quantitative and categorical data for both sexes, respectively.

p-value (Mann-Whitney U test or 2-sided Chi-2) between the 2 groups.

Only one NC-CHB patient had a confirmed sarcopenia: low muscle strength (i.e.; low HGS_A or HGS_A/BMI), low muscle quality (i.e.; low HGS_A/TMM or HGS_A/ASM), and low muscle quantity (i.e.; low ASM/TBW or ASM/BMI or ASM/Height²). No participant had a probable sarcopenia in the two groups.

7. Discussion

The present case-control study including 26 NC-CHB patients and 28 healthy participants reported that both groups had comparable HGS values whatever their mode of expression (i.e.; absolute or relative values). Only one NC-CHB patient had a confirmed sarcopenia. To the best of the authors’ knowledge, this is the first study who explored the presence of sarcopenia in NC-CHB patients, and it is the third case-control study analysing the HGS of adult patients with CHB compared to healthy participants. Table 1S (Appendix) exposes the methodologies and results of the previously published two studies [28,29].

7.1. CHB and HGS

The NC-CHB group and the control group had comparable HGS values (Table 5). Our finding is similar to that reported by the previous two case-control studies [28,29]. Therefore, NC-CHB did not affect the HGS values. The HGS_A values of our patients were different from those reported in the two previous studies [28,29]. On the one hand, our patients’ HGS_A mean of the three measurements on dominant and non-dominant hands (i.e.; 38 ± 10 and 36 ± 10 kg, respectively) were higher by ≈ 8 kg and≈9 kg when compared to those noted in the Turkish study (i.e.; 30 ± 6 and 27 ± 6 kg, respectively) (Table 1S). On the other hand, our patients’ HGS_A highest value on the non-dominant hand (i.e.; 38 ± 10 kg) was higher by ≈21 kg when compared to that noted in the Indian study (i.e.; 17 ± 5 kg). The divergence between the reported patients’ HGS values can be attributed to several points related to the patients’ characteristics, such as sex, corpulence status, and hand dominance . First, compared to the Turkish study [29], our study included a higher percentage of NC-CHB male patients (i.e.; 47 vs. 58%, respectively), and the Indian study [28] omitted to report the percentage of included males. Males are physically stronger and have higher HGS values than females [62]. Second, unlike the two previous studies [28,29], we determined the patients’ corpulence status, where 27% of our NC-CHB patients were obese, and the mean BMI was 28 ± 6 kg/m² (Table 1). We ‘speculate’ that our NC-CHB patients had a better corpulence status than those included in the previous studies [28,29] for the following three reasons: i) Being underweight is a major risk factor for sarcopenia [63], ii) Adiposity plays a key role in the onset of muscular deficiency [64], and iii) High fat mass and BMI (i.e.; >25 kg/m²) are associated with declining functional ability [64]. Therefore, the HGS could be decreased by obesity [65]. Third, both in our study and the Turkish one [29], all CHB patients were right-handed. The Indian study [28] omitted to report the percentage of right-handed patients. While in right-handed participants, the dominant hand was significantly stronger, in the left-handed participants, there was no difference between the two hands’ strengths [66]. Some methodological issues as sample size, HGS measurements and expression modes can also explain the difference between the reported HGS values in the three studies.

7.2. CHB, sarcopenia and malnutrition

In this study, only one NC-CHB participant had low HGS values. Our finding is different from that reported in patients with liver cirrhosis where the frequency of sarcopenia was 30–70% [34]. Our result can be explained by the absence of motor impairment, and by different pathophysiological mechanisms of CHB compared to liver cirrhosis. In liver cirrhosis, three reasons were advanced to explain the motor impairment: i) Muscle mass’ loss due to decreased protein synthesis and myofibrillar degradation [8,67,68], ii) Low levels of magnesium in striated muscles and increased protein degradation [67,68], and iii) Mitochondrial function impairment [69]. In chronic liver disease without liver cirrhosis, sarcopenia is associated with metabolic syndrome and advanced fibrosis [35,36]. However, sarcopenia is also associated with malnutrition, and chronic disease is one of the etiologic criteria for the diagnosis of malnutrition. The weak of muscle mass (i.e.; quantity and/or quality) found in the NC-CHB group in this study can be suggested as phenotypic criteria of malnutrition [54]. While our study was not designed to define or evaluate the malnutrition, our results showed that 46% of NC-CHB patients presented a weak in muscle quantity without impact on muscle strength. This finding can confirm the progressive decline of muscle mass in NC-CHB patients reported in one study [70], which was explicated especially by the decline of serum albumin level [70]. Therefore, the malnutrition in NC-CHB should be more explored in future studies.

8. Methodology discussion and study limitations

HGS is a simple, quick, and inexpensive method of measuring muscle strength [12–15]. Some remarks related to the HGS measurements as participant’s position, test familiarisation, evaluated hands, retained HGS value, comparison with norms, and HGS expressions’ mode should be discussed. First, contrarily to the other two studies [28,29], where the HGS measures were done in the recommended standard position (i.e.; sitting position [71]), in our study, HGS was measured in a standing position. While the sitting position represented upper body’ small muscle group strength, the standing position represented the global (i.e.; forearm, leg, and trunk) muscle strength [59]. Second, as done in one study [28], before the HGS measure, we realized a familiarization session with the dynamometer. An adequate familiarization of the strength test, omitted by Oktayoğlu et al. [29], provided stable and reproducible values [39]. Third, contrarily to the Indian study [28], where only the non-dominant hand was evaluated, and as done in the Turkish study [28], we evaluated both hands. This situation made comparison between studies somewhat difficult. Fourth, since the HGS value was closely related to body mass [59,72,73], and in order to avoid any source of confusion when interpreting the results, we have corrected the HGS for some body mass indexes, such as TMM, BMI and ASM [14,59,74]. This standardization is capital since i) rHGS was negatively associated with cardiometabolic risk elements like metabolic profile of fasting blood glucose, high-density-lipoprotein-cholesterol and triglycerides [75], ii) rHGS measurement, compared to HGS_A, is a more reasonable predictor of metabolic profile and/or disease [75], iii) Some conditions as rheumatoid arthritis, malignancy, and aging are characterized by a lean body mass loss, and a preserved or even increased fat mass [76], iv) Age-related weight loss, joined with muscle mass loss, was mostly dependable on muscle limitation [77], and v) Strength decline is associated with muscle mass’ loss [78], and since the latter can be caused by muscle quality impairment [8], it is important to opt for the HGS_A/TMM and HGS_A/ASM [14,74]. The HGS_A/BMI, which is the most relevant relative strength index in clinical settings, is a predictor of lower mobility, cardiometabolic risk, and metabolic profile and/or disease [75,79]. The HGS_A/BMI is comparable to laboratory-based approaches and can increase the translational value of HGS as a prognostic tool [80]. In practice, it is recommended to address both the confounding of strength by body mass and the concomitant health risks of increased body weight and low muscular strength [79]. Fifth, the retained HGS value varied from one study to another (e.g.; mean of three measures for each hand [29], highest value [28]). This situation makes the comparison between studies difficult. Sixth, in this study, and contrary to similar ones [28,29], HGS_A was compared with European norms for healthy persons [31] in order to diagnose participants with a low muscle strength (Table 5). The latter approach is interesting since the low HGS is a clinical marker of poor mobility and a better predictor of clinical outcomes than the low muscle mass [16].

Four remarks related to the sample size calculation, the applied statistical approach, and the number of controls should be discussed. First, our sample size (26 NC-CHB patients and 28 controls) was closer to the one of the Turkish study (32 CHB and 30 controls) [29], but lower than the one of the Indian study (80 CHB and 159 controls) [28]. Determining the sample’s finest size promises enough power to differentiate statistical significance [43], and aids avoid an inadequate power to distinguish statistical effects [81]. Second, we have calculated the effect size, which indicates the practical significance of a research outcome as HGS [61]. For example, in the Turkish study [29], if calculated, the Hedge’s unbiased ‘d’ for HGS value will be medium at −0.303. Third, we applied both ‘statistical’ and ‘clinical’ significant approaches [30] (Tables 4 and 5) and we compared the percentage of participants having abnormal HGS values between the two groups [31]. In similar studies [28,29], only the ‘significant’ statistical approach was applied with a ‘p-value’ < 0.05 being considered significant. Nowadays, the ‘significant’ statistical approach is disapproved [82]. Fourth, as done in the Turkish study [29], we included two groups of healthy participants and patients with CHB. In the Indian study [28], three groups of healthy participants, patients with chronic hepatitis, and patients with liver cirrhosis were included. Taking three groups into a single study seems to have little precedent in the literature and raises some questions, such as whether the prevalence of some clinical data like obesity, schooling level, socioeconomic level, smoking status, and biological data, in the three groups is comparable.

This study presents some limitations. The first concerns the inclusion of a younger control group. This is a major limitation since the HGS is influenced by age [83]. This point might probably have an effect on the HGS scores, as younger subjects have more muscle mass and strength than older ones [83]. However, in this study, the NC-CHB and the control groups have comparable values of muscle mass and TMM (34.4 ± 7.6 vs. 34.7 ± 7.1%, and 27.7 ± 7.5 vs. 28.3 ± 7.7 kg, respectively, Table 4). The second limitation concerns the schooling and socioeconomic levels of the two groups [i.e.; compared to the control group, the NC-CHB group included higher percentages of participants with low schooling level (3.57 vs. 30.76; respectively) and unfavorable socioeconomic level (10.71 vs. 34.61; respectively)] (Table 1). These different socioeconomically profiles may influence the results, since a disadvantaged socioeconomic level is a determinant of probable sarcopenia [84]. However, in the one hand, the unfavorable socioeconomic level is more impacted by CHB [85]. On the other hand, the socioeconomic profile of our NC-CHB patients is representative of African patients with CHB [86]. According to a Senegalese study [86], due to socio-economic difficulties, the serological status was undetermined in 34.6% of patients. The third limitation is related to the non-practice of the HBV-DNA test and a fibroscan in the control group to confirm the absence of HBV and hepatic fibrosis, respectively. This point is crucial since the 21.5% (i.e.; 6/28) of the control group were caregivers of patients with CHB, and therefore they were under the risk for hepatitis B. The practice of the HBV-DNA test and the fibroscan in the control group was ‘difficult’ for economical and ethical reasons. The fourth limitation concerns the precision of BIA in measuring muscle mass. For this reason, it would be better to realize a test allowing a better analysis of the skeletal muscle index (compared to BIA). Nevertheless, BIA’s accuracy in sarcopenia diagnosis was validated [87]. The fifth limitation is about the prediction equation to determine the skeletal mass. The prediction equation provides a valid estimation of skeletal mass varying in age and adiposity [49]. The sixth limitation is related to the non-application of the strength, assistance with walking, rising from a chair, climbing stairs, and falls questionnaire, which is used to screen for sarcopenia risk [88]. The seventh limitation is the non-use of magnetic resonance imaging or computed tomography to assess muscle quality [89]. The last limitation concerns the inclusion of two ‘apparently’ healthy females with anemia in the control group (their hemoglobin levels were 10.3 and 11.2 g/dL). This might indicate that those participants had chronic underlying conditions that may interfere with the ability to perform the HGS test [90]. However, the ‘apparently’ healthy females’ group can be representative of the general female adults’ population in Tunisia. In fact, the frequencies of anemia observed in our female control group (i.e.; 2/13) and in the general female adult population in Tunisia are comparable (e.g.; 15.4 vs.15.7%, respectively) [91].

9. Conclusion

In NC-CHB patients, no impairment in HGS was retained. Our results related to confirmed sarcopenia cannot be generalized on the total population with NC-CHB, as among our 26 patients with NC-CHB, only one got sarcopenia.

Abbreviations list

ASM	=	Appendicular skeletal muscle
BIA	=	Bioelectrical impedance meter analysis
BMI	=	Body mass index
CHB	=	Chronic hepatitis B
COVID-19	=	Coronavirus disease-19
HBeAg	=	Hepatitis B viral protein
HBV	=	Hepatitis B virus
HGS	=	Handgrip strength
HGSA	=	HGS expressed as absolute value
METAVIR	=	Meta-analysis of histological data in viral hepatitis
NC-CHB	=	Non-cirrhotic chronic hepatitis B
rHGS	=	Relative handgrip strength
SD	=	Standard deviation
TBW	=	Total body weight
TMM	=	Total muscle mass

Establishment where the work was performed

Department of physiology and functional explorations, department of infectious diseases, laboratories of biochemistry and haematology (Farhat HACHED Hospital, Sousse, Tunisia).

Authors’ contributions

JB: conception and design, analysis and interpretation of the data, drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

IL: conception and design, analysis and interpretation of the data, drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

HC: drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

JBA: drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

SM: drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

WM: drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

AL: conception and design, drafting of the paper or revising it critically for intellectual content, final approval of the version to be published.

HBS: conception and design, analysis and interpretation of the data.

All authors agree to be accountable for all aspects of the work.

Acknowledgments

The authors want to thank: i) Pr. Khalifa LIMEM for his invaluable contribution to the analysis of biochemical markers, ii) Pr. Mondher KORTAS for his appreciated contribution to the analysis of haematological markers, and iii) Pr. Ramzi GHARBI for his precious contribution in the improvement of the quality of the writing in the present article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data are available upon a reasonable request from the corresponding author ([email protected]).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19932820.2023.2204564.

Additional information

Funding

This paper was not funded.