Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis

Clinical Nutrition Experimental xxx (xxxx) xxx

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Original Article

Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis ^a, Arthur Fernandes Cortez*, Vivian Pinto de Almeida, Bruno Bordallo Corre rio Costa Reis, Gustavo Scaramuzza dos Reis, Bruno Ceza Felipe Sppezapria Barreto, Phillipe Rodrigues Bastos, ~o Mello Carlos Eduardo Branda rio Gaffr Department of Internal Medicine of the Universidade Federal do Estado do Rio de Janeiro (UNIRIO), Hospital Universita ee and Guinle, Rua Mariz e Barros, 775, Tijuca, Rio de Janeiro, 20270-004, RJ, Brazil

a r t i c l e i n f o

s u m m a r y

Article history: Received 8 September 2019 Accepted 21 November 2019 Available online xxx

Backgrounds & aims: Nutritional status of patients with chronic liver disease has been gaining prominence since it is directly associated to morbidity and mortality. However, there is still no consensus on the best method for nutritional assessment given the great variability of body composition and the several stages of the disease. The objective was to compare different methods of nutritional evaluation of subjects with chronic viral hepatitis (CVH), including handgrip strength (HGS) and adductor pollicis muscle thickness (APMT), as well as their influence factors. Methods: This cross-sectional study enrolled sixty-nine noncirrhotic patients with CVH by B or C viruses. Subjects were evaluated and classified through subjective global assessment (SGA), anthropometry, HGS and APMT. Each parameter was compared between gender by chi-square test, and by T test within the categories of SGA. Finally, simple correlation and multivariate linear regression were performed to obtain the strength of association of the collected variables and HGS.

Keywords: Chronic liver disease Nutritional assessment Chronic viral hepatitis Handgrip strength Adductor pollicis muscle thickness

* Corresponding author. Rua Conde de Itaguaí, 55 casa 02/101, Tijuca, Rio de Janeiro, 20511-200, RJ, Brazil. Fax: þ5521 21960288. E-mail addresses: [email protected] (A.F. Cortez), [email protected] (V.P. de Almeida), [email protected] ^a), [email protected] (B.C. Costa Reis), [email protected] (G.S. dos Reis), [email protected] (B.B. Corre ~o Mello). (F.S. Barreto), [email protected] (P.R. Bastos), [email protected] (C.E. Branda https://doi.org/10.1016/j.yclnex.2019.11.002 2352-9393/© 2019 The Author(s). Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Results: According to the SGA, one-third were considered at nutritional risk (SGA-B). Nutritional risk was found in 59.4%, 65.2% and 88% based on HGS, mid-arm muscle circumference (MAMC) and APMT, respectively. The dominant APMT obtained a mean of 17.2 ± 5.4 mm vs 16.2 ± 4.6 mm of the non-dominant hand. On the other hand, the dominant HGS had a mean of 27.3 ± 11.2 vs 24.2 ± 11.0 of the nondominant HGS. APMT and HGS values for any limb were significantly higher in males, but below thresholds patterns described for any gender. The HGS of both hands were correlated with sex, age, weight, height, MAMC, tricipital skinfold and APMT (p < 0.05). In multivariate linear regression, the model that best fit an exploratory analysis included: age, sex, height, MAMC, tricipital skinfold, and dominant APMT. Conclusion: There was a higher nutritional risk in patients with stable CVH when variables dependent of muscle mass were analyzed. APMT was an independent parameter to predict HGS value. We encourage malnutrition screening based on anthropometry, HGS and APMT, since SGA is not such a reliable tool for CVH. © 2019 The Author(s). Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Chronic viral hepatitis (CVH) represents a serious public health problem, with more than 257 million people affected by hepatitis B virus (HBV) and more than 71 million by hepatitis C virus (HCV) [1]. Even with the modification made by Pugh at prognostic classification in patients with chronic liver disease, the concern with nutritional status comes since the original description of Child's criteria in 1964 [2]. In the last decades, studies have shown the relationship between chronic liver disease complications and worse nutritional status [3,4]. Several tools are available for the nutritional risk screening, but there is no evidence-based method known to accurately diagnose malnutrition or sarcopenia [5e7]. Currently, most guidelines use lean body mass reduction and a combination of low handgrip strength (HGS) and/or loss of function evaluated by the gait speed test as the main criteria for the diagnosis of sarcopenia [8,9]. Recent reviews from hepatology societies stated the necessity of a rational algorithm allowing nutritional screening and assessment in all stages of chronic liver disease [10,11]. In fact, patients with chronic liver disease classified as Child C or with decompensated cirrhosis are already considered at high risk for nutritional imbalance, while other profiles still require a cost-time-effective evaluation. HGS is part of nutritional assessment and its reduction can precede the loss of muscle mass affecting morbidity and mortality in healthy people and cirrhotic patients [12e16]. On the other hand, the adductor pollicis muscle thickness (APMT), despite being an easy, cheap and reproducible method with known reference values [17], does not assess muscle strength and lacks studies in patients with CVH. Undoubtedly, there is a current need to establish a nutritional risk protocol applicable to each patient group in each clinical practice. Patients with CVH may present within a broad spectrum of disease severity, including early, stable and non-cirrhotic stages or even decompensated cirrhosis. The latter, arraying a high prevalence of malnutrition and nutritional risk [10,11]. In addition to the diagnostic difficulty due to the lack of a nutritional screening tool validated in chronic liver disease, this group of individuals presents variability in body composition, reducing the accuracy of anthropometry and imaging exams [11]. Therefore, the present study aims to verify the prevalence of malnutrition, and to compare different methods for nutritional status in patients with CVH, including HGS and APMT, as well as their influencing factors.

Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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2. Materials and methods 2.1. Study sample A cross-sectional study was conducted in an academic hospital in Rio de Janeiro State, Brazil, between March 2017 and July 2018. The study complied with the provision of the Declaration of Helsinki, Good Clinical Practice Guidelines and National Health Council Resolution 466/12. It was approved by e and Guinle University Hospital. the Ethics Committee of the Gaffre Seventy-four outpatients with chronic viral hepatitis (CVH) due to B or C viruses were enrolled, regardless of cirrhosis status. Patients with advanced stage or decompensation of heart failure, chronic renal failure or chronic obstructive pulmonary disease were not enrolled in the study, as well as those patients in whom it was not possible to measure all the anthropometric indicators. Patients with significant alcohol consumption (greater than 10 g ethanol/day) were excluded. After rational investigation, sixty-nine patients met the inclusion criteria, i.e, they had stable CVH without cirrhosis. Through careful clinical examination looking for stigmata of liver failure, laboratory tests, imaging tests or histopathological evidence suggestive of cirrhosis, five patients were excluded, two of them with HIV coinfection. 2.2. Data collection Demographic data were obtained from medical records. Nutritional status assessment, subjective global assessment (SGA) and anthropometric measurements were performed by two independent trained raters [18]. Validated SGA comprises information about current weight and weight loss, dietary intake changes, gastrointestinal symptoms, functional capacity, and anthropometric measures [19]. Patients were then classified into well nourished (A), moderately malnourished or suspected malnourished (B), and severely malnourished (C). 2.3. Anthropometry A Lange® Skinfold Caliper was used for determining tricipital skinfold and adductor pollicis muscle thickness (APMT). The assessment of mid-arm circumference (MAC) was taken following standard procedures described by Lohman and colleagues with an inelastic and flexible measuring tape on the middle third of the dominant arm [20]. In brief, tricipital skinfold was measured on the posterior surface of the arm with the elbow extended and the arm held by the side of the body at the midpoint between acromion and olecranon [21,22]. Thereafter, mid-arm muscle circumference (MAMC) in cm, arm muscle area (AMA) in cm2 and arm fat area (AFA) in cm2 were estimated through previously described formulas and derived from mid-arm circumference (MAC) [23]. MAMC, as a marker of lean muscle mass, was calculated using the standard formula: MAMC ¼ MAC e (3.14  TS thickness). The anthropometric values were compared with the NHANES (National Health and Nutrition Examination Survey). The reference range of MAMC was 25.3 cm for men and 23.2 cm for women [24]. In previous studies, these anthropometric measures have shown excellent reliability when performed by expert and trained personnel [25]. The body weight was obtained from an electronic platform anthropometric scale with 0.1 kg precision (Welmy®). Participants were barefooted and with minimum vesture. A mobile stadiometer with 0.5 cm precision (Welmy®) was used to measure height of subjects in anatomical position [21]. Body mass index (BMI) was classified accordingly to World Health Organization (WHO) [26]. 2.4. Hand-grip strength and adductor pollicis muscle thickness Participants were sitting with the hands resting on the knee and elbows flexed at 90 over the homolateral lower limb. In the vertex of an imaginary triangle formed by the extension of thumb and index finger, three graduated pressure of 10 g/mm2 were applied to both the dominant and nondominant hand, and the mean was considered as the APMT of each hand [27]. The cut-off point for men was 26.1 mm for the dominant hand (dAPMT) and 25.1 mm for the non-dominant hand (ndAPMT). Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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In females, the cutoff points were: 19.8 mm (dAPMT) and 18.7 mm (ndAPMT) [17]. Thereafter, patients were instructed to tighten the Jamar Plus digital dynamometer electronic device with maximum force in response to the voice command for HGS. The average of three measurements was considered the measurement of dominant HGS (kgf). We used the threshold <30 kgf for men and <20 kgf for women, based on the European Work Group on Sarcopenia in Older People (EWGSOP) [8,28]. 2.5. Statistical analysis The KolmogoroveSmirnov test was used to determine whether continuous data were normally distributed. Double check was performed throughout histogram and QQ-plot visual inspection. Normally distributed data (age, weight, height, BMI, AC, MAMC, AMA, HGS and APMT) were expressed as mean ± standard deviation (SD). The resources used for bivariate comparisons between independent samples were the unpaired T test or Mann Whitney for quantitative variables with normal or asymmetric distribution, respectively. In the comparisons between categorical variables, chi-square test or Fisher test were used. Anthropometric measures, dAPMT and ndAPMT, and dominant and nondominant HGS (dHGS and ndHGS) were compared between genders. We sought comparative bivariate analyzes by the T test of independent samples between continuous values of anthropometry, HGS and APMT with the categories of SGA. HGS and APMT of both hands were dichotomized by thresholds of normality comparing to the categories of SGA (A vs B). Univariate analysis was determined by potential anthropometric measures associated with dAPMT, ndAPMT, dHGS and ndHGS. For this purpose, we used Pearson's correlation. In order to obtain the strength of association of the variables collected in a predictive model of HGS, a multivariate linear regression analysis was performed. Analysis diagnosed with collinearity were set to determine the linear regression model most appropriate, since we have many variables with mutual relationships. We adopted the forward model for the exploratory variable insertion due to the persistence of collinearity in some variables and the gradual inclusion of all variables selected in order of significance. Anthropometric parameters derived from MAC were removed from the model because high collinearity was found (AMA, AFA, tricipital skinfold). Subsequently, the linear regression model was tested assuming independence of the data (DurbineWatson) and normality by residual values and constancy of variances (regression standardized residual and standardized predicted value). The model that best fits and explains 69% of dominant HGS values included: age, sex, height, MAMC, tricipital skinfold and dAPMT. The same model used for dominant hand was reproduced in the non-dominant hand. In spite of exploratory analyzes to guarantee the best multivariate linear regression model, only 35.6% of the described variables could explain ndHGS values. Weight and BMI were included in some exploratory regression models, since they are known as determinants of HGS and APMT. However, these parameters quietly reduced the strength of association and significance of other variables by their collinearity. Statistical analysis was carried out by using SPSS version 21.0 software and the level of 0.05 in twotailed test was considered significant. In tables, continuous data are expressed as mean ± (standard deviation) and categorical data are expressed as number (%) 3. Results Of the 74 patients enrolled, five presented cirrhosis and were excluded from the analysis. Among the 69 patients with stable CVH, 53 (76.8%) were infected with HCV, 14 (20.2%) were infected with HBV and 2 were coinfected with HCV and HBV. Thirty-nine (56.5%) patients received treatment for viral hepatitis, of which 31 were HCV-positive and 8 HBV-positive. Therefore, approximately 60% of the patients infected with one of the viruses were treated (HCV 31/53 and HBV 8/14). Table 1 displays the population demographic and anthropometric characteristics and compares the analyzed variables between genders. Males represented 44.9% of the sample, showing significantly higher values for weight, height and CMB compared to women. However, women were older and had higher tricipital skinfold values. According to SGA, 66.6% of sample were well nourished (46 patients), while 33.3% (23) were considered at nutritional risk (ASG-B). No patient was classified as severely malnourished by SGA. The mean BMI was 26 ± 4.6 kg/m2 and the proportion of subject classified as normal, overweight and obese by the BMI were: 44.9% (31), 40.6% (28) and 14.5% (10), respectively. Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Table 1 Baseline characteristics of the population of patients with stable CVH. Variables

Total (n ¼ 69)

Male (n ¼ 31)

Female (n ¼ 38)

p value

Age (years) Weight (kg) Height (cm) BMI (kg/m2) AC (cm) MAC (cm) MAMC (cm) TS (mm) dHGS (kgf) ndHGS (kgf) dAPMT (mm) ndAPMT (mm) SGA A SGA B

61.0 ± 12.3 69.4 ± 14.2 163 ± 11 26.0 ± 4.6 88.8 ± 12.5 30.0 ± 4.1 22.8 ± 4.4 22.4 ± 12.8 27.3 ± 11.1 24.2 ± 11.0 17.2 ± 5.4 16.2 ± 4.6 46 (66.7%) 23 (33.3%)

56.4 ± 13.5 75.2 ± 13.7 172.8 ± 0.07 25.1 ± 3.9 91.1 ± 12.9 29.8 ± 4.4 24.7 ± 4.3 15.7 ± 8.4 36.1 ± 10.3 30.4 ± 13.6 19.8 ± 6.4 17.9 ± 5.6 20 (64.5%) 11 (35.5%)

64.8 ± 9.9 64.7 ± 12.8 155.7 ± 0.07 26.6 ± 5.0 86.9 ± 12.1 30.2 ± 4.0 21.4 ± 4.0 27.9 ± 13.2 20.1 ± 5.4 19.1 ± 4.0 15 ± 3.0 14.5 ± 3.1 26 (68.4%) 12 (31.6%)

0.004 0.002 <0.001 0.164 0.218 0.729 0.002 <0.001 <0.001 <0.001 <0.001 0.004 0.8 0.8

BMI - body mass index; AC - abdominal circumference; MAC- mid-arm circumference; MAMC- mid-arm muscle circumference; TS - tricipital skinfold; HGS e Handgrip strength; APMT - adductor pollicis muscle thickness; dHGS- dominant hangrip strength; dAPMT - dominant adductor pollicis muscle thickness; ndHGS or ndAPMT - of the non-dominant arm; SGA - Subjective Global Assessment. Continuous data are expressed as mean ± (standard deviation) and categorical data are expressed as number (%).

Mean dominant APMT was 17.2 ± 5.4 mm (range: 8.3e35.3 mm). Only 7 patients had left hand as dominant. Mean APMT of the non-dominant hand was 16.2 ± 4.6 mm (range: 7.3 and 30 mm). Mean HGS of the dominant and the non-dominant hands were 27.3 ± 11.2 kgf (range: 10.4 to 61.2 kgf) and 24.2 ± 11.0 kgf (6.1e58.7 kgf), respectively. APMT and HGS values for any hand were significantly higher in males (Table 1 and graphs 1 and 2). Considering the reference thresholds from the continuous values of dHGS [41 (59.4%)], MAMC [45 (65.2%)] and dAPMT [61 (88.1%)], a higher prevalence of nutritional risk was detected when compared to SGA [23 (33.3%)]. No patient was underweight, according to BMI. Among the categories of SGA, differences between the continuous values of MAC and MAMC (p ¼ 0.014 and 0.007, respectively) can be observed, being greater when the patient is well nourished by the SGA (Table 2). Nonetheless, there was no statistically significant difference between the categories of SGA and the continuous values of HGS and APMT (Table 2). There was also no statistically significant difference between the categories of SGA and groups dichotomized by reference values (normal and below normal range) for both HGS and APMT of both hands. It was found a statistically significant difference between dHGS and virus type (Table 3). Patients with HCV had proportionately lower mean HGS than those with HBV, even though it was still within the normal range for HGS. Despite that, the HBV group had 76% chance of having values below the normal range when compared to HCV group (p ¼ 0.003) (Graph 3). There was a significant correlation between the variables sex, age, weight, height, MAMC, tricipital skinfold, APMT and the HGS of both hands (Table 4). In the multivariate linear regression analysis, the model that best fit an exploratory analysis included: age, sex, height, MAMC, tricipital skinfold, dominant APMT. This set of variables can explain 69% of dominant HGS values. In Table 5, we present the strength of association of each variable of the model (beta), the correlation coefficient (B) with the standard error (Std error) and the level of significance (sig) with the confidence interval (CI) for the association. It is possible to verify that height and APMT were positively associated with dominant HGS, while females were negatively related to dHGS, all with statistical significance (p < 0.05). In spite of exploratory analyzes to guarantee the best multivariate linear regression model, only 35.6% of the described variables can explain ndHGS values. 4. Discussion This study about the nutritional assessment of ambulatory patients with CVH highlights three major findings: 1- The prevalence of malnutrition based on SGA was consistent with previous literature reports, and nutritional risk increased based on the tools: HGS, APMT and MAMC; 2- The relevance of Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Graph 1. Variation of dHGS for both genders.

Graph 2. Variation of dAPMT for both genders.

Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Table 2 Comparison between anthropometric parameters and SGA categories. Variables

SGA A (n ¼ 46)

SGA B (n ¼ 23)

p value

Weight (kg) Height (m) BMI (kg/m2) AC (cm) MAC (cm) MAMC (cm) TSF (mm) dHGS (kgf) ndHGS (kgf) dAPMT (mm) ndAPMT (mm)

70.6 ± 12.7 1.6 ± 0.1 26.5 ± 3.7 89.8 ± 11.9 30.9 ± 4 23.8 ± 4.5 22 ± 12.5 27.9 ± 12.2 24.7 ± 11.5 17.2 ± 5.3 16.3 ± 5.0

67.1 ± 16.9 1.6 ± 0.1 24.9 ± 5.9 87.4 ± 13.6 28.3 ± 4 21 ± 3.5 23.3 ± 13.7 26.0 ± 9.1 23.1 ± 10.1 17.0 ± 5.5 15.9 ± 4.0

0.392 0.676 0.247 0.490 0.014 0.007 0.701 0.478 0.558 0.875 0.796

SGA e subjective global assessment; BMI - body mass index; AC - abdominal circumference; MAC e mid-arm circumference; MAMC - mid -arm muscle circumference; TSF - tricipital skinfold; dHGS e dominant handgrip strength; ndHGS e non-dominant handgrip strength; dAPMT - dominant adductor pollicis muscle thickness; ndAPMT: non-dominant adductor pollicis muscle thickness. Continuous data are expressed as mean ± (standard deviation).

Table 3 Bivariate comparison between continuous values of HGS, APMT and type of hepatitis virus.

dHGS (kgf) ndHGS (kgf) dAPMT (mm) ndAPMT (mm)

HCV (n ¼ 53)

SE

HBV (n ¼ 14)

25.40 ± 10.38 23.12 ± 10.36 16.78 ± 5.24 25.4 ± 10.41

1.34 1.4 0.71 0.57

34.85 28.61 18.86 17.45

± ± ± ±

11.73 13.02 5,75 6.11

SE

p valor

3.13 3.48 1.54 1.63

0.01 0.16 0.23 0.37

HCV e hepatitis C vírus; SE: standard error; HBV - hepatitis B virus; dHGS e dominant handgrip strength; ndHGS e nondominant handgrip strength; dAPMT - dominant adductor pollicis muscle thickness; ndAPMT non-dominant adductor pollicis muscle thickness. Continuous data are expressed as mean ± (standard deviation).

known factors as age, height, weight, MAMC and sex in association to HGS and APMT values; 3- The association between APMT and HGS, independently of other known factors, as early marker of nutritional risk in patients with CVH. In spite of the existence of several tools and methods for malnutrition screening, there is not a gold standard suitable to any clinical condition [5,6,29]. Lean body mass is largely assessed and endorsed by all societies in malnutrition and sarcopenia [6,8,30]. Whether through dynamometry (HGS) or gait speed test, it has been shown that both muscle strength and skeletal muscle power deteriorate earlier than LEAN BODY MASS, the other component of sarcopenia [31]. In our study, there was a higher prevalence of nutritional risk and sarcopenia considering HGS values and both indirect methods of muscle mass analysis (APMT and MAMC) when compared to SGA. This improvement in nutritional risk screening in different stages of chronic liver disease have already been demonstrated by direct comparison between SGA and nutritional multimodal evaluation [32]. It is interesting to notice the strong correlation between muscle mass measurement (APMT and MAMC) and muscle strength by HGS (Table 4), besides correlation between those factors and anthropometry parameters as sex, age, weight and height. However, there was no association between any muscle mass measurement and SGA. These data reinforce SGA as a poor accuracy tool in nutritional screening, since it underestimates the prevalence of malnutrition and sarcopenia in patients with stable CVH and chronic liver disease [8,11]. SGA still seems to have value in the thorough assessment of clinical and surgical inpatients, but more accurate tools have been proposed for the screening, especially for outpatients [29,33,34]. Notwithstanding, no single nutritional risk screening tool has been validated in chronic liver disease patients, including those with CVH [8]. The lack of correlation between muscle mass parameters and BMI is also noteworthy. Even though the use of BMI has been suggested by most hepatology societies, it was not an accurate method for the Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Graph 3. Hepatitis virus-types and dHGS threshold proportion.

nutritional risk screening in patients with stable CVH. Most of subjects (55%) were considered overweight or obese. There was not a single patient classified as underweight, attesting the adequate relationship of weight and height, without muscular quality though. The mechanism of sarcopenia in CLD can be summarized in anabolic resistance, catabolism increase and impaired muscular cells regeneration [35] Muscle mass assessment is important in chronic liver disease because of two main reasons. First, it is the only parameter suggested by all guidelines, overcoming those traditional parameters, as weight loss, reduced food intake, subcutaneous fat loss, edema and BMI [11]. Second, lean body mass and muscle strength imbalance have intrinsic mechanisms related to hyperammonemia in chronic liver disease, leading to complications such as ascites, hepatorenal syndrome, spontaneous bacterial peritonitis and hepatic encephalopathy [35]. Our stable patients with CVH presented nutritional risk prevalence of 33%, 59.4%, 65.2% and 88% based on SGA, HGS, MAMC and APMT, respectively. Comparing groups of SGA A vs SGA B, MAMC was the only factor found to have a statistically significant difference (Table 2). Hence, muscle mass evaluation is indispensable in this context. Unfortunately, current exams are still inefficient in detecting functionality, and assessing protein synthesis and proteolysis [29,36]. Integrating several methods to determine muscular disfunction is still the rational, especially in early stages of chronic inflammatory diseases. The validation of APMT values was published in a Brazilian study including 300 healthy patients, in 2010 [17]. Mean APMT of the dominant hand was 26.1 mm in men and 19.8 mm in women. In non-dominant hand, mean values were 25.1 mm in men and 18.7 mm in women. We have found lower mean values for both hands considering thresholds for APMT and MAMC [24], suggesting decreased muscle mass in this group. Although the influence between APMT and HGS is not as strong compared to height and male gender, APMT was the third largest predictor of HGS values in the regression model, including age. Reference articles for determination of normal values in the general population [8,37] consider height, weight, age and gender as the main predictors of HGS values. The thickness of the adductor pollicis muscle reduced the collinearity between weight, height, BMI, and gender as it personalizes the size of the individual's hands, whether male or female, tall or short, obese or not. Thus, the applied

Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Table 4 Correlation analysis between dHGS and ndHGS values. Variables

d HGS

Female Age (years) Height (cm) Weight (kg) BMI (kg/m2) AC (cm) MAC (cm) MAMC (cm) TS (mm) APMT (mm)

nd HGS

r

P value

R

P value

0.70 0.53 0.73 0.53 0.05 0.275 0.115 0.38 0.31 0.58

<0.001 <0.001 <0.001 <0.001 0.697 0.038 0.355 0.001 0.009 <0.001

0.51 0.44 0.52 0.31 0.05 0.07 0.03 0.30 0.30 0.45

<0.001 <0.001 <0.001 0.008 0.640 0.574 0.817 0.017 0.011 <0.001

r: correlation coefficient; dHGS e dominant handgrip strength; ndHGS e non-dominant handgrip strength; BMI - body mass index; AC - abdominal circumference; MAC e mid-arm circumference; MAMC e mid-arm muscle circumference; TS - tricipital skinfold; APMT - adductor pollicis muscle thickness.

Table 5 Multivariate regression model with prediction dHGS values. Model

B

Std. Error

Beta

Sig.

CI (95%)

Constant Age (years) Gender (F) Height (m) MAMC (cm) TS (mm) dAMPT

25.29 0.11 5.88 29.09 0.1 0.09 0.42

24.517 0.077 2.828 11.008 0.228 0.079 0.206

e 0.11 0.25 0.3 0.03 0.01 0.18

0.307 0.176 0.042 0.011 0.694 0.922 0.047

74.511 0.26 11.563 6.989 0.368 0.151 0.006

23.929 0.049 0.207 51.188 0.549 0.167 0.833

B - correlation coefficient; Std Error - standard error; Beta - force of influence in the predictive model for HGS; Sig - level of significance; CI - confidence interval; MAMC e mid-arm muscle circumference; TS - tricipital skinfold; dAPMT - adductor pollicis muscle thickness.

multivariable regression model reinforced this hypothesis, leading to the observation of functionality and strength regardless of weight, height and BMI for adults. The study design does not allow us to guarantee which muscular assessment is altered earlier or is more important (muscle mass vs muscle strength). Muscle mass depletion, measured by APMT or MAMC, was more prevalent than diminished muscle strength, measured by HGS. There is a tendency to prefer muscle mass functionality as nutritional assessment, in patients with cirrhosis, especially those with stable chronic liver disease in Child-Pugh A [38]. In other populations, including healthy subjects, APMT was not a good nor a sufficient lean body mass predictor to be used alone [39,40]. The notable advantage of parallel analysis of APMT and HGS is the practicality to evaluate a single muscle in a fast and cheap manner, not influenced by edema or subcutaneous tissue, and then, in the same limb, analyze handgrip strength with available devices. This association between APMT and HGS was demonstrated in healthy people, even after controlling by known factors such as sex, age and BMI [41]. APMT has high correlation with lean body mass evaluated by dual energy x-ray absorptiometry exam (DEXA). Augusti et al. first compared HGS, APMT and other anthropometric measurements with the lean body mass gold-standard (DEXA) in patients with cirrhosis. They found the relation between the lowest values of HGS and APMT (20.5 kgf and 6.5 mm, respectively) and emergence of hepatic encephalopathy [42]. Nevertheless, there is no prognostic evidence in chronic liver disease patients without cirrhosis [42e44]. HGS shows more solid results at nutritional assessment, specially at sarcopenia. The dynapenia, decreased muscle strength without neurological or muscular disease, have been studied beyond aging [45]. The HGS had greater capacity to detect malnutrition in people with chronic liver disease, besides the capacity to predict morbimortality [15,46,47]. Hiraoka et al. have shown that an isolated HGS Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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reduction was more prevalent than isolated skeletal muscular volume in patients with chronic liver disease. In addition, they recognized an increased frequency of these alterations with the hepatic function deterioration [48]. On multivariate linear regression analysis, age, sex, height and APMT strongly contributed for HGS values. As expected in healthy people and chronic liver disease patients [41,48], the HGS values were negatively associated with age and female gender. The notorious difference between genders is almost always recognized, with higher values in men [8].

Fig. Rational nutritional screening risk in chronic liver disease. * Nutritional Risk Screening 2002 [50]; Royal Free HospitalNutritional Prioritizing Tool [51].

Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Concerning virus type, there would be a difference between hepatitis viruses due to perpetuation of chronic inflammation more evident in HCV infected patients. In spite of being statistically significant difference between dominant HGS and virus type, HGS were within the normal range by both viruses. Besides that, approximately 60% of the patients infected with one of the viruses were treated (HCV 31/ 53 and HBV 8/14). Since there were a proportionality between groups of treatment (HBV vs HCV), the treatment of HBV infection could lead to myalgia and, consequently, lead to reduced HGS. However, there was no clinical relevance in this sample due to the small HBV infected population.

5. Conclusion The nutritional status of individuals with CVH was comparable with the literature by SGA (33% in SGA-B). However, considering dynamometry (HGS), MAMC and APMT, nutritional risk was detected in 59.4%, 65.2% and 88.1%, respectively. Several known variables interfere with HGS, such as age, sex, weight and height. However, the multivariate regression model was enhanced with the dominant APMT, constituting important and independent parameter to predict the dominant HGS values. The recommendation is to encourage the screening of malnutrition and sarcopenia in patients with CVH, creating a practical and applicable rationale in the outpatient setting, based on anthropometry, HGS and APMT.

6. Perspectives The diagnosis of malnutrition is multimodal and requires a comprehensive and structured nutritional assessment, ranging from anthropometry, dietary habits, laboratory tests for micronutrient and macronutrient deficiencies, loss of subcutaneous tissue and, mainly, muscle mass [6]. Consequently, nutritional assessment is not feasible for nutritional risk screening, especially in those who remain stable, even with chronic inflammatory disease or infectious diseases, such as hepatitis and HIV [11,49]. As suggested in recent papers, advanced stable chronic liver disease (Child-Pugh C) or decompensated chronic liver disease increase the risk of malnutrition and its harmful consequences [8,11]. Hence, during clinical stabilization, they require not a screening test, but instead, nutritional evaluation, intervention and an indispensable follow-up. Our proposal differs from other guidelines that still consider BMI as part of nutritional screening in patients with viral hepatitis and stable chronic liver disease [8]. First, the rational to nutritional risk screening is to split chronic liver disease into two groups: decompensated or CHILD C, and those with compensated disease (CHILD A or B). Secondly, we should apply nutritional risk tools [30], preferably MNA and NRS 2002, in addition to the dHGS and dAPMT measurement. Finally, the only possibility of reassessment without intervention would happen in case the patient does not have reduced levels of HGS or APMT and does not have a positive screening for nutritional risk. On the other hand, any altered nutritional parameters between HGS, APMT and screening tool, as well as decompensated cirrhosis or CHILD C patients, should be evaluated by a multidisciplinary team (Fig). Concerning this investigative proposal, we must point out the lack of researches associating nutritional screening to outcome improvement. We also need to mention that there are several screening tools, but those suggested above are endorsed by the nutrition societies [6,30].

Name and location of the institution where the study was performed e e Guinle e Universidade Federal do Estado do Rio de Janeiro Hospital Universit ario Gaffre (UNIRIO). Sources of financial support No grants and founding. Please cite this article as: Cortez AF et al., Nutritional assessment, handgrip strength and adductor pollicis muscle thickness in patients with chronic viral hepatitis, Clinical Nutrition Experimental, https://doi.org/ 10.1016/j.yclnex.2019.11.002

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Author contribution Arthur Fernandes Cortez: Study design, data analysis, preparation manuscript, and review of manuscript; Vívian Pinto de Almeida: preparation manuscript, and critical review manuscript; Bruno Bordallo Corr^ ea: literature search, data collection, study design, and manuscript preparation; Bruno Cezario Costa Reis: literature search, data collection, study design, and manuscript preparation; Gustavo Scaramuzza Reis: data collection, design study, and preparation manuscript; Felipe Sppezzapria Barreto: literature search, data collection, study design, and manuscript preparation; Phillipe Rodrigues Bastos: ~oliterature search, data collection, study design, and manuscript preparation; Carlos Eduardo Branda Mello: preparation manuscript, and review of manuscript. Conflict of Interest None of the authors have any financial or personal relationship with other parties that shall bring into question the impartiality or accuracy of their work.

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