Study of Factors Affecting Sarcopenia in Regular Hemodialysis Patients

Study of Factors Affecting Sarcopenia in Regular Hemodialysis Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study of Factors Affecting Sarcopenia in Regular Hemodialysis Patients Heba Kasem, Ahmed El Arbagy, Yassen lasheen, Hazem Hamad, Nahla Gaballa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7729859/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sarcopenia is prevalent among hemodialysis patients and is linked with decreased physical function, diminished quality of life, and a total increase of cardiovascular risk. Materials and Methods: A cross-sectional study was conducted at Menoufia University Hospitals, including 86 ESRD patients, who were classified into three groups according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria based on sarcopenia severity. Medical history, anthropometric measurements, nutritional condition, physical function tests, and laboratory investigations were included to analyze differences between groups, considering influential factors. Results: Sarcopenia’s bioimpedance parameters including ASM ( 16.21 ± 2.40) , ASMI (5.02 ± 1.19), Phase angle (°) ( 5.7 ± 0.5) hand strength (20.81 ± 5.15), were significantly lower than the non-sarcopenic group with low physical activity assessed by IPA. The multivariate analysis and the ROC curve revealed that malnutrition was the most predicted risk factor for sarcopenia in regular hemodialysis patients (OR= 3.54; CI: 1.911-5.814, P =0.000) with cut off value less than 3.5 with high sensitivity and specificity followed by diabetes mellitus (OR= 1.619; CI: 0.312-0.852, P= 0.01) and other predictors. Conclusion: Malnutrition, physical inactivity, and associated comorbidity may be significant modifiable factors in the sarcopenia development and progression in heamodialysis cases. These results emphasize the value of early screening and individualized intervention strategies for managing sarcopenia in affected patients. Hemodialysis Sarcopenia Malnutrition Figures Figure 1 Introduction Sarcopenia, defined as the gradual loss of muscle mass and functional strength, could be an exceedingly predominant condition among patients with chronic kidney disease (CKD), especially those experiencing hemodialysis (HD) [1].The predominance of sarcopenia in this population range from 11% to 30%, depending on the definition and range of renal disease utilized [2].The presence of sarcopenia in HD patients is correlated with unfavorable patient prognoses, encompassing physical disability, compromised quality of life, elevated susceptibility to cardiovascular events, and a greater risk of premature death [3].The development of sarcopenia in patients with end-stage renal disease (ESRD) arises from multiple contributing mechanisms that reflect a complex interaction between muscular, systemic, and lifestyle-related factors. Elements such as uremic toxin accumulation, persistent inflammation, insulin resistance, metabolic acidosis, and disruptions in hormonal balance collectively impair the equilibrium between muscle protein synthesis and breakdown, ultimately resulting in muscle atrophy. Additionally, coexisting conditions, including diabetes mellitus (DM), inadequate nutritional status, and reduced physical activity—further accelerate the decline in muscle mass and functional strength among individuals undergoing hemodialysis (HD) [4]. Identification and management of sarcopenia at an early stage in this high-risk group is essential to halt further decline and enhance patient outcomes. Despite its clinical significance, the underlying determinants that drive the onset and progression of sarcopenia in individuals undergoing hemodialysis (HD) are not yet fully elucidated. In light of this gap, the present study was designed to assess the prevalence of sarcopenia and identify its potential risk factors among a cohort of routine HD patients, aiming to generate insights that could guide the creation of targeted preventive and therapeutic approaches to reduce the impact of this disabling condition. Patients and methods A cross-sectional study was conducted at Menoufia University Hospitals included 86 ESRD patients, Participants were categorized into three subgroups according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) diagnostic criteria, reflecting the presence and severity of sarcopenia: Group I (without sarcopenia, n=31), Group II (pre-sarcopenia, n=14), and Group III (sarcopenia, n=41) from December 2022 till December 2023. The study protocol received approval from the local ethics committee of the Menoufia University under IRP number (11/2021 INTM59) to conduct this study and to use facilities in hospitals. All the included patients were treated According to the Helsinki Declaration of biomedical ethics. Verbal and written consents were obtained from each patient after proper orientation regarding the objectives of the study, the data confidentiality, as well as the impact of the study (World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. Revised by the 59th WMA General Assembly, Seoul, Republic of Korea Prior to enrollment, all participants were provided with a clear explanation of the study objectives, and written informed consent was obtained. The study population consisted of individuals above 18 years of age who had been on regular hemodialysis treatment for at least three months prior to the initiation of the research. Exclusion criteria comprised patients with advanced chronic liver disease presenting as cirrhosis and/or ascites, severe heart failure corresponding to New York Heart Association (NYHA) class III-IV, nephrotic syndrome, active malignancies, depressive disorders, thyroid dysfunction (either hypo- or hyperthyroidism), and conditions causing malabsorption. Patients unable to cooperate with the study, those receiving immunosuppressive medications, or those who had been hospitalized within the last six months were also excluded. Each participant underwent a comprehensive evaluation encompassing medical history, anthropometric measurements, nutritional status assessment, and physical performance tests, with sarcopenia evaluation conducted in a single session. Concurrently, biochemical parameters were gathered during this same visit. Sarcopenia was diagnosed based on the EWGSOP2 criteria , identifying the presence of both decreased muscle strength and a reduction in muscle mass [5]. Muscle strength was assessed using (the Jamar Hydraulic Hand Dynamometer, JAMAR), with participants instructed to maintain their arms by their sides and exert maximum isometric effort with the dominant hand. Low muscle strength will be characterized by a handgrip strength (HGS) measurement of less than 30 kg in males and less than 20 kg in females [6]. Bioelectrical impedance analysis (BIA) was performed using a Tanita MC-780MA device (Japan) to evaluate skeletal muscle mass, an essential parameter for diagnosing sarcopenia. The BIA measurement was conducted two hours after the patients’ mid-week hemodialysis session. Appendicular skeletal muscle mass index (ASMI) was determined using the formula: appendicular muscle mass (kg) / height² (m²). According to the Asian Working Group for Sarcopenia (AWGS) guidelines, low ASMI values were defined as <7.0 kg/m² for men and <5.7 kg/m² for women, as assessed by BIA [7]. The phase angle (PA) represents a linear parameter that quantifies the association between electrical resistance (R) and reactance (Rc). The phase angle cut-off values were 5.66° for men and 4.38° for women [8]. Body Mass Index (BMI) that was calculated as body weight (kg) divided by height (kg/m2), and waist circumference were taken to evaluate body composition and potential nutritional risks [9]. In addition, the 7-point Subjective Global Assessment (SGA) scoring system was applied to evaluate the overall nutritional status of participants [10]. To assess physical activity levels, the International Physical Activity Questionnaire (IPAQ) was utilized, enabling analysis of daily activities patterns and habitual exercise practices among the participants [11]. Laboratory records for all patients were retrieved and entered dedicated software for analysis. The laboratory assessments included a complete blood count (CBC) and testing for hepatitis C virus antibodies (HCV Ab). Renal function was evaluated through blood urea and serum creatinine measurements, Dialysis adequacy (Kt/V) was determined via the classic Daugirdas equation. Parameters related to mineral bone disease—including corrected serum calcium, phosphorus, intact parathyroid hormone (PTH), and serum albumin. Ferritin levels were measured with the Automated AAII-25 Colorimetric system (USA). All demographic, clinical, and laboratory data were systematically organized into tables for subsequent analysis. Statistical analysis: Statistical analyses, coding and data entry were carried out using IBM SPSS Statistics software, version 22.0 (IBM Corp., Armonk, NY, USA; 2013 release).Descriptive statistics for quantitative data were presented as mean (X), standard deviation (SD), and range, whereas qualitative data were reported as counts and percentages.Analytical methods included the Chi-square test (χ²) to assess associations between categorical variables, and one-way ANOVA to evaluate differences in means across multiple groups using the F distribution. Monte Carlo techniques were implemented when applicable to derive numerical results through repeated random sampling. Comparisons of continuous variables among the three groups were performed using the Kruskal–Wallis’s test, and correlations between quantitative variables were analyzed using Spearman’s correlation coefficient (r). Univariate and multivariate Binary logistic Regression analysis were used to detect predictors of sarcopenia, receiving operation characters (ROC) curve was conducted to detect area under curve (AUC), cut off point, sensitivity and specificity of the predictors. P-value of (>0.05) was considered not statistically significant. A P-value of less than 0.05 was regarded as statistically significant, whereas a P-value of 0.001 or lower was considered highly significant. Results Out of the 86 patients receiving maintenance hemodialysis, 16% were identified as having pre-sarcopenia, while 47.6% were diagnosed with sarcopenia. Age and gender distributions did not differ significantly between groups (P² = 0.002) [Table 1]. Comorbidity and laboratory assessment However, the associated comorbidities (75.6%) of sarcopenic patients were diabetic (p=0.03), had longer dialysis vintage (6.93 ± 3.62) years and among dialysis parameters, Sarcopenic patients exhibited significantly lower mean values for dry body weight (56.01 ± 12.23) and BMI (21.33 ± 3.81) compared to Group I (p=0.013 and 0.006, respectively) [Table 1]. Sarcopenic patients exhibited significantly lower mean levels of hemoglobin (Hb) (9.56 ± 1.17), creatinine (6.87 ± 2.02) Kt/v (1.15 ± 0.13), and albumin (3.42 ± 0.35) compared to non-sarcopenic patients (p2 < 0.05). Conversely, Sarcopenic patients displayed significantly higher mean levels of cholesterol (196.05 ±39.02), triglycerides (193.02 ±40.43), and CRP (17.29 ± 10.48) compared to non-sarcopenic Group (p2 < 0.05) [Table 1]. Sarcopenia assessment Appendicular Skeletal muscle mass Index (ASMI) was (5.02 ± 1.19 kg/m2), Mean Arm Circumference (MAC) was (18.59 ± 2.75 cm) and grip strength was (20.81 ± 5.15 Kg) in sarcopenic group with statistically significant differences between them and non-sarcopenic group (P2=0.014, 0.004 and 0.002; respectively). The correlation between ASMI and other parameters in sarcopenic group demonstrated significant positive correlations with BMI (r= 0.01, p value =0.019) & SGA (r= 0.24, p value =0.042) [Table 1&2]. Nutritional assessment Severely malnourished cases were predominated in pre-sarcopenic [SGA score 3.14 ± 1.51] and sarcopenic patients [SGA score (1.87 ± 0.92)] compared with non-sarcopenic ones [SGA score (5.19 ± 1.61)] (P=0.000). While the SGA correlation with clinical and laboratory parameters in sarcopenic group demonstrated a significant positive correlation with the serum albumin (r= 0.081, p value =0.014) and BMI (r= 0.17, p value =0.028) [Table 1&3]. Physical activity assessment The international physical assessment questionnaire (IPA) revealed low-grade physical activity in 19 (46.3%) sarcopenic patients relative to a moderate grade physical activity in 19 (61.3%) non-sarcopenic patients (P = 0.004) [Table 1]. In multivariate analysis and ROC curve, SGA was the most predicted risk factor for sarcopenia in regular hemodialysis patients (OR= 3.54; CI: 1.911-5.814, P =0.000) with cut off value less than 3.5 with high sensitivity and specificity followed by Diabetes mellitus (OR= 1.619; CI: 0.312-0.852, P= 0.01) and higher TG (OR=1.037; CI: 1.008-1.067). Other predictors like BMI, serum creatinine, hemoglobin level, albumin, hand grip, ASM and MAC were associated with sarcopenia [Table 4 &Figure 1]. Discussion Sarcopenia is defined by a progressive decline in skeletal muscle mass and strength, accompanied by an increased risk of adverse outcomes, including physical disability, diminished quality of life, and death-rate [12]. In contrast, pure malnutrition refers to the reduction of body weight, muscle mass and fat stores resulting from inadequate energy and nutrient intake [13]. In chronic kidney disease (CKD), the mechanisms contributing to muscle loss are multifactorial and may arise from various underlying conditions [14]. This work sought to determine the prevalence of sarcopenia and its associated risk factors in a sample of regular HD patients. The analysis indicated that 47.7% of the study population met the criteria for sarcopenia. Such prevalence is in agreement with the findings of da Silva et al. and Matsuzawa et al., which reported rates between 4% and 68% among individuals with ESRD [15&16]. The groups were well-matched in terms of age, sex, and causes of original kidney diseases, suggesting that the observed differences in sarcopenia prevalence were not attributed to these factors. Dialysis duration and the presence of comorbidities—particularly diabetes mellitus—emerged as significant contributors to the development of sarcopenia, consistent with observations. Elder et al., & Sánchez-Tocino et al . [17&18]. demonstrated that the risk of sarcopenia increases with longer dialysis duration; such conditions promote a sustained catabolic environment characterized by progressive muscle loss, diminished endurance, elevated oxidative stress, ongoing inflammation, and harmful impacts of uremic toxins on muscle integrity. Sitaš et al. [ 19] . In hemodialysis (HD) populations, diabetes mellitus (DM) has been identified as a significant contributor to the risk of sarcopenia [20&21]. In patients with DM, factors such as insulin resistance, chronic inflammatory processes, and impaired protein synthesis play a central role in driving the loss of skeletal muscle mass and functional strength [22]. A plausible explanation is that diminished insulin sensitivity plays a role in accelerating skeletal muscle protein catabolism, while disruption of insulin/IGF-I signaling pathways can lower phosphorylated Akt activity, ultimately leading to muscle loss [23]. Some studies reported that patients with insulin resistance had lower handgrip strength [24&25]. Mori et al., & Umakanthan et al., [26&27] have reported an association between low BMI and the presence of sarcopenia in ESRD patients. A low body mass index (BMI) reflects insufficient dietary intake, nutritional deficiencies, and compromised nutritional status [28], which can lead to diminished protein synthesis and subsequent muscle weakness [29]. While BMI serves as a practical screening measure, it should be noted that it does not differentiate adipose tissue from lean muscle mass, therefore, a low BMI may not necessarily indicate sarcopenia [30&31]. The assessment of sarcopenia using various measures, including Appendicular Skeletal muscle mass index (ASMI), handgrip strength, and mid-arm circumference (MAC), revealed significant differences between the studied groups, reflecting the reduced muscle mass and strength associated with sarcopenia. These findings are consistent with the diagnostic criteria for sarcopenia, which involve the assessment of both muscle mass and muscle strength [14]. These outcomes demonstrate the relevance of using a combination of measures to accurately diagnose and characterize sarcopenia in HD patients. The SGA score showed a higher prevalence of severe malnutrition in the sarcopenic patients, which is aligned with previous studies reporting a link between malnutrition and the onset of sarcopenia in patients with ESRD [32&33]. The International Physical Activity Questionnaire (IPAQ) indicated that low physical activity levels were more prevalent among individuals in the sarcopenic group. Yuenyongchaiwat & Angulo et al., [34&35] identified physical inactivity as a significant risk factor for the severity of sarcopenia in HD patients. The reduced physical function observed in the sarcopenic group can be attributed to the decline in muscle mass and strength. Correlation analysis revealed significant positive correlations between ASMI, BMI and IPA in the participants studied. So, we should interpret the overall clinical picture and in conjunction with other relevant factors, such as nutritional status, physical activity levels, and comorbidities, which can also influence muscle mass and function [14]. The regression analysis identified lower SGA scores as the most significant predicting risk factor for sarcopenia in regular HD patients, followed by DM and higher triglyceride levels, in addition to other predictors included BMI, serum creatinine, hemoglobin, and albumin, as well as lower values of ASMI, handgrip strength, and MAC. Overall, the current study demonstrates the multifactorial origin of sarcopenia in HD patients, and the importance of addressing various risk factors, including DM, malnutrition, dyslipidemia, chronic inflammation, and physical inactivity, to prevent or mitigate the development and progression of sarcopenia in this population. Several limitations need acknowledgment. Firstly, the cross-sectional nature of this study precludes determining cause-and-effect associations. In addition, modest sample size and recruitment from a single center may limit the applicability of the results to broader populations. Moreover, the use of BIA for muscle mass assessment, though common, may have accuracy issues, especially in patients with fluid imbalances. Future longitudinal studies with larger cohorts and more precise muscle mass assessment techniques are warranted for deeper insights into sarcopenia's pathophysiology and risk factors in this patient group. CONCLUSION The current study highlights the need for an integrated strategy in both diagnosing and managing sarcopenia among HD patients, given that malnutrition, diabetes mellitus, and increased triglyceride concentrations emerged as major predictive factors. Additionally, lower levels of BMI, serum creatinine, hemoglobin and albumin. Early identification of individuals at risk through routine screening for muscle mass, strength, and physical function is crucial. Moreover, addressing modifiable risk factors, such as malnutrition, physical inactivity, and metabolic abnormalities, should be a priority in the care of these patients. Interventions targeting nutritional support, including adequate protein and energy intake, as well as regular physical activity and exercise programs tailored to the specific needs of HD patients, may aid in preserving muscle mass and function. In addition, effective management of comorbidities—including diabetes mellitus and lipid disorders—could aid in the prevention and reduction of sarcopenia among these patients. Declarations Author Contribution Hazem Mahmoud Hamad performed patient enrollment and sample collection, Maymona Abd El-Wahed Al Khalifa performed electrical bioimpedance. Ahmed Mohamed kamal concerned with data interpretation and statistical analysis. Heba E Kasem, Nahla Kamal Gaballa & Ahmed Rabie El Arbagy contributed to writing and revision of the manuscript. All the authors have read and approved the final version of the manuscript. Acknowledgement No acknowledgements Data Availability The data sets used and/or analyzed during the current study are available from the Heba E kasem on reasonable request. References Larsson L, Degens H , Li M , Salviati L, Lee Y.I , Wesley Thompson W. et al. Sarcopenia: Aging-Related Loss of Muscle Mass and Function Physiol Rev. 2019 Jan 1;99(1):427-511. 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A sarcopenia index derived from malnutrition parameters in elderly haemodialysis patients. Nutrients. 2023;15(5):1115. DOI: 10.3390/nu15051115 Yuenyongchaiwat K, Jongritthiporn S, Somsamarn K, Sukkho O, Pairojkittrakul S, Traitanon O. Depression and low physical activity are related to sarcopenia in hemodialysis: a single-center study. Peer J. 2021;9: e11695. DOI: 10.7717/peerj.11695 Angulo J, El Assar M, Álvarez-Bustos A, Rodríguez-Mañas L. Physical activity and exercise: Strategies to manage frailty. Redox Biol. 2020; 35:101513. DOI: 10.1016/j.redox.2020.101513 Tables Table (1): Characteristics of the patients (n =86) P-value Test of significance Group (111) With sarcopenia (n=41) Group (11) with presarcopenia (n=14) Group (1) Without sarcopenia (n= 31) Items P = 0.176 P1 = 0.586 P2 = 0.080 P3 = 0.428 K = 3.47 45.39 ± 15.68 47 (31.59) 41.29 ± 17.50 41 (22.75-55) 38.36 ± 17.51 39 (19-51) Age(years) Mean ±SD Median (IQR) 0.373 χ 2 = 1.97 21 (51.2) 20 (48.8) 9 (64.3) 5 (35.7) 13 (41.9) 18 (58.1) Gender [No. (%) ] Male Female Comorbidities 0.03* χ2= 7.02 10 (24.4) 31 (75.6) 5 (35.7) 9 (64.3) 17 (54.8) 14 (45.2) DM No Yes 0.268 χ 2 = 2.64 13 (31.7) 28 (68.3) 7 (50) 7 (50) 15 (48.4) 16 (51.6) HTN No Yes 0.237 M= 2.88 35 (85.4) 6 (14.6) 9 (64.3) 5 (35.7) 24 (77.4) 7 (22.6) HCV infection No Yes P = 0.017* P1 = 0.065 P2 = 0.002 P3 = 0.206 K = 8.18 6.93 ± 3.62 6 (4-9) 5.71 ± 2.89 5.5 (3.75-8.0) 4.58 ± 2.25 4 (3-6) Dialysis vintage (years) Mean ± SD Median (IQR) P = 0.043* P1 = 0.459 P2=0.013* P3 = 0.245 F = 3.26 56.01 ± 12.23 61.79 ± 12.39 65.60 ± 20.86 Dry weight (kg) Mean ±SD P = 0.021* P1 = 0.219 P2=0.006* P3 = 0.369 F = 4.06 21.33 ± 3.81 22.49 ± 3.11 24.13 ± 4.87 BMI (Kg/m2) Mean ± SD P = 0.838 P1 = 0.872 P2 = 0.653 P3 = 0.608 F = 0.18 88.02 ± 12.47 89.71 ± 6.47 89.16 ± 9.22 Waist circumference (cm) Mean ±SD P = 0.035* P1 = 0.740 P2 = 0.014* P3 = 0.115 3.50 5.02 ± 1.19 5.21 ± 1.16 7.14 ± 1.38 ASMI (kg/m2) P = 0.003* P1 = 0.418 P2 = 0.361 P3 = 0.002* 6.1 5.7 ± 0.5 5.9 ± 0.45 6.18 ± 0.45 Phase angle (°) P = 0.016* P1 = 0.386 P2 = 0.004* P3 = 0.183 4.36 20.81 ± 5.15 27.00 ± 6.92 24.16 ± 7.39 Hand strength (Kg) P = 0.001** P1 = 0.427 P2 = 0.002* P3 = 0.105 7.91 18.59 ± 2.75 19.71 ± 2.01 24.42 ± 2.36 MAC (cm) P = 0.000** P1 = 0.000** P2 = 0.006* P3 = 0.000** F= 51.88 1.87 ± 0.92 3.14 ± 1.51 5.19 ± 1.61 SGA Score P= 0.004* P1 = 0.427 P2 = 0.002* P3 = 0.105 M = 15.55 19 (46.3) 18 (43.9) 4 (9.8) 2 (14.3) 5 (35.7) 7 (50) 5 (16.1) 19 (61.3) 7 (22.6) IPA Low Moderate High K: Kruskal Wallis test M= Monte Carlo test χ2: Chi-squared test SD=Standard deviation DM= Diabetes Mellitus HTN= Hypertension HCV= Hepatitis C Virus BMI= Body Mass index ASMI= Appendicular Skeletal Muscle Mass Index MAC: Mid Arm Circumference SGA= subjective global assessment IPA= international physical Activity P value: non-significant (P value > 0.05) *= significant (P value < 0.05) P1: group I vs group II P2: group I vs group III P3: group II vs group III. Table (2): Spearmen correlation between ASMI and all other clinical & lab parameters (n=86). Items ASMI in studied participants (n=86) [r] p-value Age (years) -0.04 0.770 Dialysis vintage (years) -0.17 0.157 Dry weight (kg) 0.26 0.720 BMI (Kg/m2) 0.01 0.019* Waist circumference 0.24 0.080 SGA 0.24 0.042* IPA 0.01 0.946 Hand grip (Kg) 0.02 0.850 MAC (cm) 0.07 0.559 Hb (gm/dl) 0.23 0.053 Serum creatinine (mg/dl) 0.05 0.700 Kt/V 0.27 0.200 Sodium (mg/dl) 0.11 0.630 Potassium (mg/dl) 0.11 0.360 Albumin (gm/dl) 0.01 0.946 Calcium (mg/dl) 0.06 0.610 Phosphorus (mg/dl) 0.01 0.929 Intact PTH (pg/dl) -0.17 0.165 Cholesterol (mg/dl) 0.01 0.987 Triglyceride (mg/dl) -0.14 0.229 LDL (mg/dl) -0.11 0.345 Ferritin (mg/dl) -0.09 0.434 CRP (mg/dl) -0.10 0.383 Table (3): Spearmen correlation between SGA and all other clinical & lab parameters in sarcopenic group (n=41). Items SGA score in patient with sarcopenia (n=41) R p-value Age (years) -0.044 0.78 Dialysis vintage (years) -0.209 0.29 Dry weight (kg) 0.154 0.337 BMI 0.173 0.028* IPA 0.003 0.096 Hb (gm/dl) 0.052 0.749 Serum creatinine (mg/dl) 0.258 0.104 Kt/V 0.205 0.199 Sodium (mg/dl) 0.008 0.961 Potassium (mg/dl) 0.010 0.951 Albumin (gm/dl) 0.081 0.014* Calcium (mg/dl) 0.071 0.104 Phosphorus (mg/dl) 0.194 0.225 Intact PTH (pg/dl) -0.024 0.602 Cholesterol (mg/dl) 0.219 0.17 Triglyceride (mg/dl) 0.127 0.428 LDL (mg/dl) 0.03 0.818 Ferritin (mg/dl) -0.065 0.554 CRP (mg/dl) -0.109 0.499 Table (4): Binary logistic regression analysis for relevant risk factors for sarcopenia in regular hemodialysis patients. Risk factors of sarcopenia B P-value OR 95% C.I. Lower limit Upper limit Dialysis vintage (years) 0.218 0.065 1.243 0.987 1.567 DM (yes) 0.663 0.01* 1.619 0.312 0.852 Dry weight (kg) -0.018 0.376 0.983 0.945 1.022 SGA Score 1.204 0.000** 3.450 1.911 5.814 IPA 0.599 0.071 1.82 1.115 2.969 BMI (kg/m 2 ) -0.196 0.021* 0.822 0.695 0.971 Hemoglobin (gm/dl) -0.889 0.038* 0.411 0.177 0.952 Serum creatinine (mg/dl) -0.824 0.011* 0.439 0.232 0.831 Kt/V -7.916 0.062 0.000 0.000 1.467 Albumin (gm/dl) -3.185 0.017* 0.041 0.003 0.572 Cholesterol (mg/dl) 0.034 0.073 1.035 0.997 1.074 Triglyceride (mg/dl) 0.036 0.013* 1.037 1.008 1.067 CRP (mg/dl) 0.202 0.111 1.224 0.955 1.569 Hand grip -0.110 0.025* 0.896 0.813 0.986 MAC -0.151 0.045* 0.860 0.742 0.997 OR: Odds ratio CI: confidence interval DM= Diabetes Mellitus CRP= C - reactive protein ASM= Appendicular Skletal Muscle Index MAC: Mid Arm Circumference. SGA= subjective global assessment IPA= international physical Activity. P-value: non-significant (P-value > 0.05) *= significant (P value < 0.05). **=statistically highly significant (P value ≤ 0.001). P1: group I vs group II P2: group I vs group III P3: group II vs group III Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":119583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve of subjective global assessment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-7729859/v1/8fd6a3419374657b982f8925.png"},{"id":92697431,"identity":"8f520d42-304e-46dd-9765-4228e55f8186","added_by":"auto","created_at":"2025-10-03 07:17:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7729859/v1/78b547c4-3b93-4bfb-bdf9-daac888bd1e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eStudy of Factors Affecting Sarcopenia in Regular Hemodialysis Patients\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, defined as the gradual loss of muscle mass and functional strength, could be an exceedingly predominant condition among patients with chronic kidney disease (CKD), especially those experiencing hemodialysis (HD) [1].The predominance of sarcopenia in this population range from 11% to 30%, depending on the definition and range of renal disease utilized [2].The presence of sarcopenia in HD patients is correlated with unfavorable patient prognoses, encompassing physical disability, compromised quality of life, elevated susceptibility to cardiovascular events, and a greater risk of premature death [3].The development of sarcopenia in patients with end-stage renal disease (ESRD) arises from multiple contributing mechanisms that reflect a complex interaction between muscular, systemic, and lifestyle-related factors. Elements such as uremic toxin accumulation, persistent inflammation, insulin resistance, metabolic acidosis, and disruptions in hormonal balance collectively impair the equilibrium between muscle protein synthesis and breakdown, ultimately resulting in muscle atrophy. Additionally, coexisting conditions, including diabetes mellitus (DM), inadequate nutritional status, and reduced physical activity\u0026mdash;further accelerate the decline in muscle mass and functional strength among individuals undergoing hemodialysis (HD) [4]. Identification and management of sarcopenia at an early stage in this high-risk group is essential to halt further decline and enhance patient outcomes. Despite its clinical significance, the underlying determinants that drive the onset and progression of sarcopenia in individuals undergoing hemodialysis (HD) are not yet fully elucidated. In light of this gap, the present study was designed to assess the prevalence of sarcopenia and identify its potential risk factors among a cohort of routine HD patients, aiming to generate insights that could guide the creation of targeted preventive and therapeutic approaches to reduce the impact of this disabling condition.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cp\u003eA cross-sectional study was conducted at Menoufia University Hospitals included 86 ESRD patients, Participants were categorized into three subgroups according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) diagnostic criteria, reflecting the presence and severity of sarcopenia: Group I (without sarcopenia, n=31), Group II (pre-sarcopenia, n=14), and Group III (sarcopenia, n=41) from December 2022 till December 2023. The study protocol received approval from the local ethics committee of the Menoufia University under IRP number (11/2021 INTM59) to conduct this study and to use facilities in hospitals.\u0026nbsp;All the included patients were treated According to the Helsinki Declaration of biomedical ethics. Verbal and written consents were obtained from each patient after proper orientation regarding the objectives of the study, the data confidentiality, as well as the impact of the study (World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. Revised by the 59th WMA General Assembly, Seoul, Republic of Korea Prior to enrollment, all participants were provided with a clear explanation of the study objectives, and written informed consent was obtained. The study population consisted of individuals above 18 years of age who had been on regular hemodialysis treatment for at least three months prior to the initiation of the research. Exclusion criteria comprised patients with advanced chronic liver disease presenting as cirrhosis and/or ascites, severe heart failure corresponding to New York Heart Association (NYHA) class III-IV, nephrotic syndrome, active malignancies, depressive disorders, thyroid dysfunction (either hypo- or hyperthyroidism), and conditions causing malabsorption. Patients unable to cooperate with the study, those receiving immunosuppressive medications, or those who had been hospitalized within the last six months were also excluded. Each participant underwent a comprehensive evaluation encompassing medical history, anthropometric measurements, nutritional status assessment, and physical performance tests, with sarcopenia evaluation conducted in a single session. Concurrently, biochemical parameters were gathered during this same visit. Sarcopenia was diagnosed based on the \u003cem\u003eEWGSOP2 criteria\u003c/em\u003e, identifying the presence of both decreased muscle strength and a reduction in muscle mass [5]. \u003cem\u003eMuscle strength\u003c/em\u003e was assessed using (the Jamar Hydraulic Hand Dynamometer, JAMAR), with participants instructed to maintain their arms by their sides and exert maximum isometric effort with the dominant hand. Low muscle strength will be characterized by a handgrip strength (HGS) measurement of less than 30 kg in males and less than 20 kg in females [6]. \u003cem\u003eBioelectrical impedance analysis (BIA)\u003c/em\u003e was performed using a Tanita MC-780MA device (Japan) to evaluate skeletal muscle mass, an essential parameter for diagnosing sarcopenia. The BIA measurement was conducted two hours after the patients\u0026rsquo; mid-week hemodialysis session. Appendicular skeletal muscle mass index (ASMI) was determined using the formula: appendicular muscle mass (kg) / height\u0026sup2; (m\u0026sup2;). According to the Asian Working Group for Sarcopenia (AWGS) guidelines, low ASMI values were defined as \u0026lt;7.0 kg/m\u0026sup2; for men and \u0026lt;5.7 kg/m\u0026sup2; for women, as assessed by BIA [7]. The phase angle (PA) represents a linear parameter that quantifies the association between electrical resistance (R) and reactance (Rc).\u0026nbsp;The phase angle cut-off values were 5.66\u0026deg; for men and 4.38\u0026deg; for women [8].\u0026nbsp;\u003cem\u003eBody Mass Index (BMI)\u003c/em\u003e that was calculated as body weight (kg) divided by height (kg/m2), and waist circumference were taken to evaluate body composition and potential nutritional risks [9]. In addition, the 7-point Subjective Global Assessment (SGA) scoring system was applied to evaluate the overall nutritional status of participants [10]. To assess physical activity levels, \u003cem\u003ethe International Physical Activity Questionnaire (IPAQ)\u0026nbsp;\u003c/em\u003ewas utilized, enabling analysis of daily activities patterns and habitual exercise practices among the participants [11]. Laboratory records for all patients were retrieved and entered dedicated software for analysis. The laboratory assessments included a complete blood count (CBC) and testing for hepatitis C virus antibodies (HCV Ab). Renal function was evaluated through blood urea and serum creatinine measurements, Dialysis adequacy (Kt/V) was determined via the classic Daugirdas equation. Parameters related to mineral bone disease\u0026mdash;including corrected serum calcium, phosphorus, intact parathyroid hormone (PTH), and serum albumin. Ferritin levels were measured with the Automated AAII-25 Colorimetric system (USA). All demographic, clinical, and laboratory data were systematically organized into tables for subsequent analysis.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis:\u003c/strong\u003e Statistical analyses, coding and data entry \u0026nbsp; were carried out using IBM SPSS Statistics software, version 22.0 (IBM Corp., Armonk, NY, USA; 2013 release).Descriptive statistics for quantitative data were presented as mean (X), standard deviation (SD), and range, whereas qualitative data were reported as counts and percentages.Analytical methods included the Chi-square test (\u0026chi;\u0026sup2;) to assess associations between categorical variables, and one-way ANOVA to evaluate differences in means across multiple groups using the F distribution. Monte Carlo techniques were implemented when applicable to derive numerical results through repeated random sampling. Comparisons of continuous variables among the three groups were performed using the Kruskal\u0026ndash;Wallis\u0026rsquo;s test, and correlations between quantitative variables were analyzed using Spearman\u0026rsquo;s correlation coefficient (r). Univariate and multivariate Binary logistic Regression analysis were used to detect predictors of sarcopenia, receiving operation characters (ROC) curve was conducted to detect area under curve (AUC), cut off point, sensitivity and specificity of the predictors. P-value of (\u0026gt;0.05) was considered not statistically significant. A P-value of less than 0.05 was regarded as statistically significant, whereas a P-value of 0.001 or lower was considered highly significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of the 86 patients receiving maintenance hemodialysis, 16% were identified as having pre-sarcopenia, while 47.6% were diagnosed with sarcopenia. Age and gender distributions did not differ significantly between groups (P\u0026sup2; = 0.002) [Table 1].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComorbidity and laboratory assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHowever, the associated comorbidities (75.6%) of sarcopenic patients were diabetic (p=0.03), had longer dialysis vintage (6.93 \u0026plusmn; 3.62) years and among dialysis parameters, Sarcopenic patients exhibited significantly lower mean values for dry body weight (56.01 \u0026plusmn; 12.23) and BMI (21.33 \u0026plusmn; 3.81) compared to Group I (p=0.013 and 0.006, respectively) [Table 1].\u003c/p\u003e\n\u003cp\u003eSarcopenic patients exhibited significantly lower mean levels of hemoglobin (Hb) (9.56 \u0026plusmn; 1.17), creatinine (6.87 \u0026plusmn; 2.02) Kt/v (1.15 \u0026plusmn; 0.13), and albumin (3.42 \u0026plusmn; 0.35) compared to non-sarcopenic patients (p2 \u0026lt; 0.05). Conversely, Sarcopenic patients displayed significantly higher mean levels of cholesterol (196.05 \u0026plusmn;39.02), triglycerides (193.02 \u0026plusmn;40.43), and CRP (17.29 \u0026plusmn; 10.48) compared to non-sarcopenic Group (p2 \u0026lt; 0.05) [Table 1]. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSarcopenia assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAppendicular Skeletal muscle mass Index (ASMI) was (5.02 \u0026plusmn; 1.19 kg/m2), Mean Arm Circumference (MAC) was (18.59 \u0026plusmn; 2.75 cm) and grip strength was (20.81 \u0026plusmn; 5.15 Kg) in sarcopenic group with statistically significant differences between them and non-sarcopenic group (P2=0.014, 0.004 and 0.002; respectively). The correlation between ASMI and other parameters in sarcopenic group demonstrated significant positive correlations with BMI (r= 0.01, p value =0.019) \u0026amp; SGA (r= 0.24, p value =0.042) [Table 1\u0026amp;2].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNutritional assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeverely malnourished cases were predominated in pre-sarcopenic [SGA score 3.14 \u0026plusmn; 1.51] and sarcopenic patients [SGA score (1.87 \u0026plusmn; 0.92)] compared with non-sarcopenic ones [SGA score (5.19 \u0026plusmn; 1.61)]\u003cstrong\u003e (P=0.000).\u003c/strong\u003e While the SGA correlation with clinical and laboratory parameters in sarcopenic group demonstrated a significant positive correlation with the serum albumin (r= 0.081, p value =0.014) and BMI (r= 0.17, p value =0.028) [Table 1\u0026amp;3].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhysical activity assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe international physical assessment questionnaire (IPA) revealed low-grade physical activity in 19 (46.3%) sarcopenic patients relative to a moderate grade physical activity in 19 (61.3%) non-sarcopenic patients (P = 0.004) [Table 1]. \u003c/p\u003e\n\u003cp\u003eIn multivariate analysis and ROC curve, SGA was the most predicted risk factor for sarcopenia in regular hemodialysis patients (OR= 3.54; CI: 1.911-5.814, P =0.000) with cut off value less than 3.5 with high sensitivity and specificity followed by Diabetes mellitus (OR= 1.619; CI: 0.312-0.852, P= 0.01) and higher TG (OR=1.037; CI: 1.008-1.067). Other predictors like BMI, serum creatinine, hemoglobin level, albumin, hand grip, ASM and MAC were associated with sarcopenia [Table 4 \u0026amp;Figure 1]. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSarcopenia is defined by a progressive decline in skeletal muscle mass and strength, accompanied by an increased risk of adverse outcomes, including physical disability, diminished quality of life, and death-rate [12]. In contrast, pure malnutrition refers to the reduction of body weight, muscle mass and fat stores resulting from inadequate energy and nutrient intake [13]. In chronic kidney disease (CKD), the mechanisms contributing to muscle loss are multifactorial and may arise from various underlying conditions [14]. This work sought to determine the prevalence of sarcopenia and its associated risk factors in a sample of regular HD patients. The analysis indicated that 47.7% of the study population met the criteria for sarcopenia. Such prevalence is in agreement with the findings of \u003cem\u003eda Silva et al. and Matsuzawa et al.,\u003c/em\u003e which reported rates between 4% and 68% among individuals with ESRD [15\u0026amp;16]. The groups were well-matched in terms of age, sex, and causes of original kidney diseases, suggesting that the observed differences in sarcopenia prevalence were not attributed to these factors. Dialysis duration and the presence of comorbidities\u0026mdash;particularly diabetes mellitus\u0026mdash;emerged as significant contributors to the development of sarcopenia, consistent with observations. \u0026nbsp;\u003cem\u003eElder et al., \u0026amp; S\u0026aacute;nchez-Tocino et al\u003c/em\u003e. [17\u0026amp;18]. demonstrated that the risk of sarcopenia increases with longer dialysis duration; such conditions promote a sustained catabolic environment characterized by progressive muscle loss, diminished endurance, elevated oxidative stress, ongoing inflammation, and harmful impacts of uremic toxins on muscle integrity. Sita\u0026scaron; et al. [\u003cspan dir=\"RTL\"\u003e19]\u003c/span\u003e. In hemodialysis (HD) populations, diabetes mellitus (DM) has been identified as a significant contributor to the risk of sarcopenia [20\u0026amp;21]. In patients with DM, factors such as insulin resistance, chronic inflammatory processes, and impaired protein synthesis play a central role in driving the loss of skeletal muscle mass and functional strength [22]. A plausible explanation is that diminished insulin sensitivity plays a role in accelerating skeletal muscle protein catabolism, while disruption of insulin/IGF-I signaling pathways can lower phosphorylated Akt activity, ultimately leading to muscle loss [23]. Some studies reported that patients with insulin resistance had lower handgrip strength [24\u0026amp;25]. Mori et al., \u0026amp; Umakanthan et al., [26\u0026amp;27] have reported an association between low BMI and the presence of sarcopenia in ESRD patients. A low body mass index (BMI) reflects insufficient dietary intake, nutritional deficiencies, and compromised nutritional status [28], which can lead to diminished protein synthesis and subsequent muscle weakness [29]. While BMI serves as a practical screening measure, it should be noted that it does not differentiate adipose tissue from lean muscle mass, therefore, a low BMI may not necessarily indicate sarcopenia [30\u0026amp;31]. The assessment of sarcopenia using various measures, including Appendicular Skeletal muscle mass index (ASMI), handgrip strength, and mid-arm circumference (MAC), revealed significant differences between the studied groups, reflecting the reduced muscle mass and strength associated with sarcopenia. These findings are consistent with the diagnostic criteria for sarcopenia, which involve the assessment of both muscle mass and muscle strength [14]. These outcomes demonstrate the relevance of using a combination of measures to accurately diagnose and characterize sarcopenia in HD patients. The SGA score showed a higher prevalence of severe malnutrition in the sarcopenic patients, which is aligned with previous studies reporting a link between malnutrition and the onset of sarcopenia in patients with ESRD [32\u0026amp;33]. The International Physical Activity Questionnaire (IPAQ) indicated that low physical activity levels were more prevalent among individuals in the sarcopenic group. Yuenyongchaiwat \u0026amp; Angulo et al., [34\u0026amp;35] identified physical inactivity as a significant risk factor for the severity of sarcopenia in HD patients. The reduced physical function observed in the sarcopenic group can be attributed to the decline in muscle mass and strength. Correlation analysis revealed significant positive correlations between ASMI, BMI and IPA in the participants studied. So, we should interpret the overall clinical picture and in conjunction with other relevant factors, such as nutritional status, physical activity levels, and comorbidities, which can also influence muscle mass and function [14]. The regression analysis identified lower SGA scores as the most significant predicting risk factor for sarcopenia in regular HD patients, followed by DM and higher triglyceride levels, in addition to other predictors included BMI, serum creatinine, hemoglobin, and albumin, as well as lower values of ASMI, handgrip strength, and MAC. Overall, the current study demonstrates the multifactorial origin of sarcopenia in HD patients, and the importance of addressing various risk factors, including DM, malnutrition, dyslipidemia, chronic inflammation, and physical inactivity, to prevent or mitigate the development and progression of sarcopenia in this population. Several limitations need acknowledgment. Firstly, the cross-sectional nature of this study precludes determining cause-and-effect associations. In addition, modest sample size and recruitment from a single center may limit the applicability of the results to broader populations. Moreover, the use of BIA for muscle mass assessment, though common, may have accuracy issues, especially in patients with fluid imbalances. Future longitudinal studies with larger cohorts and more precise muscle mass assessment techniques are warranted for deeper insights into sarcopenia\u0026apos;s pathophysiology and risk factors in this patient group.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe current study highlights the need for an integrated strategy in both diagnosing and managing sarcopenia among HD patients, given that malnutrition, diabetes mellitus, and increased triglyceride concentrations emerged as major predictive factors. Additionally, lower levels of BMI, serum creatinine, hemoglobin and albumin. Early identification of individuals at risk through routine screening for muscle mass, strength, and physical function is crucial. Moreover, addressing modifiable risk factors, such as malnutrition, physical inactivity, and metabolic abnormalities, should be a priority in the care of these patients. Interventions targeting nutritional support, including adequate protein and energy intake, as well as regular physical activity and exercise programs tailored to the specific needs of HD patients, may aid in preserving muscle mass and function. In addition, effective management of comorbidities\u0026mdash;including diabetes mellitus and lipid disorders\u0026mdash;could aid in the prevention and reduction of sarcopenia among these patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHazem Mahmoud Hamad performed patient enrollment and sample collection, Maymona Abd El-Wahed Al Khalifa performed electrical bioimpedance. Ahmed Mohamed kamal concerned with data interpretation and statistical analysis. Heba E Kasem, Nahla Kamal Gaballa \u0026amp; Ahmed Rabie El Arbagy contributed to writing and revision of the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNo acknowledgements\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data sets used and/or analyzed during the current study are available from the Heba E kasem on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLarsson\u003csup\u003e \u003c/sup\u003eL, Degens H\u003csup\u003e \u003c/sup\u003e, Li M\u003csup\u003e \u003c/sup\u003e, Salviati L, Lee Y.I\u003csup\u003e \u003c/sup\u003e, Wesley Thompson\u003csup\u003e \u003c/sup\u003eW. et al.\u003csup\u003e \u003c/sup\u003eSarcopenia: Aging-Related Loss of Muscle Mass and Function Physiol Rev. 2019 Jan 1;99(1):427-511. DOI: 10.1152/physrev.00061.2017\u003c/li\u003e\n\u003cli\u003eYuan S, Larsson S.C. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 144 (2023) 155533. https://doi.org/10.1016/j.metabol.2023.155533\u003c/li\u003e\n\u003cli\u003eKim J.K, Kim S.G, Oh J.E, Lee Y.K, Noh J.W, Kim H.J et al. Impact of sarcopenia on long-term mortality and cardiovascular events in patients undergoing hemodialysis. Korean J Intern Med 2017;34(3):599\u0026ndash;607. doi: 10.3904/kjim.2017.083\u003c/li\u003e\n\u003cli\u003eDomanski M and Ciechanowski K. Sarcopenia: A Major Challenge in Elderly Patients with End-Stage Renal Disease. journal of Aging Research2012; Article ID 754739. \u003c/li\u003e\n\u003cli\u003eA.J. CruzJentoft, G. Bahat, J. Bauer, Y. Boirie, O. Bruyere, T. Cederholm, \u003cem\u003eet al.\u003c/em\u003e Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019; 48 (1) :6-31. DOI: 10.1093/ageing/afy169\u003c/li\u003e\n\u003cli\u003eDodds RM, Syddall HE, Cooper R, Benzeval M, Deary IJ, Dennison EM, et al. Grip Strength across the Life Course: Normative Data from Twelve British Studies. PLoS ONE 2014; 4;9(12): e113637. DOI: 10.1371/journal.pone.0113637\u003c/li\u003e\n\u003cli\u003eChen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. \u003cem\u003eJ Am Med Dir Assoc\u003c/em\u003e 2014; 15(2): 95-101. DOI: 10.1016/j.jamda.2013.11.025\u003c/li\u003e\n\u003cli\u003eKumar S, Dutt A, Hemraj S, Bhat S, Manipadybhi MB. Phase Angle Measurement in Healthy Human Subjects through Bio-Impedance Analysis. \u003cem\u003eIran J Basic Med Sci\u003c/em\u003e 2012; 15(6): 1180-1184.\u003c/li\u003e\n\u003cli\u003eObesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894: i-xii, 1-253.\u003c/li\u003e\n\u003cli\u003eVisser R, Dekker FW, Boeschoten EW, et al: Reliability of the 7-point subjective global assessment scale in assessing nutritional status of dialysis patients. 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Update of the European consensus on sarcopenia: what has changed in diagnosis and prevalence in peritoneal dialysis? Eur J Clin Nutr. 2019;73(8):1209\u0026ndash;11. DOI: 10.1038/s41430-019-0468-z\u003c/li\u003e\n\u003cli\u003eMatsuzawa R, Yamamoto S, Suzuki Y, Imamura K, Harada M, Matsunaga A, et al. The clinical applicability of ultrasound technique for diagnosis of sarcopenia in hemodialysis patients. Clin Nutr. 2021;40(3):1161\u0026ndash;7. DOI: 10.1016/j.clnu.2020.07.025\u003c/li\u003e\n\u003cli\u003eElder M, Moonen A, Crowther S, Aleksova J, Center J, Elder GJ. Chronic kidney disease-related sarcopenia as a prognostic indicator in elderly haemodialysis patients. BMC Nephrol. 2023;24(1):138. DOI: https://doi.org/10.1186/s12882-023-03175-5. \u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Tocino ML, Mas-Fontao S, Gracia-Iguacel C, Pereira M, Gonz\u0026aacute;lez-Ibarguren I, Ortiz A, et al. A sarcopenia index derived from malnutrition parameters in elderly haemodialysis patients. Nutrients\u003cem\u003e.\u003c/em\u003e 2023;15(5):1115. DOI: 10.3390/nu15051115\u003c/li\u003e\n\u003cli\u003eSita\u0026scaron; Z, Mihaljević D. Pathophysiological and Diagnostic Aspects of Sarcopenia in Hemodialysis Patients. Southeast Eur Med J. 2023;7(1):42 -54. DOI: https://doi.org/10.26332/seemedj.v7i1.267\u003c/li\u003e\n\u003cli\u003eKarakousis ND, Biliou S, Pyrgioti EE, Georgakopoulos PN, Liakopoulos V, Papanas N. Frailty, sarcopenia and diabetic kidney disease: where do we stand? Int Urol Nephrol. 2023;55(5):1173\u0026ndash;81.DOI: https://doi.org/10.1007/s11255-022-03392-1 \u003c/li\u003e\n\u003cli\u003eXiang T, Fu P, Zhou L. Sarcopenia and osteosarcopenia among patients undergoing hemodialysis. Front Endocrinol (Lausanne). 2023; 17:14:1181139. DOI: 10.3389/fendo.2023.1181139\u003c/li\u003e\n\u003cli\u003eJames DE, St\u0026ouml;ckli J, Birnbaum MJ. The etiology and molecular landscape of insulin resistance. Nat Rev Mol Cell Biol. 2021;22(11):751\u0026ndash;71. DOI: 10.1038/s41580-021-00390-6\u003c/li\u003e\n\u003cli\u003eSugimoto K, Tabara Y, Ikegami H, Takata Y, Kamide K, Ikezoe T, et al. Hyperglycemia in non‐obese patients with type 2 diabetes is associated with low muscle mass: The Multicenter Study for Clarifying Evidence for Sarcopenia in Patients with Diabetes Mellitus. J Diabetes Investig. 2019;10(6):1471\u0026ndash;9. DOI: 10.1111/jdi.13070\u003c/li\u003e\n\u003cli\u003eJoo KC, Son DH, Park JM. Association between relative handgrip strength and insulin resistance in Korean elderly men without diabetes: findings of the 2015 Korea national health nutrition examination survey. Korean J Fam Med. 2022;43(3):199. 125. DOI: 10.4082/kjfm.21.0138\u003c/li\u003e\n\u003cli\u003eZaharia OP, Pesta DH, Bobrov P, Kupriyanova Y, Herder C, Karusheva Y, et al. Reduced muscle strength is associated with insulin resistance in type 2 diabetes patients with osteoarthritis. J Clin Endocrinol Metab. 2021;106(4): e1062\u0026ndash;73. DOI: 10.1210/clinem/dgaa912\u003c/li\u003e\n\u003cli\u003eMori K, Nishide K, Okuno S, Shoji T, Emoto M, Tsuda A, et al. Impact of diabetes on sarcopenia and mortality in patients undergoing hemodialysis. BMC Nephrol. 2019; 20:1\u0026ndash;7. DOI: 10.1186/s12882-019-1271-8\u003c/li\u003e\n\u003cli\u003eUmakanthan M, Li JW, Sud K, Duque G, Guilfoyle D, Cho K, et al. Prevalence and factors associated with sarcopenia in patients on maintenance dialysis in Australia\u0026mdash;a single centre, cross-sectional study. Nutrients. 2021;13(9):3284. DOI: 10.3390/nu13093284\u003c/li\u003e\n\u003cli\u003eAkchurin OM, Kaskel F. Update on inflammation in chronic kidney disease. Blood Purif. 2015;39(1\u0026ndash;3):84\u0026ndash;92. DOI: 10.1159/000368940\u003c/li\u003e\n\u003cli\u003eUemura K, Doi T, Lee S, Shimada H. 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Malnutrition and sarcopenia combined increases the risk for mortality in older adults on hemodialysis. Front Nutr\u003cem\u003e.\u003c/em\u003e 2021; 8:721941. DOI: 10.3389/fnut.2021.721941\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Tocino ML, Mas-Fontao S, Gracia-Iguacel C, Pereira M, Gonz\u0026aacute;lez-Ibarguren I, Ortiz A, et al. A sarcopenia index derived from malnutrition parameters in elderly haemodialysis patients. Nutrients. 2023;15(5):1115. DOI: 10.3390/nu15051115\u003c/li\u003e\n\u003cli\u003eYuenyongchaiwat K, Jongritthiporn S, Somsamarn K, Sukkho O, Pairojkittrakul S, Traitanon O. Depression and low physical activity are related to sarcopenia in hemodialysis: a single-center study. Peer J. 2021;9: e11695. DOI: 10.7717/peerj.11695 \u003c/li\u003e\n\u003cli\u003eAngulo J, El Assar M, \u0026Aacute;lvarez-Bustos A, Rodr\u0026iacute;guez-Ma\u0026ntilde;as L. Physical activity and exercise: Strategies to manage frailty. Redox Biol. 2020; 35:101513. DOI: 10.1016/j.redox.2020.101513\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable (1):\u0026nbsp;\u003c/strong\u003eCharacteristics of the patients (n =86)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"113%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTest of significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eGroup (111)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eWith sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e(n=41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eGroup (11)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003ewith presarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e(n=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eGroup (1)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eWithout sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e(n= 31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eP\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e0.176\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.586\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP2 = 0.080\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eK = 3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e45.39 \u0026plusmn; 15.68\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e47 (31.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e41.29 \u0026plusmn; 17.50\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e41 (22.75-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e38.36 \u0026plusmn; 17.51\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e39 (19-51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAge(years)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMean \u0026plusmn;SD\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e= 1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e21 (51.2)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e20 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e9 (64.3)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e13 (41.9)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e18 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eGender [No. (%)\u003c/strong\u003e]\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eComorbidities\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026chi;2= 7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e10 (24.4)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e31 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5 (35.7)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e9 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e17 (54.8)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e14 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e= 2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e13 (31.7)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e28 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e7 (50)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e15 (48.4)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e16 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHTN\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eM= 2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e35 (85.4)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e6 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e9 (64.3)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e24 (77.4)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHCV infection\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eP = 0.017*\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.065\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP2 = 0.002\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eK = 8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e6.93 \u0026plusmn; 3.62\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e6 (4-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5.71 \u0026plusmn; 2.89\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5.5 (3.75-8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e4.58 \u0026plusmn; 2.25\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e4 (3-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eDialysis vintage (years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.043*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 =\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e0.459\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2=0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eF = 3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e56.01 \u0026plusmn; 12.23\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e61.79 \u0026plusmn; 12.39\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e65.60 \u0026plusmn; 20.86\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eDry weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMean \u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.021*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1\u003cstrong\u003e=\u0026nbsp;\u003c/strong\u003e0.219\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2=0.006*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eF = 4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e21.33 \u0026plusmn; 3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e22.49 \u0026plusmn; 3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.13 \u0026plusmn; 4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eBMI (Kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eP = 0.838\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.872\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP2 = 0.653\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eF = 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e88.02 \u0026plusmn; 12.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e89.71 \u0026plusmn; 6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e89.16 \u0026plusmn; 9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eWaist circumference (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eMean \u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.035*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.740\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2 = 0.014*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.02 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.21 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.14 \u0026plusmn; 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eASMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.003*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.418\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP2 = 0.361\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP3 = 0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.7 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.9 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.18 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003ePhase angle (\u0026deg;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.016*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.386\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2 = 0.004*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3 = 0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e20.81 \u0026plusmn; 5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e27.00 \u0026plusmn; 6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.16 \u0026plusmn; 7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHand strength (Kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.427\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2 = 0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e18.59 \u0026plusmn; 2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e19.71 \u0026plusmn; 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e24.42 \u0026plusmn; 2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMAC (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP = 0.000**\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP1 = 0.000**\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2 = 0.006*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP3 = 0.000**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003eF= 51.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.87 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.14 \u0026plusmn; 1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.19 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eSGA Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP= 0.004*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP1 = 0.427\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP2 = 0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eP3\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eM = 15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e19 (46.3)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e18 (43.9)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e2 (14.3)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5 (35.7)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e5 (16.1)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e19 (61.3)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eIPA\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eModerate\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eK: Kruskal Wallis test M= Monte Carlo test \u0026chi;2: Chi-squared test SD=Standard deviation DM= Diabetes Mellitus HTN= Hypertension HCV= Hepatitis C Virus BMI= Body Mass index ASMI= Appendicular Skeletal Muscle Mass Index MAC: Mid Arm Circumference\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSGA= subjective global assessment IPA= international physical Activity \u0026nbsp; \u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u0026nbsp; P value: non-significant (P value \u0026gt; 0.05) *= significant (P value \u0026lt; 0.05) P1: group I vs group II P2: group I vs group III P3: group II vs group III.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (2):\u0026nbsp;\u003c/strong\u003eSpearmen correlation between ASMI and all other clinical \u0026amp; lab parameters (n=86).\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASMI in studied participants (n=86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[r]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDialysis vintage (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDry weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (Kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.946\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHand grip (Kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAC (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKt/V\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphorus (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntact PTH (pg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFerritin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (3):\u0026nbsp;\u003c/strong\u003eSpearmen correlation between SGA and all other clinical \u0026amp; lab parameters in sarcopenic group (n=41).\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGA score in patient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ewith sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDialysis vintage (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDry weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKt/V\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphorus (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntact PTH (pg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFerritin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4):\u0026nbsp;\u003c/strong\u003eBinary logistic regression analysis for relevant risk factors for sarcopenia in regular hemodialysis patients.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors of sarcopenia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% C.I.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDialysis vintage (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM (yes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDry weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eSGA Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eIPA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKt/V\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-7.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gm/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHand grip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOR: Odds ratio CI: confidence interval DM= Diabetes Mellitus CRP= C - reactive protein ASM= Appendicular Skletal Muscle Index MAC: Mid Arm Circumference. \u0026nbsp;SGA= subjective global assessment IPA= international physical Activity. P-value: non-significant (P-value \u0026gt; 0.05) *= significant (P value \u0026lt; 0.05). **=statistically highly significant (P value \u0026le; 0.001). P1: group I vs group II P2: group I vs group III P3: group II vs group III \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hemodialysis, Sarcopenia, Malnutrition","lastPublishedDoi":"10.21203/rs.3.rs-7729859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7729859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSarcopenia is prevalent among hemodialysis patients and is linked with decreased physical function, diminished quality of life, and a total increase of cardiovascular risk.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e A cross-sectional study was conducted at Menoufia University Hospitals, including 86 ESRD patients, who were classified into three groups according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria based on sarcopenia severity. Medical history, anthropometric measurements, nutritional condition, physical function tests, and laboratory investigations were included to analyze differences between groups, considering influential factors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSarcopenia’s bioimpedance parameters including ASM (\u003cstrong\u003e16.21 ± 2.40)\u003c/strong\u003e, ASMI (5.02 ± 1.19), Phase angle (°) (\u003cstrong\u003e5.7 ± 0.5) \u003c/strong\u003ehand strength (20.81 ± 5.15), were significantly lower than the non-sarcopenic group with low physical activity assessed by IPA. The multivariate analysis and the ROC curve revealed that malnutrition was the most predicted risk factor for sarcopenia in regular hemodialysis patients (OR= 3.54; CI: 1.911-5.814, P =0.000) with cut off value less than 3.5 with high sensitivity and specificity followed by diabetes mellitus (OR= 1.619; CI: 0.312-0.852, P= 0.01) and other predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eMalnutrition, physical inactivity, and associated comorbidity may be significant modifiable factors in the sarcopenia development and progression in heamodialysis cases. These results emphasize the value of early screening and individualized intervention strategies for managing sarcopenia in affected patients.\u003c/p\u003e","manuscriptTitle":"Study of Factors Affecting Sarcopenia in Regular Hemodialysis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:40:17","doi":"10.21203/rs.3.rs-7729859/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8e87ad5-afd7-425d-abc0-cd0957ab7d31","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-03T07:08:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:40:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7729859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7729859","identity":"rs-7729859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}