The Role of Medication Literacy and Polypharmacy in Sarcopenia Among Maintenance Hemodialysis Patients: A Cross-Sectional Analysis

The Role of Medication Literacy and Polypharmacy in Sarcopenia Among Maintenance Hemodialysis Patients: A Cross-Sectional Analysis | 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 The Role of Medication Literacy and Polypharmacy in Sarcopenia Among Maintenance Hemodialysis Patients: A Cross-Sectional Analysis Linfang Zhu, Yang Liu, Fengxue Yang, Jie Li, Huaihong Yuan, Ping Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4182028/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 Purpose Sarcopenia is significantly prevalent among maintenance hemodialysis patients, with the contributing factors of medication literacy and polypharmacy receiving limited exploration in current research. This study aims to fill this gap by assessing the impact of these factors, along with demographic and malnurtition, on sarcopenia risk. Methods Conducted at the Wenjiang Hemodialysis Center in West China Hospital, this descriptive cross-sectional study involved 236 participants. Data collection included the Chinese Medication Literacy Scale, Malnutrition-Inflammation Score assessments, bioelectrical impedance analysis, and grip strength measurements, with sarcopenia diagnosed according to the 2019 AWGS criteria. Results The study included 236 participants. Of these, 87 (36.9%) had no sarcopenia, 121 (51.3%) were pre-sarcopenia, 7 (3.0%) were sarcopenia, and 21 (8.9%) had severe sarcopenia. Ordinal logistic regression analysis identified male gender (OR = 0.557, 95% CI: 0.322 to 0.962, P = 0.036), age below 65 (OR = 0.178, 95% CI: 0.082 to 0.389, P < 0.001), and good nutritional status (OR = 0.544, 95% CI: 0.310 to 0.954, P = 0.034) as protective against severe sarcopenia. Conversely, the combination of polypharmacy and limited medication literacy (OR = 1.956, 95% CI: 1.094 to 3.496, P = 0.024) was significantly associated with an increased risk of sarcopenia progression. Conclusion The study highlights the protective role of good nutrition and the lesser susceptibility of males and younger individuals to severe sarcopenia. It underscores the necessity of targeted interventions to address the compounded risk presented by polypharmacy and limited medication literacy in patients undergoing hemodialysis. Sarcopenia Hemodialysis Polypharmacy Medication Literacy Malnutrition Introduction Sarcopenia, characterized by significant losses in skeletal muscle mass and function, is notably prevalent in maintenance hemodialysis patients (MHD), with reported rates at 28.5%, and a range between 25.9% and 34.6% [ 1 , 2 ]. This rate significantly surpasses those observed in the general elderly Asian population, which range from 11.2–18.3% [ 3 – 5 ]. Importantly, the prevalence of sarcopenia in Chinese community elderly ranged from 8.9–38.8% [ 6 ], highlighting the critical nature of sarcopenia within this specific patient group. Sarcopenia in these patients is closely associated with increased frailty, loss of independence, heightened risk of falls and fractures [ 7 , 8 ], diminished quality of life, higher hospitalization rates [ 9 ], and elevated mortality risk [ 10 ]. To manage the complexities of end-stage renal disease and associated cardiovascular and metabolic comorbidities [ 11 ], patients on maintenance hemodialysis often receive a variety of medications, such as phosphate binders, antihypertensives, erythropoiesis-stimulating agents, and diuretics [ 12 ]. However, the resultant polypharmacy can lead to an increased incidence of adverse drug reactions and complications, further complicating patient care. Recently, The 9-year Kashiwa cohort study have underscored that polypharmacy, especially when combined with the use of potentially inappropriate medications (PIMs), is strongly linked to the onset of sarcopenia, with an adjusted hazard ratio of 2.35 (95% confidence interval, 1.58–3.51) [ 13 ]. This finding underscores the significant role that medication management might play in both the prevention and treatment of sarcopenia within this patient group. The heavy burden of chronic diseases and the frequent use of medications characteristic of MHD patients render them particularly vulnerable to the detrimental effects of drug side effects and interactions [ 14 ]. Complicating matters further is the widespread issue of limited medication literacy among hemodialysis patients [ 15 , 16 ]. This limited understanding can exacerbate the negative impacts of polypharmacy on muscle health, contributing to the risk of sarcopenia. Enhancing medication literacy could therefore be instrumental in enabling patients to navigate their medication regimens more effectively, minimizing the occurrence of adverse reactions and interactions, and potentially reducing the risk of sarcopenia. However, the impact of medication literacy and polypharmacy on sarcopenia’s risk in hemodialysis patients is not well-documented. Additionally, demographic factors like age, gender, and nutritional status have been consistently pinpointed as crucial determinants in the risk of sarcopenia [ 17 , 18 ]. Older age and male gender are associated with a higher prevalence of sarcopenia [ 18 ], while malnutrition - a common issue in the hemodialysis population due to dietary restrictions, altered metabolism, and nutrient losses during dialysis - further exacerbates muscle wasting. The role of inflammation, driven by both chronic kidney disease and dialysis, in perpetuating muscle catabolism underscores the complex etiology of sarcopenia in this group [ 19 ]. Existing studies primarily explore the prevalence and outcomes of sarcopenia among MHD patients, often overlooking the influence of medication literacy and polypharmacy. This research contributes to the field by delving into the impact of these factors, as well as demographic and nutritional elements, on the risk of sarcopenia. Considering of above, this study aims to evaluate the influence of medication literacy and polypharmacy on sarcopenia prevalence among hemodialysis patients, with a particular emphasis on the roles of demographic and nutritional factors. Methods Design A descriptive cross-sectional study design with analytical components was conducted for this research. Setting and participants A cross-sectional study was conducted at the Wenjiang Hemodialysis Center in West China Hospital to examine sarcopenia in MHD patients. The inclusion criteria were: (1) Receiving MHD for at least 3 months, (2) Over 18 years old, (3) Providing voluntary informed consent. The exclusion criteria were: (1) Impaired consciousness, dementia or other mental illnesses, (2) Communication barriers, (3) Previous employment in medical or healthcare-related fields prior to retirement, (4) Contraindications for bioimpedance testing, (5) Presence of severe comorbidities, (6) Recent infections or bleeding episodes, (7) Diabetes-related amputations, (8) Severe gastrointestinal diseases. Patients were categorized into no sarcopenia, pre-sarcopenia, sarcopenia, and severe sarcopenia groups based on 2019 AWGS criteria. A total of 136 male patients (61 no sarcopenia, 63 pre-sarcopenia, 2 sarcopenic, and 10 with severe sarcopenia) and 100 female patients (26 no sarcopenia, 58 pre-sarcopenia, 5 sarcopenia, 11 severe sarcopenia) participated in this study. Data collection and procedures Data collection was conducted at the Wenjiang Hemodialysis Center in the Department of Nephrology, West China Hospital of Sichuan University in Chengdu, China. Data were collected from March 28, 2023, to May 31, 2023. Two researchers, Yang Liu and Linfang Zhu, completed the data collection through one-on-one face-to-face interviews with patients receiving hemodialysis sessions. After signing the informed consent form, participants completed the basic information and medication literacy questionnaire. Researchers then conducted the Malnutrition-Inflammation Score (MIS) assessment for each participant based on various parameters, including weight loss, dietary intake, gastrointestinal symptoms, functional capacity, comorbidities, physical examination for muscle and fat wasting, body mass index, and laboratory values. In illiterate patients, the consent form was read to them with a literate relative present, and they provided a fingerprint to indicate consent. If a participant’s dominant arm was in use for hemodialysis, the researchers read and assisted with the questionnaire. Once completed, the researchers immediately verified and collected the questionnaires. Relevant laboratory findings was obtained through the Hospital Laboratory Information System from the latest centralized examination at the hemodialysis center. The collected questionnaire responses, laboratory findings and MIS scores were compiled in an Excel spreadsheet. In the same data collection period, sarcopenia data were collected 20 minutes after the hemodialysis session. The same trained researchers used an InBodyS10 body composition analyzer to conduct bioelectrical impedance analysis on the participants. Information such as name, gender, age, height, and weight were entered into the system. Impedance measurements were taken in 5 minutes with the patient standing upright, arms slightly apart, and legs separated on electrode plates. Patients were required to fast and empty their bladder beforehand. Then, the participants completed the Handgrip Strength Test and the 6-meter Walk Test. Data on Body composition, handgrip strength and the 6-meter walk test results were copied into another Excel spreadsheet. Before any further analysis, the researchers (Linfang Zhu and Yang liu) systematically compiled all the collected patient information into a summaried Excel spreadsheet. This involved inputting details such as names, ages, and other relevant data to create a structured and organized dataset. Once this information was accurately recorded, it served as the foundation for subsequent data comparisons and analyses. After cross-referencing data such as names and ages and excluding missing data, it was found that 236 individuals had complete data. They had completed both the medication literacy questionnaire and muscle mass and strength measurements. Therefore, this study ultimately included 236 participants. This comprehensive dataset ensured the integrity of the study. Measurses Medication Literacy and Polypharmacy The Medication Literacy Scale in Chinese was used to assess medication literacy level of patients on hemodialysis. This 14-item scale presents 4 simulated drug use scenarios. Each item is scored as 1 for a correct response or 0 for incorrect. The total score ranges from 0 to 14. A score of 11 or higher indicates an adequate level of medication literacy, indicating good medication understanding. A score between 4 and 10 is considered marginal, implying that the individual’s understanding of medication is limited. A score of 3 or lower is deemed inadequate, indicating that the individual has a poor understanding of medication [ 20 ]. The Medication Literacy Scale in Chinese is a validated tool to measure medication literacy in MHD patients [ 18 ]. Polypharmacy is commonly defined as the concurrent use of five or more medications [ 21 ]. In this study, we defined ≥ 5 medications as polypharmacy. This practice may elevate the risk of drug interactions and adverse effects, particularly among patients undergoing hemodialysis [ 22 ]. Sarcopenia According to the Asian criteria and cut-off thresholds established by the Asian Working Group on Sarcopenia (AWGS) in 2019 [ 23 ], sarcopenia is diagnosed when low appendicular skeletal muscle mass coexists with either low muscle strength or physical function. Bioelectrical impedance analysis is used to assess low appendicular skeletal muscle mass, defined as < 7.0 kg/m² for men and < 5.7 kg/m² for women, measured using the InBodyS10 body composition analyzer (InBody Co., Ltd., Seoul, Republic of Korea). Low muscle strength, defined as < 28 kg for men and < 18 kg for women, is measured using the InBody Handgrip Strength Dynamometer (InBody-HGS). The standard for low physical fuction is a walking speed of < 1.0 m/s over a distance of 6 meters. These measurements are used to diagnose sarcopenia via AWGS 2019. Covariates Covariates included sex, age, education level, martial status, primary caregivers, monthly income, dialysis vintage, comorbidity, nutrional status using MIS scale. Kalantar-Zadeh et al [ 24 ] developed the MIS, which involves 7 components from the Subjective Global Assessment (SGA) and the 3 additional non-SGA components of body mass index (BMI; kg/m 2 ), serum albumin, and total iron-binding capacity (TIBC). Each MIS component had 4 levels of severity from 0 (normal) to 3 (very severe); the sum of all 10 components ranged from 0 (normal) to 30 (severely malnourished). The cutoff score for malnutrition was defined as 6 in this study, because the screening tool should be able to identify most of the patients at nutritional risk [ 25 ]. MIS has good consistency with the SGA [ 26 ]. The K/DOQI and Clinical Practice Guidelines for Nutritional Therapy in Chronic Kidney Disease in China recommend using MIS for nutritional assessment of MHD patients [ 27 , 28 ]. Statistical analysis Statistical analysis was performed using SPSS version 25.0. Baseline characteristics are presented as counts (percentages) for all variables. Differences in baseline variables among groups with varying degrees of sarcopenia were assessed using the χ² test or Fisher’s exact test for categorical variables, and the Kruskal-Wallis H test for continuous variables. Ordinal logistic regression analysis was employed to identify the factors influencing sarcopenia in MHD patients due to its suitability for ordinal outcome variables. A p-value of < 0.05 was considered statistically significant. Results In this study, a total of 297 questionnaires were distributed. After excluding 7 invalid ones, 290 valid questionnaires were collected. Out of these, 242 participants underwent muscle mass measurements using a body composition analyzer. Before any further steps, The researchers entered the collected patient information into an Excel spreadsheet. Upon cross-referencing data such as names and registration numbers and excluding missing items, it was found that 236 participants had complete data. They had filled out both the medication literacy questionnaire and participated in the muscle mass measurements. Therefore, this study ultimately included 236 participants. The baseline characteristics of patients undergoing hemodialysis were shown in Table 1 . Of the patients, 57.63% (n = 136) were male and 42.37% (n = 100) were female. The majority (80.08%) were under 65 years old. For education level, 20.76% had completed primary school or less, 34.75% had completed junior high school, 19.92% had completed senior high school, and 24.58% had completed university or higher. Most patients (83.47%) were married. For primary caregivers, 47.88% relied on self-care, 7.20% relied on parents, 38.56% relied on spouses, and 6.36% relied on children. For monthly family income in RMB, 37.29% earned 8000. For hemodialysis vintage, 11.44% had undergone dialysis for 5 years. 58.47% took < 5 medications and 41.53% took ≥ 5 medications. 75.00% had < 5 comorbidities and 25.00% had ≥ 5 comorbidities. The MIS was used to assess nutritional status. Patients were categorized based on their MIS scores, with 47.88% having MIS < 6 and 52.12% having MIS ≥ 6. Finally, 72.03% were categorized into the Limited literacy and Polypharmacy group, characterized by polypharmacy and limited medication literacy, while 27.97% fell into the Adequate literacy group, identified by non-polypharmacy or adequate medication literacy levels. Table 1 Baseline characteristics of patients undergoing hemodialysis (n = 236) Characteristics Category Number of patients (n) Percentage (%) Mean ± SD Sex Male 136 57.63 N/A Female 100 42.37 N/A Age (years) <65 189 80.08 48.20 ± 9.72 ≥ 65 47 19.92 72.45 ± 6.04 Education level Primary school or lower 49 20.76 N/A Junior high school 82 34.75 N/A Senior high school 47 19.92 N/A University or higher 58 24.58 N/A Martial status Married 197 83.47 N/A Unmarried 13 5.51 N/A Divorced 15 6.36 N/A widowed 11 4.66 N/A Primary caregivers Self 113 47.88 N/A Parents 17 7.20 N/A Spouses 91 38.56 N/A Children 15 6.36 N/A Monthly income (RMB) 8000 39 16.53 N/A Dialysis vintage (years) 5 108 45.76 N/A Number of medications < 5 138 58.47 3.02 ± 1.014 ≥ 5 98 41.53 5.94 ± 1.406 Comorbidity count < 5 177 75.00 N/A ≥ 5 59 25.00 N/A Malnutrition-Inflammation Score (MIS) MIS < 6 113 47.88 3.32 ± 1.453 MIS ≥ 6 123 52.12 8.82 ± 2.975 Medication literacy group Adequate literacy 66 27.97 N/A Limited literacy & Polypharmacy 170 72.03 N/A Note: The Medication Literacy Group was determined based on the presence of polypharmacy and assessed medication literacy levels. SD denotes standard deviation. Adequate literacy is defined by the number of correct answers ranging between 11 to 14 and encompasses 27.97% of the patients. Marginal literacy, indicated by 4 to 10 correct answers, includes more than half of the participants at 52.97%. Inadequate literacy, with 0 to 3 correct answers, is observed in 19.07% of the patients (Table 2 ). Table 2 Medication literacy assessment results in patients undergoing hemodialysis (n = 236) Response category Number of correct answers Percentage of patients Adequate literacy 11–14 66 (27.97%) Marginal literacy 4–10 125 (52.97%) Inadequate literacy 0–3 45 (19.07%) The number of participants with different characteristics at each medication literacy level was listed in Table 3 . Sex, age, martial status, primary caregivers, MIS, polypharmacy and limited medication literacy group were all associated with sarcopenia ( P 0.05). Table 3 Results of univariate analysis of sarcopenia determinants for patients undergoing hemodialysis (n = 236) Variables None (n = 87) Pre-sarcopenia (n = 121) Sarcopenia (n = 7) Severe sarcopenia (n = 21) Statistic P Sex 10.393 a 0.013* Male 61 63 2 10 Female 26 58 5 11 Age 40.918 a < 0.001* <65 85 90 5 9 ≥ 65 2 31 2 12 Education level 7.407 b 0.06 Primary school or lower 10 31 1 7 Junior high school 31 45 2 4 Senior high school 19 21 4 3 University or higher 27 24 0 7 Martial status 16.534 a Married 67 105 7 18 0.028* Unmarried 9 4 0 0 Divorced 10 5 0 0 widowed 1 7 0 3 Primary caregivers 25.367 a 0.001* Self 46 55 4 8 Parents 11 6 0 0 Spouses 29 53 3 6 Children 1 7 0 7 Monthly income 1.413 b 0.493 8000 12 23 1 3 Dialysis vintage 3.523 b 0.172 5 41 54 4 9 Number of medications 1.507 a 0.695 < 5 55 68 4 11 ≥ 5 32 53 3 10 Comorbidity 1.337 a 0.739 < 5 67 90 6 14 ≥ 5 20 31 1 7 MIS scores 9.843 a 0.017* MIS < 6 53 50 3 7 MIS ≥ 6 34 71 4 14 Medication literacy group 18.560 a < 0.001* Adequate literacy 48 98 6 18 Limited literacy & Polypharmacy 39 23 1 3 Note: a The Fisher value comes from the Fisher's exact test. b The H value is derived from the Kruskal-Wallis H test. * P < 0.05 indicates statistical significance. Utilizing ordinal logistic regression, we identified that being male (OR = 0.557, 95% CI: 0.322 to 0.962, P = 0.036), under 65 years old (OR = 0.178, 95% CI: 0.082 to 0.389, P < 0.001), and possessing good nutrition (OR = 0.544, 95% CI: 0.310 to 0.954, P = 0.034) were associated with reduced odds of progressing to severe sarcopenia. In contrast, polypharmacy combined with limited medication literacy increased the risk of sarcopenia progression (OR = 1.956, 95% CI: 1.094 to 3.496, P = 0.024) (Table 4 ). This group characterized by the concurrent challenges of managing five or more medications and having less than adequate medication literacy, demonstrated a pronounced vulnerability to sarcopenia. Table 4 Results of logistic regression analysis of sarcopenia determinants for patients undergoing hemodialysis (n = 290) Variables Coefficient (B) Standard Error (SE) Wald Statistic Odds Ratio (OR) 95% Confidence interval for OR P Lower Upper Age < 65 years (refence: ≥65) -1.723 0.397 18.816 0.178 0.082 0.389 < 0.001* Sex: Male (refence:Female) -0.586 0.279 4.410 0.557 0.322 0.962 0.036* Marriage: Married (refence: widowed) 0.412 0.716 0.331 1.510 0.371 6.146 0.565 Marriage: Unmarried (refence: widowed) -0.852 0.948 0.807 0.427 0.067 2.737 0.369 Marriage: Divorced (refence: widowed) -1.225 0.903 1.839 0.294 0.050 1.725 0.175 Caregivers: Self care (refence: Child care) -1.063 0.659 2.608 0.345 0.095 1.255 0.106 Caregivers: Parental care (refence: Child care) -1.222 0.865 1.995 0.295 0.054 1.606 0.158 Caregivers: Spousal care (refence: Child care) -1.265 0.667 3.593 0.282 0.076 1.044 0.058 Good Nutritional Status (MIS < 6) (refence: MIS scores ≥ 6) -0.609 0.286 4.517 0.544 0.310 0.954 0.034* Limited literacy & Polypharmacy (reference: Adequate literacy) 0.671 0.296 5.120 1.956 1.094 3.496 0.024* Note: The logistic regression model identifies factors associated with increased odds of sarcopenia. A negative coefficient indicates a protective effect against sarcopenia, while a positive coefficient suggests increased risk. * P < 0.05 indicates statistical significance. Discussion Polypharmacy and Medication Literacy Notably, the combination of polypharmacy (defined as the use of five or more medications) and limited medication literacy (encompassing both “marginal” and “inadequate” levels) was identified as a significant risk factor for increased sarcopenia severity. This group represents individuals who are not only dealing with the complexities associated with managing multiple medications (polypharmacy) but also face challenges due to their limited understanding or knowledge about their medications (inadequate or marginal medication literacy). This finding highlights the compound challenge posed by complex medication regimens and insufficient medication knowledge, which may contribute to suboptimal management of sarcopenia in this vulnerable group [ 29 ]. Who might be at a higher risk of adverse outcomes due to the combined effect of polypharmacy and limited medication literacy [ 30 – 33 ]. Therefore, it’s crucial to manage polypharmacy effectively and improve medication literacy among these patients to mitigate these risks. Our findings underscore the critical need for targeted interventions to improve medication literacy among hemodialysis patients, potentially mitigating the exacerbated risk of sarcopenia due to polypharmacy. The decision to examine the combined effect of polypharmacy and limited medication literacy on sarcopenia severity in MHD patients was driven by the real-world clinical context where these factors frequently coexist and may interact in complex ways. This approach was intended to capture the compounded risk that patients face when they have to manage multiple medications with potentially inadequate understanding or skills to do so effectively. Such a scenario is particularly relevant for MHD patients who often deal with complex medication regimens. The unexpected findings from this combined analysis, especially where the interaction did not fully align with our study methods, underscore the complexity of these relationships and highlight the need for further research. This nuanced understanding can inform targeted interventions and patient education strategies, ultimately aiming to reduce sarcopenia risk and improve patient outcomes. The innovative aspect of considering these combined factors lies in its potential to reveal intricate patterns of risk that might not be apparent when examining single factors in isolation [ 13 ], thereby contributing novel insights to the field. Gender and Age Differences We identified that males and those under 65 were less likely to have severe sarcopenia. In postmenopausal women, estrogen levels drop significantly, impacting bone and muscle metabolism. This decline in estrogen contributes to osteoporosis and sarcopenia [ 34 ]. Men, lacking this hormonal shift, may have a lower risk of severe sarcopenia, highlighting the role of estrogen in muscle health. As individuals age, there are natural, biological changes that contribute to muscle loss. These include hormonal changes, reduced ability to synthesize proteins, decreased physical activity, and changes in neuromuscular function [ 35 ]. These factors cumulatively contribute to the decline in muscle mass and strength, leading to sarcopenia [ 36 ]. Existing studies have also confirmed an association between these factors and the risk of sarcopenia [ 1 , 37 , 38 ]. For the elderly and high-risk groups, more targeted exercise programs and reasonable diets should be developed to delay skeletal muscle loss. Malnutrition assessment Good nutrition has been shown to be protective against sarcopenia [ 39 ]. Hemodialysis patients often experience Protein-energy wasting (PEW), a state of disordered catabolism resulting from metabolic and nutritional derangements in chronic disease states [ 40 ]. This condition leads to muscle wasting, sarcopenia, and cachexia. Adequate intake of protein, vitamin D, antioxidant nutrients, and long-chain polyunsaturated fatty acids has been shown to be beneficial for improving sarcopenia [ 41 ]. The MIS is a useful and widely employed measure of nutritional status for patients on maintenance dialysis. It has been shown to be a strong predictor of mortality in these patient [ 42 ]. By identifying nutritional deficiencies through MIS, healthcare professionals can pinpoint patients at higher risk for sarcopenia, highlighting malnutrition as a significant risk factor for the severity of sarcopenia. This underscores the critical need for personalized nutritional interventions to mitigate sarcopenia’s progression in this vulnerable population. The strength of our study lies in its ability to elucidate the intricate correlations between sarcopenia severity and factors such as polypharmacy and limited medication literacy in MHD patients, employing ordinal logistic regression analysis. Although our methodology primarily identifies associations rather than causations, it significantly advances the understanding of how these elements interplay in the context of sarcopenia. By highlighting the complex relationships among demographic, nutritional status, and medication management factors, our research offers a nuanced perspective on the potential risks contributing to sarcopenia, providing a foundation for future studies aimed at exploring these dynamics more deeply. This contribution is particularly valuable in the realm of clinical practice and patient education, where understanding these associations can inform more tailored and effective interventions. Strengths This study examines how medication literacy and polypharmacy affect sarcopenia risk in MHD patients. The use of validated scales for measuring medication literacy, malnutrition, and sarcopenia - according to the 2019 AWGS criteria - further enhances the study’s credibility and contributes valuable insights to the field. Limitations and future research Our study’s reliance on ordinal logistic regression analysis primarily identifies associations rather than causal relationships, limiting our ability to conclude definitively that polypharmacy and limited medication literacy cause increased sarcopenia severity in hemodialysis patients. The cross-sectional design further restricts our understanding of the temporal dynamics between these factors and sarcopenia progression. Future research should focus on longitudinal designs to observe sarcopenia and medication literacy over time, clarifying causal links and the evolution of these relationships. Additionally, incorporating qualitative approaches could enrich our understanding of how patients perceive and manage their medication, directly impacting their health outcomes. Conclusion Our findings highlight the protective role of good nutrition and reveal that males and younger MHD patients are less prone to severe sarcopenia. In contrast, the combination of polypharmacy and limited medication literacy significantly elevates the risk, underscoring the need for targeted interventions in this area. Specifically, the study underscores the need for healthcare providers to adopt more personalized medication strategies, which not only consider the quantity of prescriptions but also the patient’s capacity to comprehend and manage their medication regimen effectively. This could potentially involve the introduction of educational programs focused on improving medication literacy, thereby empowering patients to take an active role in their treatment processes. Declarations Acknowledgements The authors express their gratitude to the patients at the Wenjiang Hemodialysis Center, Department of Nephrology, West China Hospital of Sichuan University, Chengdu, China, for their invaluable participation in this study. Author contributions Y.L., and L.Z. conducted the data collection. L.Z., and Y.L. compiled all patient data. The study was conceptualized by F.Y., J.L., and L.Z., with the methodology developed by L.Z. Resources were provided by H.Y. and P.F. The initial draft of the manuscript was written by Y.L. and L.Z., and the review and editing were undertaken by H.Y. All authors reviewed the manuscript. Funding No funding was received for conducting this study. Data availability Data available on request from the corresponding authors. Conflicts of interest/Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate This study obtained approval from the Biomedical Ethics Committee of Sichuan University (approval number: 2020[1002]). Written informed consent was obtained from all patients, and the informed consent form was approved by the Biomedical Ethics Committee of West China Hospital, Sichuan University. The study was conducted in compliance with the principles of the Declaration of Helsinki. References Shu X, Lin T, Wang H, Zhao Y, Jiang T, Peng X, et al. Diagnosis, prevalence, and mortality of sarcopenia in dialysis patients: a systematic review and meta-analysis. Journal of Cachexia, Sarcopenia and Muscle. 2022;13(1):145–58.https://doi.org/10.1002/jcsm.12890 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601.https://doi.org/10.1093/ageing/afz046 Chen X, Hou L, Zhang Y, Dong B. Analysis of the Prevalence of Sarcopenia and Its Risk Factors in the Elderly in the Chengdu Community. J Nutr Health Aging. 2021;25(5):600–5.https://doi.org/10.1007/s12603-020-1559-1 Xin C, Sun X, Lu L, Shan L. Prevalence of sarcopenia in older Chinese adults: a systematic review and meta-analysis. BMJ Open. 2021;11(8):e041879.https://doi.org/10.1136/bmjopen-2020-041879 Chen Z, Li WY, Ho M, Chau PH. The Prevalence of Sarcopenia in Chinese Older Adults: Meta-Analysis and Meta-Regression. Nutrients. 2021;13(5):1441.https://doi.org/10.3390/nu13051441 Liu J, Ding Q, Zhou B, Liu X, Liu J, liu Y, et al. Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia(2021). Chinese Journal of Geriatrics. 2021;40(08):943–52. Yeung SSY, Reijnierse EM, Pham VK, Trappenburg MC, Lim WK, Meskers CGM, et al. Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta‐analysis. J Cachexia Sarcopenia Muscle. 2019;10(3):485–500.https://doi.org/10.1002/jcsm.12411 Lee A, McArthur C, Ioannidis G, Duque G, Adachi JD, Griffith LE, et al. Associations between Osteosarcopenia and Falls, Fractures, and Frailty in Older Adults: Results From the Canadian Longitudinal Study on Aging (CLSA). J Am Med Dir Assoc. 2024;25(1):167-176.e6.https://doi.org/10.1016/j.jamda.2023.09.027 Sousa AS, Guerra RS, Fonseca I, Pichel F, Ferreira S, Amaral TF. Financial impact of sarcopenia on hospitalization costs. Eur J Clin Nutr. 2016;70(9):1046–51.https://doi.org/10.1038/ejcn.2016.73 Kittiskulnam P, Chertow GM, Carrero JJ, Delgado C, Kaysen GA, Johansen KL. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017;92(1):238–47.https://doi.org/10.1016/j.kint.2017.01.024 Usman MS, Khan MS, Butler J. The Interplay Between Diabetes, Cardiovascular Disease, and Kidney Disease. In: Chronic Kidney Disease and Type 2 Diabetes [Internet]. Arlington (VA): American Diabetes Association; 2021 [cited 2023 Feb 20]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK571718/ Schmid H, Schiffl H, Lederer SR. Pharmacotherapy of end-stage renal disease. Expert Opin Pharmacother. 2010;11(4):597–613.https://doi.org/10.1517/14656560903544494 Tanaka T, Akishita M, Kojima T, Son BK, Iijima K. Polypharmacy with potentially inappropriate medications as a risk factor of new onset sarcopenia among community-dwelling Japanese older adults: a 9-year Kashiwa cohort study. BMC Geriatr. 2023;23:390.https://doi.org/10.1186/s12877-023-04012-y Gilad L, Haviv YS, Cohen-Glickman I, Chinitz D, Cohen MJ. Chronic drug treatment among hemodialysis patients: a qualitative study of patients, nursing and medical staff attitudes and approaches. BMC Nephrol. 2020;21:239.https://doi.org/10.1186/s12882-020-01900-y Jang SM, Parker WM, Pai AB, Jiang R, Cardone KE. Assessment of literacy and numeracy skills related to medication labels in patients on chronic in-center hemodialysis. J Am Pharm Assoc (2003). 2020;60(6):957-962.e1.https://doi.org/10.1016/j.japh.2020.07.010 Zhu L, Liu Y, Yang F, Yu S, Fu P, Yuan H. Prevalence, associated factors and clinical implications of medication literacy linked to frailty in hemodialysis patients in China: a cross-sectional study. BMC Nephrol. 2023;24:307.https://doi.org/10.1186/s12882-023-03346-4 Nasimi N, Dabbaghmanesh MH, Sohrabi Z. Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults. Exp Gerontol. 2019;122:67–73.https://doi.org/10.1016/j.exger.2019.04.009 Alatas H, Serin Y, Arslan N. Nutritional Status and Risk of Sarcopenia among Hospitalized Older Adults Residing in a Rural Region in Turkey. Ann Geriatr Med Res. 2023;27(4):293–300.https://doi.org/10.4235/agmr.23.0064 Hortegal EVF, Alves JJDA, Santos EJF, Nunes LCR, Galvão JC, Nunes RF, et al. Sarcopenia and inflammation in patients undergoing hemodialysis. Nutr Hosp. 2020;37(4):855–62.https://doi.org/10.20960/nh.03068 Zheng F, Zhong Z, Ding S, Luo A, Liu Z. [Modification and evaluation of assessment of medication literacy]. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2016;41(11):1226–31.https://doi.org/10.11817/j.issn.1672-7347.2016.11.019 Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17:230.https://doi.org/10.1186/s12877-017-0621-2 Kitamura M, Yamaguchi K, Ota Y, Notomi S, Komine M, Etoh R, et al. Prognostic impact of polypharmacy by drug essentiality in patients on hemodialysis. Sci Rep. 2021;11:24238.https://doi.org/10.1038/s41598-021-03772-0 Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300-307.e2.https://doi.org/10.1016/j.jamda.2019.12.012 Kalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis. 2001;38(6):1251–63.https://doi.org/10.1053/ajkd.2001.29222 Yamada K, Furuya R, Takita T, Maruyama Y, Yamaguchi Y, Ohkawa S, et al. Simplified nutritional screening tools for patients on maintenance hemodialysis. Am J Clin Nutr. 2008;87(1):106–13.https://doi.org/10.1093/ajcn/87.1.106 Santin FG de O, Bigogno FG, Dias Rodrigues JC, Cuppari L, Avesani CM. Concurrent and Predictive Validity of Composite Methods to Assess Nutritional Status in Older Adults on Hemodialysis. J Ren Nutr. 2016;26(1):18–25.https://doi.org/10.1053/j.jrn.2015.07.002 Wright M, Southcott E, MacLaughlin H, Wineberg S. Clinical practice guideline on undernutrition in chronic kidney disease. BMC Nephrol. 2019;20:370.https://doi.org/10.1186/s12882-019-1530-8 Chinese Medical Doctor Association Division of Nephrology, Expert Collaboration Group on Nutritional Therapy Guidelines of the Chinese Society of Integrated Traditional and Western Medicine Nephrology Committee. Clinical Practice Guidelines for Nutritional Therapy in Chronic Kidney Disease in China (2021 Edition). Chinese Medical Journal. 2021;101(08):539–59. Pana A, Sourtzi P, Kalokairinou A, Velonaki VS. Sarcopenia and polypharmacy among older adults: A scoping review of the literature. Arch Gerontol Geriatr. 2022;98:104520.https://doi.org/10.1016/j.archger.2021.104520 Toida T, Toida R, Takahashi R, Uezono S, Komatsu H, Sato Y, et al. Impact of polypharmacy on all-cause mortality and hospitalization in incident hemodialysis patients: a cohort study. Clin Exp Nephrol. 2021;25(11):1215–23.https://doi.org/10.1007/s10157-021-02094-9 Okpechi IG, Tinwala MM, Muneer S, Zaidi D, Ye F, Hamonic LN, et al. Prevalence of polypharmacy and associated adverse health outcomes in adult patients with chronic kidney disease: protocol for a systematic review and meta-analysis. Syst Rev. 2021;10:198.https://doi.org/10.1186/s13643-021-01752-z Lu L, Wang S, Chen J, Yang Y, Wang K, Zheng J, et al. Associated adverse health outcomes of polypharmacy and potentially inappropriate medications in community-dwelling older adults with diabetes. Front Pharmacol. 2023;14:1284287.https://doi.org/10.3389/fphar.2023.1284287 Lyles A, Culver N, Ivester J, Potter T. Effects of health literacy and polypharmacy on medication adherence. Consult Pharm. 2013;28(12):793–9.https://doi.org/10.4140/TCP.n.2013.793 Lu L, Tian L. Postmenopausal osteoporosis coexisting with sarcopenia: the role and mechanisms of estrogen. J Endocrinol. 2023;259(1):e230116.https://doi.org/10.1530/JOE-23-0116 Wilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev. 2018;47:123–32.https://doi.org/10.1016/j.arr.2018.07.005 Isoyama N, Qureshi AR, Avesani CM, Lindholm B, Bàràny P, Heimbürger O, et al. Comparative Associations of Muscle Mass and Muscle Strength with Mortality in Dialysis Patients. Clin J Am Soc Nephrol. 2014;9(10):1720–8.https://doi.org/10.2215/CJN.10261013 Wathanavasin W, Banjongjit A, Avihingsanon Y, Praditpornsilpa K, Tungsanga K, Eiam-Ong S, et al. Prevalence of Sarcopenia and Its Impact on Cardiovascular Events and Mortality among Dialysis Patients: A Systematic Review and Meta-Analysis. Nutrients. 2022;14(19):4077.https://doi.org/10.3390/nu14194077 Petermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, et al. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86–99.https://doi.org/10.1002/jcsm.12783 Kurajoh M, Mori K, Miyabe M, Matsufuji S, Ichii M, Morioka T, et al. Nutritional Status Association With Sarcopenia in Patients Undergoing Maintenance Hemodialysis Assessed by Nutritional Risk Index. Front Nutr. 2022;9:896427.https://doi.org/10.3389/fnut.2022.896427 Hanna RM, Ghobry L, Wassef O, Rhee CM, Kalantar-Zadeh K. A Practical Approach to Nutrition, Protein-Energy Wasting, Sarcopenia, and Cachexia in Patients with Chronic Kidney Disease. Blood Purif. 2020;49(1–2):202–11.https://doi.org/10.1159/000504240 Liu S, Zhang L, Li S. Advances in nutritional supplementation for sarcopenia management. Front Nutr. 2023;10:1189522.https://doi.org/10.3389/fnut.2023.1189522 Prelevic V, Antunovic T, Radunovic D, Gligorovic-Barhanovic N, Gledovic B, Ratkovic M, et al. Malnutrition inflammation score (MIS) is stronger predictor of mortality in hemodialysis patients than waist-to-hip ratio (WHR)-4-year follow-up. Int Urol Nephrol. 2022;54(3):695–700.https://doi.org/10.1007/s11255-021-02954-z 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4182028","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286662037,"identity":"5b836b01-42fd-41c2-a677-271397e6fc80","order_by":0,"name":"Linfang Zhu","email":"","orcid":"","institution":"Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Linfang","middleName":"","lastName":"Zhu","suffix":""},{"id":286662039,"identity":"748d5744-6bff-4f24-9c48-3df2e2ed7eda","order_by":1,"name":"Yang Liu","email":"","orcid":"","institution":"Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":286662041,"identity":"9b797297-7925-4123-bb14-297c4e6bd2cc","order_by":2,"name":"Fengxue Yang","email":"","orcid":"","institution":"Sichuan Nursing Vocational College","correspondingAuthor":false,"prefix":"","firstName":"Fengxue","middleName":"","lastName":"Yang","suffix":""},{"id":286662043,"identity":"46e01a12-dae5-4237-9048-8464648cc3c7","order_by":3,"name":"Jie Li","email":"","orcid":"","institution":"Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":286662046,"identity":"b3a0ef06-eace-46a1-b58f-fd70df5934f4","order_by":4,"name":"Huaihong Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJACZiDmMQCxPjAwJJCmhXEGKVoYQFqYeYjRYnAj+dnjwjY7GXP23sOvbdvs8uTbzxgw/NyBT0uaufHMtmQey55zada5bcnFBmdyDBh7z+DTkmAmzdvGzGNwI8fMOHfbgcQNEjwGzIxt+LSkfwNqqYdosQRqmT+DoJYckC2HQVqMHzMCtTTcIKBF8sybMmmec8d5DM6cMWPs/ZecuOFMWsHBXjxa+I6nb5PmKau2NzjeY/zhxxm7xPnthzc++IlHi8IBBJtNAsY6gKkQAeQbEGzmD/hUjoJRMApGwcgFAINaUdbxO+D+AAAAAElFTkSuQmCC","orcid":"","institution":"Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Huaihong","middleName":"","lastName":"Yuan","suffix":""},{"id":286662048,"identity":"96a52765-9393-49f6-9298-eff9436d99ca","order_by":5,"name":"Ping Fu","email":"","orcid":"","institution":"Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2024-03-28 11:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4182028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4182028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54280236,"identity":"b201203a-06ed-420d-80ca-f49578ec379a","added_by":"auto","created_at":"2024-04-08 08:46:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":607506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4182028/v1/ff74b4bc-5567-44d7-8662-a7b1d448be9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Medication Literacy and Polypharmacy in Sarcopenia Among Maintenance Hemodialysis Patients: A Cross-Sectional Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia, characterized by significant losses in skeletal muscle mass and function, is notably prevalent in maintenance hemodialysis patients (MHD), with reported rates at 28.5%, and a range between 25.9% and 34.6% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This rate significantly surpasses those observed in the general elderly Asian population, which range from 11.2\u0026ndash;18.3% [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Importantly, the prevalence of sarcopenia in Chinese community elderly ranged from 8.9\u0026ndash;38.8% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], highlighting the critical nature of sarcopenia within this specific patient group. Sarcopenia in these patients is closely associated with increased frailty, loss of independence, heightened risk of falls and fractures [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], diminished quality of life, higher hospitalization rates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and elevated mortality risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo manage the complexities of end-stage renal disease and associated cardiovascular and metabolic comorbidities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], patients on maintenance hemodialysis often receive a variety of medications, such as phosphate binders, antihypertensives, erythropoiesis-stimulating agents, and diuretics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the resultant polypharmacy can lead to an increased incidence of adverse drug reactions and complications, further complicating patient care.\u003c/p\u003e \u003cp\u003eRecently, The 9-year Kashiwa cohort study have underscored that polypharmacy, especially when combined with the use of potentially inappropriate medications (PIMs), is strongly linked to the onset of sarcopenia, with an adjusted hazard ratio of 2.35 (95% confidence interval, 1.58\u0026ndash;3.51) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This finding underscores the significant role that medication management might play in both the prevention and treatment of sarcopenia within this patient group. The heavy burden of chronic diseases and the frequent use of medications characteristic of MHD patients render them particularly vulnerable to the detrimental effects of drug side effects and interactions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplicating matters further is the widespread issue of limited medication literacy among hemodialysis patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This limited understanding can exacerbate the negative impacts of polypharmacy on muscle health, contributing to the risk of sarcopenia. Enhancing medication literacy could therefore be instrumental in enabling patients to navigate their medication regimens more effectively, minimizing the occurrence of adverse reactions and interactions, and potentially reducing the risk of sarcopenia. However, the impact of medication literacy and polypharmacy on sarcopenia\u0026rsquo;s risk in hemodialysis patients is not well-documented.\u003c/p\u003e \u003cp\u003eAdditionally, demographic factors like age, gender, and nutritional status have been consistently pinpointed as crucial determinants in the risk of sarcopenia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Older age and male gender are associated with a higher prevalence of sarcopenia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while malnutrition - a common issue in the hemodialysis population due to dietary restrictions, altered metabolism, and nutrient losses during dialysis - further exacerbates muscle wasting. The role of inflammation, driven by both chronic kidney disease and dialysis, in perpetuating muscle catabolism underscores the complex etiology of sarcopenia in this group [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting studies primarily explore the prevalence and outcomes of sarcopenia among MHD patients, often overlooking the influence of medication literacy and polypharmacy. This research contributes to the field by delving into the impact of these factors, as well as demographic and nutritional elements, on the risk of sarcopenia.\u003c/p\u003e \u003cp\u003eConsidering of above, this study aims to evaluate the influence of medication literacy and polypharmacy on sarcopenia prevalence among hemodialysis patients, with a particular emphasis on the roles of demographic and nutritional factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eA descriptive cross-sectional study design with analytical components was conducted for this research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSetting and participants\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted at the Wenjiang Hemodialysis Center in West China Hospital to examine sarcopenia in MHD patients. The inclusion criteria were: (1) Receiving MHD for at least 3 months, (2) Over 18 years old, (3) Providing voluntary informed consent. The exclusion criteria were: (1) Impaired consciousness, dementia or other mental illnesses, (2) Communication barriers, (3) Previous employment in medical or healthcare-related fields prior to retirement, (4) Contraindications for bioimpedance testing, (5) Presence of severe comorbidities, (6) Recent infections or bleeding episodes, (7) Diabetes-related amputations, (8) Severe gastrointestinal diseases. Patients were categorized into no sarcopenia, pre-sarcopenia, sarcopenia, and severe sarcopenia groups based on 2019 AWGS criteria. A total of 136 male patients (61 no sarcopenia, 63 pre-sarcopenia, 2 sarcopenic, and 10 with severe sarcopenia) and 100 female patients (26 no sarcopenia, 58 pre-sarcopenia, 5 sarcopenia, 11 severe sarcopenia) participated in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection and procedures\u003c/h2\u003e \u003cp\u003eData collection was conducted at the Wenjiang Hemodialysis Center in the Department of Nephrology, West China Hospital of Sichuan University in Chengdu, China. Data were collected from March 28, 2023, to May 31, 2023. Two researchers, Yang Liu and Linfang Zhu, completed the data collection through one-on-one face-to-face interviews with patients receiving hemodialysis sessions. After signing the informed consent form, participants completed the basic information and medication literacy questionnaire. Researchers then conducted the Malnutrition-Inflammation Score (MIS) assessment for each participant based on various parameters, including weight loss, dietary intake, gastrointestinal symptoms, functional capacity, comorbidities, physical examination for muscle and fat wasting, body mass index, and laboratory values. In illiterate patients, the consent form was read to them with a literate relative present, and they provided a fingerprint to indicate consent. If a participant\u0026rsquo;s dominant arm was in use for hemodialysis, the researchers read and assisted with the questionnaire. Once completed, the researchers immediately verified and collected the questionnaires. Relevant laboratory findings was obtained through the Hospital Laboratory Information System from the latest centralized examination at the hemodialysis center. The collected questionnaire responses, laboratory findings and MIS scores were compiled in an Excel spreadsheet.\u003c/p\u003e \u003cp\u003eIn the same data collection period, sarcopenia data were collected 20 minutes after the hemodialysis session. The same trained researchers used an InBodyS10 body composition analyzer to conduct bioelectrical impedance analysis on the participants. Information such as name, gender, age, height, and weight were entered into the system. Impedance measurements were taken in 5 minutes with the patient standing upright, arms slightly apart, and legs separated on electrode plates. Patients were required to fast and empty their bladder beforehand. Then, the participants completed the Handgrip Strength Test and the 6-meter Walk Test. Data on Body composition, handgrip strength and the 6-meter walk test results were copied into another Excel spreadsheet.\u003c/p\u003e \u003cp\u003eBefore any further analysis, the researchers (Linfang Zhu and Yang liu) systematically compiled all the collected patient information into a summaried Excel spreadsheet. This involved inputting details such as names, ages, and other relevant data to create a structured and organized dataset. Once this information was accurately recorded, it served as the foundation for subsequent data comparisons and analyses. After cross-referencing data such as names and ages and excluding missing data, it was found that 236 individuals had complete data. They had completed both the medication literacy questionnaire and muscle mass and strength measurements. Therefore, this study ultimately included 236 participants. This comprehensive dataset ensured the integrity of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurses\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMedication Literacy and Polypharmacy\u003c/h2\u003e \u003cp\u003eThe Medication Literacy Scale in Chinese was used to assess medication literacy level of patients on hemodialysis. This 14-item scale presents 4 simulated drug use scenarios. Each item is scored as 1 for a correct response or 0 for incorrect. The total score ranges from 0 to 14. A score of 11 or higher indicates an adequate level of medication literacy, indicating good medication understanding. A score between 4 and 10 is considered marginal, implying that the individual\u0026rsquo;s understanding of medication is limited. A score of 3 or lower is deemed inadequate, indicating that the individual has a poor understanding of medication [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Medication Literacy Scale in Chinese is a validated tool to measure medication literacy in MHD patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePolypharmacy is commonly defined as the concurrent use of five or more medications [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we defined\u0026thinsp;\u0026ge;\u0026thinsp;5 medications as polypharmacy. This practice may elevate the risk of drug interactions and adverse effects, particularly among patients undergoing hemodialysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSarcopenia\u003c/h2\u003e \u003cp\u003eAccording to the Asian criteria and cut-off thresholds established by the Asian Working Group on Sarcopenia (AWGS) in 2019 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], sarcopenia is diagnosed when low appendicular skeletal muscle mass coexists with either low muscle strength or physical function. Bioelectrical impedance analysis is used to assess low appendicular skeletal muscle mass, defined as \u0026lt;\u0026thinsp;7.0 kg/m\u0026sup2; for men and \u0026lt;\u0026thinsp;5.7 kg/m\u0026sup2; for women, measured using the InBodyS10 body composition analyzer (InBody Co., Ltd., Seoul, Republic of Korea). Low muscle strength, defined as \u0026lt;\u0026thinsp;28 kg for men and \u0026lt;\u0026thinsp;18 kg for women, is measured using the InBody Handgrip Strength Dynamometer (InBody-HGS). The standard for low physical fuction is a walking speed of \u0026lt;\u0026thinsp;1.0 m/s over a distance of 6 meters. These measurements are used to diagnose sarcopenia via AWGS 2019.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates included sex, age, education level, martial status, primary caregivers, monthly income, dialysis vintage, comorbidity, nutrional status using MIS scale.\u003c/p\u003e \u003cp\u003eKalantar-Zadeh et al [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] developed the MIS, which involves 7 components from the Subjective Global Assessment (SGA) and the 3 additional non-SGA components of body mass index (BMI; kg/m\u003csup\u003e2\u003c/sup\u003e), serum albumin, and total iron-binding capacity (TIBC). Each MIS component had 4 levels of severity from 0 (normal) to 3 (very severe); the sum of all 10 components ranged from 0 (normal) to 30 (severely malnourished). The cutoff score for malnutrition was defined as 6 in this study, because the screening tool should be able to identify most of the patients at nutritional risk [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. MIS has good consistency with the SGA [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The K/DOQI and Clinical Practice Guidelines for Nutritional Therapy in Chronic Kidney Disease in China recommend using MIS for nutritional assessment of MHD patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS version 25.0. Baseline characteristics are presented as counts (percentages) for all variables. Differences in baseline variables among groups with varying degrees of sarcopenia were assessed using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test for categorical variables, and the Kruskal-Wallis H test for continuous variables. Ordinal logistic regression analysis was employed to identify the factors influencing sarcopenia in MHD patients due to its suitability for ordinal outcome variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, a total of 297 questionnaires were distributed. After excluding 7 invalid ones, 290 valid questionnaires were collected. Out of these, 242 participants underwent muscle mass measurements using a body composition analyzer. Before any further steps, The researchers entered the collected patient information into an Excel spreadsheet. Upon cross-referencing data such as names and registration numbers and excluding missing items, it was found that 236 participants had complete data. They had filled out both the medication literacy questionnaire and participated in the muscle mass measurements. Therefore, this study ultimately included 236 participants.\u003c/p\u003e \u003cp\u003eThe baseline characteristics of patients undergoing hemodialysis were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the patients, 57.63% (n\u0026thinsp;=\u0026thinsp;136) were male and 42.37% (n\u0026thinsp;=\u0026thinsp;100) were female. The majority (80.08%) were under 65 years old. For education level, 20.76% had completed primary school or less, 34.75% had completed junior high school, 19.92% had completed senior high school, and 24.58% had completed university or higher. Most patients (83.47%) were married. For primary caregivers, 47.88% relied on self-care, 7.20% relied on parents, 38.56% relied on spouses, and 6.36% relied on children. For monthly family income in RMB, 37.29% earned\u0026thinsp;\u0026lt;\u0026thinsp;3000, 46.19% earned 3000\u0026ndash;8000, and 16.53% earned\u0026thinsp;\u0026gt;\u0026thinsp;8000. For hemodialysis vintage, 11.44% had undergone dialysis for \u0026lt;\u0026thinsp;1 year, 42.80% for 1\u0026ndash;5 years, and 45.76% for \u0026gt;\u0026thinsp;5 years. 58.47% took\u0026thinsp;\u0026lt;\u0026thinsp;5 medications and 41.53% took\u0026thinsp;\u0026ge;\u0026thinsp;5 medications. 75.00% had\u0026thinsp;\u0026lt;\u0026thinsp;5 comorbidities and 25.00% had\u0026thinsp;\u0026ge;\u0026thinsp;5 comorbidities. The MIS was used to assess nutritional status. Patients were categorized based on their MIS scores, with 47.88% having MIS\u0026thinsp;\u0026lt;\u0026thinsp;6 and 52.12% having MIS\u0026thinsp;\u0026ge;\u0026thinsp;6. Finally, 72.03% were categorized into the Limited literacy and Polypharmacy group, characterized by polypharmacy and limited medication literacy, while 27.97% fell into the Adequate literacy group, identified by non-polypharmacy or adequate medication literacy levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients undergoing hemodialysis (n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of patients (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMartial status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary caregivers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpouses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income (RMB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3000\u0026ndash;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDialysis vintage (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity count\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalnutrition-Inflammation Score (MIS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIS\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIS\u0026thinsp;\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication literacy group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited literacy \u0026amp; Polypharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The Medication Literacy Group was determined based on the presence of polypharmacy and assessed medication literacy levels.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD denotes standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdequate literacy is defined by the number of correct answers ranging between 11 to 14 and encompasses 27.97% of the patients. Marginal literacy, indicated by 4 to 10 correct answers, includes more than half of the participants at 52.97%. Inadequate literacy, with 0 to 3 correct answers, is observed in 19.07% of the patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedication literacy assessment results in patients undergoing hemodialysis (n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of correct answers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (27.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (52.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInadequate literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (19.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe number of participants with different characteristics at each medication literacy level was listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Sex, age, martial status, primary caregivers, MIS, polypharmacy and limited medication literacy group were all associated with sarcopenia (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, Education level, monthly income, primary illness, dialysis vintage, number of medications, comorbidity were not associated with sarcopenia (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of univariate analysis of sarcopenia determinants for patients undergoing hemodialysis (n\u0026thinsp;=\u0026thinsp;236)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.393\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.918\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.407\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMartial status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.534\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary caregivers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.367\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.413\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026thinsp;~\u0026thinsp;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDialysis vintage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.523\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.507\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.337\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMIS scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.843\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIS\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIS\u0026thinsp;\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication literacy group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.560\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited literacy \u0026amp; Polypharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: \u003csup\u003ea\u003c/sup\u003eThe Fisher value comes from the Fisher's exact test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eThe H value is derived from the Kruskal-Wallis H test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUtilizing ordinal logistic regression, we identified that being male (OR\u0026thinsp;=\u0026thinsp;0.557, 95% CI: 0.322 to 0.962, P\u0026thinsp;=\u0026thinsp;0.036), under 65 years old (OR\u0026thinsp;=\u0026thinsp;0.178, 95% CI: 0.082 to 0.389, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and possessing good nutrition (OR\u0026thinsp;=\u0026thinsp;0.544, 95% CI: 0.310 to 0.954, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) were associated with reduced odds of progressing to severe sarcopenia. In contrast, polypharmacy combined with limited medication literacy increased the risk of sarcopenia progression (OR\u0026thinsp;=\u0026thinsp;1.956, 95% CI: 1.094 to 3.496, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This group characterized by the concurrent challenges of managing five or more medications and having less than adequate medication literacy, demonstrated a pronounced vulnerability to sarcopenia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of logistic regression analysis of sarcopenia determinants for patients undergoing hemodialysis (n\u0026thinsp;=\u0026thinsp;290)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard Error (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003cp\u003efor OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;65 years\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: \u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: Male\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence:Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage: Married\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: widowed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage: Unmarried\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: widowed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarriage: Divorced\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: widowed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaregivers: Self care\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: Child care)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaregivers: Parental care\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: Child care)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCaregivers: Spousal care\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(refence: Child care)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGood Nutritional Status (MIS\u0026thinsp;\u0026lt;\u0026thinsp;6)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e (refence: MIS scores\u0026thinsp;\u0026ge;\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLimited literacy \u0026amp; Polypharmacy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e (reference: Adequate literacy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: The logistic regression model identifies factors associated with increased odds of sarcopenia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eA negative coefficient indicates a protective effect against sarcopenia, while a positive coefficient suggests increased risk.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePolypharmacy and Medication Literacy\u003c/h2\u003e \u003cp\u003eNotably, the combination of polypharmacy (defined as the use of five or more medications) and limited medication literacy (encompassing both \u0026ldquo;marginal\u0026rdquo; and \u0026ldquo;inadequate\u0026rdquo; levels) was identified as a significant risk factor for increased sarcopenia severity. This group represents individuals who are not only dealing with the complexities associated with managing multiple medications (polypharmacy) but also face challenges due to their limited understanding or knowledge about their medications (inadequate or marginal medication literacy). This finding highlights the compound challenge posed by complex medication regimens and insufficient medication knowledge, which may contribute to suboptimal management of sarcopenia in this vulnerable group [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Who might be at a higher risk of adverse outcomes due to the combined effect of polypharmacy and limited medication literacy [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, it\u0026rsquo;s crucial to manage polypharmacy effectively and improve medication literacy among these patients to mitigate these risks.\u003c/p\u003e \u003cp\u003eOur findings underscore the critical need for targeted interventions to improve medication literacy among hemodialysis patients, potentially mitigating the exacerbated risk of sarcopenia due to polypharmacy. The decision to examine the combined effect of polypharmacy and limited medication literacy on sarcopenia severity in MHD patients was driven by the real-world clinical context where these factors frequently coexist and may interact in complex ways. This approach was intended to capture the compounded risk that patients face when they have to manage multiple medications with potentially inadequate understanding or skills to do so effectively. Such a scenario is particularly relevant for MHD patients who often deal with complex medication regimens. The unexpected findings from this combined analysis, especially where the interaction did not fully align with our study methods, underscore the complexity of these relationships and highlight the need for further research. This nuanced understanding can inform targeted interventions and patient education strategies, ultimately aiming to reduce sarcopenia risk and improve patient outcomes. The innovative aspect of considering these combined factors lies in its potential to reveal intricate patterns of risk that might not be apparent when examining single factors in isolation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], thereby contributing novel insights to the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGender and Age Differences\u003c/h2\u003e \u003cp\u003eWe identified that males and those under 65 were less likely to have severe sarcopenia. In postmenopausal women, estrogen levels drop significantly, impacting bone and muscle metabolism. This decline in estrogen contributes to osteoporosis and sarcopenia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Men, lacking this hormonal shift, may have a lower risk of severe sarcopenia, highlighting the role of estrogen in muscle health. As individuals age, there are natural, biological changes that contribute to muscle loss. These include hormonal changes, reduced ability to synthesize proteins, decreased physical activity, and changes in neuromuscular function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These factors cumulatively contribute to the decline in muscle mass and strength, leading to sarcopenia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting studies have also confirmed an association between these factors and the risk of sarcopenia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For the elderly and high-risk groups, more targeted exercise programs and reasonable diets should be developed to delay skeletal muscle loss.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMalnutrition assessment\u003c/h2\u003e \u003cp\u003eGood nutrition has been shown to be protective against sarcopenia [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Hemodialysis patients often experience Protein-energy wasting (PEW), a state of disordered catabolism resulting from metabolic and nutritional derangements in chronic disease states [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This condition leads to muscle wasting, sarcopenia, and cachexia. Adequate intake of protein, vitamin D, antioxidant nutrients, and long-chain polyunsaturated fatty acids has been shown to be beneficial for improving sarcopenia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe MIS is a useful and widely employed measure of nutritional status for patients on maintenance dialysis. It has been shown to be a strong predictor of mortality in these patient [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. By identifying nutritional deficiencies through MIS, healthcare professionals can pinpoint patients at higher risk for sarcopenia, highlighting malnutrition as a significant risk factor for the severity of sarcopenia. This underscores the critical need for personalized nutritional interventions to mitigate sarcopenia\u0026rsquo;s progression in this vulnerable population.\u003c/p\u003e \u003cp\u003eThe strength of our study lies in its ability to elucidate the intricate correlations between sarcopenia severity and factors such as polypharmacy and limited medication literacy in MHD patients, employing ordinal logistic regression analysis. Although our methodology primarily identifies associations rather than causations, it significantly advances the understanding of how these elements interplay in the context of sarcopenia. By highlighting the complex relationships among demographic, nutritional status, and medication management factors, our research offers a nuanced perspective on the potential risks contributing to sarcopenia, providing a foundation for future studies aimed at exploring these dynamics more deeply. This contribution is particularly valuable in the realm of clinical practice and patient education, where understanding these associations can inform more tailored and effective interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths\u003c/h2\u003e \u003cp\u003eThis study examines how medication literacy and polypharmacy affect sarcopenia risk in MHD patients. The use of validated scales for measuring medication literacy, malnutrition, and sarcopenia - according to the 2019 AWGS criteria - further enhances the study\u0026rsquo;s credibility and contributes valuable insights to the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research\u003c/h2\u003e \u003cp\u003eOur study\u0026rsquo;s reliance on ordinal logistic regression analysis primarily identifies associations rather than causal relationships, limiting our ability to conclude definitively that polypharmacy and limited medication literacy cause increased sarcopenia severity in hemodialysis patients. The cross-sectional design further restricts our understanding of the temporal dynamics between these factors and sarcopenia progression.\u003c/p\u003e \u003cp\u003eFuture research should focus on longitudinal designs to observe sarcopenia and medication literacy over time, clarifying causal links and the evolution of these relationships. Additionally, incorporating qualitative approaches could enrich our understanding of how patients perceive and manage their medication, directly impacting their health outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings highlight the protective role of good nutrition and reveal that males and younger MHD patients are less prone to severe sarcopenia. In contrast, the combination of polypharmacy and limited medication literacy significantly elevates the risk, underscoring the need for targeted interventions in this area. Specifically, the study underscores the need for healthcare providers to adopt more personalized medication strategies, which not only consider the quantity of prescriptions but also the patient\u0026rsquo;s capacity to comprehend and manage their medication regimen effectively. This could potentially involve the introduction of educational programs focused on improving medication literacy, thereby empowering patients to take an active role in their treatment processes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the patients at the Wenjiang Hemodialysis Center, Department of Nephrology, West China Hospital of Sichuan University, Chengdu, China, for their invaluable participation in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L., and L.Z. conducted the data collection. L.Z., and Y.L. compiled all patient data. The study was conceptualized by F.Y., J.L., and L.Z., with the methodology developed by L.Z. Resources were provided by H.Y. and P.F. The initial draft of the manuscript was written by Y.L. and L.Z., and the review and editing were undertaken by H.Y. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained approval from the Biomedical Ethics Committee of Sichuan University (approval number: 2020[1002]). Written informed consent was obtained from all patients, and the informed consent form was approved by the Biomedical Ethics Committee of West China Hospital, Sichuan University. The study was conducted in compliance with the principles of the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShu X, Lin T, Wang H, Zhao Y, Jiang T, Peng X, et al. Diagnosis, prevalence, and mortality of sarcopenia in dialysis patients: a systematic review and meta-analysis. Journal of Cachexia, Sarcopenia and Muscle. 2022;13(1):145\u0026ndash;58.https://doi.org/10.1002/jcsm.12890\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruy\u0026egrave;re O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(4):601.https://doi.org/10.1093/ageing/afz046\u003c/li\u003e\n\u003cli\u003eChen X, Hou L, Zhang Y, Dong B. Analysis of the Prevalence of Sarcopenia and Its Risk Factors in the Elderly in the Chengdu Community. J Nutr Health Aging. 2021;25(5):600\u0026ndash;5.https://doi.org/10.1007/s12603-020-1559-1\u003c/li\u003e\n\u003cli\u003eXin C, Sun X, Lu L, Shan L. Prevalence of sarcopenia in older Chinese adults: a systematic review and meta-analysis. BMJ Open. 2021;11(8):e041879.https://doi.org/10.1136/bmjopen-2020-041879\u003c/li\u003e\n\u003cli\u003eChen Z, Li WY, Ho M, Chau PH. The Prevalence of Sarcopenia in Chinese Older Adults: Meta-Analysis and Meta-Regression. Nutrients. 2021;13(5):1441.https://doi.org/10.3390/nu13051441\u003c/li\u003e\n\u003cli\u003eLiu J, Ding Q, Zhou B, Liu X, Liu J, liu Y, et al. Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia(2021). Chinese Journal of Geriatrics. 2021;40(08):943\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eYeung SSY, Reijnierse EM, Pham VK, Trappenburg MC, Lim WK, Meskers CGM, et al. Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta‐analysis. J Cachexia Sarcopenia Muscle. 2019;10(3):485\u0026ndash;500.https://doi.org/10.1002/jcsm.12411\u003c/li\u003e\n\u003cli\u003eLee A, McArthur C, Ioannidis G, Duque G, Adachi JD, Griffith LE, et al. Associations between Osteosarcopenia and Falls, Fractures, and Frailty in Older Adults: Results From the Canadian Longitudinal Study on Aging (CLSA). J Am Med Dir Assoc. 2024;25(1):167-176.e6.https://doi.org/10.1016/j.jamda.2023.09.027\u003c/li\u003e\n\u003cli\u003eSousa AS, Guerra RS, Fonseca I, Pichel F, Ferreira S, Amaral TF. Financial impact of sarcopenia on hospitalization costs. Eur J Clin Nutr. 2016;70(9):1046\u0026ndash;51.https://doi.org/10.1038/ejcn.2016.73\u003c/li\u003e\n\u003cli\u003eKittiskulnam P, Chertow GM, Carrero JJ, Delgado C, Kaysen GA, Johansen KL. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017;92(1):238\u0026ndash;47.https://doi.org/10.1016/j.kint.2017.01.024\u003c/li\u003e\n\u003cli\u003eUsman MS, Khan MS, Butler J. The Interplay Between Diabetes, Cardiovascular Disease, and Kidney Disease. In: Chronic Kidney Disease and Type 2 Diabetes [Internet]. Arlington (VA): American Diabetes Association; 2021 [cited 2023 Feb 20]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK571718/\u003c/li\u003e\n\u003cli\u003eSchmid H, Schiffl H, Lederer SR. Pharmacotherapy of end-stage renal disease. Expert Opin Pharmacother. 2010;11(4):597\u0026ndash;613.https://doi.org/10.1517/14656560903544494\u003c/li\u003e\n\u003cli\u003eTanaka T, Akishita M, Kojima T, Son BK, Iijima K. Polypharmacy with potentially inappropriate medications as a risk factor of new onset sarcopenia among community-dwelling Japanese older adults: a 9-year Kashiwa cohort study. BMC Geriatr. 2023;23:390.https://doi.org/10.1186/s12877-023-04012-y\u003c/li\u003e\n\u003cli\u003eGilad L, Haviv YS, Cohen-Glickman I, Chinitz D, Cohen MJ. Chronic drug treatment among hemodialysis patients: a qualitative study of patients, nursing and medical staff attitudes and approaches. BMC Nephrol. 2020;21:239.https://doi.org/10.1186/s12882-020-01900-y\u003c/li\u003e\n\u003cli\u003eJang SM, Parker WM, Pai AB, Jiang R, Cardone KE. Assessment of literacy and numeracy skills related to medication labels in patients on chronic in-center hemodialysis. J Am Pharm Assoc (2003). 2020;60(6):957-962.e1.https://doi.org/10.1016/j.japh.2020.07.010\u003c/li\u003e\n\u003cli\u003eZhu L, Liu Y, Yang F, Yu S, Fu P, Yuan H. Prevalence, associated factors and clinical implications of medication literacy linked to frailty in hemodialysis patients in China: a cross-sectional study. BMC Nephrol. 2023;24:307.https://doi.org/10.1186/s12882-023-03346-4\u003c/li\u003e\n\u003cli\u003eNasimi N, Dabbaghmanesh MH, Sohrabi Z. Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults. Exp Gerontol. 2019;122:67\u0026ndash;73.https://doi.org/10.1016/j.exger.2019.04.009\u003c/li\u003e\n\u003cli\u003eAlatas H, Serin Y, Arslan N. Nutritional Status and Risk of Sarcopenia among Hospitalized Older Adults Residing in a Rural Region in Turkey. Ann Geriatr Med Res. 2023;27(4):293\u0026ndash;300.https://doi.org/10.4235/agmr.23.0064\u003c/li\u003e\n\u003cli\u003eHortegal EVF, Alves JJDA, Santos EJF, Nunes LCR, Galv\u0026atilde;o JC, Nunes RF, et al. Sarcopenia and inflammation in patients undergoing hemodialysis. Nutr Hosp. 2020;37(4):855\u0026ndash;62.https://doi.org/10.20960/nh.03068\u003c/li\u003e\n\u003cli\u003eZheng F, Zhong Z, Ding S, Luo A, Liu Z. [Modification and evaluation of assessment of medication literacy]. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2016;41(11):1226\u0026ndash;31.https://doi.org/10.11817/j.issn.1672-7347.2016.11.019\u003c/li\u003e\n\u003cli\u003eMasnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17:230.https://doi.org/10.1186/s12877-017-0621-2\u003c/li\u003e\n\u003cli\u003eKitamura M, Yamaguchi K, Ota Y, Notomi S, Komine M, Etoh R, et al. Prognostic impact of polypharmacy by drug essentiality in patients on hemodialysis. Sci Rep. 2021;11:24238.https://doi.org/10.1038/s41598-021-03772-0\u003c/li\u003e\n\u003cli\u003eChen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300-307.e2.https://doi.org/10.1016/j.jamda.2019.12.012\u003c/li\u003e\n\u003cli\u003eKalantar-Zadeh K, Kopple JD, Block G, Humphreys MH. A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis. 2001;38(6):1251\u0026ndash;63.https://doi.org/10.1053/ajkd.2001.29222\u003c/li\u003e\n\u003cli\u003eYamada K, Furuya R, Takita T, Maruyama Y, Yamaguchi Y, Ohkawa S, et al. Simplified nutritional screening tools for patients on maintenance hemodialysis. Am J Clin Nutr. 2008;87(1):106\u0026ndash;13.https://doi.org/10.1093/ajcn/87.1.106\u003c/li\u003e\n\u003cli\u003eSantin FG de O, Bigogno FG, Dias Rodrigues JC, Cuppari L, Avesani CM. Concurrent and Predictive Validity of Composite Methods to Assess Nutritional Status in Older Adults on Hemodialysis. J Ren Nutr. 2016;26(1):18\u0026ndash;25.https://doi.org/10.1053/j.jrn.2015.07.002\u003c/li\u003e\n\u003cli\u003eWright M, Southcott E, MacLaughlin H, Wineberg S. Clinical practice guideline on undernutrition in chronic kidney disease. BMC Nephrol. 2019;20:370.https://doi.org/10.1186/s12882-019-1530-8\u003c/li\u003e\n\u003cli\u003eChinese Medical Doctor Association Division of Nephrology, Expert Collaboration Group on Nutritional Therapy Guidelines of the Chinese Society of Integrated Traditional and Western Medicine Nephrology Committee. Clinical Practice Guidelines for Nutritional Therapy in Chronic Kidney Disease in China (2021 Edition). Chinese Medical Journal. 2021;101(08):539\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003ePana A, Sourtzi P, Kalokairinou A, Velonaki VS. Sarcopenia and polypharmacy among older adults: A scoping review of the literature. Arch Gerontol Geriatr. 2022;98:104520.https://doi.org/10.1016/j.archger.2021.104520\u003c/li\u003e\n\u003cli\u003eToida T, Toida R, Takahashi R, Uezono S, Komatsu H, Sato Y, et al. Impact of polypharmacy on all-cause mortality and hospitalization in incident hemodialysis patients: a cohort study. Clin Exp Nephrol. 2021;25(11):1215\u0026ndash;23.https://doi.org/10.1007/s10157-021-02094-9\u003c/li\u003e\n\u003cli\u003eOkpechi IG, Tinwala MM, Muneer S, Zaidi D, Ye F, Hamonic LN, et al. Prevalence of polypharmacy and associated adverse health outcomes in adult patients with chronic kidney disease: protocol for a systematic review and meta-analysis. Syst Rev. 2021;10:198.https://doi.org/10.1186/s13643-021-01752-z\u003c/li\u003e\n\u003cli\u003eLu L, Wang S, Chen J, Yang Y, Wang K, Zheng J, et al. Associated adverse health outcomes of polypharmacy and potentially inappropriate medications in community-dwelling older adults with diabetes. Front Pharmacol. 2023;14:1284287.https://doi.org/10.3389/fphar.2023.1284287\u003c/li\u003e\n\u003cli\u003eLyles A, Culver N, Ivester J, Potter T. Effects of health literacy and polypharmacy on medication adherence. Consult Pharm. 2013;28(12):793\u0026ndash;9.https://doi.org/10.4140/TCP.n.2013.793\u003c/li\u003e\n\u003cli\u003eLu L, Tian L. Postmenopausal osteoporosis coexisting with sarcopenia: the role and mechanisms of estrogen. J Endocrinol. 2023;259(1):e230116.https://doi.org/10.1530/JOE-23-0116\u003c/li\u003e\n\u003cli\u003eWilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev. 2018;47:123\u0026ndash;32.https://doi.org/10.1016/j.arr.2018.07.005\u003c/li\u003e\n\u003cli\u003eIsoyama N, Qureshi AR, Avesani CM, Lindholm B, B\u0026agrave;r\u0026agrave;ny P, Heimb\u0026uuml;rger O, et al. Comparative Associations of Muscle Mass and Muscle Strength with Mortality in Dialysis Patients. Clin J Am Soc Nephrol. 2014;9(10):1720\u0026ndash;8.https://doi.org/10.2215/CJN.10261013\u003c/li\u003e\n\u003cli\u003eWathanavasin W, Banjongjit A, Avihingsanon Y, Praditpornsilpa K, Tungsanga K, Eiam-Ong S, et al. Prevalence of Sarcopenia and Its Impact on Cardiovascular Events and Mortality among Dialysis Patients: A Systematic Review and Meta-Analysis. Nutrients. 2022;14(19):4077.https://doi.org/10.3390/nu14194077\u003c/li\u003e\n\u003cli\u003ePetermann-Rocha F, Balntzi V, Gray SR, Lara J, Ho FK, Pell JP, et al. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2022;13(1):86\u0026ndash;99.https://doi.org/10.1002/jcsm.12783\u003c/li\u003e\n\u003cli\u003eKurajoh M, Mori K, Miyabe M, Matsufuji S, Ichii M, Morioka T, et al. Nutritional Status Association With Sarcopenia in Patients Undergoing Maintenance Hemodialysis Assessed by Nutritional Risk Index. Front Nutr. 2022;9:896427.https://doi.org/10.3389/fnut.2022.896427\u003c/li\u003e\n\u003cli\u003eHanna RM, Ghobry L, Wassef O, Rhee CM, Kalantar-Zadeh K. A Practical Approach to Nutrition, Protein-Energy Wasting, Sarcopenia, and Cachexia in Patients with Chronic Kidney Disease. Blood Purif. 2020;49(1\u0026ndash;2):202\u0026ndash;11.https://doi.org/10.1159/000504240\u003c/li\u003e\n\u003cli\u003eLiu S, Zhang L, Li S. Advances in nutritional supplementation for sarcopenia management. Front Nutr. 2023;10:1189522.https://doi.org/10.3389/fnut.2023.1189522\u003c/li\u003e\n\u003cli\u003ePrelevic V, Antunovic T, Radunovic D, Gligorovic-Barhanovic N, Gledovic B, Ratkovic M, et al. Malnutrition inflammation score (MIS) is stronger predictor of mortality in hemodialysis patients than waist-to-hip ratio (WHR)-4-year follow-up. Int Urol Nephrol. 2022;54(3):695\u0026ndash;700.https://doi.org/10.1007/s11255-021-02954-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Sarcopenia, Hemodialysis, Polypharmacy, Medication Literacy, Malnutrition","lastPublishedDoi":"10.21203/rs.3.rs-4182028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4182028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSarcopenia is significantly prevalent among maintenance hemodialysis patients, with the contributing factors of medication literacy and polypharmacy receiving limited exploration in current research. This study aims to fill this gap by assessing the impact of these factors, along with demographic and malnurtition, on sarcopenia risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Conducted at the Wenjiang Hemodialysis Center in West China Hospital, this descriptive cross-sectional study involved 236 participants. Data collection included the Chinese Medication Literacy Scale, Malnutrition-Inflammation Score assessments, bioelectrical impedance analysis, and grip strength measurements, with sarcopenia diagnosed according to the 2019 AWGS criteria.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 236 participants. Of these, 87 (36.9%) had no sarcopenia, 121 (51.3%) were pre-sarcopenia, 7 (3.0%) were sarcopenia, and 21 (8.9%) had severe sarcopenia. Ordinal logistic regression analysis identified male gender (OR\u0026thinsp;=\u0026thinsp;0.557, 95% CI: 0.322 to 0.962, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), age below 65 (OR\u0026thinsp;=\u0026thinsp;0.178, 95% CI: 0.082 to 0.389, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and good nutritional status (OR\u0026thinsp;=\u0026thinsp;0.544, 95% CI: 0.310 to 0.954, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) as protective against severe sarcopenia. Conversely, the combination of polypharmacy and limited medication literacy (OR\u0026thinsp;=\u0026thinsp;1.956, 95% CI: 1.094 to 3.496, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) was significantly associated with an increased risk of sarcopenia progression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study highlights the protective role of good nutrition and the lesser susceptibility of males and younger individuals to severe sarcopenia. It underscores the necessity of targeted interventions to address the compounded risk presented by polypharmacy and limited medication literacy in patients undergoing hemodialysis.\u003c/p\u003e","manuscriptTitle":"The Role of Medication Literacy and Polypharmacy in Sarcopenia Among Maintenance Hemodialysis Patients: A Cross-Sectional Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 18:57:32","doi":"10.21203/rs.3.rs-4182028/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":"01b28d4b-4e5e-414b-b5f3-857354154c91","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T08:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 18:57:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4182028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4182028","identity":"rs-4182028","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}