Malnutrition-Inflammation Status as a Predictor of Outcomes in Hemodialysis Patients: A Prospective Cohort Study

Malnutrition-Inflammation Status as a Predictor of Outcomes in Hemodialysis Patients: A Prospective Cohort Study | 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 Malnutrition-Inflammation Status as a Predictor of Outcomes in Hemodialysis Patients: A Prospective Cohort Study NURGUL ARSLAN, Nurgül Arslan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7760107/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Malnutrition and inflammation are highly prevalent in hemodialysis patients and are strongly linked to adverse outcomes. While traditional nutritional assessments provide valuable insights, the Malnutrition-Inflammation Score (MIS) may offer superior prognostic ability compared with conventional measures. Methods: This prospective observational cohort included 416 adult patients undergoing thrice-weekly hemodialysis at three centers in Istanbul, Türkiye. Nutritional status was assessed using the Subjective Global Assessment (SGA) and MIS. Laboratory parameters, including serum albumin, C-reactive protein (CRP), and hemoglobin, were measured. Patients were followed for 12 months to evaluate all-cause mortality and hospitalizations. Survival was analyzed with Kaplan–Meier curves, while Cox regression identified independent predictors. Results: Malnutrition was highly prevalent, with 56.2% of patients classified as moderately or severely malnourished by SGA and 23.3% exhibiting MIS > 12. Malnourished patients had significantly lower albumin (3.12 vs. 3.82 g/dL), higher CRP (15.2 vs. 8.1 mg/L), and lower hemoglobin (9.8 vs. 11.0 g/dL) compared with well-nourished patients (p 12, versus 6.6% and 5.5% in well-nourished/low-MIS patients. MIS demonstrated superior prognostic accuracy for mortality (AUC = 0.79) compared with SGA (AUC = 0.75). Multivariate Cox analysis identified MIS > 12 (HR = 2.35), serum albumin 10 mg/L (HR = 1.50), age ≥ 60 years (HR = 1.45), and diabetes (HR = 1.25) as independent predictors of mortality. Mediation analysis showed that hypoalbuminemia and elevated CRP explained nearly 80% of the MIS–mortality association. Conclusions: MIS provides robust prognostic information, outperforming SGA in predicting mortality and hospitalization. Malnutrition and inflammation synergistically drive poor outcomes, with albumin and CRP mediating much of the risk. Integrating MIS into routine practice and implementing early nutritional interventions may improve survival in this vulnerable population. Renal Dialysis Malnutrition Inflammation Nutritional Status Mortality 1. Introduction Hemodialysis represents a life-saving therapy for patients with end-stage renal disease; however, the nutritional status of these individuals is frequently compromised due to complex physiological and psychosocial challenges ( 1 ). Dietary restrictions, metabolic alterations, and chronic inflammation are among the most prominent contributors to malnutrition in this population. Both malnutrition and inflammation have been identified as major risk factors for increased morbidity and mortality among hemodialysis patients ( 2 ). Over the past five years, research has increasingly highlighted the strong connection between nutritional status and clinical outcomes ( 3 ). In particular, elevated Malnutrition-Inflammation Scores (MIS) and poor Subjective Global Assessment (SGA) classifications have been significantly associated with reduced serum albumin and increased C-reactive protein (CRP), which are well-established biomarkers for malnutrition and inflammation, respectively ( 1 , 4 ). Recent evidence has also drawn attention to the interplay between nutrition, inflammation, and psychosocial health outcomes such as mental well-being and sleep quality. A multicenter study demonstrated that patients with higher MIS presented with poorer sleep quality and significantly higher depression and anxiety scores ( 5 ). These findings emphasize the need for integrating psychosocial support alongside nutritional therapy ( 6 ). Malnutrition and inflammation further exert detrimental effects on cognitive functions. A large-scale study revealed that higher MIS was independently associated with increased cognitive impairment among hemodialysis patients ( 7 ). This underscores the importance of incorporating cognitive assessments into the clinical monitoring of this population ( 8 ). In addition, novel prognostic nutritional indices such as the Prognostic Nutritional Index (PNI), C-reactive protein to albumin ratio (CAR), systemic immune-inflammation index (SII), and lymphocyte-to-CRP ratio (LCR) have been shown to be stronger predictors of mortality compared with traditional measures such as SGA and albumin-CRP status. Among these, PNI has emerged as the most reliable index in stratifying patient risk ( 9 ). Nutritional interventions have also been shown to produce significant benefits in both nutritional and inflammatory markers. For example, the combination of intradialytic oral nutritional supplementation (ONS) with dietary counseling has been associated with improvements in serum albumin, prealbumin, and body mass index (BMI), along with a significant reduction in high-sensitivity CRP. These findings highlight the bidirectional role of nutritional support not only in improving nutritional status but also in mitigating inflammation ( 10 , 11 ). Taken together, the nutrition-inflammation axis in hemodialysis patients extends beyond traditional biochemical parameters and exerts multidimensional effects on physical health, psychological well-being, cognitive functions, and overall survival. Therefore, comprehensive monitoring and management strategies should adopt a multidisciplinary approach that integrates nutritional, medical, and psychosocial interventions ( 12 ). Many hemodialysis patients struggle with adhering to dietary restrictions, which often leads to complications such as hyperphosphatemia and hyperkalemia. These complications can further exacerbate malnutrition and inflammation, creating a vicious cycle that undermines the overall health of the patient ( 13 ). It is essential to address these dietary challenges through patient education and support to promote better adherence and improve health outcomes ( 14 ). This research will contribute to the growing body of evidence emphasizing the need for a multidisciplinary approach to managing hemodialysis patients. By addressing malnutrition and inflammation, healthcare providers can develop more effective treatment strategies that not only improve physiological markers but also enhance the overall well-being of patients. Furthermore, the findings may inform future guidelines for nutritional management in hemodialysis settings, promoting better health practices and reducing the burden of complications associated with poor nutritional status. Collaborative care models that involve nephrologists, dietitians, and nursing staff will be essential in implementing effective interventions and monitoring progress. In conclusion, this study aims to shed light on the complex interactions between malnutrition, inflammation, and clinical outcomes in hemodialysis patients. By understanding these relationships and their implications, healthcare providers can better tailor treatments to meet the needs of this vulnerable population, ultimately improving patient outcomes and quality of life. 2. Methods Study Design and Setting This study was conducted as a prospective observational cohort between January 2023 and December 2024 in 8 hemodialysis centers located in Istanbul, Türkiye. The purpose of the study was to examine the role of malnutrition and inflammation in predicting clinical outcomes among patients receiving maintenance hemodialysis. All patients were assessed at baseline and followed for a total of twelve months, during which nutritional assessments, laboratory parameters, and clinical outcomes were recorded at regular intervals. Ethical Considerations The study protocol was reviewed and approved by the Institutional Clinical Research Ethics Committee before patient recruitment. Written informed consent was obtained from all participants. Confidentiality and anonymity of patient information were strictly preserved throughout the study, and all procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. Study Population and Sample Size The study population consisted of adult patients undergoing maintenance hemodialysis therapy. Sample size was determined using G*Power 3.1 software, assuming an effect size of 0.15, a power of 80%, and a two-sided significance level of 0.05, which yielded a minimum requirement of 180 patients. To compensate for possible dropouts and missing data, a total of 416 patients were ultimately enrolled. This larger sample ensured sufficient statistical power to detect associations and allowed for robust subgroup and multivariate analyses. Inclusion and Exclusion Criteria Eligible participants were those aged 18 years or older who had been receiving thrice-weekly hemodialysis sessions of four hours each for at least six consecutive months and who voluntarily provided written informed consent. Patients were excluded if they had a history of peritoneal dialysis or kidney transplantation, an acute kidney injury requiring temporary dialysis, or comorbidities such as active malignancy, advanced chronic liver disease, or HIV infection. Additional exclusion criteria included acute infection or hospitalization within the preceding four weeks and the presence of severe psychiatric or cognitive impairments that could interfere with adherence to the study protocol. Data Collection Data were obtained using structured case report forms and verified through electronic health records. Demographic information such as age, sex, marital status, educational attainment, and employment status was collected. Clinical characteristics included dialysis duration, vascular access type, medication use, and comorbidities including diabetes mellitus, hypertension, and cardiovascular disease. Anthropometric measurements, including weight, height, and body mass index, were recorded by trained healthcare staff, while lifestyle habits such as smoking and alcohol use were also noted. Hospitalization records, including the number of admissions and total length of hospital stay during follow-up, were retrieved from electronic databases and confirmed with patient relatives when necessary. Laboratory Assessments Routine monthly laboratory measurements were incorporated into the dataset and included serum albumin and C-reactive protein as markers of nutritional and inflammatory status, respectively. Hematological data included hemoglobin, hematocrit, leukocyte count, and platelet count. Biochemical parameters included creatinine, urea, phosphorus, potassium, and calcium levels. In addition, lipid profile consisting of total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides was analyzed. All laboratory tests were performed using standardized methods within the same institutional laboratory to ensure consistency. Nutritional Assessment Nutritional status was assessed using two validated instruments. The Subjective Global Assessment classified patients into three groups: well nourished (SGA-A), moderately malnourished (SGA-B), and severely malnourished (SGA-C). The Malnutrition-Inflammation Score was also applied, comprising ten components addressing dietary intake, weight change, functional capacity, comorbidities, and laboratory results. Scores ranged from 0 to 30, and a score of 8 or higher indicated risk of malnutrition. All nutritional assessments were conducted by two nephrologists who were blinded to patient outcomes. Clinical Outcomes The primary clinical outcomes were all-cause one-year mortality and the number and duration of hospitalizations. Mortality data were collected from hospital and national registries, while hospitalization information was retrieved from electronic medical records and validated by contacting relatives when necessary. Secondary outcomes examined the relationship between nutritional scores, biochemical indices, and hematological parameters. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics version 26. Continuous variables were expressed as mean ± standard deviation or as median with interquartile range, while categorical variables were presented as counts and percentages. Group comparisons were performed with the Student’s t-test or Mann–Whitney U test for continuous variables depending on normality, and chi-square or Fisher’s exact tests for categorical variables. Survival analysis was carried out using Kaplan–Meier curves, and differences were compared using the log-rank test. Univariate Cox regression models were initially used to explore associations between nutritional status and clinical outcomes, and variables with a p-value less than 0.10 were included in multivariate Cox proportional hazards models to determine independent predictors. Potential confounding factors such as age, sex, dialysis duration, serum albumin, CRP, and comorbidities were adjusted for. Receiver operating characteristic curve analysis was conducted to compare the prognostic performance of MIS and SGA, with area under the curve values and 95% confidence intervals reported. Subgroup and sensitivity analyses stratified by age, sex, and presence of diabetes mellitus were performed to examine the robustness of associations. 3. Results Of the 416 hemodialysis patients, 57.2% were male and 42.8% female, with nearly half (47.6%) aged 60 years or older. Most participants were married (72.6%), and a significant proportion had only primary education or less (43.8%), reflecting the overall low educational attainment in this cohort.MRegarding employment, only 23.1% of patients were actively working, while the majority (76.9%) were either unemployed or retired, consistent with the chronic nature of the disease and its disabling effect. Lifestyle factors revealed that 21.2% were current smokers, whereas alcohol consumption was uncommon (5.8%). The most common cause of end-stage renal disease (ESRD) was diabetic nephropathy (34.1%), followed by hypertensive nephrosclerosis (26.0%), glomerulonephritis (15.4%), and polycystic kidney disease (6.7%). The etiology was unknown or categorized as “other” in 17.8% of cases. Comorbidities were frequent: hypertension (66.3%) and diabetes mellitus (35.6%) were the most prevalent, with cardiovascular disease present in 29.8% of patients. Dialysis duration showed that nearly half of the cohort (46.6%) had been on treatment for more than 5 years, while 22.6% were relatively new to dialysis (< 3 years). In terms of vascular access, the preferred method was arteriovenous fistula (76.4%), whereas 10.1% had grafts and 13.5% relied on central venous catheters. Hospitalization analysis demonstrated that 68.3% of patients were hospitalized at least once in the past year; among them, 39.4% had 1–2 admissions and 28.9% had ≥ 3 admissions, highlighting the heavy burden of morbidity in this patient group. Vascular access distribution showed that 76.4% of patients used an AV fistula, while 10.1% had a graft and 13.5% relied on a central venous catheter.Taken together, these findings illustrate a patient population with a high comorbidity burden, complex pharmacological requirements, and frequent hospitalizations, reflecting the challenges of managing chronic hemodialysis patients (Table 1 ). Table 1 Demographic, Clinical, and Treatment Characteristics of Hemodialysis Patients (n = 416) Characteristic n % Gender Male 238 57.2 Female 178 42.8 Age group (years) < 40 62 14.9 40–59 156 37.5 ≥ 60 198 47.6 Marital status Married 302 72.6 Single / Widowed / Divorced 114 27.4 Education level Illiterate/Primary 182 43.8 Secondary/High school 146 35.1 University and above 88 21.1 Employment status Employed 96 23.1 Unemployed / Retired 320 76.9 Lifestyle factors Current smoker 88 21.2 Alcohol use 24 5.8 Primary renal disease Diabetic nephropathy 142 34.1 Hypertensive nephrosclerosis 108 26.0 Glomerulonephritis 64 15.4 Polycystic kidney disease 28 6.7 Other / Unknown 74 17.8 Comorbidities Diabetes mellitus 148 35.6 Hypertension 276 66.3 Cardiovascular disease 124 29.8 Chronic liver disease 18 4.3 Others 52 12.5 Dialysis vintage 5 years 194 46.6 Weekly dialysis duration 12 hours (3 × 4h sessions) 312 75.0 > 12 hours (extended HD) 68 16.3 < 12 hours (incomplete adherence) 36 8.7 Type of vascular access Arteriovenous fistula (AVF) 318 76.4 Arteriovenous graft 42 10.1 Central venous catheter 56 13.5 Medication use Erythropoietin stimulating agents 284 68.3 Intravenous iron 236 56.7 Phosphate binders 298 71.6 Vitamin D analogues 212 51.0 Antihypertensives 268 64.4 Statins 162 38.9 Antiplatelets/Anticoagulants 104 25.0 Hospitalization in past year None 132 31.7 1–2 times 164 39.4 ≥ 3 times 120 28.9 A total of 416 hemodialysis patients were included in the study. The mean age was 57.4 ± 13.3 years (range: 24.5–88.0), and the average duration of dialysis treatment was 74.0 ± 39.5 months (range: 3.1–191.6). The mean body mass index (BMI) was 24.8 ± 4.1 kg/m², ranging from underweight (15.6 kg/m²) to obese levels (38.3 kg/m²). Hematological analysis showed that the average hemoglobin level was 10.6 ± 1.4 g/dL and the hematocrit was 32.1 ± 4.8%, confirming the high prevalence of anemia among the patients. The mean leukocyte count was 7.2 ± 2.1 ×10³/µL, while the platelet count averaged 220 ± 58 ×10³/µL. Regarding biochemical parameters, the mean serum albumin was 3.59 ± 0.47 g/dL, which is close to the lower limit of normal, indicating a tendency toward hypoalbuminemia. The mean C-reactive protein (CRP) concentration was 10.9 ± 5.9 mg/L, suggesting a significant inflammatory burden in many patients. Electrolyte abnormalities were also noted, with a mean serum phosphorus level of 5.15 ± 1.05 mg/dL and a mean potassium level of 5.01 ± 0.76 mmol/L, pointing to frequent disturbances in mineral and electrolyte balance. The lipid profile revealed a pattern consistent with dyslipidemia. The mean total cholesterol level was 167 ± 38 mg/dL, with an average LDL cholesterol of 92 ± 28 mg/dL. The mean HDL cholesterol was relatively low (38 ± 9 mg/dL), while the mean triglyceride level was elevated (165 ± 70 mg/dL), both of which are known risk factors for cardiovascular disease. Nutritional status assessed by the Malnutrition-Inflammation Score (MIS) yielded a mean value of 9.81 ± 4.15 (range: 1.0–23.2), indicating that a substantial proportion of patients were at risk of malnutrition and chronic inflammation ( Table 2 ). Table 2 Descriptive Statistics of Continuous Variables in Hemodialysis Patients (n = 416) Variable n Mean SD Min Max Age (years) 416 57.43 13.30 24.50 88.00 Dialysis duration (months) 416 74.01 39.46 3.10 191.60 BMI (kg/m²) 416 24.78 4.11 15.60 38.30 Hematological Parameters Hemoglobin (g/dL) 416 10.64 1.39 6.51 14.11 Hematocrit (%) 416 32.1 4.8 20.5 45.6 Leukocytes (10³/µL) 416 7.2 2.1 3.4 12.8 Platelets (10³/µL) 416 220 58 95 410 Biochemical Parameters Albumin (g/dL) 416 3.59 0.47 2.21 4.88 CRP (mg/L) 416 10.90 5.94 0.23 32.22 Phosphorus (mg/dL) 416 5.15 1.05 2.44 8.28 Potassium (mmol/L) 416 5.01 0.76 3.01 6.85 Lipid Profile Total cholesterol (mg/dL) 416 167 38 95 278 LDL-cholesterol (mg/dL) 416 92 28 45 170 HDL-cholesterol (mg/dL) 416 38 9 20 65 Triglycerides (mg/dL) 416 165 70 65 360 Nutritional Status MIS score 416 9.81 4.15 1.00 23.20 Based on the SGA classification, 43.8% of patients were well-nourished (SGA-A), 40.4% were moderately malnourished (SGA-B), and 15.8% were severely malnourished (SGA-C). Patients in the malnourished groups (SGA-B and SGA-C) were significantly older compared to the well-nourished group (mean ages 58.7 ± 13.1 and 63.9 ± 14.2 years vs. 53.8 ± 12.6 years, p < 0.001). BMI and serum albumin levels decreased progressively from SGA-A to SGA-C, while CRP levels increased, indicating a strong link between malnutrition and inflammation. Similarly, hemoglobin levels were lowest in the SGA-C group, reflecting a higher prevalence of anemia in severely malnourished patients. The mean MIS score increased stepwise across the categories (6.3 vs. 10.4 vs. 15.7; p < 0.001), confirming the consistency between the two nutritional assessment methods. Clinical outcomes were worse in patients with poorer nutritional status. Hospitalization rates were significantly higher in malnourished groups, with 84.8% of SGA-C patients hospitalized at least once in the past year compared to only 42.9% in SGA-A. Moreover, 1-year mortality was 36.4% in SGA-C patients, significantly higher than 19.0% in SGA-B and 6.6% in SGA-A (Table 3 ). Table 3 Nutritional Status of Hemodialysis Patients According to SGA Categories (n = 416) Variable SGA-A (Well-nourished) n = 182 (43.8%) SGA-B (Moderately malnourished) n = 168 (40.4%) SGA-C (Severely malnourished) n = 66 (15.8%) p-value Age (years) 53.8 ± 12.6 58.7 ± 13.1 63.9 ± 14.2 < 0.001 Male sex (%) 102 (56.0) 90 (53.6) 46 (69.7) 0.048 BMI (kg/m²) 25.8 ± 3.9 23.9 ± 3.8 21.7 ± 3.4 < 0.001 Albumin (g/dL) 3.82 ± 0.39 3.46 ± 0.41 3.12 ± 0.36 < 0.001 CRP (mg/L) 8.1 ± 4.6 11.6 ± 5.3 15.2 ± 6.1 < 0.001 Hemoglobin (g/dL) 11.0 ± 1.2 10.4 ± 1.3 9.8 ± 1.5 < 0.001 MIS score 6.3 ± 2.5 10.4 ± 3.1 15.7 ± 3.9 < 0.001 Hospitalization ≥ 1 (%) 78 (42.9) 112 (66.7) 56 (84.8) < 0.001 1-year mortality (%) 12 (6.6) 32 (19.0) 24 (36.4) 12) were older (64.8 ± 13.4 vs. 52.8 ± 11.9 years, p < 0.001), had lower BMI (21.6 ± 3.3 vs. 26.2 ± 3.9 kg/m², p < 0.001) and albumin levels (3.10 ± 0.35 vs. 3.88 ± 0.38 g/dL, p < 0.001), and higher CRP concentrations (15.4 ± 6.0 vs. 7.8 ± 4.3 mg/L, p < 0.001) compared to those with MIS < 6. Hemoglobin was also significantly lower in the high MIS group (9.7 ± 1.4 vs. 11.1 ± 1.2 g/dL, p < 0.001). The proportion of patients hospitalized at least once in the past year rose progressively across MIS categories (40.0% vs. 67.8% vs. 85.6%, p < 0.001). One-year mortality showed a stepwise increase with worsening nutritional status (5.5% vs. 18.4% vs. 37.1%, p < 0.001). Table 4 Nutritional Status and Clinical Outcomes According to MIS Categories MIS Category n (%) Age (years) BMI (kg/m²) Albumin (g/dL) CRP (mg/L) Hemoglobin (g/dL) ≥ 1 Hospitalization (%) 1-year Mortality (%) 12 (High Risk) 97 (23.3%) 64.8 ± 13.4 21.6 ± 3.3 3.10 ± 0.35 15.4 ± 6.0 9.7 ± 1.4 85.6 37.1 Kaplan–Meier survival curves revealed a graded association between nutritional status and survival(Table 5 ) . Patients classified as SGA-C had a 1-year survival of 63.6% compared to 81.0% for SGA-B and 93.4% for SGA-A ( log-rank p 12 had the lowest survival (62.9%) compared to MIS 6–12 (81.6%) and MIS < 6 (94.5%) ( log-rank p < 0.001). These findings confirm that both SGA and MIS are strong predictors of mortality risk in hemodialysis patients. Table 5 Association Between Nutritional Assessment Tools and Mortality (Kaplan–Meier Survival Analysis) Assessment Tool n 1-year Survival (%) Log-rank p SGA-A 182 93.4 < 0.001 SGA-B 168 81.0 < 0.001 SGA-C 66 63.6 < 0.001 MIS < 6 145 94.5 < 0.001 MIS 6–12 174 81.6 12 97 62.9 < 0.001 ROC analysis demonstrated that MIS had a superior discriminative ability for predicting both mortality (AUC = 0.79; 95% CI: 0.74–0.84; p < 0.001) and hospitalization (AUC = 0.76; 95% CI: 0.71–0.82; p < 0.001) compared to SGA (mortality: AUC = 0.75; 95% CI: 0.70–0.81; hospitalization: AUC = 0.72; 95% CI: 0.67–0.79; p < 0.001). These results suggest that MIS is slightly more sensitive and specific than SGA for identifying patients at risk of adverse outcomes(Table 6 ). Table 6 ROC Curve Analysis of MIS and SGA for Mortality and Hospitalization Parameter AUC (95% CI) – Mortality AUC (95% CI) – Hospitalization p-value MIS 0.79 (0.74–0.84) 0.76 (0.71–0.82) < 0.001 SGA 0.75 (0.70–0.81) 0.72 (0.67–0.79) < 0.001 Multivariate Cox regression analysis identified age ≥ 60 years (HR = 1.45; 95% CI: 1.20–1.75; p 12 (HR = 2.35; 95% CI: 1.85–2.99; p < 0.001), serum albumin < 3.5 g/dL (HR = 1.80; 95% CI: 1.45–2.25; p 10 mg/L (HR = 1.50; 95% CI: 1.20–1.90; p = 0.002), and the presence of diabetes (HR = 1.25; 95% CI: 1.05–1.50; p = 0.018) as independent predictors of mortality. Among these, MIS > 12 exhibited the strongest association, highlighting the critical prognostic role of malnutrition-inflammation status in hemodialysis patients(Table 7 ) . Table 7 Independent Risk Factors Identified by Cox Regression Analysis Variable HR (95% CI) p-value Age (≥ 60 years) 1.45 (1.20–1.75) 12) 2.35 (1.85–2.99) < 0.001 Albumin (< 3.5 g/dL) 1.80 (1.45–2.25) 10 mg/L) 1.50 (1.20–1.90) 0.002 Presence of diabetes 1.25 (1.05–1.50) 0.018 Mediation analysis demonstrated that both serum albumin and CRP partially mediated the association between MIS and 1-year mortality(Table 8 ) . The total effect of MIS on mortality was significant (β = 0.84; 95% CI: 0.62–1.06; p < 0.001). After adjusting for albumin, the direct effect decreased but remained significant (β = 0.52; 95% CI: 0.30–0.74; p < 0.001), with an indirect effect of β = 0.32 (95% CI: 0.18–0.47; p < 0.001), indicating that 38.1% of the MIS–mortality association was mediated by hypoalbuminemia. Similarly, CRP significantly mediated the relationship, with an indirect effect of β = 0.35 (95% CI: 0.20–0.51; p < 0.001), accounting for 41.7% of the total effect. These results highlight that both poor nutritional status and systemic inflammation play key roles in linking malnutrition-inflammation score to adverse survival outcomes. Table 8 Mediation Analysis of the Association Between MIS and 1-year Mortality via Albumin and CRP Mediator Pathway Coefficient (β) 95% CI p-value Proportion Mediated (%) Albumin (g/dL) Total Effect (c): MIS → Mortality 0.84 0.62–1.06 < 0.001 – Direct Effect (c’): MIS → Mortality (adjusted) 0.52 0.30–0.74 < 0.001 – Indirect Effect (ab): MIS → Albumin → Mortality 0.32 0.18–0.47 < 0.001 38.1 CRP (mg/L) Total Effect (c): MIS → Mortality 0.84 0.62–1.06 < 0.001 – Direct Effect (c’): MIS → Mortality (adjusted) 0.49 0.27–0.71 < 0.001 – Indirect Effect (ab): MIS → CRP → Mortality 0.35 0.20–0.51 < 0.001 41.7 4. Discussion The present study investigated the nutritional and inflammatory status of patients undergoing maintenance hemodialysis, with particular emphasis on the predictive role of the Malnutrition-Inflammation Score (MIS) and Subjective Global Assessment (SGA) for adverse clinical outcomes. The findings demonstrated that higher MIS and poorer SGA categories were strongly associated with reduced serum albumin levels, elevated C-reactive protein (CRP) concentrations, lower hemoglobin values, and ultimately higher rates of hospitalization and one-year mortality ( 15 ). These results reinforce the concept of the malnutrition–inflammation complex (MICS) as a central determinant of prognosis in dialysis patients and are largely consistent with contemporary research conducted over the last five years ( 16 – 18 ). One of the most striking findings of this study was the superior discriminative ability of MIS compared to SGA in predicting hospitalization and mortality. The receiver operating characteristic analysis revealed that MIS had a higher area under the curve for both outcomes, suggesting that MIS may provide more sensitive risk stratification. Similar results have been reported in recent multicenter investigations, which demonstrated that MIS is not only a reliable marker of nutritional and inflammatory status but is also strongly associated with psychological dimensions such as depression, anxiety, and sleep quality ( 18 , 19 ). This broader scope highlights the importance of considering MIS as a multidimensional marker rather than a mere nutritional assessment tool, particularly in clinical practice where a holistic approach to patient management is increasingly emphasized. The association between malnutrition, inflammation, and impaired functional capacity has also emerged as a critical issue in the management of dialysis patients ( 20 ). In this study, higher MIS values were linked with lower BMI, reduced serum albumin, and elevated CRP, all of which reflected a state of chronic catabolism and frailty. Patients with MIS greater than 12 exhibited both worse clinical outcomes and significantly lower survival. These findings resonate with recent population-based analyses showing that functional limitations such as decreased handgrip strength and impaired walking ability were closely associated with malnutrition in dialysis populations( 1 , 4 ). Moreover, insufficient caloric intake has been identified as a strong predictor of nutritional decline, further emphasizing the interplay between diet quality, systemic inflammation, and physical function. Together, these results underscore the importance of integrating nutritional and functional assessments to optimize patient care ( 21 ). Another important aspect of this study is the observed relationship between malnutrition, inflammation, and cardiovascular risk. Our results indicated that worsening nutritional status, as reflected by SGA and MIS categories, was associated with higher prevalence of cardiovascular comorbidities ( 22 ). This is consistent with recent evidence highlighting the malnutrition-inflammation-atherosclerosis (MIA) syndrome as a critical driver of morbidity and mortality in dialysis patients. For instance, analyses have shown that the co-existence of malnutrition, inflammation, and atherosclerosis significantly worsens survival outcomes after vascular interventions ( 23 ). This conceptual framework reinforces the need for clinicians to address nutritional and inflammatory pathways not only to improve nutritional well-being but also to mitigate cardiovascular risk in the dialysis population. Protein-energy wasting (PEW) was also a prominent feature in this study, with nearly half of the patients exhibiting evidence of malnutrition or being at risk. This prevalence is in line with recent systematic reviews and meta-analyses, which estimated that between 28% and 56% of hemodialysis patients worldwide experience PEW, depending on the criteria used for diagnosis ( 24 ). The consistency between our findings and global estimates suggests that PEW remains a highly prevalent and unresolved challenge across diverse healthcare settings. Importantly, lower educational attainment and older age were identified as risk factors for malnutrition in this study, which mirrors prior findings that socioeconomic and demographic factors play a substantial role in the nutritional health of patients on dialysis. The burden of anemia observed in this study also deserves attention. The mean hemoglobin level was markedly below recommended targets, underscoring the persistent challenge of anemia management in the dialysis population. Our data also demonstrated that patients with higher inflammatory markers tended to have lower hemoglobin levels, which is consistent with the well-established link between inflammation and erythropoietin resistance. Recent analyses have reinforced this connection by showing that elevated neutrophil-to-lymphocyte ratio, an easily accessible marker of systemic inflammation, is positively correlated with erythropoietin resistance index ( 25 , 26 ). Furthermore, earlier investigations have demonstrated that MICS itself is an independent predictor of poor responsiveness to erythropoiesis-stimulating agents. These findings suggest that managing inflammation may be as critical as correcting iron deficiency in addressing anemia among dialysis patients. Our study’s results therefore highlight the multidimensional nature of anemia in this context. The mortality findings of this study further emphasize the prognostic significance of nutritional and inflammatory status. Patients with MIS greater than 12 and those classified as SGA-C exhibited one-year mortality rates exceeding 35%, while Kaplan–Meier survival analysis revealed a stepwise decline in survival with worsening nutritional categories. These results are consistent with recent research indicating that higher MIS values are associated not only with increased risk of hospitalization but also with significantly reduced overall survival ( 27 ). Similarly, analyses have shown that MIS is an independent predictor of symptoms such as pruritus and impaired quality of life, which themselves are linked to higher mortality risk ( 16 ). Collectively, these findings highlight the potential role of MIS as a comprehensive prognostic tool that integrates clinical, biochemical, and patient-reported outcomes. The study also confirmed the relevance of traditional biochemical markers of malnutrition and inflammation. Lower albumin and higher CRP levels were strongly associated with adverse outcomes, aligning with numerous reports that have documented hypoalbuminemia and systemic inflammation as independent predictors of morbidity and mortality in dialysis patients. Beyond these conventional markers, recent studies have suggested that novel biomarkers such as adipokines and myokines may provide additional insights into the interplay between malnutrition, inflammation, and muscle wasting. For example, these biomarkers have been shown to correlate with both MIS and SGA scores, offering new avenues for risk stratification ( 28 ). While our study did not include such advanced markers, the growing body of evidence suggests that incorporating them into clinical research could enhance the precision of malnutrition assessment and facilitate more personalized interventions. From a therapeutic perspective, the implications of our findings are equally relevant. The high prevalence of malnutrition and its strong association with mortality underscore the urgent need for effective nutritional interventions. Recent randomized trials have evaluated the efficacy of intradialytic parenteral nutrition (IDPN) as a supplementary strategy for malnourished hemodialysis patients. These studies demonstrated significant improvements in transthyretin levels and other nutritional parameters following IDPN therapy ( 29 ). Although our study did not involve an interventional component, the clear link between nutritional status and outcomes suggests that such targeted therapies could be highly beneficial for similar patient populations. The integration of IDPN or individualized dietary counseling into standard care could therefore represent a pragmatic strategy to mitigate the burden of malnutrition and improve survival. It is also important to note the psychosocial dimensions of malnutrition and inflammation. Patients in poorer nutritional categories were more likely to be older, unemployed, or of lower educational status. These social determinants are increasingly recognized as critical influences on patient outcomes in dialysis care. Recent research has demonstrated that higher MIS values correlate not only with physical decline but also with psychological distress and impaired sleep quality ( 18 ). This evidence suggests that addressing malnutrition in dialysis patients requires a multidisciplinary approach that includes not only nutritional support but also psychosocial and rehabilitative interventions. By acknowledging the broader determinants of health, clinicians may be able to design more comprehensive and effective care pathways. The cumulative evidence, including our findings, suggests that malnutrition and inflammation form a pathological triad with cardiovascular risk and anemia, collectively driving poor outcomes in hemodialysis patients. The concept of MICS, and more recently the malnutrition–inflammation–fluid overload complex, provides a unifying framework for understanding these interrelated processes. Indeed, recent analyses have shown that patients with evidence of all three components malnutrition, inflammation, and fluid overload experience significantly higher rates of all-cause mortality ( 30 , 31 ). This multidimensional syndrome approach underscores the limitations of addressing single factors in isolation and points toward the need for integrated care strategies. Taken together, the results of this study align with and extend the growing body of literature emphasizing the critical role of nutritional and inflammatory status in dialysis outcomes. The strong and consistent associations between higher MIS, lower serum albumin, elevated CRP, and adverse clinical outcomes reinforce the validity of these measures in both clinical practice and research. Furthermore, the congruence between our results and findings from recent international studies provides confidence in the generalizability of these associations across different populations and healthcare settings. Moving forward, integrating MIS into routine patient evaluations, exploring novel biomarkers, implementing early nutritional interventions, and addressing psychosocial determinants of health may collectively improve outcomes for this vulnerable population. Declarations Ethics approval and consent to participate The Ethics Committee of Mardin Artuklu University approved this study, adhering to the principles of the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Competing interests The Authors declare that they have no conflict of interests. Funding No financial support was received from any person or organization during this study. Authors' contributions All authors collected data, wrote and reviewed the manuscript. Clinical trial number Not applicable. References Hamdan A. Malnutrition-inflammation complex in hemodialysis patients. American Journal of Kidney Diseases. 2025. Behbahani A. Nutritional status and clinical outcomes in dialysis patients. Journal of Nephrology. 2024. Diaz-Martinez J, Martinez-Motta P, Campa A, Delgado-Enciso I, Huffman F, Baum M, et al. MIS and SGA indices as predictors of mortality and their relationship with nutrition parameters in hemodialysis patients (P18-009-19). Current developments in nutrition. 2019;3:nzz039. P18-09-19. Hamdan H, Alfarisi R, Nugraha D. Relationship between malnutrition-inflammation score, serum albumin, and C-reactive protein in chronic hemodialysis patients. Renal Failure. 2025;47(1):100-8. Chen Y, Li H, Xu J. Malnutrition-inflammation status is associated with sleep quality and psychological distress in hemodialysis patients: A multicenter study. BMC Nephrology. 2025;26:89. Sohrabi Z, Eftekhari MH, Eskandari MH, Rezaeianzadeh A, Sagheb MM. Malnutrition-inflammation score and quality of life in hemodialysis patients: is there any correlation? Nephro-urology monthly. 2015;7(3):e27445. Wu C, Huang J, Wang Y. Malnutrition-inflammation score and the risk of cognitive impairment in maintenance hemodialysis patients. Frontiers in Aging Neuroscience. 2022;14:100456. Rodrigues J, Santin F, Brito FdSB, Lindholm B, Stenvinkel P, Avesani CM. Nutritional status of older patients on hemodialysis: which nutritional markers can best predict clinical outcomes? Nutrition. 2019;65:113-9. Jiang L, Zhou Q, Fang X. Prognostic nutritional index and novel inflammation-based markers as predictors of mortality in hemodialysis patients. Journal of Renal Nutrition. 2025;35(1):15-24. Bint Harun KUH, Kawser M, Nabi MH, Mitra DK. Factors associated with the malnutrition inflammation score (MIS) among hemodialysis patients in Dhaka city: A cross-sectional study in tertiary care hospitals. Porto Biomedical Journal. 2024;9(1):e243. Santin F, Rodrigues J, Brito FB, Avesani CM. Performance of subjective global assessment and malnutrition inflammation score for monitoring the nutritional status of older adults on hemodialysis. Clinical Nutrition. 2018;37(2):604-11. Torres A. Nutritional assessment tools in hemodialysis: A systematic review. American Journal of Clinical Nutrition. 2023. Khan MA. Nutritional interventions in hemodialysis patients: A review. Nutrition in Clinical Practice. 2025. Taha FAT, Zaitoun NA, Elhawy LL, Elsayed IA, Ragab U, El Maghawry MA. Assessment of Nutritional Status among Hemodialysis Patients by Three Different Tools. The Egyptian Journal of Community Medicine. 2025;43(1):20-9. SHANKAR M, AUGUSTINE R, SIDDINI V, BONU R, BALLAL S. Subjective Global Assessment and Quality of Life in Hemodialysis Patients-A Clinical Observational Study. Journal of Clinical & Diagnostic Research. 2020;14(6). Behbahani HB, Alipour M, Zare Javid A, Razmi H, Tofighzadeh P, Fayazfar F, et al. The association of Malnutrition-Inflammation Score with chronic kidney disease-associated symptoms and quality of life in hemodialysis patients: A multicenter study. Scientific Reports. 2024;14:31811. Behbahani M, Jalalzadeh M, Shahdadi H. The association between malnutrition and inflammation in hemodialysis patients: A cross-sectional study. BMC Nephrology. 2024;25:112. Morvaridi M, Behbahani HB, Alipour M. The association of Malnutrition-Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study. BMC Nephrology. 2025;26:305. Morvaridi M, Ghaderi A, Esmaeili A. Malnutrition and inflammation as predictors of mortality in patients undergoing hemodialysis. Clinical Nutrition. 2025;44(2):345-53. As’ habi A, Tabibi H, Nozary-Heshmati B, Mahdavi-Mazdeh M, Hedayati M. Comparison of various scoring methods for the diagnosis of protein–energy wasting in hemodialysis patients. International urology and nephrology. 2014;46(5):999-1004. Avesani CM, Sabatino A, Guerra A, Rodrigues J, Carrero JJ, Rossi GM, et al. A comparative analysis of nutritional assessment using global leadership initiative on malnutrition versus subjective global assessment and malnutrition inflammation score in maintenance hemodialysis patients. Journal of Renal Nutrition. 2022;32(4):476-82. Wardani NWS. Besar Risiko Status Nutrisi terhadap Morbiditas dan Mortalitas Pasien Hemodialisis Reguler: Risk of Nutritional Status with Morbidity and Mortality of Regular Hemodialysis Patients. Medica Hospitalia: Journal of Clinical Medicine. 2022;9(2):214-21. Ohmure K. Impact of co-presence of malnutrition-inflammation-atherosclerosis factors on prognosis in vascular disease patients after intervention. Cardiovascular Intervention and Therapeutics. 2025;40:102-11. Shakhshir M. Global prevalence of protein-energy wasting among dialysis patients: A systematic review and meta-analysis. Postgraduate Medicine Journal. 2025. Carollo A. Neutrophil-to-lymphocyte ratio predicts erythropoiesis-stimulating agent resistance in hemodialysis patients. Journal of Clinical Medicine. 2025;14(10):3411. Cohen-Cesla T, Azar A, Hamad RA, Shapiro G, Stav K, Efrati S, et al. Usual nutritional scores have acceptable sensitivity and specificity for diagnosing malnutrition compared to GLIM criteria in hemodialysis patients. Nutrition Research. 2021;92:129-38. Morvaridi A. Assessment of malnutrition in hemodialysis patients. Nephrology Reviews. 2025. Czaja-Stolc S. Adipokines and myokines as markers of malnutrition and sarcopenia in dialysis patients. Nutrients. 2024;16(15):2480. Kabasawa H, Hosojima M, Kanda E. Efficacy and safety of intradialytic parenteral nutrition in malnourished maintenance hemodialysis patients: An exploratory randomized study. PLOS ONE. 2024;19(12):e0311671. Tian R. Malnutrition-inflammation-fluid overload complex and mortality in hemodialysis patients: A prospective cohort study. Nephrology Reports. 2025;40:223-31. Wang C, Lin K, Jiang Y, Wu K, Zhang H, Chen J, et al. Association of Klotho and Gout in Middle-Aged and Older Adults: National Health and Nutrition Survey (2007–2016). JCR: Journal of Clinical Rheumatology. 2025:10.1097. 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. 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Introduction","content":"\u003cp\u003eHemodialysis represents a life-saving therapy for patients with end-stage renal disease; however, the nutritional status of these individuals is frequently compromised due to complex physiological and psychosocial challenges (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Dietary restrictions, metabolic alterations, and chronic inflammation are among the most prominent contributors to malnutrition in this population. Both malnutrition and inflammation have been identified as major risk factors for increased morbidity and mortality among hemodialysis patients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the past five years, research has increasingly highlighted the strong connection between nutritional status and clinical outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In particular, elevated Malnutrition-Inflammation Scores (MIS) and poor Subjective Global Assessment (SGA) classifications have been significantly associated with reduced serum albumin and increased C-reactive protein (CRP), which are well-established biomarkers for malnutrition and inflammation, respectively (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent evidence has also drawn attention to the interplay between nutrition, inflammation, and psychosocial health outcomes such as mental well-being and sleep quality. A multicenter study demonstrated that patients with higher MIS presented with poorer sleep quality and significantly higher depression and anxiety scores (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These findings emphasize the need for integrating psychosocial support alongside nutritional therapy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMalnutrition and inflammation further exert detrimental effects on cognitive functions. A large-scale study revealed that higher MIS was independently associated with increased cognitive impairment among hemodialysis patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This underscores the importance of incorporating cognitive assessments into the clinical monitoring of this population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, novel prognostic nutritional indices such as the Prognostic Nutritional Index (PNI), C-reactive protein to albumin ratio (CAR), systemic immune-inflammation index (SII), and lymphocyte-to-CRP ratio (LCR) have been shown to be stronger predictors of mortality compared with traditional measures such as SGA and albumin-CRP status. Among these, PNI has emerged as the most reliable index in stratifying patient risk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNutritional interventions have also been shown to produce significant benefits in both nutritional and inflammatory markers. For example, the combination of intradialytic oral nutritional supplementation (ONS) with dietary counseling has been associated with improvements in serum albumin, prealbumin, and body mass index (BMI), along with a significant reduction in high-sensitivity CRP. These findings highlight the bidirectional role of nutritional support not only in improving nutritional status but also in mitigating inflammation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTaken together, the nutrition-inflammation axis in hemodialysis patients extends beyond traditional biochemical parameters and exerts multidimensional effects on physical health, psychological well-being, cognitive functions, and overall survival. Therefore, comprehensive monitoring and management strategies should adopt a multidisciplinary approach that integrates nutritional, medical, and psychosocial interventions (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMany hemodialysis patients struggle with adhering to dietary restrictions, which often leads to complications such as hyperphosphatemia and hyperkalemia. These complications can further exacerbate malnutrition and inflammation, creating a vicious cycle that undermines the overall health of the patient (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It is essential to address these dietary challenges through patient education and support to promote better adherence and improve health outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis research will contribute to the growing body of evidence emphasizing the need for a multidisciplinary approach to managing hemodialysis patients. By addressing malnutrition and inflammation, healthcare providers can develop more effective treatment strategies that not only improve physiological markers but also enhance the overall well-being of patients. Furthermore, the findings may inform future guidelines for nutritional management in hemodialysis settings, promoting better health practices and reducing the burden of complications associated with poor nutritional status. Collaborative care models that involve nephrologists, dietitians, and nursing staff will be essential in implementing effective interventions and monitoring progress.\u003c/p\u003e\u003cp\u003eIn conclusion, this study aims to shed light on the complex interactions between malnutrition, inflammation, and clinical outcomes in hemodialysis patients. By understanding these relationships and their implications, healthcare providers can better tailor treatments to meet the needs of this vulnerable population, ultimately improving patient outcomes and quality of life.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was conducted as a prospective observational cohort between January 2023 and December 2024 in 8 hemodialysis centers located in Istanbul, T\u0026uuml;rkiye. The purpose of the study was to examine the role of malnutrition and inflammation in predicting clinical outcomes among patients receiving maintenance hemodialysis. All patients were assessed at baseline and followed for a total of twelve months, during which nutritional assessments, laboratory parameters, and clinical outcomes were recorded at regular intervals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The study protocol was reviewed and approved by the Institutional Clinical Research Ethics Committee before patient recruitment. Written informed consent was obtained from all participants. Confidentiality and anonymity of patient information were strictly preserved throughout the study, and all procedures were performed in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Population and Sample Size\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population consisted of adult patients undergoing maintenance hemodialysis therapy. Sample size was determined using G*Power 3.1 software, assuming an effect size of 0.15, a power of 80%, and a two-sided significance level of 0.05, which yielded a minimum requirement of 180 patients. To compensate for possible dropouts and missing data, a total of 416 patients were ultimately enrolled. This larger sample ensured sufficient statistical power to detect associations and allowed for robust subgroup and multivariate analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and Exclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEligible participants were those aged 18 years or older who had been receiving thrice-weekly hemodialysis sessions of four hours each for at least six consecutive months and who voluntarily provided written informed consent. Patients were excluded if they had a history of peritoneal dialysis or kidney transplantation, an acute kidney injury requiring temporary dialysis, or comorbidities such as active malignancy, advanced chronic liver disease, or HIV infection. Additional exclusion criteria included acute infection or hospitalization within the preceding four weeks and the presence of severe psychiatric or cognitive impairments that could interfere with adherence to the study protocol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were obtained using structured case report forms and verified through electronic health records. Demographic information such as age, sex, marital status, educational attainment, and employment status was collected. Clinical characteristics included dialysis duration, vascular access type, medication use, and comorbidities including diabetes mellitus, hypertension, and cardiovascular disease. Anthropometric measurements, including weight, height, and body mass index, were recorded by trained healthcare staff, while lifestyle habits such as smoking and alcohol use were also noted. Hospitalization records, including the number of admissions and total length of hospital stay during follow-up, were retrieved from electronic databases and confirmed with patient relatives when necessary.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory Assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRoutine monthly laboratory measurements were incorporated into the dataset and included serum albumin and C-reactive protein as markers of nutritional and inflammatory status, respectively. Hematological data included hemoglobin, hematocrit, leukocyte count, and platelet count. Biochemical parameters included creatinine, urea, phosphorus, potassium, and calcium levels. In addition, lipid profile consisting of total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides was analyzed. All laboratory tests were performed using standardized methods within the same institutional laboratory to ensure consistency.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNutritional Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNutritional status was assessed using two validated instruments. The Subjective Global Assessment classified patients into three groups: well nourished (SGA-A), moderately malnourished (SGA-B), and severely malnourished (SGA-C). The Malnutrition-Inflammation Score was also applied, comprising ten components addressing dietary intake, weight change, functional capacity, comorbidities, and laboratory results. Scores ranged from 0 to 30, and a score of 8 or higher indicated risk of malnutrition. All nutritional assessments were conducted by two nephrologists who were blinded to patient outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary clinical outcomes were all-cause one-year mortality and the number and duration of hospitalizations. Mortality data were collected from hospital and national registries, while hospitalization information was retrieved from electronic medical records and validated by contacting relatives when necessary. Secondary outcomes examined the relationship between nutritional scores, biochemical indices, and hematological parameters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 26. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or as median with interquartile range, while categorical variables were presented as counts and percentages. Group comparisons were performed with the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test for continuous variables depending on normality, and chi-square or Fisher\u0026rsquo;s exact tests for categorical variables. Survival analysis was carried out using Kaplan\u0026ndash;Meier curves, and differences were compared using the log-rank test. Univariate Cox regression models were initially used to explore associations between nutritional status and clinical outcomes, and variables with a p-value less than 0.10 were included in multivariate Cox proportional hazards models to determine independent predictors. Potential confounding factors such as age, sex, dialysis duration, serum albumin, CRP, and comorbidities were adjusted for. Receiver operating characteristic curve analysis was conducted to compare the prognostic performance of MIS and SGA, with area under the curve values and 95% confidence intervals reported. Subgroup and sensitivity analyses stratified by age, sex, and presence of diabetes mellitus were performed to examine the robustness of associations.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eOf the 416 hemodialysis patients, 57.2% were male and 42.8% female, with nearly half (47.6%) aged 60 years or older. Most participants were married (72.6%), and a significant proportion had only primary education or less (43.8%), reflecting the overall low educational attainment in this cohort.MRegarding employment, only 23.1% of patients were actively working, while the majority (76.9%) were either unemployed or retired, consistent with the chronic nature of the disease and its disabling effect. Lifestyle factors revealed that 21.2% were current smokers, whereas alcohol consumption was uncommon (5.8%). The most common cause of end-stage renal disease (ESRD) was diabetic nephropathy (34.1%), followed by hypertensive nephrosclerosis (26.0%), glomerulonephritis (15.4%), and polycystic kidney disease (6.7%). The etiology was unknown or categorized as \u0026ldquo;other\u0026rdquo; in 17.8% of cases. Comorbidities were frequent: hypertension (66.3%) and diabetes mellitus (35.6%) were the most prevalent, with cardiovascular disease present in 29.8% of patients. Dialysis duration showed that nearly half of the cohort (46.6%) had been on treatment for more than 5 years, while 22.6% were relatively new to dialysis (\u0026lt;\u0026thinsp;3 years). In terms of vascular access, the preferred method was arteriovenous fistula (76.4%), whereas 10.1% had grafts and 13.5% relied on central venous catheters. Hospitalization analysis demonstrated that 68.3% of patients were hospitalized at least once in the past year; among them, 39.4% had 1\u0026ndash;2 admissions and 28.9% had\u0026thinsp;\u0026ge;\u0026thinsp;3 admissions, highlighting the heavy burden of morbidity in this patient group. Vascular access distribution showed that 76.4% of patients used an AV fistula, while 10.1% had a graft and 13.5% relied on a central venous catheter.Taken together, these findings illustrate a patient population with a high comorbidity burden, complex pharmacological requirements, and frequent hospitalizations, reflecting the challenges of managing chronic hemodialysis patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDemographic, Clinical, and Treatment Characteristics of Hemodialysis Patients (n\u0026thinsp;=\u0026thinsp;416)\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003e238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.2\u003c/p\u003e\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\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group (years)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital 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\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\u003e302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle / Widowed / Divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.4\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate/Primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary/High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment 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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed / Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLifestyle factors\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary renal disease\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive nephrosclerosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerulonephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolycystic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther / Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic liver disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeekly dialysis duration\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12 hours (3 \u0026times; 4h sessions)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;12 hours (extended HD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12 hours (incomplete adherence)\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\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eType of vascular access\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteriovenous fistula (AVF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteriovenous graft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral venous catheter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedication use\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythropoietin stimulating agents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntravenous iron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate binders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin D analogues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntihypertensives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelets/Anticoagulants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHospitalization in past year\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3 times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.9\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\u003eA total of 416 hemodialysis patients were included in the study. The mean age was 57.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3 years (range: 24.5\u0026ndash;88.0), and the average duration of dialysis treatment was 74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;39.5 months (range: 3.1\u0026ndash;191.6). The mean body mass index (BMI) was 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 kg/m\u0026sup2;, ranging from underweight (15.6 kg/m\u0026sup2;) to obese levels (38.3 kg/m\u0026sup2;).\u003c/p\u003e\u003cp\u003eHematological analysis showed that the average hemoglobin level was 10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 g/dL and the hematocrit was 32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8%, confirming the high prevalence of anemia among the patients. The mean leukocyte count was 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 \u0026times;10\u0026sup3;/\u0026micro;L, while the platelet count averaged 220\u0026thinsp;\u0026plusmn;\u0026thinsp;58 \u0026times;10\u0026sup3;/\u0026micro;L.\u003c/p\u003e\u003cp\u003eRegarding biochemical parameters, the mean serum albumin was 3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 g/dL, which is close to the lower limit of normal, indicating a tendency toward hypoalbuminemia. The mean C-reactive protein (CRP) concentration was 10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 mg/L, suggesting a significant inflammatory burden in many patients. Electrolyte abnormalities were also noted, with a mean serum phosphorus level of 5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 mg/dL and a mean potassium level of 5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 mmol/L, pointing to frequent disturbances in mineral and electrolyte balance.\u003c/p\u003e\u003cp\u003eThe lipid profile revealed a pattern consistent with dyslipidemia. The mean total cholesterol level was 167\u0026thinsp;\u0026plusmn;\u0026thinsp;38 mg/dL, with an average LDL cholesterol of 92\u0026thinsp;\u0026plusmn;\u0026thinsp;28 mg/dL. The mean HDL cholesterol was relatively low (38\u0026thinsp;\u0026plusmn;\u0026thinsp;9 mg/dL), while the mean triglyceride level was elevated (165\u0026thinsp;\u0026plusmn;\u0026thinsp;70 mg/dL), both of which are known risk factors for cardiovascular disease.\u003c/p\u003e\u003cp\u003eNutritional status assessed by the Malnutrition-Inflammation Score (MIS) yielded a mean value of 9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15 (range: 1.0\u0026ndash;23.2), indicating that a substantial proportion of patients were at risk of malnutrition and chronic inflammation \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\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\u003eDescriptive Statistics of Continuous Variables in Hemodialysis Patients (n\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis duration (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e191.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHematological Parameters\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocytes (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBiochemical Parameters\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLipid Profile\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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNutritional 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\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.20\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\u003eBased on the SGA classification, 43.8% of patients were well-nourished (SGA-A), 40.4% were moderately malnourished (SGA-B), and 15.8% were severely malnourished (SGA-C). Patients in the malnourished groups (SGA-B and SGA-C) were significantly older compared to the well-nourished group (mean ages 58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1 and 63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 years vs. 53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). BMI and serum albumin levels decreased progressively from SGA-A to SGA-C, while CRP levels increased, indicating a strong link between malnutrition and inflammation.\u003c/p\u003e\u003cp\u003eSimilarly, hemoglobin levels were lowest in the SGA-C group, reflecting a higher prevalence of anemia in severely malnourished patients. The mean MIS score increased stepwise across the categories (6.3 vs. 10.4 vs. 15.7; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the consistency between the two nutritional assessment methods. Clinical outcomes were worse in patients with poorer nutritional status. Hospitalization rates were significantly higher in malnourished groups, with 84.8% of SGA-C patients hospitalized at least once in the past year compared to only 42.9% in SGA-A. Moreover, 1-year mortality was 36.4% in SGA-C patients, significantly higher than 19.0% in SGA-B and 6.6% in SGA-A (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eNutritional Status of Hemodialysis Patients According to SGA Categories (n\u0026thinsp;=\u0026thinsp;416)\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSGA-A (Well-nourished) n\u0026thinsp;=\u0026thinsp;182 (43.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSGA-B (Moderately malnourished) n\u0026thinsp;=\u0026thinsp;168 (40.4%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSGA-C (Severely malnourished) n\u0026thinsp;=\u0026thinsp;66 (15.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\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 (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eMale sex (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (53.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (69.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eAlbumin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eMIS score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHospitalization\u0026thinsp;\u0026ge;\u0026thinsp;1 (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e1-year mortality (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eNutritional status assessed by MIS was significantly associated with clinical outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Patients with high MIS scores (\u0026gt;\u0026thinsp;12) were older (64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 vs. 52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had lower BMI (21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 vs. 26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 kg/m\u0026sup2;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and albumin levels (3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 vs. 3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 g/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher CRP concentrations (15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 vs. 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 mg/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with MIS\u0026thinsp;\u0026lt;\u0026thinsp;6. Hemoglobin was also significantly lower in the high MIS group (9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 vs. 11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 g/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of patients hospitalized at least once in the past year rose progressively across MIS categories (40.0% vs. 67.8% vs. 85.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). One-year mortality showed a stepwise increase with worsening nutritional status (5.5% vs. 18.4% vs. 37.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eNutritional Status and Clinical Outcomes According to MIS Categories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIS Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1 Hospitalization (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1-year Mortality (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;6 (Low Risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;12 (Moderate Risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;12 (High Risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e85.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e37.1\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\u003eKaplan\u0026ndash;Meier survival curves revealed a graded association between nutritional status and survival(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Patients classified as SGA-C had a 1-year survival of 63.6% compared to 81.0% for SGA-B and 93.4% for SGA-A (\u003cem\u003elog-rank p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, patients with MIS\u0026thinsp;\u0026gt;\u0026thinsp;12 had the lowest survival (62.9%) compared to MIS 6\u0026ndash;12 (81.6%) and MIS\u0026thinsp;\u0026lt;\u0026thinsp;6 (94.5%) (\u003cem\u003elog-rank p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings confirm that both SGA and MIS are strong predictors of mortality risk in hemodialysis patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation Between Nutritional Assessment Tools and Mortality (Kaplan\u0026ndash;Meier Survival Analysis)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssessment Tool\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1-year Survival (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLog-rank p\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eSGA-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eSGA-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMIS\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMIS 6\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMIS\u0026thinsp;\u0026gt;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eROC analysis demonstrated that MIS had a superior discriminative ability for predicting both mortality (AUC\u0026thinsp;=\u0026thinsp;0.79; 95% CI: 0.74\u0026ndash;0.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hospitalization (AUC\u0026thinsp;=\u0026thinsp;0.76; 95% CI: 0.71\u0026ndash;0.82; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to SGA (mortality: AUC\u0026thinsp;=\u0026thinsp;0.75; 95% CI: 0.70\u0026ndash;0.81; hospitalization: AUC\u0026thinsp;=\u0026thinsp;0.72; 95% CI: 0.67\u0026ndash;0.79; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results suggest that MIS is slightly more sensitive and specific than SGA for identifying patients at risk of adverse outcomes(Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC Curve Analysis of MIS and SGA for Mortality and Hospitalization\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC (95% CI) \u0026ndash; Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUC (95% CI) \u0026ndash; Hospitalization\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.74\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76 (0.71\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eSGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.70\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.67\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eMultivariate Cox regression analysis identified age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;1.45; 95% CI: 1.20\u0026ndash;1.75; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MIS\u0026thinsp;\u0026gt;\u0026thinsp;12 (HR\u0026thinsp;=\u0026thinsp;2.35; 95% CI: 1.85\u0026ndash;2.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL (HR\u0026thinsp;=\u0026thinsp;1.80; 95% CI: 1.45\u0026ndash;2.25; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L (HR\u0026thinsp;=\u0026thinsp;1.50; 95% CI: 1.20\u0026ndash;1.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and the presence of diabetes (HR\u0026thinsp;=\u0026thinsp;1.25; 95% CI: 1.05\u0026ndash;1.50; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) as independent predictors of mortality. Among these, MIS\u0026thinsp;\u0026gt;\u0026thinsp;12 exhibited the strongest association, highlighting the critical prognostic role of malnutrition-inflammation status in hemodialysis patients(Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Risk Factors Identified by Cox Regression Analysis\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;60 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.45 (1.20\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eMIS (\u0026gt;\u0026thinsp;12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.35 (1.85\u0026ndash;2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eAlbumin (\u0026lt;\u0026thinsp;3.5 g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.80 (1.45\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eCRP (\u0026gt;\u0026thinsp;10 mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.50 (1.20\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25 (1.05\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\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\u003eMediation analysis demonstrated that both serum albumin and CRP partially mediated the association between MIS and 1-year mortality(Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The total effect of MIS on mortality was significant (β\u0026thinsp;=\u0026thinsp;0.84; 95% CI: 0.62\u0026ndash;1.06; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for albumin, the direct effect decreased but remained significant (β\u0026thinsp;=\u0026thinsp;0.52; 95% CI: 0.30\u0026ndash;0.74; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an indirect effect of β\u0026thinsp;=\u0026thinsp;0.32 (95% CI: 0.18\u0026ndash;0.47; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that 38.1% of the MIS\u0026ndash;mortality association was mediated by hypoalbuminemia. Similarly, CRP significantly mediated the relationship, with an indirect effect of β\u0026thinsp;=\u0026thinsp;0.35 (95% CI: 0.20\u0026ndash;0.51; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 41.7% of the total effect. These results highlight that both poor nutritional status and systemic inflammation play key roles in linking malnutrition-inflammation score to adverse survival outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Analysis of the Association Between MIS and 1-year Mortality via Albumin and CRP\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProportion Mediated (%)\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\u003eAlbumin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Effect (c): MIS \u0026rarr; Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.62\u0026ndash;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\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\u003eDirect Effect (c\u0026rsquo;): MIS \u0026rarr; Mortality (adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u0026ndash;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\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\u003eIndirect Effect (ab): MIS \u0026rarr; Albumin \u0026rarr; Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u0026ndash;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e38.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Effect (c): MIS \u0026rarr; Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.62\u0026ndash;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\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\u003eDirect Effect (c\u0026rsquo;): MIS \u0026rarr; Mortality (adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u0026ndash;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\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\u003eIndirect Effect (ab): MIS \u0026rarr; CRP \u0026rarr; Mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.20\u0026ndash;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e41.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study investigated the nutritional and inflammatory status of patients undergoing maintenance hemodialysis, with particular emphasis on the predictive role of the Malnutrition-Inflammation Score (MIS) and Subjective Global Assessment (SGA) for adverse clinical outcomes. The findings demonstrated that higher MIS and poorer SGA categories were strongly associated with reduced serum albumin levels, elevated C-reactive protein (CRP) concentrations, lower hemoglobin values, and ultimately higher rates of hospitalization and one-year mortality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These results reinforce the concept of the malnutrition\u0026ndash;inflammation complex (MICS) as a central determinant of prognosis in dialysis patients and are largely consistent with contemporary research conducted over the last five years (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the most striking findings of this study was the superior discriminative ability of MIS compared to SGA in predicting hospitalization and mortality. The receiver operating characteristic analysis revealed that MIS had a higher area under the curve for both outcomes, suggesting that MIS may provide more sensitive risk stratification. Similar results have been reported in recent multicenter investigations, which demonstrated that MIS is not only a reliable marker of nutritional and inflammatory status but is also strongly associated with psychological dimensions such as depression, anxiety, and sleep quality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This broader scope highlights the importance of considering MIS as a multidimensional marker rather than a mere nutritional assessment tool, particularly in clinical practice where a holistic approach to patient management is increasingly emphasized.\u003c/p\u003e\u003cp\u003eThe association between malnutrition, inflammation, and impaired functional capacity has also emerged as a critical issue in the management of dialysis patients (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this study, higher MIS values were linked with lower BMI, reduced serum albumin, and elevated CRP, all of which reflected a state of chronic catabolism and frailty. Patients with MIS greater than 12 exhibited both worse clinical outcomes and significantly lower survival. These findings resonate with recent population-based analyses showing that functional limitations such as decreased handgrip strength and impaired walking ability were closely associated with malnutrition in dialysis populations(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, insufficient caloric intake has been identified as a strong predictor of nutritional decline, further emphasizing the interplay between diet quality, systemic inflammation, and physical function. Together, these results underscore the importance of integrating nutritional and functional assessments to optimize patient care (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother important aspect of this study is the observed relationship between malnutrition, inflammation, and cardiovascular risk. Our results indicated that worsening nutritional status, as reflected by SGA and MIS categories, was associated with higher prevalence of cardiovascular comorbidities (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This is consistent with recent evidence highlighting the malnutrition-inflammation-atherosclerosis (MIA) syndrome as a critical driver of morbidity and mortality in dialysis patients. For instance, analyses have shown that the co-existence of malnutrition, inflammation, and atherosclerosis significantly worsens survival outcomes after vascular interventions (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This conceptual framework reinforces the need for clinicians to address nutritional and inflammatory pathways not only to improve nutritional well-being but also to mitigate cardiovascular risk in the dialysis population.\u003c/p\u003e\u003cp\u003eProtein-energy wasting (PEW) was also a prominent feature in this study, with nearly half of the patients exhibiting evidence of malnutrition or being at risk. This prevalence is in line with recent systematic reviews and meta-analyses, which estimated that between 28% and 56% of hemodialysis patients worldwide experience PEW, depending on the criteria used for diagnosis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The consistency between our findings and global estimates suggests that PEW remains a highly prevalent and unresolved challenge across diverse healthcare settings. Importantly, lower educational attainment and older age were identified as risk factors for malnutrition in this study, which mirrors prior findings that socioeconomic and demographic factors play a substantial role in the nutritional health of patients on dialysis.\u003c/p\u003e\u003cp\u003eThe burden of anemia observed in this study also deserves attention. The mean hemoglobin level was markedly below recommended targets, underscoring the persistent challenge of anemia management in the dialysis population. Our data also demonstrated that patients with higher inflammatory markers tended to have lower hemoglobin levels, which is consistent with the well-established link between inflammation and erythropoietin resistance. Recent analyses have reinforced this connection by showing that elevated neutrophil-to-lymphocyte ratio, an easily accessible marker of systemic inflammation, is positively correlated with erythropoietin resistance index (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Furthermore, earlier investigations have demonstrated that MICS itself is an independent predictor of poor responsiveness to erythropoiesis-stimulating agents. These findings suggest that managing inflammation may be as critical as correcting iron deficiency in addressing anemia among dialysis patients. Our study\u0026rsquo;s results therefore highlight the multidimensional nature of anemia in this context.\u003c/p\u003e\u003cp\u003eThe mortality findings of this study further emphasize the prognostic significance of nutritional and inflammatory status. Patients with MIS greater than 12 and those classified as SGA-C exhibited one-year mortality rates exceeding 35%, while Kaplan\u0026ndash;Meier survival analysis revealed a stepwise decline in survival with worsening nutritional categories. These results are consistent with recent research indicating that higher MIS values are associated not only with increased risk of hospitalization but also with significantly reduced overall survival (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, analyses have shown that MIS is an independent predictor of symptoms such as pruritus and impaired quality of life, which themselves are linked to higher mortality risk (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Collectively, these findings highlight the potential role of MIS as a comprehensive prognostic tool that integrates clinical, biochemical, and patient-reported outcomes.\u003c/p\u003e\u003cp\u003eThe study also confirmed the relevance of traditional biochemical markers of malnutrition and inflammation. Lower albumin and higher CRP levels were strongly associated with adverse outcomes, aligning with numerous reports that have documented hypoalbuminemia and systemic inflammation as independent predictors of morbidity and mortality in dialysis patients. Beyond these conventional markers, recent studies have suggested that novel biomarkers such as adipokines and myokines may provide additional insights into the interplay between malnutrition, inflammation, and muscle wasting. For example, these biomarkers have been shown to correlate with both MIS and SGA scores, offering new avenues for risk stratification (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While our study did not include such advanced markers, the growing body of evidence suggests that incorporating them into clinical research could enhance the precision of malnutrition assessment and facilitate more personalized interventions.\u003c/p\u003e\u003cp\u003eFrom a therapeutic perspective, the implications of our findings are equally relevant. The high prevalence of malnutrition and its strong association with mortality underscore the urgent need for effective nutritional interventions. Recent randomized trials have evaluated the efficacy of intradialytic parenteral nutrition (IDPN) as a supplementary strategy for malnourished hemodialysis patients. These studies demonstrated significant improvements in transthyretin levels and other nutritional parameters following IDPN therapy (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although our study did not involve an interventional component, the clear link between nutritional status and outcomes suggests that such targeted therapies could be highly beneficial for similar patient populations. The integration of IDPN or individualized dietary counseling into standard care could therefore represent a pragmatic strategy to mitigate the burden of malnutrition and improve survival.\u003c/p\u003e\u003cp\u003eIt is also important to note the psychosocial dimensions of malnutrition and inflammation. Patients in poorer nutritional categories were more likely to be older, unemployed, or of lower educational status. These social determinants are increasingly recognized as critical influences on patient outcomes in dialysis care. Recent research has demonstrated that higher MIS values correlate not only with physical decline but also with psychological distress and impaired sleep quality (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This evidence suggests that addressing malnutrition in dialysis patients requires a multidisciplinary approach that includes not only nutritional support but also psychosocial and rehabilitative interventions. By acknowledging the broader determinants of health, clinicians may be able to design more comprehensive and effective care pathways.\u003c/p\u003e\u003cp\u003eThe cumulative evidence, including our findings, suggests that malnutrition and inflammation form a pathological triad with cardiovascular risk and anemia, collectively driving poor outcomes in hemodialysis patients. The concept of MICS, and more recently the malnutrition\u0026ndash;inflammation\u0026ndash;fluid overload complex, provides a unifying framework for understanding these interrelated processes. Indeed, recent analyses have shown that patients with evidence of all three components malnutrition, inflammation, and fluid overload experience significantly higher rates of all-cause mortality (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This multidimensional syndrome approach underscores the limitations of addressing single factors in isolation and points toward the need for integrated care strategies.\u003c/p\u003e\u003cp\u003eTaken together, the results of this study align with and extend the growing body of literature emphasizing the critical role of nutritional and inflammatory status in dialysis outcomes. The strong and consistent associations between higher MIS, lower serum albumin, elevated CRP, and adverse clinical outcomes reinforce the validity of these measures in both clinical practice and research. Furthermore, the congruence between our results and findings from recent international studies provides confidence in the generalizability of these associations across different populations and healthcare settings. Moving forward, integrating MIS into routine patient evaluations, exploring novel biomarkers, implementing early nutritional interventions, and addressing psychosocial determinants of health may collectively improve outcomes for this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Mardin Artuklu University approved this study, adhering to the principles of the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support was received from any person or organization during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors collected data, wrote and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHamdan A. Malnutrition-inflammation complex in hemodialysis patients. American Journal of Kidney Diseases. 2025.\u003c/li\u003e\n\u003cli\u003eBehbahani A. Nutritional status and clinical outcomes in dialysis patients. Journal of Nephrology. 2024.\u003c/li\u003e\n\u003cli\u003eDiaz-Martinez J, Martinez-Motta P, Campa A, Delgado-Enciso I, Huffman F, Baum M, et al. MIS and SGA indices as predictors of mortality and their relationship with nutrition parameters in hemodialysis patients (P18-009-19). Current developments in nutrition. 2019;3:nzz039. P18-09-19.\u003c/li\u003e\n\u003cli\u003eHamdan H, Alfarisi R, Nugraha D. Relationship between malnutrition-inflammation score, serum albumin, and C-reactive protein in chronic hemodialysis patients. Renal Failure. 2025;47(1):100-8.\u003c/li\u003e\n\u003cli\u003eChen Y, Li H, Xu J. Malnutrition-inflammation status is associated with sleep quality and psychological distress in hemodialysis patients: A multicenter study. BMC Nephrology. 2025;26:89.\u003c/li\u003e\n\u003cli\u003eSohrabi Z, Eftekhari MH, Eskandari MH, Rezaeianzadeh A, Sagheb MM. 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Factors associated with the malnutrition inflammation score (MIS) among hemodialysis patients in Dhaka city: A cross-sectional study in tertiary care hospitals. Porto Biomedical Journal. 2024;9(1):e243.\u003c/li\u003e\n\u003cli\u003eSantin F, Rodrigues J, Brito FB, Avesani CM. Performance of subjective global assessment and malnutrition inflammation score for monitoring the nutritional status of older adults on hemodialysis. Clinical Nutrition. 2018;37(2):604-11.\u003c/li\u003e\n\u003cli\u003eTorres A. Nutritional assessment tools in hemodialysis: A systematic review. American Journal of Clinical Nutrition. 2023.\u003c/li\u003e\n\u003cli\u003eKhan MA. Nutritional interventions in hemodialysis patients: A review. Nutrition in Clinical Practice. 2025.\u003c/li\u003e\n\u003cli\u003eTaha FAT, Zaitoun NA, Elhawy LL, Elsayed IA, Ragab U, El Maghawry MA. Assessment of Nutritional Status among Hemodialysis Patients by Three Different Tools. The Egyptian Journal of Community Medicine. 2025;43(1):20-9.\u003c/li\u003e\n\u003cli\u003eSHANKAR M, AUGUSTINE R, SIDDINI V, BONU R, BALLAL S. Subjective Global Assessment and Quality of Life in Hemodialysis Patients-A Clinical Observational Study. Journal of Clinical \u0026amp; Diagnostic Research. 2020;14(6).\u003c/li\u003e\n\u003cli\u003eBehbahani HB, Alipour M, Zare Javid A, Razmi H, Tofighzadeh P, Fayazfar F, et al. The association of Malnutrition-Inflammation Score with chronic kidney disease-associated symptoms and quality of life in hemodialysis patients: A multicenter study. Scientific Reports. 2024;14:31811.\u003c/li\u003e\n\u003cli\u003eBehbahani M, Jalalzadeh M, Shahdadi H. The association between malnutrition and inflammation in hemodialysis patients: A cross-sectional study. BMC Nephrology. 2024;25:112.\u003c/li\u003e\n\u003cli\u003eMorvaridi M, Behbahani HB, Alipour M. The association of Malnutrition-Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study. BMC Nephrology. 2025;26:305.\u003c/li\u003e\n\u003cli\u003eMorvaridi M, Ghaderi A, Esmaeili A. Malnutrition and inflammation as predictors of mortality in patients undergoing hemodialysis. Clinical Nutrition. 2025;44(2):345-53.\u003c/li\u003e\n\u003cli\u003eAs\u0026rsquo; habi A, Tabibi H, Nozary-Heshmati B, Mahdavi-Mazdeh M, Hedayati M. Comparison of various scoring methods for the diagnosis of protein\u0026ndash;energy wasting in hemodialysis patients. International urology and nephrology. 2014;46(5):999-1004.\u003c/li\u003e\n\u003cli\u003eAvesani CM, Sabatino A, Guerra A, Rodrigues J, Carrero JJ, Rossi GM, et al. A comparative analysis of nutritional assessment using global leadership initiative on malnutrition versus subjective global assessment and malnutrition inflammation score in maintenance hemodialysis patients. 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Journal of Clinical Medicine. 2025;14(10):3411.\u003c/li\u003e\n\u003cli\u003eCohen-Cesla T, Azar A, Hamad RA, Shapiro G, Stav K, Efrati S, et al. Usual nutritional scores have acceptable sensitivity and specificity for diagnosing malnutrition compared to GLIM criteria in hemodialysis patients. Nutrition Research. 2021;92:129-38.\u003c/li\u003e\n\u003cli\u003eMorvaridi A. Assessment of malnutrition in hemodialysis patients. Nephrology Reviews. 2025.\u003c/li\u003e\n\u003cli\u003eCzaja-Stolc S. Adipokines and myokines as markers of malnutrition and sarcopenia in dialysis patients. Nutrients. 2024;16(15):2480.\u003c/li\u003e\n\u003cli\u003eKabasawa H, Hosojima M, Kanda E. Efficacy and safety of intradialytic parenteral nutrition in malnourished maintenance hemodialysis patients: An exploratory randomized study. PLOS ONE. 2024;19(12):e0311671.\u003c/li\u003e\n\u003cli\u003eTian R. Malnutrition-inflammation-fluid overload complex and mortality in hemodialysis patients: A prospective cohort study. Nephrology Reports. 2025;40:223-31.\u003c/li\u003e\n\u003cli\u003eWang C, Lin K, Jiang Y, Wu K, Zhang H, Chen J, et al. Association of Klotho and Gout in Middle-Aged and Older Adults: National Health and Nutrition Survey (2007\u0026ndash;2016). JCR: Journal of Clinical Rheumatology. 2025:10.1097.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renal Dialysis, Malnutrition, Inflammation, Nutritional Status, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-7760107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7760107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMalnutrition and inflammation are highly prevalent in hemodialysis patients and are strongly linked to adverse outcomes. While traditional nutritional assessments provide valuable insights, the Malnutrition-Inflammation Score (MIS) may offer superior prognostic ability compared with conventional measures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis prospective observational cohort included 416 adult patients undergoing thrice-weekly hemodialysis at three centers in Istanbul, T\u0026uuml;rkiye. Nutritional status was assessed using the Subjective Global Assessment (SGA) and MIS. Laboratory parameters, including serum albumin, C-reactive protein (CRP), and hemoglobin, were measured. Patients were followed for 12 months to evaluate all-cause mortality and hospitalizations. Survival was analyzed with Kaplan\u0026ndash;Meier curves, while Cox regression identified independent predictors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMalnutrition was highly prevalent, with 56.2% of patients classified as moderately or severely malnourished by SGA and 23.3% exhibiting MIS\u0026thinsp;\u0026gt;\u0026thinsp;12. Malnourished patients had significantly lower albumin (3.12 vs. 3.82 g/dL), higher CRP (15.2 vs. 8.1 mg/L), and lower hemoglobin (9.8 vs. 11.0 g/dL) compared with well-nourished patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). One-year mortality was 36.4% in SGA-C and 37.1% in MIS\u0026thinsp;\u0026gt;\u0026thinsp;12, versus 6.6% and 5.5% in well-nourished/low-MIS patients. MIS demonstrated superior prognostic accuracy for mortality (AUC\u0026thinsp;=\u0026thinsp;0.79) compared with SGA (AUC\u0026thinsp;=\u0026thinsp;0.75). Multivariate Cox analysis identified MIS\u0026thinsp;\u0026gt;\u0026thinsp;12 (HR\u0026thinsp;=\u0026thinsp;2.35), serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL (HR\u0026thinsp;=\u0026thinsp;1.80), CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L (HR\u0026thinsp;=\u0026thinsp;1.50), age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;1.45), and diabetes (HR\u0026thinsp;=\u0026thinsp;1.25) as independent predictors of mortality. Mediation analysis showed that hypoalbuminemia and elevated CRP explained nearly 80% of the MIS\u0026ndash;mortality association.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMIS provides robust prognostic information, outperforming SGA in predicting mortality and hospitalization. Malnutrition and inflammation synergistically drive poor outcomes, with albumin and CRP mediating much of the risk. Integrating MIS into routine practice and implementing early nutritional interventions may improve survival in this vulnerable population.\u003c/p\u003e","manuscriptTitle":"Malnutrition-Inflammation Status as a Predictor of Outcomes in Hemodialysis Patients: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 05:55:50","doi":"10.21203/rs.3.rs-7760107/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":"91cd45d2-7d4d-48a6-88c3-17a55bedc1c0","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T05:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 05:55:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7760107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7760107","identity":"rs-7760107","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}