Nutritional Predictors of One-Year Survival in Hemodialysis Patients: Insights from an Age-Matched Cohort Analysis

Nutritional Predictors of One-Year Survival in Hemodialysis Patients: Insights from an Age-Matched Cohort Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nutritional Predictors of One-Year Survival in Hemodialysis Patients: Insights from an Age-Matched Cohort Analysis Georgios Kosmadakis, Aura Necoara, Julien Baudenon, Clemence Deville, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7488060/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Abstract Background Malnutrition and protein-energy wasting are major predictors of mortality in hemodialysis patients. While several nutritional markers have been proposed, the independent prognostic value of individual variables remains debated, especially in the context of age-related confounding. Methods We retrospectively analyzed a cohort of 350 hemodialysis patients (237M/128F, mean age ± Standard deviation (SD) : 69.78 ± 13.47 years old). One-year survival was assessed, and a 1:1 age-matched selection (59 survivors and 59 non-survivors) (76M/42F, mean age ± SD :77.70 ± 11.64) was performed. Nutritional variables including GNRI, serum albumin, prealbumin, handgrip strength, body composition parametrs and weight variation were evaluated. Logistic regression models were used to assess their association with survival, with emphasis on simplified multivariate models to avoid overfitting. Results Univariate comparisons showed significantly better nutritional profiles among survivors. After matching for age, GNRI, prealbumin, and handgrip strength remained significantly associated with survival. Multivariate models including GNRI combined with either 6-month weight change or handgrip strength yielded significant and stable associations with survival (OR for GNRI: 1.10, p = 0.001; OR for handgrip: 1.08, p = 0.028). Conclusion In an age-matched hemodialysis cohort, GNRI, dynamic weight change, and muscle strength emerged as independent predictors of one-year survival. Simplified models provide robust prognostic insight and are preferable to overparameterized regressions in small datasets. Hemodialysis Nutritional status Longitudinal study Malnutrition Figures Figure 1 Figure 2 Introduction Protein-energy wasting (PEW) is a common and serious complication of chronic kidney disease (CKD), particularly among individuals undergoing maintenance hemodialysis. PEW reflects a complex metabolic state characterized by reduced body protein and energy stores, which has been repeatedly linked to increased hospitalization and mortality in this population [ 1 ]. The identification of reliable, practical, and early markers of nutritional deterioration is crucial for timely intervention and risk stratification in clinical nephrology. Traditional nutritional biomarkers such as serum albumin and prealbumin have long been associated with outcomes in dialysis patients. However, these markers are limited by their sensitivity to inflammation, volume status, and non-nutritional influences [ 2 , 3 ]. More comprehensive or functional markers, including the Geriatric Nutritional Risk Index (GNRI) and handgrip strength, have emerged as promising alternatives. GNRI, derived from serum albumin and body weight relative to ideal body weight, has shown consistent predictive value for mortality across various dialysis cohorts [ 4 , 5 ]. Handgrip strength, meanwhile, reflects functional muscle status and has been proposed as a more accurate indicator of sarcopenia-related risks [ 6 , 7 ]. Despite these advances, the relative and independent prognostic value of nutritional markers remains uncertain due to confounding by age and other covariates. Age, in particular, is a dominant determinant of mortality in hemodialysis patients and can obscure associations between nutritional indicators and survival. While traditional multivariable adjustment is commonly employed, it is subject to limitations in small datasets, especially when nutritional variables are collinear or incomplete. In this study, we addressed these limitations by applying an age-matching strategy to isolate the role of nutritional factors in predicting one-year survival in hemodialysis patients. Using logistic regression models with a focus on simplicity and clinical feasibility, we aimed to identify nutritional parameters that offer independent prognostic information, ultimately informing risk-based care strategies in this vulnerable population. Methods This study was designed as a retrospective, observational analysis conducted at a single dialysis center. The initial study population included 350 adult patients undergoing maintenance hemodialysis between 01/01/2024 and 31/12/2024. The patients were informed of the study and signed an non opposition statement. The study had the local Ethics Committee approval number 2024 :11241. Patients with incomplete data on nutritional variables or lacking follow-up at one year were excluded.All the included patients were metabolically stable and were on dialysis for at least 6 months. Mean dialysis vintage (±SD) was 28±19 months. All patients were on thrice weekly conventional high-flux hemodialysis and the mean dialysis sessions’duration (±SD) was 228±22 minutes. In the present real life cohort 40% of the participants were diabetic. Demographic and clinical variables were collected from electronic medical records. Nutritional assessments included the evolution of body weight at one end six months prior to the present measurment, Geriatric Nutritional Risk Index (GNRI), serum albumin, prealbumin, handgrip strength (measured in the dominant hand using a hydraulic dynamometer-Jamar®) as well as Lean Tissue Index (LTI) and Fat Tissue Index (FTI), measured by the Body Composition Monitor (BCM; Fresenius Medical Care, Sankt Wendel, Germany) approximately 20–30 minutes before a hemodialysis session. GNRI was calculated as: GNRI = [1.489 × albumin (g/L)] + [41.7 × (weight/ideal weight)], where ideal weight was based on a BMI of 22 kg/m². Additional laboratory variables included CRP, potassium, phosphorus, bicarbonate, and normalized protein catabolic rate (nPCR) measured from the biological values in a montly basis. The primary endpoint was all-cause survival at one year. For the main analysis, a 1:1 age-matching strategy was employed using nearest-neighbor matching without replacement, based on age (±1 year). This yielded two comparable groups: 59 survivors and 59 non-survivors. Descriptive statistics were used to compare demographic and nutritional characteristics between groups. Continuous variables were expressed as mean ± standard deviation or median (IQR), and categorical variables as frequencies or percentages. Group comparisons were made using independent-samples t-tests or Mann–Whitney U tests as appropriate. Normality was assessed using the Shapiro–Wilk test. Univariate logistic regression was used to identify individual predictors of survival. Variables significant at p<0.10 were entered into multivariate logistic regression models. Due to collinearity between nutritional variables and limited sample size, simplified models with 2–3 variables were built to maximize interpretability and stability. All analyses were conducted using IBM SPSS Statistics (version 20.0), with a significance threshold set at p<0.05. Ethical Approval This study was approved by the local Ethics Committee (approval number 2024:11241) and conducted in accordance with the Declaration of Helsinki. All patients provided a signed non-opposition statement. Patient confidentiality and anonymity were strictly maintained. Results A total of 118 hemodialysis patients (76M/42F, were analyzed following 1:1 age and sex matching, yielding two subgroups of 59 survivors (38M/21F) and 59 non-survivors (38M/21F) after one year of follow up. The mean age of the matched population was 77.70 ± 11.64 years, with no significant difference between survivors and non-survivors (p = 0.914). The male-to-female ratio and other demographic variables were well balanced (data not shown). Baseline Nutritional and Biochemical Characteristics Detailed descriptive and comparative statistics of the matched population are summarized in Table 1. Survivors consistently showed a more favorable nutritional and inflammatory profile compared to non-survivors. GNRI was significantly higher among survivors (109.5 ± 12.2) than non-survivors (100.0 ± 16.9), with a strong statistical association (p = 0.001), suggesting its robust predictive capacity. Handgrip strength also differed significantly between groups, with survivors exhibiting greater muscle strength (26.8 ± 11.0 kg) compared to non-survivors (20.8 ± 6.9 kg, p = 0.016), reinforcing the association between functional capacity and survival. The direction and magnitude of 6-month weight change further highlighted this nutritional divergence: survivors gained weight (+2.1 ± 5.7 kg), whereas non-survivors lost weight (-0.9 ± 3.1 kg), with a statistically significant difference (p = 0.003). From a biochemical standpoint, serum albumin and prealbumin levels were significantly higher in the survivor group, at 37.9 ± 4.6 g/L and 30.8 ± 6.7 mg/dL, respectively, compared to 33.7 ± 6.7 g/L and 24.8 ± 7.2 mg/dL among non-survivors (p < 0.001 for both). CRP levels, an index of systemic inflammation, were nearly twice as high in the non-survivor group (16.6 ± 20.7 mg/L) versus survivors (8.4 ± 10.9 mg/L), with this difference reaching statistical significance (p = 0.008). In contrast, other parameters such as Lean Tissue Index (LTI), Fat Tissue Index (FTI), serum potassium, phosphorus, bicarbonate, and nPCR, while differing in absolute values, did not achieve statistical significance (all p > 0.05). Predictors of Survival To determine which variables independently predicted 1-year survival, we conducted logistic regression analysis using simplified models. As reported in Table 2 and Figure 1, Model A identified GNRI (OR: 1.105, 95% CI not shown, p = 0.001) and 6-month weight change (OR: 0.954, p = 0.039) as significant and independent predictors. The odds ratio for GNRI implies that each unit increase is associated with approximately a 10% higher odds of survival. Conversely, weight loss was associated with increased mortality risk. In Model B, GNRI remained significant (OR: 1.047, p = 0.041), and handgrip strength emerged as an independent predictor as well (OR: 1.075, p = 0.028), indicating the importance of functional muscle mass as a prognostic indicator. GNRI Distribution The prognostic value of GNRI is further illustrated in Figure 2, where the distribution of GNRI categories is stratified by survival status. Survivors were predominantly represented in the higher GNRI brackets (>108), while non-survivors were concentrated in the lower GNRI categories (<98), clearly demonstrating the clinical discriminative capacity of this nutritional index. Discussion This study demonstrates that key nutritional parameters—specifically the Geriatric Nutritional Risk Index (GNRI), 6-month weight change, and handgrip strength—are significant and independent predictors of one-year mortality in hemodialysis patients when age is carefully controlled. These findings align with and expand upon previous research linking protein-energy wasting (PEW) with poor outcomes in this population [1,2]. GNRI consistently emerged as the most robust nutritional predictor across models. This index combines serum albumin with weight-for-height status and has been validated in several dialysis cohorts [5,8,9]. Its predictive utility in our study remained strong even after adjusting for other markers, emphasizing its value as a simple, objective, and clinically feasible tool. Notably, GNRI performed better than serum albumin or prealbumin alone, likely because it integrates both static and body composition elements of nutrition [4]. Handgrip strength also proved to be an independent predictor of survival, reinforcing the growing recognition of sarcopenia as a determinant of poor prognosis in CKD patients [6,7]. Unlike biochemical markers, handgrip reflects functional capacity and physical resilience, making it a complementary tool to GNRI. Its inclusion in multivariate models improved predictive accuracy and supports the routine incorporation of muscle strength assessments in dialysis units. The inclusion of weight change as a predictor is particularly relevant. Unintentional weight loss has long been recognized as a marker of frailty and deteriorating nutritional status. Our results confirm that greater weight loss over six months is associated with higher mortality, consistent with previous longitudinal studies [10,11]. Unlike GNRI, which provides a snapshot, weight change reflects dynamic trends and patient trajectory. Importantly, models including combinations of GNRI, albumin, and prealbumin were unstable, likely due to high intercorrelation. These findings highlight the potential risks of overfitting when using multiple overlapping nutritional variables in small samples. By focusing on simplified, non-redundant models, we were able to identify interpretable and statistically robust predictors of outcome. Our use of age-matching, rather than statistical adjustment alone, strengthened the internal validity of this study. Age is a powerful confounder in survival studies, especially in the dialysis population where mortality risk increases sharply with advancing age [12]. By matching survivors and non-survivors within a narrow age range, we were able to isolate the contribution of nutritional variables more effectively. Nevertheless, this study has limitations. It is retrospective and single-centered, which may limit generalizability. Nutritional interventions during the follow-up period were not recorded, and inflammation markers such as IL-6 were not routinely available. Despite these constraints, our approach provides a pragmatic model for nutritional risk assessment in real-world dialysis practice. Conclusion In conclusion, GNRI, handgrip strength, and recent weight change are complementary and independent predictors of one-year survival in hemodialysis patients. Their integration into routine clinical monitoring may enhance early identification of at-risk individuals and prompt timely nutritional and rehabilitative interventions. Declarations Compliance with Ethical Standards -Disclosure of potential conflicts of interest The authors have declared that no conflict of interest exists. - Research involving Human Participants and/or Animals Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee at which the studies were conducted (IRB approval number 2024 :11241) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standard - Informed consent Informed consent was obtained from all individual participants included in the study. References Ikizler TA, Cano NJ, Franch H, et al. Prevention and treatment of protein-energy wasting in chronic kidney disease patients: a consensus statement by the International Society of Renal Nutrition and Metabolism. Kidney Int . 2013;84(6):1096–1107. Kalantar-Zadeh K, Ikizler TA, Block G, Avram MM, Kopple JD. Malnutrition-inflammation complex syndrome in dialysis patients: causes and consequences. Am J Kidney Dis . 2003;42(5):864–81. Kaysen GA. Biochemistry and biomarkers of inflamed patients: why look, what to assess. Clin J Am Soc Nephrol . 2009;4(Suppl 1):S56–61. Bouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr . 2005;82(4):777–83. Xiong J, Wang M, Zhang Y, et al. Association of Geriatric Nutritional Risk Index with mortality in hemodialysis patients: a meta-analysis of cohort studies. Kidney Blood Press Res . 2018;43(6):1878–89. Lamarca F, Carrero JJ, Rodrigues JC, et al. Prevalence of sarcopenia in elderly maintenance hemodialysis patients: the impact of different diagnostic criteria. J Nutr Health Aging . 2014;18(7):710–7. Kang SH, Cho KH, Park JW, Yoon KW, Do JY. Geriatric Nutritional Risk Index as a prognostic factor in peritoneal dialysis patients. Perit Dial Int . 2013;33(4):405–10. Yoshida M, Nakashima A, Doi S, et al. Lower Geriatric Nutritional Risk Index is associated with higher risk of fractures in patients undergoing hemodialysis. Nutrients . 2021;13(8):2847. Singer R, Huang HC. Weight change in chronic kidney disease: association with mortality and kidney function. Obes Sci Pract . 2023;10(1):e723. de Mutsert R, Grootendorst DC, Indemans F, Boeschoten EW, Krediet RT, Dekker FW. Association between serum albumin and mortality in dialysis patients is partly explained by inflammation, and not by malnutrition. J Ren Nutr . 2009;19(2):127–35. Goodkin DA, Young EW, Kurokawa K, Prutz KG, Levin NW. Mortality among hemodialysis patients in Europe, Japan, and the United States: case-mix effects. Am J Kidney Dis . 2004;44(5 Suppl 2):16–21. Foley RN, Collins AJ. End-stage renal disease in the United States: an update from the USRDS. J Am Soc Nephrol . 2007;18:2644–8. Tables Table 1. Combined Baseline Characteristics and Nutritional Comparisons Variable Total (Mean ± SD) Survivors (Mean ± SD) Non-survivors (Mean ± SD) p-value Age (years) 77.7 ± 11.7 77.6 ± 11.8 77.8 ± 11.6 0.914 GNRI 104.8 ± 15.1 109.5 ± 12.2 100.0 ± 16.9 0.001 6-Month Weight Change (kg) 0.6 ± 4.8 2.1 ± 5.7 -0.9 ± 3.1 0.003 Handgrip Strength (kg) 23.8 ± 9.3 26.8 ± 11.0 20.8 ± 6.9 0.016 LTI (kg/m²) 12.5 ± 3.1 13.0 ± 3.0 11.8 ± 1.8 0.094 FTI (kg/m²) 13.0 ± 6.5 14.7 ± 6.0 14.3 ± 8.0 0.840 Albumin (g/L) 35.8 ± 5.8 37.9 ± 4.6 33.7 ± 6.7 0.000 Prealbumin (mg/dL) 27.8 ± 7.0 30.8 ± 6.7 24.8 ± 7.2 0.000 CRP (mg/L) 12.5 ± 18.2 8.4 ± 10.9 16.6 ± 20.7 0.008 Potassium (mmol/L) 4.9 ± 0.7 4.85 ± 0.6 4.63 ± 0.7 0.074 Phosphorus (mg/dL) 49.6 ± 15.7 50.5 ± 15.9 48.8 ± 15.5 0.548 Bicarbonate (mmol/L) 22.8 ± 2.8 23.0 ± 2.2 22.6 ± 3.2 0.408 nPCR (g/kg/day) 1.09 ± 0.27 1.04 ± 0.23 1.14 ± 0.31 0.110 Table 1. Combined Baseline Characteristics and Nutritional Comparisons. LTI: Lean Rissue Index, FTI: Fat tissue Index). CRP: C-reactive protein. nPCR: normalized Protein catabolic rate. p values <0.05 were considered statistically significant. Table 2. Simplified Multivariate Logistic Regression Models Model Variable Odds Ratio (Exp(B)) p-value A GNRI 1.105 0.001 A 6-Month Weight Change 0.954 0.039 B GNRI 1.047 0.041 B Handgrip Strength 1.075 0.028 Table 2 – Simplified Multivariate Logistic Regression Models , fully †Model A includes GNRI and 6-month weight change; both were independently associated with survival. ‡Model B includes GNRI and handgrip strength, each showing significant predictive value. §Odds ratios (Exp(B)) represent the change in odds of survival per unit increase in the predictor variable. ¶GNRI = Geriatric Nutritional Risk Index. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7488060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514082035,"identity":"2c980a90-79af-4894-9fd3-1c7d9f968404","order_by":0,"name":"Georgios Kosmadakis","email":"data:image/png;base64,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","orcid":"","institution":"AURA SANTE","correspondingAuthor":true,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Kosmadakis","suffix":""},{"id":514082036,"identity":"effc7ea1-aa60-43c0-bfa4-c2517c7eda44","order_by":1,"name":"Aura Necoara","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Aura","middleName":"","lastName":"Necoara","suffix":""},{"id":514082037,"identity":"4847d11e-fdbb-4ed5-935d-b08b945af75f","order_by":2,"name":"Julien Baudenon","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Baudenon","suffix":""},{"id":514082038,"identity":"5ee118df-a28d-4d35-8c35-b2b53ab539a5","order_by":3,"name":"Clemence Deville","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Clemence","middleName":"","lastName":"Deville","suffix":""},{"id":514082039,"identity":"1de6662e-5337-4c8e-9a9f-ba1f1e8976e6","order_by":4,"name":"Ioana Enache","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Ioana","middleName":"","lastName":"Enache","suffix":""},{"id":514082040,"identity":"958c8592-cf6a-4cc6-8523-d6f773ad11d6","order_by":5,"name":"Fatimza El Kanouni","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Fatimza","middleName":"El","lastName":"Kanouni","suffix":""},{"id":514082041,"identity":"a1ce5edc-bc5c-454a-ba09-67ed89de38ad","order_by":6,"name":"Joseph El Hajal","email":"","orcid":"","institution":"AURA SANTE","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"El","lastName":"Hajal","suffix":""}],"badges":[],"createdAt":"2025-08-29 11:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7488060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7488060/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11255-025-04856-w","type":"published","date":"2025-10-24T16:16:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91816885,"identity":"55f2e070-baeb-491f-9ea0-1df242325772","added_by":"auto","created_at":"2025-09-22 06:52:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28663,"visible":true,"origin":"","legend":"","description":"","filename":"Table2LogisticModels.docx","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/08cf68bbdbea208e5934a8c2.docx"},{"id":91816587,"identity":"e867190f-ff06-4cd1-9f76-a7c616002e3f","added_by":"auto","created_at":"2025-09-22 06:52:01","extension":"pptx","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122757,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1ForestPlot.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/662cdc991a15543478fcbe19.pptx"},{"id":91817087,"identity":"d44ad079-6a66-462f-81f1-7f55c1bc1c25","added_by":"auto","created_at":"2025-09-22 06:53:37","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52887,"visible":true,"origin":"","legend":"","description":"","filename":"1c81635c97284b288adcb2d3985ce9641structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/5473f7c32c4ecb981ade3308.xml"},{"id":91816710,"identity":"7b698e29-40ab-452b-8b50-258bb5cba154","added_by":"auto","created_at":"2025-09-22 06:52:42","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61294,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/553c9c6d324ce7aebc3c87c5.html"},{"id":91488809,"identity":"9c708268-90b7-4e1e-8a7e-7fb2b64e0575","added_by":"auto","created_at":"2025-09-17 05:09:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45305,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Independent Predictors of Survival\u003c/p\u003e","description":"","filename":"Figure1ForestPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/8d47a109e8d0c9823ea9d569.png"},{"id":91488808,"identity":"73715bdd-d9c2-4571-bffd-bbf27db0db45","added_by":"auto","created_at":"2025-09-17 05:09:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54588,"visible":true,"origin":"","legend":"\u003cp\u003eGNRI Distribution by Survival Status\u003c/p\u003e","description":"","filename":"Figure2GNRIDistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/56d30d61313acf404e91ce9d.png"},{"id":94490395,"identity":"56db21e1-97cb-4f9c-b296-8051c54c1b6d","added_by":"auto","created_at":"2025-10-27 17:09:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":700870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7488060/v1/f19911a1-824c-41dd-a124-245d89b303ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nutritional Predictors of One-Year Survival in Hemodialysis Patients: Insights from an Age-Matched Cohort Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProtein-energy wasting (PEW) is a common and serious complication of chronic kidney disease (CKD), particularly among individuals undergoing maintenance hemodialysis. PEW reflects a complex metabolic state characterized by reduced body protein and energy stores, which has been repeatedly linked to increased hospitalization and mortality in this population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The identification of reliable, practical, and early markers of nutritional deterioration is crucial for timely intervention and risk stratification in clinical nephrology.\u003c/p\u003e\u003cp\u003eTraditional nutritional biomarkers such as serum albumin and prealbumin have long been associated with outcomes in dialysis patients. However, these markers are limited by their sensitivity to inflammation, volume status, and non-nutritional influences [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. More comprehensive or functional markers, including the Geriatric Nutritional Risk Index (GNRI) and handgrip strength, have emerged as promising alternatives. GNRI, derived from serum albumin and body weight relative to ideal body weight, has shown consistent predictive value for mortality across various dialysis cohorts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Handgrip strength, meanwhile, reflects functional muscle status and has been proposed as a more accurate indicator of sarcopenia-related risks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these advances, the relative and independent prognostic value of nutritional markers remains uncertain due to confounding by age and other covariates. Age, in particular, is a dominant determinant of mortality in hemodialysis patients and can obscure associations between nutritional indicators and survival. While traditional multivariable adjustment is commonly employed, it is subject to limitations in small datasets, especially when nutritional variables are collinear or incomplete.\u003c/p\u003e\u003cp\u003eIn this study, we addressed these limitations by applying an age-matching strategy to isolate the role of nutritional factors in predicting one-year survival in hemodialysis patients. Using logistic regression models with a focus on simplicity and clinical feasibility, we aimed to identify nutritional parameters that offer independent prognostic information, ultimately informing risk-based care strategies in this vulnerable population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was designed as a retrospective, observational analysis conducted at a single dialysis center. The initial study population included 350 adult patients undergoing maintenance hemodialysis between 01/01/2024 and 31/12/2024. The patients were informed of the study and signed an non opposition statement. The study had the local Ethics Committee approval number 2024 :11241. Patients with incomplete data on nutritional variables or lacking follow-up at one year were excluded.All the included patients were metabolically stable and were on dialysis for at least 6 months. Mean dialysis vintage (\u0026plusmn;SD) was 28\u0026plusmn;19 months. All patients were on thrice weekly conventional high-flux hemodialysis and the mean dialysis sessions\u0026rsquo;duration (\u0026plusmn;SD) was 228\u0026plusmn;22 minutes. In the present real life cohort 40% of the participants were diabetic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic and clinical variables were collected from electronic medical records. Nutritional assessments included the evolution of body weight at one end six months prior to the present measurment, Geriatric Nutritional Risk Index (GNRI), serum albumin, prealbumin, handgrip strength (measured in the dominant hand using a hydraulic dynamometer-Jamar\u0026reg;) as well as Lean Tissue Index (LTI) and Fat Tissue Index (FTI), measured by the \u0026nbsp;Body Composition Monitor (BCM; Fresenius Medical Care, Sankt Wendel, Germany) approximately 20\u0026ndash;30 minutes before a hemodialysis session. GNRI was calculated as: GNRI = [1.489 \u0026times; albumin (g/L)] + [41.7 \u0026times; (weight/ideal weight)], where ideal weight was based on a BMI of 22 kg/m\u0026sup2;. Additional laboratory variables included CRP, potassium, phosphorus, bicarbonate, and normalized protein catabolic rate (nPCR) measured from the biological values in a montly basis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was all-cause survival at one year. For the main analysis, a 1:1 age-matching strategy was employed using nearest-neighbor matching without replacement, based on age (\u0026plusmn;1 year). This yielded two comparable groups: 59 survivors and 59 non-survivors.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to compare demographic and nutritional characteristics between groups. Continuous variables were expressed as mean \u0026plusmn; standard deviation or median (IQR), and categorical variables as frequencies or percentages. Group comparisons were made using independent-samples t-tests or Mann\u0026ndash;Whitney U tests as appropriate. Normality was assessed using the Shapiro\u0026ndash;Wilk test.\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression was used to identify individual predictors of survival. Variables significant at p\u0026lt;0.10 were entered into multivariate logistic regression models. Due to collinearity between nutritional variables and limited sample size, simplified models with 2\u0026ndash;3 variables were built to maximize interpretability and stability. All analyses were conducted using IBM SPSS Statistics (version 20.0), with a significance threshold set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003cbr\u003e\u0026nbsp;This study was approved by the local Ethics Committee (approval number 2024:11241) and conducted in accordance with the Declaration of Helsinki. All patients provided a signed non-opposition statement. Patient confidentiality and anonymity were strictly maintained.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 118 hemodialysis patients (76M/42F, were analyzed following 1:1 age and sex matching, yielding two subgroups of 59 survivors (38M/21F) and 59 non-survivors (38M/21F) after one year of follow up. The mean age of the matched population was 77.70 \u0026plusmn; 11.64 years, with no significant difference between survivors and non-survivors (p = 0.914). The male-to-female ratio and other demographic variables were well balanced (data not shown).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBaseline Nutritional and Biochemical Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDetailed descriptive and comparative statistics of the matched population are summarized in Table 1. Survivors consistently showed a more favorable nutritional and inflammatory profile compared to non-survivors. GNRI was significantly higher among survivors (109.5 \u0026plusmn; 12.2) than non-survivors (100.0 \u0026plusmn; 16.9), with a strong statistical association (p = 0.001), suggesting its robust predictive capacity. Handgrip strength also differed significantly between groups, with survivors exhibiting greater muscle strength (26.8 \u0026plusmn; 11.0 kg) compared to non-survivors (20.8 \u0026plusmn; 6.9 kg, p = 0.016), reinforcing the association between functional capacity and survival. The direction and magnitude of 6-month weight change further highlighted this nutritional divergence: survivors gained weight (+2.1 \u0026plusmn; 5.7 kg), whereas non-survivors lost weight (-0.9 \u0026plusmn; 3.1 kg), with a statistically significant difference (p = 0.003).\u003c/p\u003e\n\u003cp\u003eFrom a biochemical standpoint, serum albumin and prealbumin levels were significantly higher in the survivor group, at 37.9 \u0026plusmn; 4.6 g/L and 30.8 \u0026plusmn; 6.7 mg/dL, respectively, compared to 33.7 \u0026plusmn; 6.7 g/L and 24.8 \u0026plusmn; 7.2 mg/dL among non-survivors (p \u0026lt; 0.001 for both). \u0026nbsp;CRP levels, an index of systemic inflammation, were nearly twice as high in the non-survivor group (16.6 \u0026plusmn; 20.7 mg/L) versus survivors (8.4 \u0026plusmn; 10.9 mg/L), with this difference reaching statistical significance (p = 0.008). In contrast, other parameters such as Lean Tissue Index (LTI), Fat Tissue Index (FTI), serum potassium, phosphorus, bicarbonate, and nPCR, while differing in absolute values, did not achieve statistical significance (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredictors of Survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine which variables independently predicted 1-year survival, we conducted logistic regression analysis using simplified models. As reported in Table 2 and Figure 1, Model A identified GNRI (OR: 1.105, 95% CI not shown, p = 0.001) and 6-month weight change (OR: 0.954, p = 0.039) as significant and independent predictors. The odds ratio for GNRI implies that each unit increase is associated with approximately a 10% higher odds of survival. Conversely, weight loss was associated with increased mortality risk. In Model B, GNRI remained significant (OR: 1.047, p = 0.041), and handgrip strength emerged as an independent predictor as well (OR: 1.075, p = 0.028), indicating the importance of functional muscle mass as a prognostic indicator.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGNRI Distribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic value of GNRI is further illustrated in Figure 2, where the distribution of GNRI categories is stratified by survival status. Survivors were predominantly represented in the higher GNRI brackets (\u0026gt;108), while non-survivors were concentrated in the lower GNRI categories (\u0026lt;98), clearly demonstrating the clinical discriminative capacity of this nutritional index.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that key nutritional parameters\u0026mdash;specifically the Geriatric Nutritional Risk Index (GNRI), 6-month weight change, and handgrip strength\u0026mdash;are significant and independent predictors of one-year mortality in hemodialysis patients when age is carefully controlled. These findings align with and expand upon previous research linking protein-energy wasting (PEW) with poor outcomes in this population [1,2].\u003c/p\u003e\n\u003cp\u003eGNRI consistently emerged as the most robust nutritional predictor across models. This index combines serum albumin with weight-for-height status and has been validated in several dialysis cohorts [5,8,9]. Its predictive utility in our study remained strong even after adjusting for other markers, emphasizing its value as a simple, objective, and clinically feasible tool. Notably, GNRI performed better than serum albumin or prealbumin alone, likely because it integrates both static and body composition elements of nutrition [4].\u003c/p\u003e\n\u003cp\u003eHandgrip strength also proved to be an independent predictor of survival, reinforcing the growing recognition of sarcopenia as a determinant of poor prognosis in CKD patients [6,7]. Unlike biochemical markers, handgrip reflects functional capacity and physical resilience, making it a complementary tool to GNRI. Its inclusion in multivariate models improved predictive accuracy and supports the routine incorporation of muscle strength assessments in dialysis units.\u003c/p\u003e\n\u003cp\u003eThe inclusion of weight change as a predictor is particularly relevant. Unintentional weight loss has long been recognized as a marker of frailty and deteriorating nutritional status. Our results confirm that greater weight loss over six months is associated with higher mortality, consistent with previous longitudinal studies [10,11]. Unlike GNRI, which provides a snapshot, weight change reflects dynamic trends and patient trajectory.\u003c/p\u003e\n\u003cp\u003eImportantly, models including combinations of GNRI, albumin, and prealbumin were unstable, likely due to high intercorrelation. These findings highlight the potential risks of overfitting when using multiple overlapping nutritional variables in small samples. By focusing on simplified, non-redundant models, we were able to identify interpretable and statistically robust predictors of outcome.\u003c/p\u003e\n\u003cp\u003eOur use of age-matching, rather than statistical adjustment alone, strengthened the internal validity of this study. Age is a powerful confounder in survival studies, especially in the dialysis population where mortality risk increases sharply with advancing age [12]. By matching survivors and non-survivors within a narrow age range, we were able to isolate the contribution of nutritional variables more effectively.\u003c/p\u003e\n\u003cp\u003eNevertheless, this study has limitations. It is retrospective and single-centered, which may limit generalizability. Nutritional interventions during the follow-up period were not recorded, and inflammation markers such as IL-6 were not routinely available. Despite these constraints, our approach provides a pragmatic model for nutritional risk assessment in real-world dialysis practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, GNRI, handgrip strength, and recent weight change are complementary and independent predictors of one-year survival in hemodialysis patients. Their integration into routine clinical monitoring may enhance early identification of at-risk individuals and prompt timely nutritional and rehabilitative interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e-Disclosure of potential conflicts of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no conflict of interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e- Research involving Human Participants and/or Animals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee at which the studies were conducted (IRB approval number 2024\u0026nbsp;:11241) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standard\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e- Informed consent\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIkizler TA, Cano NJ, Franch H, et al. Prevention and treatment of protein-energy wasting in chronic kidney disease patients: a consensus statement by the International Society of Renal Nutrition and Metabolism. \u003cem\u003eKidney Int\u003c/em\u003e. 2013;84(6):1096\u0026ndash;1107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalantar-Zadeh K, Ikizler TA, Block G, Avram MM, Kopple JD. Malnutrition-inflammation complex syndrome in dialysis patients: causes and consequences. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2003;42(5):864\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaysen GA. Biochemistry and biomarkers of inflamed patients: why look, what to assess. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2009;4(Suppl 1):S56\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. \u003cem\u003eAm J Clin Nutr\u003c/em\u003e. 2005;82(4):777\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong J, Wang M, Zhang Y, et al. Association of Geriatric Nutritional Risk Index with mortality in hemodialysis patients: a meta-analysis of cohort studies. \u003cem\u003eKidney Blood Press Res\u003c/em\u003e. 2018;43(6):1878\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamarca F, Carrero JJ, Rodrigues JC, et al. Prevalence of sarcopenia in elderly maintenance hemodialysis patients: the impact of different diagnostic criteria. \u003cem\u003eJ Nutr Health Aging\u003c/em\u003e. 2014;18(7):710\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang SH, Cho KH, Park JW, Yoon KW, Do JY. Geriatric Nutritional Risk Index as a prognostic factor in peritoneal dialysis patients. \u003cem\u003ePerit Dial Int\u003c/em\u003e. 2013;33(4):405\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshida M, Nakashima A, Doi S, et al. Lower Geriatric Nutritional Risk Index is associated with higher risk of fractures in patients undergoing hemodialysis. \u003cem\u003eNutrients\u003c/em\u003e. 2021;13(8):2847.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinger R, Huang HC. Weight change in chronic kidney disease: association with mortality and kidney function. \u003cem\u003eObes Sci Pract\u003c/em\u003e. 2023;10(1):e723.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Mutsert R, Grootendorst DC, Indemans F, Boeschoten EW, Krediet RT, Dekker FW. Association between serum albumin and mortality in dialysis patients is partly explained by inflammation, and not by malnutrition. \u003cem\u003eJ Ren Nutr\u003c/em\u003e. 2009;19(2):127\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoodkin DA, Young EW, Kurokawa K, Prutz KG, Levin NW. Mortality among hemodialysis patients in Europe, Japan, and the United States: case-mix effects. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2004;44(5 Suppl 2):16\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFoley RN, Collins AJ. End-stage renal disease in the United States: an update from the USRDS. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e. 2007;18:2644\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ch3\u003eTable 1. Combined Baseline Characteristics and Nutritional Comparisons\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors (Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-survivors (Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e77.7 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e77.6 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e77.8 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e104.8 \u0026plusmn; 15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e109.5 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e100.0 \u0026plusmn; 16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-Month Weight Change (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e0.6 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e-0.9 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHandgrip Strength (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e23.8 \u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e26.8 \u0026plusmn; 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e20.8 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e11.8 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFTI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e14.7 \u0026plusmn; 6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e14.3 \u0026plusmn; 8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e35.8 \u0026plusmn; 5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e37.9 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e33.7 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrealbumin (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e27.8 \u0026plusmn; 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e30.8 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e24.8 \u0026plusmn; 7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e8.4 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e16.6 \u0026plusmn; 20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e4.85 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e4.63 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhosphorus (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e49.6 \u0026plusmn; 15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e50.5 \u0026plusmn; 15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e48.8 \u0026plusmn; 15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBicarbonate (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e22.8 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e23.0 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e22.6 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.4949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enPCR (g/kg/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7474%;\"\u003e\n \u003cp\u003e1.09 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2116%;\"\u003e\n \u003cp\u003e1.04 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e1.14 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2628%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1. Combined Baseline Characteristics and Nutritional Comparisons. LTI: Lean Rissue Index, FTI: Fat tissue Index). CRP: C-reactive protein. nPCR: normalized Protein catabolic rate. p values \u0026lt;0.05 were considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eTable 2. Simplified Multivariate Logistic Regression Models\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (Exp(B))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.735%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2671%;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.735%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2671%;\"\u003e\n \u003cp\u003e6-Month Weight Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.735%;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2671%;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.735%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2671%;\"\u003e\n \u003cp\u003eHandgrip Strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.735%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash; Simplified Multivariate Logistic Regression Models\u003c/strong\u003e, fully \u0026dagger;Model A includes GNRI and 6-month weight change; both were independently associated with survival.\u003cbr\u003e\u0026Dagger;Model B includes GNRI and handgrip strength, each showing significant predictive value.\u003cbr\u003e\u0026sect;Odds ratios (Exp(B)) represent the change in odds of survival per unit increase in the predictor variable.\u003cbr\u003e\u0026para;GNRI = Geriatric Nutritional Risk Index.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hemodialysis, Nutritional status, Longitudinal study, Malnutrition","lastPublishedDoi":"10.21203/rs.3.rs-7488060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7488060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMalnutrition and protein-energy wasting are major predictors of mortality in hemodialysis patients. While several nutritional markers have been proposed, the independent prognostic value of individual variables remains debated, especially in the context of age-related confounding.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed a cohort of 350 hemodialysis patients (237M/128F, mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard deviation (SD) : 69.78\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47 years old). One-year survival was assessed, and a 1:1 age-matched selection (59 survivors and 59 non-survivors) (76M/42F, mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD :77.70\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64) was performed. Nutritional variables including GNRI, serum albumin, prealbumin, handgrip strength, body composition parametrs and weight variation were evaluated. Logistic regression models were used to assess their association with survival, with emphasis on simplified multivariate models to avoid overfitting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eUnivariate comparisons showed significantly better nutritional profiles among survivors. After matching for age, GNRI, prealbumin, and handgrip strength remained significantly associated with survival. Multivariate models including GNRI combined with either 6-month weight change or handgrip strength yielded significant and stable associations with survival (OR for GNRI: 1.10, p\u0026thinsp;=\u0026thinsp;0.001; OR for handgrip: 1.08, p\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn an age-matched hemodialysis cohort, GNRI, dynamic weight change, and muscle strength emerged as independent predictors of one-year survival. Simplified models provide robust prognostic insight and are preferable to overparameterized regressions in small datasets.\u003c/p\u003e","manuscriptTitle":"Nutritional Predictors of One-Year Survival in Hemodialysis Patients: Insights from an Age-Matched Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 05:09:14","doi":"10.21203/rs.3.rs-7488060/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":"e3e89a3d-bfa3-4708-94d0-9001fe9baa25","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:31:08+00:00","versionOfRecord":{"articleIdentity":"rs-7488060","link":"https://doi.org/10.1007/s11255-025-04856-w","journal":{"identity":"international-urology-and-nephrology","isVorOnly":false,"title":"International Urology and Nephrology"},"publishedOn":"2025-10-24 16:16:51","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-09-17 05:09:14","video":"","vorDoi":"10.1007/s11255-025-04856-w","vorDoiUrl":"https://doi.org/10.1007/s11255-025-04856-w","workflowStages":[]},"version":"v1","identity":"rs-7488060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7488060","identity":"rs-7488060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}