Identifying Malnutrition Risk Factors: A Comparative Analysis of Malnutrition Diagnostic Tools

Identifying Malnutrition Risk Factors: A Comparative Analysis of Malnutrition Diagnostic Tools | 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 Article Identifying Malnutrition Risk Factors: A Comparative Analysis of Malnutrition Diagnostic Tools Lama Mattar, Dayana El Chaar, Cosette Fakih El-Khoury This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7179279/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Evidence recognizing associations of certain risk factors with malnutrition diagnostic tools [European Society of Clinical Nutrition and Metabolism (ESPEN), Academy of Nutrition and Dietetics/American Society of Parenteral and Enteral Nutrition (AND/ASPEN), and Global Leadership Initiative on Malnutrition (GLIM)] and their combinations of criteria, is limited. This study aims to identify the association of certain sociodemographic, clinical, and other risk factors with various combinations of malnutrition criteria. Methods This prospective study assessed 11 risk factors against various combinations of malnutrition criteria from three tools in a sample of 578 patients. Multiple logistic regression analyses were used to assess this association and identify significant risk factors. Results Comorbidities were the only associated risk factor across all combinations of criteria (p < 0.02). Similarly, being female was associated with malnutrition (ESPEN: 95%CI:1.070–4.701, p = 0.032; ESPEN-1: 95%CI:1.231–16.318, p = 0.023; AND/ASPEN: 95%CI:1.357–3.810, p = 0.002; GLIM: 95%CI:1.310–3.471, p = 0.002; GLIM-1: 95%CI:1.230–3.302, p = 0.005; GLIM-2 95%CI:1.181–5.735, p = 0.018) except when using unintentional weight loss with low BMI-for-age (ESPEN-2). The combination of unintentional weight loss with reduced food intake (AND/ASPEN) or reduced food assimilation/absorption (GLIM-1) identified females, having pressure ulcers (AND/ASPEN: 95%CI:2.897–58.861, p = 0.001; GLIM-1: CI:3.389–64.825, p < 0.001), and the presence of comorbidities as significant risk factors (AND/ASPEN: 95%CI:1.131–1.444, p < 0.0001; GLIM-1: 95%CI:1.130–1.419, p < 0.001). Low BMI-for-age combinations showed between-group differences in the identified risk factors. In contrast, using low BMI as the sole indicator to diagnose malnutrition revealed different risk factors than other combinations. Conclusion ESPEN, AND/ASPEN, and GLIM combination of criteria are mostly associated with being female and having multiple comorbidities and pressure ulcers. Health sciences/Risk factors Health sciences/Signs and symptoms malnutrition risk factors malnutrition diagnostic tools combination of criteria Introduction Malnutrition is estimated to impact 20–50% of patients admitted to the hospital.(1–3) The high prevalence of malnutrition highlights its significance as a frequent and severe complication that deteriorates clinical and economic outcomes.(4–7) It is primarily caused by reduced dietary intake, altered nutrient absorption, increased energy expenditure, or loss of nutrients.(7) Malnutrition diagnostic tools utilize multiple criteria to identify malnutrition.(8–10) These include low body mass index (BMI), low-BMI-for age, unintentional weight loss, muscle loss, reduced food intake, reduced food assimilation, and increased inflammation.(8–10) In addition to the well-known clinical criteria and risk factors, the diagnosis of malnutrition has been commonly associated with sociodemographic, clinical, and other risk factors. Malnutrition diagnostic tools utilize multiple criteria to identify malnutrition.(8–10) These include low body mass index (BMI), low-BMI-for age, unintentional weight loss, muscle loss, reduced food intake, reduced food assimilation, and increased inflammation.(8–10) In addition to the well-known clinical criteria and risk factors, the diagnosis of malnutrition has been commonly associated with sociodemographic, clinical, and other risk factors. For example, older age, morbidity, living alone, marital status, particularly being unmarried, separated, or divorced, lower educational status, and polypharmacy are among the identified risk factors of hospital malnutrition. (6, 11–16) Despite evidence of the association of such risk factors with hospital malnutrition, their importance remains underrecognized in clinical settings, particularly when clinicians screen and assess individuals for malnutrition. As malnutrition diagnostic tools, such as the European Society of Clinical Nutrition and Metabolism (ESPEN)(10), Academy of Nutrition and Dietetics/American Society of Parenteral and Enteral Nutrition (AND/ASPEN),(9) and the Global Leadership Initiative on Malnutrition (GLIM)(8), vary in the criteria they use, they may identify different subsets of malnourished individuals. Therefore, understanding how certain sociodemographic, clinical, and other risk factors are associated with the combinations of criteria of the three most utilized malnutrition diagnostic tools can support a more equitable and comprehensive malnutrition identification. Therefore, this study aims to identify differences in the association of certain sociodemographic, clinical, and other risk factors with the multiple combinations of criteria of three malnutrition diagnostic tools. Materials and Methods Study Design This study is a single-center prospective study that was conducted in Beirut-Lebanon in a private hospital (Lebanese American University Medical Center-Rizk Hospital). The study protocol was approved by the Lebanese American University Institutional Review Board (LAU.SAS.LM1.1/Mar/2019). Sample Size A methodology for calculating sample size in multiple regression models was used. The number of assessed risk factors (n=11) in this study was used as a parameter for the calculation. The anticipated effect size ( f 2 ) was set at 0.15, the statistical level power at 0.8, and the probability level at 0.05. The sample size calculation yielded a target sample of 122 malnourished patients.(17) Inclusion and Exclusion Criteria All newly admitted patients during the study period who were above the age of 18 years were considered for inclusion. Patients were excluded if they were pregnant, <18 years, admitted directly to the intensive care unit, cardiac care unit, or outpatient wards, admitted to undergo bariatric or elective surgeries, expected to have a short length of stay (less than 72 hours), and diagnosed with eating disorders. (18) Risk Factors 11 risk factors were collected: (1) patients age, (2) sex, (3) presence of caregiver, (4) socioeconomic status, (5) educational status, (6) presence of comorbidities, (7) admission unit, (8) presence of pressure ulcers, (9) the risk of development of pressure ulcers, (10) number of medications, and (11) presence of polypharmacy. Polypharmacy was identified based on the numerical definition of 5 or more medications daily.(19) Routine assessment of the risk of pressure ulcers was performed for all patients using the Braden Scale, a valid, reliable and frequently used assessment tool for the risk of pressure ulcers.(20, 21) The tool classifies individuals as having no risk, mild, moderate, high, or very high risk of developing pressure ulcers.(22) Patients’ comorbidities were compiled to estimate the Charlson Comorbidity Index (CCI). The CCI, a valid and reliable method of classifying comorbidities and estimating the risk of mortality, (23-25) suggests that the higher the score, the higher the risk of mortality.(26) Malnutrition Screening and Diagnosis Newly admitted patients were approached for malnutrition screening. Patients who were enrolled in the study provided a written informed consent before the screening process. To minimize the risk of bias, licensed and trained dietitians only screened and assessed patients for malnutrition. This was performed according to the hospital protocol, using the Mini-Nutritional Assessment-Short Form (MNA-SF) or the Nutrition Risk Screening-2002 (NRS-2002). Subsequently, individuals with a nutritional risk, identified by one of the screening tools, was subjected to an in-depth malnutrition assessment using all three malnutrition diagnostic tools: ESPEN(10), AND/ASPEN,(9) and GLIM(8). Subsequently, individuals with a nutritional risk, identified by one of the screening tools, was subjected to an in-depth malnutrition assessment using all three malnutrition diagnostic tools: ESPEN(10), AND/ASPEN,(9) and GLIM(8). Combinations of Malnutrition Diagnostic Criteria A subsequent analysis of the validity and reliability of the combinations of criteria within the three malnutrition diagnostic tools was also performed.(27) The methodology of the combination of A subsequent analysis of the validity and reliability of the combinations of criteria within the three malnutrition diagnostic tools was also performed.(27) The methodology of the combination ofcriteria has been previously described(28) and validated through sensitivity and specific analyses in our published research.(27) The combinations of criteria for the three diagnostic tools are presented in Table 1.(27) The ones that are most commonly assessed across these combinations include unintentional weight loss, lower body mass index (BMI), lower BMI-for-age, decreased food intake, and reduced food assimilation. In addition to the overall tools, two combinations of criteria from the ESPEN tool (ESPEN-1 and ESPEN-2), one fromfrom AND/ASPEN tool, and two from GLIM tool (GLIM-1 and GLIM-2) will be used to compare the differences in certain sociodemographic, clinical, and other risk factors across the multiple combinations of criteria of malnutrition tools. Statistical Analysis Statistical analysis was conducted using SPSS, version 24. Categorical variables were reported as frequencies and percentages [n (%)], while continuous variables were reported as means ± standard deviations [Mean± SD]. Descriptive tests were performed for the 11 risk factors, and normality was assessed for age, CCI, and number of medications. Normality tests and bivariate analyses were conducted for the assessed malnutrition risk factors on all tools. Pearson Chi-square, Fisher Exact, and Mann-Whitney tests were used for the bivariate analysis. Logistic regression analyses were performed to assess the association of the risk factors with malnutrition, identified through various diagnostic tools and combinations of indicators within the same tool. If the p-value was less than 0.2 in the bivariate analysis, variables were included in the logistic regression models. An unexpected change in direction after running the logistic regression for the “age” variable was identified. Results of diagnostic analyses revealed an interaction of the “age” with the “CCI”. As the index encompasses age within its score, such interaction was deemed a logical one. Therefore, two models were created and compared in each logistic regression: (1) a model with CCI excluding age, (2) and a model with age excluding CCI. The model with the CCI alone conveyed the highest Cox & Snell R 2 and was reported in the results. The model containing the age variable was used to report the association of “age” with malnutrition diagnosis alone. Results A total of 578 patients were screened for malnutrition, of which 124 were nutritionally at risk according to the MNA and NRS-2002 screening tools. Table 1 in the supplemental material shows the differences in sociodemographic, clinical, and other risk factors based on nutritional risk. Results of all bivariate analyses of the risk factors with the screening and diagnostic tools and the logistic regression of the screening tools are described in the supplemental material (Tables 1-5). 1. Multiple Logistic Regression of Risk-Factors with ESPEN Malnutrition Diagnostic Tool Females (OR=2.243, 95% CI 1.070 -4.701, p=0.032) and a higher CCI score (OR=1.217, 95% CI 1.046 -1.416, p=0.011) were associated with a higher likelihood of malnutrition diagnosis using the overall ESPEN tool (Table 2). Similarly, ESPEN-1 ((BMI) <18.5 kg/m 2 ) revealed the same likelihood of malnutrition in females (OR=1.500, 95% CI 1.231-16.318, p=0.023) and individuals with higher comorbidities (OR=1.329, 95% CI 1.089 -1.625, p=0.006). Results of the model also showed that there are lower odds of malnutrition, diagnosed with low BMI, when there is an absence of a caregiver (OR=0.281, 95% CI 0.097-0.812, p=0.019) and polypharmacy (OR=0.237, 95% CI 0.072-0.775, p=0.017). The logistic regression of ESPEN-2 identified only the CCI score as a risk factor that was associated with malnutrition diagnosis, using the combination of unintentional weight loss and low BMI-for-age (OR=1.220, 95% CI 1.034-1.440, p=0.018) (Table 2). 2. Multiple Logistic Regression of Malnutrition Risk-Factors with AND/ASPEN Risk factors that were significantly related to higher odds of being diagnosed with malnutrition on the AND/ASPEN criteria of unintentional weight loss combined with decreased food intake criteria are being female (OR=2.274, 95% CI 1.357-3.810, p=0.002), having pressure ulcers (OR=13.058, 95% CI 2.897-58.861, p=0.001), and higher CCI score (OR=1.278, 95% CI 1.131-1.444, p<0.0001) (Table 3). Individuals admitted to wards other than internal medicine and surgery were less likely to be malnourished using this combination of criteria (OR=0. 053, 95% CI 0. 006-0. 492, p=0.010) (Table 3). 3. Multiple Logistic Regression of Malnutrition Risk-Factors with GLIM The risk factors of being females (OR=2.133, 95% CI 1.310-3.471, p=0.002; OR=2.015, 95% CI 1.230-3.302, p=0.005; OR=2.602, 95% CI 1.181-5.735, p=0.018) and having a higher CCI score (OR=1.285, 95% CI 1.148-1.439, p<0.001; OR=1.266, 95% CI 1.130-1.419, p<0.001; OR=1.179, 95% CI 1.005-1.382, p=0.043) were positively associated with an higher risk of malnutrition, identified through overall GLIM, GLIM-1 (unintentional weight combined with reduced food assimilation or absorption) and GLIM 2 (low BMI-for age combined with reduced food assimilation or absorption) respectively (Table 4). Both GLIM and GLIM-1 classified the presence of pressure ulcers as a risk factor that was associated with malnutrition diagnosis (OR: 13.325, 95%CI 3.049-58.235, p<0.001; OR: 14.822, 95%CI 3.389-64.825, p<0.001, respectively). Additionally, both combinations of criteria revealed that the risk of malnutrition was lower when individuals were admitted to units other than the internal medicine and surgery units (GLIM: OR: 0.057, 95%CI: 0.006-0.517, p=0.011; GLIM-1: OR: 0.059, 95%CI: 0.006-0.531, p=0.012) (Table 4). 4. Comparison of Malnutrition Risk-Factors Identified with the Three Diagnostic Tool: A higher CCI score, indicating higher comorbidities, was the only risk factor that was identified by all combinations of criteria using the three malnutrition diagnostic tools (Table 5). Being female also appeared to be a commonly identified risk factor on all combinations, except for ESPEN-2 (unintentional weight loss and low BMI-for-age). Combinations of criteria involving the use of unintentional weight loss (AND/ASPEN), or low BMI-for-age (GLIM-2) with reduced food assimilation/absorption identified similar risk factors: female, having pressure ulcers, higher CCI score. AND/ASPEN (unintentional weight loss with reduced food intake) and GLIM-2 (low BMI-for-age with reduced food assimilation or absorption) showed that being admitted to wards other than internal medicine and surgery was protective of malnutrition. On the other hand, diagnostic criteria utilizing low BMI-for-age (ESPEN-2 and GLIM-2) did not identify the same risk factors, with only ESPEN-2 revealing females as a risk factor of malnutrition. Despite the common criterion of reduced food assimilation or absorption between GLIM-1 (unintentional weight loss combined with reduced food assimilation/ absorption) and GLIM 2 (low BMI-for age combined with reduced food assimilation/ absorption) (low BMI-for age combined with reduced food assimilation/ absorption), there is a notable difference in the identified risk factors of GLIM-1, which revealed more common risk factors with AND/ASPEN combination of criteria (unintentional weight loss with reduced food intake) than GLIM-2 (Table 5). Discussion Our findings revealed that combinations of malnutrition diagnostic criteria identified female gender, higher CCI score, and the presence of pressure ulcers as the most recognized demographic and clinical risk factors. The CCI was the only common clinical risk factor among all combinations of the diagnostic tools. Female gender was a frequently recognized demographic risk factor identified through all combinations of criteria except for ESPEN-2. Additionally, a higher risk of pressure ulcers was also associated with a diagnosis of malnutrition using the malnutrition screening tools, AND/ASPEN, overall, GLIM, and GLIM-1. Our results revealed that females are at a higher malnutrition risk, identified through multiple combinations of criteria from the tools, in comparison to male patients. This is consistent with the literature showing that females were also at a higher risk of malnutrition when admitted to the hospital.(29, 30) This might be related to longer life expectancy in females compared to men, and a greater risk of economic and social burdens in older adult females.(31) Additionally, biological differences such as increased energy, protein, and certain micronutrient requirements in adult women pose a higher risk of malnutrition compared to men.(32, 33) The association of the risk of pressure ulcer with different combinations of criteria is strongly consistent with previous findings.(34, 35) Likewise, our data on the correlation of higher presence of comorbidities with malnutrition diagnosed has been demonstrated in the literature.(6, 36) Unlike other studies,(6, 11, 37) age did not remain a significant risk factor in our models for the various combinations of malnutrition criteria. Additionally, our results did not detect any association between educational status and socioeconomic status with malnutrition. On the other hand, Ouaijan et al. found that educational status, employment status, and type of health coverage in patients were significant malnutrition risk factors when using GLIM to diagnose malnutrition in the same population.(38) Similarly, Bardon et al. revealed a potential association between educational status and marital status with malnutrition.(14) The presence of a caregiver was not associated with malnutrition in our results, in contrast to a previous research that emphasized that being single, widowed, or divorced was a significant social risk factor of malnutrition.(39) However, our findings demonstrated that the absence of a caregiver was protective of malnutrition when using ESPEN-1 for malnutrition diagnosis, when BMI was used as a diagnostic measure of malnutrition. In addition, polypharmacy was associated with malnutrition only when a BMI < 18.5 kg/m 2 had been used as a diagnostic criterion (ESPEN-1). Similarly, Pirlich et al. demonstrated that polypharmacy is a significant risk factor of malnutrition.(11) This variability in risk factors when utilizing BMI as a sole criterion for malnutrition diagnosis was evident in our findings in comparison to risk factors identified with other criterion combinations. The use of BMI alone for malnutrition diagnosis (ESPEN-1) identified some associations that were challenging to explain. For instance, the absence of a caregiver was identified as a protective factor, and the association of polypharmacy with malnutrition was not observed with any other combinations of criteria. In contrast, when low BMI-for-age was used with other criteria, unintentional weight loss (ESPEN-2) or reduced food assimilation and absorption (GLIM-2), different risk factors were associated with the diagnosis compared to when BMI was used alone. The identified risk factors with ESPEN-2 and GLIM-2 were like those identified with AND/ASPEN (unintentional weight loss combined with decreased food intake), which utilized the combination of unintentional weight loss and reduced food intake. This finding might reinforce the value of combining BMI cut-offs with other criteria for a more comprehensive assessment. The results of this study offer robust insight into the multifactorial nature of malnutrition in hospitalized patients. By identifying sociodemographic, clinical, and other risk factors through the different combinations of criteria, specifically BMI, BMI-for-age, unintentional weight loss, decreased food intake, and reduced food absorption/absorption/assimilation, we demonstrate how certain risk factors may be more closely associated with specific diagnostic approaches. Based on the results of this study, clinicians should consider female gender, multiple comorbidities, and pressure ulcers as significant demographic and clinical risk factors, respectively, particularly when utilizing unintentional weight loss, decreased food intake, and reduced food assimilation as criteria of malnutrition diagnosis. These findings suggest that, depending on the criteria used, some individuals may be more readily identified with a nutritional risk as compared to others who lack it, thus ensuring a more equitable identification of malnutrition in this population. There are multiple limitations to this study. First, most of our sample are individuals of middle to high socioeconomic status, have at least a high school degree, are admitted to the internal medicine and surgery units, and have a caregiver. Therefore, our results might not be generalizable to individuals with lower economic and educational status, admitted to other hospital wards, and who do not have a caregiver. Moreover, not all combinations of malnutrition diagnostic criteria are evaluated in this study; this hinders our ability to perform a comprehensive review of the risk factors for all combinations of criteria. Additionally, the magnitude of the odds ratio, particularly the ones for the risk of pressure ulcers, remains challenging to fully apprehend. Such results might reflect on the complexity of the malnutrition diagnosis and the various risk various risk factors that interplay to influence the latter. Conclusion This analysis highlighted how malnutrition diagnostic tools (ESPEN, AND/ASPEN, and GLIM) and their combinations of criteria (unintentional weight loss, reduced food intake, assimilation or absorption, low BMI, and low BMI-for-age) might be associated with multiple risk factors, primarily female gender, comorbidities, and presence of pressure ulcers. This would assist clinicians in capturing varying risk profiles in newly admitted hospital patients, thus supporting more equitable and comprehensive identification of malnutrition. Lastly, our results address the increasing call for mandating proper nutrition screening to effectively target malnutrition and its burden on individuals and the healthcare system.(1, 6) Declarations Ethical Approval: The study protocol was approved by the Lebanese American University Institutional Review Board (LAU.SAS.LM1.1/Mar/2019). Competing Interests: The authors declare no competing interests related to this work. Funding: No financial assistance was provided to support this study Author contribution Dayana El Chaar: data curation, formal analysis, investigation, writing – original draft, review and editing. Acknowledgement: None Data Availability Statement The data supporting the findings of this study are available in the results section and supplementary tables of this manuscript. Additional details on the demographic characteristics of the population are available in the following publication: El Chaar D, Mattar L, Fakih El Khoury C. AND/ASPEN and the GLIM malnutrition diagnostic criteria have a high degree of criterion validity and reliability for the identification of malnutrition in a hospital setting: A single-center prospective study. Journal of Parenteral and Enteral Nutrition. 2022 Jul;46(5):1061–1070. Further data are available from the corresponding author upon reasonable request. References Barker LA, Gout BS, Crowe TC. 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Ouaijan K, Hwalla N, Kandala N-B, Abi Kharma J, Kabengele Mpinga E. Analysis of predictors of malnutrition in adult hospitalized patients: social determinants and food security. Frontiers in Nutrition. 2023;10:1149579. Amaral TF, Matos LC, Teixeira MA, Tavares MM, Álvares L, Antunes A. Undernutrition and associated factors among hospitalized patients. Clinical nutrition. 2010;29(5):580-5. Tables Table 1 to 5 are available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Table1.docx Table 1 Table2.docx Table 2 Table3.docx Table 3 Table4.docx Table 4 Table5.docx Table 5 SupplementaryTablesTable15.docx Supplementary Tables 1-5 Cite Share Download PDF Status: Under Review Version 1 posted Review # 1 received at journal 20 Oct, 2025 Reviewer # 1 agreed at journal 27 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 22 Jul, 2025 Unknown event 22 Jul, 2025 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. 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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-7179279","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519585110,"identity":"9bfc2581-7682-46a1-9fc6-e1755f10483b","order_by":0,"name":"Lama Mattar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPiQ244MHYDqBgeFhA24tbFBagoeBgdkgAaYlkUgtbBLEaWE/nfiBMceuzp797LOKhJrDDPzsOQYMiTvwaOHJ3SzBuC1Zgocn3exGwrHDDJI9b4BazuBzWO4GoBZmoMPS2G4ksB1mMLgBsqUNjxb+t5t/MG6rl+Dhf8ZWkPDvMIM9QS0SuduAthyW4JFIYwOqBNoiQVDL220WiduOS/bceMYskdiXziNx5lnBAXx+4efP3Xzj47Zqfvb+NMYPH75Zy/G3J2988BFPiIFBAhKbB0QcIKBhFIyCUTAKRgEBAADIP0tjNEi7wwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8813-1798","institution":"Lebanese American University","correspondingAuthor":true,"prefix":"","firstName":"Lama","middleName":"","lastName":"Mattar","suffix":""},{"id":519585111,"identity":"e5a92a7e-914d-4844-8e3d-5429f1a40b6f","order_by":1,"name":"Dayana El Chaar","email":"","orcid":"","institution":"University of 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1","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/3bb8a88e627630b38ffb15b7.docx"},{"id":92870752,"identity":"f8f65e17-8923-4841-9782-5612d7f7f6db","added_by":"auto","created_at":"2025-10-06 14:02:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23105,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2\u003c/p\u003e","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/5306c9c3d6030dbc7278fd56.docx"},{"id":92871945,"identity":"ea452297-0c3b-4588-b6f6-c6f04d6198a8","added_by":"auto","created_at":"2025-10-06 14:10:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18557,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3\u003c/p\u003e","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/51db3cddcc69aeeeb7b3fb6b.docx"},{"id":92872554,"identity":"13474007-e7fc-4b36-a8ef-f21c82347a11","added_by":"auto","created_at":"2025-10-06 14:18:56","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":24133,"visible":true,"origin":"","legend":"\u003cp\u003eTable 4\u003c/p\u003e","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/deda4c9bf4a0a14f500f187a.docx"},{"id":92871956,"identity":"021e0350-49bc-4bc5-8076-8aae792a374e","added_by":"auto","created_at":"2025-10-06 14:10:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18715,"visible":true,"origin":"","legend":"\u003cp\u003eTable 5\u003c/p\u003e","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/2176a3e0bcadf775f737eed6.docx"},{"id":92870754,"identity":"e02ef005-f747-4f94-9868-8c15d5339888","added_by":"auto","created_at":"2025-10-06 14:02:56","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":23449,"visible":true,"origin":"","legend":"Supplementary Tables 1-5","description":"","filename":"SupplementaryTablesTable15.docx","url":"https://assets-eu.researchsquare.com/files/rs-7179279/v1/be21494dee88bf2d19518675.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Identifying Malnutrition Risk Factors: A Comparative Analysis of Malnutrition Diagnostic Tools","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition is estimated to impact 20\u0026ndash;50% of patients admitted to the hospital.(1\u0026ndash;3) The high prevalence of malnutrition highlights its significance as a frequent and severe complication that deteriorates clinical and economic outcomes.(4\u0026ndash;7) It is primarily caused by reduced dietary intake, altered nutrient absorption, increased energy expenditure, or loss of nutrients.(7)\u003c/p\u003e\u003cp\u003eMalnutrition diagnostic tools utilize multiple criteria to identify malnutrition.(8\u0026ndash;10) These include low body mass index (BMI), low-BMI-for age, unintentional weight loss, muscle loss, reduced food intake, reduced food assimilation, and increased inflammation.(8\u0026ndash;10) In addition to the well-known clinical criteria and risk factors, the diagnosis of malnutrition has been commonly associated with sociodemographic, clinical, and other risk factors. Malnutrition diagnostic tools utilize multiple criteria to identify malnutrition.(8\u0026ndash;10) These include low body mass index (BMI), low-BMI-for age, unintentional weight loss, muscle loss, reduced food intake, reduced food assimilation, and increased inflammation.(8\u0026ndash;10) In addition to the well-known clinical criteria and risk factors, the diagnosis of malnutrition has been commonly associated with sociodemographic, clinical, and other risk factors. For example, older age, morbidity, living alone, marital status, particularly being unmarried, separated, or divorced, lower educational status, and polypharmacy are among the identified risk factors of hospital malnutrition. (6, 11\u0026ndash;16) Despite evidence of the association of such risk factors with hospital malnutrition, their importance remains underrecognized in clinical settings, particularly when clinicians screen and assess individuals for malnutrition. As malnutrition diagnostic tools, such as the European Society of Clinical Nutrition and Metabolism (ESPEN)(10), Academy of Nutrition and Dietetics/American Society of Parenteral and Enteral Nutrition (AND/ASPEN),(9) and the Global Leadership Initiative on Malnutrition (GLIM)(8), vary in the criteria they use, they may identify different subsets of malnourished individuals. Therefore, understanding how certain sociodemographic, clinical, and other risk factors are associated with the combinations of criteria of the three most utilized malnutrition diagnostic tools can support a more equitable and comprehensive malnutrition identification. Therefore, this study aims to identify differences in the association of certain sociodemographic, clinical, and other risk factors with the multiple combinations of criteria of three malnutrition diagnostic tools.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy Design\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a single-center prospective study that was conducted in Beirut-Lebanon in a private hospital (Lebanese American University Medical Center-Rizk Hospital).\u0026nbsp;The study protocol was approved by the Lebanese American University Institutional Review Board (LAU.SAS.LM1.1/Mar/2019).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample Size\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA methodology for calculating sample size in multiple regression models was used. The number of assessed risk factors (n=11) in this study was used as a parameter for the calculation. The anticipated effect size (\u003cem\u003ef\u0026nbsp;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was set at 0.15, the statistical level power at 0.8, and the probability level at 0.05. The sample size calculation yielded a target sample of 122 malnourished patients.(17)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInclusion and Exclusion Criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll newly admitted patients during the study period who were above the age of 18 years were considered for inclusion. Patients were excluded if they were pregnant, \u0026lt;18 years, admitted directly to the intensive care unit, cardiac care unit, or outpatient wards, admitted to undergo bariatric or elective surgeries, expected to have a short length of stay (less than 72 hours), and diagnosed with eating disorders. (18)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk Factors\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e11 risk factors were collected: (1) patients age, (2) sex, (3) presence of caregiver, (4) socioeconomic status, (5) educational status, (6) presence of comorbidities, (7) admission unit, (8) presence of pressure ulcers, (9) the risk of development of pressure ulcers, (10) number of medications, and (11) presence of polypharmacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolypharmacy was identified based on the numerical definition of 5 or more medications daily.(19) Routine assessment of the risk of pressure ulcers was performed for all patients using the Braden Scale, a valid, reliable and frequently used assessment tool for the risk of pressure ulcers.(20, 21) The tool classifies individuals as having no risk, mild, moderate, high, or very high risk of developing pressure ulcers.(22)\u003c/p\u003e\n\u003cp\u003ePatients\u0026rsquo; comorbidities were compiled to estimate the Charlson Comorbidity Index (CCI). The CCI, \u0026nbsp;a valid and reliable method of classifying comorbidities and estimating the risk of mortality, (23-25) suggests that the higher the score, the higher the risk of mortality.(26)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMalnutrition Screening and Diagnosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNewly admitted patients were approached for malnutrition screening. Patients who were enrolled in the study provided a written informed consent before the screening process. To minimize the risk of bias, licensed and trained dietitians only screened and assessed patients for malnutrition. This was performed according to the hospital protocol, using the Mini-Nutritional Assessment-Short Form (MNA-SF) or the Nutrition Risk Screening-2002 (NRS-2002). Subsequently, individuals with a nutritional risk, identified by one of the screening tools, was subjected to an in-depth malnutrition assessment using all three malnutrition diagnostic tools: ESPEN(10), AND/ASPEN,(9)\u0026nbsp; \u0026nbsp;and GLIM(8).\u0026nbsp;Subsequently, individuals with a nutritional risk, identified by one of the screening tools, was subjected to an in-depth malnutrition assessment using all three malnutrition diagnostic tools:\u0026nbsp;ESPEN(10), AND/ASPEN,(9)\u0026nbsp; \u0026nbsp;and GLIM(8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCombinations of Malnutrition Diagnostic Criteria\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA subsequent analysis of the validity and reliability of the combinations of criteria within the three malnutrition diagnostic tools was also performed.(27) The methodology of the combination of A subsequent analysis of the validity and reliability of the combinations of criteria within the three malnutrition diagnostic tools was also performed.(27) The methodology of the combination ofcriteria has been previously described(28) and validated through sensitivity and specific analyses in our published research.(27) The combinations of criteria for the three diagnostic tools are presented in Table 1.(27)\u0026nbsp; The ones that are most commonly assessed across these combinations include unintentional weight loss, lower body mass index (BMI), lower BMI-for-age, decreased food intake, and reduced food assimilation.\u003c/p\u003e\n\u003cp\u003eIn addition to the overall tools, two combinations of criteria from the ESPEN tool (ESPEN-1 and ESPEN-2), one fromfrom AND/ASPEN tool, and two from GLIM tool (GLIM-1 and GLIM-2) will be used to compare the differences in certain sociodemographic, clinical, and other risk factors across the multiple combinations of criteria of malnutrition tools.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using SPSS, version 24.\u0026nbsp;Categorical variables were reported as frequencies and percentages [n (%)], while continuous variables were reported as means\u0026nbsp;\u0026plusmn; standard deviations [Mean\u0026plusmn; SD].\u0026nbsp;Descriptive tests were performed for the 11 risk factors, and normality was assessed for age, CCI, and number of medications. Normality tests and bivariate analyses were conducted for the assessed malnutrition risk factors on all tools. Pearson Chi-square, Fisher Exact, and Mann-Whitney tests were used for the bivariate analysis. Logistic regression analyses were performed to assess the association of the risk factors with malnutrition, identified through various diagnostic tools and combinations of indicators within the same tool. If the p-value was less than 0.2 in the bivariate analysis, variables were included in the logistic regression models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn unexpected change in direction after running the logistic regression for the \u0026ldquo;age\u0026rdquo; variable was identified. Results of diagnostic analyses revealed an interaction of the \u0026ldquo;age\u0026rdquo; with the \u0026ldquo;CCI\u0026rdquo;. As the index encompasses age within its score, such interaction was deemed a logical one. Therefore, two models were created and compared in each logistic regression: (1) a model with CCI excluding age, (2) and a model with age excluding CCI. \u0026nbsp;The model with the CCI alone conveyed the highest Cox \u0026amp; Snell R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eand was reported in the results. The model containing the age variable was used to report the association of \u0026ldquo;age\u0026rdquo; with malnutrition diagnosis alone.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 578 patients were screened for malnutrition, of which 124 were nutritionally at risk according to the MNA and NRS-2002 screening tools. Table 1 in the supplemental material shows the differences in sociodemographic, clinical, and other risk factors based on nutritional risk. \u0026nbsp;\u0026nbsp;Results of all bivariate analyses of the risk factors with the screening and diagnostic tools and the logistic regression of the screening tools are described in the supplemental material (Tables 1-5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Multiple Logistic Regression of Risk-Factors with ESPEN Malnutrition Diagnostic Tool\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFemales (OR=2.243, 95% CI 1.070 -4.701, p=0.032) and a higher CCI score (OR=1.217, 95% CI 1.046 -1.416, p=0.011) were associated with a higher likelihood of malnutrition diagnosis using the overall ESPEN tool (Table 2). Similarly, ESPEN-1 ((BMI) \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e) revealed the same likelihood of malnutrition in females (OR=1.500, 95% CI 1.231-16.318, p=0.023) and individuals with higher comorbidities (OR=1.329, 95% CI 1.089 -1.625, p=0.006). Results of the model also showed that there are lower odds of malnutrition, diagnosed with low BMI, when there is an absence of a caregiver (OR=0.281, 95% CI 0.097-0.812, p=0.019) and polypharmacy (OR=0.237, 95% CI 0.072-0.775, p=0.017). The logistic regression of ESPEN-2 identified only the CCI score as a risk factor that was associated with malnutrition diagnosis, using the combination of unintentional weight loss and low BMI-for-age (OR=1.220, 95% CI 1.034-1.440, p=0.018) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Multiple Logistic Regression of Malnutrition Risk-Factors with AND/ASPEN\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRisk factors that were significantly related to higher odds of being diagnosed with malnutrition on the AND/ASPEN criteria of unintentional weight loss combined with decreased food intake criteria are being female (OR=2.274, 95% CI 1.357-3.810, p=0.002), having pressure ulcers (OR=13.058, 95% CI 2.897-58.861, p=0.001), and higher CCI score (OR=1.278, 95% CI 1.131-1.444, p\u0026lt;0.0001) (Table 3). Individuals admitted to wards other than internal medicine and surgery were less likely to be malnourished using this combination of criteria (OR=0. 053, 95% CI 0. 006-0. 492, p=0.010) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Multiple Logistic Regression of Malnutrition Risk-Factors with GLIM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe risk factors of being females (OR=2.133, 95% CI 1.310-3.471, p=0.002; OR=2.015, 95% CI 1.230-3.302, p=0.005; OR=2.602, 95% CI 1.181-5.735, p=0.018) and having a higher CCI score (OR=1.285, 95% CI 1.148-1.439, p\u0026lt;0.001; OR=1.266, 95% CI 1.130-1.419, p\u0026lt;0.001; OR=1.179, 95% CI 1.005-1.382, p=0.043) were positively associated with an higher risk of malnutrition, identified through overall GLIM, GLIM-1 (unintentional weight combined with reduced food assimilation or absorption) and GLIM 2 (low BMI-for age combined with reduced food assimilation or absorption) respectively \u0026nbsp;(Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth GLIM and GLIM-1 classified the presence of pressure ulcers as a risk factor that was associated with malnutrition diagnosis (OR: 13.325, 95%CI 3.049-58.235, p\u0026lt;0.001; OR: 14.822, 95%CI 3.389-64.825, p\u0026lt;0.001, respectively). \u0026nbsp;Additionally, both combinations of criteria revealed that the risk of malnutrition was lower when individuals were admitted to units other than the internal medicine and surgery units (GLIM: OR: 0.057, 95%CI: 0.006-0.517, p=0.011; GLIM-1: OR: 0.059, 95%CI: 0.006-0.531, p=0.012) (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Comparison of Malnutrition Risk-Factors Identified with the Three Diagnostic Tool:\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA higher CCI score, indicating higher comorbidities, was the only risk factor that was identified by all combinations of criteria using the three malnutrition diagnostic tools (Table 5). Being female also appeared to be a commonly identified risk factor on all combinations, except for ESPEN-2 (unintentional weight loss and low BMI-for-age). Combinations of criteria involving the use of unintentional weight loss (AND/ASPEN), or low BMI-for-age (GLIM-2) with reduced food assimilation/absorption identified similar risk factors: female, having pressure ulcers, higher CCI score. AND/ASPEN (unintentional weight loss with reduced food intake) and GLIM-2 (low BMI-for-age with reduced food assimilation or absorption) showed that being admitted to wards other than internal medicine and surgery was protective of malnutrition. On the other hand, diagnostic criteria utilizing low BMI-for-age (ESPEN-2 and GLIM-2) did not identify the same risk factors, with only ESPEN-2 revealing females as a risk factor of malnutrition. Despite the common criterion of reduced food assimilation or absorption between GLIM-1 (unintentional weight loss combined with reduced food assimilation/ absorption) and GLIM 2 (low BMI-for age combined with reduced food assimilation/ absorption) (low BMI-for age combined with reduced food assimilation/ absorption), there is a notable difference in the identified risk factors of GLIM-1, which revealed more common risk factors with AND/ASPEN combination of criteria (unintentional weight loss with reduced food intake) than GLIM-2 (Table 5).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings revealed that combinations of malnutrition diagnostic criteria identified female gender, higher CCI score, and the presence of pressure ulcers as the most recognized demographic and clinical risk factors. The CCI was the only common clinical risk factor among all combinations of the diagnostic tools. Female gender was a frequently recognized demographic risk factor identified through all combinations of criteria except for ESPEN-2. Additionally, a higher risk of pressure ulcers was also associated with a diagnosis of malnutrition using the malnutrition screening tools, AND/ASPEN, overall, GLIM, and GLIM-1.\u003c/p\u003e\u003cp\u003eOur results revealed that females are at a higher malnutrition risk, identified through multiple combinations of criteria from the tools, in comparison to male patients. This is consistent with the literature showing that females were also at a higher risk of malnutrition when admitted to the hospital.(29, 30) This might be related to longer life expectancy in females compared to men, and a greater risk of economic and social burdens in older adult females.(31) Additionally, biological differences such as increased energy, protein, and certain micronutrient requirements in adult women pose a higher risk of malnutrition compared to men.(32, 33) The association of the risk of pressure ulcer with different combinations of criteria is strongly consistent with previous findings.(34, 35) Likewise, our data on the correlation of higher presence of comorbidities with malnutrition diagnosed has been demonstrated in the literature.(6, 36) Unlike other studies,(6, 11, 37) age did not remain a significant risk factor in our models for the various combinations of malnutrition criteria. Additionally, our results did not detect any association between educational status and socioeconomic status with malnutrition. On the other hand, Ouaijan et al. found that educational status, employment status, and type of health coverage in patients were significant malnutrition risk factors when using GLIM to diagnose malnutrition in the same population.(38) Similarly, Bardon et al. revealed a potential association between educational status and marital status with malnutrition.(14) The presence of a caregiver was not associated with malnutrition in our results, in contrast to a previous research that emphasized that being single, widowed, or divorced was a significant social risk factor of malnutrition.(39) However, our findings demonstrated that the absence of a caregiver was protective of malnutrition when using ESPEN-1 for malnutrition diagnosis, when BMI was used as a diagnostic measure of malnutrition. In addition, polypharmacy was associated with malnutrition only when a BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e had been used as a diagnostic criterion (ESPEN-1). Similarly, Pirlich et al. demonstrated that polypharmacy is a significant risk factor of malnutrition.(11)\u003c/p\u003e\u003cp\u003eThis variability in risk factors when utilizing BMI as a sole criterion for malnutrition diagnosis was evident in our findings in comparison to risk factors identified with other criterion combinations. The use of BMI alone for malnutrition diagnosis (ESPEN-1) identified some associations that were challenging to explain. For instance, the absence of a caregiver was identified as a protective factor, and the association of polypharmacy with malnutrition was not observed with any other combinations of criteria. In contrast, when low BMI-for-age was used with other criteria, unintentional weight loss (ESPEN-2) or reduced food assimilation and absorption (GLIM-2), different risk factors were associated with the diagnosis compared to when BMI was used alone. The identified risk factors with ESPEN-2 and GLIM-2 were like those identified with AND/ASPEN (unintentional weight loss combined with decreased food intake), which utilized the combination of unintentional weight loss and reduced food intake. This finding might reinforce the value of combining BMI cut-offs with other criteria for a more comprehensive assessment.\u003c/p\u003e\u003cp\u003eThe results of this study offer robust insight into the multifactorial nature of malnutrition in hospitalized patients. By identifying sociodemographic, clinical, and other risk factors through the different combinations of criteria, specifically BMI, BMI-for-age, unintentional weight loss, decreased food intake, and reduced food absorption/absorption/assimilation, we demonstrate how certain risk factors may be more closely associated with specific diagnostic approaches. Based on the results of this study, clinicians should consider female gender, multiple comorbidities, and pressure ulcers as significant demographic and clinical risk factors, respectively, particularly when utilizing unintentional weight loss, decreased food intake, and reduced food assimilation as criteria of malnutrition diagnosis. These findings suggest that, depending on the criteria used, some individuals may be more readily identified with a nutritional risk as compared to others who lack it, thus ensuring a more equitable identification of malnutrition in this population.\u003c/p\u003e\u003cp\u003eThere are multiple limitations to this study. First, most of our sample are individuals of middle to high socioeconomic status, have at least a high school degree, are admitted to the internal medicine and surgery units, and have a caregiver. Therefore, our results might not be generalizable to individuals with lower economic and educational status, admitted to other hospital wards, and who do not have a caregiver. Moreover, not all combinations of malnutrition diagnostic criteria are evaluated in this study; this hinders our ability to perform a comprehensive review of the risk factors for all combinations of criteria. Additionally, the magnitude of the odds ratio, particularly the ones for the risk of pressure ulcers, remains challenging to fully apprehend. Such results might reflect on the complexity of the malnutrition diagnosis and the various risk various risk factors that interplay to influence the latter.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis analysis highlighted how malnutrition diagnostic tools (ESPEN, AND/ASPEN, and GLIM) and their combinations of criteria (unintentional weight loss, reduced food intake, assimilation or absorption, low BMI, and low BMI-for-age) might be associated with multiple risk factors, primarily female gender, comorbidities, and presence of pressure ulcers. This would assist clinicians in capturing varying risk profiles in newly admitted hospital patients, thus supporting more equitable and comprehensive identification of malnutrition. Lastly, our results address the increasing call for mandating proper nutrition screening to effectively target malnutrition and its burden on individuals and the healthcare system.(1, 6)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\u003cp\u003eThe study protocol was approved by the Lebanese American University Institutional Review Board (LAU.SAS.LM1.1/Mar/2019).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests related to this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo financial assistance was provided to support this study\u003c/p\u003e\u003ch2\u003eAuthor contribution\u003c/h2\u003e\u003cp\u003eDayana El Chaar: data curation, formal analysis, investigation, writing \u0026ndash; original draft, review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available in the results section and supplementary tables of this manuscript. Additional details on the demographic characteristics of the population are available in the following publication:\u003c/p\u003e\u003cp\u003eEl Chaar D, Mattar L, Fakih El Khoury C. \u003cem\u003eAND/ASPEN and the GLIM malnutrition diagnostic criteria have a high degree of criterion validity and reliability for the identification of malnutrition in a hospital setting: A single-center prospective study.\u003c/em\u003e Journal of Parenteral and Enteral Nutrition. 2022 Jul;46(5):1061\u0026ndash;1070.\u003c/p\u003e\u003cp\u003eFurther data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarker LA, Gout BS, Crowe TC. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. 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AND/ASPEN and the GLIM malnutrition diagnostic criteria have a high degree of criterion validity and reliability for the identification of malnutrition in a hospital setting: A single-center prospective study. Journal of Parenteral and Enteral Nutrition. 2022;46(5):1061-70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeller H, de van der Schueren MA, Consortium G, Jensen GL, Barazzoni R, Compher C, et al. Global Leadership Initiative on Malnutrition (GLIM): guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. Journal of Parenteral and Enteral Nutrition. 2020;44(6):992\u0026ndash;1003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVajdi M, Farhangi MA. Gender-specific difference among socio-demographic determinants of malnutrition and anemia among hospitalized children. 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Rome, Italy: Food and Agriculture Organization; 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJabbour J, Khalil M, Ronzoni AR, Mabry R, Al-Jawaldeh A, El-Adawy M, et al. Malnutrition and gender disparities in the Eastern Mediterranean Region: The need for action. Frontiers in Nutrition. 2023;10:1113662.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIizaka S, Okuwa M, Sugama J, Sanada H. The impact of malnutrition and nutrition-related factors on the development and severity of pressure ulcers in older patients receiving home care. Clinical Nutrition. 2010;29(1):47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsaousi G, Stavrou G, Ioannidis A, Salonikidis S, Kotzampassi K. Pressure ulcers and malnutrition: results from a snapshot sampling in a university hospital. Medical Principles and Practice. 2015;24(1):11\u0026thinsp;\u0026minus;\u0026thinsp;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmasene M, Medrano M, Echeverria I, Urquiza M, Rodriguez-Larrad A, Diez A, et al. Malnutrition and poor physical function are associated with higher comorbidity index in hospitalized older adults. Frontiers in nutrition. 2022;9:920485.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeandro-Merhi VA, Costa CL, Saragiotto L, AQUINO JLBd. Nutritional indicators of malnutrition in hospitalized patients. Arquivos de Gastroenterologia. 2019;56(4):447\u0026thinsp;\u0026minus;\u0026thinsp;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOuaijan K, Hwalla N, Kandala N-B, Abi Kharma J, Kabengele Mpinga E. Analysis of predictors of malnutrition in adult hospitalized patients: social determinants and food security. Frontiers in Nutrition. 2023;10:1149579.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmaral TF, Matos LC, Teixeira MA, Tavares MM, \u0026Aacute;lvares L, Antunes A. Undernutrition and associated factors among hospitalized patients. Clinical nutrition. 2010;29(5):580-5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"malnutrition, risk factors, malnutrition diagnostic tools, combination of criteria","lastPublishedDoi":"10.21203/rs.3.rs-7179279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7179279/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\u003eEvidence recognizing associations of certain risk factors with malnutrition diagnostic tools [European Society of Clinical Nutrition and Metabolism (ESPEN), Academy of Nutrition and Dietetics/American Society of Parenteral and Enteral Nutrition (AND/ASPEN), and Global Leadership Initiative on Malnutrition (GLIM)] and their combinations of criteria, is limited. This study aims to identify the association of certain sociodemographic, clinical, and other risk factors with various combinations of malnutrition criteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis prospective study assessed 11 risk factors against various combinations of malnutrition criteria from three tools in a sample of 578 patients. Multiple logistic regression analyses were used to assess this association and identify significant risk factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComorbidities were the only associated risk factor across all combinations of criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;0.02). Similarly, being female was associated with malnutrition (ESPEN: 95%CI:1.070\u0026ndash;4.701, p\u0026thinsp;=\u0026thinsp;0.032; ESPEN-1: 95%CI:1.231\u0026ndash;16.318, p\u0026thinsp;=\u0026thinsp;0.023; AND/ASPEN: 95%CI:1.357\u0026ndash;3.810, p\u0026thinsp;=\u0026thinsp;0.002; GLIM: 95%CI:1.310\u0026ndash;3.471, p\u0026thinsp;=\u0026thinsp;0.002; GLIM-1: 95%CI:1.230\u0026ndash;3.302, p\u0026thinsp;=\u0026thinsp;0.005; GLIM-2 95%CI:1.181\u0026ndash;5.735, p\u0026thinsp;=\u0026thinsp;0.018) except when using unintentional weight loss with low BMI-for-age (ESPEN-2). The combination of unintentional weight loss with reduced food intake (AND/ASPEN) or reduced food assimilation/absorption (GLIM-1) identified females, having pressure ulcers (AND/ASPEN: 95%CI:2.897\u0026ndash;58.861, p\u0026thinsp;=\u0026thinsp;0.001; GLIM-1: CI:3.389\u0026ndash;64.825, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the presence of comorbidities as significant risk factors (AND/ASPEN: 95%CI:1.131\u0026ndash;1.444, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; GLIM-1: 95%CI:1.130\u0026ndash;1.419, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Low BMI-for-age combinations showed between-group differences in the identified risk factors. In contrast, using low BMI as the sole indicator to diagnose malnutrition revealed different risk factors than other combinations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eESPEN, AND/ASPEN, and GLIM combination of criteria are mostly associated with being female and having multiple comorbidities and pressure ulcers.\u003c/p\u003e","manuscriptTitle":"Identifying Malnutrition Risk Factors: A Comparative Analysis of Malnutrition Diagnostic Tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 14:02:51","doi":"10.21203/rs.3.rs-7179279/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-10-20T17:54:14+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-28T02:20:13+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-09-23T19:17:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T09:46:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T09:43:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-07-22T18:56:35+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-07-22T11:36:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5d2d92ae-2f3e-4800-92df-6a03e6f344d3","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55222845,"name":"Health sciences/Risk factors"},{"id":55222846,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2025-10-06T14:02:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 14:02:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7179279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7179279","identity":"rs-7179279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}