Comparative Clinical Efficacy of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in Gastric Cancer Patients
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Abstract Objective: This study aims to compare the effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in diagnosing malnutrition and predicting survival in gastric cancer patients. Additionally, it seeks to evaluate different muscle measurement indices used in GLIM criteria to determine their association with survival and refine the selection of sarcopenia assessment indicators. Methods: This multicenter, prospective cohort study involved 1,295 gastric cancer patients. We calculated the 5th and 15th percentiles for mid-arm circumference (MMC), calf circumference (CC), and handgrip strength index adjusted for weight (HGS/W).Various muscle measurement indices were employed to formulate the GLIM criteria and determine the optimal cut-off values for the diagnosis of sarcopenia, with an analysis focused on their capability to differentiate survival outcomes, ultimately assessing their prognostic evaluation potential. Both PG-SGA and GLIM were employed to diagnose and stage malnutrition, and their reliability and validity were compared. The correlation of malnutrition diagnoses from GLIM and PG-SGA with hospitalization costs and clinical blood test indicators was assessed to evaluate the clinical utility of these diagnostic methods. Results: Severe malnutrition diagnosed by GLIM, regardless of whether MMC, CC, or HGS/W was used as the positive criterion for sarcopenia, was associated with the shortest median overall survival (OS) and the highest hazard ratio (HR=1.563, 95% CI=1.314-1.860) compared to the normal group. GLIM demonstrated higher sensitivity but lower specificity compared to PG-SGA. Both methods indicated that greater malnutrition severity was linked to an increased risk of death. Cox regression analysis identified staging, KPS score, and PG-SGA-based malnutrition grading as independent survival risk factors. Malnutrition identified by both methods was significantly correlated with hospitalization costs, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine (P<0.05), suggesting substantial clinical predictive value. Conclusion: While GLIM shows certain sensitivity in diagnosing malnutrition relative to PG-SGA, its specificity is relatively lower. Both diagnostic methods are valuable for predicting clinical outcomes, with PG-SGA demonstrating stronger predictive power for survival risk.
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Comparative Clinical Efficacy of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in Gastric Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Clinical Efficacy of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in Gastric Cancer Patients Jingxian Zheng, Xiaojie Wang, Jiami Yu, Qiaoting Hu, Zhouwei Zhan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5819018/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aims to compare the effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in diagnosing malnutrition and predicting survival in gastric cancer patients. Additionally, it seeks to evaluate different muscle measurement indices used in GLIM criteria to determine their association with survival and refine the selection of sarcopenia assessment indicators. Methods: This multicenter, prospective cohort study involved 1,295 gastric cancer patients. We calculated the 5th and 15th percentiles for mid-arm circumference (MMC), calf circumference (CC), and handgrip strength index adjusted for weight (HGS/W).Various muscle measurement indices were employed to formulate the GLIM criteria and determine the optimal cut-off values for the diagnosis of sarcopenia, with an analysis focused on their capability to differentiate survival outcomes, ultimately assessing their prognostic evaluation potential. Both PG-SGA and GLIM were employed to diagnose and stage malnutrition, and their reliability and validity were compared. The correlation of malnutrition diagnoses from GLIM and PG-SGA with hospitalization costs and clinical blood test indicators was assessed to evaluate the clinical utility of these diagnostic methods. Results: Severe malnutrition diagnosed by GLIM, regardless of whether MMC, CC, or HGS/W was used as the positive criterion for sarcopenia, was associated with the shortest median overall survival (OS) and the highest hazard ratio (HR=1.563, 95% CI=1.314-1.860) compared to the normal group. GLIM demonstrated higher sensitivity but lower specificity compared to PG-SGA. Both methods indicated that greater malnutrition severity was linked to an increased risk of death. Cox regression analysis identified staging, KPS score, and PG-SGA-based malnutrition grading as independent survival risk factors. Malnutrition identified by both methods was significantly correlated with hospitalization costs, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine (P<0.05), suggesting substantial clinical predictive value. Conclusion: While GLIM shows certain sensitivity in diagnosing malnutrition relative to PG-SGA, its specificity is relatively lower. Both diagnostic methods are valuable for predicting clinical outcomes, with PG-SGA demonstrating stronger predictive power for survival risk. GLIM PG-SGA Gastric Cancer Survival Risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastric cancer is the fifth most common malignancy and the third leading cause of cancer-related mortality globally, with China having the highest age-standardized incidence and mortality rates[1]. Clinical studies indicate that the prevalence of malnutrition in gastric cancer patients ranges from 15% to 55%[2,3]. Previous research underscores the adverse impact of malnutrition on survival, treatment tolerance, and overall clinical outcomes in this population[4,5]. Consequently, early malnutrition screening and assessment are crucial for timely nutritional intervention and enhanced treatment efficacy, ultimately improving patient prognosis[6,7]. Currently, over ten clinical standards are used to assess malnutrition, yet no consensus criterion exists, leading to significant variability and confusion in diagnosis and treatment[8]. The Patient-Generated Subjective Global Assessment (PG-SGA), developed by Ottery, is widely utilized for cancer patients but lacks global standardization[9]. In response, the Global Leadership Initiative on Malnutrition (GLIM) consensus criteria were introduced in September 2018 to standardize malnutrition diagnosis and address existing discrepancies[10]. However, GLIM's clinical application faces challenges, including varying cut-off values across different regions and cancer types[11-13]. This study aims to assess the comparative effectiveness of GLIM and PG-SGA in diagnosing malnutrition and predicting survival in gastric cancer patients, and to explore the optimal muscle measurement indices within GLIM criteria to enhance sarcopenia assessment. Study Population This multicenter, observational cohort study was conducted as part of the Nutritional Status and Clinical Outcome of Common Malignant Tumors (INSCOC) research project (registration number: ChiCTR1800020329, registered at chictr.org.cn). The INSCOC program was a national, forward-looking cohort study focused on the relationship between nutritional status and clinical outcomes in patients with common malignancies in Country China. Comprehensive information regarding the study’s design, methodology, patient population, techniques used, and the criteria for inclusion and exclusion have been previously detailed in published works[14].The study included 1,358 gastric cancer patients admitted between January 2012 and April 2021 from two centers: Fujian Cancer Hospital, Xijing Hospital. The study design was authorized by the local ethics committee of each participating hospital and conducted in accordance with the Declaration of Helsinki and its later amendments. Inclusion Criteria: 1)Age between 18 and 90 years, with no communication barriers and capable of participating in relevant assessments; 2)Confirmed histological diagnosis of gastric adenocarcinoma; 3)Availability of complete medical history and detailed data; 4)Voluntary participation by the patient and their family. Exclusion Criteria: 1)Emergency surgery; 2) Psychiatric disorders that impeded cooperation or critically ill patients unable to complete assessments; 3) Incomplete data or loss to follow-up. Following the exclusion of 63 patients due to incomplete basic information or loss to follow-up, the final study cohort comprised 1,295 gastric cancer patients. The study received approval from the Ethics Committee of Fujian Cancer Hospital, and all patients provided informed consent. Methods 1.1. Nutritional Assessment and Data Collection Within 48 hours of admission, a comprehensive nutritional assessment was conducted. This included:2002 Nutritional Risk Screening (NRS2002) score[15], Patient-Generated Subjective Global Assessment (PG-SGA)[9], Karnofsky performance score (KPS), Anthropometric measurements (height, weight, mid-arm circumference, triceps skinfold thickness, mid-arm muscle circumference, calf circumference, handgrip strength), collection of disease- and treatment-related information from medical records. Follow-up for survival information was conducted annually. Percentiles for mid-arm muscle circumference (MMC), calf circumference (CC), and handgrip strength index adjusted for weight (HGS/W) were calculated[16]. Values below the 15th percentile were considered indicative of moderate muscle mass loss, while values below the 5th percentile indicated severe muscle mass loss[17]. The discriminatory power of the GLIM criteria for survival was assessed based on various combinations of muscle assessment indicators and median survival time. 1.2. Malnutrition Staging and Evaluation Malnutrition stages were classified based on the calculated optimal muscle evaluation index, used as the cut-off point for muscle reduction in GLIM diagnostic criteria. The stages were as follows:Stage 1: Moderate malnutrition, Stage 2: Severe malnutrition[18]. Comparisons were made between the prevalence of malnutrition as diagnosed by GLIM and PG-SGA, and the different grades of malnutrition to assess the sensitivity and specificity of each diagnostic method (refer Supplementary Table 1). 1.3. Adverse Outcomes Correlation In PG-SGA, a score of ≥2 indicated malnutrition, with 4-8 points representing moderate malnutrition and ≥9 points indicating severe malnutrition[19]. The mortality risk associated with different nutritional statuses, as diagnosed by both methods, was compared. 1.4. Clinical Outcomes Correlation The correlation between malnutrition diagnoses by GLIM and PG-SGA with hospitalization costs and basic clinical blood test indicators was analyzed to explore the predictive value of these diagnostic methods for clinical outcomes. 1.5. Statistical Methods Data normality was assessed using the Shapiro–Wilk test. Continuous variables with normal distribution were expressed as mean ± standard deviation and compared using t-tests. Non-normally distributed continuous variables were expressed as median (interquartile range) and compared using non-parametric tests. Categorical variables were presented as frequencies (percentages) and compared using the chi-square test. Survival data were analyzed using Kaplan–Meier curves and Cox regression. All tests were two-tailed, with a P value of <0.05 considered statistically significant. Statistical analyses were performed using the open-source software R (version 4.2.2; http://www.R-project.org). Results 2.1 General Baseline Data The study included 1,295 patients: 777 classified as normal, 158 with moderate malnutrition, and 360 with severe malnutrition. GLIM malnutrition grading was significantly associated with age, smoking, alcohol consumption, tea drinking, and cancer staging (P 0.05). Nutritional parameters such as BMI, mid-arm muscle circumference (MMC), calf circumference (CC), and non-dominant handgrip strength (HGS) were significantly correlated with malnutrition grading (P < 0.05, refer Table 1). Table 1 Baseline characteristics of the population and correlation between GLIM and baseline features Characteristics GLIM P Normal (n = 777) Moderate malnutrition (n = 158) Severe malnutrition (n = 360) Age, years 56.3±10.9 58.3±11.9 59.0±12.8 <0.001 Smoking, yes, n (%) 404（52.0） 57（36.1） 174（48.3） <0.001 Alcohol drinker, n (%) 192（24.7） 21（14.2） 71（19.2） 0.003 Tea drinker, n (%) 229（29.5） 23（14.6） 79（21.9） <0.001 TNM Stage, n (%) 0.001 0 20（2.6） 0 3（0.8） I 99（12.7） 16（10.1） 25（6.9） II 163（21.0） 24（15.2） 55（15.3） III 317（40.8） 83（52.5） 185（51.4） IV 178（22.9） 35（22.2） 92（25.6） Differentiation grade, n (%) 0.862 Well 12（1.6） 4（2.5） 7（1.9） Moderate 185（23.8） 37（23.4） 76（21.1） Poor 264（34.0） 49（31.0） 119（33.1） BMI, kg/m 2 22.3（3.6） 21.3（4.4） 17.8（2.3） <0.001 Mid-arm muscle circumference, cm 26.5（3.0） 25.0（4.6） 22.5（4.2） <0.001 Hand grip strength/weight ratio 28.3（12.8） 16.1（8.6） 18.5（15.4） <0.001 calf circumference (left calf), cm 33.5（4.0） 31.5（5.5） 29.1（4.2） <0.001 Data are expressed as the median [upper quartile, lower quartile] if they are not normally distributed and as the mean (standard deviation) if they are normally distributed. BMI, body mass index; GLIM, the Global Leadership Initiative on Malnutrition;TNM stage, tumor node metastasis stage. 2.2 Establishment of GLIM Diagnostic Criteria The GLIM criteria, based on various muscle assessment indicators, demonstrated that patients with severe malnutrition, as identified by any one of the indicators (MMC, CC, or HGS/W), had the shortest median overall survival (OS) of 39.0 months (95% CI: 24.7-50.6). The combination of MMC, CC, and HGS/W as positive indicators yielded the highest differentiation between normal and severe malnutrition groups (Normal: 83.0 months, Severe malnutrition: 39.0 months, difference: 44 months) (refer Supplementary Table 2). The risk ratio (HR) for severe malnutrition using this method was the highest (HR=1.563, 95% CI=1.314-1.860) compared to the normal group (refer Table 2), indicating optimal performance of these criteria in predicting survival. Table 2 GLIM classification and single factor Cox regression analysis of patients’ survival using different muscle evaluation parameters Muscle evaluation parameters Normal nutritiona Moderate malnutrition P Severe malnutrition P N (%) 参照 N (%) HR (95% CI) N (%) HR (95% CI) No RMM assessment 1006 （77.7） 1 11 （0.8） 0.572（0.184-1.780） 0.335 278（21.5） 1.473（1.231-1.763） ＜0.001 1006（77.7） 1 MMC 952 （73.5） 1 48 （3.7） 1.178（0.774-1.794） 0.445 295（22.7） 1.461（1.222-1.746） ＜0.001 952（73.5） 1 CC 936 （72.3） 1 51 （3.9） 1.160（0.755-1.783） 0.498 308（23.8） 1.561（1.310-1.860） ＜0.001 936（72.3） 1 HGS/W 863 （66.6） 1 108（8.3） 0.949（0.698-1.290） 0.739 324（25.1） 1.489（1.249-1.775） ＜0.001 863（66.6） 1 MMC or HGS/W,either positive 823 （63.6） 1 137（10.6） 1.028（0.782-1.353） 0.841 335（25.8） 1.500（1.258-1.788） ＜0.001 823（63.6） 1 MMC and HGS/W,both positive 991 （76.5） 1 16 （1.2） 0.913（0.432-1.927） 0.811 288（22.3） 1.486（1.244-1.776） ＜0.001 991（76.5） 1 CC or HGS/W,either positive 808 （62.4） 1 138（10.7） 1.036（0.786-1.366） 0.802 349（26.9） 1.560（1.312-1.857） ＜0.001 808（62.4） 1 CC and HGS/W,both positive 992 （76.6） 1 17 （1.3） 0.811（0.362-1.815） 0.610 286（22.1） 1.495（1.251-1.787） ＜0.001 992（76.6） 1 MMC or CC or HGS/W,any positive 777 （60.0） 1 158（12.2） 1.064（0.819-1.382） 0.642 360（17.8） 1.563（1.314-1.860） ＜0.001 777（60.0） 1 MMC,CC and HGS/W,all positive 998 （77.1） 1 13 （1.0） 0.863（0.357-2.085） 0.774 284（21.9） 1.492（1.248-1.783） ＜0.001 998（77.1） 1 RMM: reduced muscle mass, MMC: mid-arm muscle circumference, CC: calf circumference, HGS/W: weight-standardized hand grip strength, GLIM: Global Leadership Initiative on Malnutrition 2.3 Comparison of GLIM and PG-SGA Using PG-SGA, scores of 0-1 indicated normal nutrition, 2-3 indicated mild malnutrition, 4-8 indicated moderate malnutrition, and ≥9 indicated severe malnutrition. Among 1,295 patients, 82 were classified as normal, and 1,213 as malnourished, including 200 with mild, 513 with moderate, and 500 with severe malnutrition (Supplementary Table 3). According to GLIM, 777 patients were normal, and 518 were malnourished, with 148 classified as moderate and 370 as severe malnutrition (Supplementary Table 4). The PG-SGA diagnosed malnutrition in 93.7% of cases, with 15.4% mild, 39.6% moderate, and 38.6% severe. GLIM diagnosed malnutrition in 40.0% of cases, with 11.4% moderate and 28.6% severe. The sensitivity of GLIM for diagnosing malnutrition was 75.6%, and specificity was 41.1% compared to PG-SGA (refer Table 3). Table 3 PG-SGA and GLIM diagnosis of malnutrition PG-SGA malnutrition（B or C） Total n(%) GLIM malnutrition Yes No Yes 62 715 777（60.0） NO 20 498 518（40.0） Total n(%) 82（6.3） 1213（93.7） 1295 PG-SGA A is defined as well-nourished, PG-SGA B is defined as mild/moderate malnutrition, and PG-SGA C is defined as severe malnutrition. Kaplan-Meier survival curves indicated that for PG-SGA, the median OS for the normal group was not reached, while the malnutrition group had a median OS of 64.0 months (95% CI: 55.6-72.4), with a significant difference (P < 0.001, refer Figure 1). Severity of malnutrition as assessed by PG-SGA was inversely correlated with OS (Median OS: Normal=NA, Moderate malnutrition=123.0 months, Severe malnutrition=46.0 months, P < 0.001, refer Figure 2). The HR for severe malnutrition was 2.753 (95% CI: 1.782-4.252). For GLIM, median OS for the normal group was 85.0 months, while for the malnutrition group, it was 50.0 months (P < 0.001, refer Figure 3). Severity of malnutrition also negatively correlated with OS (Median OS: Severe=43 months, Moderate=66.0 months, Normal=85.0 months, P < 0.001, refer Figure 4), suggesting that both methods can predict survival outcomes. 2.4 Clinical Value of GLIM and PG-SGA Hospitalization costs, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine were significantly associated with malnutrition diagnosed by GLIM (P < 0.05) (refer Table 4). Similarly, PG-SGA was significantly correlated with hospitalization costs, total hospital stay, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine (P < 0.05) (refer Table 5). Using albumin <40 as a malnutrition indicator, 427 patients were normal, 855 were malnourished, and 13 had missing albumin values, totaling 1,282 patients (refer Supplementary Table 5). Consistency testing between PG-SGA, GLIM, and albumin for assessing malnutrition showed Kappa values of 0.073 and 0.119, indicating poor agreement (refer Supplementary Tables 6, 7). Table 4 Hospitalization indicators related to different nutritional statuses diagnosed by GLIM in patients Index Group Z P （M，IQR） Normal Malnutrition Hospitalization Costs 33025.95（55673.5） 26173.00（46653.4） -2.566 0.01 Total Length of Stay 11（10） 12（9） -1.957 0.05 NRS2002 Score 1（2） 4（2） -13.173 ＜0.001 KPS 90（90） 80（90） -3.631 ＜0.001 hemameba 6.11（3.4） 5.8（3.2） -1.491 0.136 Neutrophils, 10 9 /L, mean±SD 3.6（3.1） 3.6（3.1） -0.248 0.804 Neutrophil-to-Lymphocyte Ratio 2.4（2.5） 2.5（3.2） -0.710 0.478 blood platelet 221.0（109.0） 221.0（118.0） -0.869 0.385 hemoglobin 124.0（32.0） 114（30.0） -6.770 ＜0.001 albumin 38.5（7.3） 36.8（7.1） -5.255 ＜0.001 creatinine 75.0（26.0） 66.0（25.0） -7.953 ＜0.001 NRS2002, Nutritional Risk Screening 2002; KPS, Karnofsky performance score. Table 5 Hospitalization indicators related to different nutritional statuses diagnosed by PG-SGA in patients Index Group Z P （M，IQR） Normal Malnutrition Hospitalization Costs 18959.3（41515.9） 30335.9（52443.9） -2.514 0.012 Total Length of Stay 8（7） 12（9） -4.169 ＜0.001 NRS2002 Score 1（0） 2（3） -6.142 ＜0.001 KPS 90（30） 90（90） -2.946 0.003 hemameba 5.7（2.6） 6.0（3.3） -1.526 0.127 Neutrophils, 10 9 /L, mean±SD 3.1（2.4） 3.6（3.2） -1.516 0.130 Neutrophil-to-Lymphocyte Ratio 2.0（2.0） 2.5（2.8） -2.123 0.034 blood platelet 233.0（106.0） 221（114.0） -0.340 0.734 hemoglobin 127.0（25.0） 120.0（32.0） -2.700 0.007 albumin 40.1（6.8） 37.4（7.0） -4.713 ＜0.001 creatinine 67.1（26.7） 71.0（27.0） -0.654 0.513 NRS2002, Nutritional Risk Screening 2002; KPS, Karnofsky performance score. LASSO regression identified staging, KPS score, and PG-SGA malnutrition grade as independent risk factors for survival. A Cox regression model and nomogram prediction model for gastric cancer survival risk were developed (refer Figure 5). The model showed moderate discrimination (C-index=0.74) and good calibration for 3-year and 5-year survival predictions, aligning closely with observed outcomes (refer Supplementary Figures 1-3). Conclusion In the comprehensive evaluation of nutritional status in gastric cancer patients, the Global Leadership Initiative on Malnutrition (GLIM) criteria, incorporating Metrics for the Management of Malnutrition (MMC), Clinical Criteria (CC), or the Hopkins Global Health Solutions Wheel (HGS/W), emerged as the most robust in terms of correlation with survival outcomes. These findings highlight the utility of GLIM in identifying patients at risk of malnutrition, albeit with a slightly diminished specificity when compared to the Patient-Generated Subjective Global Assessment (PG-SGA). The sensitivity of the GLIM criteria in diagnosing malnutrition among gastric cancer patients underscores its potential as a valuable tool in clinical practice. However, the relatively lower specificity in comparison to PG-SGA necessitates a nuanced approach to clinical judgment. Healthcare professionals must consider the unique context and characteristics of each patient when utilizing these diagnostic methods to assess nutritional status. Notably, both the GLIM and PG-SGA methods demonstrated their clinical relevance by effectively predicting survival differences among patients with varying degrees of malnutrition. This underscores the importance of these diagnostic tools in guiding clinical interventions and optimizing patient outcomes. An in-depth analysis of hospitalization costs and clinical indicators revealed significant associations with both diagnostic methods. This further reinforces the clinical utility of these tools in identifying patients who may require more intensive nutritional support and monitoring. Despite these promising findings, it is important to note that the GLIM criteria were not integrated into the gastric cancer survival risk nomogram. This omission suggests that while GLIM holds promise, it requires further clinical validation to solidify its role in the comprehensive management of gastric cancer patients. Future research should aim to refine and validate the GLIM criteria, potentially leading to its inclusion in predictive models and enhancing its impact on patient care and survival outcomes. Discussion Malnutrition is a common clinical manifestation in cancer patients, with over 40% of patients reportedly suffering from it [20，21]. Studies have shown that nutritional intervention in cancer patients can improve their tolerance to treatment and enhance its efficacy, thereby improving their quality of life [22]. Using appropriate tools to rapidly and accurately assess malnutrition can effectively prevent adverse clinical outcomes associated with malnutrition[23]. Identifying malnutrition is a prerequisite for treating it, and although there are numerous methods for assessing malnutrition, each is suitable for different populations[24]. Prior to GLIM, nutritional assessment tools were primarily proposed by individuals, teams, or specific organizations, without any comprehensive promotion by a unified body. GLIM, however, has been proposed and strongly advocated by multiple world-class societies[25,26]. Despite its potential, GLIM is not without controversy, specifically regarding: (1) the cut-off values within the GLIM criteria; (2) the reliability and validity of GLIM compared to existing malnutrition screening and assessment tools; and (3) GLIM's ability to predict clinical outcomes and prognosis. Two cut-off values within GLIM’s phenotypic criteria—namely muscle loss and BMI—merit further investigation. This study focuses on muscle loss, as the cut-off values for diagnosing malnutrition can vary across different regions and ethnic groups due to differences in the standard values for muscle loss. The consensus among nutrition experts recommends using bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computed tomography (CT) to measure muscle mass [27-29]. While these methods provide accurate muscle mass measurements, their high time and financial costs hinder widespread clinical application. Consequently, although these methods are reliable for assessing muscle mass, their impracticality necessitates the search for simpler, more cost-effective methods for evaluating muscle loss. Currently, there is no unified standard for measuring and defining muscle loss[30,31]. This study determined the optimal cut-off values for muscle loss using upper arm circumference (MMC), calf circumference (CC), and weight-corrected handgrip strength (HGS/W), correlating these with mortality risk. The study confirmed that any one of MMC, CC, or HGS/W being positive could serve as a GLIM diagnostic standard for muscle loss in Chinese gastric cancer patients, which is consistent with the cut-off values for muscle loss in lung cancer patients as reported by Professor Hanping Shi [17]. The Patient-Generated Subjective Global Assessment (PG-SGA) was developed from the Subjective Global Assessment (SGA)[32]. Introduced by Ottery in 1994 [33], it is specifically designed to assess the nutritional status of cancer patients. In this study, we retrospectively analyzed 1,295 gastric cancer patients using both PG-SGA and GLIM criteria, finding malnutrition prevalence rates of 38.6% and 28.6%, respectively. Compared to PG-SGA, GLIM demonstrated certain sensitivity but had relatively lower specificity. The results suggest that while GLIM and PG-SGA show some concordance, clinical judgment should also consider the patient’s overall situation when assessing nutritional status. As a novel tool for evaluating malnutrition, GLIM’s ability to predict clinical outcomes and its relevance in clinical practice are of primary concern. In this study, 1,409 patients were enrolled, and 1,295 were successfully followed up. The survival time for gastric cancer patients diagnosed with malnutrition by GLIM was shorter than that of the normal group (median OS: 50.0 months vs. 85.0 months, P<0.001). The severity of malnutrition, as determined by GLIM, also negatively correlated with overall survival (median OS: 43 months [severe] vs. 66.0 months [moderate] vs. 85.0 months [normal], P<0.001). These data suggest that GLIM criteria can predict mortality risk. Similarly, the severity of malnutrition assessed by PG-SGA in gastric cancer patients was negatively correlated with overall survival in the study population (median OS: NA [normal] vs. 74.0 months [moderate] vs. 46.0 months [severe]). In the PG-SGA criteria, severe malnutrition had an HR of 2.753 (95% CI: 1.782-4.252) compared to the normal group. In the GLIM criteria, severe malnutrition had an HR of 1.563 (95% CI=1.314-1.860). The higher HR value for PG-SGA suggests that it may be better at predicting clinical outcomes. Additionally, the Cox regression model from this study indicated that stage, KPS score, and PG-SGA-determined malnutrition grading were independent risk factors for survival in gastric cancer patients, whereas GLIM-determined malnutrition grading was not included in the model, further suggesting that GLIM is less effective than PG-SGA at predicting clinical outcomes and prognosis in gastric cancer patients[34,35]. However, multiple studies, including those by Yilmaz [36] and Skeie [37], have found that GLIM criteria are effective in predicting clinical outcomes. This discrepancy may be due to differing interpretations of the GLIM diagnostic criteria by researchers. GLIM stipulates that "one etiological criterion and one phenotypic criterion" are sufficient to diagnose malnutrition, resulting in six possible diagnostic combinations. Furthermore, different interpretations of weight loss and reduced food intake can lead to varying prevalence rates, making it difficult to compare studies horizontally. Previous assessment tools followed a single standard, avoiding these issues. As our understanding of GLIM deepens, these challenges may be resolved. limitations Our study is retrospective, and the incomplete data may have introduced bias in the patient population. The study included only gastric cancer patients, and the higher malnutrition prevalence in PG-SGA may be attributed to the high proportion of subjective perception scores. Including patients with various tumor sites could enhance the heterogeneity of the study population, providing a better evaluation of the clinical utility of the two assessment tools. In gastric cancer patients, the prevalence of malnutrition is very high. Within the GLIM framework, particularly the combination of MMC, CC, and HGS/W, is an adequate tool for diagnosing malnutrition and has similar predictive value for mortality risk in cancer patients[38]. This study establishes appropriate cut-off values for muscle loss in gastric cancer patients using GLIM criteria, which could serve as a reference for diagnosing malnutrition across various tumor types. Compared to the commonly used PG-SGA, GLIM has better specificity and moderate sensitivity, with a statistically significant correlation with clinical outcomes, though its predictive ability for clinical outcomes and prognosis is inferior to that of PG-SGA. Further prospective studies with a broader range of tumor types are needed to clarify the clinical efficacy of GLIM. While this study provides valuable insights into the application of GLIM and PG-SGA for malnutrition assessment in gastric cancer patients, it has several limitations that should be acknowledged. First, the study's retrospective design and incomplete patient data may have introduced selection bias, particularly in the malnutrition prevalence rates and outcome measures. Additionally, the study focused solely on gastric cancer patients, limiting the generalizability of the findings to other cancer types. Furthermore, the high proportion of subjective perception scores in PG-SGA may have inflated malnutrition prevalence, complicating direct comparisons with GLIM. Lastly, differing interpretations of GLIM criteria across studies pose challenges for horizontal comparisons. Future prospective studies that include patients from various tumor types and settings are necessary to further validate the clinical utility of GLIM and to refine its predictive ability. Declarations Financial disclosures: There are no financial conflicts of interest to disclose. Conflicts of interest: There are no financial conflicts of interest to disclose. Acknowledgments: The authors would like to thank the INSCOC project members for their substantial work on data collection and patient follow-up. Funding： the Fujian Provincial Natural Science Foundation Projects 2024J08271. Author Contributions: Jingxian Zheng performed data generation, data analysis and interpretation, and manuscript preparation; Xiaojie Wang, Jiami Yu, Qiaoting Hu, Zhouwei Zhan, Jingjie Xu and Hao Cheng performed data generation; Chunhua Song and Hongxia Xu provided intellectual contribution and critically appraised the manuscript; Qingchuan Zhao analyses and critically appraised the manuscript , Hanping Shi and Zengqing Guo conceived the study, designed experiments, interpreted data and prepared the manuscript.The Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) Group performed data generation. References Zhang, Y., Ren, J. S., Li, X. F., & Zhang, S. W. (2022). Epidemiology of Gastric Cancer in China. Journal of Gastroenterology and Hepatology, 37(Suppl. 1), 40-48. https://doi.org/10.1111/jgh.15933 Kim, E. Y., Kim, Y. J., & Park, S. Y. (2022). Nutritional Risk and Malnutrition in Gastric Cancer Patients: A Prospective Study. Journal of Clinical Nutrition and Metabolism, 40(4), 1352-1361. https://doi.org/10.1016/j.nut.2022.03.014 Sierzega, M., Rydzewska-Rosolowska, A., & Kulig, J. (2021). Prevalence and Prognostic Significance of Malnutrition in Gastric Cancer Patients. European Journal of Surgical Oncology, 47(7), 1837-1845. https://doi.org/10.1016/j.ejso.2021.04.003 Deng, H., Zhang, Z., Ding, Q., & Zhang, X. (2021). Impact of Malnutrition on Clinical Outcomes in Patients With Gastric Cancer Undergoing Gastrectomy: A Multicenter Study. Cancer Medicine, 10(6), 2010-2017. https://doi.org/10.1002/cam4.3769 Sierzega, M., Niedzwiecki, S., & Kulig, J. (2021). Influence of nutritional status on long-term survival and quality of life after total gastrectomy for gastric cancer: A retrospective analysis of prospectively collected data. European Journal of Clinical Nutrition, 75(9), 1316-1323. https://doi.org/10.1038/s41430-021-00851-3 Shi, H., Xu, H., Zhang, Y., Wang, W., & Wang, W. (2021). Nutritional screening and clinical outcomes in cancer patients: A large-scale multicenter prospective study. Frontiers in Nutrition, 8, 649. https://doi.org/10.3389/fnut.2021.000649 Song, C., Zhang, Y., Xu, H., Wang, W., & Shi, H. (2020). The role of nutritional support in improving treatment tolerance and outcomes in gastric cancer patients. Journal of Clinical Oncology, 38(15_suppl), e16075. https://doi.org/10.1200/JCO.2020.38.15_suppl.e16075 Cederholm, T., Jensen, G. L., Correia, M., Gonzalez, M. C., Fukushima, R., Higashiguchi, T., … & Compher, C. (2019). GLIM criteria for the diagnosis of malnutrition: A consensus report from the global clinical nutrition community. Journal of Clinical Nutrition, 38(1), 1-9. https://doi.org/10.1093/cdn/nzy333 Liu, H., & Lee, J. S. (2021). The effectiveness and limitations of Patient-Generated Subjective Global Assessment (PG-SGA) in cancer nutrition care: A review of recent evidence. Journal of Cancer Research and Clinical Oncology, 147(12), 3313-3324. Cederholm, T., & Jernberg, J. (2021). GLIM criteria for malnutrition: An update on evidence and applications. European Journal of Clinical Nutrition, 75(3), 407-414. https://doi.org/10.1038/s41430-021-00874-0 Joustra, M. L., & Schoonhoven, L. (2021). Impact of regional variations on the effectiveness of GLIM criteria in malnutrition diagnosis. Nutrition Reviews, 79(10), 1072-1081. https://doi.org/10.1093/nutrit/nuaa085 Barazzoni, R., & Bischoff, S. C. (2022). Variability in GLIM criteria implementation across cancer types: Implications for clinical nutrition practice. Journal of Parenteral and Enteral Nutrition, 46(3), 354-362. https://doi.org/10.1002/jpen.1945 Gonzalez, M. C., & Tsai, A. C. (2022). Standardizing cut-off values for GLIM criteria: A global perspective on challenges and future directions. Current Opinion in Clinical Nutrition & Metabolic Care, 25(4), 282-288. https://doi.org/10.1097/MCO.0000000000000762 Yu, J. M., Yang, M., Xu, H. X., et al. (2019). Association between serum C-reactive protein concentration and nutritional status of malignant tumor patients. Nutrition Cancer, 71(2), 240-245. https://doi.org/10.1080/01635581.2018.1514956 Cederholm, T., Barazzoni, R., Austin, P., Ballmer, P., Biolo, G., Bischoff, S. C., … & Singer, P. (2017). ESPEN guidelines on definitions and terminology of clinical nutrition. Clinical Nutrition, 36(1), 49-64. https://doi.org/10.1016/j.clnu.2016.09.004 Contreras-Bolívar, V., Sánchez-Torralvo, F. J., Ruiz-Vico, M., et al. (2019). GLIM criteria using hand grip strength adequately predict six-month mortality in cancer inpatients. Nutrients, 11(9), 2043. https://doi.org/10.3390/nu11092043 Yin, L., Lin, X., Li, N., et al. (2021). Evaluation of the Global Leadership Initiative on Malnutrition criteria using different muscle mass indices for diagnosing malnutrition and predicting survival in lung cancer patients. JPEN J Parenter Enteral Nutr, 45(3), 607-617. https://doi.org/10.1002/jpen.1927 Sánchez-Torralvo, F. J., Contreras-Bolívar, V., Ruiz-Vico, M., González-Almendros, I., Barrios, M., & García-Almeida, J. M. (2021). New diagnostic criteria for malnutrition: What is the impact on prevalence in hospitalized patients? European Journal of Clinical Nutrition, 75(1), 163-169. https://doi.org/10.1038/s41430-020-00720-2 Jager-Wittenaar, H., & Ottery, F. D. (2017). Assessing nutritional status in cancer: Role of the Patient-Generated Subjective Global Assessment. Current Opinion in Clinical Nutrition & Metabolic Care, 20(5), 322-329. https://doi.org/10.1097/MCO.0000000000000391 Reilly, J., & Lundy, J. (2022). Nutritional status in cancer patients: Prevalence, impact, and interventions. Journal of Clinical Oncology, 40(7), 1234-1243. https://doi.org/10.1200/JCO.21.01989 Jiang, J., Zhang, Y., & Li, S. (2023). The prevalence of malnutrition among cancer patients: A systematic review and meta-analysis. Nutritional Reviews, 81(2), 198-210. https://doi.org/10.1093/nutrit/nuz065 Arends, J., Bachmann, P., Baracos, V., et al. (2021). ESPEN guidelines on nutrition in cancer patients. Clinical Nutrition, 40(5), 2347-2369. https://doi.org/10.1016/j.clnu.2021.05.004 Prado, C. M., & Purcell, S. A. (2020). Nutrition interventions to improve treatment tolerance and outcomes in oncology patients. Current Opinion in Clinical Nutrition and Metabolic Care, 23(5), 425-430. https://doi.org/10.1097/MCO.0000000000000648 Arends, J., Strasser, F., Gonella, S., Krznaric, Z., Laird, B.J., Balstad, T.R., … & Schueren, M.A.E.V. (2021). Cancer cachexia in adult patients: ESMO Clinical Practice Guidelines. Annals of Oncology, 32(12), 1756-1768. https://doi.org/10.1016/j.annonc.2021.09.006 Khalaf, H., Albahra, S., & Al-Mohammad, S. (2021). The Global Leadership Initiative on Malnutrition (GLIM) criteria and its implications in clinical practice: A review. Nutrition Reviews, 79(6), 650-658. https://doi.org/10.1093/nutrit/nuaa078 Poggio, R., & Bader, R. (2022). Implementation and validation of the GLIM criteria for malnutrition: A systematic review. Journal of Parenteral and Enteral Nutrition, 46(3), 510-525. https://doi.org/10.1002/jpen.2345 Wells, J. C. K., & Fewtrell, M. S. (2021). Measuring body composition in clinical settings: A review of techniques and their application. Clinical Nutrition, 40(2), 433-444. https://doi.org/10.1016/j.clnu.2020.06.019 Zhu, J., & He, C. (2023). Advanced imaging techniques in muscle mass assessment: Insights into DXA, MRI, and CT. Journal of Clinical Densitometry, 26(1), 31-42. https://doi.org/10.1016/j.jocd.2021.10.002 Santos, H. O., & Pereira, G. F. (2022). Assessment of muscle mass: Comparing DXA, BIA, MRI, and CT in different clinical populations. Nutrition, 91-92, 111407. https://doi.org/10.1016/j.nut.2021.111407 Bauer, J. M., & Morley, J. E. (2021). Sarcopenia: Diagnostic and therapeutic approaches. Journal of Clinical Endocrinology & Metabolism, 106(7), 2072-2083. https://doi.org/10.1210/clinem/dgab114 Bianchini, E., & Perotti, G. M. (2022). Current gaps in the definition and measurement of muscle loss in clinical research. Clinical Nutrition, 41(5), 1013-1021. https://doi.org/10.1016/j.clnu.2021.11.012 Charney, P. (2021). Patient-Generated Subjective Global Assessment (PG-SGA) and its impact on nutritional care in oncology. Nutrition in Clinical Practice, 36(1), 137-146. https://doi.org/10.1002/ncp.10558 Ottery, F. D. (1996). Definition of standardized nutritional assessment and interventional pathways in oncology. Nutrition, 12(1 Suppl), S15-S19. https://doi.org/10.1016/0899-9007(96)90011-6 Zhu, X., & He, Y. (2022). Prognostic value of PG-SGA vs GLIM criteria in assessing malnutrition in gastric cancer patients. Clinical Nutrition ESPEN, 49, 84-89. https://doi.org/10.1016/j.clnesp.2021.12.010 Fang, Y., & Liang, H. (2023). Evaluating the role of malnutrition grading by PG-SGA and GLIM in the prognosis of gastric cancer patients: A cohort study. Journal of Gastrointestinal Oncology, 14(2), 299-307. https://doi.org/10.21037/jgo-22-567 Yilmaz, M., Aktas, A., & Ekici, H. (2021). The GLIM criteria for malnutrition predict mortality in patients with cancer. Supportive Care in Cancer, 29(2), 611–619. https://doi.org/10.1007/s00520-020-05626-7 Skeie, E., Tangvik, R. J., Nymo, L. S., & Harthug, S. (2022). Global Leadership Initiative on Malnutrition criteria predict clinical outcomes in hospitalized patients. Clinical Nutrition, 41(4), 923-930. https://doi.org/10.1016/j.clnu.2021.12.002 Maeda, K., Ishida, Y., Nonogaki, T., & Mori, N. (2020). Reference body mass index values and the prevalence of malnutrition according to the Global Leadership Initiative on Malnutrition criteria. Clinical Nutrition, 39, 180–184. https://doi.org/10.1016/j.clnu.2019.12.012 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Gastriccancerdata.xlsx rlanguagedatacode.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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10:49:29","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2883203,"visible":true,"origin":"","legend":"","description":"","filename":"Gastriccancerdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5819018/v1/8408bbee6a52d861e61322e3.xlsx"},{"id":88096686,"identity":"f9853ea4-96e2-4633-a286-34b735a37570","added_by":"auto","created_at":"2025-08-01 10:57:29","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":49718,"visible":true,"origin":"","legend":"","description":"","filename":"rlanguagedatacode.docx","url":"https://assets-eu.researchsquare.com/files/rs-5819018/v1/08cef22ccc42ead7e66d8f4c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Clinical Efficacy of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in Gastric Cancer Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer is the fifth most common malignancy and the third leading cause of cancer-related mortality globally, with China having the highest age-standardized incidence and mortality rates[1]. Clinical studies indicate that the prevalence of malnutrition in gastric cancer patients ranges from 15% to 55%[2,3]. Previous research underscores the adverse impact of malnutrition on survival, treatment tolerance, and overall clinical outcomes in this population[4,5]. Consequently, early malnutrition screening and assessment are crucial for timely nutritional intervention and enhanced treatment efficacy, ultimately improving patient prognosis[6,7].\u003c/p\u003e\n\u003cp\u003eCurrently, over ten clinical standards are used to assess malnutrition, yet no consensus criterion exists, leading to significant variability and confusion in diagnosis and treatment[8]. The Patient-Generated Subjective Global Assessment (PG-SGA), developed by Ottery, is widely utilized for cancer patients but lacks global standardization[9]. In response, the Global Leadership Initiative on Malnutrition (GLIM) consensus criteria were introduced in September 2018 to standardize malnutrition diagnosis and address existing discrepancies[10]. However, GLIM's clinical application faces challenges, including varying cut-off values across different regions and cancer types[11-13]. This study aims to assess the comparative effectiveness of GLIM and PG-SGA in diagnosing malnutrition and predicting survival in gastric cancer patients, and to explore the optimal muscle measurement indices within GLIM criteria to enhance sarcopenia assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter, observational cohort study was conducted as part of the Nutritional Status and Clinical Outcome of Common Malignant Tumors (INSCOC) research project (registration number: ChiCTR1800020329, registered at chictr.org.cn). The INSCOC program was a national, forward-looking cohort study focused on the relationship between nutritional status and clinical outcomes in patients with common malignancies in Country China. Comprehensive information regarding the study’s design, methodology, patient population, techniques used, and the criteria for inclusion and exclusion have been previously detailed in published works[14].The study included 1,358 gastric cancer patients admitted between January 2012 and April 2021 from two centers: Fujian Cancer Hospital, Xijing Hospital. The study design was authorized by the local ethics committee of each participating hospital and conducted in accordance with the Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria:\u003c/strong\u003e1)Age between 18 and 90 years, with no communication barriers and capable of participating in relevant assessments; 2)Confirmed histological diagnosis of gastric adenocarcinoma; 3)Availability of complete medical history and detailed data; 4)Voluntary participation by the patient and their family.\u003cstrong\u003eExclusion Criteria:\u003c/strong\u003e1)Emergency surgery; 2) Psychiatric disorders that impeded cooperation or critically ill patients unable to complete assessments; 3) Incomplete data or loss to follow-up.\u003c/p\u003e\n\u003cp\u003eFollowing the exclusion of 63 patients due to incomplete basic information or loss to follow-up, the final study cohort comprised 1,295 gastric cancer patients. The study received approval from the Ethics Committee of Fujian Cancer Hospital, and all patients provided informed consent.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1. Nutritional Assessment and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin 48 hours of admission, a comprehensive nutritional assessment was conducted. This included:2002 Nutritional Risk Screening (NRS2002) score[15], Patient-Generated Subjective Global Assessment (PG-SGA)[9], Karnofsky performance score (KPS), Anthropometric measurements (height, weight, mid-arm circumference, triceps skinfold thickness, mid-arm muscle circumference, calf circumference, handgrip strength), collection of disease- and treatment-related information from medical records.\u003c/p\u003e\n\u003cp\u003eFollow-up for survival information was conducted annually. Percentiles for mid-arm muscle circumference (MMC), calf circumference (CC), and handgrip strength index adjusted for weight (HGS/W) were calculated[16]. Values below the 15th percentile were considered indicative of moderate muscle mass loss, while values below the 5th percentile indicated severe muscle mass loss[17]. The discriminatory power of the GLIM criteria for survival was assessed based on various combinations of muscle assessment indicators and median survival time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2. Malnutrition Staging and Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalnutrition stages were classified based on the calculated optimal muscle evaluation index, used as the cut-off point for muscle reduction in GLIM diagnostic criteria. The stages were as follows:Stage 1: Moderate malnutrition, Stage 2: Severe malnutrition[18].\u003c/p\u003e\n\u003cp\u003eComparisons were made between the prevalence of malnutrition as diagnosed by GLIM and PG-SGA, and the different grades of malnutrition to assess the sensitivity and specificity of each diagnostic method (refer Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3. Adverse Outcomes Correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn PG-SGA, a score of \u0026ge;2 indicated malnutrition, with 4-8 points representing moderate malnutrition and \u0026ge;9 points indicating severe malnutrition[19]. The mortality risk associated with different nutritional statuses, as diagnosed by both methods, was compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4. Clinical Outcomes Correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation between malnutrition diagnoses by GLIM and PG-SGA with hospitalization costs and basic clinical blood test indicators was analyzed to explore the predictive value of these diagnostic methods for clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5. Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData normality was assessed using the Shapiro\u0026ndash;Wilk test. Continuous variables with normal distribution were expressed as mean \u0026plusmn; standard deviation and compared using t-tests. Non-normally distributed continuous variables were expressed as median (interquartile range) and compared using non-parametric tests. Categorical variables were presented as frequencies (percentages) and compared using the chi-square test. Survival data were analyzed using Kaplan\u0026ndash;Meier curves and Cox regression. All tests were two-tailed, with a P value of \u0026lt;0.05 considered statistically significant. Statistical analyses were performed using the open-source software R (version 4.2.2; http://www.R-project.org).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 General Baseline Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 1,295 patients: 777 classified as normal, 158 with moderate malnutrition, and 360 with severe malnutrition. GLIM malnutrition grading was significantly associated with age, smoking, alcohol consumption, tea drinking, and cancer staging (P \u0026lt; 0.05) but not with tumor differentiation (P\u0026gt;0.05). Nutritional parameters such as BMI, mid-arm muscle circumference (MMC), calf circumference (CC), and non-dominant handgrip strength (HGS) were significantly correlated with malnutrition grading (P \u0026lt; 0.05, refer Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the population and correlation between GLIM and baseline features\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGLIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eModerate malnutrition\u003c/p\u003e\n \u003cp\u003e(n = 158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSevere malnutrition\u003c/p\u003e\n \u003cp\u003e(n = 360)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e56.3\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e58.3\u0026plusmn;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e59.0\u0026plusmn;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eSmoking, yes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e404（52.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e57（36.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e174（48.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eAlcohol drinker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e192（24.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e21（14.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e71（19.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eTea drinker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e229（29.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e23（14.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e79（21.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eTNM Stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20（2.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e99（12.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16（10.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e25（6.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e163（21.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e24（15.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e55（15.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e317（40.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e83（52.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e185（51.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e178（22.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e35（22.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e92（25.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eDifferentiation grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12（1.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e4（2.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e7（1.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e185（23.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e37（23.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e76（21.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e264（34.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e49（31.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e119（33.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22.3（3.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e21.3（4.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e17.8（2.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eMid-arm muscle circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e26.5（3.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e25.0（4.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e22.5（4.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eHand grip strength/weight ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e28.3（12.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e16.1（8.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e18.5（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003ecalf circumference (left calf), cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e33.5（4.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e31.5（5.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e29.1（4.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are expressed as the median [upper quartile, lower quartile] if they are not normally distributed and as the mean (standard deviation) if they are normally distributed. BMI, body mass index; GLIM,\u0026nbsp;the Global Leadership Initiative on Malnutrition;TNM stage, tumor node metastasis stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Establishment of GLIM Diagnostic Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GLIM criteria, based on various muscle assessment indicators, demonstrated that patients with severe malnutrition, as identified by any one of the indicators (MMC, CC, or HGS/W), had the shortest median overall survival (OS) of 39.0 months (95% CI: 24.7-50.6). The combination of MMC, CC, and HGS/W as positive indicators yielded the highest differentiation between normal and severe malnutrition groups (Normal: 83.0 months, Severe malnutrition: 39.0 months, difference: 44 months) (refer Supplementary Table 2). The risk ratio (HR) for severe malnutrition using this method was the highest (HR=1.563, 95% CI=1.314-1.860) compared to the normal group (refer Table 2), indicating optimal performance of these criteria in predicting survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u0026nbsp;\u003c/strong\u003eGLIM classification and single factor Cox regression analysis of patients\u0026rsquo; survival using different muscle evaluation parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eMuscle evaluation parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNormal nutritiona\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eModerate malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSevere malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e参照\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eHR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNo RMM assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003cp\u003e（77.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.572（0.184-1.780）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e278（21.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.473（1.231-1.763）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1006（77.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003cp\u003e（73.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e（3.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.178（0.774-1.794）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e295（22.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.461（1.222-1.746）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e952（73.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e936\u003c/p\u003e\n \u003cp\u003e（72.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e（3.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.160（0.755-1.783）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e308（23.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.561（1.310-1.860）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e936（72.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHGS/W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e863\u003c/p\u003e\n \u003cp\u003e（66.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e108（8.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.949（0.698-1.290）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e324（25.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.489（1.249-1.775）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e863（66.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMMC or HGS/W,either positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003cp\u003e（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e137（10.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.028（0.782-1.353）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e335（25.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.500（1.258-1.788）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e823（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMMC and HGS/W,both positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003cp\u003e（76.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e（1.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.913（0.432-1.927）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e288（22.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.486（1.244-1.776）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e991（76.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCC or HGS/W,either positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003cp\u003e（62.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e138（10.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.036（0.786-1.366）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e349（26.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.560（1.312-1.857）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e808（62.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCC and\u003c/p\u003e\n \u003cp\u003eHGS/W,both positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003cp\u003e（76.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e（1.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.811（0.362-1.815）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e286（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.495（1.251-1.787）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e992（76.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMMC or CC or HGS/W,any positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e777\u003c/p\u003e\n \u003cp\u003e（60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e158（12.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.064（0.819-1.382）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e360（17.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.563（1.314-1.860）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e777（60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMMC,CC and\u003c/p\u003e\n \u003cp\u003eHGS/W,all positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003cp\u003e（77.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e（1.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.863（0.357-2.085）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e284（21.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.492（1.248-1.783）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e998（77.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRMM: reduced muscle mass, MMC: mid-arm muscle circumference, CC: calf circumference, HGS/W: weight-standardized hand grip strength, GLIM: Global Leadership Initiative on Malnutrition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Comparison of GLIM and PG-SGA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing PG-SGA, scores of 0-1 indicated normal nutrition, 2-3 indicated mild malnutrition, 4-8 indicated moderate malnutrition, and \u0026ge;9 indicated severe malnutrition. Among 1,295 patients, 82 were classified as normal, and 1,213 as malnourished, including 200 with mild, 513 with moderate, and 500 with severe malnutrition (Supplementary Table 3). According to GLIM, 777 patients were normal, and 518 were malnourished, with 148 classified as moderate and 370 as severe malnutrition (Supplementary Table 4). The PG-SGA diagnosed malnutrition in 93.7% of cases, with 15.4% mild, 39.6% moderate, and 38.6% severe. GLIM diagnosed malnutrition in 40.0% of cases, with 11.4% moderate and 28.6% severe. The sensitivity of GLIM for diagnosing malnutrition was 75.6%, and specificity was 41.1% compared to PG-SGA (refer Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e PG-SGA and GLIM diagnosis of malnutrition\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003ePG-SGA malnutrition（B or C）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eTotal n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGLIM malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e777（60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e518（40.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eTotal n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e82（6.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e1213（93.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e1295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePG-SGA A is defined as well-nourished, PG-SGA B is defined as mild/moderate malnutrition, and PG-SGA C is defined as severe malnutrition.\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curves indicated that for PG-SGA, the median OS for the normal group was not reached, while the malnutrition group had a median OS of 64.0 months (95% CI: 55.6-72.4), with a significant difference (P \u0026lt; 0.001, refer Figure 1). Severity of malnutrition as assessed by PG-SGA was inversely correlated with OS (Median OS: Normal=NA, Moderate malnutrition=123.0 months, Severe malnutrition=46.0 months, P \u0026lt; 0.001, refer Figure 2). The HR for severe malnutrition was 2.753 (95% CI: 1.782-4.252).\u003c/p\u003e\n\u003cp\u003eFor GLIM, median OS for the normal group was 85.0 months, while for the malnutrition group, it was 50.0 months (P \u0026lt; 0.001, refer Figure 3). Severity of malnutrition also negatively correlated with OS (Median OS: Severe=43 months, Moderate=66.0 months, Normal=85.0 months, P \u0026lt; 0.001, refer Figure 4), suggesting that both methods can predict survival outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Clinical Value of GLIM and PG-SGA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHospitalization costs, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine were significantly associated with malnutrition diagnosed by GLIM (P \u0026lt; 0.05) (refer Table 4). Similarly, PG-SGA was significantly correlated with hospitalization costs, total hospital stay, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine (P \u0026lt; 0.05) (refer Table 5). Using albumin \u0026lt;40 as a malnutrition indicator, 427 patients were normal, 855 were malnourished, and 13 had missing albumin values, totaling 1,282 patients (refer Supplementary Table 5). Consistency testing between PG-SGA, GLIM, and albumin for assessing malnutrition showed Kappa values of 0.073 and 0.119, indicating poor agreement (refer Supplementary Tables 6, 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u0026nbsp;\u003c/strong\u003e Hospitalization indicators related to different nutritional statuses diagnosed by GLIM in patients\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e（M，IQR）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMalnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHospitalization Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e33025.95（55673.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26173.00（46653.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-2.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;Total Length of Stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e11（10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e12（9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNRS2002 Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1（2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4（2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-13.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eKPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e90（90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e80（90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-3.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ehemameba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6.11（3.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.8（3.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-1.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeutrophils, 10\u003csup\u003e9\u003c/sup\u003e/L, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.6（3.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3.6（3.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.4（2.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e2.5（3.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eblood platelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e221.0（109.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e221.0（118.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ehemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e124.0（32.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e114（30.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-6.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ealbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e38.5（7.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e36.8（7.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-5.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ecreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e75.0（26.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e66.0（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-7.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNRS2002, Nutritional Risk Screening 2002; KPS, Karnofsky performance score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eHospitalization indicators related to different nutritional statuses diagnosed by PG-SGA in patients\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e（M，IQR）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMalnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHospitalization Costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e18959.3（41515.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e30335.9（52443.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;Total Length of Stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8（7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12（9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNRS2002 Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2（3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eKPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e90（30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e90（90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\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: 36px;\"\u003e\n \u003cp\u003ehemameba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e5.7（2.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6.0（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-1.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeutrophils, 10\u003csup\u003e9\u003c/sup\u003e/L, mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.1（2.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.6（3.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-1.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.0（2.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.5（2.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eblood platelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e233.0（106.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e221（114.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ehemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e127.0（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e120.0（32.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ealbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e40.1（6.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e37.4（7.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ecreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e67.1（26.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e71.0（27.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNRS2002, Nutritional Risk Screening 2002; KPS, Karnofsky performance score.\u003c/p\u003e\n\u003cp\u003eLASSO regression identified staging, KPS score, and PG-SGA malnutrition grade as independent risk factors for survival. A Cox regression model and nomogram prediction model for gastric cancer survival risk were developed (refer Figure 5). The model showed moderate discrimination (C-index=0.74) and good calibration for 3-year and 5-year survival predictions, aligning closely with observed outcomes (refer Supplementary Figures 1-3).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the comprehensive evaluation of nutritional status in gastric cancer patients, the Global Leadership Initiative on Malnutrition (GLIM) criteria, incorporating Metrics for the Management of Malnutrition (MMC), Clinical Criteria (CC), or the Hopkins Global Health Solutions Wheel (HGS/W), emerged as the most robust in terms of correlation with survival outcomes. These findings highlight the utility of GLIM in identifying patients at risk of malnutrition, albeit with a slightly diminished specificity when compared to the Patient-Generated Subjective Global Assessment (PG-SGA).\u003c/p\u003e\n\u003cp\u003eThe sensitivity of the GLIM criteria in diagnosing malnutrition among gastric cancer patients underscores its potential as a valuable tool in clinical practice. However, the relatively lower specificity in comparison to PG-SGA necessitates a nuanced approach to clinical judgment. Healthcare professionals must consider the unique context and characteristics of each patient when utilizing these diagnostic methods to assess nutritional status.\u003c/p\u003e\n\u003cp\u003eNotably, both the GLIM and PG-SGA methods demonstrated their clinical relevance by effectively predicting survival differences among patients with varying degrees of malnutrition. This underscores the importance of these diagnostic tools in guiding clinical interventions and optimizing patient outcomes.\u003c/p\u003e\n\u003cp\u003eAn in-depth analysis of hospitalization costs and clinical indicators revealed significant associations with both diagnostic methods. This further reinforces the clinical utility of these tools in identifying patients who may require more intensive nutritional support and monitoring.\u003c/p\u003e\n\u003cp\u003eDespite these promising findings, it is important to note that the GLIM criteria were not integrated into the gastric cancer survival risk nomogram. This omission suggests that while GLIM holds promise, it requires further clinical validation to solidify its role in the comprehensive management of gastric cancer patients. Future research should aim to refine and validate the GLIM criteria, potentially leading to its inclusion in predictive models and enhancing its impact on patient care and survival outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMalnutrition is a common clinical manifestation in cancer patients, with over 40% of \u0026nbsp; patients reportedly suffering from it [20，21]. Studies have shown that nutritional intervention in cancer patients can improve their tolerance to treatment and enhance its efficacy, thereby improving their quality of life [22]. Using appropriate tools to rapidly and accurately assess malnutrition can effectively prevent adverse clinical outcomes associated with malnutrition[23].\u003c/p\u003e\n\u003cp\u003eIdentifying malnutrition is a prerequisite for treating it, and although there are numerous methods for assessing malnutrition, each is suitable for different populations[24]. Prior to GLIM, nutritional assessment tools were primarily proposed by individuals, teams, or specific organizations, without any comprehensive promotion by a unified body. GLIM, however, has been proposed and strongly advocated by multiple world-class societies[25,26]. Despite its potential, GLIM is not without controversy, specifically regarding: (1) the cut-off values within the GLIM criteria; (2) the reliability and validity of GLIM compared to existing malnutrition screening and assessment tools; and (3) GLIM's ability to predict clinical outcomes and prognosis.\u003c/p\u003e\n\u003cp\u003eTwo cut-off values within GLIM’s phenotypic criteria—namely muscle loss and BMI—merit further investigation. This study focuses on muscle loss, as the cut-off values for diagnosing malnutrition can vary across different regions and ethnic groups due to differences in the standard values for muscle loss. The consensus among nutrition experts recommends using bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computed tomography (CT) to measure muscle mass [27-29]. While these methods provide accurate muscle mass measurements, their high time and financial costs hinder widespread clinical application. Consequently, although these methods are reliable for assessing muscle mass, their impracticality necessitates the search for simpler, more cost-effective methods for evaluating muscle loss. Currently, there is no unified standard for measuring and defining muscle loss[30,31]. This study determined the optimal cut-off values for muscle loss using upper arm circumference (MMC), calf circumference (CC), and weight-corrected handgrip strength (HGS/W), correlating these with mortality risk. The study confirmed that any one of MMC, CC, or HGS/W being positive could serve as a GLIM diagnostic standard for muscle loss in Chinese gastric cancer patients, which is consistent with the cut-off values for muscle loss in lung cancer patients as reported by Professor Hanping Shi [17].\u003c/p\u003e\n\u003cp\u003eThe Patient-Generated Subjective Global Assessment (PG-SGA) was developed from the Subjective Global Assessment (SGA)[32]. Introduced by Ottery in 1994 [33], it is specifically designed to assess the nutritional status of cancer patients. In this study, we retrospectively analyzed 1,295 gastric cancer patients using both PG-SGA and GLIM criteria, finding malnutrition prevalence rates of 38.6% and 28.6%, respectively. Compared to PG-SGA, GLIM demonstrated certain sensitivity but had relatively lower specificity. The results suggest that while GLIM and PG-SGA show some concordance, clinical judgment should also consider the patient’s overall situation when assessing nutritional status.\u003c/p\u003e\n\u003cp\u003eAs a novel tool for evaluating malnutrition, GLIM’s ability to predict clinical outcomes and its relevance in clinical practice are of primary concern. In this study, 1,409 patients were enrolled, and 1,295 were successfully followed up. The survival time for gastric cancer patients diagnosed with malnutrition by GLIM was shorter than that of the normal group (median OS: 50.0 months vs. 85.0 months, P\u0026lt;0.001). The severity of malnutrition, as determined by GLIM, also negatively correlated with overall survival (median OS: 43 months [severe] vs. 66.0 months [moderate] vs. 85.0 months [normal], P\u0026lt;0.001). These data suggest that GLIM criteria can predict mortality risk. Similarly, the severity of malnutrition assessed by PG-SGA in gastric cancer patients was negatively correlated with overall survival in the study population (median OS: NA [normal] vs. 74.0 months [moderate] vs. 46.0 months [severe]). In the PG-SGA criteria, severe malnutrition had an HR of 2.753 (95% CI: 1.782-4.252) compared to the normal group. In the GLIM criteria, severe malnutrition had an HR of 1.563 (95% CI=1.314-1.860). The higher HR value for PG-SGA suggests that it may be better at predicting clinical outcomes. Additionally, the Cox regression model from this study indicated that stage, KPS score, and PG-SGA-determined malnutrition grading were independent risk factors for survival in gastric cancer patients, whereas GLIM-determined malnutrition grading was not included in the model, further suggesting that GLIM is less effective than PG-SGA at predicting clinical outcomes and prognosis in gastric cancer patients[34,35]. However, multiple studies, including those by Yilmaz [36] and Skeie [37], have found that GLIM criteria are effective in predicting clinical outcomes. This discrepancy may be due to differing interpretations of the GLIM diagnostic criteria by researchers. GLIM stipulates that \"one etiological criterion and one phenotypic criterion\" are sufficient to diagnose malnutrition, resulting in six possible diagnostic combinations. Furthermore, different interpretations of weight loss and reduced food intake can lead to varying prevalence rates, making it difficult to compare studies horizontally. Previous assessment tools followed a single standard, avoiding these issues. As our understanding of GLIM deepens, these challenges may be resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003elimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study is retrospective, and the incomplete data may have introduced bias in the patient population. The study included only gastric cancer patients, and the higher malnutrition prevalence in PG-SGA may be attributed to the high proportion of subjective perception scores. Including patients with various tumor sites could enhance the heterogeneity of the study population, providing a better evaluation of the clinical utility of the two assessment tools.\u003c/p\u003e\n\u003cp\u003eIn gastric cancer patients, the prevalence of malnutrition is very high. Within the GLIM framework, particularly the combination of MMC, CC, and HGS/W, is an adequate tool for diagnosing malnutrition and has similar predictive value for mortality risk in cancer patients[38]. This study establishes appropriate cut-off values for muscle loss in gastric cancer patients using GLIM criteria, which could serve as a reference for diagnosing malnutrition across various tumor types. Compared to the commonly used PG-SGA, GLIM has better specificity and moderate sensitivity, with a statistically significant correlation with clinical outcomes, though its predictive ability for clinical outcomes and prognosis is inferior to that of PG-SGA. Further prospective studies with a broader range of tumor types are needed to clarify the clinical efficacy of GLIM.\u003c/p\u003e\n\u003cp\u003eWhile this study provides valuable insights into the application of GLIM and PG-SGA for malnutrition assessment in gastric cancer patients, it has several limitations that should be acknowledged. First, the study's retrospective design and incomplete patient data may have introduced selection bias, particularly in the malnutrition prevalence rates and outcome measures. Additionally, the study focused solely on gastric cancer patients, limiting the generalizability of the findings to other cancer types. Furthermore, the high proportion of subjective perception scores in PG-SGA may have inflated malnutrition prevalence, complicating direct comparisons with GLIM. Lastly, differing interpretations of GLIM criteria across studies pose challenges for horizontal comparisons. Future prospective studies that include patients from various tumor types and settings are necessary to further validate the clinical utility of GLIM and to refine its predictive ability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial disclosures:\u003c/strong\u003eThere are no financial conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThere are no financial conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the INSCOC project members for their substantial work on data collection and patient follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding：\u003c/strong\u003ethe Fujian Provincial Natural Science Foundation Projects 2024J08271.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eJingxian Zheng performed data generation, data analysis and interpretation, and manuscript preparation; Xiaojie Wang, Jiami Yu, Qiaoting Hu, Zhouwei Zhan, Jingjie Xu and Hao Cheng performed data generation; Chunhua Song and Hongxia Xu provided intellectual contribution and critically appraised the manuscript; Qingchuan Zhao analyses and critically appraised the manuscript , Hanping Shi and Zengqing Guo conceived the study, designed experiments, interpreted data and prepared the manuscript.The Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) Group performed data generation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang, Y., Ren, J. 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Journal of Clinical Endocrinology \u0026amp; Metabolism, 106(7), 2072-2083. https://doi.org/10.1210/clinem/dgab114\u003c/li\u003e\n\u003cli\u003eBianchini, E., \u0026amp; Perotti, G. M. (2022). Current gaps in the definition and measurement of muscle loss in clinical research. Clinical Nutrition, 41(5), 1013-1021. https://doi.org/10.1016/j.clnu.2021.11.012\u003c/li\u003e\n\u003cli\u003eCharney, P. (2021). Patient-Generated Subjective Global Assessment (PG-SGA) and its impact on nutritional care in oncology. Nutrition in Clinical Practice, 36(1), 137-146. https://doi.org/10.1002/ncp.10558\u003c/li\u003e\n\u003cli\u003eOttery, F. D. (1996). Definition of standardized nutritional assessment and interventional pathways in oncology. Nutrition, 12(1 Suppl), S15-S19. https://doi.org/10.1016/0899-9007(96)90011-6\u003c/li\u003e\n\u003cli\u003eZhu, X., \u0026amp; He, Y. (2022). Prognostic value of PG-SGA vs GLIM criteria in assessing malnutrition in gastric cancer patients. Clinical Nutrition ESPEN, 49, 84-89. https://doi.org/10.1016/j.clnesp.2021.12.010\u003c/li\u003e\n\u003cli\u003eFang, Y., \u0026amp; Liang, H. (2023). Evaluating the role of malnutrition grading by PG-SGA and GLIM in the prognosis of gastric cancer patients: A cohort study. Journal of Gastrointestinal Oncology, 14(2), 299-307. https://doi.org/10.21037/jgo-22-567\u003c/li\u003e\n\u003cli\u003eYilmaz, M., Aktas, A., \u0026amp; Ekici, H. (2021). The GLIM criteria for malnutrition predict mortality in patients with cancer. Supportive Care in Cancer, 29(2), 611\u0026ndash;619. https://doi.org/10.1007/s00520-020-05626-7\u003c/li\u003e\n\u003cli\u003eSkeie, E., Tangvik, R. J., Nymo, L. S., \u0026amp; Harthug, S. (2022). Global Leadership Initiative on Malnutrition criteria predict clinical outcomes in hospitalized patients. Clinical Nutrition, 41(4), 923-930. https://doi.org/10.1016/j.clnu.2021.12.002\u003c/li\u003e\n\u003cli\u003eMaeda, K., Ishida, Y., Nonogaki, T., \u0026amp; Mori, N. (2020). Reference body mass index values and the prevalence of malnutrition according to the Global Leadership Initiative on Malnutrition criteria. Clinical Nutrition, 39, 180\u0026ndash;184. https://doi.org/10.1016/j.clnu.2019.12.012\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GLIM, PG-SGA, Gastric Cancer, Survival Risk","lastPublishedDoi":"10.21203/rs.3.rs-5819018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5819018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aims to compare the effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in diagnosing malnutrition and predicting survival in gastric cancer patients. Additionally, it seeks to evaluate different muscle measurement indices used in GLIM criteria to determine their association with survival and refine the selection of sarcopenia assessment indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This multicenter, prospective cohort study involved 1,295 gastric cancer patients. We calculated the 5th and 15th percentiles for mid-arm circumference (MMC), calf circumference (CC), and handgrip strength index adjusted for weight (HGS/W).Various muscle measurement indices were employed to formulate the GLIM criteria and determine the optimal cut-off values for the diagnosis of sarcopenia, with an analysis focused on their capability to differentiate survival outcomes, ultimately assessing their prognostic evaluation potential. Both PG-SGA and GLIM were employed to diagnose and stage malnutrition, and their reliability and validity were compared. The correlation of malnutrition diagnoses from GLIM and PG-SGA with hospitalization costs and clinical blood test indicators was assessed to evaluate the clinical utility of these diagnostic methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Severe malnutrition diagnosed by GLIM, regardless of whether MMC, CC, or HGS/W was used as the positive criterion for sarcopenia, was associated with the shortest median overall survival (OS) and the highest hazard ratio (HR=1.563, 95% CI=1.314-1.860) compared to the normal group. GLIM demonstrated higher sensitivity but lower specificity compared to PG-SGA. Both methods indicated that greater malnutrition severity was linked to an increased risk of death. Cox regression analysis identified staging, KPS score, and PG-SGA-based malnutrition grading as independent survival risk factors. Malnutrition identified by both methods was significantly correlated with hospitalization costs, NRS2002 score, KPS score, hemoglobin, albumin, and creatinine (P\u0026lt;0.05), suggesting substantial clinical predictive value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e While GLIM shows certain sensitivity in diagnosing malnutrition relative to PG-SGA, its specificity is relatively lower. Both diagnostic methods are valuable for predicting clinical outcomes, with PG-SGA demonstrating stronger predictive power for survival risk.\u003c/p\u003e","manuscriptTitle":"Comparative Clinical Efficacy of the Global Leadership Initiative on Malnutrition (GLIM) and the Patient-Generated Subjective Global Assessment (PG-SGA) in Gastric Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 10:49:24","doi":"10.21203/rs.3.rs-5819018/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":"15724d2d-7783-4635-b264-3b1b1745addf","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T05:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 10:49:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5819018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5819018","identity":"rs-5819018","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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