Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer Zuo Junbo, Zuo Junbo, Huang Yan, Huang Yan, Huang Zhenhua, Huang Zhenhua, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4313120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2024 Read the published version in European Journal of Clinical Nutrition → Version 1 posted 9 You are reading this latest preprint version Abstract Objective This study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC). Method Objective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria. Results A total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 patients (47.8%) based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition. Conclusions Compared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients. Health sciences/Diseases/Nutrition disorders/Malnutrition Health sciences/Biomarkers Health sciences/Diseases/Nutrition disorders/Malnutrition Health sciences/Biomarkers geriatric nutritional risk index objective nutritional screening tool GLIM-defined malnutrition gastric cancer Figures Figure 1 Figure 2 1. Introduction Gastric cancer (GC) is a prevalent malignant tumor, ranking fifth in terms of incidence and fourth in terms of mortality globally 1 . Due to the impact of the disease itself and the anti-tumor treatment process, GC patients are at a higher risk of experiencing malnutrition. It has been reported that the prevalence of malnutrition in GC patients can reach as high as 65%-85% 2 . Numerous studies have demonstrated that malnutrition is not only significantly associated with a higher risk of complications and mortality but also has an adverse effect on patients' treatment outcomes and quality of life 3–6 . Therefore, early screening and identification of malnourished patients followed by effective intervention measures play a pivotal role in multimodal cancer treatment and are currently gaining increasing attention. The Global Leadership Initiative on Malnutrition (GLIM) presents a novel diagnostic framework aimed at identifying malnutrition through a two-step approach 7 . Firstly, validated screening tools are used to identify patients who are at risk of malnutrition. Secondly, the diagnosis of malnutrition is established based on meeting at least one phenotypic criterion (involuntary weight loss, low body mass index or reduced muscle mass) and one etiological criterion (reduced food intake or assimilation, disease burden or inflammation). At present, the subjective tools used for nutritional risk screening include Nutrition Risk Screening-2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Malnutrition Screening Tool (MST) 2, 7 . Although these tools have been widely used in clinical practice, there are still some limitations in practical application. For example, the assessment of weight loss, previous dietary intake, and disease history relies on patient recall and subjective descriptions, which may introduce potential biases. In addition, to ensure accuracy and reliability, it is imperative that these assessment tools are utilized by qualified medical professionals, which may pose challenges in areas with insufficient medical and healthcare resources. Currently, various objective nutritional tools have been utilized for screening and evaluating malnutrition, as well as predicting clinical outcomes. The geriatric nutritional risk index (GNRI) is a simple nutritional screening tool that evaluates the risk of malnutrition by combining serum albumin levels with ideal body weight 8 . It has been demonstrated to be associated with adverse outcomes in various malignancies and can be applicable for both young and elderly patients 9 . The prognostic nutritional index (PNI), which is calculated based on total lymphocyte counts and serum albumin levels, has been used to assess nutritional status and serve as a prognostic indicator for different types of malignancies 10 . The controlling nutritional status (CONUT) score, a conveniently calculated tool using three blood parameters (albumin level, total cholesterol level, and total lymphocyte count), has also been utilized as a valuable tool for evaluating nutritional status and predicting outcomes in patients with diverse cancer types 11 . Based on laboratory examinations and anthropometric measurements, these objective nutritional tools can be easily performed in the clinical setting and used for dynamic surveillance. However, there is a paucity of studies evaluating the efficacy of these objective nutritional screening tools in identifying malnutrition based on the GLIM criteria. Furthermore, it remains unclear which objective nutritional screening tool is most effective for detecting GLIM-defined malnutrition in patients with GC. Therefore, this study aims to compare three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the optimal tool for identifying GLIM-defined malnutrition in GC patients. 2. Materials and methods 2.1 Study Patients This cross-sectional study enrolled consecutive patients diagnosed with GC in our Department from October 2021 to March 2023. Inclusion criteria were: ( 1 ) age between 18 and 80 years; ( 2 ) confirmed pathological diagnosis of gastric adenocarcinoma through gastroscopic biopsy; and ( 3 ) no prior neoadjuvant therapy. Exclusion criteria were: ( 1 ) absence of abdominal CT scans from our institution; ( 2 ) presence of severe comorbidities including heart failure, chronic kidney disease, or liver cirrhosis; and ( 3 ) concurrent presence of other malignancies or a history of other malignancies within the past 5 years. All patients provided written informed consent for data collection and analysis. This study followed the Declaration of Helsinki and obtained approval from the Ethics Committee of The Affiliated People's Hospital of Jiangsu University (No. K-20220028-Y). 2.2 Data collection Demographic and clinical data were collected, including age, sex, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, Charlson Comorbidity Index (CCI) score, tumor-node-metastasis (TNM) stage according to the eighth edition of the American Joint Committee on Cancer (AJCC). Additionally, laboratory data were also obtained, including albumin level, hemoglobin level, C-reactive protein (CRP) with the cut-off value set at 5.0 mg/L, neutrophil and lymphocyte counts and neutrophil/lymphocyte ratio (NLR). 2.3 Objective nutritional screening tools The GNRI is calculated as 14.87 × serum albumin concentration (g/L) + 41.7 × weight/ideal weight (kg) 8 . The ideal weight was determined using the formula: for males, it was calculated as 0.75×height (cm) – 62.5; and for females, it was calculated as 0.60×height (cm) – 40 8 . The PNI is calculated as 10 × serum albumin (g/dL) + 5 × lymphocyte count (10 9 /L) 12 . The CONUT score was calculated based on the concentrations of albumin, lymphocyte count, and total cholesterol, with each parameter being assigned scores as previously described 13 . The cumulative sum of these scores constituted the CONUT score. 2.4 Body composition analysis Body composition analysis was performed on CT images at the L3 level using Slice-O-Matic software V 5.0 (Tomovision, Magog, QC, Canada). Tissue-specific Hounsfield unit (HU) thresholds were applied to identify the cross-sectional areas of skeletal muscle (-29 to + 150 HU), subcutaneous fat (-190 to -30 HU), and visceral fat (-150 to -50 HU). These cross-sectional areas (cm 2 ) of L3 were then normalized for height squared (m 2 ) to calculate skeletal muscle index (L3-SMI, cm 2 /m 2 ), subcutaneous fat index (L3-SFI, cm 2 /m 2 ), and visceral fat index (L3-VFI, cm 2 /m 2 ). 2.5 Diagnosis of malnutrition Malnutrition was diagnosed according to the GLIM criteria 7 , and the diagnostic procedure followed the description provided in our previous study 14 . Briefly, patients with GC were considered to fulfil the etiological criterion of inflammation or disease burden, and the diagnosis of malnutrition was based on meeting at least one of the phenotypic criteria: ( 1 ) involuntary weight loss defined as > 5% within the past 6 months or > 10% beyond 6 months; ( 2 ) low BMI defined as < 18.5 kg/m 2 for patients under 70 years old and < 20 kg/m 2 for those over 70 years; ( 3 ) reduced muscle mass defined as ≤ 40.8 cm 2 /m 2 in males and ≤ 34.9 cm 2 /m 2 in females using sex-specific cut-off values of L3-SMI 14 . 2.6 Statistical analysis Continuous variables that follow a normal distribution (assessed using the Shapiro-Wilk test) are reported as mean ± standard deviation (SD) and analyzed using an independent t-test. Non-normally distributed continuous variables are presented as median (interquartile range) and analyzed using the Mann-Whitney U-test. Categorical variables were expressed as numbers (percentages) and analyzed using the chi-squared test. Univariate or multivariate logistic regression analyses were further employed to evaluate the association between these nutritional indices and GLIM-defined malnutrition. The adjusted model included age, sex, BMI, ECOG performance status, CCI, and TNM stage. The presence of collinearity among independent variables was assessed by calculating the Variance Inflation Factor (VIF) and correlation coefficients. If the VIF is ≥ 10 or the correlation coefficients are > 0.7, it indicates the existence of collinearity between independent variables, which are subsequently excluded from analysis 15 . The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The optimal cut-off value was established based on the maximal Youden's index formula: sensitivity + specificity − 1. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM malnutrition criteria. The statistical analyses were performed using SPSS version 25.0 (IBM Corp, Armonk, NY, USA), and statistical significance was defined as p < 0.05. 3. Result 3.1 Patient characteristics As shown in Fig. 1 , a total of 316 patients diagnosed with GC were analyzed in this study. Their demographic and clinical characteristics are presented in Table 1 . Among them, there were 224 (70.9%) males and 92 (29.1%) females, with a median age of 68 (62-72.75) years and a mean BMI of 23.44 ± 3.33 kg/m 2 . A total of 151 patients (47.8%) were diagnosed with malnutrition based on GLIM criteria. Compared to patients without GLIM-defined malnutrition, those with GLIM-defined malnutrition exhibited a higher proportion of advanced TNM stage, elevated CRP levels (≥ 5mg/L), and poor ECOG performance status (p < 0.05, Table 1 ). Additionally, the group with GLIM malnutrition demonstrated significantly lower levels of BMI, albumin, hemoglobin, lymphocyte count, L3-SMI, L3-VFI, L3-SFI, GNRI, and PNI; while showing higher levels of age and COUNT score (p < 0.05, Table 1 ). Table 1 Clinical characteristics of study patients based on GLIM-defined malnutrition. Total (n = 316) With malnutrition (n = 151) Without malnutrition (n = 165) P -value Age, years 68(62-72.75) 69(64–73) 67(59.5–71.5) 0.008 Sex, n (%) 0.627 Male 224(70.9) 109(72.2) 115(69.7) Female 92(29.1) 42(27.8) 50(30.3) BMI, kg/m 2 23.44 ± 3.33 21.66 ± 2.75 25.06 ± 2.98 < 0.001 CCI score, n (%) 0.187 0 240(75.9) 111(73.5) 129(78.2) 1 53(16.8) 31(20.5) 22(13.3) 2 23(7.3) 9(6.0) 14(8.5) ECOG performance status, n (%) < 0.001 0 190(60.1) 61(40.4) 129(78.2) 1 84(26.6) 52(34.4) 32(19.4) 2 34(10.8) 30(19.9) 4(2.4) 3 8(2.5) 8(5.3) 0(0) TNM stage, n (%) < 0.001 Ⅰ 78(24.7) 17(11.3) 61(37.0) Ⅱ 62(19.6) 35(23.2) 27(16.4) Ⅲ 134(42.4) 73(48.3) 61(37.0) Ⅳ 42(13.3) 26(17.2) 16(9.7) NRS-2002 score 3( 2 – 4 ) 4( 3 – 5 ) 2( 2 – 3 ) < 0.001 Albumin, g/L 37.09 ± 4.34 35.82 ± 4.12 38.24 ± 4.22 < 0.001 Hemoglobin, g/L 119.5(103–135) 112(90–123) 127(114.5–137) < 0.001 Neutrophil, 10 9 /L 3.7(2.8–4.58) 3.6(2.6–4.6) 3.8(2.95–4.5) 0.080 Lymphocyte, 10 9 /L 1.45(1.13–1.8) 1.4(1.1–1.7) 1.6(1.2-2.0) < 0.001 NLR 2.37(1.80–3.33) 2.38(1.77–3.69) 2.36(1.83–3.20) 0.479 CRP ≥ 5mg/L, n (%) 73(23.1) 43(28.5) 30(18.2) 0.030 L3-SMI, cm 2 /m 2 44.91 ± 7.57 40.99 ± 6.41 48.51 ± 6.73 < 0.001 L3-VFI, cm 2 /m 2 41.39(25.01–61.93) 28.24(12.09–46.20) 52.40(37.37–71.65) < 0.001 L3-SFI, cm 2 /m 2 38.78(25.86–55.45) 30.16(18.32–45.09) 46.35(34.99–60.41) < 0.001 GNRI 99.03 ± 9.49 93.83 ± 8.00 103.79 ± 8.17 < 0.001 PNI 44.82 ± 5.30 42.98 ± 5.11 46.51 ± 4.91 < 0.001 COUNT score 2 ( 1 – 4 ) 3 ( 2 – 4 ) 2 ( 1 – 3 ) < 0.001 BMI, body mass index; CCI, Charlson Comorbidity Index; CONUT, controlling nutritional status; CRP, C-reactive protein; ECOG, Eastern cooperative oncology group; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; L3, the third lumbar vertebra; NLR, neutrophil-to-lymphocyte ratio; NRS-2002, Nutritional Risk Screening 2002; PNI, prognostic nutritional index. SFI, subcutaneous fat index. SMI, skeletal muscle index. TNM, tumor–node–metastasis; VFI, visceral fat index. 3.2 ROC curves for predicting GLIM malnutrition As shown in Fig. 2 , the diagnostic accuracy of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed using ROC curves. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. The optimal cut-off values for GNRI, PNI, and COUNT score to identify GLIM-defined malnutrition were determined as 97, 45.4, and 3, correspondingly. Based on these defined thresholds, the prevalence rates of low GNRI (< 97), low PNI (< 45.4), and high CONUT score (≥ 3) were found to be 41.5%, 53.5%, and 49.4%, respectively. 3.3 Association between objective nutritional indicators and GLIM-defined malnutrition The results of crude and adjusted logistic regression analyses evaluating the association between objective nutritional indicators and GLIM-defined malnutrition are presented in Table 2 . After adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage, a one-unit decrease in PNI was found to be significantly associated with GLIM-defined malnutrition (OR = 1.16, 95% CI: 1.12–1.21, p = 0.010). Furthermore, there was also a significant association between a one-unit increase in COUNT score and GLIM-defined malnutrition (OR = 1.22, 95% CI: 1.02–1.44, p = 0.025). Due to the high correlation between GNRI and BMI (r > 0.7), BMI was excluded from the multiple analysis model for GNRI; nevertheless, a one-unit decrease in GNRI remained significantly associated with GLIM-defined malnutrition after multivariable adjustments (OR = 1.13, 95% CI: 1.09–1.18, p < 0.001). Additionally, it was observed that patients with low GNRI (OR = 4.69, 95% CI: 2.46–8.33, p < 0.001) exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI (OR = 2.10, 95% CI: 1.12–3.93, p = 0.021). However, there was no significant association between high CONUT score and the risk of GLIM-defined malnutrition (OR = 1.59, 95% CI: 0.86–2.93, p = 0.140). Table 2 Crude and adjusted logistic regression analysis assessing the association between objective nutritional indicators and GLIM malnutrition. Units of increase or decrease Crude OR (95% CI) P -value Adjusted OR (95% CI) P -value GNRI a 1 unit ↓ 1.16 (1.12–1.21) < 0.001 1.13 (1.09–1.18) < 0.001 < 97 7.40 (4.45–12.28) < 0.001 4.69 (2.64–8.33) < 0.001 PNI b 1 unit ↓ 1.15 (1.10–1.21) < 0.001 1.09 (1.02–1.16) 0.010 < 45.4 4.04 (2.52–6.48) < 0.001 2.10 (1.12–3.93) 0.021 CONUT score b 1 unit ↑ 1.37 (1.21–1.56) < 0.001 1.22 (1.02–1.44) 0.025 ≥ 3 3.06 (1.19–4.83) < 0.001 1.59 (0.86–2.93) 0.140 a Adjusting for age, sex, CCI, ECOG performance status, and TNM stage. b Adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage. CONUT, controlling nutritional status; GNRI, geriatric nutritional risk index; PNI, prognostic nutritional index. 3.4 Comparative analysis of three objective nutritional screening tools Table 3 presents statistical evaluations for comparative analysis of three objective nutritional screening tools. The prevalence of GLIM-defined malnutrition was 74.8% (98/131), 63.3% (107/169), and 61.5% (96/156) in patients with low GNRI, low PNI, and high CONUT score, respectively. Based on the GLIM criterion for diagnosing malnutrition, GNRI accurately identified 72.8% (230/316) of the patients, demonstrating a sensitivity of 64.9% and a specificity of 80%. PNI correctly identified 66.5% (230/316) of the patients with a sensitivity of 70.9% and a specificity of 62.4%. Furthermore, the CONUT score accurately identified 63.6% (201/316) of the patients, exhibiting both sensitivity and specificity at 63.6%. The kappa statistics analysis revealed a moderate agreement (k = 0.452) between low GNRI and GLIM-defined malnutrition, while low PNI (k = 0.331) and high CONUT score (k = 0.272) demonstrated fair agreement with GLIM-defined malnutrition. Therefore, among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity, accuracy, PPV, NPV, and consistency with GLIM-defined malnutrition. Table 3 statistical evaluations for comparative analysis of three objective nutritional screening tools. GLIM malnutrition Sensitivity (%) Specificity (%) Accuracy (%) PPV (%) NPV (%) Kappa P -value Yes NO GNRI < 97 98 33 64.9 80 72.8 74.8 71.4 0.452 <0.001 ≥ 97 53 132 PNI < 45.4 107 62 70.9 62.4 66.5 63.3 70.1 0.331 <0.001 ≥ 45.4 44 103 COUNT score ≥ 3 96 60 63.6 63.6 63.6 61.5 65.6 0.272 <0.001 < 3 55 105 CONUT, controlling nutritional status; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; NPV, negative predictive value; PNI, prognostic nutritional index; PPV, positive predictive value. 4. Discussion This is one of the few studies that compare different objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with GC. The findings of this study indicate that GNRI, PNI, and COUNT scores were independently associated with the risk of malnutrition based on the GLIM criteria, thereby potentially serving as predictive indicators for GLIM-defined malnutrition. Moreover, patients with low GNRI exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI and high CONUT score. Amony these tools, GNRI demonstrated the highest diagnostic accuracy for detecting GLIM-defined malnutrition (AUC = 0.805, 95% CI: 0.758–0.852). Furthermore, the GNRI-based malnutrition risk assessment showed the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition. Thus, GNRI is considered the most effective objective screening tool for identifying GLIM-defined malnutrition in this study. According to a recent systematic review and meta-analysis, the prevalence of GLIM-defined malnutrition varied widely from 11.9–87.9% in patients with cancer 16 . Xu et al. revealed that the prevalence of malnutrition according to the GLIM criteria was 38.3% (343 out of 895) in patients with GC 5 , which is lower than the observed prevalence of 47.8% reported in this study. This difference may be attributed to their inclusion of patients who underwent radical gastrectomy while excluding those with stage IV cancer. As the global consensus on diagnostic criteria for malnutrition, GLIM-defined malnutrition has been demonstrated to be significantly associated with an increased risk of postoperative complications and poor survival in various cancer 16–18 , including gastrointestinal cancer 19 . The application of GLIM criteria for assessing malnutrition in cancer patients can provide valuable insights for guiding nutrition management and intervention strategies. Therefore, timely identification of cancer patients with GLIM-defined malnutrition is crucial for the effective implementation of targeted nutritional interventions. The GLIM proposes a two-step strategy for diagnosing malnutrition: initial screening using any validated tool to identify patients at risk, followed by evaluating five phenotypic/etiologic criteria to establish a diagnosis of malnutrition 7 . However, the GLIM does not specify a particular screening tool for the first step. Zhang and colleagues conducted a comparative study evaluating the efficacy of three commonly used malnutrition risk screening tools in identifying GLIM-defined malnutrition among patients diagnosed with gastrointestinal cancer. Their findings suggest that NRS-2002 is the optimal tool for detecting malnutrition in gastrointestinal cancer patients under the age of 65, while MNA-SF is most effective for those over 65 years old 20 . However, it is crucial to acknowledge that both NRS-2002 and MNA-SF involve subjective assessments, which may be subject to bias and heavily rely on the expertise of the examiner. Therefore, there is a requirement of objective screening tools that are user-friendly, time-efficient, and operator-independent for identify patients at risk of malnutrition. Further studies are required to validate these objective tools in screening patients with GLIM-defined malnutrition. In the present study, we conducted a comparative analysis of three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the most effective tool for identifying GLIM-defined malnutrition in patients with GC. The results of our study demonstrated that GNRI exhibited superior performance as an objective nutritional screening tool compared to PNI and CONUT scores in identifying GLIM-defined malnutrition. Recently, Chen et al. investigated GNRI, PNI, and advanced lung cancer inflammation index (ALI) for detecting malnutrition based on GLIM criteria among rectal cancer patients. Consistent with our study, their findings also revealed that GNRI exhibited optimal performance among the three nutritional tools 21 . Furthermore, Cohen-Cesla et al. conducted a study aiming to assess the concurrent validity of four nutritional scores - malnutrition-inflammation score (MIS), objective score of nutrition on dialysis (OSND), GNRI, and nutritional risk index (NRI) - in relation to the GLIM criteria for diagnosing malnutrition in maintenance hemodialysis patients. Importantly, their results also indicated that GNRI exhibited superior sensitivity and had the largest AUC for predicting malnutrition based on the GLIM criteria 22 . Hence, the GNRI can serve as an optimal objective nutritional screening tool for identifying GLIM-defined malnutrition. The initial aim of GNRI was to evaluate the morbidity and mortality risk in elderly patients during hospitalization, with cut-off values of 98 established for identifying nutrition-related risks 8 . Subsequent studies have demonstrated that GNRI can effectively serve as a prognostic tool in patients with different types of cancers, including gastrointestinal cancer 23 , rectal cancer 24 , head and neck cancer 25 , and lung cancer 26 . However, the association between GNRI and GLIM-defined malnutrition remains unclear. Our study is the first to demonstrate that the GNRI exhibits good predictive power in identifying GLIM-defined malnutrition in patients with GC. Furthermore, the GNRI cut-off value of 97 for identifying GLIM-defined malnutrition in this study closely approximated the original classification value of 98. Based on this cut-off value, GNRI exhibited a sensitivity of 64.9%, specificity of 80%, PPV of 72.8% and NPV of 71.4% for identifying GLIM-defined malnutrition. Therefore, the GNRI-based assessment may facilitate the identification of GLIM-defined malnutrition and enable early implementation of nutritional interventions to improve outcomes in patients with GC. There are also several limitations in the present study. Firstly, due to the single-center design of our study, it is imperative to validate our findings through prospective multi-center studies. Additionally, the nutritional screening tools were evaluated solely upon admission. It is crucial to further explore the clinical significance of these tools in patients' treatment and follow-up outcomes. Lastly, muscle mass assessment in this study was based on CT measurements; however, there is a lack of generally accepted standardized cut-off values for defining low muscle mass 27 . The cut-off values used in this study were defined based on a large cohort of GC patients in China 28 . However, it should be noted that these cut-off values may not be applicable to other tumor types or ethnic groups. In conclusion, our study showed that compare to PNI and COUNT scores, GNRI was the best objective nutritional screening tool for identifying GLIM-defined malnutrition in patients with GC. The use of GNRI is recommended due to its simplicity and cost-effectiveness, as it can be easily calculated using the parameters commonly employed in daily clinical practice. The results of our study provide further evidence supporting the selection of GNRI as an effective screening tool for identifying GLIM-defined malnutrition in cancer patients. Further studies with larger sample sizes are warranted to establish its validity and reliability. Declarations Competing Interests The authors declare no competing interests. Author Contribution Xuefeng Bu and Junbo Zuo designed the study. Zhenhua Huang, JingXin Zhang, Wenji Hou, Chen Wang, and Xiuhua Wang collected the data. Junbo Zuo, Yan Huang, and Xuefeng Bu analyzed and interpreted the data. Junbo Zuo and Yan Huang wrote the manuscript. All the authors critically reviewed and approved the final manuscript. Acknowledgments We would like to thank all the physicians and nurses in general surgery and patients for their great cooperation. Data Availability Applications for data access for non-commercial use can be submitted to author Junbo Zuo, [email protected] . References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Lung 2022; 200(5): 661–669. e-pub ahead of print 2022/09/17; doi: 10.1007/s00408-022-00567-6 Barazzoni R, Jensen GL, Correia M, Gonzalez MC, Higashiguchi T, Shi HP et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition (GLIM) diagnosis of malnutrition. Clinical nutrition (Edinburgh, Scotland) 2022; 41(6): 1425–1433. e-pub ahead of print 2022/04/23; doi: 10.1016/j.clnu.2022.02.001 Zhuang CL, Huang DD, Pang WY, Zhou CJ, Wang SL, Lou N et al. Sarcopenia is an Independent Predictor of Severe Postoperative Complications and Long-Term Survival After Radical Gastrectomy for Gastric Cancer: Analysis from a Large-Scale Cohort. Medicine 2016; 95(13): e3164. e-pub ahead of print 2016/04/05; doi: 10.1097/md.0000000000003164 Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2024 Read the published version in European Journal of Clinical Nutrition → Version 1 posted Editorial decision: revise 15 Jul, 2024 Review # 2 received at journal 20 May, 2024 Reviewer # 2 agreed at journal 07 May, 2024 Review # 1 received at journal 06 May, 2024 Reviewer # 1 agreed at journal 30 Apr, 2024 Reviewers invited by journal 30 Apr, 2024 Editor assigned by journal 24 Apr, 2024 Submission checks completed at journal 24 Apr, 2024 First submitted to journal 23 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4313120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297033465,"identity":"27ff8c6e-6bf4-4bff-8757-b1621c9ee86a","order_by":0,"name":"Zuo 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Xuefeng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bu","middleName":"","lastName":"Xuefeng","suffix":""},{"id":297033480,"identity":"56378785-d389-48dc-8459-7878c456a06d","order_by":15,"name":"Bu Xuefeng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bu","middleName":"","lastName":"Xuefeng","suffix":""}],"badges":[],"createdAt":"2024-04-23 15:41:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4313120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4313120/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41430-024-01514-9","type":"published","date":"2024-09-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56040858,"identity":"27b3fd45-33e7-449a-9bc6-bf89383d7676","added_by":"auto","created_at":"2024-05-07 19:18:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169638,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing patients enrollment process. CT, computed tomography; GLIM, Global Leadership Initiative on Malnutrition\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4313120/v1/75438d04f9a61bd67a1aa7fa.jpeg"},{"id":56041194,"identity":"57630618-cc08-433d-b9e9-a8aeda85f933","added_by":"auto","created_at":"2024-05-07 19:26:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99704,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve for geriatric nutritional risk index (GNRI) (A), prognostic nutritional index (PNI) (B), and controlling nutritional status (COUNT) scores (C) in identifying GLIM-defined malnutrition\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4313120/v1/80903b533a443accb3ed8df4.jpeg"},{"id":65573200,"identity":"cfdbc128-7f41-4cbe-835f-be53808b899c","added_by":"auto","created_at":"2024-09-30 07:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4313120/v1/e8b98c98-6251-4f9d-99ec-6981d82dff7f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is a prevalent malignant tumor, ranking fifth in terms of incidence and fourth in terms of mortality globally\u003csup\u003e1\u003c/sup\u003e. Due to the impact of the disease itself and the anti-tumor treatment process, GC patients are at a higher risk of experiencing malnutrition. It has been reported that the prevalence of malnutrition in GC patients can reach as high as 65%-85%\u003csup\u003e2\u003c/sup\u003e. Numerous studies have demonstrated that malnutrition is not only significantly associated with a higher risk of complications and mortality but also has an adverse effect on patients' treatment outcomes and quality of life\u003csup\u003e3\u0026ndash;6\u003c/sup\u003e. Therefore, early screening and identification of malnourished patients followed by effective intervention measures play a pivotal role in multimodal cancer treatment and are currently gaining increasing attention.\u003c/p\u003e \u003cp\u003eThe Global Leadership Initiative on Malnutrition (GLIM) presents a novel diagnostic framework aimed at identifying malnutrition through a two-step approach\u003csup\u003e7\u003c/sup\u003e. Firstly, validated screening tools are used to identify patients who are at risk of malnutrition. Secondly, the diagnosis of malnutrition is established based on meeting at least one phenotypic criterion (involuntary weight loss, low body mass index or reduced muscle mass) and one etiological criterion (reduced food intake or assimilation, disease burden or inflammation). At present, the subjective tools used for nutritional risk screening include Nutrition Risk Screening-2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Malnutrition Screening Tool (MST)\u003csup\u003e2, 7\u003c/sup\u003e. Although these tools have been widely used in clinical practice, there are still some limitations in practical application. For example, the assessment of weight loss, previous dietary intake, and disease history relies on patient recall and subjective descriptions, which may introduce potential biases. In addition, to ensure accuracy and reliability, it is imperative that these assessment tools are utilized by qualified medical professionals, which may pose challenges in areas with insufficient medical and healthcare resources.\u003c/p\u003e \u003cp\u003eCurrently, various objective nutritional tools have been utilized for screening and evaluating malnutrition, as well as predicting clinical outcomes. The geriatric nutritional risk index (GNRI) is a simple nutritional screening tool that evaluates the risk of malnutrition by combining serum albumin levels with ideal body weight\u003csup\u003e8\u003c/sup\u003e. It has been demonstrated to be associated with adverse outcomes in various malignancies and can be applicable for both young and elderly patients\u003csup\u003e9\u003c/sup\u003e. The prognostic nutritional index (PNI), which is calculated based on total lymphocyte counts and serum albumin levels, has been used to assess nutritional status and serve as a prognostic indicator for different types of malignancies\u003csup\u003e10\u003c/sup\u003e. The controlling nutritional status (CONUT) score, a conveniently calculated tool using three blood parameters (albumin level, total cholesterol level, and total lymphocyte count), has also been utilized as a valuable tool for evaluating nutritional status and predicting outcomes in patients with diverse cancer types\u003csup\u003e11\u003c/sup\u003e. Based on laboratory examinations and anthropometric measurements, these objective nutritional tools can be easily performed in the clinical setting and used for dynamic surveillance.\u003c/p\u003e \u003cp\u003eHowever, there is a paucity of studies evaluating the efficacy of these objective nutritional screening tools in identifying malnutrition based on the GLIM criteria. Furthermore, it remains unclear which objective nutritional screening tool is most effective for detecting GLIM-defined malnutrition in patients with GC. Therefore, this study aims to compare three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the optimal tool for identifying GLIM-defined malnutrition in GC patients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eStudy Patients\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled consecutive patients diagnosed with GC in our Department from October 2021 to March 2023. Inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age between 18 and 80 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) confirmed pathological diagnosis of gastric adenocarcinoma through gastroscopic biopsy; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) no prior neoadjuvant therapy. Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) absence of abdominal CT scans from our institution; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) presence of severe comorbidities including heart failure, chronic kidney disease, or liver cirrhosis; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) concurrent presence of other malignancies or a history of other malignancies within the past 5 years. All patients provided written informed consent for data collection and analysis. This study followed the Declaration of Helsinki and obtained approval from the Ethics Committee of The Affiliated People's Hospital of Jiangsu University (No. K-20220028-Y).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eData collection\u003c/em\u003e\u003c/h2\u003e \u003cp\u003e Demographic and clinical data were collected, including age, sex, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, Charlson Comorbidity Index (CCI) score, tumor-node-metastasis (TNM) stage according to the eighth edition of the American Joint Committee on Cancer (AJCC). Additionally, laboratory data were also obtained, including albumin level, hemoglobin level, C-reactive protein (CRP) with the cut-off value set at 5.0 mg/L, neutrophil and lymphocyte counts and neutrophil/lymphocyte ratio (NLR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Objective nutritional screening tools\u003c/h2\u003e \u003cp\u003eThe GNRI is calculated as 14.87 \u0026times; serum albumin concentration (g/L)\u0026thinsp;+\u0026thinsp;41.7 \u0026times; weight/ideal weight (kg) \u003csup\u003e8\u003c/sup\u003e. The ideal weight was determined using the formula: for males, it was calculated as 0.75\u0026times;height (cm) \u0026ndash; 62.5; and for females, it was calculated as 0.60\u0026times;height (cm) \u0026ndash; 40\u003csup\u003e8\u003c/sup\u003e. The PNI is calculated as 10 \u0026times; serum albumin (g/dL)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003csup\u003e12\u003c/sup\u003e. The CONUT score was calculated based on the concentrations of albumin, lymphocyte count, and total cholesterol, with each parameter being assigned scores as previously described\u003csup\u003e13\u003c/sup\u003e. The cumulative sum of these scores constituted the CONUT score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Body composition analysis\u003c/h2\u003e \u003cp\u003eBody composition analysis was performed on CT images at the L3 level using Slice-O-Matic software V 5.0 (Tomovision, Magog, QC, Canada). Tissue-specific Hounsfield unit (HU) thresholds were applied to identify the cross-sectional areas of skeletal muscle (-29 to +\u0026thinsp;150 HU), subcutaneous fat (-190 to -30 HU), and visceral fat (-150 to -50 HU). These cross-sectional areas (cm\u003csup\u003e2\u003c/sup\u003e) of L3 were then normalized for height squared (m\u003csup\u003e2\u003c/sup\u003e) to calculate skeletal muscle index (L3-SMI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e), subcutaneous fat index (L3-SFI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e), and visceral fat index (L3-VFI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Diagnosis of malnutrition\u003c/h2\u003e \u003cp\u003eMalnutrition was diagnosed according to the GLIM criteria\u003csup\u003e7\u003c/sup\u003e, and the diagnostic procedure followed the description provided in our previous study\u003csup\u003e14\u003c/sup\u003e. Briefly, patients with GC were considered to fulfil the etiological criterion of inflammation or disease burden, and the diagnosis of malnutrition was based on meeting at least one of the phenotypic criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) involuntary weight loss defined as \u0026gt;\u0026thinsp;5% within the past 6 months or \u0026gt;\u0026thinsp;10% beyond 6 months; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) low BMI defined as \u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e for patients under 70 years old and \u0026lt;\u0026thinsp;20 kg/m\u003csup\u003e2\u003c/sup\u003e for those over 70 years; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) reduced muscle mass defined as \u0026le;\u0026thinsp;40.8 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e in males and \u0026le;\u0026thinsp;34.9 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e in females using sex-specific cut-off values of L3-SMI\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables that follow a normal distribution (assessed using the Shapiro-Wilk test) are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and analyzed using an independent t-test. Non-normally distributed continuous variables are presented as median (interquartile range) and analyzed using the Mann-Whitney U-test. Categorical variables were expressed as numbers (percentages) and analyzed using the chi-squared test. Univariate or multivariate logistic regression analyses were further employed to evaluate the association between these nutritional indices and GLIM-defined malnutrition. The adjusted model included age, sex, BMI, ECOG performance status, CCI, and TNM stage. The presence of collinearity among independent variables was assessed by calculating the Variance Inflation Factor (VIF) and correlation coefficients. If the VIF is \u0026ge;\u0026thinsp;10 or the correlation coefficients are \u0026gt;\u0026thinsp;0.7, it indicates the existence of collinearity between independent variables, which are subsequently excluded from analysis\u003csup\u003e15\u003c/sup\u003e. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The optimal cut-off value was established based on the maximal Youden's index formula: sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM malnutrition criteria. The statistical analyses were performed using SPSS version 25.0 (IBM Corp, Armonk, NY, USA), and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 316 patients diagnosed with GC were analyzed in this study. Their demographic and clinical characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, there were 224 (70.9%) males and 92 (29.1%) females, with a median age of 68 (62-72.75) years and a mean BMI of 23.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33 kg/m\u003csup\u003e2\u003c/sup\u003e. A total of 151 patients (47.8%) were diagnosed with malnutrition based on GLIM criteria. Compared to patients without GLIM-defined malnutrition, those with GLIM-defined malnutrition exhibited a higher proportion of advanced TNM stage, elevated CRP levels (\u0026ge;\u0026thinsp;5mg/L), and poor ECOG performance status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the group with GLIM malnutrition demonstrated significantly lower levels of BMI, albumin, hemoglobin, lymphocyte count, L3-SMI, L3-VFI, L3-SFI, GNRI, and PNI; while showing higher levels of age and COUNT score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of study patients based on GLIM-defined malnutrition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;316)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith malnutrition (n\u0026thinsp;=\u0026thinsp;151)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithout malnutrition (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(62-72.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(64\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(59.5\u0026ndash;71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224(70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115(69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240(75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG performance status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190(60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRS-2002 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.24\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.5(103\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112(90\u0026ndash;123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127(114.5\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7(2.8\u0026ndash;4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6(2.6\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8(2.95\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45(1.13\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(1.1\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6(1.2-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37(1.80\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38(1.77\u0026ndash;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36(1.83\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;5mg/L, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3-SMI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3-VFI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.39(25.01\u0026ndash;61.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.24(12.09\u0026ndash;46.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.40(37.37\u0026ndash;71.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3-SFI, cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.78(25.86\u0026ndash;55.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.16(18.32\u0026ndash;45.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.35(34.99\u0026ndash;60.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.79\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOUNT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; CCI, Charlson Comorbidity Index; CONUT, controlling nutritional status; CRP, C-reactive protein; ECOG, Eastern cooperative oncology group; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; L3, the third lumbar vertebra; NLR, neutrophil-to-lymphocyte ratio; NRS-2002, Nutritional Risk Screening 2002; PNI, prognostic nutritional index. SFI, subcutaneous fat index. SMI, skeletal muscle index. TNM, tumor\u0026ndash;node\u0026ndash;metastasis; VFI, visceral fat index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ROC curves for predicting GLIM malnutrition\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the diagnostic accuracy of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed using ROC curves. The GNRI demonstrated good diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.805, 95% CI: 0.758\u0026ndash;0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641\u0026ndash;0.757) and 0.665 (95% CI: 0.605\u0026ndash;0.725) respectively. The optimal cut-off values for GNRI, PNI, and COUNT score to identify GLIM-defined malnutrition were determined as 97, 45.4, and 3, correspondingly. Based on these defined thresholds, the prevalence rates of low GNRI (\u0026lt;\u0026thinsp;97), low PNI (\u0026lt;\u0026thinsp;45.4), and high CONUT score (\u0026ge;\u0026thinsp;3) were found to be 41.5%, 53.5%, and 49.4%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between objective nutritional indicators and GLIM-defined malnutrition\u003c/h2\u003e \u003cp\u003eThe results of crude and adjusted logistic regression analyses evaluating the association between objective nutritional indicators and GLIM-defined malnutrition are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage, a one-unit decrease in PNI was found to be significantly associated with GLIM-defined malnutrition (OR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.12\u0026ndash;1.21, p\u0026thinsp;=\u0026thinsp;0.010). Furthermore, there was also a significant association between a one-unit increase in COUNT score and GLIM-defined malnutrition (OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.02\u0026ndash;1.44, p\u0026thinsp;=\u0026thinsp;0.025). Due to the high correlation between GNRI and BMI (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7), BMI was excluded from the multiple analysis model for GNRI; nevertheless, a one-unit decrease in GNRI remained significantly associated with GLIM-defined malnutrition after multivariable adjustments (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI: 1.09\u0026ndash;1.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, it was observed that patients with low GNRI (OR\u0026thinsp;=\u0026thinsp;4.69, 95% CI: 2.46\u0026ndash;8.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI (OR\u0026thinsp;=\u0026thinsp;2.10, 95% CI: 1.12\u0026ndash;3.93, p\u0026thinsp;=\u0026thinsp;0.021). However, there was no significant association between high CONUT score and the risk of GLIM-defined malnutrition (OR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 0.86\u0026ndash;2.93, p\u0026thinsp;=\u0026thinsp;0.140).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude and adjusted logistic regression analysis assessing the association between objective nutritional indicators and GLIM malnutrition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnits of increase or decrease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 unit \u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.12\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13 (1.09\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.40 (4.45\u0026ndash;12.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.69 (2.64\u0026ndash;8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 unit \u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15 (1.10\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.04 (2.52\u0026ndash;6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10 (1.12\u0026ndash;3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCONUT score \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 unit \u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.21\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22 (1.02\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.06 (1.19\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.59 (0.86\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea Adjusting for age, sex, CCI, ECOG performance status, and TNM stage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eb Adjusting for age, sex, BMI, CCI, ECOG performance status, and TNM stage.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCONUT, controlling nutritional status; GNRI, geriatric nutritional risk index; PNI, prognostic nutritional index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparative analysis of three objective nutritional screening tools\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents statistical evaluations for comparative analysis of three objective nutritional screening tools. The prevalence of GLIM-defined malnutrition was 74.8% (98/131), 63.3% (107/169), and 61.5% (96/156) in patients with low GNRI, low PNI, and high CONUT score, respectively. Based on the GLIM criterion for diagnosing malnutrition, GNRI accurately identified 72.8% (230/316) of the patients, demonstrating a sensitivity of 64.9% and a specificity of 80%. PNI correctly identified 66.5% (230/316) of the patients with a sensitivity of 70.9% and a specificity of 62.4%. Furthermore, the CONUT score accurately identified 63.6% (201/316) of the patients, exhibiting both sensitivity and specificity at 63.6%. The kappa statistics analysis revealed a moderate agreement (k\u0026thinsp;=\u0026thinsp;0.452) between low GNRI and GLIM-defined malnutrition, while low PNI (k\u0026thinsp;=\u0026thinsp;0.331) and high CONUT score (k\u0026thinsp;=\u0026thinsp;0.272) demonstrated fair agreement with GLIM-defined malnutrition. Therefore, among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity, accuracy, PPV, NPV, and consistency with GLIM-defined malnutrition.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003estatistical evaluations for comparative analysis of three objective nutritional screening tools.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGLIM malnutrition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eGNRI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e72.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eCOUNT score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCONUT, controlling nutritional status; GLIM, Global Leadership Initiative on Malnutrition; GNRI, geriatric nutritional risk index; NPV, negative predictive value; PNI, prognostic nutritional index; PPV, positive predictive value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis is one of the few studies that compare different objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with GC. The findings of this study indicate that GNRI, PNI, and COUNT scores were independently associated with the risk of malnutrition based on the GLIM criteria, thereby potentially serving as predictive indicators for GLIM-defined malnutrition. Moreover, patients with low GNRI exhibited a significantly higher risk of GLIM-defined malnutrition compared to those with low PNI and high CONUT score. Amony these tools, GNRI demonstrated the highest diagnostic accuracy for detecting GLIM-defined malnutrition (AUC\u0026thinsp;=\u0026thinsp;0.805, 95% CI: 0.758\u0026ndash;0.852). Furthermore, the GNRI-based malnutrition risk assessment showed the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k\u0026thinsp;=\u0026thinsp;0.452) with GLIM-defined malnutrition. Thus, GNRI is considered the most effective objective screening tool for identifying GLIM-defined malnutrition in this study.\u003c/p\u003e \u003cp\u003eAccording to a recent systematic review and meta-analysis, the prevalence of GLIM-defined malnutrition varied widely from 11.9\u0026ndash;87.9% in patients with cancer \u003csup\u003e16\u003c/sup\u003e. Xu et al. revealed that the prevalence of malnutrition according to the GLIM criteria was 38.3% (343 out of 895) in patients with GC\u003csup\u003e5\u003c/sup\u003e, which is lower than the observed prevalence of 47.8% reported in this study. This difference may be attributed to their inclusion of patients who underwent radical gastrectomy while excluding those with stage IV cancer. As the global consensus on diagnostic criteria for malnutrition, GLIM-defined malnutrition has been demonstrated to be significantly associated with an increased risk of postoperative complications and poor survival in various cancer \u003csup\u003e16\u0026ndash;18\u003c/sup\u003e, including gastrointestinal cancer \u003csup\u003e19\u003c/sup\u003e. The application of GLIM criteria for assessing malnutrition in cancer patients can provide valuable insights for guiding nutrition management and intervention strategies. Therefore, timely identification of cancer patients with GLIM-defined malnutrition is crucial for the effective implementation of targeted nutritional interventions.\u003c/p\u003e \u003cp\u003eThe GLIM proposes a two-step strategy for diagnosing malnutrition: initial screening using any validated tool to identify patients at risk, followed by evaluating five phenotypic/etiologic criteria to establish a diagnosis of malnutrition\u003csup\u003e7\u003c/sup\u003e. However, the GLIM does not specify a particular screening tool for the first step. Zhang and colleagues conducted a comparative study evaluating the efficacy of three commonly used malnutrition risk screening tools in identifying GLIM-defined malnutrition among patients diagnosed with gastrointestinal cancer. Their findings suggest that NRS-2002 is the optimal tool for detecting malnutrition in gastrointestinal cancer patients under the age of 65, while MNA-SF is most effective for those over 65 years old\u003csup\u003e20\u003c/sup\u003e. However, it is crucial to acknowledge that both NRS-2002 and MNA-SF involve subjective assessments, which may be subject to bias and heavily rely on the expertise of the examiner. Therefore, there is a requirement of objective screening tools that are user-friendly, time-efficient, and operator-independent for identify patients at risk of malnutrition. Further studies are required to validate these objective tools in screening patients with GLIM-defined malnutrition.\u003c/p\u003e \u003cp\u003eIn the present study, we conducted a comparative analysis of three commonly used objective nutritional screening tools (GNRI, PNI, and CONUT score) to determine the most effective tool for identifying GLIM-defined malnutrition in patients with GC. The results of our study demonstrated that GNRI exhibited superior performance as an objective nutritional screening tool compared to PNI and CONUT scores in identifying GLIM-defined malnutrition. Recently, Chen et al. investigated GNRI, PNI, and advanced lung cancer inflammation index (ALI) for detecting malnutrition based on GLIM criteria among rectal cancer patients. Consistent with our study, their findings also revealed that GNRI exhibited optimal performance among the three nutritional tools \u003csup\u003e21\u003c/sup\u003e. Furthermore, Cohen-Cesla et al. conducted a study aiming to assess the concurrent validity of four nutritional scores - malnutrition-inflammation score (MIS), objective score of nutrition on dialysis (OSND), GNRI, and nutritional risk index (NRI) - in relation to the GLIM criteria for diagnosing malnutrition in maintenance hemodialysis patients. Importantly, their results also indicated that GNRI exhibited superior sensitivity and had the largest AUC for predicting malnutrition based on the GLIM criteria \u003csup\u003e22\u003c/sup\u003e. Hence, the GNRI can serve as an optimal objective nutritional screening tool for identifying GLIM-defined malnutrition.\u003c/p\u003e \u003cp\u003eThe initial aim of GNRI was to evaluate the morbidity and mortality risk in elderly patients during hospitalization, with cut-off values of 98 established for identifying nutrition-related risks\u003csup\u003e8\u003c/sup\u003e. Subsequent studies have demonstrated that GNRI can effectively serve as a prognostic tool in patients with different types of cancers, including gastrointestinal cancer\u003csup\u003e23\u003c/sup\u003e, rectal cancer\u003csup\u003e24\u003c/sup\u003e, head and neck cancer\u003csup\u003e25\u003c/sup\u003e, and lung cancer\u003csup\u003e26\u003c/sup\u003e. However, the association between GNRI and GLIM-defined malnutrition remains unclear. Our study is the first to demonstrate that the GNRI exhibits good predictive power in identifying GLIM-defined malnutrition in patients with GC. Furthermore, the GNRI cut-off value of 97 for identifying GLIM-defined malnutrition in this study closely approximated the original classification value of 98. Based on this cut-off value, GNRI exhibited a sensitivity of 64.9%, specificity of 80%, PPV of 72.8% and NPV of 71.4% for identifying GLIM-defined malnutrition. Therefore, the GNRI-based assessment may facilitate the identification of GLIM-defined malnutrition and enable early implementation of nutritional interventions to improve outcomes in patients with GC.\u003c/p\u003e \u003cp\u003eThere are also several limitations in the present study. Firstly, due to the single-center design of our study, it is imperative to validate our findings through prospective multi-center studies. Additionally, the nutritional screening tools were evaluated solely upon admission. It is crucial to further explore the clinical significance of these tools in patients' treatment and follow-up outcomes. Lastly, muscle mass assessment in this study was based on CT measurements; however, there is a lack of generally accepted standardized cut-off values for defining low muscle mass \u003csup\u003e27\u003c/sup\u003e. The cut-off values used in this study were defined based on a large cohort of GC patients in China \u003csup\u003e28\u003c/sup\u003e. However, it should be noted that these cut-off values may not be applicable to other tumor types or ethnic groups.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed that compare to PNI and COUNT scores, GNRI was the best objective nutritional screening tool for identifying GLIM-defined malnutrition in patients with GC. The use of GNRI is recommended due to its simplicity and cost-effectiveness, as it can be easily calculated using the parameters commonly employed in daily clinical practice. The results of our study provide further evidence supporting the selection of GNRI as an effective screening tool for identifying GLIM-defined malnutrition in cancer patients. Further studies with larger sample sizes are warranted to establish its validity and reliability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e \u003cp\u003eXuefeng Bu and Junbo Zuo designed the study. Zhenhua Huang, JingXin Zhang, Wenji Hou, Chen Wang, and Xiuhua Wang collected the data. Junbo Zuo, Yan Huang, and Xuefeng Bu analyzed and interpreted the data. Junbo Zuo and Yan Huang wrote the manuscript. All the authors critically reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to thank all the physicians and nurses in general surgery and patients for their great cooperation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eApplications for data access for non-commercial use can be submitted to author Junbo Zuo, [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A \u003cem\u003eet al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e 2021; 71(3): 209\u0026ndash;249. e-pub ahead of print 2021/02/05; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu R, Chen XD, Ding Z. 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Medicine 2016; 95(13): e3164. e-pub ahead of print 2016/04/05; doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/md.0000000000003164\u003c/span\u003e\u003cspan address=\"10.1097/md.0000000000003164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"geriatric nutritional risk index, objective nutritional screening tool, GLIM-defined malnutrition, gastric cancer","lastPublishedDoi":"10.21203/rs.3.rs-4313120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4313120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC).\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eObjective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 patients (47.8%) based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.805, 95% CI: 0.758\u0026ndash;0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641\u0026ndash;0.757) and 0.665 (95% CI: 0.605\u0026ndash;0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k\u0026thinsp;=\u0026thinsp;0.452) with GLIM-defined malnutrition.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCompared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients.\u003c/p\u003e","manuscriptTitle":"Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 19:18:25","doi":"10.21203/rs.3.rs-4313120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-07-15T15:09:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-20T14:42:46+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-07T13:57:16+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-07T00:52:12+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-04-30T23:49:26+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-04-30T05:34:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T11:30:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-24T11:30:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2024-04-23T15:39:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"99d41b41-73a5-49e0-8378-9c0be5e2e5ff","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31330422,"name":"Health sciences/Diseases/Nutrition disorders/Malnutrition"},{"id":31330423,"name":"Health sciences/Biomarkers"},{"id":31330424,"name":"Health sciences/Diseases/Nutrition disorders/Malnutrition"},{"id":31330425,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2024-09-30T07:06:39+00:00","versionOfRecord":{"articleIdentity":"rs-4313120","link":"https://doi.org/10.1038/s41430-024-01514-9","journal":{"identity":"european-journal-of-clinical-nutrition","isVorOnly":false,"title":"European Journal of Clinical Nutrition"},"publishedOn":"2024-09-29 04:00:00","publishedOnDateReadable":"September 29th, 2024"},"versionCreatedAt":"2024-05-07 19:18:25","video":"","vorDoi":"10.1038/s41430-024-01514-9","vorDoiUrl":"https://doi.org/10.1038/s41430-024-01514-9","workflowStages":[]},"version":"v1","identity":"rs-4313120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4313120","identity":"rs-4313120","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}