Objective Evaluation of the Prognostic Value of Common Nutritional Indicators in Patients with Different Inflammatory States after Radical Gastrectomy for Gastric Cancer: A Real-world Study Running Head：Nutritional Indicators in Inflammatory State

Objective Evaluation of the Prognostic Value of Common Nutritional Indicators in Patients with Different Inflammatory States after Radical Gastrectomy for Gastric Cancer: A Real-world Study Running Head：Nutritional Indicators in Inflammatory State | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Objective Evaluation of the Prognostic Value of Common Nutritional Indicators in Patients with Different Inflammatory States after Radical Gastrectomy for Gastric Cancer: A Real-world Study Running Head：Nutritional Indicators in Inflammatory State Hua-Long Zheng, Zhi-Wei Zheng, Ling-Hua Wei, Jia-Bin Wang, Jian-Xian Lin, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5492406/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aim: Few studies have investigated the prognostic significance of nutritional indicators in patients with various inflammatory states. Methods Patients who underwent radical gastrectomy for TNM stages I–III gastric cancer were included. Nutritional assessment was performed using commonly used indicators. The patients were categorized into two groups with high and low inflammatory status using the X-tile analysis. Results A total of 1327 patients were enrolled in this study, including 843 and 484 patients in the low- and high-SII groups, respectively. Compared with the SII-low group, the SII-high group exhibited significantly lower GNRI, PNI, CXI, and SMI indices and a higher proportion of patients with malnutrition based on the GLIM and CONUT criteria(all P < 0.05). Multivariate COX analysis revealed that GLIM criteria (overall survival [OS]: P = 0.002; recurrence-free survival [RFS]: P = 0.007) and CONUT (OS: P = 0.010; RFS:P = 0.001) were independent prognostic factors for OS and RFS in the SII-low group. In the SII-high group, the GLIM criteria, GNRI, and SMI were the independent prognostic factors for OS(all P < 0.05), the GLIM criteria and SMI were the independent influencing factors for RFS(all P < 0.05). The TimeROC curve and AUC demonstrated the robustness of the GLIM criteria in predicting prognosis across various inflammatory states. Conclusions Different nutritional indicators should be considered while evaluating the prognosis of patients with gastric cancer with varying inflammatory states. Compared with other nutritional indicators, the GLIM criteria are more suitable for patients with different inflammatory conditions. nutritional indicators inflammation malnutrition overall survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Gastric cancer ranks fifth among the most common cancers and the fifth leading cause of cancer-related deaths worldwide, thereby posing a significant threat to human health [ 1 ]. Further, patients with gastric cancer exhibit 41.6–86.1% of the incidence of malnutrition [ 2 ]. Notably, malnutrition increases the risk of surgical complications and also leads to reduced quality of life and shorter overall survival [ 3 – 4 ]. Therefore, selecting appropriate indicators for assessing the nutritional status of patients with gastric cancer in order to manage their condition effectively is crucial. Currently, the Global Leadership Initiative on Malnutrition (GLIM), Controlling Nutritional Status (CONUT), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), Cachexia Index (CXI), and Skeletal Muscle Mass Index (SMI) are the commonly used nutrition assessment tools in clinical practice [ 5 – 10 ]. The GLIM criteria, which incorporate both phenotypic and etiologic criteria as standards, are a novel approach for comprehensive nutritional status evaluation. This offers a convenient and efficient nutritional assessment guide that can be easily implemented [ 11 ].The CONUT score involves three important immunonutrition indicators based on serum albumin, total lymphocyte count, and cholesterol levels [ 12 ]. The GNRI considers factors such as serum albumin levels and anthropometric indicators, making it easily applicable in clinical practice, and thus has been widely used to assess the nutritional status of various patients [ 13 ]. The PNI is calculated from albumin and lymphocyte counts, which are easy to obtain and calculate [ 14 ]. The CXI is mainly used to assess the nutritional status of patients with tumor burden [ 15 ]. The SMI directly reflects the skeletal muscle mass (SMA) in patients with gastric cancer [ 16 ]. Notably, all these indicators reflect the nutritional status of patients from different perspectives; however, their value in predicting the prognosis of patients with gastric cancer remains controversial [ 17 – 19 ]. The inflammatory state also plays a crucial role in determining the prognosis of patients with gastric cancer. Further, cytokines, chemokines, and angiogenic factors in the inflammatory microenvironment drive the growth of tumor cells, playing a significant role in tumorigenesis in the gastrointestinal tract and hepatobiliary organs [ 20 ]. The Systemic Immune Inflammation Index (SII), which evaluates the overall inflammatory state of patients based on neutrophil, lymphocyte, and platelet counts in peripheral blood, is also widely used [ 21 ]. Notably, high SII values are associated with a poor prognosis [ 22 – 24 ]. Tumors produce proinflammatory cytokines that disrupt carbohydrate, fat, and protein metabolism throughout the body and exacerbate catabolism, leading to muscle breakdown [ 25 ]. This indicates a potential association between nutritional status and inflammatory state in patients with cancer. However, research on the prognostic value of nutritional indicators in different inflammatory states in patients with gastric cancer is limited. Therefore, this study aimed to evaluate the commonly used nutritional indices for predicting prognosis under various inflammatory conditions in patients with gastric cancer. Additionally, we explored how nutrition affects prognostic indicators through an intermediary effect analysis. The findings of this study may provide a basis for selecting appropriate nutritional assessment indicators in clinical practice to guide individualized nutritional interventions and partly improve the prognosis of patients with gastric cancer. Materials and Methods Patients The clinical data of patients diagnosed with gastric adenocarcinoma using gastroscopy and pathology who underwent radical resection at Fujian Medical University Union Hospital between January 2010 and December 2015 were prospectively collected and retrospectively analyzed. The inclusion criteria were as follows: (1) Postoperative pathology-confirmed gastric adenocarcinoma; (2) No distant metastasis found on preoperative chest radiography, abdominal ultrasound, or upper abdominal CT; and (3) Radical resection. The exclusion criteria were as follows: (1) Patients who received neoadjuvant chemotherapy or radiotherapy before surgery; (2) Incomplete clinical data, CT images, and follow-up data; and (3) Patients with gastric stump cancer. Finally, 1327 patients were included in the analysis (Supplement Fig. 1 ). The study protocol was approved by the ethics committees of all the participating hospitals and was conducted in accordance with the Declaration of Helsinki. Patient characteristics and outcomes Baseline demographic information, clinical parameters, laboratory tests, and physical measurements along with the age, sex, presence of hypertension and diabetes, history of cardiovascular and cerebrovascular diseases, tumor pathological stage, ASA score, ECOG score, extent of surgical resection, and nutritional indicators of enrolled patients were collected. All data were extracted from the electronic medical records. Overall survival (OS) was defined as the time from the date of surgery to death or final follow-up. Relapse-free survival (RFS) was defined as the time from the date of surgery to recurrence, death from any cause, or the last follow-up visit. The patients were assessed at follow-up appointments every 3 months for 2 years after surgery, every 6 months for 3–5 years, and annually after 5 years. Most patient follow-up appointments included physical examinations, laboratory tests (including measurement of tumor markers such as CA19-9, CEA, and CA72-4), imaging tests (including chest X-ray/CT, whole abdomen ultrasound/CT, and MR), and endoscopy annually. The disease recurrence was diagnosed based on radiological findings from cross-sectional imaging or biopsy results of the suspicious lesions. Nutrition index and definition of malnutrition and inflammation The GLIM diagnostic criteria include etiological (reduced food intake or assimilation, inflammation, or disease burden) and phenotypic (weight loss, low body mass index (BMI), and reduced muscle mass) criteria. As cancer is an etiologic component of the GLIM criteria, patients who met any phenotypic criterion were diagnosed with malnutrition in this study [ 26 ]. The following phenotypic criteria were used based on available data: weight loss of > 5% within the past 6 months or > 10% in > 6 months, low BMI of < 18.5 for patients aged < 70 years or 70 years, and reduced muscle mass was determined based on SMI. CT images of the L3 vertebral section were selected for the quantitative analysis of SMA, and the SMI was calculated using the formula SMI = SMA/height 2 , expressed in cm 2 /m 2 . The cutoff values for muscle deficit were 45.9 and 31.1 in males and females, respectively. At least one phenotypic and one etiological criteria were required to diagnose malnutrition. The CONUT scores were determined based on albumin, lymphocyte, and total cholesterol levels. Albumin levels of > 35, 30–34, 25–29, and < 25 g/L, lymphocyte count of ≥ 1.6, 1.2–1.59, 0.8–1.19, and < 0.8 × 10 9 /L, and total cholesterol levels of ≥ 180, 140–180, 100–139, and < 100 mmol/L were assigned a score of 0, 2, 4, and 6, respectively. Albumin, lymphocyte, and total cholesterol levels were then combined. A total score of ≥ 2 points indicated malnutrition. The GNRI, PNI, and CXI were calculated using the following formulas: GNRI = 1.487 × albumin (g/L) + 41.7 × current weight/IBW, where IBW = [height (m)]² × 22. The cutoff point for the GNRI was 97.5. Patients with GNRI score of > 97.5 and ≤ 97.5 were assigned to the normal and malnutrition groups, respectively. PNI = albumin (g/L) + 5 × lymphocyte count (×10 9 ). The cutoff point for PNI was 45.8. Patients with PNI score of > 45.8 and ≤ 45.8 were assigned to the normal and malnutrition groups, respectively. CXI = SMI (cm 2 /m 2 ) × serum albumin (g/dL)/NLR. The cutoff point for the CXI was 55.2. Patients with CXI score of < 55.2 and ≥ 55.2 were assigned to the normal and malnutrition groups, respectively. SII was calculated as platelet count × neutrophil count/lymphocyte count. The cutoff point for the SII was 582.5. Patients with SII score of < 582.5 and ≥ 582.5 were assigned to the SII-low and SII-high groups, respectively (Supplement Table 1 ). Statistical analysis Continuous variables were expressed as mean ± standard deviation (± SD). Continuous variables with normal distribution were evaluated using Student’s t-test. Categorical variables were presented as frequencies or percentages, and χ2 tests or Fisher’s exact tests were applied. The linear regression model was used to test associations between SII and other covariates. Inflammation and nutritional indicators were dichotomized based on optimal cut-offs, determined using X-tile software ( http://www.tissuearray.org/rimmlab/ ). The time dependent receiver operating characteristic curve (ROC) was utilized to evaluate the predictive power of the different nutritional assessment indices for OS and RFS. A machine learning method was employed to screen variables and to construct new and improved indicators. Kaplan–Meier curves and log-rank tests were used to compare survival between the groups. Univariate and multivariate Cox regression analyses were conducted to analyze the independent prognostic value of nutritional indicators for OS and RFS in patients. All data were processed using SPSS software (version 26.0) and R software (version 3.5.0). Results Clinicopathological characteristics Table 1 presents the general clinical data of patients in the two groups. A total of 1327 patients with gastric cancer met the inclusion criteria, including 1001 males and 326 females. The mean age of the study population was 60.7 ± 11.1 years. Notably, 843 (63.5%) and 484 (36.5%) patients were included in the SII-low and SII-high groups. Compared to the SII-low group, patients in the SII-high group were younger (59.8 ± 11.5 vs. 62.3 ± 10.2, P < 0.001), with a lower prevalence of hypertension (76.7% vs. 81.4%, P = 0.04) and higher proportions of patients with total gastrectomy (51.0% vs. 42.3%, P = 0.002), postoperative chemotherapy (45.2% vs. 35.3%, P < 0.001), and advanced pathological stages (80.4% vs. 65.5%, P < 0.001). Significant differences were also observed in common nutritional indicators, such as GNRI scores (95.8 ± 8.8 vs. 99.0 ± 7.4, P < 0.001), PNI values (45.4 ± 5.9 vs. 49.5 ± 5.6, P < 0.001), and CXI levels (48.6 ± 2.8 vs. 107.2 ± 58.3, P < 0.001), between both groups. Additionally, patients in the SII-high group exhibited lower SMI scores (39.3 ± 7.7 vs. 41.0 ± 9.1, P < 0.001), higher rates of malnutrition based on GLIM criteria (38.9% vs. 31.7%, P < 0.001), and an increased incidence of malnutrition based on the CONUT assessment results (43.3% vs. 22.7%, P < 0.001). Figure 1 shows the distribution of different nutritional indicators in patients with different inflammatory states. Notably, the nutritional status of patients with a low inflammatory state was higher than that of patients with a high inflammatory state (Fig. 1 A–D). According to the GLIM (SII-low = 31.7% vs. SII-high = 38.9%, P = 0.009) and CONUT (SII-low = 22.7% vs. SII-high = 43.3%, P < 0.001) criteria, a higher proportion of patients exhibited a high inflammatory state (Fig. 1 E, F). Influence of nutritional indicators on prognosis in different inflammatory states Table 2 presents the impact of each nutritional assessment tool on the OS in different inflammatory states. The univariate Cox analysis revealed that the GNRI, PNI, GLIM, CONUT, CXI, and SMI were significantly associated with OS in the total population (all P < 0.05). Further, multivariate Cox analysis revealed that GLIM (adjusted HR = 1.862, 95% CI 1.408–2.461; P < 0.001), PNI (adjusted HR = 0.981, 95% CI 0.962–1.000, P = 0.04), GNRI (adjusted HR = 1.862, 95% CI 1.408–2.461, P < 0.001), and CONUT (adjusted HR: 1.503, 95% CI 1.170–1.931, P = 0.001) were the independent influencing factors for OS. In the SII-low group, GLIM (adjusted HR = 1.774, 95% CI 1.227–2.564, P = 0.002) and CONUT score (adjusted HR = 1.505, 95% CI 1.103–2.054, P = 0.010) were independent predictors of OS. In the SII-high group, GLIM (adjusted HR = 2.047; 95% CI 1.314–3.188, P = 0.002), GNRI (adjusted HR 0.980; 95% CI 0.961–1.000, P = 0.045), and SMI (adjusted HR 0.963, 95% CI: 0.937–0.989, P = 0.006) were independent prognostic factors of OS. Moreover, multivariate cox regression analysis after adjusting for RFS revealed that GLIM (adjusted HR = 1.638, 95% CI 1.318–2.036, P < 0.001) and CONUT (adjusted HR = 1.463, 95% CI 1.194–1.793, P < 0.001) were independent predictors of RFS. In the SII-low group, GLIM (adjusted HR = 1.469, 95% CI 1.109–1.947, P = 0.007), CONUT (adjusted HR = 1.523, 95% CI 1.184–1.959, P = 0.001) were independent predictors of RFS. In the SII-high group, GLIM (adjusted HR = 2.000, 95% CI 1.400–2.859, P < 0.001), and SMI (adjusted HR = 0.971, 95% CI 0.951–0.992, P = 0.008) were the independent influencing factors of RFS (Supplement Table 2 ). Comparison of prognostic ability of nutritional tools Multivariate analysis was employed to assess the prognostic power of several nutritional indicators to independently predict OS (Fig. 2 ). Notably, the GNRI, PNI, and GLIM exhibited relatively stable predictive abilities in the entire population. In the SII-low group, only GLIM exhibited relatively stable predictive capability, whereas in the SII-high group, only GNRI and GLIM exhibited relatively stable predictive ability. Notably, the GLIM was applicable to patients with different inflammatory states and reliably predicted OS (1-year AUC: overall = 0.673, SII-low = 0.657, SII-high = 0.679; 3-year AUC: overall = 0.635, SII-low = 0.632, SII-high = 0.631; 5-year AUC: overall = 0.632, SII-low = 0.635, SII-high = 0.616) (Table 3). Subgroup survival analysis Kaplan–Meier curves for OS in different subgroups of patients with and without malnutrition based on the GLIM criteria are presented in Fig. 3 . Notably, patients with malnutrition demonstrated significantly worse OS than patients without malnutrition in the overall population (57.9% vs. 76.7%, P < 0.001), as well as in the SII-low (63.8% vs. 79.3%, P < 0.001), SII-high (48.7% vs. 71.0%, P < 0.001), II (65.9% vs. 90.0%, P = 0.004), and III (39.3% vs. 56.4%, P < 0.001) subgroups. Kaplan–Meier curves for RFS in different subgroups of patients with and without malnutrition based on the GLIM criteria are presented in Supplement Fig. 3 . Notably, patients with malnutrition demonstrated significantly worse RFS than patients without malnutrition in the overall population (47.9% vs. 67.6%, P < 0.001), as well as in the SII-low (53.1% vs. 70.8%, P < 0.001), SII-high (40.4% vs. 61.8%, P < 0.001), I (78.2% vs. 87.9%, P < 0.001), II (60.0% vs. 71.6%, P = 0.001), and III (27.4% vs. 48.7%, P < 0.001) subgroups. Mediation analyses A machine learning approach (random forest) was employed to evaluate the significance of baseline measures, excluding nutritional indicators on OS. ASA grade considerably influenced the OS of patients with gastric cancer (Supplement Fig. 4 ). Mediation analysis revealed that the ASA score partially mediated the impact of GLIM, GNRI, PNI, and CONUT on prognosis in all patients (the proportion of mediation of GLIM, GNRI, PNI, and CONUT was 19.21%, 14.95%, 20.05%, and 22.45%, respectively). In the SII-low group, the ASA grade partly mediated the influence of GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 21.54% and 31.06%, respectively). In the SII-high group, the ASA partially mediated the effect of GLIM on prognosis (the proportion of mediation of GLIM was 16.54%) (Supplement Figs. 6 and 7). ASA grade significantly impacted RFS among patients with gastric cancer (Supplement Fig. 5). Mediation effect analysis for the entire population indicated that the ASA score partially mediated the effects exerted by GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 10.30% and 10.90%, respectively) (Supplement Fig. 8). In the SII-low group, the ASA score partially mediated the impact of GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 12.20% and 9.77%, respectively). In the SII-high group, the ASA score also partially mediated the effect of GLIM on prognosis (the proportion of mediation of GLIM was 10.08%) (Supplement Figs. 9 and 10). Discussion This study revealed that the predictive ability of various nutritional indicators for prognosis varies depending on the inflammatory status of patients. Notably, the GLIM criteria demonstrated consistent prognostic stability across different inflammatory states and time points. Further, the association between nutritional indicators and OS and RFS was partially mediated by ASA scores. To the best of our knowledge, this is the first study to investigate the prognostic value of nutritional indicators in patients with gastric cancer with different inflammatory states, thereby providing a crucial foundation for assessing their nutritional status. Inflammation may be associated with nutritional status, and as part of the systemic inflammatory response in tumors, proinflammatory cytokines promote host catabolism [ 27 ]. Subsequently, the hypermetabolic environment generated by this proinflammatory environment may induce cachexia, anorexia, and digestive dysfunction, further aggravating malnutrition [ 28 ]. Ruan et al. showed that, among the patients with 11 types of cancer, including gastric cancer, those with sarcopenia exhibited a higher inflammatory state [ 29 ]. In a study involving patients with colorectal cancer, the GNRI was significantly associated with the SII, and the GNRI was significantly lower in the SII-high group than in the SII-low group [ 30 ]. Xie et al. reported that systemic inflammation is closely associated with involuntary weight loss in patients with cancer, including gastric cancer [ 31 ]. Nevertheless, the relationship between multiple nutritional indicators and inflammatory states has not been explored in patients with gastric cancer. The nutritional indicators included in this study were assessed based on hematological indicators, CT imaging data, and anthropometric indicators, reflecting the metabolic state of the body. Notably, the nutritional status of patients with gastric cancer with high inflammation was generally worse than that of patients with low inflammation, which was consistent with the results of the previous studies on the mechanism of inflammation, reflecting the widespread effect of inflammation on the metabolism of the body. The preoperative identification of a high inflammatory state and low nutritional status in patients and timely treatment intervention may positively affect patient prognosis [ 32 ]. The present study also demonstrated the consistent prognostic value of the GLIM standard. Cai et al. found that malnutrition, as defined by GLIM, is an independent risk factor for OS in patients with gastric cancer undergoing surgical treatment [ 33 ]. Zhang et al. revealed that the GLIM criteria effectively predict OS in older patients with cancer [ 34 ], whereas Xu et al. reported a significant association between weight loss according to the GLIM criteria and poor OS and disease-free survival in patients with gastric cancer [ 35 ]. Our cox regression analysis results for OS and RFS indicated an independent association between the GLIM criteria and these outcomes across all subgroups, including the low- and high-SII populations, after controlling for confounding variables. Time-dependent ROC curve analysis further confirmed the stable prognostic value of the GLIM criteria at various time points in all subgroups. The determination of nutritional status using the GLIM standard is closely linked to physical components, and preoperative weight loss, low BMI, and low muscle mass are unfavorable prognostic factors in patients with gastrointestinal tumors [ 36 – 39 ]. Perioperative nutritional supplementation is an effective strategy for improving the nutritional status of patients with gastric cancer and preventing perioperative body component loss [ 40 – 42 ]. Therefore, early preoperative identification of malnutrition, followed by appropriate nutritional intervention strategies during perioperative care, is crucial [ 43 ]. The ASA scoring standard refers to the scoring system developed by the American Society of Anesthesiologists to assess the risk of preoperative anesthesia in patients. This comprehensive scoring system evaluates various factors, including overall health, significant complications, recent physiological status, and other relevant factors in patients. Notably, ASA scoring is a prognostic risk factor for poor outcomes in patients with gastric carcinoma [ 44 – 45 ]. Previous studies have demonstrated that inadequate nutrient intake can result in reduced levels of growth factors and anabolic hormones (including IGF-I), thereby increasing the risk of cardiovascular disease [ 46 ]. Vinciane et al. revealed that malnutrition among older individuals may result in the loss of microbiota and exacerbate lung–gut associations, consequently increasing the susceptibility to respiratory diseases [ 47 ]. Further, malnutrition is associated with an increased risk of Parkinson's disease, chronic kidney disease, osteoporosis, and other ailments [ 48 – 50 ]. In this study, malnutrition exhibited a direct negative impact on survival and also worsened the prognosis by elevating the ASA grade, indicating the complexity of the mechanism through which nutritional status affects prognosis and the multisystemic impact of nutritional status on the human body. Therefore, clinicians should consider the possibility of systemic functional disorders in patients with malnutrition. This study has some limitations. First, this retrospective study was conducted at a single center,the generalizability and clinical applicability of our results are limited due to the absence of validation data from other centers or Western populations.Therefore, the findings should be further validated in a prospective multicenter cohort. Second, as this study lacked clinical data on the impact of nutritional interventions on various nutritional indicators, we could not conduct relevant analyses. Third, the neoadjuvant population was not included in this study, neoadjuvant patients are a unique group with dynamic changes in nutritional indicators before and after chemotherapy. Consequently, we plan to specifically address this patient population in our future research. Lastly, We used the SII as a reference index to assess inflammatory status in this study and did not explore the reference values for other inflammatory markers, such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and C-reactive protein. Nevertheless,this study comprehensively incorporated commonly used nutritional indicators in clinical practice and represents the first investigation to explore the prognostic significance of nutritional indicators in patients with gastric cancer based on their inflammatory status, thereby identifying distinct nutritional markers suitable for assessing the prognosis of patients with gastric cancer with varying levels of inflammation. Conclusion Different nutritional indicators should be selected to evaluate the prognosis of patients with gastric cancer and varying inflammatory states. Further, the GLIM criteria are more suitable than the CONUT, GNRI, PNI, CXI, and SMI criteria for patients with diverse inflammatory conditions. Declarations Conflict of Interest The authors declare that they have no conflicts of interest with the contents of this article. Funding: 1.Fujian Provincial Medical "Building High-level Hospitals, High-level Clinical Medical Centers and Key Clinical Specialty Projects" ([2021] No. 76) Author Contribution Zheng HL ,Zheng ZW , Wei LH, Xu Z, Xu BB, Wang JB, and Li P conceived the study, analyzed the data, and drafted the manuscript. Lin J,Shen LL,and Zhang LK helped collect data and design the study. Lin JX, Huang CM, and Li P helped critically revise the manuscript for important intellectual content. Acknowledgements We are grateful to the patients and their families for their participation in this study and to the doctors and nurses at our center. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. 10.3322/caac.21834 . da Silva JB, Maurício SF, Bering T, Correia MI. The relationship between nutritional status and the Glasgow prognostic score in patients with cancer of the esophagus and stomach. Nutr Cancer. 2013;65(1):25–33. 10.1080/01635581.2013.741755 . Zheng HL, Lu J, Li P, et al. Effects of Preoperative Malnutrition on Short- and Long-Term Outcomes of Patients with Gastric Cancer: Can We Do Better? Ann Surg Oncol. 2017;24(11):3376–85. 10.1245/s10434-017-5998-9 . Guo ZQ, Yu JM, Li W, et al. Survey and analysis of the nutritional status in hospitalized patients with malignant gastric tumors and its influence on the quality of life. Support Care Cancer. 2020;28(1):373–80. 10.1007/s00520-019-04803-32 . Cederholm T, Jensen GL, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9. 10.1016/j.clnu.2018.08.002 . Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38–45. Bouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–83. 10.1093/ajcn/82.4.777 . Onodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 1984;85(9):1001–5. Jafri SH, Previgliano C, Khandelwal K, Shi R. Cachexia index in advanced non-small-cell lung cancer patients. Clin Med Insights Oncol. 2015;9:87–93. Matsunaga T, Satio H, Miyauchi W, et al. Impact of skeletal muscle mass in patients with recurrent gastric cancer. World J Surg Oncol. 2021;19(1):170. 10.1186/s12957-021-02283- . Published 2021 Jun 11. Cederholm T, Jensen GL, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9. 10.1016/j.clnu.2018.08.002 . Kuroda D, Sawayama H, Kurashige J, et al. Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer. 2018;21(2):204–12. 10.1007/s10120-017-0744-3 . Bouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–83. 10.1093/ajcn/82.4.777666 . Jiang N, Deng JY, Ding XW, et al. Prognostic nutritional index predicts postoperative complications and long-term outcomes of gastric cancer. World J Gastroenterol. 2014;20(30):10537–44. 10.3748/wjg.v20.i30.10537 . Gong C, Wan Q, Zhao R, Zuo X, Chen Y, Li T. Cachexia Index as a Prognostic Indicator in Patients with Gastric Cancer: A Retrospective Study. Cancers (Basel). 2022;14(18):4400. Published 2022 Sep 10. 10.3390/cancers14184400 Dong QT, Cai HY, Zhang Z, et al. Influence of body composition, muscle strength, and physical performance on the postoperative complications and survival after radical gastrectomy for gastric cancer: A comprehensive analysis from a large-scale prospective study. Clin Nutr. 2021;40(5):3360–9. 10.1016/j.clnu.2020.11.007 . Zheng ZF, Lu J, Xie JW, et al. Preoperative skeletal muscle index vs the controlling nutritional status score: Which is a better objective predictor of long-term survival for gastric cancer patients after radical gastrectomy? Cancer Med. 2018;7(8):3537–47. 10.1002/cam4.1548 . Zhao Y, Ge N, Xie D et al. The geriatric nutrition risk index versus the mini-nutritional assessment short form in predicting postoperative delirium and hospital length of stay among older non-cardiac surgical patients: a prospective cohort study. BMC Geriatr. 2020;20(1):107. Published 2020 Mar 17. 10.1186/s12877-020-1501-8 Allard JP, Keller H, Gramlich L, Jeejeebhoy KN, Laporte M, Duerksen DR. GLIM criteria has fair sensitivity and specificity for diagnosing malnutrition when using SGA as comparator. Clin Nutr. 2020;39(9):2771–7. 10.1016/j.clnu.2019.12.004 . Marusawa H, Jenkins BJ. Inflammation and gastrointestinal cancer: an overview. Cancer Lett. 2014;345(2):153–6. 10.1016/j.canlet.2013.08.0254.7 . Zeng QY, Qin Y, Shi Y, et al. Systemic immune-inflammation index and all-cause and cause-specific mortality in sarcopenia: a study from National Health and Nutrition Examination Survey 1999–2018. Front Immunol. 2024;15:1376544. 10.3389/fimmu.2024.1376544 . Published 2024 Apr 4. Jomrich G, Paireder M, Kristo I, et al. High Systemic Immune-Inflammation Index is an Adverse Prognostic Factor for Patients With Gastroesophageal Adenocarcinoma. Ann Surg. 2021;273(3):532–41. 10.1097/SLA.0000000000003370 . Xu Z, Chen X, Yuan J, Wang C, An J, Ma X. Correlations of preoperative systematic immuno-inflammatory index and prognostic nutrition index with a prognosis of patients after radical gastric cancer surgery. Surgery. 2022;172(1):150–9. 10.1016/j.surg.2022.01.006 . Wang H, Yin X, Ma K, et al. Nomogram Based on Preoperative Fibrinogen and Systemic Immune-Inflammation Index Predicting Recurrence and Prognosis of Patients with Borrmann Type III Advanced Gastric Cancer. J Inflamm Res. 2023;16:1059–75. 10.2147/JIR.S404585 . Published 2023 Mar 12. Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. 10.1186/1475-2891-9-69 . Zhang X, Tang M, Zhang Q, et al. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients. Clin Nutr. 2021;40(3):1224–32. 10.1016/j.clnu.2020.08.004 . Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. 10.1186/1475-2891-9-69 . Published 2010 Dec 22. Ruan GT, Xie HL, Yuan KT, et al. Prognostic value of systemic inflammation and for patients with colorectal cancer cachexia. J Cachexia Sarcopenia Muscle. 2023;14(6):2813–23. 10.1002/jcsm.13358 . Ruan GT, Ge YZ, Xie HL, et al. Association Between Systemic Inflammation and Malnutrition With Survival in Patients With Cancer Sarcopenia-A Prospective Multicenter Study. Front Nutr. 2022;8:811288. 10.3389/fnut.2021.811288 . Published 2022 Feb 7. Xiang S, Yang YX, Pan WJ, et al. Prognostic value of systemic immune inflammation index and geriatric nutrition risk index in early-onset colorectal cancer. Front Nutr. 2023;10:1134300. 10.3389/fnut.2023.1134300 . Published 2023 Apr 18. Xie H, Zhang H, Ruan G, et al. Individualized threshold of the involuntary weight loss in prognostic assessment of cancer. J Cachexia Sarcopenia Muscle. 2023;14(6):2948–58. 10.1002/jcsm.13368 . Merker M, Felder M, Gueissaz L, et al. Association of Baseline Inflammation With Effectiveness of Nutritional Support Among Patients With Disease-Related Malnutrition: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2020;3(3):e200663. 10.1001/jamanetworkopen.2020.0663 . Published 2020 Mar 2. Cai W, Yang H, Zheng J, et al. Global leaders malnutrition initiative-defined malnutrition affects long-term survival of different subgroups of patients with gastric cancer: A propensity score-matched analysis. Front Nutr. 2022;9:995295. 10.3389/fnut.2022.995295 . Published 2022 Sep 30. Xu LB, Mei TT, Cai YQ, et al. Correlation Between Components of Malnutrition Diagnosed by Global Leadership Initiative on Malnutrition Criteria and the Clinical Outcomes in Gastric Cancer Patients: A Propensity Score Matching Analysis. Front Oncol. 2022;12:851091. 10.3389/fonc.2022.851091 . Published 2022 Mar 3. Zhang X, Tang M, Zhang Q, et al. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients. Clin Nutr. 2021;40(3):1224–32. 10.1016/j.clnu.2020.08.004 . Zhang HL, Yang YS, Duan JN, et al. Prognostic value of preoperative weight loss-adjusted body mass index on survival after esophagectomy for esophageal squamous cell carcinoma. World J Gastroenterol. 2020;26(8):839–49. 10.3748/wjg.v26.i8.839 . van Vugt JLA, van den Coebergh RRJ, Lalmahomed ZS, et al. Impact of low skeletal muscle mass and density on short and long-term outcome after resection of stage I-III colorectal cancer. Eur J Surg Oncol. 2018;44(9):1354–60. 10.1016/j.ejso.2018.05.029 . Zhang W, Cui X, Li R, Ji W, Shi H, Cui J. Association between ICW/TBW ratio and cancer prognosis: Subanalysis of a population-based retrospective multicenter study. Clin Nutr. 2024;43(2):322–31. 10.1016/j.clnu.2023.12.004 . Seo D, Kim HS, Ahn JB, Park YR. Investigation of the Trajectory of Muscle and Body Mass as a Prognostic Factor in Patients With Colorectal Cancer: Longitudinal Cohort Study. JMIR Public Health Surveill. 2023;9:e43409. 10.2196/43409 . Published 2023 Mar 22. Ritch CR, Cookson MS, Clark PE, et al. Perioperative Oral Nutrition Supplementation Reduces Prevalence of Sarcopenia following Radical Cystectomy: Results of a Prospective Randomized Controlled Trial. J Urol. 2019;201(3):470–7. 10.1016/j.juro.2018.10.010 . Rinninella E, Biondi A, Cintoni M et al. NutriCatt Protocol Improves Body Composition and Clinical Outcomes in Elderly Patients Undergoing Colorectal Surgery in ERAS Program: A Retrospective Cohort Study. Nutrients . 2021;13(6):1781. Published 2021 May 23. 10.3390/nu13061781 Ryan AM, Reynolds JV, Healy L, et al. Enteral nutrition enriched with eicosapentaenoic acid (EPA) preserves lean body mass following esophageal cancer surgery: results of a double-blinded randomized controlled trial. Ann Surg. 2009;249(3):355–63. 10.1097/SLA.0b013e31819a4789 . Weimann A, Braga M, Carli F, et al. ESPEN guideline: Clinical nutrition in surgery. Clin Nutr. 2017;36(3):623–50. 10.1016/j.clnu.2017.02.013 . Macalindong SS, Kim KH, Nam BH et al. Effect of total number of harvested lymph nodes on survival outcomes after curative resection for gastric adenocarcinoma: findings from an eastern high-volume gastric cancer center. BMC Cancer . 2018;18(1):73. Published 2018 Jan 12. 10.1186/s12885-017-3872-6 Bian L, Wu D, Chen Y, et al. Associations of radiological features of adipose tissues with postoperative complications and overall survival of gastric cancer patients. Eur Radiol. 2022;32(12):8569–78. 10.1007/s00330-022-08918-w . Fontana L, Vinciguerra M, Longo VD. Growth factors, nutrient signaling, and cardiovascular aging. Circ Res. 2012;110(8):1139–50. 10.1161/CIRCRESAHA.111.246470 . Saint-Criq V, Lugo-Villarino G, Thomas M. Dysbiosis, malnutrition and enhanced gut-lung axis contribute to age-related respiratory diseases. Ageing Res Rev. 2021;66:101235. 10.1016/j.arr.2020.101235 . Ojo O, Adegboye ARA. The Effects of Nutrition on Chronic Conditions. Nutrients. 2023;15(5):1066. 10.3390/nu15051066 . Published 2023 Feb 21. Di Renzo L, Gualtieri P, De Lorenzo A. Diet, Nutrition and Chronic Degenerative Diseases. Nutrients . 2021;13(4):1372. Published 2021 Apr 20. 10.3390/nu13041372 Atkinson SA, Ward WE. Clinical nutrition: 2. The role of nutrition in the prevention and treatment of adult osteoporosis. CMAJ. 2001;165(11):1511–4. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files tables.doc Supplementtablesandfigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5492406","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382823404,"identity":"f9ed0b1a-bf7e-46a2-ac65-b81f9a71ea28","order_by":0,"name":"Hua-Long Zheng","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hua-Long","middleName":"","lastName":"Zheng","suffix":""},{"id":382823405,"identity":"14bd411d-bec2-4727-a1a0-eba50bddc8b0","order_by":1,"name":"Zhi-Wei Zheng","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Wei","middleName":"","lastName":"Zheng","suffix":""},{"id":382823406,"identity":"076d3991-63a4-46dc-9378-4537d8e30508","order_by":2,"name":"Ling-Hua Wei","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling-Hua","middleName":"","lastName":"Wei","suffix":""},{"id":382823407,"identity":"e1befdb1-b792-4cbc-9fb8-e7c6da878b83","order_by":3,"name":"Jia-Bin Wang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia-Bin","middleName":"","lastName":"Wang","suffix":""},{"id":382823408,"identity":"a8853c06-209a-4fd1-b072-efac70b367a8","order_by":4,"name":"Jian-Xian Lin","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian-Xian","middleName":"","lastName":"Lin","suffix":""},{"id":382823409,"identity":"2ea4bb41-05a4-4e2d-bb92-3bb6089b6631","order_by":5,"name":"Zhen Xue","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xue","suffix":""},{"id":382823410,"identity":"a0bca062-3696-4598-8b32-58c49ea7ca42","order_by":6,"name":"Bin-Bin Xu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bin-Bin","middleName":"","lastName":"Xu","suffix":""},{"id":382823411,"identity":"8a0fc551-2166-4190-a832-dca47c586a9f","order_by":7,"name":"Li-Li Shen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li-Li","middleName":"","lastName":"Shen","suffix":""},{"id":382823412,"identity":"b91e041b-b634-44a6-aff4-99970aaacb04","order_by":8,"name":"Jia Lin","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Lin","suffix":""},{"id":382823413,"identity":"d09f8609-47c3-4dab-bc3b-8bef1510b5ec","order_by":9,"name":"Ling-Kang Zhang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling-Kang","middleName":"","lastName":"Zhang","suffix":""},{"id":382823414,"identity":"e60bea74-fff5-4709-aa53-72f196f54ee3","order_by":10,"name":"Chang-Ming Huang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chang-Ming","middleName":"","lastName":"Huang","suffix":""},{"id":382823415,"identity":"5233e25f-d8f5-47a0-8451-e849cf512498","order_by":11,"name":"Ping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZiB+YGDDw8bM2Pjgg4GNHXFaEgzSZPjYmw8bzihISybOpgSGwzZyPMfSpHk+HGJsIKTanJ338IuEgjQeNokcA2kbgwPMDOyHj27Ap8WymS/NIgHkF6AW4xyDO3wMPGlpN/BpMTjMY2YA9AtYS3KOwTNmBgkeM2K0HAZrOWxhcJixgQgtxg/AWniOJTYzEKPFspnHjAHsMGAgM/YYpCWzEfKLOf8Z4w8f/tjYyzcztv/48cfGjp/98DH8DmNgYJNAEWHDpxyqhfkDIUWjYBSMglEwwgEAgeBElhgpjs4AAAAASUVORK5CYII=","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-11-20 16:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5492406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5492406/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71890168,"identity":"44dc1929-8be5-4b32-a238-361dafa11465","added_by":"auto","created_at":"2024-12-19 12:55:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":514928,"visible":true,"origin":"","legend":"\u003cp\u003eThe distributions of nutritional indicators between SII-High and SII-Low patients\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/8e5c73a6658906c99e023fed.jpeg"},{"id":71890116,"identity":"99042b69-46ef-464c-a2c0-25a050e56e5d","added_by":"auto","created_at":"2024-12-19 12:54:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328578,"visible":true,"origin":"","legend":"\u003cp\u003eThe time-depend ROCcurves of nutritional indicators about overall survival in overall, SII-High and SII-Low patients.\u003c/p\u003e\n\u003cp\u003e(A) Overall patients (B) SII-Low patients (C) SII-High patients\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/d35ea111d77311ca532441c8.jpeg"},{"id":71890109,"identity":"e4f2038d-bd43-469e-a27a-21c252355fb5","added_by":"auto","created_at":"2024-12-19 12:54:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":471312,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier curves of patients with malnutrition and normal status for overall survival based on GLIM criteria.\u003c/p\u003e\n\u003cp\u003eNotes: A. total population. B. SII-Low patients. C. SII-High patients. D. Stage I patients. E. Stage II patients. F. Stage III patients\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/e63cb192d94783b3b9224abb.jpeg"},{"id":71890079,"identity":"c19f946b-5f44-486a-9eff-bfbcb4408c88","added_by":"auto","created_at":"2024-12-19 12:54:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":499691,"visible":true,"origin":"","legend":"\u003cp\u003eThe mediation proportion of ASA Score in nutritional indicators attributed to OS in overall patients.\u003c/p\u003e\n\u003cp\u003e(A)GLIM criteria. (B)GNRI. (C)PNI. (D)CONUT. Notes: ASA,American Society of Anaesthesiologists;GLIM, the Global Leadership Initiative on Malnutrition; CONUT, the Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index, PNI, the Prognostic Nutritional Index\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/f009c98a3f0d80046a2a214c.jpeg"},{"id":79919819,"identity":"860002e9-a5a8-4b2b-b81b-8f946ef8a59e","added_by":"auto","created_at":"2025-04-04 13:24:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2488965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/b82dc0d9-6bb3-4f8a-bb69-974c187996f5.pdf"},{"id":71890078,"identity":"4ed10983-2dd9-47b1-b023-0badf3eef0d3","added_by":"auto","created_at":"2024-12-19 12:54:52","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":87278,"visible":true,"origin":"","legend":"","description":"","filename":"tables.doc","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/896fe98c0c9d76f43a5665a9.doc"},{"id":71890115,"identity":"f3ec452b-f94c-415d-b32c-789e72ad18eb","added_by":"auto","created_at":"2024-12-19 12:54:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":120499257,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtablesandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5492406/v1/6e1f01e8348ae0bf09f2f268.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Objective Evaluation of the Prognostic Value of Common Nutritional Indicators in Patients with Different Inflammatory States after Radical Gastrectomy for Gastric Cancer: A Real-world Study Running Head：Nutritional Indicators in Inflammatory State","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer ranks fifth among the most common cancers and the fifth leading cause of cancer-related deaths worldwide, thereby posing a significant threat to human health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Further, patients with gastric cancer exhibit 41.6\u0026ndash;86.1% of the incidence of malnutrition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, malnutrition increases the risk of surgical complications and also leads to reduced quality of life and shorter overall survival [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, selecting appropriate indicators for assessing the nutritional status of patients with gastric cancer in order to manage their condition effectively is crucial.\u003c/p\u003e \u003cp\u003eCurrently, the Global Leadership Initiative on Malnutrition (GLIM), Controlling Nutritional Status (CONUT), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), Cachexia Index (CXI), and Skeletal Muscle Mass Index (SMI) are the commonly used nutrition assessment tools in clinical practice [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GLIM criteria, which incorporate both phenotypic and etiologic criteria as standards, are a novel approach for comprehensive nutritional status evaluation. This offers a convenient and efficient nutritional assessment guide that can be easily implemented [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].The CONUT score involves three important immunonutrition indicators based on serum albumin, total lymphocyte count, and cholesterol levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The GNRI considers factors such as serum albumin levels and anthropometric indicators, making it easily applicable in clinical practice, and thus has been widely used to assess the nutritional status of various patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The PNI is calculated from albumin and lymphocyte counts, which are easy to obtain and calculate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The CXI is mainly used to assess the nutritional status of patients with tumor burden [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The SMI directly reflects the skeletal muscle mass (SMA) in patients with gastric cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, all these indicators reflect the nutritional status of patients from different perspectives; however, their value in predicting the prognosis of patients with gastric cancer remains controversial [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inflammatory state also plays a crucial role in determining the prognosis of patients with gastric cancer. Further, cytokines, chemokines, and angiogenic factors in the inflammatory microenvironment drive the growth of tumor cells, playing a significant role in tumorigenesis in the gastrointestinal tract and hepatobiliary organs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Systemic Immune Inflammation Index (SII), which evaluates the overall inflammatory state of patients based on neutrophil, lymphocyte, and platelet counts in peripheral blood, is also widely used [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, high SII values are associated with a poor prognosis [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Tumors produce proinflammatory cytokines that disrupt carbohydrate, fat, and protein metabolism throughout the body and exacerbate catabolism, leading to muscle breakdown [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This indicates a potential association between nutritional status and inflammatory state in patients with cancer. However, research on the prognostic value of nutritional indicators in different inflammatory states in patients with gastric cancer is limited. Therefore, this study aimed to evaluate the commonly used nutritional indices for predicting prognosis under various inflammatory conditions in patients with gastric cancer. Additionally, we explored how nutrition affects prognostic indicators through an intermediary effect analysis. The findings of this study may provide a basis for selecting appropriate nutritional assessment indicators in clinical practice to guide individualized nutritional interventions and partly improve the prognosis of patients with gastric cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThe clinical data of patients diagnosed with gastric adenocarcinoma using gastroscopy and pathology who underwent radical resection at Fujian Medical University Union Hospital between January 2010 and December 2015 were prospectively collected and retrospectively analyzed. The inclusion criteria were as follows: (1) Postoperative pathology-confirmed gastric adenocarcinoma; (2) No distant metastasis found on preoperative chest radiography, abdominal ultrasound, or upper abdominal CT; and (3) Radical resection. The exclusion criteria were as follows: (1) Patients who received neoadjuvant chemotherapy or radiotherapy before surgery; (2) Incomplete clinical data, CT images, and follow-up data; and (3) Patients with gastric stump cancer. Finally, 1327 patients were included in the analysis (Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study protocol was approved by the ethics committees of all the participating hospitals and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient characteristics and outcomes\u003c/h3\u003e\n\u003cp\u003eBaseline demographic information, clinical parameters, laboratory tests, and physical measurements along with the age, sex, presence of hypertension and diabetes, history of cardiovascular and cerebrovascular diseases, tumor pathological stage, ASA score, ECOG score, extent of surgical resection, and nutritional indicators of enrolled patients were collected. All data were extracted from the electronic medical records. Overall survival (OS) was defined as the time from the date of surgery to death or final follow-up. Relapse-free survival (RFS) was defined as the time from the date of surgery to recurrence, death from any cause, or the last follow-up visit. The patients were assessed at follow-up appointments every 3 months for 2 years after surgery, every 6 months for 3\u0026ndash;5 years, and annually after 5 years. Most patient follow-up appointments included physical examinations, laboratory tests (including measurement of tumor markers such as CA19-9, CEA, and CA72-4), imaging tests (including chest X-ray/CT, whole abdomen ultrasound/CT, and MR), and endoscopy annually. The disease recurrence was diagnosed based on radiological findings from cross-sectional imaging or biopsy results of the suspicious lesions.\u003c/p\u003e\n\u003ch3\u003eNutrition index and definition of malnutrition and inflammation\u003c/h3\u003e\n\u003cp\u003eThe GLIM diagnostic criteria include etiological (reduced food intake or assimilation, inflammation, or disease burden) and phenotypic (weight loss, low body mass index (BMI), and reduced muscle mass) criteria. As cancer is an etiologic component of the GLIM criteria, patients who met any phenotypic criterion were diagnosed with malnutrition in this study [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The following phenotypic criteria were used based on available data: weight loss of \u0026gt;\u0026thinsp;5% within the past 6 months or \u0026gt;\u0026thinsp;10% in \u0026gt;\u0026thinsp;6 months, low BMI of \u0026lt;\u0026thinsp;18.5 for patients aged\u0026thinsp;\u0026lt;\u0026thinsp;70 years or \u0026lt;\u0026thinsp;20 for patients aged\u0026thinsp;\u0026gt;\u0026thinsp;70 years, and reduced muscle mass was determined based on SMI. CT images of the L3 vertebral section were selected for the quantitative analysis of SMA, and the SMI was calculated using the formula SMI\u0026thinsp;=\u0026thinsp;SMA/height\u003csup\u003e2\u003c/sup\u003e, expressed in cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e. The cutoff values for muscle deficit were 45.9 and 31.1 in males and females, respectively. At least one phenotypic and one etiological criteria were required to diagnose malnutrition. The CONUT scores were determined based on albumin, lymphocyte, and total cholesterol levels. Albumin levels of \u0026gt;\u0026thinsp;35, 30\u0026ndash;34, 25\u0026ndash;29, and \u0026lt;\u0026thinsp;25 g/L, lymphocyte count of \u0026ge;\u0026thinsp;1.6, 1.2\u0026ndash;1.59, 0.8\u0026ndash;1.19, and \u0026lt;\u0026thinsp;0.8 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, and total cholesterol levels of \u0026ge;\u0026thinsp;180, 140\u0026ndash;180, 100\u0026ndash;139, and \u0026lt;\u0026thinsp;100 mmol/L were assigned a score of 0, 2, 4, and 6, respectively. Albumin, lymphocyte, and total cholesterol levels were then combined. A total score of \u0026ge;\u0026thinsp;2 points indicated malnutrition. The GNRI, PNI, and CXI were calculated using the following formulas: GNRI\u0026thinsp;=\u0026thinsp;1.487 \u0026times; albumin (g/L)\u0026thinsp;+\u0026thinsp;41.7 \u0026times; current weight/IBW, where IBW = [height (m)]\u0026sup2; \u0026times; 22. The cutoff point for the GNRI was 97.5. Patients with GNRI score of \u0026gt;\u0026thinsp;97.5 and \u0026le;\u0026thinsp;97.5 were assigned to the normal and malnutrition groups, respectively. PNI\u0026thinsp;=\u0026thinsp;albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte count (\u0026times;10\u003csup\u003e9\u003c/sup\u003e). The cutoff point for PNI was 45.8. Patients with PNI score of \u0026gt;\u0026thinsp;45.8 and \u0026le;\u0026thinsp;45.8 were assigned to the normal and malnutrition groups, respectively. CXI\u0026thinsp;=\u0026thinsp;SMI (cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e) \u0026times; serum albumin (g/dL)/NLR. The cutoff point for the CXI was 55.2. Patients with CXI score of \u0026lt;\u0026thinsp;55.2 and \u0026ge;\u0026thinsp;55.2 were assigned to the normal and malnutrition groups, respectively. SII was calculated as platelet count \u0026times; neutrophil count/lymphocyte count. The cutoff point for the SII was 582.5. Patients with SII score of \u0026lt;\u0026thinsp;582.5 and \u0026ge;\u0026thinsp;582.5 were assigned to the SII-low and SII-high groups, respectively (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;SD). Continuous variables with normal distribution were evaluated using Student\u0026rsquo;s t-test. Categorical variables were presented as frequencies or percentages, and χ2 tests or Fisher\u0026rsquo;s exact tests were applied. The linear regression model was used to test associations between SII and other covariates. Inflammation and nutritional indicators were dichotomized based on optimal cut-offs, determined using X-tile software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tissuearray.org/rimmlab/\u003c/span\u003e\u003cspan address=\"http://www.tissuearray.org/rimmlab/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The time dependent receiver operating characteristic curve (ROC) was utilized to evaluate the predictive power of the different nutritional assessment indices for OS and RFS. A machine learning method was employed to screen variables and to construct new and improved indicators. Kaplan\u0026ndash;Meier curves and log-rank tests were used to compare survival between the groups. Univariate and multivariate Cox regression analyses were conducted to analyze the independent prognostic value of nutritional indicators for OS and RFS in patients. All data were processed using SPSS software (version 26.0) and R software (version 3.5.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the general clinical data of patients in the two groups. A total of 1327 patients with gastric cancer met the inclusion criteria, including 1001 males and 326 females. The mean age of the study population was 60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 years. Notably, 843 (63.5%) and 484 (36.5%) patients were included in the SII-low and SII-high groups. Compared to the SII-low group, patients in the SII-high group were younger (59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 vs. 62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a lower prevalence of hypertension (76.7% vs. 81.4%, P\u0026thinsp;=\u0026thinsp;0.04) and higher proportions of patients with total gastrectomy (51.0% vs. 42.3%, P\u0026thinsp;=\u0026thinsp;0.002), postoperative chemotherapy (45.2% vs. 35.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and advanced pathological stages (80.4% vs. 65.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences were also observed in common nutritional indicators, such as GNRI scores (95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 vs. 99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PNI values (45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 vs. 49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CXI levels (48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 vs. 107.2\u0026thinsp;\u0026plusmn;\u0026thinsp;58.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), between both groups. Additionally, patients in the SII-high group exhibited lower SMI scores (39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 vs. 41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher rates of malnutrition based on GLIM criteria (38.9% vs. 31.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and an increased incidence of malnutrition based on the CONUT assessment results (43.3% vs. 22.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of different nutritional indicators in patients with different inflammatory states. Notably, the nutritional status of patients with a low inflammatory state was higher than that of patients with a high inflammatory state (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;D). According to the GLIM (SII-low\u0026thinsp;=\u0026thinsp;31.7% vs. SII-high\u0026thinsp;=\u0026thinsp;38.9%, P\u0026thinsp;=\u0026thinsp;0.009) and CONUT (SII-low\u0026thinsp;=\u0026thinsp;22.7% vs. SII-high\u0026thinsp;=\u0026thinsp;43.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) criteria, a higher proportion of patients exhibited a high inflammatory state (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInfluence of nutritional indicators on prognosis in different inflammatory states\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the impact of each nutritional assessment tool on the OS in different inflammatory states. The univariate Cox analysis revealed that the GNRI, PNI, GLIM, CONUT, CXI, and SMI were significantly associated with OS in the total population (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further, multivariate Cox analysis revealed that GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;1.862, 95% CI 1.408\u0026ndash;2.461; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PNI (adjusted HR\u0026thinsp;=\u0026thinsp;0.981, 95% CI 0.962\u0026ndash;1.000, P\u0026thinsp;=\u0026thinsp;0.04), GNRI (adjusted HR\u0026thinsp;=\u0026thinsp;1.862, 95% CI 1.408\u0026ndash;2.461, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CONUT (adjusted HR: 1.503, 95% CI 1.170\u0026ndash;1.931, P\u0026thinsp;=\u0026thinsp;0.001) were the independent influencing factors for OS. In the SII-low group, GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;1.774, 95% CI 1.227\u0026ndash;2.564, P\u0026thinsp;=\u0026thinsp;0.002) and CONUT score (adjusted HR\u0026thinsp;=\u0026thinsp;1.505, 95% CI 1.103\u0026ndash;2.054, P\u0026thinsp;=\u0026thinsp;0.010) were independent predictors of OS. In the SII-high group, GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;2.047; 95% CI 1.314\u0026ndash;3.188, P\u0026thinsp;=\u0026thinsp;0.002), GNRI (adjusted HR 0.980; 95% CI 0.961\u0026ndash;1.000, P\u0026thinsp;=\u0026thinsp;0.045), and SMI (adjusted HR 0.963, 95% CI: 0.937\u0026ndash;0.989, P\u0026thinsp;=\u0026thinsp;0.006) were independent prognostic factors of OS. Moreover, multivariate cox regression analysis after adjusting for RFS revealed that GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;1.638, 95% CI 1.318\u0026ndash;2.036, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CONUT (adjusted HR\u0026thinsp;=\u0026thinsp;1.463, 95% CI 1.194\u0026ndash;1.793, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent predictors of RFS. In the SII-low group, GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;1.469, 95% CI 1.109\u0026ndash;1.947, P\u0026thinsp;=\u0026thinsp;0.007), CONUT (adjusted HR\u0026thinsp;=\u0026thinsp;1.523, 95% CI 1.184\u0026ndash;1.959, P\u0026thinsp;=\u0026thinsp;0.001) were independent predictors of RFS. In the SII-high group, GLIM (adjusted HR\u0026thinsp;=\u0026thinsp;2.000, 95% CI 1.400\u0026ndash;2.859, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SMI (adjusted HR\u0026thinsp;=\u0026thinsp;0.971, 95% CI 0.951\u0026ndash;0.992, P\u0026thinsp;=\u0026thinsp;0.008) were the independent influencing factors of RFS (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eComparison of prognostic ability of nutritional tools\u003c/h3\u003e\n\u003cp\u003eMultivariate analysis was employed to assess the prognostic power of several nutritional indicators to independently predict OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the GNRI, PNI, and GLIM exhibited relatively stable predictive abilities in the entire population. In the SII-low group, only GLIM exhibited relatively stable predictive capability, whereas in the SII-high group, only GNRI and GLIM exhibited relatively stable predictive ability. Notably, the GLIM was applicable to patients with different inflammatory states and reliably predicted OS (1-year AUC: overall\u0026thinsp;=\u0026thinsp;0.673, SII-low\u0026thinsp;=\u0026thinsp;0.657, SII-high\u0026thinsp;=\u0026thinsp;0.679; 3-year AUC: overall\u0026thinsp;=\u0026thinsp;0.635, SII-low\u0026thinsp;=\u0026thinsp;0.632, SII-high\u0026thinsp;=\u0026thinsp;0.631; 5-year AUC: overall\u0026thinsp;=\u0026thinsp;0.632, SII-low\u0026thinsp;=\u0026thinsp;0.635, SII-high\u0026thinsp;=\u0026thinsp;0.616) (Table\u0026nbsp;3).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup survival analysis\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier curves for OS in different subgroups of patients with and without malnutrition based on the GLIM criteria are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Notably, patients with malnutrition demonstrated significantly worse OS than patients without malnutrition in the overall population (57.9% vs. 76.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as in the SII-low (63.8% vs. 79.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SII-high (48.7% vs. 71.0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), II (65.9% vs. 90.0%, P\u0026thinsp;=\u0026thinsp;0.004), and III (39.3% vs. 56.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) subgroups.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier curves for RFS in different subgroups of patients with and without malnutrition based on the GLIM criteria are presented in Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Notably, patients with malnutrition demonstrated significantly worse RFS than patients without malnutrition in the overall population (47.9% vs. 67.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as in the SII-low (53.1% vs. 70.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SII-high (40.4% vs. 61.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), I (78.2% vs. 87.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), II (60.0% vs. 71.6%, P\u0026thinsp;=\u0026thinsp;0.001), and III (27.4% vs. 48.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMediation analyses\u003c/h2\u003e \u003cp\u003eA machine learning approach (random forest) was employed to evaluate the significance of baseline measures, excluding nutritional indicators on OS. ASA grade considerably influenced the OS of patients with gastric cancer (Supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mediation analysis revealed that the ASA score partially mediated the impact of GLIM, GNRI, PNI, and CONUT on prognosis in all patients (the proportion of mediation of GLIM, GNRI, PNI, and CONUT was 19.21%, 14.95%, 20.05%, and 22.45%, respectively). In the SII-low group, the ASA grade partly mediated the influence of GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 21.54% and 31.06%, respectively). In the SII-high group, the ASA partially mediated the effect of GLIM on prognosis (the proportion of mediation of GLIM was 16.54%) (Supplement Figs.\u0026nbsp;6 and 7).\u003c/p\u003e \u003cp\u003eASA grade significantly impacted RFS among patients with gastric cancer (Supplement Fig.\u0026nbsp;5). Mediation effect analysis for the entire population indicated that the ASA score partially mediated the effects exerted by GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 10.30% and 10.90%, respectively) (Supplement Fig.\u0026nbsp;8). In the SII-low group, the ASA score partially mediated the impact of GLIM and CONUT on prognosis (the proportion of mediation of GLIM and CONUT was 12.20% and 9.77%, respectively). In the SII-high group, the ASA score also partially mediated the effect of GLIM on prognosis (the proportion of mediation of GLIM was 10.08%) (Supplement Figs.\u0026nbsp;9 and 10).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed that the predictive ability of various nutritional indicators for prognosis varies depending on the inflammatory status of patients. Notably, the GLIM criteria demonstrated consistent prognostic stability across different inflammatory states and time points. Further, the association between nutritional indicators and OS and RFS was partially mediated by ASA scores. To the best of our knowledge, this is the first study to investigate the prognostic value of nutritional indicators in patients with gastric cancer with different inflammatory states, thereby providing a crucial foundation for assessing their nutritional status.\u003c/p\u003e \u003cp\u003eInflammation may be associated with nutritional status, and as part of the systemic inflammatory response in tumors, proinflammatory cytokines promote host catabolism [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Subsequently, the hypermetabolic environment generated by this proinflammatory environment may induce cachexia, anorexia, and digestive dysfunction, further aggravating malnutrition [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Ruan et al. showed that, among the patients with 11 types of cancer, including gastric cancer, those with sarcopenia exhibited a higher inflammatory state [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In a study involving patients with colorectal cancer, the GNRI was significantly associated with the SII, and the GNRI was significantly lower in the SII-high group than in the SII-low group [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Xie et al. reported that systemic inflammation is closely associated with involuntary weight loss in patients with cancer, including gastric cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nevertheless, the relationship between multiple nutritional indicators and inflammatory states has not been explored in patients with gastric cancer. The nutritional indicators included in this study were assessed based on hematological indicators, CT imaging data, and anthropometric indicators, reflecting the metabolic state of the body. Notably, the nutritional status of patients with gastric cancer with high inflammation was generally worse than that of patients with low inflammation, which was consistent with the results of the previous studies on the mechanism of inflammation, reflecting the widespread effect of inflammation on the metabolism of the body. The preoperative identification of a high inflammatory state and low nutritional status in patients and timely treatment intervention may positively affect patient prognosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study also demonstrated the consistent prognostic value of the GLIM standard. Cai et al. found that malnutrition, as defined by GLIM, is an independent risk factor for OS in patients with gastric cancer undergoing surgical treatment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Zhang et al. revealed that the GLIM criteria effectively predict OS in older patients with cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], whereas Xu et al. reported a significant association between weight loss according to the GLIM criteria and poor OS and disease-free survival in patients with gastric cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our cox regression analysis results for OS and RFS indicated an independent association between the GLIM criteria and these outcomes across all subgroups, including the low- and high-SII populations, after controlling for confounding variables. Time-dependent ROC curve analysis further confirmed the stable prognostic value of the GLIM criteria at various time points in all subgroups. The determination of nutritional status using the GLIM standard is closely linked to physical components, and preoperative weight loss, low BMI, and low muscle mass are unfavorable prognostic factors in patients with gastrointestinal tumors [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Perioperative nutritional supplementation is an effective strategy for improving the nutritional status of patients with gastric cancer and preventing perioperative body component loss [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, early preoperative identification of malnutrition, followed by appropriate nutritional intervention strategies during perioperative care, is crucial [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ASA scoring standard refers to the scoring system developed by the American Society of Anesthesiologists to assess the risk of preoperative anesthesia in patients. This comprehensive scoring system evaluates various factors, including overall health, significant complications, recent physiological status, and other relevant factors in patients. Notably, ASA scoring is a prognostic risk factor for poor outcomes in patients with gastric carcinoma [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies have demonstrated that inadequate nutrient intake can result in reduced levels of growth factors and anabolic hormones (including IGF-I), thereby increasing the risk of cardiovascular disease [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Vinciane et al. revealed that malnutrition among older individuals may result in the loss of microbiota and exacerbate lung\u0026ndash;gut associations, consequently increasing the susceptibility to respiratory diseases [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Further, malnutrition is associated with an increased risk of Parkinson's disease, chronic kidney disease, osteoporosis, and other ailments [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In this study, malnutrition exhibited a direct negative impact on survival and also worsened the prognosis by elevating the ASA grade, indicating the complexity of the mechanism through which nutritional status affects prognosis and the multisystemic impact of nutritional status on the human body. Therefore, clinicians should consider the possibility of systemic functional disorders in patients with malnutrition.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, this retrospective study was conducted at a single center,the generalizability and clinical applicability of our results are limited due to the absence of validation data from other centers or Western populations.Therefore, the findings should be further validated in a prospective multicenter cohort. Second, as this study lacked clinical data on the impact of nutritional interventions on various nutritional indicators, we could not conduct relevant analyses. Third, the neoadjuvant population was not included in this study, neoadjuvant patients are a unique group with dynamic changes in nutritional indicators before and after chemotherapy. Consequently, we plan to specifically address this patient population in our future research. Lastly, We used the SII as a reference index to assess inflammatory status in this study and did not explore the reference values for other inflammatory markers, such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and C-reactive protein.\u003c/p\u003e \u003cp\u003eNevertheless,this study comprehensively incorporated commonly used nutritional indicators in clinical practice and represents the first investigation to explore the prognostic significance of nutritional indicators in patients with gastric cancer based on their inflammatory status, thereby identifying distinct nutritional markers suitable for assessing the prognosis of patients with gastric cancer with varying levels of inflammation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDifferent nutritional indicators should be selected to evaluate the prognosis of patients with gastric cancer and varying inflammatory states. Further, the GLIM criteria are more suitable than the CONUT, GNRI, PNI, CXI, and SMI criteria for patients with diverse inflammatory conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest with the contents of this article.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003e1.Fujian Provincial Medical \u0026quot;Building High-level Hospitals, High-level Clinical Medical Centers and Key Clinical Specialty Projects\u0026quot; ([2021] No. 76)\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZheng HL ,Zheng ZW , Wei LH, Xu Z, Xu BB, Wang JB, and Li P conceived the study, analyzed the data, and drafted the manuscript. Lin J,Shen LL,and Zhang LK helped collect data and design the study. Lin JX, Huang CM, and Li P helped critically revise the manuscript for important intellectual content.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe are grateful to the patients and their families for their participation in this study and to the doctors and nurses at our center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Silva JB, Maur\u0026iacute;cio SF, Bering T, Correia MI. The relationship between nutritional status and the Glasgow prognostic score in patients with cancer of the esophagus and stomach. Nutr Cancer. 2013;65(1):25\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/01635581.2013.741755\u003c/span\u003e\u003cspan address=\"10.1080/01635581.2013.741755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng HL, Lu J, Li P, et al. Effects of Preoperative Malnutrition on Short- and Long-Term Outcomes of Patients with Gastric Cancer: Can We Do Better? Ann Surg Oncol. 2017;24(11):3376\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1245/s10434-017-5998-9\u003c/span\u003e\u003cspan address=\"10.1245/s10434-017-5998-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo ZQ, Yu JM, Li W, et al. Survey and analysis of the nutritional status in hospitalized patients with malignant gastric tumors and its influence on the quality of life. Support Care Cancer. 2020;28(1):373\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00520-019-04803-32\u003c/span\u003e\u003cspan address=\"10.1007/s00520-019-04803-32\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCederholm T, Jensen GL, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2018.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2018.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgnacio de Ul\u0026iacute;barri J, Gonz\u0026aacute;lez-Madro\u0026ntilde;o A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ajcn/82.4.777\u003c/span\u003e\u003cspan address=\"10.1093/ajcn/82.4.777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 1984;85(9):1001\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJafri SH, Previgliano C, Khandelwal K, Shi R. Cachexia index in advanced non-small-cell lung cancer patients. Clin Med Insights Oncol. 2015;9:87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsunaga T, Satio H, Miyauchi W, et al. Impact of skeletal muscle mass in patients with recurrent gastric cancer. World J Surg Oncol. 2021;19(1):170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12957-021-02283-\u003c/span\u003e\u003cspan address=\"10.1186/s12957-021-02283-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2021 Jun 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCederholm T, Jensen GL, Correia MITD, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2018.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2018.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuroda D, Sawayama H, Kurashige J, et al. Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer. 2018;21(2):204\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10120-017-0744-3\u003c/span\u003e\u003cspan address=\"10.1007/s10120-017-0744-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouillanne O, Morineau G, Dupont C, et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ajcn/82.4.777666\u003c/span\u003e\u003cspan address=\"10.1093/ajcn/82.4.777666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang N, Deng JY, Ding XW, et al. Prognostic nutritional index predicts postoperative complications and long-term outcomes of gastric cancer. World J Gastroenterol. 2014;20(30):10537\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v20.i30.10537\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v20.i30.10537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong C, Wan Q, Zhao R, Zuo X, Chen Y, Li T. Cachexia Index as a Prognostic Indicator in Patients with Gastric Cancer: A Retrospective Study. Cancers (Basel). 2022;14(18):4400. Published 2022 Sep 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14184400\u003c/span\u003e\u003cspan address=\"10.3390/cancers14184400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong QT, Cai HY, Zhang Z, et al. Influence of body composition, muscle strength, and physical performance on the postoperative complications and survival after radical gastrectomy for gastric cancer: A comprehensive analysis from a large-scale prospective study. Clin Nutr. 2021;40(5):3360\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2020.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2020.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng ZF, Lu J, Xie JW, et al. Preoperative skeletal muscle index vs the controlling nutritional status score: Which is a better objective predictor of long-term survival for gastric cancer patients after radical gastrectomy? Cancer Med. 2018;7(8):3537\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.1548\u003c/span\u003e\u003cspan address=\"10.1002/cam4.1548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Ge N, Xie D et al. The geriatric nutrition risk index versus the mini-nutritional assessment short form in predicting postoperative delirium and hospital length of stay among older non-cardiac surgical patients: a prospective cohort study. BMC Geriatr. 2020;20(1):107. Published 2020 Mar 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-020-1501-8\u003c/span\u003e\u003cspan address=\"10.1186/s12877-020-1501-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllard JP, Keller H, Gramlich L, Jeejeebhoy KN, Laporte M, Duerksen DR. GLIM criteria has fair sensitivity and specificity for diagnosing malnutrition when using SGA as comparator. Clin Nutr. 2020;39(9):2771\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2019.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2019.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarusawa H, Jenkins BJ. Inflammation and gastrointestinal cancer: an overview. Cancer Lett. 2014;345(2):153\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canlet.2013.08.0254.7\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2013.08.0254.7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng QY, Qin Y, Shi Y, et al. Systemic immune-inflammation index and all-cause and cause-specific mortality in sarcopenia: a study from National Health and Nutrition Examination Survey 1999\u0026ndash;2018. Front Immunol. 2024;15:1376544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2024.1376544\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2024.1376544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2024 Apr 4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJomrich G, Paireder M, Kristo I, et al. High Systemic Immune-Inflammation Index is an Adverse Prognostic Factor for Patients With Gastroesophageal Adenocarcinoma. Ann Surg. 2021;273(3):532\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SLA.0000000000003370\u003c/span\u003e\u003cspan address=\"10.1097/SLA.0000000000003370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Chen X, Yuan J, Wang C, An J, Ma X. Correlations of preoperative systematic immuno-inflammatory index and prognostic nutrition index with a prognosis of patients after radical gastric cancer surgery. Surgery. 2022;172(1):150\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.surg.2022.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.surg.2022.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Yin X, Ma K, et al. Nomogram Based on Preoperative Fibrinogen and Systemic Immune-Inflammation Index Predicting Recurrence and Prognosis of Patients with Borrmann Type III Advanced Gastric Cancer. J Inflamm Res. 2023;16:1059\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/JIR.S404585\u003c/span\u003e\u003cspan address=\"10.2147/JIR.S404585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2023 Mar 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1475-2891-9-69\u003c/span\u003e\u003cspan address=\"10.1186/1475-2891-9-69\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Tang M, Zhang Q, et al. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients. Clin Nutr. 2021;40(3):1224\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2020.08.004\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2020.08.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1475-2891-9-69\u003c/span\u003e\u003cspan address=\"10.1186/1475-2891-9-69\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2010 Dec 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan GT, Xie HL, Yuan KT, et al. Prognostic value of systemic inflammation and for patients with colorectal cancer cachexia. J Cachexia Sarcopenia Muscle. 2023;14(6):2813\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcsm.13358\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan GT, Ge YZ, Xie HL, et al. Association Between Systemic Inflammation and Malnutrition With Survival in Patients With Cancer Sarcopenia-A Prospective Multicenter Study. Front Nutr. 2022;8:811288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2021.811288\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.811288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2022 Feb 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang S, Yang YX, Pan WJ, et al. Prognostic value of systemic immune inflammation index and geriatric nutrition risk index in early-onset colorectal cancer. Front Nutr. 2023;10:1134300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2023.1134300\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2023.1134300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2023 Apr 18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie H, Zhang H, Ruan G, et al. Individualized threshold of the involuntary weight loss in prognostic assessment of cancer. J Cachexia Sarcopenia Muscle. 2023;14(6):2948\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcsm.13368\u003c/span\u003e\u003cspan address=\"10.1002/jcsm.13368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerker M, Felder M, Gueissaz L, et al. Association of Baseline Inflammation With Effectiveness of Nutritional Support Among Patients With Disease-Related Malnutrition: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2020;3(3):e200663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2020.0663\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2020.0663\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2020 Mar 2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, Yang H, Zheng J, et al. Global leaders malnutrition initiative-defined malnutrition affects long-term survival of different subgroups of patients with gastric cancer: A propensity score-matched analysis. Front Nutr. 2022;9:995295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2022.995295\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.995295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2022 Sep 30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu LB, Mei TT, Cai YQ, et al. Correlation Between Components of Malnutrition Diagnosed by Global Leadership Initiative on Malnutrition Criteria and the Clinical Outcomes in Gastric Cancer Patients: A Propensity Score Matching Analysis. Front Oncol. 2022;12:851091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2022.851091\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.851091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2022 Mar 3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Tang M, Zhang Q, et al. The GLIM criteria as an effective tool for nutrition assessment and survival prediction in older adult cancer patients. Clin Nutr. 2021;40(3):1224\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2020.08.004\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2020.08.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang HL, Yang YS, Duan JN, et al. Prognostic value of preoperative weight loss-adjusted body mass index on survival after esophagectomy for esophageal squamous cell carcinoma. World J Gastroenterol. 2020;26(8):839\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v26.i8.839\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v26.i8.839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Vugt JLA, van den Coebergh RRJ, Lalmahomed ZS, et al. Impact of low skeletal muscle mass and density on short and long-term outcome after resection of stage I-III colorectal cancer. Eur J Surg Oncol. 2018;44(9):1354\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejso.2018.05.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ejso.2018.05.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Cui X, Li R, Ji W, Shi H, Cui J. Association between ICW/TBW ratio and cancer prognosis: Subanalysis of a population-based retrospective multicenter study. Clin Nutr. 2024;43(2):322\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2023.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2023.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo D, Kim HS, Ahn JB, Park YR. Investigation of the Trajectory of Muscle and Body Mass as a Prognostic Factor in Patients With Colorectal Cancer: Longitudinal Cohort Study. JMIR Public Health Surveill. 2023;9:e43409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/43409\u003c/span\u003e\u003cspan address=\"10.2196/43409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2023 Mar 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitch CR, Cookson MS, Clark PE, et al. Perioperative Oral Nutrition Supplementation Reduces Prevalence of Sarcopenia following Radical Cystectomy: Results of a Prospective Randomized Controlled Trial. J Urol. 2019;201(3):470\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.juro.2018.10.010\u003c/span\u003e\u003cspan address=\"10.1016/j.juro.2018.10.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinninella E, Biondi A, Cintoni M et al. NutriCatt Protocol Improves Body Composition and Clinical Outcomes in Elderly Patients Undergoing Colorectal Surgery in ERAS Program: A Retrospective Cohort Study. \u003cem\u003eNutrients\u003c/em\u003e. 2021;13(6):1781. Published 2021 May 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu13061781\u003c/span\u003e\u003cspan address=\"10.3390/nu13061781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan AM, Reynolds JV, Healy L, et al. Enteral nutrition enriched with eicosapentaenoic acid (EPA) preserves lean body mass following esophageal cancer surgery: results of a double-blinded randomized controlled trial. Ann Surg. 2009;249(3):355\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SLA.0b013e31819a4789\u003c/span\u003e\u003cspan address=\"10.1097/SLA.0b013e31819a4789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeimann A, Braga M, Carli F, et al. ESPEN guideline: Clinical nutrition in surgery. Clin Nutr. 2017;36(3):623\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2017.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2017.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacalindong SS, Kim KH, Nam BH et al. Effect of total number of harvested lymph nodes on survival outcomes after curative resection for gastric adenocarcinoma: findings from an eastern high-volume gastric cancer center. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2018;18(1):73. Published 2018 Jan 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12885-017-3872-6\u003c/span\u003e\u003cspan address=\"10.1186/s12885-017-3872-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBian L, Wu D, Chen Y, et al. Associations of radiological features of adipose tissues with postoperative complications and overall survival of gastric cancer patients. Eur Radiol. 2022;32(12):8569\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-022-08918-w\u003c/span\u003e\u003cspan address=\"10.1007/s00330-022-08918-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFontana L, Vinciguerra M, Longo VD. Growth factors, nutrient signaling, and cardiovascular aging. Circ Res. 2012;110(8):1139\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCRESAHA.111.246470\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.111.246470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaint-Criq V, Lugo-Villarino G, Thomas M. Dysbiosis, malnutrition and enhanced gut-lung axis contribute to age-related respiratory diseases. Ageing Res Rev. 2021;66:101235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2020.101235\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2020.101235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjo O, Adegboye ARA. The Effects of Nutrition on Chronic Conditions. Nutrients. 2023;15(5):1066. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15051066\u003c/span\u003e\u003cspan address=\"10.3390/nu15051066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2023 Feb 21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Renzo L, Gualtieri P, De Lorenzo A. Diet, Nutrition and Chronic Degenerative Diseases. \u003cem\u003eNutrients\u003c/em\u003e. 2021;13(4):1372. Published 2021 Apr 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu13041372\u003c/span\u003e\u003cspan address=\"10.3390/nu13041372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson SA, Ward WE. Clinical nutrition: 2. The role of nutrition in the prevention and treatment of adult osteoporosis. CMAJ. 2001;165(11):1511\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"nutritional indicators, inflammation, malnutrition, overall survival","lastPublishedDoi":"10.21203/rs.3.rs-5492406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5492406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aim:\u003c/h2\u003e \u003cp\u003eFew studies have investigated the prognostic significance of nutritional indicators in patients with various inflammatory states.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients who underwent radical gastrectomy for TNM stages I\u0026ndash;III gastric cancer were included. Nutritional assessment was performed using commonly used indicators. The patients were categorized into two groups with high and low inflammatory status using the X-tile analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1327 patients were enrolled in this study, including 843 and 484 patients in the low- and high-SII groups, respectively. Compared with the SII-low group, the SII-high group exhibited significantly lower GNRI, PNI, CXI, and SMI indices and a higher proportion of patients with malnutrition based on the GLIM and CONUT criteria(all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate COX analysis revealed that GLIM criteria (overall survival [OS]: P\u0026thinsp;=\u0026thinsp;0.002; recurrence-free survival [RFS]: P\u0026thinsp;=\u0026thinsp;0.007) and CONUT (OS: P\u0026thinsp;=\u0026thinsp;0.010; RFS:P\u0026thinsp;=\u0026thinsp;0.001) were independent prognostic factors for OS and RFS in the SII-low group. In the SII-high group, the GLIM criteria, GNRI, and SMI were the independent prognostic factors for OS(all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the GLIM criteria and SMI were the independent influencing factors for RFS(all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The TimeROC curve and AUC demonstrated the robustness of the GLIM criteria in predicting prognosis across various inflammatory states.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDifferent nutritional indicators should be considered while evaluating the prognosis of patients with gastric cancer with varying inflammatory states. Compared with other nutritional indicators, the GLIM criteria are more suitable for patients with different inflammatory conditions.\u003c/p\u003e","manuscriptTitle":"Objective Evaluation of the Prognostic Value of Common Nutritional Indicators in Patients with Different Inflammatory States after Radical Gastrectomy for Gastric Cancer: A Real-world Study Running Head：Nutritional Indicators in Inflammatory State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 12:54:17","doi":"10.21203/rs.3.rs-5492406/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa3bff00-d2ff-4f4b-b9d1-bb2639bfc0f7","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-04T13:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-19 12:54:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5492406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5492406","identity":"rs-5492406","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}