Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients Huazhen Tang, Zhenpeng Yang, Xibo Sun, Shuai Lu, Bing Wang, Wanni Zhao, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-719491/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: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors. Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated. Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately. Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic. Cancer Biology Oncology Prediction Model Metabolism Cancer Clinical Prognosis Integration of Data Figures Figure 1 Figure 2 Figure 3 Introduction Cancer is the most threatening disease to human beings. Its incidence rate has been rising globally, and it is the most fatal disease in the world. Some experts believe that cancer is mainly caused by gene mutation, but the effect of gene targeted therapy is not significant in the fight against cancer 1 – 2 . The mutations in cancer are diverse, and the gene mutations found in cancer are complex and heterogeneous, it is difficult to identify the key rate limiting genes for targeted treatment of tumors 3 . This suggests that genetic mutations may be not the origin of cancer. A few decades ago, Warburg 4 first theorized that mitochondrial damage causes energy metabolism defects and leads to cancer. When the respiration of tumor cells is damaged, the retrograde response (RTG) is activated, which transmit signals from mitochondria to nucleus, affecting the stability of the genome and leading metabolic reprogramming 5 – 6 . This propound a theory that cancer is essentially a metabolic disease. Metabolic reprogramming in cancer cells alters glucose metabolism, lipid metabolism, amino acid metabolism, and tumor microenvironment (TME), leading to cancer progression 7 . The rapidly proliferation of cancer cells needs to balance the decomposition and anabolic at the same time. Therefore, metabolism related indicators can reflect tumor growth. Recent reports have suggested that patient prognosis is associated with certain molecular biomarkers involved in tumor metabolism. However, expensive and time-consuming laboratory metabolomics technology is required. In contrast, blood tests from clinical patients are convenient and can be widely used in clinical application. The changes of metabolic markers in blood test can reflect tumor metabolic reprogramming and the prognosis of patients. Therefore, we developed and validated a metabolic based prognostic prediction model to predict the survival of patients with solid tumors and support the decision making on early therapy. Materials And Methods Materials This retrospective analysis was conducted on a primary cohort of solid tumor patients from INSCOC(Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers)database. The INSCOC is a nation-wide cross-sectional survey on nutritional status and clinical outcome in patients with malignant tumors. Patients were derived from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project of China (registered at chictr.org.cn, ChiCTR1800020329). Patients were evaluated from January 2013 to August 2018 at 30 tertiary public hospitals in China. Inclusion criteria: 1) a histologic diagnosis of malignant solid tumors; 2) a complete medical history record and follow-up data available. An independent cohort of cancer patients with the same inclusion criteria were enrolled from the Fujian Cancer Hospital, and this cohort was used to form the external validation cohort. The follow-up time was 1-60 months in both primary cohort and validation cohort, and the outcome was patient’s death. The study was approved by the Ethics Committees of all participating institutions and all data was analyzed anonymously. The study is reported in accordance with the TRIPOD guidance for transparent reporting of prediction models 8 . Data collection Demographic and clinicopathological data were collected, including sex, age, smoking history, drinking history, PGSGA score (Patient Generated Subjective Global Assessment score), NRS2002 score (Nutrition risk screening score), primary tumor site, tumor metastasis, TP (total protein), ALB (albumin), PAB (prealbumin), FBG (fasting blood-glucose), TC (total cholesterol), TG (triglyceride), HDL-C (high density lipoprotein cholesterol), LDL-C (low density lipoprotein cholesterol), WBC(white blood cell), NLR(neutrophil/lymphocyte ratio). All continuous variables were converted to categorical variables according to clinical standard. Regardless of tumor type or origin, metabolic abnormalities are common features of most cancer cells. Therefore, 15 kinds of malignant solid tumors were included in the study and classified by human systems. The NLR is an inflammatory marker which has been investigated as a prognostic indicator in post-therapeutic recurrence and survival of patients with cancer 9 – 10 . In our study, NLR was classified according to the optimum cutoff value(Figure.S1). PGSGA was adapted from the SGA (Subjective Global Assessment) and widely used for clinical assessment of malnutrition in cancer patients 11 . Patients with PGSGA score≥4 need nutritional interventions and symptomatic treatment. NRS 2002 is recommended by The European Society of Clinical Nutrition and Metabolism (ESPEN) as a nutritional risk screening method for patients 12 . Patients with NRS 2002 score ≥3 are at risk of malnutrition and require nutritional support. Variables where >10% of values were missing, or patients with a missing value for a specific variable, were excluded from the analysis. The missing data in selected variables were multiply imputed to generate a complete data set. Statistical analysis We used a primary cohort of solid tumor patients from INSCOC database to develop a clinical prediction model. Categorical variables were reported as whole numbers and proportions. The overall survival (OS) was calculated using the Kaplane-Meier method and the log-rank test. Univariate analysis was performed for all variables, and the variables with P values < 0.05 were included in multivariate analysis. A multivariate Cox regression analysis was performed to test the independent significance of different factors. The variables were selected by stepwise regression and then fit a more parsimonious model. Nomograms are a pictorial representation of a complex mathematical formula that use two or more known variables to calculate an outcome. The resulting model was simplified into a nomogram to predict the 1-, 3-and 5-years OS rates. We also did a decision-curve analysis to assess the clinical usefulness of the model. The area under ROC curve (AUC) was used to evaluate the predictive accuracy. Calibration curves were assessed graphically by plotting the observed rates against the predicted probabilities to evaluate the agreement. Brier score was used to evaluate probability calibration. Nomograms are a pictorial representation of a complex mathematical formula that uses two or more known variables to calculate an outcome. The resulting model was simplified into a nomogram to predict the survival of patients with solid tumors. To assess the performance of our model, the discriminative performance of the model was measured using Harrell’s C-statistic. An internal validation step was performed to counteract the possible overfitting of our model to the data. The bootstrapping techniques (B = 100) was used to validate and correct the over-optimism of the models ,and obtained the optimism-corrected measures of C- statistic. In all analyses, p values < 0.05 were considered to indicate statistical significance. All analyses were performed using the R software, version 3.6.1. Results Clinicopathological characteristics In the primary cohort, there were 5006 patients who met the inclusion criteria were finally enrolled in the study, the median follow-up time was 30.3 months. For the validation cohort, we studied 1703 patients admitted to a single institution, the median follow-up time was 27.0 months. The demographic features and clinical characteristics of the primary and validation cohorts are presented in Table 1 . Figure S2 shows the cumulative survival free between primary cohort and validation cohort. Log-Rank test showed that P=0.052. This means there was no significant difference between the two cohorts. Table 1 Patient characteristics Demographic or clinical characteristic Primary cohort (n =5006) Validation cohort (n = 1703) No. of Patients % No. of Patients % Sex Male 2517 50.3 942 55.3 Female 2489 49.7 761 44.7 Primary tumor site Digestive system 1797 35.9 1343 78.9 Reproductive system 1067 21.3 88 5.2 Respiratory system 1339 26.8 184 10.8 Nervous system 302 6.0 4 0.2 Urogenital system 72 1.4 10 0.6 Others 429 8.6 74 4.3 Age ≤65yr 3328 66.5 1281 75.2 >65yr 1678 33.5 422 24.8 Smoking history No 3025 60.4 996 58.4 Yes 1981 39.6 707 41.6 Drinking history No 4053 81.0 1377 80.9 Yes 953 19.0 326 19.1 Tumor stage I 694 13.9 139 8.1 II 1011 20.2 304 17.9 III 1348 26.9 553 32.5 IV 1953 39.0 707 41.5 Tumor metastasis No 3726 74.4 1193 70.1 Yes 1280 25.6 510 29.9 PGSGA score <4 2475 49.4 967 56.8 ≥4 2531 50.6 736 43.2 NRS2002 score <3 3993 79.8 1140 66.9 ≥3 1013 20.2 563 33.1 TP 80 g/L 143 2.9 105 6.1 ALB <35g/L 814 16.3 473 27.8 ≥35 g/L 4192 83.7 1230 72.2 PAB <280mg/L 4373 87.4 1340 78.7 ≥280 mg/L 633 12.6 363 21.3 FBG <6.1 mmol/L 3392 67.8 1322 77.6 ≥6.1 mmol/L 1614 32.2 381 22.4 TC <5.2 mmol/L 4275 85.4 1367 80.3 ≥5.2 mmol/L 731 14.6 336 19.7 TG <1.7 mmol/L 3502 70.0 1265 74.3 ≥1.7 mmol/L 1504 30.0 438 25.7 HDL-C <1.55 mmol/L 2390 47.7 689 40.5 ≥1.55 mmol/L 2616 52.3 1014 59.5 LDL-C <3.4 mmol/L 3955 79.0 1039 61.0 ≥3.4 mmol/L 1051 21.0 664 39.0 WBC 10×10^9/L 395 7.9 157 9.2 NLR <3.08 3503 70.0 1217 71.5 ≥3.08 1503 30.0 486 28.5 Model development All variables listed in Table 2 were used for univariate and multivariate Cox regression analysis. On multivariate analysis, the presence of age > 65yr (HR 1.69, 95%CI 1.53-1.87), smoking history (HR 1.45, 95%CI 1.32-1.59), the tumor was in stage III (HR 1.40, 95%CI 1.17-1.66) and stage IV (HR 1.46, 95%CI 1.25-1.72), tumor metastasis (HR 2.36, 95%CI 2.14-2.61), PGSGA score≥4(HR 1.46, 95%CI 1.32-1.61), FBG≥6.1 mmol/L(HR 2.63, 95%CI 2.39-2.91), NLR≥3.08(HR 1.25, 95%CI 1.13-1.38) were the independent risk factors of overall survival. In addition, ALB≥35 g/L (HR 0.71, 95%CI 0.63-0.80), TG≥1.7 mmol/L(HR 0.77, 95%CI 0.69-0.86), HDL-C≥1.55 mmol/L (HR 0.65, 95%CI 0.59-0.72) were the protective factors of overall survival. The HR of the prediction model was shown by forest plot (Figure 1 ). We simplified the model into a nomogram (Figure 2 ). The nomogram based on these ten factors was developed to predict the 1, 3, 5 years of OS in cancer patients. The scales of the nomogram reflect coefficients from the Cox model rescaled to a user-friendly 100-pointrange. Table 2 The results of the univariate and multivariate Cox regression analysis of the primary cohort Variables Univariable model Multivariable analysis HR (95% CI) P-value HR (95% CI) P-value Sex Male Reference Female 0.65(0.59-0.71) <0.001 Primary tumor site Digestive system Reference Nervous system 0.91(0.73-1.14) 0.40 Reproductive system 0.41(0.35-0.49) <0.001 Respiratory system 1.82(1.63-2.03) 65yr 2.35(2.14-2.58) <0.001 1.69(1.53-1.87) <0.001 Smoking history No Reference Reference Yes 1.63(1.49-1.80) <0.001 1.45(1.32-1.59) <0.001 Drinking history No Reference Yes 1.35(1.21-1.52) <0.001 Tumor stage I Reference Reference II 1.08(0.89-1.31) 0.43 1.09(0.90-1.31) 0.395 III 1.59(1.34-1.89) <0.001 1.40(1.17-1.66) <0.001 IV 1.82(1.54-2.13) <0.001 1.46(1.24-1.72) <0.001 Tumor metastasis No Reference Reference Yes 2.89(2.62-3.18) <0.001 2.30(2.08-2.54) <0.001 PGSGA score <4 Reference Reference ≥4 1.89(1.72-2.09) <0.001 1.44(1.30-1.59) <0.001 NRS2002 score <3 Reference ≥3 1.35(1.21-1.51) <0.001 TP <60 g/L Reference 60-80 g/L 0.77(0.67-0.88) 80 g/L 0.86(0.63-1.16) 0.31 ALB <35g/L Reference Reference ≥35 g/L 0.43(0.39-0.48) <0.001 0.65(0.58-0.73) <0.001 PAB <280mg/L Reference ≥280 mg/L 0.69(0.59-0.81) <0.001 FBG <6.1 mmol/L Reference Reference ≥6.1 mmol/L 3.59(3.27-3.96) <0.001 2.82(2.55-3.11) <0.001 TC <5.2 mmol/L Reference ≥5.2 mmol/L 0.88(0.77-1.01) 0.068 TG <1.7 mmol/L Reference Reference ≥1.7 mmol/L 0.73(0.65-0.81) <0.001 0.73(0.65-0.82) <0.001 HDL-C <1.55 mmol/L Reference Reference ≥1.55 mmol/L 0.59(0.54-0.65) <0.001 0.64(0.58-0.71) <0.001 LDL-C <3.4 mmol/L Reference ≥3.4 mmol/L 0.88(0.78-0.99) 0.043 WBC 10×10^9/L 1.72(1.44-2.05) <0.001 NLR <3.08 Reference Reference ≥3.08 1.80(1.63-1.98) <0.001 1.23(1.12-1.36) <0.001 Model validation In primary cohort, Harrell's C- statistic was 0.775 (95% CI, 0.765-0.786). Furthermore, the calibration plot for the probability of survival at 5 years was closely follow the ideal line of 45 degrees, indicating optimal agreement between the prediction by model and the actual observed survival (Figure. 3A). The Brier score was 0.169 (95% CI, 0.152-0.186). The model yielded AUC values of 0.812 (95% CI, 0.794-0.830) for predicting mortality at 5 years after admission (Figure. 3C). Internal validation with bootstrapping revealed the optimism-corrected C-statistic of the predictive model was 0.776, and the optimism-corrected Brier score was 0.169. Both the average optimism of C-statistic and Brier score were less than 0.001, reflecting a small degree of over-optimism. In the external validation cohort, there was also a good calibration curve for the risk estimation, indicating that the model is well-calibrated (Figure. 3B). The Brier score was 0.185 (95% CI, 0.158-0.212), and Harrell's C- statistic was 0.771 (95% CI, 0.752-0.790). The AUC values for predicting mortality at 5 years was 0.786 (95% CI, 0.741-0.832) (Figure. 3D). The decision-curve analysis showed that the prediction model is the higher line on the decision curve, which indicates that the prediction model leads to a higher net benefit and greater clinical utility (Figure.S3). Discussion Cancer is a significant public health problem and is the second leading cause of death globally 13 . Metabolic alterations of tumors are recognized as one of the hallmarks of cancer 14 . Cancer cells support energy to maintain tumor progression and proliferation by adopting to metabolic changes. A huge number of cancer cells show metabolic reprogramming, including the reprogrammed glucose, lipid and amino acid metabolism to satisfy high proliferation requests. FBG, ALB, TG and HDL-C can well reflect metabolic changes as routine clinical detection items, so they were included in this study. In addition, tumor cells need to survive drastic changes in the microenvironment such as hypoxia, nutrient storage, acidic pH and chronic inflammation 15 . The tumor microenvironment enforces metabolic plasticity and promotes tumor proliferation and progression 16 . Therefore, this study included PGSGA score as a sensitive to evaluate nutritional status, and included NLR as an inflammatory marker. Besides, we included some other indicators related to the survival and prognosis of tumor patients, such as age, smoking history, tumor stage and tumor metastasis, so as to more comprehensively predict the prognosis of cancer patients. There were also many indicators that can reflect tumor metabolism and microenvironment, but they were not included in this model due to the incomplete data, missing values>10%, being removed by stepwise regression and the results of multivariate analysis were meaningless. A clinical prediction model can provide tailored estimation on prognosis and help physician with associated decision making in daily practice. At present, many indicators of tumor metabolism are based on experimental metabolomics technologies, which cannot be widely popularized in clinical practice because of its high cost and long detection cycle. Therefore, we used routine clinical detection indicators in prediction model. In order to develop an accurate clinical prediction model, we conducted strict quality control. First, in order to build a reliable and accurate prediction model, the sample size should be large enough and the data should be complete. We screened from INSCOC database and included a large sample of 5006 in this study to ensure the quality of data development. Second, we ensured that the data covered common types of tumors, and all factors included in the prediction model are commonly assessed in routine clinical examinations. In this way, the universality of the prediction model can be guaranteed to the greatest extent and can be widely used in clinical practice, which is clearly a practical advantage. The model is available as a nomogram. Nomograms have emerged as a simpler, yet more advanced method to calculate the prognosis of different cancers. By integrating diverse prognostic and determinant variables to generate the probability of a clinical outcome, the nomogram fulfills a necessary role in oncological personalized medicine. In this study, the nomogram could predict an individual's 1-, 3- and 5-year survival rates with good accuracy which was verified in the validation cohort. This nomogram can evaluate the prognosis of patients conveniently and help clinicians adopt preventive and therapeutic strategies. Normally, the main way for the body to obtain energy is the oxidative phosphorylation of glucose under aerobic conditions. In cancer cells, even in the presence of oxygen, the main pathway of glucose metabolism is aerobic glycolysis, termed Warburg effect 4 , which reflects the reprogramming of tumor glucose metabolism. Hyperglycemia is a common phenomenon in patients with advanced cancer 17 . Hyperglycemia can provide cancer cells with a high glucose fuel source to support rapid proliferation, drive glycolysis metabolic pathway, and lead to worse prognosis. Hyperglycemia can indirectly influence cancer cells through an increase in the levels of insulin/IGF-1, thus activating the PI3K/AKT/mTOR signaling pathway and promoting the development of cancer 18 . Beyond that, hyperglycemia has a direct impact on cancer cell proliferation, metastasis, invasiveness, and antiapoptotic qualities 19 – 21 . In our study, FBG ≥ 6.1 mmol / L (HR 2.63, 95% CI 2.39-2.91) is considered as one of the risk factors affecting the survival of tumor patients, which confirms the harm of hyperglycemia. Hyperglycemia can promote glycolysis and raise the prevalence and mortality of certain malignancies. FBG is the most intuitive index to reflect blood glucose, which can be a prediction index for cancer progression and glucose metabolism 22 – 23 . Therefore, patients with FBG ≥ 6.1 mmol / L should be carried out appropriate diet or drug intervention to improve the prognosis. In cancer cells, the protein synthesis and decomposition are enhanced, but the anabolism exceeds the catabolism, and can even capture the protein from normal tissues, in order to meet the needs of their own growth. The amino acid metabolism of the tumor was also changed, tumor cells can obtain energy through glutamine and other amino acids. These changes will lead to severe protein consumption, negative nitrogen balance and hypoproteinemia. Patients with hypoproteinemia have a greater risk of recurrence and mortality, which can be corrected by albumin supplementation. Albumin (Alb) is an acute phase protein that decreases with inflammation and due to other reasons, such as malnutrition, increased age and metabolic disorder. Albumin reflects nutritional state and response to amino acid metabolism, and is associated with the prognosis of cancer patients. In our study, ALB≥35 g/L (HR 0.71, 95%CI 0.63-0.80) was the protective factors of overall survival. Kao HK et al. 24 showed that patients with increased serum albumin level can have better prognosis. Therefore, albumin can not only reflect amino acid metabolism, but also predict survival and prognosis as a biomarker 25 – 26 . Nowadays, there are increasing evidences of the role of lipid metabolism alterations as biomarkers of cancer prognosis and survival. Together with the Warburg effect and the increased glutaminolysis, lipid metabolism plays a key role in cancer metabolic reprogramming. Extremely proliferative cancer cells exhibit an intense lipid and cholesterol avidity, which they satisfy by increasing the uptake of dietary or exogenous lipids and lipoproteins 27 . In addition, the increase of de novo fatty acid synthesis and lipid synthesis in cancer cells requires efficient and complementary lipolytic mechanisms to accommodate the intracellular lipid content and provide materials for tumor cell proliferation 28 . This long-term metabolic change will lead to the depletion of stored fat, and promote cancer cell metastasis 29 . TG and HDL, as lipid indexes reflecting lipid metabolism, are closely related to prognosis. Studies showed that a high level of HDL-C can reduce the risk and progression of cancer 30 – 31 . In our study, HDL-C≥1.55 mmol/L (HR 0.65, 95%CI 0.59-0.72) were protective factor of survival. However, the association between TG and the survival of tumor patients is contradictory. Some studies observed that high level TG can improve the survival of cancer patients 32 – 33 . On the contrary, other studies 30 – 34 has shown that high TG can lead to poor prognosis in cancer patients. In our study, TG≥1.7 mmol/L(HR 0.77, 95%CI 0.69-0.86) were protective factor of survival. Rhonda Arthur et al. 35 showed that TG were not associated with cancer death, but associated with risk of cardiovascular death. This reduced the proportion of cancer cases of death in subjects with elevated TG levels. However, the detailed mechanisms and the biological significance of them require further investigation. In conclusion, Lipid-metabolic can associate with cancer survival and have been proposed as prognosis biomarkers of cancer 36 . In our study, PGSGA score≥4(HR 1.46, 95%CI 1.32-1.61) and NLR≥3.08(HR 1.25, 95%CI 1.13-1.38) were the independent risk factors of overall survival. PGSGA score≥4 indicates the malnutrition in cancer patients, and these patients often have poor prognosis and low survival 37 . In this study, increased NLR was associated with decreased OS. NLR is the ratio of lymphocytes to neutrophils, the two types of cells are part of the human immune system and play a key role in TME. The immune cells in the TME can detect and eliminate the abnormal cells or tumor cells and protect the body from damage caused by tumor cells. Kao HK et al 24 . considered that an elevated NLR indicates an imbalance between the innate and acquired immune response, which might be linked to a poorer prognosis. This elevation may reflect an inflammatory microenvironment that lymphocytes can have tumor suppressing effects and have been linked with better prognosis 38 , whereas neutrophils can create a favorable tumor microenvironment by remodeling the extracellular matrix and angiogenesis, thus enabling the tumor to growth and spread 39 – 40 . Therefore, NLR can be used as a biological indicator of inflammation to predict the prognosis of cancer patients. Some limitations of this study should be discussed when considering the results. First, cancer patients still have a lot of laboratory indicators reflecting the metabolic situation in clinic, so it is necessary to add more factors to improve the model in the future. In addition, although internal validation was performed to prevent over-interpreting the data, and external validation verify our findings are applicable in single center, we need a prospective multicenter study to confirm the results in the future. Conclusion Although the interactions between cancer metabolism and clinical prognosis are intricate. We emphasize their importance and develop a model based on cancer metabolism to predict the prognosis of tumor patients. This proves the correlation between cancer metabolism and clinical prognosis. This prognosis prediction model can accurately predict the prognosis through rapid and economical blood tests, and can be widely used in clinical practice. Declarations DATA AVAILABILITY STATEMENT The INSCOC data that support the findings of this study are available from Chinese Cancer Society Nutrition and Support Committee but restrictions apply to the availability of these data, which were used under license for the current study, and this INSCOC data relates to the confidentiality of multiple clinical center and patient privacy, so it is not convenient to disclose. Data are however available from the authors upon reasonable request. ETHICS STATEMENT The studies involving human participants were reviewed and approved by Ethics Committee of Capital Medical University Affiliated Beijing Shijitan Hospital. The patients/participants provided their written informed consent to participate in this study. All methods were carried out in accordance with relevant guidelines and regulations. We don’t consent for the publication of identifying images or other personal or clinical details of participants that compromise anonymity. COMPETING INTERESTS The authors declare no conflicts of financial and non-financial competing interests. AUTHOR CONTRIBUTIONS (I) Conception and design: HT, ZY, HS, BR. (II) Administrative support: HS, BR. (III) Provision of study materials or patients: XS, SL, BW, WZ. (IV) Collection and assembly of data: BZ, XW, ZZ, PJ, LW, LD, NG. (V) Data analysis and interpretation: HT, ZY. (VI) Manuscript writing: HT; (VII) Final approval of manuscript: HS, BR. All authors contributed to the article and approved the submitted version. FUNDING This work was financially supported by National Key Research and Development Program to Dr. Hanping Shi (No. 2017YFC1309200); The National Natural Science Foundation of China to Dr. Hanping Shi (81672888); The National Natural Science Foundation of China to Dr. Benqiang Rao (81660484), and Beijing Natural Science Foundation Proposed Program to Dr. Benqiang Rao (7202076). ACKNOWLEDGEMENTS The authors would like to thank the INSCOC project members for their substantial work on data collection and patient follow-up. Disclosure of conflicts of interest The authors have nothing to disclose. References Vitucci M, Hayes DN, Miller CR. Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy. Br J Cancer. 2011 Feb 15;104(4):545-53. doi: 10.1038/sj.bjc.6606031. Epub 2010 Nov 30. Koscielny S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci Transl Med. 2010 Jan 13;2(14):14ps2. doi: 10.1126/scitranslmed.3000313. Salk JJ, Fox EJ, Loeb LA. Mutational heterogeneity in human cancers: origin and consequences. Annu Rev Pathol. 2010;5:51-75. doi: 10.1146/annurev-pathol-121808-102113. Warburg O,Wind F,Negelstein E. The metabolism of tumors in the body[J]. J Gen Physiol,1927,8(6):519-530. Seyfried, T.N., Shelton, L.M. Cancer as a metabolic disease. Nutr Metab (Lond) 7, 7 (2010). https://doi.org/10.1186/1743-7075-7-7 Veatch JR, McMurray MA, Nelson ZW, Gottschling DE. Mitochondrial dysfunction leads to nuclear genome instability via an iron-sulfur cluster defect. Cell. 2009 Jun 26;137(7):1247-58. doi: 10.1016/j.cell.2009.04.014. Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Science. 2020 Apr 10;368(6487):eaaw5473. doi: 10.1126/science.aaw5473. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015 Jan 7;350:g7594. doi: 10.1136/bmj.g7594. Dell'Aquila E, Cremolini C, Zeppola T, Lonardi S, Bergamo F, Masi G, et al. Prognostic and predictive role of neutrophil/lymphocytes ratio in metastatic colorectal cancer: a retrospective analysis of the TRIBE study by GONO. Ann Oncol. 2018 Apr 1;29(4):924-930. doi: 10.1093/annonc/mdy004. Simonaggio A, Elaidi R, Fournier L, Fabre E, Ferrari V, Borchiellini D, et al. Variation in neutrophil to lymphocyte ratio (NLR) as predictor of outcomes in metastatic renal cell carcinoma (mRCC) and non-small cell lung cancer (mNSCLC) patients treated with nivolumab. Cancer Immunol Immunother. 2020 Dec;69(12):2513-2522. doi: 10.1007/s00262-020-02637-1. Epub 2020 Jun 19. Ottery FD. Rethinking nutritional support of the cancer patient: the new field of nutritional oncology. Semin Oncol. 1994 Dec;21(6):770-8. Kondrup J, Rasmussen HH, Hamberg O, Stanga Z; Ad Hoc ESPEN Working Group. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003 Jun;22(3):321-36. doi: 10.1016/s0261-5614(02)00214-5. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. Cancer J Clin. (2020) 70:7–30. doi: 10.3322/caac.21590 Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011 Mar 4;144(5):646-74. doi: 10.1016/j.cell.2011.02.013. Oizel K, Chauvin C, Oliver L, Gratas C, Geraldo F, Jarry U, et al. Efficient mitochondrial glutamine targeting prevails over glioblastoma metabolic plasticity. Clin Cancer Res. (2017) 23:6292–304. doi: 10.1158/1078-0432.CCR-16-3102 Lin X, Xiao Z, Chen T, Liang SH, Guo H. Glucose Metabolism on Tumor Plasticity, Diagnosis, and Treatment. Front Oncol. 2020 Mar 6;10:317. doi: 10.3389/fonc.2020.00317. Duan W, Shen X, Lei J, Xu Q, Xu Q, Yu Y. Hyperglycemia, a neglected factor during cancer progresssion. Biomed Ress Int. 2014;2014:461917. Li G, Zhang K, Gong F, Jin H. A study on changes and clinical significance of blood glucose, blood lipid and inflammation in patients with ovarian cancer. J BUON. 2019 Nov-Dec;24(6):2322-2326. Johnson JA, Carstensen B, Witte D, Bowker SL, Lipscombe L, Renehan AG; Diabetes and Cancer Research Consortium. Diabetes and cancer (1): evaluating the temporal relationship between type 2 diabetes and cancer incidence. Diabetologia 2012;55:1607-18. doi: 10.1007/s00125-012-2525-1. Epub 2012 Apr 4. Dong C, Yuan T, Wu Y, Wang Y, Fan TW, Miriyala S, et al. Loss of FBP1 by Snail-mediated repression provides metabolic advantages in basal-like breast cancer. Cancer Cell 2013;23:316-31. doi: 10.1016/j.ccr.2013.01.022. Epub 2013 Feb 28. Catrina SB, Okamoto K, Pereira T, Brismar K, Poellinger L. Hyperglycemia regulates hypoxia-inducible factor-1alpha protein stability and function. Diabetes 2004;53:3226-32. doi: 10.2337/diabetes.53.12.3226. Chang SC, Yang WV. Hyperglycemia, tumorigenesis, and chronic inflammation. Crit Rev Oncol Hematol. 2016;108:146-153. doi:10.1016/j.critrevonc.2016.11.003 Ryu TY, Park J, Scherer PE. Hyperglycemia as a risk factor for cancer progression. Diabetes Metab J. 2014;38(5):330-336. doi:10.4093/dmj.2014.38.5.330 Kao HK, Löfstrand J, Loh CY, Lao WW, Yi JS, Chang YL, et al. Nomogram based on albumin and neutrophil-to-lymphocyte ratio for predicting the prognosis of patients with oral cavity squamous cell carcinoma. Sci Rep. 2018 Aug 30;8(1):13081. doi: 10.1038/s41598-018-31498-z. Li SQ, Jiang YH, Lin J, Zhang J, Sun F, Gao QF, et al. Albumin-to-fibrinogen ratio as a promising biomarker to predict clinical outcome of non-small cell lung cancer individuals. Cancer Med. 2018 Apr;7(4):1221-1231. doi: 10.1002/cam4.1428. Epub 2018 Mar 13. Yang JR, Xu JY, Chen GC, Yu N, Yang J, Zeng DX, et al. Post-diagnostic C-reactive protein and albumin predict survival in Chinese patients with non-small cell lung cancer: a prospective cohort study. Sci Rep. 2019 May 31;9(1):8143. doi: 10.1038/s41598-019-44653-x. Beloribi-Djefaflia S, Vasseur S, Guillaumond F. Lipid metabolic reprogramming in cancer cells. Oncogenesis. (2016) 5:e189. doi: 10.1038/oncsis.2015.49 Menendez JA,Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis.Nat Rev.Cancer.2007;7(10):763-777. doi: 10.1038/nrc2222. Luo X, Cheng C, Tan Z, Li N, Tang M, Y ang L, et al. Emerging roles of lipid metabolism in cancer metastasis. Mol Cancer. (2017) 16:76. doi: 10.1186/s12943-017-0646-3 Lofterød T, Mortensen ES, Nalwoga H, Wilsgaard T, Frydenberg H, Risberg T, et al. Impact of pre-diagnostic triglycerides and HDL-cholesterol on breast cancer recurrence and survival by breast cancer subtypes. BMC Cancer. 2018 Jun 15;18(1):654. doi: 10.1186/s12885-018-4568-2. Wang Y, Sun XQ, Lin HC, Wang DS, Wang ZQ, Shao Q, et al. Correlation between immune signature and high-density lipoprotein cholesterol level in stage II/III colorectal cancer. Cancer Med. 2019 Mar;8(3):1209-1217. doi: 10.1002/cam4.1987. Epub 2019 Feb 7. Li X, Tang H, Wang J, Xie X, Liu P, Kong Y, et al. The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer. Breast. 2017 Apr;32:1-6. doi: 10.1016/j.breast.2016.11.024. Epub 2016 Dec 8. Liu X, Li M, Wang X, Dang Z, Jiang Y, Wang X, et al. Effect of serum triglyceride level on the prognosis of patients with hepatocellular carcinoma in the absence of cirrhosis. Lipids Health Dis. 2018 Nov 6;17(1):248. doi: 10.1186/s12944-018-0898-y. His M, Dartois L, Fagherazzi G, Boutten A, Dupré T, Mesrine S, et al. Associations between serum lipids and breast cancer incidence and survival in the E3N prospective cohort study. Cancer Causes Control. 2017 Jan;28(1):77-88. doi: 10.1007/s10552-016-0832-4. Epub 2016 Nov 18. Arthur R, Møller H, Garmo H, Häggström C, Holmberg L, Stattin P, et al. Serum glucose, triglycerides, and cholesterol in relation to prostate cancer death in the Swedish AMORIS study. Cancer Causes Control. 2019 Feb;30(2):195-206. doi: 10.1007/s10552-018-1093-1. Epub 2018 Nov 12. Fernández LP, Gómez de Cedrón M, Ramírez de Molina A. Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment. Front Oncol. 2020 Oct 28;10:577420. doi: 10.3389/fonc.2020.577420. Arends J. Struggling with nutrition in patients with advanced cancer: nutrition and nourishment-focusing on metabolism and supportive care. Ann Oncol. 2018 Feb 1;29(suppl_2):ii27-ii34. doi: 10.1093/annonc/mdy093. Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol. 2013 Mar 1;31(7):860-7. doi: 10.1200/JCO.2011.41.0902. Epub 2013 Jan 22. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008 Jul 24;454(7203):436-44. doi: 10.1038/nature07205. Dumitru CA, Lang S, Brandau S. Modulation of neutrophil granulocytes in the tumor microenvironment: mechanisms and consequences for tumor progression. Semin Cancer Biol. 2013 Jun;23(3):141-8. doi: 10.1016/j.semcancer.2013.02.005. Epub 2013 Feb 26. Additional Declarations No competing interests reported. Supplementary Files Fig.S1.jpeg Figure S1. The optimum cutoff value of NLR. The optimal critical value of NLR was 3.08 determined by time-dependent ROC curve. Fig.S2.jpeg Figure S2. Kaplan-Meier survival analysis for patients in primary cohort and validation cohort. Survival rates in 5 years(60 months) for cancer patients in the primary cohort and validation cohort were analyzed by Kaplane-Meier survival analysis. FigS3.jpeg Figure S3: Decision-curve analysis for the prediction model. The prediction model is the higher line on the decision curve. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-719491","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":56420665,"identity":"fb0aeb39-30df-414e-86be-a0b0534070c0","order_by":0,"name":"Huazhen Tang","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huazhen","middleName":"","lastName":"Tang","suffix":""},{"id":56420668,"identity":"2f07107e-2bbc-49ea-bde4-edbfc243b04a","order_by":1,"name":"Zhenpeng Yang","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenpeng","middleName":"","lastName":"Yang","suffix":""},{"id":56420670,"identity":"6592db09-1e47-4286-b3bf-ee1856bb11fa","order_by":2,"name":"Xibo Sun","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xibo","middleName":"","lastName":"Sun","suffix":""},{"id":56420672,"identity":"80fc5421-5840-4534-b9df-9053f07f9474","order_by":3,"name":"Shuai Lu","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Lu","suffix":""},{"id":56420673,"identity":"2bbeef49-750b-4f63-b6a7-6411a8aacea8","order_by":4,"name":"Bing Wang","email":"","orcid":"","institution":"Capital Medical University Affiliated Beijing Shijitan 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02:59:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-719491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-719491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14945532,"identity":"002f877f-5b6e-4f8e-99af-72cf1a284d6d","added_by":"auto","created_at":"2021-10-27 14:17:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265212,"visible":true,"origin":"","legend":"The forest plot showed the results of the prediction model. The HR of the multivariate Cox regression analysis model was shown by forest plot.","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/b6efa1e33428a0afedc4c564.jpeg"},{"id":14945118,"identity":"4acf65ef-c0a0-4432-baa8-a2459acb5791","added_by":"auto","created_at":"2021-10-27 14:14:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132808,"visible":true,"origin":"","legend":"The nomogram developed to predict the overall survival of cancer patients with metabolic reprogramming. To use the nomogram, an individual patient's value is located on each variable axis, and a line is drawn upward to determine the number of points received for each variable's value. The sum of these numbers is located on the Total Points axis, and a line is drawn downward to the survival axes to determine the likelihood of survival at 1, 3 or 5 years.","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/765237240f5f31832a255d86.jpeg"},{"id":14945123,"identity":"fc245537-f8b1-449c-a575-5d4d5d680a37","added_by":"auto","created_at":"2021-10-27 14:14:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167844,"visible":true,"origin":"","legend":"The calibration curve and area under the ROC curves (AUC) for predicting 5 years survival in primary cohort and validation cohort. Calibration plot showing the optimal agreement between the prediction and actual observation in primary cohort(A) and validation cohort(B).The ideal line with 45° slope represents a perfect prediction (the predicted probability equals the observed probability). Area under the ROC curves (AUC) for predicting the survival at 5 years in the primary cohort (C) and the validation cohort (D).","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/6d7bda82977620f414dfd20a.jpg"},{"id":20007429,"identity":"34b510b6-a745-472d-a9db-c41a7f81e058","added_by":"auto","created_at":"2022-04-06 06:59:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":606902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/eb7a6550-1b8b-4ed4-a16d-81ee9be02525.pdf"},{"id":14945533,"identity":"93e350c3-6d5c-4fba-a904-046dcdda43e9","added_by":"auto","created_at":"2021-10-27 14:17:17","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8373,"visible":true,"origin":"","legend":"Figure S1. The optimum cutoff value of NLR. The optimal critical value of NLR was 3.08 determined by time-dependent ROC curve.","description":"","filename":"Fig.S1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/740bba2ef651f69bf3f912d9.jpeg"},{"id":14945531,"identity":"5cc901f7-62d3-4848-b0a0-75c54d8ec2a0","added_by":"auto","created_at":"2021-10-27 14:17:17","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":139462,"visible":true,"origin":"","legend":"Figure S2. Kaplan-Meier survival analysis for patients in primary cohort and validation cohort. Survival rates in 5 years(60 months) for cancer patients in the primary cohort and validation cohort were analyzed by Kaplane-Meier survival analysis. ","description":"","filename":"Fig.S2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/6ffb65025e50d9c4a866c914.jpeg"},{"id":14945120,"identity":"af601310-107b-4878-ace6-dad30f7faa3a","added_by":"auto","created_at":"2021-10-27 14:14:17","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":64133,"visible":true,"origin":"","legend":"Figure S3: Decision-curve analysis for the prediction model. The prediction model is the higher line on the decision curve.","description":"","filename":"FigS3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-719491/v1/b2624faf64b1b37260c5531b.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is the most threatening disease to human beings. Its incidence rate has been rising globally, and it is the most fatal disease in the world. Some experts believe that cancer is mainly caused by gene mutation, but the effect of gene targeted therapy is not significant in the fight against cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The mutations in cancer are diverse, and the gene mutations found in cancer are complex and heterogeneous, it is difficult to identify the key rate limiting genes for targeted treatment of tumors \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This suggests that genetic mutations may be not the origin of cancer. A few decades ago, Warburg\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e first theorized that mitochondrial damage causes energy metabolism defects and leads to cancer. When the respiration of tumor cells is damaged, the retrograde response (RTG) is activated, which transmit signals from mitochondria to nucleus, affecting the stability of the genome and leading metabolic reprogramming \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This propound a theory that cancer is essentially a metabolic disease.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming in cancer cells alters glucose metabolism, lipid metabolism, amino acid metabolism, and tumor microenvironment (TME), leading to cancer progression\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The rapidly proliferation of cancer cells needs to balance the decomposition and anabolic at the same time. Therefore, metabolism related indicators can reflect tumor growth. Recent reports have suggested that patient prognosis is associated with certain molecular biomarkers involved in tumor metabolism. However, expensive and time-consuming laboratory metabolomics technology is required. In contrast, blood tests from clinical patients are convenient and can be widely used in clinical application. The changes of metabolic markers in blood test can reflect tumor metabolic reprogramming and the prognosis of patients. Therefore, we developed and validated a metabolic based prognostic prediction model to predict the survival of patients with solid tumors and support the decision making on early therapy.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eThis retrospective analysis was conducted on a primary cohort of solid tumor patients from INSCOC(Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers)database. The INSCOC is a nation-wide cross-sectional survey on nutritional status and clinical outcome in patients with malignant tumors. Patients were derived from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project of China (registered at chictr.org.cn, ChiCTR1800020329). Patients were evaluated from January 2013 to August 2018 at 30 tertiary public hospitals in China. Inclusion criteria: 1) a histologic diagnosis of malignant solid tumors; 2) a complete medical history record and follow-up data available. An independent cohort of cancer patients with the same inclusion criteria were enrolled from the Fujian Cancer Hospital, and this cohort was used to form the external validation cohort. The follow-up time was 1-60 months in both primary cohort and validation cohort, and the outcome was patient\u0026rsquo;s death. The study was approved by the Ethics Committees of all participating institutions and all data was analyzed anonymously. The study is reported in accordance with the TRIPOD guidance for transparent reporting of prediction models\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eDemographic and clinicopathological data were collected, including sex, age, smoking history, drinking history, PGSGA score (Patient Generated Subjective Global Assessment score), NRS2002 score (Nutrition risk screening score), primary tumor site, tumor metastasis, TP (total protein), ALB (albumin), PAB (prealbumin), FBG (fasting blood-glucose), TC (total cholesterol), TG (triglyceride), HDL-C (high density lipoprotein cholesterol), LDL-C (low density lipoprotein cholesterol), WBC(white blood cell), NLR(neutrophil/lymphocyte ratio). All continuous variables were converted to categorical variables according to clinical standard. Regardless of tumor type or origin, metabolic abnormalities are common features of most cancer cells. Therefore, 15 kinds of malignant solid tumors were included in the study and classified by human systems. The NLR is an inflammatory marker which has been investigated as a prognostic indicator in post-therapeutic recurrence and survival of patients with cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In our study, NLR was classified according to the optimum cutoff value(Figure.S1). PGSGA was adapted from the SGA (Subjective Global Assessment) and widely used for clinical assessment of malnutrition in cancer patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Patients with PGSGA score\u0026ge;4 need nutritional interventions and symptomatic treatment. NRS 2002 is recommended by The European Society of Clinical Nutrition and Metabolism (ESPEN) as a nutritional risk screening method for patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Patients with NRS 2002 score \u0026ge;3 are at risk of malnutrition and require nutritional support. Variables where \u0026gt;10% of values were missing, or patients with a missing value for a specific variable, were excluded from the analysis. The missing data in selected variables were multiply imputed to generate a complete data set.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe used a primary cohort of solid tumor patients from INSCOC database to develop a clinical prediction model. Categorical variables were reported as whole numbers and proportions. The overall survival (OS) was calculated using the Kaplane-Meier method and the log-rank test. Univariate analysis was performed for all variables, and the variables with P values \u0026lt; 0.05 were included in multivariate analysis. A multivariate Cox regression analysis was performed to test the independent significance of different factors. The variables were selected by stepwise regression and then fit a more parsimonious model. Nomograms are a pictorial representation of a complex mathematical formula that use two or more known variables to calculate an outcome. The resulting model was simplified into a nomogram to predict the 1-, 3-and 5-years OS rates. We also did a decision-curve analysis to assess the clinical usefulness of the model.\u003c/p\u003e\n \u003cp\u003eThe area under ROC curve (AUC) was used to evaluate the predictive accuracy. Calibration curves were assessed graphically by plotting the observed rates against the predicted probabilities to evaluate the agreement. Brier score was used to evaluate probability calibration. Nomograms are a pictorial representation of a complex mathematical formula that uses two or more known variables to calculate an outcome. The resulting model was simplified into a nomogram to predict the survival of patients with solid tumors. To assess the performance of our model, the discriminative performance of the model was measured using Harrell\u0026rsquo;s C-statistic. An internal validation step was performed to counteract the possible overfitting of our model to the data. The bootstrapping techniques (B = 100) was used to validate and correct the over-optimism of the models ,and obtained the optimism-corrected measures of C- statistic.\u003c/p\u003e\n \u003cp\u003eIn all analyses, p values \u0026lt; 0.05 were considered to indicate statistical significance. All analyses were performed using the R software, version 3.6.1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eClinicopathological characteristics\u003c/h2\u003e\n\u003cp\u003eIn the primary cohort, there were 5006 patients who met the inclusion criteria were finally enrolled in the study, the median follow-up time was 30.3 months. For the validation cohort, we studied 1703 patients admitted to a single institution, the median follow-up time was 27.0 months. The demographic features and clinical characteristics of the primary and validation cohorts are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure S2 shows the cumulative survival free between primary cohort and validation cohort. Log-Rank test showed that P=0.052. This means there was no significant difference between the two cohorts.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDemographic or\u003c/p\u003e\n \u003cp\u003eclinical characteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePrimary cohort (n =5006)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eValidation cohort (n = 1703)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary tumor site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReproductive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrogenital system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;65yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;65yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePGSGA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRS2002 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;60 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60-80 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;80 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;35g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;35 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;280mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;280 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;6.1 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;6.1 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;5.2 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;5.2 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1.7 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;1.7 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1.55 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;1.55 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3.4 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3.4 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;4\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-10\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;10\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eModel development\u003c/h2\u003e\n\u003cp\u003eAll variables listed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e were used for univariate and multivariate Cox regression analysis. On multivariate analysis, the presence of age \u0026gt; 65yr (HR 1.69, 95%CI 1.53-1.87), smoking history (HR 1.45, 95%CI 1.32-1.59), the tumor was in stage III (HR 1.40, 95%CI 1.17-1.66) and stage IV (HR 1.46, 95%CI 1.25-1.72), tumor metastasis (HR 2.36, 95%CI 2.14-2.61), PGSGA score\u0026ge;4(HR 1.46, 95%CI 1.32-1.61), FBG\u0026ge;6.1 mmol/L(HR 2.63, 95%CI 2.39-2.91), NLR\u0026ge;3.08(HR 1.25, 95%CI 1.13-1.38) were the independent risk factors of overall survival. In addition, ALB\u0026ge;35 g/L (HR 0.71, 95%CI 0.63-0.80), TG\u0026ge;1.7 mmol/L(HR 0.77, 95%CI 0.69-0.86), HDL-C\u0026ge;1.55 mmol/L (HR 0.65, 95%CI 0.59-0.72) were the protective factors of overall survival. The HR of the prediction model was shown by forest plot (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). We simplified the model into a nomogram (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The nomogram based on these ten factors was developed to predict the 1, 3, 5 years of OS in cancer patients. The scales of the nomogram reflect coefficients from the Cox model rescaled to a user-friendly 100-pointrange.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe results of the univariate and multivariate Cox regression analysis of the primary cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariable model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariable analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65(0.59-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary tumor site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91(0.73-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReproductive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41(0.35-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82(1.63-2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrogenital system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69(0.41-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21(1.02-1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;65yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;65yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35(2.14-2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69(1.53-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63(1.49-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45(1.32-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35(1.21-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08(0.89-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09(0.90-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59(1.34-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(1.17-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82(1.54-2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46(1.24-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89(2.62-3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30(2.08-2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePGSGA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89(1.72-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44(1.30-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRS2002 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35(1.21-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;60 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60-80 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77(0.67-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;80 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86(0.63-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;35g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;35 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43(0.39-0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65(0.58-0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;280mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;280 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69(0.59-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;6.1 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;6.1 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59(3.27-3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82(2.55-3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;5.2 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;5.2 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.77-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1.7 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;1.7 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73(0.65-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73(0.65-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1.55 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;1.55 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59(0.54-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64(0.58-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3.4 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3.4 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88(0.78-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;4\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-10\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99(0.88-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;10\u0026times;10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72(1.44-2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80(1.63-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23(1.12-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eModel validation\u003c/h2\u003e\n\u003cp\u003eIn primary cohort, Harrell\u0026apos;s C- statistic was 0.775 (95% CI, 0.765-0.786). Furthermore, the calibration plot for the probability of survival at 5 years was closely follow the ideal line of 45 degrees, indicating optimal agreement between the prediction by model and the actual observed survival (Figure. 3A). The Brier score was 0.169 (95% CI, 0.152-0.186). The model yielded AUC values of 0.812 (95% CI, 0.794-0.830) for predicting mortality at 5 years after admission (Figure. 3C).\u003c/p\u003e\n\u003cp\u003eInternal validation with bootstrapping revealed the optimism-corrected C-statistic of the predictive model was 0.776, and the optimism-corrected Brier score was 0.169. Both the average optimism of C-statistic and Brier score were less than 0.001, reflecting a small degree of over-optimism. In the external validation cohort, there was also a good calibration curve for the risk estimation, indicating that the model is well-calibrated (Figure. 3B). The Brier score was 0.185 (95% CI, 0.158-0.212), and Harrell\u0026apos;s C- statistic was 0.771 (95% CI, 0.752-0.790). The AUC values for predicting mortality at 5 years was 0.786 (95% CI, 0.741-0.832) (Figure. 3D). The decision-curve analysis showed that the prediction model is the higher line on the decision curve, which indicates that the prediction model leads to a higher net benefit and greater clinical utility (Figure.S3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer is a significant public health problem and is the second leading cause of death globally\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Metabolic alterations of tumors are recognized as one of the hallmarks of cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Cancer cells support energy to maintain tumor progression and proliferation by adopting to metabolic changes. A huge number of cancer cells show metabolic reprogramming, including the reprogrammed glucose, lipid and amino acid metabolism to satisfy high proliferation requests. FBG, ALB, TG and HDL-C can well reflect metabolic changes as routine clinical detection items, so they were included in this study. In addition, tumor cells need to survive drastic changes in the microenvironment such as hypoxia, nutrient storage, acidic pH and chronic inflammation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The tumor microenvironment enforces metabolic plasticity and promotes tumor proliferation and progression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, this study included PGSGA score as a sensitive to evaluate nutritional status, and included NLR as an inflammatory marker. Besides, we included some other indicators related to the survival and prognosis of tumor patients, such as age, smoking history, tumor stage and tumor metastasis, so as to more comprehensively predict the prognosis of cancer patients. There were also many indicators that can reflect tumor metabolism and microenvironment, but they were not included in this model due to the incomplete data, missing values\u0026gt;10%, being removed by stepwise regression and the results of multivariate analysis were meaningless.\u003c/p\u003e \u003cp\u003eA clinical prediction model can provide tailored estimation on prognosis and help physician with associated decision making in daily practice. At present, many indicators of tumor metabolism are based on experimental metabolomics technologies, which cannot be widely popularized in clinical practice because of its high cost and long detection cycle. Therefore, we used routine clinical detection indicators in prediction model. In order to develop an accurate clinical prediction model, we conducted strict quality control. First, in order to build a reliable and accurate prediction model, the sample size should be large enough and the data should be complete. We screened from INSCOC database and included a large sample of 5006 in this study to ensure the quality of data development. Second, we ensured that the data covered common types of tumors, and all factors included in the prediction model are commonly assessed in routine clinical examinations. In this way, the universality of the prediction model can be guaranteed to the greatest extent and can be widely used in clinical practice, which is clearly a practical advantage. The model is available as a nomogram. Nomograms have emerged as a simpler, yet more advanced method to calculate the prognosis of different cancers. By integrating diverse prognostic and determinant variables to generate the probability of a clinical outcome, the nomogram fulfills a necessary role in oncological personalized medicine. In this study, the nomogram could predict an individual's 1-, 3- and 5-year survival rates with good accuracy which was verified in the validation cohort. This nomogram can evaluate the prognosis of patients conveniently and help clinicians adopt preventive and therapeutic strategies.\u003c/p\u003e \u003cp\u003eNormally, the main way for the body to obtain energy is the oxidative phosphorylation of glucose under aerobic conditions. In cancer cells, even in the presence of oxygen, the main pathway of glucose metabolism is aerobic glycolysis, termed Warburg effect\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which reflects the reprogramming of tumor glucose metabolism. Hyperglycemia is a common phenomenon in patients with advanced cancer\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Hyperglycemia can provide cancer cells with a high glucose fuel source to support rapid proliferation, drive glycolysis metabolic pathway, and lead to worse prognosis. Hyperglycemia can indirectly influence cancer cells through an increase in the levels of insulin/IGF-1, thus activating the PI3K/AKT/mTOR signaling pathway and promoting the development of cancer\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Beyond that, hyperglycemia has a direct impact on cancer cell proliferation, metastasis, invasiveness, and antiapoptotic qualities \u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In our study, FBG \u0026ge; 6.1 mmol / L (HR 2.63, 95% CI 2.39-2.91) is considered as one of the risk factors affecting the survival of tumor patients, which confirms the harm of hyperglycemia. Hyperglycemia can promote glycolysis and raise the prevalence and mortality of certain malignancies. FBG is the most intuitive index to reflect blood glucose, which can be a prediction index for cancer progression and glucose metabolism \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, patients with FBG \u0026ge; 6.1 mmol / L should be carried out appropriate diet or drug intervention to improve the prognosis.\u003c/p\u003e \u003cp\u003eIn cancer cells, the protein synthesis and decomposition are enhanced, but the anabolism exceeds the catabolism, and can even capture the protein from normal tissues, in order to meet the needs of their own growth. The amino acid metabolism of the tumor was also changed, tumor cells can obtain energy through glutamine and other amino acids. These changes will lead to severe protein consumption, negative nitrogen balance and hypoproteinemia. Patients with hypoproteinemia have a greater risk of recurrence and mortality, which can be corrected by albumin supplementation. Albumin (Alb) is an acute phase protein that decreases with inflammation and due to other reasons, such as malnutrition, increased age and metabolic disorder. Albumin reflects nutritional state and response to amino acid metabolism, and is associated with the prognosis of cancer patients. In our study, ALB\u0026ge;35 g/L (HR 0.71, 95%CI 0.63-0.80) was the protective factors of overall survival. Kao HK et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e showed that patients with increased serum albumin level can have better prognosis. Therefore, albumin can not only reflect amino acid metabolism, but also predict survival and prognosis as a biomarker \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNowadays, there are increasing evidences of the role of lipid metabolism alterations as biomarkers of cancer prognosis and survival. Together with the Warburg effect and the increased glutaminolysis, lipid metabolism plays a key role in cancer metabolic reprogramming. Extremely proliferative cancer cells exhibit an intense lipid and cholesterol avidity, which they satisfy by increasing the uptake of dietary or exogenous lipids and lipoproteins\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, the increase of de novo fatty acid synthesis and lipid synthesis in cancer cells requires efficient and complementary lipolytic mechanisms to accommodate the intracellular lipid content and provide materials for tumor cell proliferation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This long-term metabolic change will lead to the depletion of stored fat, and promote cancer cell metastasis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. TG and HDL, as lipid indexes reflecting lipid metabolism, are closely related to prognosis. Studies showed that a high level of HDL-C can reduce the risk and progression of cancer\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In our study, HDL-C\u0026ge;1.55 mmol/L (HR 0.65, 95%CI 0.59-0.72) were protective factor of survival. However, the association between TG and the survival of tumor patients is contradictory. Some studies observed that high level TG can improve the survival of cancer patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. On the contrary, other studies\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e has shown that high TG can lead to poor prognosis in cancer patients. In our study, TG\u0026ge;1.7 mmol/L(HR 0.77, 95%CI 0.69-0.86) were protective factor of survival. Rhonda Arthur et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e showed that TG were not associated with cancer death, but associated with risk of cardiovascular death. This reduced the proportion of cancer cases of death in subjects with elevated TG levels. However, the detailed mechanisms and the biological significance of them require further investigation. In conclusion, Lipid-metabolic can associate with cancer survival and have been proposed as prognosis biomarkers of cancer\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, PGSGA score\u0026ge;4(HR 1.46, 95%CI 1.32-1.61) and NLR\u0026ge;3.08(HR 1.25, 95%CI 1.13-1.38) were the independent risk factors of overall survival. PGSGA score\u0026ge;4 indicates the malnutrition in cancer patients, and these patients often have poor prognosis and low survival\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In this study, increased NLR was associated with decreased OS. NLR is the ratio of lymphocytes to neutrophils, the two types of cells are part of the human immune system and play a key role in TME. The immune cells in the TME can detect and eliminate the abnormal cells or tumor cells and protect the body from damage caused by tumor cells. Kao HK et al\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. considered that an elevated NLR indicates an imbalance between the innate and acquired immune response, which might be linked to a poorer prognosis. This elevation may reflect an inflammatory microenvironment that lymphocytes can have tumor suppressing effects and have been linked with better prognosis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, whereas neutrophils can create a favorable tumor microenvironment by remodeling the extracellular matrix and angiogenesis, thus enabling the tumor to growth and spread\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Therefore, NLR can be used as a biological indicator of inflammation to predict the prognosis of cancer patients.\u003c/p\u003e \u003cp\u003eSome limitations of this study should be discussed when considering the results. First, cancer patients still have a lot of laboratory indicators reflecting the metabolic situation in clinic, so it is necessary to add more factors to improve the model in the future. In addition, although internal validation was performed to prevent over-interpreting the data, and external validation verify our findings are applicable in single center, we need a prospective multicenter study to confirm the results in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAlthough the interactions between cancer metabolism and clinical prognosis are intricate. We emphasize their importance and develop a model based on cancer metabolism to predict the prognosis of tumor patients. This proves the correlation between cancer metabolism and clinical prognosis. This prognosis prediction model can accurately predict the prognosis through rapid and economical blood tests, and can be widely used in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e\n\u003cp\u003eThe INSCOC data that support the findings of this study are available from Chinese Cancer Society Nutrition and Support Committee but restrictions apply to the availability of these data, which were used under license for the current study, and this INSCOC data relates to the confidentiality of multiple clinical center and patient privacy, so it is not convenient to disclose. Data are however available from the authors upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Ethics Committee of Capital Medical University Affiliated Beijing Shijitan Hospital. The patients/participants provided their written informed consent to participate in this study. All methods were carried out in accordance with relevant guidelines and regulations. We don\u0026rsquo;t consent for the publication of identifying images or other personal or clinical details of participants that compromise anonymity.\u003c/p\u003e\n\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of financial and non-financial competing interests.\u003c/p\u003e\n\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e\n\u003cp\u003e(I) Conception and design: HT, ZY, HS, BR. (II) Administrative support: HS, BR. (III) Provision of study materials or patients: XS, SL, BW, WZ. (IV) Collection and assembly of data: BZ, XW, ZZ, PJ, LW, LD, NG. (V) Data analysis and interpretation: HT, ZY. (VI) Manuscript writing: HT; (VII) Final approval of manuscript: HS, BR. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis work was financially supported by National Key Research and Development Program to Dr. Hanping Shi (No. 2017YFC1309200); The National Natural Science Foundation of China to Dr. Hanping Shi (81672888); The National Natural Science Foundation of China to Dr. Benqiang Rao (81660484), and Beijing Natural Science Foundation Proposed Program to Dr. Benqiang Rao (7202076).\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank the INSCOC project members for their substantial work on data collection and patient follow-up.\u003c/p\u003e\n\u003ch2\u003eDisclosure of conflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eVitucci M, Hayes DN, Miller CR. Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy. Br J Cancer. 2011 Feb 15;104(4):545-53. doi: 10.1038/sj.bjc.6606031. Epub 2010 Nov 30.\u003c/li\u003e\n \u003cli\u003eKoscielny S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci Transl Med. 2010 Jan 13;2(14):14ps2. doi: 10.1126/scitranslmed.3000313.\u003c/li\u003e\n \u003cli\u003eSalk JJ, Fox EJ, Loeb LA. Mutational heterogeneity in human cancers: origin and consequences. Annu Rev Pathol. 2010;5:51-75. doi: 10.1146/annurev-pathol-121808-102113.\u003c/li\u003e\n \u003cli\u003eWarburg O,Wind F,Negelstein E. The metabolism of tumors in the body[J]. J Gen Physiol,1927,8(6):519-530.\u003c/li\u003e\n \u003cli\u003eSeyfried, T.N., Shelton, L.M. Cancer as a metabolic disease. Nutr Metab (Lond) 7, 7 (2010). \u003ca href=\"https://doi.org/10.1186/1743-7075-7-7\"\u003ehttps://doi.org/10.1186/1743-7075-7-7\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eVeatch JR, McMurray MA, Nelson ZW, Gottschling DE. Mitochondrial dysfunction leads to nuclear genome instability via an iron-sulfur cluster defect. Cell. 2009 Jun 26;137(7):1247-58. doi: 10.1016/j.cell.2009.04.014.\u003c/li\u003e\n \u003cli\u003eFaubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Science. 2020 Apr 10;368(6487):eaaw5473. doi: 10.1126/science.aaw5473.\u003c/li\u003e\n \u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015 Jan 7;350:g7594. doi: 10.1136/bmj.g7594.\u003c/li\u003e\n \u003cli\u003eDell\u0026apos;Aquila E, Cremolini C, Zeppola T, Lonardi S, Bergamo F, Masi G, et al. Prognostic and predictive role of neutrophil/lymphocytes ratio in metastatic colorectal cancer: a retrospective analysis of the TRIBE study by GONO. Ann Oncol. 2018 Apr 1;29(4):924-930. doi: 10.1093/annonc/mdy004.\u003c/li\u003e\n \u003cli\u003eSimonaggio A, Elaidi R, Fournier L, Fabre E, Ferrari V, Borchiellini D, et al. Variation in neutrophil to lymphocyte ratio (NLR) as predictor of outcomes in metastatic renal cell carcinoma (mRCC) and non-small cell lung cancer (mNSCLC) patients treated with nivolumab. Cancer Immunol Immunother. 2020 Dec;69(12):2513-2522. doi: 10.1007/s00262-020-02637-1. Epub 2020 Jun 19.\u003c/li\u003e\n \u003cli\u003eOttery FD. Rethinking nutritional support of the cancer patient: the new field of nutritional oncology. Semin Oncol. 1994 Dec;21(6):770-8.\u003c/li\u003e\n \u003cli\u003eKondrup J, Rasmussen HH, Hamberg O, Stanga Z; Ad Hoc ESPEN Working Group. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003 Jun;22(3):321-36. doi: 10.1016/s0261-5614(02)00214-5.\u003c/li\u003e\n \u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. Cancer J Clin. (2020) 70:7\u0026ndash;30. doi: 10.3322/caac.21590\u003c/li\u003e\n \u003cli\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011 Mar 4;144(5):646-74. doi: 10.1016/j.cell.2011.02.013.\u003c/li\u003e\n \u003cli\u003eOizel K, Chauvin C, Oliver L, Gratas C, Geraldo F, Jarry U, et al. \u0026nbsp;Efficient mitochondrial glutamine targeting prevails over glioblastoma metabolic plasticity. Clin Cancer Res. (2017) 23:6292\u0026ndash;304. doi: 10.1158/1078-0432.CCR-16-3102\u003c/li\u003e\n \u003cli\u003eLin X, Xiao Z, Chen T, Liang SH, Guo H. Glucose Metabolism on Tumor Plasticity, Diagnosis, and Treatment. Front Oncol. 2020 Mar 6;10:317. doi: 10.3389/fonc.2020.00317.\u003c/li\u003e\n \u003cli\u003eDuan W, Shen X, Lei J, Xu Q, Xu Q, Yu Y. Hyperglycemia, a neglected factor during cancer progresssion. Biomed Ress Int. 2014;2014:461917.\u003c/li\u003e\n \u003cli\u003eLi G, Zhang K, Gong F, Jin H. A study on changes and clinical significance of blood glucose, blood lipid and inflammation in patients with ovarian cancer. J BUON. 2019 Nov-Dec;24(6):2322-2326.\u003c/li\u003e\n \u003cli\u003eJohnson JA, Carstensen B, Witte D, Bowker SL, Lipscombe L, Renehan AG; Diabetes and Cancer Research Consortium. Diabetes and cancer (1): evaluating the temporal relationship between type 2 diabetes and cancer incidence. Diabetologia 2012;55:1607-18. doi: 10.1007/s00125-012-2525-1. Epub 2012 Apr 4.\u003c/li\u003e\n \u003cli\u003eDong C, Yuan T, Wu Y, Wang Y, Fan TW, Miriyala S, et al. Loss of FBP1 by Snail-mediated repression provides metabolic advantages in basal-like breast cancer. Cancer Cell 2013;23:316-31.\u0026nbsp;doi: 10.1016/j.ccr.2013.01.022. Epub 2013 Feb 28.\u003c/li\u003e\n \u003cli\u003eCatrina SB, Okamoto K, Pereira T, Brismar K, Poellinger L. Hyperglycemia regulates hypoxia-inducible factor-1alpha protein stability and function. Diabetes 2004;53:3226-32. doi: 10.2337/diabetes.53.12.3226.\u003c/li\u003e\n \u003cli\u003eChang SC, Yang WV. Hyperglycemia, tumorigenesis, and chronic inflammation. Crit Rev Oncol Hematol. 2016;108:146-153. doi:10.1016/j.critrevonc.2016.11.003\u003c/li\u003e\n \u003cli\u003eRyu TY, Park J, Scherer PE. Hyperglycemia as a risk factor for cancer progression. Diabetes Metab J. 2014;38(5):330-336. doi:10.4093/dmj.2014.38.5.330\u003c/li\u003e\n \u003cli\u003eKao HK, L\u0026ouml;fstrand J, Loh CY, Lao WW, Yi JS, Chang YL, et al. Nomogram based on albumin and neutrophil-to-lymphocyte ratio for predicting the prognosis of patients with oral cavity squamous cell carcinoma. Sci Rep. 2018 Aug 30;8(1):13081. doi: 10.1038/s41598-018-31498-z.\u003c/li\u003e\n \u003cli\u003eLi SQ, Jiang YH, Lin J, Zhang J, Sun F, Gao QF, et al. Albumin-to-fibrinogen ratio as a promising biomarker to predict clinical outcome of non-small cell lung cancer individuals. Cancer Med. 2018 Apr;7(4):1221-1231. doi: 10.1002/cam4.1428. Epub 2018 Mar 13.\u003c/li\u003e\n \u003cli\u003eYang JR, Xu JY, Chen GC, Yu N, Yang J, Zeng DX, et al. Post-diagnostic C-reactive protein and albumin predict survival in Chinese patients with non-small cell lung cancer: a prospective cohort study. Sci Rep. 2019 May 31;9(1):8143. doi: 10.1038/s41598-019-44653-x.\u003c/li\u003e\n \u003cli\u003eBeloribi-Djefaflia S, Vasseur S, Guillaumond F. Lipid metabolic reprogramming in cancer cells. Oncogenesis. (2016) 5:e189. doi: 10.1038/oncsis.2015.49\u003c/li\u003e\n \u003cli\u003eMenendez JA,Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis.Nat Rev.Cancer.2007;7(10):763-777.\u0026nbsp;doi: 10.1038/nrc2222.\u003c/li\u003e\n \u003cli\u003eLuo X, Cheng C, Tan Z, Li N, Tang M, Y ang L, et al. Emerging roles of lipid metabolism in cancer metastasis. Mol Cancer. (2017) 16:76. doi: 10.1186/s12943-017-0646-3\u003c/li\u003e\n \u003cli\u003eLofter\u0026oslash;d T, Mortensen ES, Nalwoga H, Wilsgaard T, Frydenberg H, Risberg T, et al. Impact of pre-diagnostic triglycerides and HDL-cholesterol on breast cancer recurrence and survival by breast cancer subtypes. BMC Cancer. 2018 Jun 15;18(1):654. doi: 10.1186/s12885-018-4568-2.\u003c/li\u003e\n \u003cli\u003eWang Y, Sun XQ, Lin HC, Wang DS, Wang ZQ, Shao Q, et al. Correlation between immune signature and high-density lipoprotein cholesterol level in stage II/III colorectal cancer. Cancer Med. 2019 Mar;8(3):1209-1217. doi: 10.1002/cam4.1987. Epub 2019 Feb 7.\u003c/li\u003e\n \u003cli\u003eLi X, Tang H, Wang J, Xie X, Liu P, Kong Y, et al. The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer. Breast. 2017 Apr;32:1-6. doi: 10.1016/j.breast.2016.11.024. Epub 2016 Dec 8.\u003c/li\u003e\n \u003cli\u003eLiu X, Li M, Wang X, Dang Z, Jiang Y, Wang X, et al. Effect of serum triglyceride level on the prognosis of patients with hepatocellular carcinoma in the absence of cirrhosis. Lipids Health Dis. 2018 Nov 6;17(1):248. doi: 10.1186/s12944-018-0898-y.\u003c/li\u003e\n \u003cli\u003eHis M, Dartois L, Fagherazzi G, Boutten A, Dupr\u0026eacute; T, Mesrine S, et al. Associations between serum lipids and breast cancer incidence and survival in the E3N prospective cohort study. Cancer Causes Control. 2017 Jan;28(1):77-88. doi: 10.1007/s10552-016-0832-4. Epub 2016 Nov 18.\u003c/li\u003e\n \u003cli\u003eArthur R, M\u0026oslash;ller H, Garmo H, H\u0026auml;ggstr\u0026ouml;m C, Holmberg L, Stattin P, et al. Serum glucose, triglycerides, and cholesterol in relation to prostate cancer death in the Swedish AMORIS study. Cancer Causes Control. 2019 Feb;30(2):195-206. doi: 10.1007/s10552-018-1093-1. Epub 2018 Nov 12.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFern\u0026aacute;ndez LP, G\u0026oacute;mez de Cedr\u0026oacute;n M, Ram\u0026iacute;rez de Molina A. Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment. Front Oncol. 2020 Oct 28;10:577420. doi: 10.3389/fonc.2020.577420.\u003c/li\u003e\n \u003cli\u003eArends J. Struggling with nutrition in patients with advanced cancer: nutrition and nourishment-focusing on metabolism and supportive care. Ann Oncol. 2018 Feb 1;29(suppl_2):ii27-ii34. doi: 10.1093/annonc/mdy093.\u003c/li\u003e\n \u003cli\u003eLoi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol. 2013 Mar 1;31(7):860-7. doi: 10.1200/JCO.2011.41.0902. Epub 2013 Jan 22.\u003c/li\u003e\n \u003cli\u003eMantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008 Jul 24;454(7203):436-44. doi: 10.1038/nature07205.\u003c/li\u003e\n \u003cli\u003eDumitru CA, Lang S, Brandau S. Modulation of neutrophil granulocytes in the tumor microenvironment: mechanisms and consequences for tumor progression. Semin Cancer Biol. 2013 Jun;23(3):141-8. doi: 10.1016/j.semcancer.2013.02.005. Epub 2013 Feb 26.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prediction Model, Metabolism, Cancer, Clinical Prognosis, Integration of Data","lastPublishedDoi":"10.21203/rs.3.rs-719491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-719491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.\u003c/p\u003e\u003cp\u003eMethods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.\u003c/p\u003e\u003cp\u003eResults: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.\u003c/p\u003e\u003cp\u003eConclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-27 14:14:14","doi":"10.21203/rs.3.rs-719491/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":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"October 27th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8130161,"name":"Cancer Biology"},{"id":8130162,"name":"Oncology"}],"tags":[],"updatedAt":"2022-04-06T06:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-27 14:14:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-719491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-719491","identity":"rs-719491","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}