Association of systemic immune-inflammatory index with cancer prevalence and mortality: Results from NHANES 1999-2010

Association of systemic immune-inflammatory index with cancer prevalence and mortality: Results from NHANES 1999-2010 | 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 Association of systemic immune-inflammatory index with cancer prevalence and mortality: Results from NHANES 1999-2010 Yuzhuo Zhang, Wenyu Jia, Tian Lan, Xinru Zhang, Bangjie Li, Ju Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3507394/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To study the predictive effect of systemic immune-inflammatory index (SII) on cancer prevalence and mortality. Methods: Participants with SII and cancer status were screened from the National Health and Nutrition Examination Survey database from 1999 to 2010, and their baseline characteristics were analyzed according to the SII tertile. Multivariable logistical or Cox proportional hazards models were used to analyze the associations between SII with cancer prevalence or mortality. The mortality was followed through December 31 2018. For further evaluation on associations of SII with specified outcomes, restricted cubic spline and two piecewise regression models were adopted. Results: This study eventually included 26,049 individuals, of whom 2,432 were cancer patients. According to the SII tertile group, cancer prevalence increased significantly with the rise of SII. In the highest tertile of SII, SII was significantly related to cancer prevalence (OR=1.45, 95%CI= 1.31 - 1.61, p<0.05). Moreover, during a median follow-up of 12.75 year, 329, 351, and 1,202 cardiovascular, cancer and all-cause deaths occurred among cancer patients, respectively. The results indicated that highest level of SII was significantly associated with increased cardiovascular mortality (HR=1.80, 95%CI= 1.28 - 2.52), cancer mortality (HR=1.64, 95%CI= 1.28 - 2.09) and all-cause mortality (HR=1.56, 95%CI= 1.31- 1.85). The model adjusted for multiple covariates still showed the same trend. U-shaped dose-response associations between log-transformed SII (ln-SII) with prevalence and mortality of cancer were detected. The threshold values of ln-SII for the lowest risk associated with cancer prevalence, cardiovascular mortality cancer mortality and all-cause mortality were 5.44, 6.21, 6.27 and 6.21, respectively. Above thresholds, SII was positively associated with increased risk of above outcomes. Conclusion: SII may be a potential earlier warning marker for the prevalence and mortality of total cancers. Figures Figure 1 Figure 2 1. Introduction As a global public health problem, cancer remains a major challenge. According to cancer statistics, cancer is still the second leading cause of death in the United States, with 1,958,310 new cancer cases and 609,820 cancer deaths expected in 2023 ( 1 ). Based on the heavy economic and social burden caused by cancer, it is important to identify risk factors associated with cancer incidence and death. Inflammation is listed as a hallmark of cancer ( 2 ). Chronic inflammation has been found to be closely related to the occurrence and outcome of malignant tumors, and inflammatory cells are important members of the tumor microenvironment ( 3 , 4 ). Inflammation drives tumor occurrence, growth, progression and metastasis, and participates in all stages of the tumor ( 5 ). It is of great significance to explore convenient and precise indicators that can reflect the inflammatory state of the system. In this situation, systemic immune-inflammatory index (SII) is a suitable indicator that can be used to clarify the association between inflammation and cancer. SII is defined as (platelet count × neutrophil count)/ lymphocyte count. Platelets, neutrophils, and lymphocytes were once considered tools for tumor prognosis, respectively ( 6 ). SII combines three independent prognostic factors, providing a more powerful tool. SII is a convenient indicator to evaluate the systemic inflammatory response ( 7 , 8 ), which is believed to be related to the occurrence and outcome of various diseases, such as rheumatoid arthritis, hepatic steatosis, hypertension etc. ( 9 – 11 ). SII has been reported to be associated with adverse outcomes for a variety of cancers, including cancers of the digestive, respiratory, reproductive, urinary, and nervous systems ( 8 , 12 – 14 ), but no studies have been conducted on SII and the related risks of morbidity and death for all cancers. Here, we analyzed the associations of SII with the risks of cancer prevalence and death in the adult population by a large-scale epidemiological analysis. 2. Materials and Methods 2.1. Sample Population National Health and Nutrition Examination Survey (NHANES) was a periodic survey and used a complex, stratified, multi-stage probabilistic sampling method to assess the health status of population in US. The NHANES project began in the early 1960s and is still ongoing today. Informed consent was provided by each participant and the relevant data on NHANES was partially opened up for further study. This study used the publicly available data from 1999 to 2010 in NHANES. A total of 62,160 participants were included in the NHANES database from 1999 to 2010, of which those under the age of 20 were excluded first (n = 26,781). Of the remaining 32,464 participants, 6,415 were excluded due to unavailability information of cancer status (n = 46), unavailability information of systemic immune-inflammatory index (n = 3,453), total energy intake 5,000 kcal/day (n = 1,871), and pregnant women (n = 1,045). After that, 26,049 participants with information of cancer status and systemic immune-inflammation index were screened. Of these, 2,432 and 23,617 participants with or without cancer were analyzed for prevalence. At the same time, mortality of 2,432 cancer patients was analyzed. The entire process of data filtering is shown in Fig. 1 . 2.2 Systemic immune-inflammation index SII is based on data from complete blood count with 5-part differential-Whole Blood (LAB25) and relates to the number of neutrophils, lymphocytes and platelets. Beckman Coulter method of counting and sizing were used for complete blood count. The specific calculation formula is as follows: SII = platelet count × neutrophil count / lymphocyte count (× 10 9 cells/µl). Here, SII was set as an exposure variable. 2.3 Covariates Various covariates that might affect the outcomes were included here. All analyses were fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. They were obtained from the demographics data, questionnaire data and laboratory data of NHANES, respectively. BMI was divided into three categories according to 30.0; family income was divided into three categories according to 3.0; race was divided into Mexican American, non-Hispanic White, non-Hispanic Black and other race; education was divided into lower than high school, high school and more than high school; physical activity was divided into does not meet guidelines and meets guidelines. All information is available at www.cdc.gov/nchs/nhanes . 2.4 Outcomes Outcomes of this study focused on cancer prevalence and mortality, including cardiovascular mortality, cancer mortality and all-cause mortality. The final mortality status of every survey participant was determined by NHANES through December 31 2019. 2.5. Statistical analysis All participants included in this study were classified according to tertile of SII. When conducting data analysis, chi-square test, one-way ANOVA and Kruskal-Wallis H-test were used to compare the differences among the three groups. All estimates accounted for complex survey designs, and all percentages were weighted. The Values for categorical and continuous variables are expressed as n (%) and weighted mean ± standard error, respectively. The associations between SII with cancer prevalence and mortality were analyzed using multivariate logistical or Cox proportional regression model to estimate odds ratios (ORs), hazard ratios (HRs) and 95% corresponding confidence intervals (CIs). Proportional hazard assumption was examined using Schoenfeld residual methods. The tertile 1 of SII was set as the reference group in categorical analyses. Three models with different adjustments were as following: a, crude model without any adjustments; b, adjusted for age, sex and race; c, fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. Since the distribution of SII is specifically skewed to the right, SII values were natural log-transformed in continuous analysis. Next, we performed restricted cubic spline model with fully multivariable adjustments to investigate dose-response associations between SII and study specified outcomes. The number of nodes was determined based on the lowest value of the Akaike information criterion. Likelihood ratio test was used to test nonlinearity. If nonlinearity was detected, two-piecewise logistical or Cox proportional regression models were constructed according to the inflection point. All statistical analyses were performed using R Version 3.6.2, and p < 0.05 was considered statistically significant. 3. Results 3.1 Baseline characteristics of study participants Our study ultimately included 26,049 individuals with information on cancer status and systemic immune-inflammatory index. The weighted mean ± standard error of SII and log-transformed SII (ln-SII) were 587.92 ± 3.46 and 6.24 ± 0.01, respectively. Of all the participants, 51.68% were male, with an average age of 46.78 years, of whom 2,432 had cancer. Table S1 shows the baseline characteristics of participants stratified according to SII tertile, and it can be seen that the number of cancer patients increased significantly with the rise of SII ( p < 0.05). The baseline characteristics of participants with cancer according to SII tertile is presented in Table S2 . The results showed that cardiovascular mortality, cancer mortality and all-cause mortality were significantly higher in cancer patients in the SII tertile 3 group compared to tertile 1 and tertile 2 groups ( p < 0.05). 3.2 Associations of SII with cancer prevalence and mortality Three different models were used in multivariate logistical or Cox regression models to analyze the associations of SII with cancer prevalence and death. Table 1 indicated that high levels of SII can significantly increase the prevalence of cancer, especially in tertile 3 group (OR = 1.45, 95%CI = 1.31–1.61, p < 0.0001). In addition, SII was strongly associated with an increased risk of cardiovascular mortality (HR = 1.80, 95%CI = 1.28–2.52, p = 0.0007), cancer mortality (HR = 1.64, 95%CI = 1.28–2.09, p < 0.0001) and all-cause mortality (HR = 1.56, 95%CI = 1.31–1.85, p < 0.0001) in the crude model. Furthermore, as compared to the tertile 1 group in fully adjusted Model 3, SII in tertile 3 group was still positively related to elevated risks of cancer prevalence, cancer and all-cause mortality ( p < 0.05), while there seen to be a negative association but significant trend of cardiovascular mortality. 3.3 Dose-response associations of ln-SII with cancer prevalence and mortality After adjusting for multiple covariates in Model 3, the nonlinear U-shaped associations between ln-SII and study specified outcomes were shown in Fig. 2 . For further evaluation, two piecewise logistical or Cox proportional regression models were used to detect the difference in the relationship at the threshold. As showed in Table 2 , the risk of cancer prevalence decreased until the minimum (ln-SII = 5.44) and then increased as SII increased (HR = 0.59, 95%CI = 0.43–0.80, p = 0.0007; HR = 1.15, 95%CI = 1.04–1.27, p = 0.0061, respectively). Similarly, the cut-off values of ln-SII for the lowest risks related to all-cause mortality, cancer mortality and cardiovascular mortality were 6.21, 6.27 and 6.21, respectively. The risks of these outcomes also showed a trend of first decreasing and then increasing before and after the cut-off values ( p < 0.05). 4. Discussion As one of the main causes of death, cancer has brought great burden to all mankind and aroused wide concern. The relationship between inflammation and cancer is a new understanding of the occurrence and progression of cancer, and inflammation has become one of hallmarks of cancer ( 2 , 15 ). There are no systematic indicators to comprehensively reflect the role of inflammation in all cancer morbidity and mortality. This study examined the associations between systemic inflammation and risk of cancer and death, with SII as a marker of systemic inflammation. The study found that both too high and too low levels of SII increased the prevalence of cancer, cancer mortality and all-cause mortality, but only when lnSII exceeded 6.21, the cardiovascular mortality of tumor patients increased. Immune-inflammatory response is an important way for the body to adapt to the environment and fight against diseases, and it also plays a complex role in the occurrence, development and treatment of tumors. Chronic inflammation is involved in the induction of early carcinogenesis of malignant tumors, tumor growth and metastasis, and promote tumor drug resistance ( 16 ). Researchers have explored many different markers of immune-inflammatory response, and SII is a readily available and appropriate parameter. SII is a composite index, based on blood cell analysis, that correlates with neutrophils, platelets, and lymphocyte counts ( 17 ). Neutrophils are believed to contribute to various aspects of cancer, including growth and metastasis of the primary tumor, impaired immune surveillance, and treatment resistance ( 18 ). Platelets inhibit the anti-tumor immunity of T cells and promote the metastasis of tumor cells ( 19 ). Lymphocytes are important participants in tumor immunity. Logically, high levels of neutrophils and platelets and low levels of lymphocytes are positively related to the risk of cancer and death. Here, we analyzed publicly available data from the NHANES 1999–2010 to explore the impact of SII levels on cancer prevalence and death in U.S. adults. After tripartite grouping of SII and adjusting for multiple covariates, our results showed that cancer prevalence, cancer mortality and all-cause mortality were significantly increased in the group with higher SII level, while the cardiovascular mortality only showed an increasing trend without statistical significance. These results are consistent with the majority of published literature. In 2015, SII was first found to be positively associated with overall survival in non-small cell lung cancer, with high levels of SII indicating high mortality ( 17 ). Subsequent studies have shown that SII directly affects the overall survival and progression-free survival of nasopharyngeal carcinoma, and is an independent risk factor for lymph node metastasis of endometrial cancer ( 20 , 21 ). Other studies have shown that SII is strongly associated with overall survival and tumor stage of colorectal cancer, and significantly associated with poor survival and adverse pathological features in patients with bladder cancer ( 22 , 23 ). The U-shaped dose-response associations between SII and the specified outcomes attracted our attention. Previously, a prospective cohort study conducted in Rotterdam, the Netherlands, considered that SII was significantly positively correlated with the incidence of cancer ( 24 ). However, in our study, when lnSII was less than 5.44, SII increased and cancer prevalence decreased, while when lnSII was greater than 5.44, SII increased and cancer prevalence increased. We considered the possible reasons for the differences, first of all, the included population was different. The population in this study was from the United States, and the included population was over 20 years old, while the former target was Dutch people over 45. In addition, the included covariates were different. The associations of SII with cancer and all-cause mortality showed the same trend. The same phenomenon was observed in a previous study of SII in hypertensive patients in the NHANES database in relation to all-cause mortality and cancer mortality ( 25 ), suggesting that low levels of immune inflammation in the body is another risk factor for cancer and all-cause death, which deserves further confirmation by more studies. Interestingly, the analysis showed that there was a little change of cardiovascular mortality with SII in cancer patients before the threshold ( p ＞0.05), after which high SII levels were associated with high mortality. This has been reported for the first time in cancer patients, but there are similarities to studies in other populations ( 25 – 27 ). There are some limitations in our study. First, SII was obtained only once according to the complete blood count with 5-part differential-Whole Blood, which may lead to accidental bias in the conclusion. Second, although covariates are included as much as possible for adjustment, the possibility of other confounding factors cannot be completely avoided. Finally, the analysis of the database only in the United States may not be applicable to people in other regions. 5. Conclusion Here, we suggest that SII may be a potential earlier warning marker for the prevalence and mortality of total cancers. Declarations Authors’ contributions ZY, JW, JC, LY and TH conceived and designed the study. ZY, JW, LT, ZX, and LB collected and analysed the data. ZY drafted the paper and TH revised the manuscript. All authors have reviewed the final manuscript. Funding This research was funded by the PhD research startup foundation of the Third Affiliated Hospital of Zhengzhou University (grant BS20230101). Data availability The detailed datasets about the surveys are available at www.cdc.gov/nchs/nhanes. Declarations Ethics approval and consent to participate This study was approved by the ethics review board of the National Center for Health Statistics and written informed consents were obtained from each participant. Consent for publication Not applicable. Competing interest All authors declare that there is no conflict of interest involved. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. Hanahan D, Weinberg RA. 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Tables Table 1 Associations between systemic immune-inflammation index with cancer prevalence and mortality Variable Odds ratio / Hazard ratio (95% CI), P -value Model I a Model II b Model III c Cancer prevalence Tertile 1 Reference Reference Reference Tertile 2 1.15 (1.03, 1.28) 0.0124 1.07 (0.96, 1.20) 0.2427 1.07 (0.96, 1.20) 0.2277 Tertile 3 1.45 (1.31, 1.61) <0.0001 1.17 (1.05, 1.31) 0.0045 1.15 (1.03, 1.29) 0.0168 P for trend <0.001 0.004 0.017 Cardiovascular mortality Tertile 1 Reference Reference Reference Tertile 2 0.99 (0.72, 1.35) 0.9298 1.00 (0.77, 1.30) 0.9929 0.96 (0.71, 1.28) 0.7677 Tertile 3 1.80 (1.28, 2.52) 0.0007 1.48 (1.13, 1.94) 0.0041 1.38 (0.99, 1.93) 0.0601 P for trend <0.001 <0.001 0.009 Cancer mortality Tertile 1 Reference Reference Reference Tertile 2 0.81 (0.62, 1.07) 0.1366 0.77 (0.58, 1.02) 0.0721 0.72 (0.54, 0.97) 0.0281 Tertile 3 1.64 (1.28, 2.09) <0.0001 1.58 (1.21, 2.06) 0.0008 1.43 (1.09, 1.87) 0.0091 P for trend <0.001 <0.001 0.006 All-cause mortality Tertile 1 Reference Reference Reference Tertile 2 0.97 (0.82, 1.16) 0.7560 0.97 (0.83, 1.13) 0.6663 0.96 (0.83, 1.11) 0.5929 Tertile 3 1.56 (1.31, 1.85) <0.0001 1.44 (1.25, 1.65) <0.0001 1.35 (1.16, 1.56) <0.0001 P for trend <0.001 <0.001 0.049 a Crude model; b Adjusted for age, sex and race; c Adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. Table 2 The results of two piecewise regression model of systemic immune-inflammation index with cancer prevalence and mortality a Variable Odds ratio / Hazard ratio (95% CI), P -value Cancer prevalence Cardiovascular mortality Cancer mortality All-cause mortality Cut-off value (as continuous variables, ln-transformed SII) 5.44 6.21 6.27 6.21 Cut-off value (per 1 ln-transformed SIIl increment 1.15 (1.04, 1.27) 0.0061 1.71 (1.22, 2.38) 0.0018 2.22 (1.58, 3.12) <0.0001 1.59 (1.35, 1.88) <0.0001 Log Likelihood Ratio Tests <0.001 0.029 <0.001 <0.001 a All analyses were fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. Additional Declarations No competing interests reported. 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Flow chart showing the process of participants selection. Of 62,160 participants from 1999 to 2010 of National Health and Nutrition Examination Survey (NHANES), 2,432 and 23,617 participants with and without cancers remained in the final analysis.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3507394/v1/c88d62c7cde8b345a38463cc.png"},{"id":46052312,"identity":"af2c5024-0503-4352-b735-981962da0d83","added_by":"auto","created_at":"2023-11-08 00:08:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71398,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline fitting for the associations between ln-SII with\u003c/p\u003e\n\u003cp\u003ecancer prevalence and mortality. Association of ln-SII levels with the cancer prevalence (A), cardiovascular (CVD) mortality (B), cancer mortality (C) and all-cause mortality.\u003c/p\u003e","description":"","filename":"OnlineFigure2new.png","url":"https://assets-eu.researchsquare.com/files/rs-3507394/v1/2b5edb87287b3b3b72dd2294.png"},{"id":46397260,"identity":"a202323a-1f5c-4797-bc49-1c529cc4c3ca","added_by":"auto","created_at":"2023-11-14 07:37:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717564,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3507394/v1/72463036-cba6-45c9-af14-2eb7a9ad7b1b.pdf"},{"id":46052313,"identity":"c2e53b2f-6258-4747-a71f-b4d0c8916198","added_by":"auto","created_at":"2023-11-08 00:08:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27424,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-3507394/v1/ecdde4745bb8e83dcd3d5cf9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of systemic immune-inflammatory index with cancer prevalence and mortality: Results from NHANES 1999-2010","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs a global public health problem, cancer remains a major challenge. According to cancer statistics, cancer is still the second leading cause of death in the United States, with 1,958,310 new cancer cases and 609,820 cancer deaths expected in 2023 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Based on the heavy economic and social burden caused by cancer, it is important to identify risk factors associated with cancer incidence and death. Inflammation is listed as a hallmark of cancer (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Chronic inflammation has been found to be closely related to the occurrence and outcome of malignant tumors, and inflammatory cells are important members of the tumor microenvironment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Inflammation drives tumor occurrence, growth, progression and metastasis, and participates in all stages of the tumor (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It is of great significance to explore convenient and precise indicators that can reflect the inflammatory state of the system. In this situation, systemic immune-inflammatory index (SII) is a suitable indicator that can be used to clarify the association between inflammation and cancer.\u003c/p\u003e \u003cp\u003eSII is defined as (platelet count \u0026times; neutrophil count)/ lymphocyte count. Platelets, neutrophils, and lymphocytes were once considered tools for tumor prognosis, respectively (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). SII combines three independent prognostic factors, providing a more powerful tool. SII is a convenient indicator to evaluate the systemic inflammatory response (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), which is believed to be related to the occurrence and outcome of various diseases, such as rheumatoid arthritis, hepatic steatosis, hypertension etc. (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). SII has been reported to be associated with adverse outcomes for a variety of cancers, including cancers of the digestive, respiratory, reproductive, urinary, and nervous systems (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), but no studies have been conducted on SII and the related risks of morbidity and death for all cancers. Here, we analyzed the associations of SII with the risks of cancer prevalence and death in the adult population by a large-scale epidemiological analysis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample Population\u003c/h2\u003e \u003cp\u003eNational Health and Nutrition Examination Survey (NHANES) was a periodic survey and used a complex, stratified, multi-stage probabilistic sampling method to assess the health status of population in US. The NHANES project began in the early 1960s and is still ongoing today. Informed consent was provided by each participant and the relevant data on NHANES was partially opened up for further study. This study used the publicly available data from 1999 to 2010 in NHANES.\u003c/p\u003e \u003cp\u003eA total of 62,160 participants were included in the NHANES database from 1999 to 2010, of which those under the age of 20 were excluded first (n\u0026thinsp;=\u0026thinsp;26,781). Of the remaining 32,464 participants, 6,415 were excluded due to unavailability information of cancer status (n\u0026thinsp;=\u0026thinsp;46), unavailability information of systemic immune-inflammatory index (n\u0026thinsp;=\u0026thinsp;3,453), total energy intake\u0026thinsp;\u0026lt;\u0026thinsp;500 or \u0026gt;\u0026thinsp;5,000 kcal/day (n\u0026thinsp;=\u0026thinsp;1,871), and pregnant women (n\u0026thinsp;=\u0026thinsp;1,045). After that, 26,049 participants with information of cancer status and systemic immune-inflammation index were screened. Of these, 2,432 and 23,617 participants with or without cancer were analyzed for prevalence. At the same time, mortality of 2,432 cancer patients was analyzed. The entire process of data filtering is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Systemic immune-inflammation index\u003c/h2\u003e \u003cp\u003eSII is based on data from complete blood count with 5-part differential-Whole Blood (LAB25) and relates to the number of neutrophils, lymphocytes and platelets. Beckman Coulter method of counting and sizing were used for complete blood count. The specific calculation formula is as follows: SII\u0026thinsp;=\u0026thinsp;platelet count \u0026times; neutrophil count / lymphocyte count (\u0026times; 10\u003csup\u003e9\u003c/sup\u003e cells/\u0026micro;l). Here, SII was set as an exposure variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Covariates\u003c/h2\u003e \u003cp\u003eVarious covariates that might affect the outcomes were included here. All analyses were fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. They were obtained from the demographics data, questionnaire data and laboratory data of NHANES, respectively. BMI was divided into three categories according to \u0026lt;\u0026thinsp;25.0, 25.0\u0026thinsp;~\u0026thinsp;30.0 and \u0026gt;\u0026thinsp;30.0; family income was divided into three categories according to \u0026lt;\u0026thinsp;1.0, 1.0\u0026thinsp;~\u0026thinsp;3.0 and \u0026gt;\u0026thinsp;3.0; race was divided into Mexican American, non-Hispanic White, non-Hispanic Black and other race; education was divided into lower than high school, high school and more than high school; physical activity was divided into does not meet guidelines and meets guidelines. All information is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcomes\u003c/h2\u003e \u003cp\u003eOutcomes of this study focused on cancer prevalence and mortality, including cardiovascular mortality, cancer mortality and all-cause mortality. The final mortality status of every survey participant was determined by NHANES through December 31 2019.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll participants included in this study were classified according to tertile of SII. When conducting data analysis, chi-square test, one-way ANOVA and Kruskal-Wallis H-test were used to compare the differences among the three groups. All estimates accounted for complex survey designs, and all percentages were weighted. The Values for categorical and continuous variables are expressed as n (%) and weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error, respectively.\u003c/p\u003e \u003cp\u003eThe associations between SII with cancer prevalence and mortality were analyzed using multivariate logistical or Cox proportional regression model to estimate odds ratios (ORs), hazard ratios (HRs) and 95% corresponding confidence intervals (CIs). Proportional hazard assumption was examined using Schoenfeld residual methods. The tertile 1 of SII was set as the reference group in categorical analyses. Three models with different adjustments were as following: a, crude model without any adjustments; b, adjusted for age, sex and race; c, fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein. Since the distribution of SII is specifically skewed to the right, SII values were natural log-transformed in continuous analysis. Next, we performed restricted cubic spline model with fully multivariable adjustments to investigate dose-response associations between SII and study specified outcomes. The number of nodes was determined based on the lowest value of the Akaike information criterion. Likelihood ratio test was used to test nonlinearity. If nonlinearity was detected, two-piecewise logistical or Cox proportional regression models were constructed according to the inflection point. All statistical analyses were performed using R Version 3.6.2, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eOur study ultimately included 26,049 individuals with information on cancer status and systemic immune-inflammatory index. The weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of SII and log-transformed SII (ln-SII) were 587.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46 and 6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, respectively. Of all the participants, 51.68% were male, with an average age of 46.78 years, of whom 2,432 had cancer. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e shows the baseline characteristics of participants stratified according to SII tertile, and it can be seen that the number of cancer patients increased significantly with the rise of SII (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The baseline characteristics of participants with cancer according to SII tertile is presented in \u003cb\u003eTable S2\u003c/b\u003e. The results showed that cardiovascular mortality, cancer mortality and all-cause mortality were significantly higher in cancer patients in the SII tertile 3 group compared to tertile 1 and tertile 2 groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations of SII with cancer prevalence and mortality\u003c/h2\u003e \u003cp\u003eThree different models were used in multivariate logistical or Cox regression models to analyze the associations of SII with cancer prevalence and death. \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e indicated that high levels of SII can significantly increase the prevalence of cancer, especially in tertile 3 group (OR\u0026thinsp;=\u0026thinsp;1.45, 95%CI\u0026thinsp;=\u0026thinsp;1.31\u0026ndash;1.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In addition, SII was strongly associated with an increased risk of cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.80, 95%CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007), cancer mortality (HR\u0026thinsp;=\u0026thinsp;1.64, 95%CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.56, 95%CI\u0026thinsp;=\u0026thinsp;1.31\u0026ndash;1.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in the crude model. Furthermore, as compared to the tertile 1 group in fully adjusted Model 3, SII in tertile 3 group was still positively related to elevated risks of cancer prevalence, cancer and all-cause mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there seen to be a negative association but significant trend of cardiovascular mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Dose-response associations of ln-SII with cancer prevalence and mortality\u003c/h2\u003e \u003cp\u003eAfter adjusting for multiple covariates in Model 3, the nonlinear U-shaped associations between ln-SII and study specified outcomes were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For further evaluation, two piecewise logistical or Cox proportional regression models were used to detect the difference in the relationship at the threshold. As showed in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e, the risk of cancer prevalence decreased until the minimum (ln-SII\u0026thinsp;=\u0026thinsp;5.44) and then increased as SII increased (HR\u0026thinsp;=\u0026thinsp;0.59, 95%CI\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007; HR\u0026thinsp;=\u0026thinsp;1.15, 95%CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0061, respectively). Similarly, the cut-off values of ln-SII for the lowest risks related to all-cause mortality, cancer mortality and cardiovascular mortality were 6.21, 6.27 and 6.21, respectively. The risks of these outcomes also showed a trend of first decreasing and then increasing before and after the cut-off values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs one of the main causes of death, cancer has brought great burden to all mankind and aroused wide concern. The relationship between inflammation and cancer is a new understanding of the occurrence and progression of cancer, and inflammation has become one of hallmarks of cancer (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). There are no systematic indicators to comprehensively reflect the role of inflammation in all cancer morbidity and mortality. This study examined the associations between systemic inflammation and risk of cancer and death, with SII as a marker of systemic inflammation. The study found that both too high and too low levels of SII increased the prevalence of cancer, cancer mortality and all-cause mortality, but only when lnSII exceeded 6.21, the cardiovascular mortality of tumor patients increased.\u003c/p\u003e \u003cp\u003eImmune-inflammatory response is an important way for the body to adapt to the environment and fight against diseases, and it also plays a complex role in the occurrence, development and treatment of tumors. Chronic inflammation is involved in the induction of early carcinogenesis of malignant tumors, tumor growth and metastasis, and promote tumor drug resistance (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Researchers have explored many different markers of immune-inflammatory response, and SII is a readily available and appropriate parameter. SII is a composite index, based on blood cell analysis, that correlates with neutrophils, platelets, and lymphocyte counts (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Neutrophils are believed to contribute to various aspects of cancer, including growth and metastasis of the primary tumor, impaired immune surveillance, and treatment resistance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Platelets inhibit the anti-tumor immunity of T cells and promote the metastasis of tumor cells (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Lymphocytes are important participants in tumor immunity. Logically, high levels of neutrophils and platelets and low levels of lymphocytes are positively related to the risk of cancer and death.\u003c/p\u003e \u003cp\u003eHere, we analyzed publicly available data from the NHANES 1999\u0026ndash;2010 to explore the impact of SII levels on cancer prevalence and death in U.S. adults. After tripartite grouping of SII and adjusting for multiple covariates, our results showed that cancer prevalence, cancer mortality and all-cause mortality were significantly increased in the group with higher SII level, while the cardiovascular mortality only showed an increasing trend without statistical significance. These results are consistent with the majority of published literature. In 2015, SII was first found to be positively associated with overall survival in non-small cell lung cancer, with high levels of SII indicating high mortality (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Subsequent studies have shown that SII directly affects the overall survival and progression-free survival of nasopharyngeal carcinoma, and is an independent risk factor for lymph node metastasis of endometrial cancer (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Other studies have shown that SII is strongly associated with overall survival and tumor stage of colorectal cancer, and significantly associated with poor survival and adverse pathological features in patients with bladder cancer (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe U-shaped dose-response associations between SII and the specified outcomes attracted our attention. Previously, a prospective cohort study conducted in Rotterdam, the Netherlands, considered that SII was significantly positively correlated with the incidence of cancer (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, in our study, when lnSII was less than 5.44, SII increased and cancer prevalence decreased, while when lnSII was greater than 5.44, SII increased and cancer prevalence increased. We considered the possible reasons for the differences, first of all, the included population was different. The population in this study was from the United States, and the included population was over 20 years old, while the former target was Dutch people over 45. In addition, the included covariates were different. The associations of SII with cancer and all-cause mortality showed the same trend. The same phenomenon was observed in a previous study of SII in hypertensive patients in the NHANES database in relation to all-cause mortality and cancer mortality (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), suggesting that low levels of immune inflammation in the body is another risk factor for cancer and all-cause death, which deserves further confirmation by more studies. Interestingly, the analysis showed that there was a little change of cardiovascular mortality with SII in cancer patients before the threshold (\u003cem\u003ep\u003c/em\u003e＞0.05), after which high SII levels were associated with high mortality. This has been reported for the first time in cancer patients, but there are similarities to studies in other populations (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are some limitations in our study. First, SII was obtained only once according to the complete blood count with 5-part differential-Whole Blood, which may lead to accidental bias in the conclusion. Second, although covariates are included as much as possible for adjustment, the possibility of other confounding factors cannot be completely avoided. Finally, the analysis of the database only in the United States may not be applicable to people in other regions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eHere, we suggest that SII may be a potential earlier warning marker for the prevalence and mortality of total cancers.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZY, JW, JC, LY and TH conceived and designed the study. ZY, JW, LT, ZX, and LB collected and analysed the data. ZY drafted the paper and TH revised the manuscript. All authors have reviewed the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the PhD research startup foundation of the Third Affiliated Hospital of Zhengzhou University (grant BS20230101).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed datasets about the surveys are available at www.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics review board of the National Center for Health Statistics and written informed consents were obtained from each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there is no conflict of interest involved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng T, Lyon CJ, Bergin S, Caligiuri MA, Hsueh WA. Obesity, Inflammation, and Cancer. Annu Rev Pathol. 2016;11:421\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerwenka A, Lanier LL. Natural killer cell memory in infection, inflammation and cancer. Nat Rev Immunol. 2016;16(2):112\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChovanec M, Cierna Z, Miskovska V, et al. Systemic immune-inflammation index in germ-cell tumours. 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Systemic immune-inflammation index is associated with hepatic steatosis: Evidence from NHANES 2015\u0026ndash;2018. Front Immunol. 2022;13:1058779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu JP, Zeng RX, Zhang YZ, et al. Systemic inflammation markers and the prevalence of hypertension: A NHANES cross-sectional study. Hypertens Res. 2023;46(4):1009\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan R, Chen Y, Xu G, Pan W, Lv Y, Zhang Z. Combined systemic immune-inflammatory index and prognostic nutritional index predict outcomes in advanced non-small cell lung cancer patients receiving platinum-doublet chemotherapy. Front Oncol. 2023;13:996312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Gao Y, Wu Y, Lin H. Prognostic value of systemic immune-inflammation index in patients with urologic cancers: a meta-analysis. Cancer Cell Int. 2020;20:499.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei H, Xu S, Mao X, Chen X, Chen Y, Sun X, Sun P. Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer. J Inflamm Res. 2021;14:7131\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenga SS, Grose RP. Hallmarks of cancer-the new testament. Open Biol. 2021;11(1):200358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao H, Wu L, Yan G, Chen Y, Zhou M, Wu Y, Li Y. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal Transduct Target Ther. 2021;6(1):263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong X, Cui B, Wang M, Yang Z, Wang L, Xu Q. Systemic Immune-inflammation Index, Based on Platelet Counts and Neutrophil-Lymphocyte Ratio, Is Useful for Predicting Prognosis in Small Cell Lung Cancer. Tohoku J Exp Med. 2015;236(4):297\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuail DF, Amulic B, Aziz M, et al. Neutrophil phenotypes and functions in cancer: A consensus statement. J Exp Med. 2022;219(6):e20220011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordon Y. Tumour immunology: Platelets - a new target in cancer immunotherapy? Nat Rev Immunol. 2017;17(6):348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Qin X, Zhang Y, Xue S, Song X. The prognostic predictive value of systemic immune index and systemic inflammatory response index in nasopharyngeal carcinoma: A systematic review and meta-analysis. Front Oncol. 2023;13:1006233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei H, Xu S, Mao X, Chen X, Chen Y, Sun X, Sun P. Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer. J Inflamm Res. 2021;14:7131\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang S, Yang YX, Pan WJ, Li Y, Zhang JH, Gao Y, Liu S. Prognostic value of systemic immune inflammation index and geriatric nutrition risk index in early-onset colorectal cancer. Front Nutr. 2023;10:1134300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Cao D, Huang Y, Xiong Q, Tan D, Liu L, Lin T, Wei Q. The Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Bladder Cancer. Front Immunol. 2022;13:865643.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFest J, Ruiter R, Mulder M, Groot Koerkamp B, Ikram MA, Stricker BH, van Eijck CHJ. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int J Cancer. 2020;146(3):692\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Li P, Zhang Y, et al. Association of systemic immune inflammatory index with all-cause and cause-specific mortality in hypertensive individuals: Results from NHANES. Front Immunol. 2023;14:1087345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKou J, Huang J, Li J, Wu Z, Ni L. Systemic immune-inflammation index predicts prognosis and responsiveness to immunotherapy in cancer patients: a systematic review and meta\u0026ndash;analysis, Clin Exp Med, 2023 Mar 26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia Y, Xia C, Wu L, Li Z, Li H, Zhang J. Systemic Immune Inflammation Index (SII), System Inflammation Response Index (SIRI) and Risk of All-Cause Mortality and Cardiovascular Mortality: A 20-Year Follow-Up Cohort Study of 42,875 US Adults. J Clin Med. 2023;12(3):1128.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.13419913419914%\" colspan=\"4\"\u003e\n \u003cp\u003eTable 1 Associations between systemic immune-inflammation index with cancer prevalence and mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8658008658008658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.057720057720058%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.07647907647907%\" colspan=\"3\"\u003e\n \u003cp\u003eOdds ratio / Hazard ratio (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8658008658008658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.972972972972975%\"\u003e\n \u003cp\u003eModel I\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.972972972972975%\"\u003e\n \u003cp\u003eModel II\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.972972972972975%\"\u003e\n \u003cp\u003eModel III\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.0810810810810811%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.13419913419914%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eCancer prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8658008658008658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.03, 1.28) 0.0124\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e1.07 (0.96, 1.20) 0.2427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e1.07 (0.96, 1.20) 0.2277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.45 (1.31, 1.61) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.17 (1.05, 1.31) 0.0045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.03, 1.29) 0.0168\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.13419913419914%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eCardiovascular mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8658008658008658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.99 (0.72, 1.35) 0.9298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e1.00 (0.77, 1.30) 0.9929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.96 (0.71, 1.28) 0.7677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.80 (1.28, 2.52) 0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.13, 1.94) 0.0041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e1.38 (0.99, 1.93) 0.0601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eCancer mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.81 (0.62, 1.07) 0.1366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.77 (0.58, 1.02) 0.0721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.72 (0.54, 0.97) 0.0281\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.64 (1.28, 2.09) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.58 (1.21, 2.06) 0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.43 (1.09, 1.87) 0.0091\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.97 (0.82, 1.16) 0.7560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.97 (0.83, 1.13) 0.6663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.96 (0.83, 1.11) 0.5929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003eTertile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.56 (1.31, 1.85) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.44 (1.25, 1.65) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.35 (1.16, 1.56) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.028818443804035%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.368876080691642%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8645533141210374%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.13419913419914%\" colspan=\"4\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCrude model;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age, sex and race;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.8658008658008658%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"764\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eTable 2 The results of two piecewise regression model of systemic immune-inflammation index with cancer prevalence and mortality\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.15013404825737%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.84986595174263%\" colspan=\"4\"\u003e\n \u003cp\u003eOdds ratio / Hazard ratio (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.067164179104477%\"\u003e\n \u003cp\u003eCancer prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.813432835820894%\"\u003e\n \u003cp\u003eCardiovascular mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.559701492537314%\"\u003e\n \u003cp\u003eCancer mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.559701492537314%\"\u003e\n \u003cp\u003eAll-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.15013404825737%\"\u003e\n \u003cp\u003eCut-off value \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (as continuous variables, ln-transformed SII)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.292225201072387%\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.828418230563003%\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.15013404825737%\"\u003e\n \u003cp\u003e\u0026lt; Cut-off value \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (per 1 ln-transformed SII increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.292225201072387%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59 (0.43, 0.80) 0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.828418230563003%\"\u003e\n \u003cp\u003e0.88 (0.62, 1.26) 0.4820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.69 (0.56, 0.85) 0.0003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86 (0.74, 0.99) 0.0411\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.15013404825737%\"\u003e\n \u003cp\u003e\u0026gt;\u0026nbsp;Cut-off value \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(per 1 ln-transformed SIIl increment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.292225201072387%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15 (1.04, 1.27) 0.0061\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.828418230563003%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71 (1.22, 2.38) 0.0018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.22 (1.58, 3.12) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.59 (1.35, 1.88) \u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.15013404825737%\"\u003e\n \u003cp\u003eLog Likelihood Ratio Tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.292225201072387%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.828418230563003%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.364611260053618%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAll analyses were fully adjusted for age, sex, body mass index, race, family income, education, smoking, drinking, physical activity, diabetes, cardiovascular diseases, hypertension, hyperlipidemia, chronic kidney disease, serum albumin, total bilirubin, white blood cell count, red blood cell count, hemoglobin and C-Reactive protein.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"","lastPublishedDoi":"10.21203/rs.3.rs-3507394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3507394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To study the predictive effect of systemic immune-inflammatory index (SII) on cancer prevalence and mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Participants with SII and cancer status were screened from the National Health and Nutrition Examination Survey database from 1999 to 2010, and their baseline characteristics were analyzed according to the SII tertile. Multivariable logistical or Cox proportional hazards models were used to analyze the associations between SII with cancer prevalence or mortality. The mortality was followed through December 31 2018. For further evaluation on associations of SII with specified outcomes, restricted cubic spline and two piecewise regression models were adopted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis study eventually included 26,049 individuals, of whom 2,432 were cancer patients. According to the SII tertile group, cancer prevalence increased significantly with the rise of SII. In the highest tertile of SII, SII was significantly related to cancer prevalence (OR=1.45, 95%CI= 1.31 - 1.61, p\u0026lt;0.05). Moreover, during a median follow-up of 12.75 year, 329, 351, and 1,202 cardiovascular, cancer and all-cause deaths occurred among cancer patients, respectively. The results indicated that highest level of SII was significantly associated with increased cardiovascular mortality (HR=1.80, 95%CI= 1.28 - 2.52), cancer mortality (HR=1.64, 95%CI= 1.28 - 2.09) and all-cause mortality (HR=1.56, 95%CI= 1.31- 1.85). The model adjusted for multiple covariates still showed the same trend. U-shaped dose-response associations between log-transformed SII (ln-SII) with prevalence and mortality of cancer were detected. The threshold values of ln-SII for the lowest risk associated with cancer prevalence, cardiovascular mortality cancer mortality and all-cause mortality were 5.44, 6.21, 6.27 and 6.21, respectively. Above thresholds, SII was positively associated with increased risk of above outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e SII may be a potential earlier warning marker for the prevalence and mortality of total cancers.\u003c/p\u003e","manuscriptTitle":"Association of systemic immune-inflammatory index with cancer prevalence and mortality: Results from NHANES 1999-2010","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-08 00:07:58","doi":"10.21203/rs.3.rs-3507394/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":"d0110bac-958e-451a-9f5f-f14690788a03","owner":[],"postedDate":"November 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-14T07:29:35+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-08 00:07:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3507394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3507394","identity":"rs-3507394","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}