Prevalence of Anemia in Patients With Cancer and Its Association With Dietary Inflammatory Index: A Population-Based Study From NHANES 1999 to 2023

Prevalence of Anemia in Patients With Cancer and Its Association With Dietary Inflammatory Index: A Population-Based Study From NHANES 1999 to 2023 | 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 Prevalence of Anemia in Patients With Cancer and Its Association With Dietary Inflammatory Index: A Population-Based Study From NHANES 1999 to 2023 Qingling Guo#, Qingcong Guo#, Li Zhou, Youping Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7244002/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 : Anemia frequently occurs among cancer patients, yet whether the dietary inflammatory index (DII) is related to anemia remains unclear, highlighting a significant gap in current research. Objective : This study sought to determine the prevalence of anemia among cancer patients and its correlation with the DII. Methods : This study, which included 4600 cancer survivors, utilized data sourced from the National Health and Nutrition Examination Survey database covering the period from 1999 to 2023. Information on age, sex, race, education level, body mass index, smoking status, drinking status, hypertension, and diabetes was gathered for all participants. The relationship between anemia risk and DII was examined using logistic regression analysis. Results : Anemic cancer patients were found to have higher DII levels compared to their nonanemic counterparts.In multivariate regression models, the adjusted odds ratios (ORs) for the second (Q2), third (Q3), and fourth (Q4) quartiles of DII were 1.24 (95% CI, 0.95–1.63; P = 0.118), 1.12 (95% CI, 0.84–1.48; P = 0.441), and 1.68 (95% CI, 1.28–2.21; P < 0.001), respectively. This finding indicates that for every 1-unit increase in the DII, the incidence of anemia in Q4 is 68% higher than that in Q1.Similar patterns of association were observed for subgroup analysis (all P values for interaction > 0.05). Conclusion : Among cancer patients, elevated DII levels have been linked to an increased risk of anemia. Therefore, greater emphasis should be placed on managing dietary inflammation as a means to more effectively prevent and manage anemia.This association may be important to consider in the context of clinicians prescribing for cancer patients prevalence of anemia management. anemia dietary inflammatory index cancer NHANES association cohort study Figures Figure 1 Figure 2 1. Introduction Anemia is a common comorbidity in patients with cancer, significantly impacting their quality of life, treatment tolerance, and overall prognosis. The etiology of cancer-related anemia is multifactorial, encompassing factors such as chronic inflammation, nutritional deficiencies, and the direct effects of cancer treatments. Chronic inflammation, often present in cancer patients [1], has been implicated in the pathogenesis of anemia through various mechanisms, including the suppression of erythropoiesis, alterations in iron metabolism, and the production of pro-inflammatory cytokines that interfere with red blood cell production [2–5]. Dietary factors have long been recognized as important determinants of health outcomes, including the risk of developing anemia. The Dietary Inflammatory Index (DII) is a composite score that quantifies the inflammatory potential of an individual's diet based on the intake of various nutrients and bioactive compounds. Prior research has indicated that a diet with a higher Dietary Inflammatory Index (DII) score, which is a marker of a pro-inflammatory diet, may be linked to a heightened risk of chronic conditions such as cardiovascular disease, diabetes, and specific cancers [6–8]. However, the relationship between DII and anemia, particularly in the context of cancer, remains underexplored. Given the high prevalence of anemia in cancer patients and the potential role of diet in modulating inflammation and anemia risk, understanding the association between DII and anemia in this population is of significant clinical and public health importance. A useful tool for investigating this relationship in a sizable, nationally representative sample of the American population is the National Health and Nutrition Examination Survey (NHANES). Using data from NHANES 1999 to 2023, we hope to learn more about the prevalence of anemia in cancer patients and how it relates to DII. Our findings may contribute to the development of dietary interventions aimed at reducing the burden of anemia in cancer patients and improving their overall health outcomes. 2. Methods 2.1. Study Population The NHANES [9] (https://www.cdc.gov/nchs/nhanes/index.htm) provided the data used in this study. The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) carried out a cross-sectional survey to ascertain disease prevalence and risk factors in the U.S. population, NHANES. Since 1999, NHANES has been carried out every two years. Two 24-hour dietary recalls, health behaviors, laboratory tests, and physical examinations were among the data submitted by qualified participants. Mobile exam centers (MECs) are where these health measurement data are gathered.Data from nine 2-year NHANES cycles and two 3-year NHANES cycles (1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, 2017–2020 and 2021–2023) were combined in this study. Figure 1 depicts the thorough inclusion and exclusion procedure. All participants gave written informed consent, and NHANES was approved by the National Center for Health Statistics Ethics Review Board. 2.2. Calculation of DII Comprehensive information about the development and validation of the Dietary Inflammatory Index (DII) can be found in earlier studies [10–13]. In summary, the Z-score is calculated by taking an individual's daily intake, subtracting the global average daily intake, and then dividing the result by the standard deviation. [14]Then, it is converted to a percentile score, which is subsequently doubled and subtracted by '1' to achieve a symmetrical distribution. Further, the percentile value is multiplied by the corresponding overall inflammation effect score. Finally, by adding up each DII score, we can obtain an individual 'overall DII score'. To gather dietary data for the current investigation, we used two 24HRs from the NHANES database. The DII score was derived from 26 out of 45 food parameters, which include carbohydrates, protein, fat, alcohol, fiber, cholesterol, and various fatty acids (saturated, monounsaturated, and polyunsaturated), as well as omega-3 and omega-6 fatty acids. Additionally, the parameters considered include niacin, vitamins A, B1, B2, B6, B12, C, E, along with iron, magnesium, zinc, selenium, folic acid, beta carotene, and caffeine. Notably, even if fewer than 30 nutrients are used in the DII calculation, scores can still be determined. A low DII score suggests an anti-inflammatory dietary pattern, while a high DII score indicates a pro-inflammatory dietary pattern. 2.3. Anemia Definition Hemoglobin data were obtained from the complete Blood Count with a 5-part Differential available in the NHANES database. Anemia was defined based on the serum hemoglobin (Hb) threshold (g/dL) as recommended by the World Health Organization (WHO) [15,16]. 2.4. Covariates In our study analyses, we included demographic characteristics identified as potential confounders on the NHANES website as covariates. These characteristics encompassed age, gender (male, female), race (non-Hispanic Black, non-Hispanic White, Mexican Americans, and other races), educational attainment (less than 12th grade, high school graduate/GED or equivalent, and other), smoking status (yes, no), drinking status (yes, no), hypertension status (yes, no), diabetes status (yes, no), and Body Mass Index (BMI) categorized as <25, 25–30, and ≥30. This information was gathered from the relevant questionnaire and informed by a review of the literature as well as clinical experience [17,18]. 2.5. Statistical Analyses All participants underwent a descriptive analysis. Depending on the data type, continuous data were analyzed using either the mean and standard deviation (SD) or the median and interquartile range (IQR). Categorical variables were expressed as proportions (%). The χ² test was employed to compare categorical variables. For comparisons involving normally distributed data, one-way analysis of variance (ANOVA) was utilized, while the Kruskal–Wallis test was applied for skewed distributions. The relationship between the Dietary Inflammatory Index (DII) and anemia was examined using logistic regression models. Both non-adjusted and multivariate adjusted models were used: Model 1, without adjustment for any covariates; Model 2, was adjusted for Model 1 plus age, sex, race, and education level; Model 3 adjusted for covariates in Model 2 and body mass index, smoking status, and drinking status; Model 4 was adjusted for Model 3 plus hypertension, and diabetes. Subgroup analyses were conducted based on age, gender, smoking status, drinking status, as well as hypertension and diabetes, to assess the consistency of the association between the Dietary Inflammatory Index (DII) and anemia. Statistical significance was evaluated by comparing the adjusted odds ratios (ORs) to 1.0, along with reporting 95% confidence intervals (CIs). All analyses were performed using the statistical software R (http://www.R-project.org, The R Foundation) and Free Statistics software version 1.7. A two-tailed test was employed, with a significance level set at P < .05. 3. Results 3.1. Characteristics of Participants The detailed process of inclusion and exclusion is shown in Figure 1. Initially, 119,555 potential participants were identified from eleven cycles of NHANES (NHANES 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018). After excluding 114,955 participants with missing DII data, anemia data and other covariates data, or participants who were non-cancer, we recruited the remaining 4600 eligible participants. Table 1 describes the basic characteristics of the study participants with or without anemia. Of the total 4600 participants, 551 individuals met the criteria for the diagnosis of anemia in this study and the prevalence of anemia was 11.98% overall. There were significant differences between the two groups in the distribution of DII, quartile grouping of DII, age, gender, race, education level, diabetes status and hypertension status ( P < 0.001). Importantly, compared with the cancer survivors without anemia, those with anemia had higher DII. 3.2. Associations Between DII and Anemia Associations between DII and anemia using logistic regression are shown in Table 2. In the crude model 1 without adjustment, odds ratios (ORs) (95% CI) between the risk of anemia and DII across quartile 2, 3 and 4（Q2, Q3 and Q4） compared with quartile 1(Q1) were 1.28 (95% CI, 0.98–1.67; P = 0.069), 1.16 (95% CI, 0.98–1.52; P = 0.272) and 1.73 (95% CI, 1.34–2.23; P < 0.001), respectively. ORs (95% CI) of model 2 between the risk of anemia and DII after adjustment for age, gender, and education level across Q2, Q3 and Q4 compared with Q1 were 1.26 (95% CI, 0.96–1.65; P = 0.094), 1.13 (95% CI, 0.85–1.49; P = 0.396) and 1.73 (95% CI, 1.32–2.26; P < 0.001), respectively. Model 3 adjusted for covariates in Model 2 and body mass index, smoking status, and drinking status, ORs (95% CI) between the risk of anemia and DII across Q2, Q3 and Q4 compared with Q1 were 1.27 (95% CI, 0.97–1.67; P = 0.085), 1.14 (95% CI, 0.86–1.51; P = 0.354) and 1.74 (95% CI, 1.33–2.29; P < 0.001), respectively. While, after adjustment for all the covariates of interest in model 4, the ORs (95% CI) between the risk of anemia and DII across Q2, Q3 and Q4 compared with Q1 were 1.24 (95% CI, 0.95–1.63; P = 0.118), 1.12 (95% CI, 0.84–1.48; P = 0.441) and 1.68 (95% CI, 1.28–2.21; P < 0.001), respectively. This finding indicates that for every 1-unit increase in the DII, the incidence of anemia in Q4 is 68% higher than that in Q1.Additionally, the trend test also showed that the risk of anemia and DII were all statistically significant in these four models ( P for trend < 0.001 in the crude model 1; P = 0.001 in model 2; P = 0.001 in model 3; P = 0.002 in model 4). 3.3. Stratified Analyses Stratified analyses were performed across different subgroups to assess potential effect modifications in the association between the Dietary Inflammatory Index (DII) and anemia among individuals with cancer. However, no significant interactions were identified in any subgroup when stratified by gender, age, BMI, or hypertension (Figure 2). 4. Discussion An increasing amount of evidence has established a connection between dietary inflammation levels and anemia [19,20]; however, the relationship between the Dietary Inflammatory Index (DII) and anemia in cancer patients remains a subject of interest. In this cross-sectional study, we aimed to address this knowledge gap. A total of 4,600 participants from the NHANES database were included, and the key findings are as follows: (1) the prevalence of anemia among cancer patients in the US from 1999 to 2023 was 11.98%.This prevalence is consistent with previous studies highlighting the significant burden of anemia in cancer patients. (2) A higher mean DII was found in anemia patients than in nonanemia patients. (3) DII was positively correlated with the risk of anemia in cancer patients, both before and after adjusting for covariates, increased the risk by 9%. (4) Compared with Q1, the risk of anemia in Q4 increased by 73%, 73%, 74%, and 68% in model 1, model 2, model 3, and model 4, respectively. The subgroup analysis further confirmed the validity of the results. This suggests that a pro-inflammatory diet may contribute to the development of anemia in this population. In recent decades, an increasing number of studies have emphasized a crucial link: the connection between dietary patterns, types of food, inflammation, and disease risk [21,22]. Against this backdrop, the DII, as a tool for assessing the inflammatory potential of diet, can reflect the impact of diet on chronic diseases. A high-inflammatory diet is closely related to an increased risk of developing chronic diseases such as rheumatoid arthritis, diabetes, and cancer [23–25]. The mechanisms mainly involve the activation of inflammatory cells, the release of inflammatory cytokines, the increase of oxidative stress, and the regulatory effects of nutrients on inflammation and metabolism. A high-inflammatory diet is associated with an increased risk of developing various types of cancer. People with a higher DII score have a significantly higher risk of developing cancer than those with a lower DII score. Therefore, adjusting dietary habits to reduce the DII score may help prevent and improve these chronic diseases. Pro-inflammatory diets have been reported to be associated with the development of cancer. This is because chronic inflammation can promote the occurrence and development of tumors. Inflammatory cytokines (such as IL-6, TNF-α) can promote the proliferation, survival, and invasion of tumor cells [26,27]. A high-inflammatory diet can increase the level of oxidative stress, leading to DNA damage, thereby increasing the risk of developing cancer [28]. Chronic inflammation is considered a potential cause of anemia, as it can lead to dysfunction of red blood cells. Here, they are unable to effectively absorb and utilize iron. Moreover, in an inflammatory state, the body cannot normally respond to erythropoietin, a hormone produced by the kidneys that prompts the bone marrow to produce red blood cells. Our data suggest that a high DII pro-inflammatory diet may be a potential risk factor for anemia in cancer patients. Anemia is a prevalent global health concern, and recent research has highlighted a significant link between anemia and inflammation. In both animal models and patients with inflammatory diseases, increased levels of hepcidin are associated with diminished expression of iron transporters in duodenal enterocytes and macrophages, resulting in impaired dietary iron absorption [29,30].Moreover, even low-grade inflammation can hinder iron absorption in the gut, thereby reducing the supply of iron needed for erythropoiesis and consequently having a negative impact on anemia [31]. Inflammatory cytokines can also shorten the lifespan of red blood cells by activating macrophages. There is a close relationship between anemia and inflammation, with inflammation interfering with the production and function of red blood cells through various mechanisms, thereby causing anemia. Pro-inflammatory diets increase the body's inflammatory response, leading to elevated hepcidin levels, which in turn inhibit the absorption and utilization of iron. This reduces the supply of iron, affects erythropoiesis, and thus increases the risk of anemia. Certain components in pro-inflammatory diets (such as saturated fats and high sugar) can increase oxidative stress levels, leading to DNA damage and thereby affecting erythropoiesis [32–34]. Pro-inflammatory diets may also lead to insufficient intake of certain nutrients (such as vitamin C), which are crucial for iron absorption and utilization [35,36]. Consequently, modifying dietary habits by decreasing the consumption of pro-inflammatory foods and increasing the intake of anti-inflammatory options may aid in the prevention and management of anemia. It is important to note that while following a vegetarian diet may contribute to a lower Dietary Inflammatory Index (DII), individuals susceptible to anemia should carefully regulate their diet and supplements to ensure optimal health. After adjusting for multiple covariates, including age, gender, race, education level, BMI, smoking status, drinking status, hypertension, and diabetes, we observed a positive association between DII and anemia risk in cancer patients. Specifically, the adjusted odds ratios (ORs) for the second (Q2), third (Q3), and fourth (Q4) quartiles of DII were 1.24, 1.12, and 1.68, respectively. This indicates that higher DII levels are associated with a greater risk of anemia in cancer patients. The trend test also showed that the risk of anemia increased with higher DII levels, further supporting the positive association. Subgroup analyses were conducted to evaluate potential effect modifications of the association between DII and anemia in different demographic and clinical groups. No significant interactions were found in any subgroup following stratification by gender, age, BMI, hypertension history, and diabetes history. This suggests that the association between DII and anemia risk is consistent across various subgroups of cancer patients. These findings have important clinical implications. Given the high prevalence of anemia in cancer patients and the potential role of diet in modulating inflammation and anemia risk, controlling dietary inflammation may be a valuable strategy for preventing and treating anemia in this population. Clinicians should consider dietary interventions aimed at reducing inflammation as part of the comprehensive management of cancer patients. Future research should explore the potential mechanisms underlying the association between DII and anemia in cancer patients and investigate the effectiveness of dietary interventions in reducing anemia risk. 5. Conclusion In conclusion, our study demonstrates that high DII levels are associated with an increased risk of anemia in cancer patients. This association highlights the importance of dietary inflammation in the development of anemia among cancer patients. Future research should focus on elucidating the underlying mechanisms and evaluating the potential benefits of dietary interventions in managing anemia in this population. Clinicians should consider incorporating dietary assessments and recommendations to reduce inflammation as part of the standard care for cancer patients. This approach may help to mitigate the burden of anemia and improve the overall health outcomes of cancer patients. Declarations Acknowledgements The authors thank the NHANES staff, investigators, and participants. Thanks to the Free Statistics team for providing technical assistance and valuable tools for data analysis and visualization. Author contributions Qingling Guo and Qingcong Guo performed data collection, analysis and drafted the manuscript. Youping Lin and Li Zhou participated in data analysis.Qingling Guo conceived the study and revised the manu script. All authors approved the submitted version. D isclosure statement No potential conflict of interest was reported by the author(s). Ethics approval and consent to participate Not applicable. Funding Funding support hasn’t been received for this study. Data availability statement The authors confirm that data supporting the findings of this study are available within the article References Knight K, Wade S, Balducci L. Chronic Inflammation as a Driver of Anemia in Cancer Patients: Mechanisms and Therapeutic Implications. Blood Rev. 2021;49:100888. doi:10.1016/j.blre.2021.100888. Zhang Q, Kuang W, Liu L, Wang J, Feng L. The Role of Pro-inflammatory Cytokines in Cancer-Related Anemia: Focus on IL-6 and Hepcidin. Cancers (Basel). 2020;12(8):2113. doi:10.3390/cancers12082113. Tessitore A, Girelli D, Campostrini N. 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High-sucrose diet inhibits erythroid differentiation via oxidative stress and DNA damage. Antioxidants (Basel). 2023;12(2):417. doi:10.3390/antiox12020417. Moser SC, van Dijk BS, Willems PHGM, Swinkels DW, Fleming RE, van Swelm RPL. Vitamin C deficiency exacerbates anemia in inflammation by downregulating ferroportin expression. Nutrients. 2022;14(17):3476. doi:10.3390/nu14173476. Blanco-Rojo R, Toxqui L, López-Parra AM, Pérez-Granados AM, Arroyo-Pardo E, Vaquero MP. Combined vitamin C and iron deficiency in pro-inflammatory diets impairs hematopoietic function. J Nutr. 2019;149(12):2207-2215. doi:10.1093/jn/nxz185. Tables Table 1. Descriptive characteristics of participants with and without anemia in the enrolled population of NHANES. Variables Total (n = 4600) No anemia (n = 4049) Anemia (n = 551) p Age, Mean ± SD 65.9 ± 13.7 65.3 ± 13.8 70.1 ± 12.5 < 0.001 Gender, n (%) < 0.001 Male 2157 (46.9) 1847 (45.6) 310 (56.3) Female 2443 (53.1) 2202 (54.4) 241 (43.7) Race2, n (%) < 0.001 Non-Hispanic black 3344 (72.7) 3016 (74.5) 328 (59.5) Non-Hispanic white 563 (12.2) 420 (10.4) 143 (26) Mexican American 270 ( 5.9) 239 (5.9) 31 (5.6) Other races 423 ( 9.2) 374 (9.2) 49 (8.9) Education2, n (%) < 0.001 Less than 12th grade 905 (19.7) 755 (18.6) 150 (27.2) High school graduate/GED or equivalent 1018 (22.1) 896 (22.1) 122 (22.1) Other 2677 (58.2) 2398 (59.2) 279 (50.6) Smoke, n (%) 0.891 No 2108 (45.8) 1857 (45.9) 251 (45.6) Yes 2492 (54.2) 2192 (54.1) 300 (54.4) Drink, n (%) 0.063 No 595 (12.9) 510 (12.6) 85 (15.4) Yes 4005 (87.1) 3539 (87.4) 466 (84.6) Hypertension, n (%) < 0.001 No 1697 (36.9) 1571 (38.8) 126 (22.9) Yes 2903 (63.1) 2478 (61.2) 425 (77.1) Diabetes, n (%) < 0.001 No 3457 (75.2) 3089 (76.3) 368 (66.8) Yes 1143 (24.8) 960 (23.7) 183 (33.2) BMI, Mean ± SD 28.9 ± 6.5 29.0 ± 6.4 28.5 ± 7.0 0.063 DII, Median (IQR) 1.7 (0.2, 3.0) 1.7 (0.1, 2.9) 2.1 (0.6, 3.2) < 0.001 DII, n (%) < 0.001 Q1 1150 (25.0) 1040 (25.7) 110 (20) Q2 1150 (25.0) 1013 (25) 137 (24.9) Q3 1150 (25.0) 1024 (25.3) 126 (22.9) Q4 1150 (25.0) 972 (24) 178 (32.3) Note Data presented are Mean ± SD,Median (IQR),or n (%). Abbreviations:BMI,body mass index.DII,dietary inflammatory index. Table 2 ： Association between DII level with anemia among participants with cancer in the NHANES 1999 – 2023. Variable OR(95%CI) Model 1 p-Value Model 2 p-Value Model 3 p-Value Model 4 p-Value DII 1.09(1.04~1.15) <0.001 1.09 (1.03~1.15) 0.001 1.09 (1.04~1.15) 0.001 1.09 (1.03~1.14) 0.002 Quartiles Q1 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 1.28 (0.98~1.67) 0.069 1.26 (0.96~1.65) 0.094 1.27 (0.97~1.67) 0.085 1.24 (0.95~1.63) 0.118 Q3 1.16 (0.89~1.52) 0.272 1.13 (0.85~1.49) 0.396 1.14 (0.86~1.51) 0.354 1.12 (0.84~1.48) 0.441 Q4 1.73 (1.34~2.23) <0.001 1.73 (1.32~2.26) <0.001 1.74 (1.33~2.29) <0.001 1.68 (1.28~2.21) <0.001 P for trend <0.001 <0.001 <0.001 <0.001 Notes: Model 1 wascrude model. Model 2 was adjusted for Model 1 plus age, sex, race, and education level. Model 3 was adjusted for Model 2 plus body mass index , smoking status,and drinking status. Model 4 was adjusted for Model 3 plus hypertension, and diabetes. Abbreviations: OR, hazard ratio; 95% CI, 95% confidence interval. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7244002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514655531,"identity":"d0a8cd7a-5203-4ff2-9e3c-c21ed9121c9e","order_by":0,"name":"Qingling Guo#","email":"","orcid":"","institution":"Binhaiwan Central Hospital of Dongguan ， Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Qingling","middleName":"","lastName":"Guo#","suffix":""},{"id":514655533,"identity":"58f5ff07-fa10-4f2b-a74b-4370f9ad299f","order_by":1,"name":"Qingcong Guo#","email":"","orcid":"","institution":"Daojiao Hospital of Dongguan ， Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Qingcong","middleName":"","lastName":"Guo#","suffix":""},{"id":514655535,"identity":"4a67d766-0b4a-4226-87a2-73bbf76ce949","order_by":2,"name":"Li Zhou","email":"","orcid":"","institution":"Binhaiwan Central Hospital of Dongguan ， Dongguan","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhou","suffix":""},{"id":514655536,"identity":"c73b226d-cabc-4b5d-a50c-9f0c88c2d15d","order_by":3,"name":"Youping Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3QsQrCMBCA4SuF6nCQTU4UfIUUQToIvoAP0S5ODo6dRBA61V1x8BX6CJFAXCquBZeKL5Cxo9ZVsXFzyD/n4+4CYLP9YewghQhjQtZaC6ErA0JCRWWZB/1uqqLjLjUgHPKhf0viMS+mQ9n2TIizGlGYE0KBWgLCgHVEA3FhRvUtzn6TyUUA/m4ffie+AvWa4vbPmdwihPzaQHjuJBQlhB7NS4meCSlcl9cEaQ5mhArPKevFCBV/fjI138IOF32s4uVkclrfta7GA9ZrIG9Tf3tus9lsts89AIIwSrvK+bikAAAAAElFTkSuQmCC","orcid":"","institution":"Binhaiwan Central Hospital of Dongguan ， Dongguan","correspondingAuthor":true,"prefix":"","firstName":"Youping","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-07-29 13:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7244002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7244002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91817138,"identity":"7c3e19f0-ecde-4949-969d-7b5f086991cb","added_by":"auto","created_at":"2025-09-22 06:53:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87836,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the patient selection process.(For details, see the attached page)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7244002/v1/dfd03d9e4199478cfbddaa5a.png"},{"id":91489051,"identity":"8ae2f6af-5314-4c5e-a3ce-7f7c33877b07","added_by":"auto","created_at":"2025-09-17 05:12:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89363,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analyses of the association between DII level with anemia according to baseline characteristics.(For details, see the attached page)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7244002/v1/1b47353eaba463e82dd6b0fc.png"},{"id":97894974,"identity":"908564ec-d0b8-4324-bae7-6626c3505ad3","added_by":"auto","created_at":"2025-12-10 15:33:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1696795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7244002/v1/3dcd58c5-43c3-461e-a159-79322b546bcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of Anemia in Patients With Cancer and Its Association With Dietary Inflammatory Index: A Population-Based Study From NHANES 1999 to 2023","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAnemia is a common comorbidity in patients with cancer, significantly impacting their quality of life, treatment tolerance, and overall prognosis. The etiology of cancer-related anemia is multifactorial, encompassing factors such as chronic inflammation, nutritional deficiencies, and the direct effects of cancer treatments. Chronic inflammation, often present in cancer patients [1], has been implicated in the pathogenesis of anemia through various mechanisms, including the suppression of erythropoiesis, alterations in iron metabolism, and the production of pro-inflammatory cytokines that interfere with red blood cell production [2\u0026ndash;5].\u003c/p\u003e\n\u003cp\u003eDietary factors have long been recognized as important determinants of health outcomes, including the risk of developing anemia. The Dietary Inflammatory Index (DII) is a composite score that quantifies the inflammatory potential of an individual\u0026apos;s diet based on the intake of various nutrients and bioactive compounds. Prior research has indicated that a diet with a higher Dietary Inflammatory Index (DII) score, which is a marker of a pro-inflammatory diet, may be linked to a heightened risk of chronic conditions such as cardiovascular disease, diabetes, and specific cancers [6\u0026ndash;8]. However, the relationship between DII and anemia, particularly in the context of cancer, remains underexplored.\u003c/p\u003e\n\u003cp\u003eGiven the high prevalence of anemia in cancer patients and the potential role of diet in modulating inflammation and anemia risk, understanding the association between DII and anemia in this population is of significant clinical and public health importance. A useful tool for investigating this relationship in a sizable, nationally representative sample of the American population is the National Health and Nutrition Examination Survey (NHANES). Using data from NHANES 1999 to 2023, we hope to learn more about the prevalence of anemia in cancer patients and how it relates to DII. Our findings may contribute to the development of dietary interventions aimed at reducing the burden of anemia in cancer patients and improving their overall health outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch4\u003e\u003cem\u003e2.1. Study Population\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe NHANES [9] (https://www.cdc.gov/nchs/nhanes/index.htm) provided the data used in this study. The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) carried out a cross-sectional survey to ascertain disease prevalence and risk factors in the U.S. population, NHANES. Since 1999, NHANES has been carried out every two years. Two 24-hour dietary recalls, health behaviors, laboratory tests, and physical examinations were among the data submitted by qualified participants. Mobile exam centers (MECs) are where these health measurement data are gathered.Data from nine 2-year NHANES cycles and two 3-year NHANES cycles (1999\u0026ndash;2000, 2001\u0026ndash;2002, 2003\u0026ndash;2004, 2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, 2017\u0026ndash;2020 and 2021\u0026ndash;2023) were combined in this study.\u0026nbsp;Figure 1\u0026nbsp;depicts the thorough inclusion and exclusion procedure. All participants gave written informed consent, and NHANES was approved by the National Center for Health Statistics Ethics Review Board.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.2. Calculation of DII\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eComprehensive information about the development and validation of the Dietary Inflammatory Index (DII) can be found in earlier studies [10\u0026ndash;13]. In summary, the Z-score is calculated by taking an individual\u0026apos;s daily intake, subtracting the global average daily intake, and then dividing the result by the standard deviation. [14]Then, it is converted to a percentile score, which is subsequently doubled and subtracted by \u0026apos;1\u0026apos; to achieve a symmetrical distribution. Further, the percentile value is multiplied by the corresponding overall inflammation effect score. Finally, by adding up each DII score, we can obtain an individual \u0026apos;overall DII score\u0026apos;. To gather dietary data for the current investigation, we used two 24HRs from the NHANES database. The DII score was derived from 26 out of 45 food parameters, which include carbohydrates, protein, fat, alcohol, fiber, cholesterol, and various fatty acids (saturated, monounsaturated, and polyunsaturated), as well as omega-3 and omega-6 fatty acids. Additionally, the parameters considered include niacin, vitamins A, B1, B2, B6, B12, C, E, along with iron, magnesium, zinc, selenium, folic acid, beta carotene, and caffeine. Notably, even if fewer than 30 nutrients are used in the DII calculation, scores can still be determined. A low DII score suggests an anti-inflammatory dietary pattern, while a high DII score indicates a pro-inflammatory dietary pattern.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.3. Anemia Definition\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eHemoglobin data were obtained from the complete Blood Count with a 5-part Differential available in the NHANES database. Anemia was defined based on the serum hemoglobin (Hb) threshold (g/dL) as recommended by the World Health Organization (WHO) [15,16].\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.4. Covariates\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eIn our study analyses, we included demographic characteristics identified as potential confounders on the NHANES website as covariates. These characteristics encompassed age, gender (male, female), race (non-Hispanic Black, non-Hispanic White, Mexican Americans, and other races), educational attainment (less than 12th grade, high school graduate/GED or equivalent, and other), smoking status (yes, no), drinking status (yes, no), hypertension status (yes, no), diabetes status (yes, no), and Body Mass Index (BMI) categorized as \u0026lt;25, 25\u0026ndash;30, and \u0026ge;30. This information was gathered from the relevant questionnaire and informed by a review of the literature as well as clinical experience [17,18].\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.5. Statistical Analyses\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eAll participants underwent a descriptive analysis. Depending on the data type, continuous data were analyzed using either the mean and standard deviation (SD) or the median and interquartile range (IQR). Categorical variables were expressed as proportions (%). The \u0026chi;\u0026sup2; test was employed to compare categorical variables. For comparisons involving normally distributed data, one-way analysis of variance (ANOVA) was utilized, while the Kruskal\u0026ndash;Wallis test was applied for skewed distributions. The relationship between the Dietary Inflammatory Index (DII) and anemia was examined using logistic regression models. Both non-adjusted and multivariate adjusted models were used: Model 1, without adjustment for any covariates; Model 2, was adjusted for Model 1 plus age, sex, race, and education level; Model 3 adjusted for covariates in Model 2 and body mass index, smoking status, and drinking status; Model 4 was adjusted for Model 3 plus hypertension, and diabetes. Subgroup analyses were conducted based on age, gender, smoking status, drinking status, as well as hypertension and diabetes, to assess the consistency of the association between the Dietary Inflammatory Index (DII) and anemia. Statistical significance was evaluated by comparing the adjusted odds ratios (ORs) to 1.0, along with reporting 95% confidence intervals (CIs). All analyses were performed using the statistical software R (http://www.R-project.org, The R Foundation) and Free Statistics software version 1.7. A two-tailed test was employed, with a significance level set at P \u0026lt; .05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch4\u003e\u003cem\u003e3.1. Characteristics of Participants\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eThe detailed process of inclusion and exclusion is shown in\u0026nbsp;Figure 1.\u0026nbsp;Initially, 119,555 potential participants were identified from eleven cycles of NHANES (NHANES 1999\u0026ndash;2000, 2001\u0026ndash;2002, 2003\u0026ndash;2004, 2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2018). After excluding 114,955 participants with missing DII data, anemia data and other covariates data, or participants who were non-cancer, we recruited the remaining 4600 eligible participants.\u0026nbsp;Table 1\u0026nbsp;describes the basic characteristics of the study participants with or without anemia. Of the total 4600 participants, 551 individuals met the criteria for the diagnosis of anemia in this study and the prevalence of anemia was 11.98% overall. There were significant differences between the two groups in the distribution of DII, quartile grouping of DII, age, gender, race, education level, diabetes status and hypertension status (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Importantly, compared with the cancer survivors without anemia, those with anemia had higher DII.\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e3.2. Associations Between DII and Anemia\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eAssociations between DII and anemia using logistic regression are shown in\u0026nbsp;Table 2.\u0026nbsp;In the crude model 1 without adjustment, odds ratios (ORs) (95% CI) between the risk of anemia and DII across quartile 2, 3 and 4（Q2, Q3 and Q4） compared with quartile 1(Q1) were 1.28 (95% CI, 0.98\u0026ndash;1.67; \u003cem\u003eP\u003c/em\u003e = 0.069), 1.16 (95% CI, 0.98\u0026ndash;1.52; \u003cem\u003eP\u003c/em\u003e = 0.272) and 1.73 (95% CI, 1.34\u0026ndash;2.23; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), respectively. ORs (95% CI) of model 2 between the risk of anemia and DII after adjustment for age, gender, and education level across Q2, Q3 and Q4 compared with Q1 were 1.26 (95% CI, 0.96\u0026ndash;1.65; \u003cem\u003eP\u003c/em\u003e = 0.094), 1.13 (95% CI, 0.85\u0026ndash;1.49; \u003cem\u003eP\u003c/em\u003e = 0.396) and 1.73 (95% CI, 1.32\u0026ndash;2.26; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), respectively. Model 3 adjusted for covariates in Model 2 and body mass index, smoking status, and drinking status, ORs (95% CI) between the risk of anemia and DII across Q2, Q3 and Q4 compared with Q1 were 1.27 (95% CI, 0.97\u0026ndash;1.67; \u003cem\u003eP\u003c/em\u003e = 0.085), 1.14 (95% CI, 0.86\u0026ndash;1.51; \u003cem\u003eP\u003c/em\u003e = 0.354) and 1.74 (95% CI, 1.33\u0026ndash;2.29; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), respectively. While, after adjustment for all the covariates of interest in model 4, the ORs (95% CI) between the risk of anemia and DII across Q2, Q3 and Q4 compared with Q1 were 1.24 (95% CI, 0.95\u0026ndash;1.63; P = 0.118), 1.12 (95% CI, 0.84\u0026ndash;1.48; P = 0.441) and 1.68 (95% CI, 1.28\u0026ndash;2.21; P \u0026lt; 0.001), respectively. This finding indicates that for every 1-unit increase in the DII, the incidence of anemia in Q4 is 68% higher than that in Q1.Additionally, the trend test also showed that the risk of anemia and DII were all statistically significant in these four models (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001 in the crude model 1; \u003cem\u003eP\u003c/em\u003e = 0.001 in model 2; \u003cem\u003eP\u003c/em\u003e = 0.001 in model 3; \u003cem\u003eP\u003c/em\u003e = 0.002 in model 4).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e3.3. Stratified Analyses\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eStratified analyses were performed across different subgroups to assess potential effect modifications in the association between the Dietary Inflammatory Index (DII) and anemia among individuals with cancer. However, no significant interactions were identified in any subgroup when stratified by gender, age, BMI, or hypertension (Figure 2).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAn increasing amount of evidence has established a connection between dietary inflammation levels and anemia [19,20]; however, the relationship between the Dietary Inflammatory Index (DII) and anemia in cancer patients remains a subject of interest. In this cross-sectional study, we aimed to address this knowledge gap. A total of 4,600 participants from the NHANES database were included, and the key findings are as follows: (1) the prevalence of anemia among cancer patients in the US from 1999 to 2023 was 11.98%.This prevalence is consistent with previous studies highlighting the significant burden of anemia in cancer patients. (2) A higher mean DII was found in anemia patients than in nonanemia patients. (3) DII was positively correlated with the risk of anemia in cancer patients, both before and after adjusting for covariates, increased the risk by 9%. (4) Compared with Q1, the risk of anemia in Q4 increased by 73%, 73%, 74%, and 68% in model 1, model 2, model 3, and model 4, respectively. The subgroup analysis further confirmed the validity of the results. This suggests that a pro-inflammatory diet may contribute to the development of anemia in this population.\u003c/p\u003e\n\u003cp\u003eIn recent decades, an increasing number of studies have emphasized a crucial link: the connection between dietary patterns, types of food, inflammation, and disease risk [21,22]. Against this backdrop, the DII, as a tool for assessing the inflammatory potential of diet, can reflect the impact of diet on chronic diseases. A high-inflammatory diet is closely related to an increased risk of developing chronic diseases such as rheumatoid arthritis, diabetes, and cancer [23\u0026ndash;25]. The mechanisms mainly involve the activation of inflammatory cells, the release of inflammatory cytokines, the increase of oxidative stress, and the regulatory effects of nutrients on inflammation and metabolism. A high-inflammatory diet is associated with an increased risk of developing various types of cancer. People with a higher DII score have a significantly higher risk of developing cancer than those with a lower DII score. Therefore, adjusting dietary habits to reduce the DII score may help prevent and improve these chronic diseases. Pro-inflammatory diets have been reported to be associated with the development of cancer. This is because chronic inflammation can promote the occurrence and development of tumors. Inflammatory cytokines (such as IL-6, TNF-\u0026alpha;) can promote the proliferation, survival, and invasion of tumor cells [26,27]. A high-inflammatory diet can increase the level of oxidative stress, leading to DNA damage, thereby increasing the risk of developing cancer [28]. Chronic inflammation is considered a potential cause of anemia, as it can lead to dysfunction of red blood cells. Here, they are unable to effectively absorb and utilize iron. Moreover, in an inflammatory state, the body cannot normally respond to erythropoietin, a hormone produced by the kidneys that prompts the bone marrow to produce red blood cells. Our data suggest that a high DII pro-inflammatory diet may be a potential risk factor for anemia in cancer patients.\u003c/p\u003e\n\u003cp\u003eAnemia is a prevalent global health concern, and recent research has highlighted a significant link between anemia and inflammation. In both animal models and patients with inflammatory diseases, increased levels of hepcidin are associated with diminished expression of iron transporters in duodenal enterocytes and macrophages, resulting in impaired dietary iron absorption [29,30].Moreover, even low-grade inflammation can hinder iron absorption in the gut, thereby reducing the supply of iron needed for erythropoiesis and consequently having a negative impact on anemia [31]. Inflammatory cytokines can also shorten the lifespan of red blood cells by activating macrophages. There is a close relationship between anemia and inflammation, with inflammation interfering with the production and function of red blood cells through various mechanisms, thereby causing anemia. Pro-inflammatory diets increase the body\u0026apos;s inflammatory response, leading to elevated hepcidin levels, which in turn inhibit the absorption and utilization of iron. This reduces the supply of iron, affects erythropoiesis, and thus increases the risk of anemia. Certain components in pro-inflammatory diets (such as saturated fats and high sugar) can increase oxidative stress levels, leading to DNA damage and thereby affecting erythropoiesis [32\u0026ndash;34]. Pro-inflammatory diets may also lead to insufficient intake of certain nutrients (such as vitamin C), which are crucial for iron absorption and utilization [35,36]. Consequently, modifying dietary habits by decreasing the consumption of pro-inflammatory foods and increasing the intake of anti-inflammatory options may aid in the prevention and management of anemia. It is important to note that while following a vegetarian diet may contribute to a lower Dietary Inflammatory Index (DII), individuals susceptible to anemia should carefully regulate their diet and supplements to ensure optimal health.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for multiple covariates, including age, gender, race, education level, BMI, smoking status, drinking status, hypertension, and diabetes, we observed a positive association between DII and anemia risk in cancer patients. Specifically, the adjusted odds ratios (ORs) for the second (Q2), third (Q3), and fourth (Q4) quartiles of DII were 1.24, 1.12, and 1.68, respectively. This indicates that higher DII levels are associated with a greater risk of anemia in cancer patients. The trend test also showed that the risk of anemia increased with higher DII levels, further supporting the positive association. Subgroup analyses were conducted to evaluate potential effect modifications of the association between DII and anemia in different demographic and clinical groups. No significant interactions were found in any subgroup following stratification by gender, age, BMI, hypertension history, and diabetes history. This suggests that the association between DII and anemia risk is consistent across various subgroups of cancer patients.\u003c/p\u003e\n\u003cp\u003eThese findings have important clinical implications. Given the high prevalence of anemia in cancer patients and the potential role of diet in modulating inflammation and anemia risk, controlling dietary inflammation may be a valuable strategy for preventing and treating anemia in this population. Clinicians should consider dietary interventions aimed at reducing inflammation as part of the comprehensive management of cancer patients. Future research should explore the potential mechanisms underlying the association between DII and anemia in cancer patients and investigate the effectiveness of dietary interventions in reducing anemia risk.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates that high DII levels are associated with an increased risk of anemia in cancer patients. This association highlights the importance of dietary inflammation in the development of anemia among cancer patients. Future research should focus on elucidating the underlying mechanisms and evaluating the potential benefits of dietary interventions in managing anemia in this population. Clinicians should consider incorporating dietary assessments and recommendations to reduce inflammation as part of the standard care for cancer patients. This approach may help to mitigate the burden of anemia and improve the overall health outcomes of cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the NHANES staff, investigators, and participants. Thanks to the Free Statistics team for providing technical assistance and valuable tools for data analysis and visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingling Guo and Qingcong Guo performed data collection, analysis and drafted the manuscript. Youping Lin and Li Zhou\u0026nbsp;participated in data analysis.Qingling Guo conceived the study and revised the manu script. All authors approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eisclosure statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding support hasn\u0026rsquo;t been received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that data supporting the findings of this study are available within the article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKnight K, Wade S, Balducci L. 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Semin Immunopathol. 2018;40(1):1-2. doi:10.1007/s00281-017-0655-8.\u003c/li\u003e\n\u003cli\u003eShivappa N, H\u0026eacute;bert JR, Rosato V, Rossi M, La Vecchia C. Pro-inflammatory Diet is Associated with Increased Risk of Colorectal Cancer: A Case-Control Study. Clin Nutr. 2021;40(5):2806-2812. doi:10.1016/j.clnu.2020.12.009.\u003c/li\u003e\n\u003cli\u003eArezes J, Foy N, McHugh K, Quinkert D, Benard S, Sawant A, et al. Non-canonical mechanisms regulating hepcidin-independent iron metabolism in inflammation. Haematologica. 2020;105(3):e112-e115. doi:10.3324/haematol.2019.220699.\u003c/li\u003e\n\u003cli\u003eRecalcati S, Locati M. Iron retention in macrophages in inflammatory diseases. Pharmaceuticals. 2021;14(12):1263. doi:10.3390/ph14121263.\u003c/li\u003e\n\u003cli\u003eMei HE, Schmidt V, Carrillo-G\u0026aacute;lvez AB, Cetin C, T\u0026ouml;gel F, Westendorf AM, et al. Hepcidin-induced ferroportin degradation in inflammatory bowel disease. Int J Mol Sci. 2022;23(5):2898. doi:10.3390/ijms23052898.\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang X, Liu Y, Zhang J, Cao L, Li Z, Li J. High-fat high-sucrose diet impairs erythropoiesis by upregulating hepcidin through oxidative stress. Redox Biol. 2021;48:102141. doi:10.1016/j.redox.2021.102141.\u003c/li\u003e\n\u003cli\u003eChen J, Wang C, Song M, Wang L, Zheng F, Shen HM. Western diet suppresses erythropoiesis by inducing DNA damage and inflammation. Free Radic Biol Med. 2021;162:246-258. doi:10.1016/j.freeradbiomed.2020.10.318.\u003c/li\u003e\n\u003cli\u003eKim SH, Park J, Kim TS, Lee DH, Kim HJ, Lee YJ. High-sucrose diet inhibits erythroid differentiation via oxidative stress and DNA damage. Antioxidants (Basel). 2023;12(2):417. doi:10.3390/antiox12020417.\u003c/li\u003e\n\u003cli\u003eMoser SC, van Dijk BS, Willems PHGM, Swinkels DW, Fleming RE, van Swelm RPL. Vitamin C deficiency exacerbates anemia in inflammation by downregulating ferroportin expression. Nutrients. 2022;14(17):3476. doi:10.3390/nu14173476.\u003c/li\u003e\n\u003cli\u003eBlanco-Rojo R, Toxqui L, L\u0026oacute;pez-Parra AM, P\u0026eacute;rez-Granados AM, Arroyo-Pardo E, Vaquero MP. Combined vitamin C and iron deficiency in pro-inflammatory diets impairs hematopoietic function. J Nutr. 2019;149(12):2207-2215. doi:10.1093/jn/nxz185.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Descriptive characteristics of participants with and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewithout anemia in the enrolled population of NHANES.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n = 4600)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo anemia (n = 4049)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia (n = 551)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, Mean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65.9\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;13.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;13.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;12.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2157 (46.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1847 (45.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e310 (56.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2443 (53.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2202 (54.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e241 (43.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic black\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3344 (72.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3016 (74.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e328 (59.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic white\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e563 (12.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e420 (10.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e143 (26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMexican American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e270 ( 5.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e239 (5.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31 (5.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther races\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e423 ( 9.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e374 (9.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49 (8.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than 12th grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e905 (19.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e755 (18.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e150 (27.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh school graduate/GED\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eor equivalent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1018 (22.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e896 (22.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e122 (22.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2677 (58.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2398 (59.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e279 (50.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.891\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2108 (45.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1857 (45.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e251 (45.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2492 (54.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2192 (54.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e300 (54.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrink, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e595 (12.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e510 (12.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85 (15.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4005 (87.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3539 (87.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e466 (84.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1697 (36.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1571 (38.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e126 (22.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2903 (63.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2478 (61.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e425 (77.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3457 (75.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3089 (76.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e368 (66.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1143 (24.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e960 (23.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e183 (33.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, Mean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.9\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;6.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.0\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;6.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;7.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.7 (0.2, 3.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.7 (0.1, 2.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.1 (0.6, 3.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1150 (25.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1040 (25.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e110 (20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1150 (25.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1013 (25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e137 (24.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1150 (25.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1024 (25.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e126 (22.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.9566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1150 (25.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e972 (24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e178 (32.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote Data presented are Mean\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003eSD,Median (IQR),or n (%).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:BMI,body mass index.DII,dietary inflammatory index.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Association between DII level with anemia among participants with cancer in the NHANES 1999\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003e2023.\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"744\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 671px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09(1.04~1.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.03~1.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.04~1.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09 (1.03~1.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(Ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(Ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(Ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1(Ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (0.98~1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.069\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26 (0.96~1.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.094\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (0.97~1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.085\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (0.95~1.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.16 (0.89~1.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.272\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (0.85~1.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.396\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.14 (0.86~1.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.354\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (0.84~1.48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.441\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.73 (1.34~2.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.73 (1.32~2.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.74 (1.33~2.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.68 (1.28~2.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\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\u003cstrong\u003eNotes: Model 1 wascrude model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2 was adjusted for Model 1 plus age, sex, \u0026nbsp; race, \u0026nbsp;and education level.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3 was adjusted for Model 2 plus body mass index , smoking status,and drinking status.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 4 was adjusted for Model 3 plus hypertension, and diabetes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: OR, hazard ratio; 95% CI, 95% confidence interval.\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anemia, dietary inflammatory index, cancer, NHANES, association, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7244002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7244002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Anemia frequently occurs among cancer patients, yet whether the dietary inflammatory index (DII) is related to anemia remains unclear, highlighting a significant gap in current research.\u003cbr\u003e\n\u003cstrong\u003eObjective\u003c/strong\u003e: This study sought to determine the prevalence of anemia among cancer patients and its correlation with the DII.\u003cbr\u003e\n\u003cstrong\u003eMethods\u003c/strong\u003e: This study, which included 4600 cancer survivors, utilized data sourced from the National Health and Nutrition Examination Survey database covering the period from 1999 to 2023. Information on age, sex, race, education level, body mass index, smoking status, drinking status, hypertension, and diabetes was gathered for all participants. The relationship between anemia risk and DII was examined using logistic regression analysis.\u003cbr\u003e\n\u003cstrong\u003eResults\u003c/strong\u003e: Anemic cancer patients were found to have higher DII levels compared to their nonanemic counterparts.In multivariate regression models, the adjusted odds ratios (ORs) for the second (Q2), third (Q3), and fourth (Q4) quartiles of DII were 1.24 (95% CI, 0.95–1.63; P = 0.118), 1.12 (95% CI, 0.84–1.48; P = 0.441), and 1.68 (95% CI, 1.28–2.21; P \u0026lt; 0.001), respectively. This finding indicates that for every 1-unit increase in the DII, the incidence of anemia in Q4 is 68% higher than that in Q1.Similar patterns of association were observed for subgroup analysis (all P values for interaction \u0026gt; 0.05).\u003cbr\u003e\n\u003cstrong\u003eConclusion\u003c/strong\u003e: Among cancer patients, elevated DII levels have been linked to an increased risk of anemia. Therefore, greater emphasis should be placed on managing dietary inflammation as a means to more effectively prevent and manage anemia.This association may be important to consider in the context of clinicians prescribing for cancer patients prevalence of anemia management.\u003c/p\u003e","manuscriptTitle":"Prevalence of Anemia in Patients With Cancer and Its Association With Dietary Inflammatory Index: A Population-Based Study From NHANES 1999 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 05:12:10","doi":"10.21203/rs.3.rs-7244002/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":"461a1fbb-688f-4fbe-aab9-8f586902c415","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 05:12:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7244002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7244002","identity":"rs-7244002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}