Inflammatory Burden Index and Cancer Prevalence: Insights from a Nationally Representative Study on the Predictive Role of Systemic Inflammation

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Chronic systemic inflammation plays a pivotal role in tumor initiation and progression. The Inflammatory Burden Index (IBI), a composite biomarker derived from routine blood parameters, has shown promise in cancer prognosis. However, evidence from large-scale, population-based studies on its association with cancer prevalence is scarce. Objective To investigate the association between IBI levels and cancer prevalence in a representative U.S. population, providing insights into the role of systemic inflammation in cancer risk stratification. Methods Data from 10,196 participants of the National Health and Nutrition Examination Survey (NHANES) 2005–2020 were analyzed. Cancer prevalence was based on self-reported diagnoses, and IBI was calculated using established formulas. Multivariable logistic regression and restricted cubic spline (RCS) analyses were employed to evaluate the relationship between IBI levels and overall and site-specific cancer prevalence. Results Higher IBI levels were significantly associated with increased cancer prevalence (9.92%). Adjusted logistic models confirmed a positive relationship between IBI and cancer risk (OR: 1.37, 95% CI: 1.04–1.80; P = 0.02). Notably, stronger associations were observed for breast (OR: 1.99) and prostate cancer (OR: 2.02). Subgroup analysis revealed significant interactions between IBI and body mass index (BMI), with amplified risk among individuals with BMI ≥ 25. Conclusions Elevated IBI levels are independently associated with higher cancer prevalence, particularly for breast and prostate cancers. These findings support the utility of IBI as a non-invasive, cost-effective marker for cancer risk stratification in clinical and public health settings. Inflammatory Burden Index cancer prevalence systemic inflammation NHANES breast cancer prostate cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Cancer remains a major public health burden worldwide, contributing significantly to morbidity, mortality, and healthcare expenditures. Despite considerable advancements in diagnostics and therapeutics, the global cancer burden is projected to escalate, with estimates indicating over 26 million new cases and 17 million deaths by 2030 [ 1 , 2 ] . Beyond its physical and psychological toll on patients [ 3 , 4 ] , cancer imposes substantial economic burdens, including both direct medical costs and indirect losses due to decreased productivity and premature mortality [ 5 – 7 ] . Consequently, effective prevention strategies—particularly early detection and timely intervention—are essential not only to reduce cancer-related mortality but also to alleviate long-term treatment burden and improve quality of life [ 8 , 9 ] . Chronic systemic inflammation has been widely recognized as a key driver in the initiation and progression of cancer. Mechanistically, it promotes tumorigenesis by altering gene expression, modulating immune responses, and shaping the tumor microenvironment [ 10 ] . Experimental studies have demonstrated that inflammatory pathways, such as those involving the complement receptor C5aR1 in non-alcoholic steatohepatitis (NASH) mouse models, contribute to hepatic fibrosis and may promote malignancy [ 11 ] . Moreover, dysregulated immune responses mediated by inflammatory microRNAs—such as miR-31 implicated in chronic graft-versus-host disease (cGVHD)—underscore the complex link between persistent inflammation and oncogenesis [ 12 ] . IBI, a composite marker derived from routine hematological parameters, provides a quantifiable measure of systemic inflammation. It has demonstrated strong prognostic value in oncology, outperforming traditional single inflammatory markers in stratifying colorectal cancer risk [ 13 ] . In a cohort of 2,748 cancer patients, elevated IBI levels were independently associated with increased mortality [14]. Similarly, IBI has been validated as a reliable predictor of survival outcomes in patients with gastric cancer [ 15 ] . Despite the multifactorial etiology of cancer, inflammation has emerged as a central contributor to its pathogenesis. Nevertheless, large-scale, population-based investigations into the relationship between systemic inflammation and cancer remain scarce. To address this gap, the present study utilizes the IBI as a quantitative proxy for systemic inflammation, leveraging data from the NHANES to assess its association with cancer prevalence. By elucidating this relationship, we aim to enhance understanding of inflammation-driven carcinogenesis and provide an evidence-based rationale for integrating IBI into cancer risk assessment and prevention strategies. 2. Materials and methods 2..1 Study design and sample Data for this study were obtained from the NHANES conducted between 2005 and 2020. The NHANES protocol received approval from the Institutional Review Board, and all participants provided written informed consent [ 16 ] . A total of 26,731 individuals who underwent dental examinations were initially considered. Participants were excluded sequentially due to missing laboratory data (n = 69), incomplete cancer questionnaire responses (n = 9,007), missing demographic information (n = 2,294), unavailable dietary data (n = 5,053), and incomplete disease-related variables (n = 112). After applying all exclusion criteria, 10,196 participants were included in the final analysis. The participant selection process is detailed in Fig. 1 . 2.2 Laboratory Data and Analysis Laboratory data were obtained from serum samples collected during NHANES physical examinations. Samples were processed at Mobile Examination Centers (MEC), maintained under cold chain conditions (2–8°C), and transported to the Advanced Research and Diagnostic Laboratory (ARDL) at the University of Minnesota for analysis. Complete blood counts (CBC) were conducted using the Coulter® DxH 800 analyzer by certified personnel. Biomarkers assessed included C-reactive protein (CRP), neutrophil and lymphocyte count, serum albumin, serum globulin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). The Inflammatory Burden Index (IBI) was calculated as: CRP × neutrophil count / lymphocyte count, following previously validated formulas [ 17 , 18 ] . 2.3 Cancer Diagnosis and Definition Cancer diagnosis was based on participants’ self-reported responses to the NHANES medical conditions questionnaire. Participants were asked, “Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?” A “yes” response was classified as a cancer case. The specific cancer type was identified based on responses to the follow-up question, “What kind of cancer was it?” 2.4 Demographic Information and Behavioral Characteristics Demographic and behavioral variables were obtained from standardized NHANES questionnaires. Collected information included age, sex (male/female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), educational attainment (less than high school, high school graduate, more than high school), marital status (married/partnered, widowed/divorced/separated, never married), and poverty-to-income ratio (PIR). Behavioral variables included smoking and alcohol consumption. Smoking status was classified as: never smokers (fewer than 100 cigarettes in lifetime), former smokers (≥ 100 cigarettes but no current use), and current smokers (≥ 100 cigarettes and current use). Alcohol intake was categorized as never, former, light, moderate, or heavy, based on daily consumption thresholds described previously [ 19 ] . 2.5 Physical Measures and Dietary Intake Obesity status was determined using BMI, measured by certified technicians at the MEC. Dietary intake data were collected via two 24-hour dietary recall interviews—the first conducted in-person at the MEC and the second via telephone 3–10 days later. Nutrient values were calculated based on the USDA Food and Nutrient Database for Dietary Studies (FNDDS), and the average energy intake from both recalls was used in the analysis. 2.6 Disease Diagnosis Criteria Hypertension was defined as a mean blood pressure ≥ 140/90 mmHg, calculated by excluding zero diastolic values; if all values were zero, the mean was set to zero. When only one measurement was available, it was used directly; for multiple readings, the first was excluded before averaging. Diabetes was defined based on any of the following: a self-reported physician diagnosis, hemoglobin A1c > 6.5%, fasting glucose ≥ 7.0 mmol/L, random glucose ≥ 11.1 mmol/L, 2-hour OGTT ≥ 11.1 mmol/L, or the use of glucose-lowering medications or insulin [ 20 ] . 2.7 Statistical analyses This study addressed randomly missing data using multiple imputation methods, while non-randomly missing data were excluded to maximize the representativeness of the sample. The screened data were analyzed using R software (v.4.2.1). Continuous variables were described as mean (standard deviation) and compared between groups using t-tests. Categorical variables were expressed as frequency (percentage), and group differences were evaluated using the chi-square test. In multivariable-adjusted analyses, binary logistic regression models were employed, accompanied by multiple sensitivity analyses to validate the robustness of the results. Covariates (a total of 16) were incrementally included in the model in a stepwise manner to ensure the reliability of the study conclusions. Additionally, the IBI levels were categorized into high and low groups using ROC curve analysis, with a cutoff value of 2.45, to further delineate their distribution characteristics. We also developed models to investigate the relationship between IBI levels and the risk of various cancers, focusing on the most prevalent cancers in the NHANES database. Logistic regression analyses were performed, with subgroup analyses and cross-validation employed to refine the target populations. To assess the potential nonlinear association between IBI levels and the overall risk of cancer or specific cancer types (e.g., breast cancer, prostate cancer), restricted cubic splines (RCS) were applied, adjusting for all 16 covariates in the process. All statistical analyses accounted for the complex, multistage sampling design of NHANES and were weighted appropriately to ensure representativeness. Weighted multivariate logistic regression models were used to strengthen the generalizability of the inferences. A significance threshold of P < 0.05 was set for all statistical tests. 3. Result 3.1 Characteristics of the included population A total of 10,196 participants from the NHANES database were included, representing approximately 142.5 million U.S. adults. The overall cancer prevalence in the study population was 9.92%. Baseline characteristics are summarized in Table 1. Significant differences in cancer prevalence were observed across subgroups defined by age, sex, race/ethnicity, educational attainment, marital status, poverty-to-income ratio (PIR), smoking status, alcohol consumption, energy intake, hypertension, diabetes, serum globulin, albumin, ALT, AST, and IBI levels. Notably, individuals with higher IBI values showed an increased prevalence of cancer. Table.1. Table of baseline characteristics of the population. Characteristics Cancer P value no yes Total 9185(90.08) 1011(9.92) Age ~ years 46.12(0.40) 62.82(0.67) < 0.01 Race~% < 0.01 Non-Hispanic White 3221(65.65) 643(86.22) Non-Hispanic Black 2176(10.65) 164(5.26) Mexican American 1312(8.39) 62(1.98) Other Hispanic 993(6.36) 71(2.84) Other Race 1483(8.95) 71(3.71) Gender~% 0.01 Male 4429(49.00) 485(41.47) Female 4756(51.00) 526(58.53) Education level~% 0.01 Less than high school 1480(9.32) 142(7.19) High school 2116(24.54) 199(19.71) More than high school 5589(66.14) 670(73.10) Marital Status~% < 0.01 Married/ living with partner 5548(64.34) 580(64.90) Widowed/Divorced/Separated 1826(16.22) 351(29.80) Never married 1811(19.43) 80(5.29) Family PIR 3.17(0.05) 3.57(0.07) < 0.01 BMI ~ kg/m2 29.79(0.16) 29.81(0.29) 0.96 Smoking behavior~% < 0.01 now 1698(16.42) 149(12.80) former 2034(24.12) 385(38.17) never 5453(59.46) 477(49.03) Alcohol consumption~% < 0.01 never 1140(9.25) 108(7.29) former 468(4.87) 91(7.66) mild 3753(42.20) 543(54.62) moderate 1829(21.41) 156(18.50) heavy 1995(22.26) 113(11.93) Energy intake ~ kcal 2105.81(13.33) 2012.74(35.11) 0.01 DM~% < 0.01 yes 1742(14.06) 267(23.17) no 7443(85.94) 744(76.83) Hypertension~% < 0.01 no 5655(66.12) 398(46.51) yes 3530(33.88) 613(53.49) Neutrophil ~ 1000 cells/uL 4.24(0.04) 4.34(0.08) 0.27 Blood platelet ~ 1000 cells/uL 2.20(0.01) 2.50(0.23) 0.19 C-Reactive Proteinmg/L 3.63(0.10) 4.08(0.31) 0.17 ALT ~ U/L 24.48(0.25) 21.26(0.38) < 0.01 AST ~ U/L 23.68(0.21) 22.62(0.30) 0.01 Albumin ~ g/dL 4.21(0.01) 4.16(0.01) < 0.01 Globulin ~ g/dL 2.90(0.01) 2.80(0.02) < 0.01 3.2 Association Between IBI Levels and Cancer Risk Table 2 presents the results of univariate and multivariable logistic regression analyses assessing the association between IBI levels and cancer prevalence. In unadjusted analyses, higher IBI levels were significantly associated with increased cancer prevalence (OR: 1.57; 95% CI: 1.29–1.91; P < 0.001). This association remained robust across stepwise multivariable models: Model 1 (adjusted for age, sex, race): OR = 1.31; 95% CI: 1.03–1.66; P = 0.03 Model 2 (additional adjustment for education, marital status, BMI, PIR, energy intake): OR = 1.40; 95% CI: 1.08–1.82; P = 0.01 Model 3 (further adjustment for smoking, alcohol, hypertension, diabetes): OR = 1.36; 95% CI: 1.04–1.78; P = 0.03 Model 4 (additionally adjusted for albumin, globulin, ALT, AST): OR = 1.37; 95% CI: 1.04–1.80; P = 0.02 Table.2. Association between IBI levels and risk of cancer. Variable Model OR (95%CI) P value Cancer Crude 1.57(1.29,1.91) < 0.01 Model1 1.31(1.03,1.66) 0.03 Model2 1.40(1.08,1.82) 0.01 Model3 1.36(1.04,1.78) 0.03 Model4 1.37(1.04,1.80) 0.02 3.3 IBI Levels and Specific Cancer Types Subtype-specific analyses, adjusting for the same covariates as in the primary models, demonstrated significant associations between higher IBI levels and increased prevalence of breast cancer (OR = 1.99; 95% CI: 1.06–3.72) and prostate cancer (OR = 2.02; 95% CI: 1.21–3.39). No significant associations were observed for skin cancer (P = 0.95), colorectal cancer (P = 0.35), melanoma (P = 0.85), uterine cancer (P = 0.99), or cervical cancer (P = 0.83), as shown in Fig. 2 . 3.4 Subgroup Analyses Subgroup analyses ( Table 3 ) revealed a generally consistent positive association between elevated IBI levels and cancer prevalence across demographic and clinical subgroups. A significant interaction was observed between IBI and body mass index (BMI). Specifically, in individuals with BMI ≥ 25, higher IBI levels were significantly associated with increased cancer risk (OR = 1.54; 95% CI: 1.12–2.11). Table.3 OR, 95% CI, and P values for subgroup analyses of cancer prevalence and IBI. Subgroup Variable Cancer OR (95%CI) P value P for interaction Age 0.40 <65 1.51(0.99,2.32) 0.06 ≥65 1.33(0.96,1.86) 0.09 Gender 0.66 Male 1.11(0.77,1.60) 0.56 Female 1.62(1.14,2.31) 0.01 Race 0.19 Non-Hispanic White 1.34(0.99,1.83) 0.06 Non-Hispanic Black 1.19(0.77,1.86) 0.42 Mexican American 2.45(1.07, 5.62) 0.04 Other Hispanic 1.81(1.01,3.23) 0.05 Other Race 2.04(0.99, 4.19) 0.05 Education level 0.63 Less than high school 1.36(0.72,2.59) 0.33 High school 1.55(0.92,2.62) 0.09 More than high school 1.35(0.96,1.90) 0.08 Marital Status 0.09 Married or living with partner 1.35(0.97,1.87) 0.08 Never married 3.38(1.32,8.65) 0.01 Widowed/Divorced/Separated 1.32(0.95,1.82) 0.09 Family PIR 0.56 2.3 1.47(1.00,2.17) 0.05 ≥ 2.3 1.33(0.94,1.87) 0.10 BMI 0.05 <25 1.01(0.66,1.53) 0.97 ≥25 1.54(1.12,2.11) 0.01 Energy intake 0.46 <1900 1.33(0.91,1.95) 0.14 ≥1900 1.38(0.94,2.01) 0.10 Smoking behavior 0.74 never 1.49(1.11,2.00) 0.01 former 1.41(0.86,2.31) 0.17 now 1.08(0.57, 2.05) 0.80 Alcohol consumption 0.50 never 1.36(0.59,3.13) 0.46 former 0.94(0.38, 2.36) 0.89 mild 1.24(0.89,1.73) 0.20 moderate 2.12(1.15,3.90) 0.02 heavy 1.22(0.67, 2.22) 0.50 Hypertension 0.97 yes 1.31(1.00,1.72) 0.05 no 1.51(1.02,2.23) 0.04 Diabetes 0.22 yes 1.68(1.05,2.69) 0.03 no 1.35(0.99,1.84) 0.06 * PIR, energy intake were grouped according to median 3.5 Subgroup Analyses for Breast and Prostate Cancer Further stratified analyses for breast and prostate cancer ( Tables 4 and 5 ) identified significant effect modifications. Among women with BMI ≥ 25, elevated IBI levels were strongly associated with breast cancer prevalence (OR = 2.90; 95% CI: 1.34–6.30). For prostate cancer, significant interactions were observed with energy intake and diabetes. Men with higher energy intake (OR = 4.03; 95% CI: 1.91–8.54) or diabetes (OR = 4.85; 95% CI: 1.91–12.35) showed markedly increased prostate cancer prevalence in the context of elevated IBI. Table.4 OR, 95% CI, and P values for subgroup analyses of breast cancer prevalence and IBI. Subgroup Variable Cancer = Breast OR (95%CI) P value P for interaction Age 0.39 <65 2.76(0.93, 8.17) 0.07 ≥65 1.50(0.77, 2.90) 0.22 Education level 0.89 Less than high school 1.90(0.52,6.98) 0.32 High school 2.28(0.73,7.14) 0.15 More than high school 2.52(1.07,5.97) 0.04 Family PIR 0.50 <2.3 1.38(0.64, 2.99) 0.4 ≥ 2.3 2.40(1.11, 5.16) 0.03 BMI 0.03 <25 0.87(0.32, 2.36) 0.78 ≥25 2.90(1.34, 6.30) 0.01 Energy intake 0.74 <1900 1.62(0.82,3.20) 0.16 ≥1900 2.36(0.68, 8.20) 0.17 Smoking behavior 0.68 never 1.84(0.85, 4.00) 0.12 former 3.58(1.12,11.43) 0.03 now 0.38(0.01,27.15) 0.65 Hypertension 0.92 yes 1.72(0.90, 3.28) 0.10 no 2.73(0.95, 7.84) 0.06 Diabetes 0.63 yes 0.83(0.21, 3.26) 0.78 no 2.28(1.10, 4.71) 0.03 * PIR, energy intake were grouped according to median Table.5 OR, 95% CI, and P values for subgroup analyses of prostate cancer prevalence and IBI. Subgroup Variable Cancer = Prostate OR (95%CI) P value P for interaction Age 0.14 <65 3.57(1.26, 10.16) 0.02 ≥65 1.52 0.80, 2.90) 0.19 Education level 0.34 Less than high school 1.35(0.39, 4.67) 0.63 High school 2.01(0.71, 5.66) 0.18 More than high school 2.41(1.06, 5.49) 0.04 Family PIR 0.21 <2.3 3.90(1.70, 8.90) < 0.01 ≥ 2.3 1.67(0.79,3.53) 0.17 BMI 0.10 <25 1.13(0.54,2.36) 0.73 ≥25 2.63(1.32, 5.23) 0.01 Energy intake < 0.01 <1900 0.79(0.44, 1.41) 0.41 ≥1900 4.03(1.91, 8.54) < 0.01 Smoking behavior 0.47 never 2.37(1.09, 5.12) 0.03 former 1.59(0.76, 3.33) 0.21 now 0.72(0.19,2.64) 0.61 Hypertension 0.57 yes 1.84(1.03, 3.32) 0.30 no 1.77(0.59, 5.29) 0.04 Diabetes < 0.01 yes 4.85(1.91, 12.35) < 0.01 no 1.20(0.58, 2.50) 0.61 * PIR, energy intake were grouped according to median 3.6 Nonlinear Relationship Between IBI Levels and Cancer Risk RCS analyses (Fig. 3 ) demonstrated a significant positive linear relationship between IBI levels and overall cancer prevalence. A similar trend was observed for breast cancer among females (P for non-linearity = 0.21; Fig. 4 ), suggesting a linear association. However, no significant nonlinear association was detected for prostate cancer in males ( Fig. 5 ). 4. Discussion This study utilized a nationally representative cohort from the NHANES dataset to explore the association between the IBI and cancer prevalence. Our findings indicate that higher IBI levels are independently associated with a greater likelihood of cancer, reinforcing the role of systemic inflammation in carcinogenesis. The robust association between elevated IBI levels and cancer prevalence persisted across both unadjusted and multivariable-adjusted models. Importantly, the association remained significant even after sequential adjustment for demographic, behavioral, and biochemical confounders, underscoring the independent contribution of systemic inflammation to cancer burden. The strongest associations were observed for breast and prostate cancers. Additionally, stratified analyses revealed that this association was particularly prominent among individuals with BMI ≥ 25, suggesting a potential synergistic interaction between obesity-induced inflammation and tumor development. These findings are consistent with the established literature identifying inflammation as a key driver of tumorigenesis [ 21 , 22 ] . Chronic inflammatory states facilitate cancer initiation and progression by promoting genomic instability, enhancing angiogenesis, and inducing immunosuppressive microenvironments [ 23 – 25 ] . The stronger associations observed with breast and prostate cancers may reflect underlying hormonal regulation of inflammatory signaling pathways unique to these tumor types. The interaction between IBI and BMI emphasizes the intricate relationship between metabolic dysfunction and systemic inflammation [ 26 ] . Obesity-related inflammatory pathways—marked by dysregulated adipokine secretion and persistent low-grade inflammation—may potentiate cancer susceptibility in individuals with elevated IBI levels [ 27 – 29 ] . Moreover, the observed effect modification by energy intake and diabetes status in prostate cancer further supports the existence of a complex metabolic-inflammatory axis that merits deeper mechanistic investigation [ 30 ] . These results carry important clinical and public health implications. As a non-invasive and cost-efficient biomarker, IBI could be integrated into routine screening protocols to aid in the early identification of individuals at elevated risk for cancer [ 31 , 32 ] . This is particularly relevant for populations with obesity or metabolic syndrome, who may benefit from enhanced risk stratification. Furthermore, these findings underscore the need to target systemic inflammation through lifestyle-based interventions—such as dietary optimization, physical activity, and weight management—as part of a comprehensive cancer prevention framework. The strengths of this study include the use of a nationally representative sample and rigorous analytical approaches, enhancing both generalizability and internal validity. However, its cross-sectional design precludes causal inference, and the possibility of residual confounding cannot be excluded. Moreover, although IBI serves as a practical proxy for systemic inflammation, it may not fully encapsulate the multifaceted nature of inflammatory processes [ 33 , 34 ] . Future studies—particularly prospective and longitudinal designs—are warranted to validate the predictive utility of IBI and elucidate its mechanistic links to cancer development. Building on these findings, future research should focus on elucidating the underlying mechanisms linking IBI to specific cancer types through experimental and molecular studies. Prospective cohort studies are also needed to establish causality and assess the predictive value of IBI in cancer incidence and progression. Finally, exploring the role of targeted anti-inflammatory interventions in mitigating cancer risk among individuals with high IBI levels represents a promising avenue for translational research. 5. Conclusion In conclusion, this nationally representative analysis demonstrates that elevated IBI levels are significantly associated with increased cancer prevalence, particularly breast and prostate cancers. These findings reinforce the role of systemic inflammation in cancer pathophysiology and highlight the potential of IBI as a practical, population-level biomarker for cancer risk assessment and stratification. Declarations Acknowledgments The author thanks the staff and the participants of the NHANES study for their valuable contributions. Availability of data and materials The NHANES protocol was approved by the Institutional Review Board of the National Center for Health Statistics (NCHS). As the present study used publicly available de-identified data, it was exempt from additional ethical approval. Author Contributions MW: Methodology, Data Analysis; ZL and XC managed and cleaned the data. HS criticized and revised the manuscript. All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest. Funding Beijing Natural Science Foundation (J230038) Data Availability Statement The NHANES datasets analyzed during the current study are publicly available from the National Center for Health Statistics (NCHS) (https:// www. cdc.gov/ nchs/ nhanes/ index. htm), except for geographic data (latitude) that are restricted to use through the NCHS Research Data Center (http:// www. cdc.gov/ rdc/) per NCHS, Centers for Disease Control and Prevention policy. Ethics approval and consent to participate All the data were from NHANES and ethics were exempt in this study. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable References Yang X, Chen H, Sang S, Chen H, Li L, Yang X. Burden of All Cancers Along With Attributable Risk Factors in China From 1990 to 2019: Comparison With Japan, European Union, and USA. Front Public Health . 2022;10:862165. 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Published 2024 Aug 20. doi: 10.1186/s12890-024-03211-6 Xiong Z, Xu W, Wang Y, Cao S, Zeng X, Yang P. Inflammatory burden index: associations between osteoarthritis and all-cause mortality among individuals with osteoarthritis. BMC Public Health . 2024;24(1):2203. Published 2024 Aug 13. doi: 10.1186/s12889-024-19632-1 Rattan P, Penrice DD, Ahn JC, et al. Inverse Association of Telomere Length With Liver Disease and Mortality in the US Population. Hepatol Commun . 2022;6(2):399–410. doi: 10.1002/hep4.1803 ElSayed NA, Aleppo G, Aroda VR, et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023 [published correction appears in Diabetes Care. 2023;46(5):1106. doi: 10.2337/dc23-er05] [published correction appears in Diabetes Care. 2023;46(9):1715. doi: 10.2337/dc23-ad08]. Diabetes Care . 2023;46(Suppl 1):S19-S40. doi: 10.2337/dc23-S002 Meng Q, Shen J, Ren Y, et al. EZH2 directly methylates PARP1 and regulates its activity in cancer. Sci Adv . 2024;10(48):eadl2804. doi: 10.1126/sciadv.adl2804 Shahgoli VK, Noorolyai S, Ahmadpour Youshanlui M, et al. Inflammatory bowel disease, colitis, and cancer: unmasking the chronic inflammation link. Int J Colorectal Dis . 2024;39(1):173. Published 2024 Oct 28. doi: 10.1007/s00384-024-04748-y Cao C, Tian B, Geng X, et al. IL-17-Mediated Inflammation Promotes Cigarette Smoke-Induced Genomic Instability. Cells . 2021;10(5):1173. Published 2021 May 12. doi: 10.3390/cells10051173 Desai AS, Sagar V, Lysy B, et al. Inflammatory bowel disease induces inflammatory and pre-neoplastic changes in the prostate [published correction appears in Prostate Cancer Prostatic Dis. 2022;25(2):375. doi: 10.1038/s41391-021-00409-1]. Prostate Cancer Prostatic Dis . 2022;25(3):463–471. doi: 10.1038/s41391-021-00392-7 De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. J Obes . 2013;2013:291546. doi: 10.1155/2013/291546 Neganova M, Liu J, Aleksandrova Y, Klochkov S, Fan R. Therapeutic Influence on Important Targets Associated with Chronic Inflammation and Oxidative Stress in Cancer Treatment. Cancers (Basel) . 2021;13(23):6062. Published 2021 Dec 1. doi: 10.3390/cancers13236062 Liermann-Wooldrik KT, Kosmacek EA, Oberley-Deegan RE. Adipose Tissues Have Been Overlooked as Players in Prostate Cancer Progression. Int J Mol Sci . 2024;25(22):12137. Published 2024 Nov 12. doi: 10.3390/ijms252212137 Gilani A, Stoll L, Homan EA, Lo JC. Adipose Signals Regulating Distal Organ Health and Disease. Diabetes . 2024;73(2):169–177. doi: 10.2337/dbi23-0005 Fazolini NP, Cruz AL, Werneck MB, Viola JP, Maya-Monteiro CM, Bozza PT. Leptin activation of mTOR pathway in intestinal epithelial cell triggers lipid droplet formation, cytokine production and increased cell proliferation. Cell Cycle . 2015;14(16):2667–2676. doi: 10.1080/15384101.2015.1041684 Liermann-Wooldrik KT, Kosmacek EA, Oberley-Deegan RE. Adipose Tissues Have Been Overlooked as Players in Prostate Cancer Progression. Int J Mol Sci . 2024;25(22):12137. Published 2024 Nov 12. doi: 10.3390/ijms252212137 Yamashita S, Okugawa Y, Mizuno N, et al. Inflammatory Burden Index as a promising new marker for predicting surgical and oncological outcomes in colorectal cancer. Ann Gastroenterol Surg . 2024;8(5):826–835. Published 2024 May 28. doi: 10.1002/ags3.12829 Esmaeili MH, Seyednejad F, Mahboub-Ahari A, et al. Cost-effectiveness analysis of lung cancer screening with low-dose computed tomography in an Iranian high-risk population. J Med Screen . 2021;28(4):494–501. doi: 10.1177/09691413211018253 Tuomisto AE, Mäkinen MJ, Väyrynen JP. Systemic inflammation in colorectal cancer: Underlying factors, effects, and prognostic significance. World J Gastroenterol . 2019;25(31):4383–4404. doi: 10.3748/wjg.v25.i31.4383 Assery NM, Jurado CA, Assery MK, Afrashtehfar KI. Peri-implantitis and systemic inflammation: A critical update. Saudi Dent J . 2023;35(5):443–450. doi: 10.1016/j.sdentj.2023.04.005 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-6694086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471127685,"identity":"9bc9bfec-c07c-4a51-971c-316e3fa28ce5","order_by":0,"name":"Mengmeng Wang","email":"","orcid":"","institution":"Chinese Academy of traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengmeng","middleName":"","lastName":"Wang","suffix":""},{"id":471127686,"identity":"0bad39fb-f8b5-424c-89c1-0affba7c3866","order_by":1,"name":"Zongyao Li","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zongyao","middleName":"","lastName":"Li","suffix":""},{"id":471127687,"identity":"57084581-b347-41cb-a968-e771bd9ef610","order_by":2,"name":"Xinjing Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfvnDBx98/FfDw8beQKQWyRlsyYYz2I7J8PMcIFKLwQweM2kONmYbyRkJxGqR7jGQZuBh4zG4+XjjDYYam2iCWsxljhUYF0jI8BjcTiu2YDiWlttASItlQ/KG5BkGQFtu55hJMDYcJqzF4ECCwWGeBGagw84Qq+VGimEzzwFmHklgOBCnRbLnWDLjzIZjPPw8QL8kEOMXfvbm4z8+NtTYs7Ef3njjQ40NYS0ojpRIIEU5RAupOkbBKBgFo2BkAADGmj4ykni2BwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Xinjing","middleName":"","lastName":"Cui","suffix":""},{"id":471127688,"identity":"d6d64367-15fd-4a52-b091-391b0f6476e2","order_by":3,"name":"Hao Sun","email":"","orcid":"","institution":"University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-05-19 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6694086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6694086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84680467,"identity":"a2ec5c70-6b3b-42b1-b6c5-9cf8e171820e","added_by":"auto","created_at":"2025-06-16 08:15:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64062,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for screening the study population.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/1f9b4f3b58fde0d3fb965a1c.png"},{"id":84680468,"identity":"a6a21bae-94b9-4673-b781-a29a4aaa0b09","added_by":"auto","created_at":"2025-06-16 08:15:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172581,"visible":true,"origin":"","legend":"\u003cp\u003eResults analysed between IBI and prevalence of cancer (OR (95% CI) and P value).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/fdea33efd24d1ffd192dc0f3.png"},{"id":84680473,"identity":"e924ff10-7518-4cbe-a698-adae6688a7e4","added_by":"auto","created_at":"2025-06-16 08:15:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19564,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline Showing Log-IBI and Cancer Prevalence\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/36a687f30dc023ca20cf6a56.png"},{"id":84681233,"identity":"a4ddfc55-2b5d-4853-bbf5-cc16131c195c","added_by":"auto","created_at":"2025-06-16 08:23:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20949,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline Curve showing the relationship between Log-IBI and the prevalence of breast cancer in women.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/a7f376b85408b37a29258c1b.png"},{"id":84680479,"identity":"8d403cba-fdee-46b6-aff5-5a5fd721454c","added_by":"auto","created_at":"2025-06-16 08:15:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20388,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted Cubic Spline showing the association between Log-IBI and the prevalence of prostate cancer in men.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/d5fd3818abbce4c492fbc23d.png"},{"id":96918312,"identity":"67d81aaf-4cfc-4303-8bd2-9f74fe927944","added_by":"auto","created_at":"2025-11-27 14:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1807939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6694086/v1/2b42b3d2-07c7-4343-8a89-9acf4af5eee8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inflammatory Burden Index and Cancer Prevalence: Insights from a Nationally Representative Study on the Predictive Role of Systemic Inflammation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer remains a major public health burden worldwide, contributing significantly to morbidity, mortality, and healthcare expenditures. Despite considerable advancements in diagnostics and therapeutics, the global cancer burden is projected to escalate, with estimates indicating over 26\u0026nbsp;million new cases and 17\u0026nbsp;million deaths by 2030 \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Beyond its physical and psychological toll on patients \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, cancer imposes substantial economic burdens, including both direct medical costs and indirect losses due to decreased productivity and premature mortality \u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Consequently, effective prevention strategies\u0026mdash;particularly early detection and timely intervention\u0026mdash;are essential not only to reduce cancer-related mortality but also to alleviate long-term treatment burden and improve quality of life \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronic systemic inflammation has been widely recognized as a key driver in the initiation and progression of cancer. Mechanistically, it promotes tumorigenesis by altering gene expression, modulating immune responses, and shaping the tumor microenvironment \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Experimental studies have demonstrated that inflammatory pathways, such as those involving the complement receptor C5aR1 in non-alcoholic steatohepatitis (NASH) mouse models, contribute to hepatic fibrosis and may promote malignancy \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Moreover, dysregulated immune responses mediated by inflammatory microRNAs\u0026mdash;such as miR-31 implicated in chronic graft-versus-host disease (cGVHD)\u0026mdash;underscore the complex link between persistent inflammation and oncogenesis \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIBI, a composite marker derived from routine hematological parameters, provides a quantifiable measure of systemic inflammation. It has demonstrated strong prognostic value in oncology, outperforming traditional single inflammatory markers in stratifying colorectal cancer risk \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In a cohort of 2,748 cancer patients, elevated IBI levels were independently associated with increased mortality [14]. Similarly, IBI has been validated as a reliable predictor of survival outcomes in patients with gastric cancer \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the multifactorial etiology of cancer, inflammation has emerged as a central contributor to its pathogenesis. Nevertheless, large-scale, population-based investigations into the relationship between systemic inflammation and cancer remain scarce. To address this gap, the present study utilizes the IBI as a quantitative proxy for systemic inflammation, leveraging data from the NHANES to assess its association with cancer prevalence. By elucidating this relationship, we aim to enhance understanding of inflammation-driven carcinogenesis and provide an evidence-based rationale for integrating IBI into cancer risk assessment and prevention strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\n\u003ch3\u003e2..1 Study design and sample\u003c/h3\u003e\n\u003cp\u003eData for this study were obtained from the NHANES conducted between 2005 and 2020. The NHANES protocol received approval from the Institutional Review Board, and all participants provided written informed consent \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. A total of 26,731 individuals who underwent dental examinations were initially considered. Participants were excluded sequentially due to missing laboratory data (n\u0026thinsp;=\u0026thinsp;69), incomplete cancer questionnaire responses (n\u0026thinsp;=\u0026thinsp;9,007), missing demographic information (n\u0026thinsp;=\u0026thinsp;2,294), unavailable dietary data (n\u0026thinsp;=\u0026thinsp;5,053), and incomplete disease-related variables (n\u0026thinsp;=\u0026thinsp;112). After applying all exclusion criteria, 10,196 participants were included in the final analysis. The participant selection process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Laboratory Data and Analysis\u003c/h2\u003e \u003cp\u003eLaboratory data were obtained from serum samples collected during NHANES physical examinations. Samples were processed at Mobile Examination Centers (MEC), maintained under cold chain conditions (2\u0026ndash;8\u0026deg;C), and transported to the Advanced Research and Diagnostic Laboratory (ARDL) at the University of Minnesota for analysis. Complete blood counts (CBC) were conducted using the Coulter\u0026reg; DxH 800 analyzer by certified personnel. Biomarkers assessed included C-reactive protein (CRP), neutrophil and lymphocyte count, serum albumin, serum globulin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). The Inflammatory Burden Index (IBI) was calculated as: CRP \u0026times; neutrophil count / lymphocyte count, following previously validated formulas \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cancer Diagnosis and Definition\u003c/h2\u003e \u003cp\u003eCancer diagnosis was based on participants\u0026rsquo; self-reported responses to the NHANES medical conditions questionnaire. Participants were asked, \u0026ldquo;Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?\u0026rdquo; A \u0026ldquo;yes\u0026rdquo; response was classified as a cancer case. The specific cancer type was identified based on responses to the follow-up question, \u0026ldquo;What kind of cancer was it?\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Demographic Information and Behavioral Characteristics\u003c/h2\u003e \u003cp\u003eDemographic and behavioral variables were obtained from standardized NHANES questionnaires. Collected information included age, sex (male/female), race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), educational attainment (less than high school, high school graduate, more than high school), marital status (married/partnered, widowed/divorced/separated, never married), and poverty-to-income ratio (PIR). Behavioral variables included smoking and alcohol consumption. Smoking status was classified as: never smokers (fewer than 100 cigarettes in lifetime), former smokers (\u0026ge;\u0026thinsp;100 cigarettes but no current use), and current smokers (\u0026ge;\u0026thinsp;100 cigarettes and current use). Alcohol intake was categorized as never, former, light, moderate, or heavy, based on daily consumption thresholds described previously \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Physical Measures and Dietary Intake\u003c/h2\u003e \u003cp\u003eObesity status was determined using BMI, measured by certified technicians at the MEC. Dietary intake data were collected via two 24-hour dietary recall interviews\u0026mdash;the first conducted in-person at the MEC and the second via telephone 3\u0026ndash;10 days later. Nutrient values were calculated based on the USDA Food and Nutrient Database for Dietary Studies (FNDDS), and the average energy intake from both recalls was used in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Disease Diagnosis Criteria\u003c/h2\u003e \u003cp\u003eHypertension was defined as a mean blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg, calculated by excluding zero diastolic values; if all values were zero, the mean was set to zero. When only one measurement was available, it was used directly; for multiple readings, the first was excluded before averaging. Diabetes was defined based on any of the following: a self-reported physician diagnosis, hemoglobin A1c\u0026thinsp;\u0026gt;\u0026thinsp;6.5%, fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, random glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, 2-hour OGTT\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, or the use of glucose-lowering medications or insulin \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analyses\u003c/h2\u003e \u003cp\u003eThis study addressed randomly missing data using multiple imputation methods, while non-randomly missing data were excluded to maximize the representativeness of the sample. The screened data were analyzed using R software (v.4.2.1). Continuous variables were described as mean (standard deviation) and compared between groups using t-tests. Categorical variables were expressed as frequency (percentage), and group differences were evaluated using the chi-square test. In multivariable-adjusted analyses, binary logistic regression models were employed, accompanied by multiple sensitivity analyses to validate the robustness of the results. Covariates (a total of 16) were incrementally included in the model in a stepwise manner to ensure the reliability of the study conclusions. Additionally, the IBI levels were categorized into high and low groups using ROC curve analysis, with a cutoff value of 2.45, to further delineate their distribution characteristics.\u003c/p\u003e \u003cp\u003eWe also developed models to investigate the relationship between IBI levels and the risk of various cancers, focusing on the most prevalent cancers in the NHANES database. Logistic regression analyses were performed, with subgroup analyses and cross-validation employed to refine the target populations. To assess the potential nonlinear association between IBI levels and the overall risk of cancer or specific cancer types (e.g., breast cancer, prostate cancer), restricted cubic splines (RCS) were applied, adjusting for all 16 covariates in the process.\u003c/p\u003e \u003cp\u003eAll statistical analyses accounted for the complex, multistage sampling design of NHANES and were weighted appropriately to ensure representativeness. Weighted multivariate logistic regression models were used to strengthen the generalizability of the inferences. A significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set for all statistical tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the included population\u003c/h2\u003e \u003cp\u003eA total of 10,196 participants from the NHANES database were included, representing approximately 142.5\u0026nbsp;million U.S. adults. The overall cancer prevalence in the study population was 9.92%. Baseline characteristics are summarized in Table\u0026nbsp;1. Significant differences in cancer prevalence were observed across subgroups defined by age, sex, race/ethnicity, educational attainment, marital status, poverty-to-income ratio (PIR), smoking status, alcohol consumption, energy intake, hypertension, diabetes, serum globulin, albumin, ALT, AST, and IBI levels. Notably, individuals with higher IBI values showed an increased prevalence of cancer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1.\u003c/b\u003e Table of baseline characteristics of the population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9185(90.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1011(9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026thinsp;~\u0026thinsp;years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.12(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.82(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3221(65.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e643(86.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2176(10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164(5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1312(8.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e993(6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1483(8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4429(49.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e485(41.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4756(51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526(58.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1480(9.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142(7.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2116(24.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199(19.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5589(66.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e670(73.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/ living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5548(64.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e580(64.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1826(16.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351(29.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1811(19.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.17(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u0026thinsp;~\u0026thinsp;kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.79(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.81(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1698(16.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149(12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2034(24.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385(38.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5453(59.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e477(49.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1140(9.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468(4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3753(42.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e543(54.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1829(21.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156(18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995(22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(11.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake\u0026thinsp;~\u0026thinsp;kcal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2105.81(13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2012.74(35.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1742(14.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267(23.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7443(85.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e744(76.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension~%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5655(66.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398(46.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3530(33.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e613(53.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil\u0026thinsp;~\u0026thinsp;1000 cells/uL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.24(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood platelet\u0026thinsp;~\u0026thinsp;1000 cells/uL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC-Reactive Proteinmg/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.08(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u0026thinsp;~\u0026thinsp;U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.48(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.26(0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u0026thinsp;~\u0026thinsp;U/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.68(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.62(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u0026thinsp;~\u0026thinsp;g/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.16(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobulin\u0026thinsp;~\u0026thinsp;g/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association Between IBI Levels and Cancer Risk\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e presents the results of univariate and multivariable logistic regression analyses assessing the association between IBI levels and cancer prevalence. In unadjusted analyses, higher IBI levels were significantly associated with increased cancer prevalence (OR: 1.57; 95% CI: 1.29\u0026ndash;1.91; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained robust across stepwise multivariable models:\u003c/p\u003e \u003cp\u003eModel 1 (adjusted for age, sex, race): OR\u0026thinsp;=\u0026thinsp;1.31; 95% CI: 1.03\u0026ndash;1.66; P\u0026thinsp;=\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003eModel 2 (additional adjustment for education, marital status, BMI, PIR, energy intake): OR\u0026thinsp;=\u0026thinsp;1.40; 95% CI: 1.08\u0026ndash;1.82; P\u0026thinsp;=\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003eModel 3 (further adjustment for smoking, alcohol, hypertension, diabetes): OR\u0026thinsp;=\u0026thinsp;1.36; 95% CI: 1.04\u0026ndash;1.78; P\u0026thinsp;=\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003eModel 4 (additionally adjusted for albumin, globulin, ALT, AST): OR\u0026thinsp;=\u0026thinsp;1.37; 95% CI: 1.04\u0026ndash;1.80; P\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2.\u003c/b\u003e Association between IBI levels and risk of cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57(1.29,1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.31(1.03,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.08,1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36(1.04,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37(1.04,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 IBI Levels and Specific Cancer Types\u003c/h2\u003e \u003cp\u003eSubtype-specific analyses, adjusting for the same covariates as in the primary models, demonstrated significant associations between higher IBI levels and increased prevalence of breast cancer (OR\u0026thinsp;=\u0026thinsp;1.99; 95% CI: 1.06\u0026ndash;3.72) and prostate cancer (OR\u0026thinsp;=\u0026thinsp;2.02; 95% CI: 1.21\u0026ndash;3.39). No significant associations were observed for skin cancer (P\u0026thinsp;=\u0026thinsp;0.95), colorectal cancer (P\u0026thinsp;=\u0026thinsp;0.35), melanoma (P\u0026thinsp;=\u0026thinsp;0.85), uterine cancer (P\u0026thinsp;=\u0026thinsp;0.99), or cervical cancer (P\u0026thinsp;=\u0026thinsp;0.83), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup Analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e) revealed a generally consistent positive association between elevated IBI levels and cancer prevalence across demographic and clinical subgroups. A significant interaction was observed between IBI and body mass index (BMI). Specifically, in individuals with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, higher IBI levels were significantly associated with increased cancer risk (OR\u0026thinsp;=\u0026thinsp;1.54; 95% CI: 1.12\u0026ndash;2.11).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.3\u003c/b\u003e OR, 95% CI, and P values for subgroup analyses of cancer prevalence and IBI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP for interaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(0.99,2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(0.96,1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.77,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62(1.14,2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34(0.99,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19(0.77,1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45(1.07, 5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81(1.01,3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04(0.99, 4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36(0.72,2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55(0.92,2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(0.96,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(0.97,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38(1.32,8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(0.95,1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47(1.00,2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(0.94,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.66,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54(1.12,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(0.91,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38(0.94,2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49(1.11,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41(0.86,2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08(0.57, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36(0.59,3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.38, 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(0.89,1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12(1.15,3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22(0.67, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31(1.00,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(1.02,2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68(1.05,2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(0.99,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* PIR, energy intake were grouped according to median\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup Analyses for Breast and Prostate Cancer\u003c/h2\u003e \u003cp\u003eFurther stratified analyses for breast and prostate cancer (\u003cb\u003eTables\u0026nbsp;4 and 5\u003c/b\u003e) identified significant effect modifications. Among women with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, elevated IBI levels were strongly associated with breast cancer prevalence (OR\u0026thinsp;=\u0026thinsp;2.90; 95% CI: 1.34\u0026ndash;6.30). For prostate cancer, significant interactions were observed with energy intake and diabetes. Men with higher energy intake (OR\u0026thinsp;=\u0026thinsp;4.03; 95% CI: 1.91\u0026ndash;8.54) or diabetes (OR\u0026thinsp;=\u0026thinsp;4.85; 95% CI: 1.91\u0026ndash;12.35) showed markedly increased prostate cancer prevalence in the context of elevated IBI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.4\u003c/b\u003e OR, 95% CI, and P values for subgroup analyses of breast cancer prevalence and IBI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCancer\u0026thinsp;=\u0026thinsp;Breast\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP for interaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76(0.93, 8.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50(0.77, 2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90(0.52,6.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28(0.73,7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52(1.07,5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38(0.64, 2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40(1.11, 5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.32, 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90(1.34, 6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62(0.82,3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36(0.68, 8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84(0.85, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.58(1.12,11.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38(0.01,27.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72(0.90, 3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73(0.95, 7.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.21, 3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28(1.10, 4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* PIR, energy intake were grouped according to median\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.5\u003c/b\u003e OR, 95% CI, and P values for subgroup analyses of prostate cancer prevalence and IBI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCancer\u0026thinsp;=\u0026thinsp;Prostate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP for interaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.57(1.26, 10.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 0.80, 2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(0.39, 4.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01(0.71, 5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41(1.06, 5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily PIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90(1.70, 8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67(0.79,3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.54,2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63(1.32, 5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.44, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.03(1.91, 8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37(1.09, 5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59(0.76, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.19,2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84(1.03, 3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77(0.59, 5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.85(1.91, 12.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20(0.58, 2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* PIR, energy intake were grouped according to median\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Nonlinear Relationship Between IBI Levels and Cancer Risk\u003c/h2\u003e \u003cp\u003eRCS analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated a significant positive linear relationship between IBI levels and overall cancer prevalence. A similar trend was observed for breast cancer among females (P for non-linearity\u0026thinsp;=\u0026thinsp;0.21; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a linear association. However, no significant nonlinear association was detected for prostate cancer in males \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study utilized a nationally representative cohort from the NHANES dataset to explore the association between the IBI and cancer prevalence. Our findings indicate that higher IBI levels are independently associated with a greater likelihood of cancer, reinforcing the role of systemic inflammation in carcinogenesis.\u003c/p\u003e \u003cp\u003eThe robust association between elevated IBI levels and cancer prevalence persisted across both unadjusted and multivariable-adjusted models. Importantly, the association remained significant even after sequential adjustment for demographic, behavioral, and biochemical confounders, underscoring the independent contribution of systemic inflammation to cancer burden. The strongest associations were observed for breast and prostate cancers. Additionally, stratified analyses revealed that this association was particularly prominent among individuals with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, suggesting a potential synergistic interaction between obesity-induced inflammation and tumor development.\u003c/p\u003e \u003cp\u003eThese findings are consistent with the established literature identifying inflammation as a key driver of tumorigenesis \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Chronic inflammatory states facilitate cancer initiation and progression by promoting genomic instability, enhancing angiogenesis, and inducing immunosuppressive microenvironments \u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The stronger associations observed with breast and prostate cancers may reflect underlying hormonal regulation of inflammatory signaling pathways unique to these tumor types.\u003c/p\u003e \u003cp\u003eThe interaction between IBI and BMI emphasizes the intricate relationship between metabolic dysfunction and systemic inflammation \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Obesity-related inflammatory pathways\u0026mdash;marked by dysregulated adipokine secretion and persistent low-grade inflammation\u0026mdash;may potentiate cancer susceptibility in individuals with elevated IBI levels \u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Moreover, the observed effect modification by energy intake and diabetes status in prostate cancer further supports the existence of a complex metabolic-inflammatory axis that merits deeper mechanistic investigation \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese results carry important clinical and public health implications. As a non-invasive and cost-efficient biomarker, IBI could be integrated into routine screening protocols to aid in the early identification of individuals at elevated risk for cancer \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. This is particularly relevant for populations with obesity or metabolic syndrome, who may benefit from enhanced risk stratification. Furthermore, these findings underscore the need to target systemic inflammation through lifestyle-based interventions\u0026mdash;such as dietary optimization, physical activity, and weight management\u0026mdash;as part of a comprehensive cancer prevention framework.\u003c/p\u003e \u003cp\u003eThe strengths of this study include the use of a nationally representative sample and rigorous analytical approaches, enhancing both generalizability and internal validity. However, its cross-sectional design precludes causal inference, and the possibility of residual confounding cannot be excluded. Moreover, although IBI serves as a practical proxy for systemic inflammation, it may not fully encapsulate the multifaceted nature of inflammatory processes \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Future studies\u0026mdash;particularly prospective and longitudinal designs\u0026mdash;are warranted to validate the predictive utility of IBI and elucidate its mechanistic links to cancer development.\u003c/p\u003e \u003cp\u003eBuilding on these findings, future research should focus on elucidating the underlying mechanisms linking IBI to specific cancer types through experimental and molecular studies. Prospective cohort studies are also needed to establish causality and assess the predictive value of IBI in cancer incidence and progression. Finally, exploring the role of targeted anti-inflammatory interventions in mitigating cancer risk among individuals with high IBI levels represents a promising avenue for translational research.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this nationally representative analysis demonstrates that elevated IBI levels are significantly associated with increased cancer prevalence, particularly breast and prostate cancers. These findings reinforce the role of systemic inflammation in cancer pathophysiology and highlight the potential of IBI as a practical, population-level biomarker for cancer risk assessment and stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the staff and the participants of the NHANES study for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES protocol was approved by the Institutional Review Board of the National Center for Health Statistics (NCHS). As the present study used publicly available de-identified data, it was exempt from additional ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMW:\u0026nbsp;Methodology,\u0026nbsp;Data Analysis; ZL and XC managed and cleaned the data. HS criticized and revised the manuscript.\u0026nbsp;All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeijing Natural Science Foundation (J230038)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES datasets analyzed during the current study are publicly available from the National Center for Health Statistics (NCHS) (https:// www. cdc.gov/ nchs/ nhanes/ index. htm), except for geographic data (latitude) that are restricted to use through the NCHS Research Data Center (http:// www. cdc.gov/ rdc/) per NCHS, Centers for Disease Control and Prevention policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data were from NHANES and ethics were exempt in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang X, Chen H, Sang S, Chen H, Li L, Yang X. 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Peri-implantitis and systemic inflammation: A critical update. \u003cem\u003eSaudi Dent J\u003c/em\u003e. 2023;35(5):443\u0026ndash;450. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sdentj.2023.04.005\u003c/span\u003e\u003cspan address=\"10.1016/j.sdentj.2023.04.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Inflammatory Burden Index, cancer prevalence, systemic inflammation, NHANES, breast cancer, prostate cancer","lastPublishedDoi":"10.21203/rs.3.rs-6694086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6694086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer remains a major global health burden with persistently high incidence and mortality rates. Chronic systemic inflammation plays a pivotal role in tumor initiation and progression. The Inflammatory Burden Index (IBI), a composite biomarker derived from routine blood parameters, has shown promise in cancer prognosis. However, evidence from large-scale, population-based studies on its association with cancer prevalence is scarce.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the association between IBI levels and cancer prevalence in a representative U.S. population, providing insights into the role of systemic inflammation in cancer risk stratification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 10,196 participants of the National Health and Nutrition Examination Survey (NHANES) 2005\u0026ndash;2020 were analyzed. Cancer prevalence was based on self-reported diagnoses, and IBI was calculated using established formulas. Multivariable logistic regression and restricted cubic spline (RCS) analyses were employed to evaluate the relationship between IBI levels and overall and site-specific cancer prevalence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher IBI levels were significantly associated with increased cancer prevalence (9.92%). Adjusted logistic models confirmed a positive relationship between IBI and cancer risk (OR: 1.37, 95% CI: 1.04\u0026ndash;1.80; P\u0026thinsp;=\u0026thinsp;0.02). Notably, stronger associations were observed for breast (OR: 1.99) and prostate cancer (OR: 2.02). Subgroup analysis revealed significant interactions between IBI and body mass index (BMI), with amplified risk among individuals with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated IBI levels are independently associated with higher cancer prevalence, particularly for breast and prostate cancers. These findings support the utility of IBI as a non-invasive, cost-effective marker for cancer risk stratification in clinical and public health settings.\u003c/p\u003e","manuscriptTitle":"Inflammatory Burden Index and Cancer Prevalence: Insights from a Nationally Representative Study on the Predictive Role of Systemic Inflammation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:15:28","doi":"10.21203/rs.3.rs-6694086/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":"1d570519-f538-4f21-aa07-9f5842af285c","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T11:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:15:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6694086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6694086","identity":"rs-6694086","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}