Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016

Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016 Wei Li, Zheng-mei Qiao, Xiao-ting Wei, Miao-jia Wang, Duo-xiang Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664819/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With the rising incidence and mortality rates of cancer, there is an urgent need for effective biomarkers to predict cancer occurrence and monitor its prognosis. The red blood cell distribution width to albumin ratio (RAR), a novel inflammatory biomarker, has unclear associations with both cancer occurrence and prognosis. This study aims to explore the relationship between RAR and cancer incidence, as well as the prognosis of cancer survivors. Methods This study included 21,452 adult participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2016, of whom 1,910 had cancer. Weighted multivariable logistic regression was used to assess the association between RAR and cancer incidence. To evaluate the relationship between RAR and cancer prognosis, weighted multivariable Cox regression, restricted cubic splines (RCS), and subgroup analysis were employed. Additionally, propensity score matching (PSM) was conducted for sensitivity analysis. Results In the unadjusted model, RAR was significantly positively correlated with cancer incidence; however, this association became non-significant after adjusting for confounding factors. After fully adjusting for potential confounders, RAR was significantly associated with both all-cause and cancer-specific mortality in cancer survivors. Specifically, each additional unit increase in RAR was associated with a 2.42-fold increase in all-cause mortality (HR 2.42, 95% CI: 1.93, 3.03) and a 2.49-fold increase in cancer-specific mortality (HR 2.49, 95% CI: 1.79, 3.47). Subgroup analysis showed that higher RAR was associated with increased mortality risk across all subgroups. The prognostic model based on RAR had a C-index of 0.76, with AUC values of 0.77 for 5 years and 0.83 for 10 years. Conclusion RAR is significantly positively correlated with both all-cause and cancer-specific mortality in cancer survivors. The prognostic model based on RAR effectively predicts cancer survival and provides a basis for early intervention, particularly for populations at higher risk of poor outcomes. Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Cancer prevention Biological sciences/Cancer/Tumour biomarkers The red blood cell distribution width to albumin ratio (RAR) cancer incidence cancer prognosis cancer survivors NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer is one of the most pressing public health challenges worldwide and is the second leading cause of death globally, following cardiovascular diseases 1 , 2 . In most countries with a very high Human Development Index (HDI), cancer has already surpassed cardiovascular diseases as the leading cause of death 2 . According to the International Agency for Research on Cancer (IARC) 3 , by 2050, the number of cancer cases and deaths worldwide is projected to reach 35.3 million and 18.5 million, respectively, representing increases of 76.6% and 89.7% compared to 2022. As cancer incidence and mortality continue to rise, it is expected to become the leading cause of death globally in the future. Therefore, there is an urgent need for effective indicators to predict the occurrence of cancer and to monitor and improve long-term health outcomes for cancer patients. Oxidative stress and inflammation can impair erythropoiesis and lead to abnormal red blood cell survival, resulting in increased Red Cell Distribution Width (RDW). Additionally, nutritional deficiencies can also cause elevated RDW 4 . RDW is associated with the incidence and prognosis of various diseases 4 , 5 . Albumin possesses antioxidant, anti-inflammatory, antiplatelet aggregation, and anticoagulant properties 6 . A decrease in albumin levels usually indicates a high level of inflammation, poor nutritional status, and unfavorable therapeutic outcomes. RDW is positively correlated with chronological age, while albumin is negatively correlated with age 7 . RDW and albumin are common laboratory markers, and the red blood cell distribution width to albumin ratio (RAR) combines the benefits of both markers, providing a comprehensive reflection of the body’s inflammatory and nutritional status. As a novel inflammatory biomarker, RAR has been significantly correlated with the prevalence of various diseases, including dementia 8 , 9 , asthma 10 , kidney stones 11 , metabolic syndrome (MetS) 12 , and erectile dysfunction 13 . Furthermore, RAR has also been significantly associated with the prevalence and prognosis of depression 14 , 15 , Parkinson’s disease 16 , and diabetes 17 . Inflammation plays a crucial role in the occurrence, development, and prognosis of tumors 18 – 20 . As a novel inflammatory biomarker, whether RAR is associated with the incidence and prognosis of cancer remains inconclusive. A study in Japan involving 573 patients with acute coronary syndrome (ACS) found that elevated RAR levels were an independent positive predictor of cancer occurrence 21 . Another study showed that RAR was associated with the postoperative prognosis of cervical cancer patients 22 . However, these studies had small sample sizes, and some did not sufficiently adjust for confounding factors. Given the continued rise in cancer incidence and mortality, along with the growing global cancer burden, this study utilizes data from the National Health and Nutrition Examination Survey (NHANES) to conduct a nationwide representative study to assess whether RAR can effectively predict cancer occurrence and the prognosis of cancer survivors. As a potential biomarker, if RAR can accurately predict cancer onset and assess long-term outcomes for cancer survivors, it could help improve early cancer screening, monitoring, and intervention strategies, ultimately enhancing the quality of life and survival rates of cancer survivors. Methods Study Population The National Health and Nutrition Examination Survey (NHANES) is an investigation initiated by the National Center for Health Statistics (NCHS) aimed at assessing the health and nutritional status of American adults and children through interviews, physical examinations, and laboratory tests. The survey uses a complex, multi-stage probability sampling strategy, randomly selecting participants from different populations and geographic regions across the United States. This sampling method ensures that the survey results are representative of the entire American population. The study protocol was approved by the NCHS Research Ethics Review Board, and all participants provided written informed consent. This study analyzed data from six NHANES cycles (2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, and 2015–2016), which included 60,936 participants. To ensure the accuracy of the study, strict exclusion criteria were applied: 1) Age less than 20 years (N = 26,756); 2) Missing RAR data (N = 3,432); 3) Missing cancer and mortality status information (N = 70); 4) Missing covariate data (N = 9,226). Ultimately, 21,452 participants were included in the study for subsequent analysis (Fig. 1 ). RAR Assessment In the NHANES survey, healthcare personnel at the Mobile Examination Center (MEC) measured participants' peripheral blood Red Cell Distribution Width (RDW, %) using a Coulter Analyzer. Albumin concentration was determined using the DcX 800 method, a two-color endpoint method. In this method, albumin reacts with bromocresol purple (BCP) reagent to form a complex, and the system measures the absorbance change at 600 nm. This absorbance is proportional to the albumin concentration in the blood sample, allowing the reflection of serum albumin levels (g/dL). The RAR was calculated using the following formula: [RDW (%) / serum albumin (g/dL)]. Cancer Survivors and Mortality Status In the medical conditions section of the interview, data on cancer diagnosis history and cancer types were collected. Participants were asked: “Has a doctor or other healthcare professional ever told you that you had cancer or any type of malignant tumor?” Those who answered "yes" were defined as cancer survivors, and the specific types of cancer they had (up to three types) were recorded. NHANES data were linked with the National Death Index (NDI), recording participants' mortality status and causes of death from the date of the survey to December 31, 2019. The causes of death were differentiated using International Classification of Diseases (ICD-10) codes 23 , categorizing deaths as all-cause mortality, cancer-related deaths, cardiovascular deaths, and other types of death. Covariates The following covariates were selected for analysis based on relevant literature: age, sex, race/ethnicity, education level, marital status, Family Poverty Income Ratio (PIR), body mass index (BMI), drinking Status, smoking status, physical activity, diabetes, hypertension, hyperlipidemia, and cardiovascular disease. Race/ethnicity: Non-Hispanic White, Non-Hispanic Black, or Other. Marital Status: Married/cohabiting, Never Married, Widowed/divorced/separated. Education Level: Less than high school, High school, College or higher. Smoking Status: "Yes" (smoked more than 100 cigarettes in their lifetime) and "No" (never smoked or smoked fewer than 100 cigarettes). Drinking Status: Confirmed by the question: "In any given year, have you consumed at least 12 alcoholic drinks?" Participants who answered "yes" were defined as drinkers. Family PIR: Categorized into three levels based on the Family Poverty Income Ratio (PIR): <1.3, 1.3–3.5, and ≥ 3.5. BMI: Categorized into three levels: <25, 25–30, and ≥ 30 kg/m². Physical Activity: Categorized as vigorous activity, moderate activity, or inactivity. Hypertension: Defined based on self-reported hypertension history, use of antihypertensive medications, or a systolic blood pressure (SBP) > 130 mmHg or diastolic blood pressure (DBP) > 80 mmHg 24 (PMID: 29146535). Diabetes: Defined based on self-reported diabetes history, use of insulin or antidiabetic medications, or laboratory results showing HbA1c ≥ 6.5%, fasting blood glucose (FBG) ≥ 7.0 mmol/L, or 2-hour postprandial glucose (2hPG) ≥ 11.1 mmol/L 25 (PMID: 34135016). Hyperlipidemia: Defined based on self-reported high cholesterol, use of cholesterol-lowering medications, or laboratory results showing triglycerides ≥ 150 mg/dL, low-density lipoprotein cholesterol (LDL-C) ≥ 130 mg/dL, high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL (male) or < 50 mg/dL (female), and total cholesterol (TC) ≥ 200 mg/dL 9 . Cardiovascular Disease: Defined based on the medical history of heart failure, coronary heart disease, angina pectoris, heart attack, and stroke reported in the survey. Statistical Analysis To ensure the study population is representative of the American population, we accounted for the complex sampling design and sampling weights in the analysis. Normality tests were conducted for continuous variables. For normally distributed data, the mean (standard error [SE]) is presented, and for skewed data, the median (interquartile range [IQR]) is presented. For group comparisons, continuous variables with a normal distribution were analyzed with the Student's t-test, and continuous variables with a skewed distribution were analyzed with the Mann-Whitney U test. Categorical variables were presented as weighted percentages and compared using the chi-square test. To assess the association between RAR, cancer incidence, and prognosis, we used weighted logistic regression and Cox regression models to calculate odds ratios (OR) and hazard ratios (HR), respectively. RAR was also converted into categorical variables based on quartiles, and trend P-values were calculated. To ensure comprehensive analysis, this study used three models: Model 1, an unadjusted crude model; Model 2, adjusted for age, sex, race, education level, marital status, family PIR, and BMI; Model 3, a fully adjusted model that further adjusted for Drinking Status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases in addition to the variables in Model 2. Considering that the relationship between RAR and cancer survival may not be a simple linear one, we used Restricted Cubic Splines (RCS) to capture complex nonlinear relationships, thereby improving model fit and predictive accuracy. Additionally, we performed subgroup analyses and interaction analyses based on different covariates for RAR and cancer survival. Although NHANES uses a complex sampling method to improve the representativeness and applicability of the results, conclusions drawn from weighted and unweighted analyses may differ. Therefore, we also used unweighted data and performed propensity score matching (PSM) to further control for confounding factors, to compare the impact of RAR on survival. Finally, we constructed a prognostic model for cancer survivors based on RAR. All statistical analyses were performed using R statistical software (version 4.2.2). A P-value of < 0.05 (two-sided) was considered statistically significant for all tests. Results Baseline Characteristics This study included 21,452 adult participants from the 2005–2016 NHANES database (weighted to represent 154,935,519 American adults), of whom 1,910 had cancer (Supplementary Table 1). Statistically significant differences were observed between the cancer and non-cancer groups in terms of RAR values, as well as in age, sex, race, education level, Family PIR, smoking status, blood pressure, hyperlipidemia, hyperglycemia, and cardiovascular disease. However, no significant differences were found between the two groups in terms of Drinking Status, BMI, and marital status. Table 1 shows the baseline characteristics of the 1,910 cancer survivors, which correspond to 14,805,089 American adults in the weighted population. The median age of the participants was 64 years [IQR: 53, 73], with 58.3% female and 1,367 (88.0%) non-Hispanic White. Participants with higher RAR values were generally characterized by being older, female, non-Hispanic Black, unmarried or single, with lower family PIR, higher BMI, and less physical activity. Additionally, participants with higher RAR values were more likely to have hypertension, diabetes, and cardiovascular disease. Table 1 Characteristics of Cancer Survivors Characteristic Quartiles of RAR Overall,N = 1910 Q1(3.41),N = 480 P Age, M (Q₁, Q₃) 64 (53, 73) 57(48, 67) 63(53, 74) 66(56, 76) 68(57, 78) < 0.001 Sex, n (%) Male 858 (41.7) 224 ( 44.2) 230 ( 45.6) 218 ( 42.3) 186 ( 32.3) 0.006 Female 1052 (58.3) 254 ( 55.8) 231 ( 54.4) 273 ( 57.7) 294 ( 67.7) Race/ethnicity, n (%) Non-Hispanic White 1367 (88.0) 381 ( 91.8) 335 ( 87.6) 357 ( 90.1) 294 ( 79.6) < 0.001 Non-Hispanic Black 238 (4.4) 19 ( 1.2) 52 ( 3.8) 62 ( 4.6) 105 ( 9.7) Other race 305 (7.6) 78 ( 7.0) 74 ( 8.6) 72 ( 5.3) 81 ( 10.4) Education level, n (%) Below high school 326 ( 9.6) 59 ( 6.8) 74 ( 8.4) 73 ( 8.9) 120 ( 16.3) < 0.001 High school 409 (19.0) 103 ( 19.1) 87 ( 15.4) 119 ( 21.1) 100 ( 20.6) Above high school 1175 (71.4) 316 ( 74.1) 300 ( 76.2) 299 ( 70.0) 260 ( 63.1) Marital status, n (%) Married/living with partner 1158 (65.7) 320 ( 71.6) 306 ( 70.8) 280 ( 60.9) 252 ( 56.6) < 0.001 Single/divorced/widowed 752 (34.3) 158 ( 28.4) 155 ( 29.2) 211 ( 39.1) 228 ( 43.4) Family PIR, n (%) ≤ 1.0 425 (13.6) 88 ( 11.2) 87 ( 9.1) 107 ( 14.6) 143 ( 21.8) 3.0 734 (51.7) 228 ( 61.1) 191 ( 55.0) 181 ( 47.3) 134 ( 38.5) Smoking status, n (%) No 916 (48.7) 251 ( 52.4) 216 ( 46.3) 220 ( 46.1) 229 ( 49.2) 0.332 Yes 994 (51.3) 227 ( 47.6) 245 ( 53.7) 271 ( 53.9) 251 ( 50.8) BMI(kg/m2), n (%) < 25 541 (30.1) 181 ( 41.2) 144 ( 31.5) 127 ( 24.8) 89 ( 17.8) 30 698 (34.9) 106 ( 18.8) 146 ( 31.5) 200 ( 43.5) 246 ( 53.3) Drinking status, n (%) No 285 (11.2) 62 ( 10.0) 59 ( 9.3) 74 ( 11.4) 90 ( 15.3) 0.094 Yes 1625 (88.8) 416 ( 90.0) 402 ( 90.7) 417 ( 88.6) 390 ( 84.7) Physical activity, n (%) Inactive 1100 (53.2) 240 ( 48.1) 255 ( 50.7) 296 ( 54.9) 309 ( 62.2) 0.008 Moderate 385 (23.3) 114 ( 25.7) 90 ( 21.0) 92 ( 22.8) 89 ( 23.3) Vigorous 425 (23.4) 124 ( 26.2) 116 ( 28.3) 103 ( 22.2) 82 ( 14.5) Hypertension, n (%) No 550 (33.6) 171 ( 42.5) 134 ( 32.1) 130 ( 29.4) 115 ( 27.2) 0.002 Yes 1360 (66.4) 307 ( 57.5) 327 ( 67.9) 361 ( 70.6) 365 ( 72.8) Diabetes, n (%) No 1470 (81.7) 402 ( 89.3) 366 ( 82.9) 386 ( 82.2) 316 ( 67.7) < 0.001 Yes 440 (18.3) 76 ( 10.7) 95 ( 17.1) 105 ( 17.8) 164 ( 32.3) Hyperlipidemia, n (%) No 330 (16.5) 82 ( 17.7) 83 ( 16.4) 80 ( 14.4) 85 ( 17.7) 0.627 Yes 1580 (83.5) 396 ( 82.3) 378 ( 83.6) 411 ( 85.6) 395 ( 82.3) Cardiovascular disease, n (%) No 1502 (83.7) 419 ( 92.2) 367 ( 83.5) 377 ( 80.3) 339 ( 75.1) < 0.001 Yes 408 (16.3) 59 ( 7.8) 94 ( 16.5) 114 ( 19.7) 141 ( 24.9) all-cause mortality,n (%) No 1443 (82.7) 406 ( 90.1) 367 ( 86.3) 365 ( 79.7) 305 ( 70.6) < 0.001 Yes 467 (17.3) 72 ( 9.9) 94 ( 15.7) 126 ( 20.3) 175 ( 29.4) cancer mortality ,n(%) No 1757 (94.1) 451 ( 95.8) 425 ( 94.6) 452 ( 94.2) 429 ( 90.9) 0.053 Yes 153 ( 5.9) 27 ( 4.2) 36 ( 5.4) 39 ( 5.8) 51 ( 9.1) Association Between RAR and Cancer Incidence Table 2 presents the results of the logistic regression analysis, which examined the association between RAR and adult cancer incidence. In Model 1 (crude model), when RAR was treated as a continuous variable, higher RAR was associated with a 44% increased risk of cancer (OR: 1.44 [1.29, 1.59], P < 0.001). When RAR was treated as a categorical variable, the Q1 group was used as the reference, with the OR for the Q2 group being 1.34 (1.12, 1.62) (P = 0.002), the OR for the Q3 group being 1.75 (1.46, 2.10) (P < 0.001), and the OR for the Q4 group being 2.07 (1.74, 2.47) (P < 0.001), with a trend P-value of 0.05). Table 2 . The Association Between RAR and Cancer Incidence Variables Model 1 Model 2 Model 3 OR (95%CI) P OR (95%CI) P OR (95%CI) P RDW 1.11( 1.08,1.15) <0.001 1.03( 0.98,1.08) 0.249 1.03( 0.98,1.08) 0.300 ALB 0.59( 0.51,0.69) <0.001 0.86( 0.70,1.07) 0.179 0.85( 0.69,1.06) 0.152 RAR(Continuous) 1.44( 1.29,1.59) <0.001 1.11( 0.97,1.27) 0.131 1.11( 0.97,1.28) 0.133 Quartiles of RAR Q1(<2.83) Reference Reference Reference Q2(2.83–3.04) 1.34(1.12,1.62) 0.002 0.93( 0.76,1.13) 0.448 0.93( 0.76,1.13) 0.462 Q3(3.05–3.31) 1.75(1.46,2.10) 3.31) 2.07(1.74,2.47) <0.001 1.15(0.94,1.41) 0.179 1.15( 0.94,1.42) 0.180 P trend <0.001 0.068 0.072 Model 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family poverty-income ratio (PIR), and body mass index (BMI); Model 3: Fully adjusted model, further adjusted for drinking status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases, in addition to the variables in Model 2. Results are presented as odds ratios (OR) and 95% confidence intervals (CI). OR, odds ratio; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio. Association Between RAR and Prognosis of Cancer Survivors The median follow-up time was 84 months (IQR: 55, 123), during which 467 participants died, including 153 from cancer. Kaplan-Meier curves showed that patients with higher RAR had significantly higher all-cause mortality and cancer-specific mortality (P < 0.001) (Fig. 2 ). Cox proportional hazards models indicated that in all models, patients with higher RAR had a significantly increased risk of death (Table 3). Specifically, in the multivariable-adjusted Model 3, higher levels of RAR were significantly associated with both all-cause mortality and cancer-specific mortality in cancer patients. When RAR was treated as a continuous variable, each additional unit increase in RAR was associated with a 2.42-fold increase in all-cause mortality and a 2.49-fold increase in cancer-specific mortality. For all-cause mortality, with Q1 as the reference group, the HR for Q2 was 1.05 (0.74, 1.50), for Q3 was 1.56 (1.10, 2.20), and for Q4 was 2.62 (1.86, 3.69), showing a significant upward trend (P trend < 0.001); for cancer-specific mortality, with Q1 as the reference group, the HR for Q2 was 1.23 (0.67, 2.27), for Q3 was 1.30 (0.71, 2.37), and for Q4 was 2.43 (1.26, 4.67), also showing a significant upward trend (P trend < 0.001). Additionally, in all models, RAR had a higher HR value than RDW and albumin. Table 3. The Relationship Between RAR and Mortality in Cancer Survivors Variables Model 1 Model 2 Model 3 HR(95%CI) P HR(95%CI) P HR(95%CI) P All-cause mortality RDW 1.34( 1.22,1.46) <0.001 1.28( 1.20,1.37) <0.001 1.28( 1.19,1.37) <0.001 ALB 0.31( 0.21,0.46) <0.001 0.39( 0.25,0.58) <0.001 0.40( 0.26,0.61) <0.001 RAR(Continuous) 2.90( 2.31,3.63) <0.001 2.483(1.99,3.10) <0.001 2.42(1.93,3.03) <0.001 Quartiles of RAR Q1(<2.93) Reference Reference Reference Q2(2.93–3.13) 1.68( 1.15,2.46) 0.008 1.12( 0.78,1.60) 0.539 1.05(0.74,1.50) 0.779 Q3(3.14–3.41) 2.64( 1.83,3.81) 3.41) 4.72( 3.33,6.70) <0.001 2.78( 1.96,3.94) <0.001 2.62 (1.86,3.69) <0.001 P trend <0.001 <0.001 <0.001 Cancer-specific mortality RDW 1.32( 1.20,1.46) <0.001 1.29( 1.17,1.41) <0.001 1.29( 1.17,1.42) <0.001 ALB 0.39( 0.17,0.86) 0.019 0.40( 0.16,0.99) 0.048 0.41( 0.16,1.05) 0.063 RAR(Continuous) 2.78( 2.09,3.70) <0.001 2.50( 1.82,3.43) <0.001 2.49 (1.79,3.47) <0.001 Quartiles of RAR Q1(<2.93) Reference Reference Reference Q2(2.93–3.13) 1.55( 0.83,2.89) 0.172 1.25(0.68,2.30) 0.476 1.23( 0.67,2.27) 0.502 Q3(3.14–3.41) 1.75( 0.93,3.31) 0.083 1.33( 0.72,2.46) 0.365 1.30 ( 0.71,2.37) 0.396 Q4(>3.41) 3.25( 1.77,5.95) <0.001 2.48(1.30,4.75) 0.006 2.43 (1.26,4.67) 0.008 P trend <0.001 0.005 <0.001 Model 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family poverty-income ratio (PIR), and body mass index (BMI); Model 3: Fully adjusted model, further adjusted for drinking status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases, in addition to the variables in Model 2. Results are presented as hazard ratios (HR) and 95% confidence intervals (CI). HR, hazard ratios; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio. Dose-Response Analysis of RAR and Mortality in Cancer Survivors RCS analysis explored the dose-response relationship between RAR levels and all-cause mortality as well as cancer-specific mortality in the study population (Figs. 3 A, B). The results showed a significant statistical relationship between RAR levels and both all-cause mortality and cancer-specific mortality (P overall 0.05). Subgroup Analysis This study evaluated the relationship between RAR levels and the mortality risk of cancer survivors in different subgroups and explored potential interactions between RAR and other variables (Fig. 4 ). In all subgroups, higher RAR was significantly associated with increased all-cause mortality; additionally, most cancer survivor subgroups also showed statistically significant differences in cancer-specific mortality, with other subgroups showing a higher trend of mortality. Notably, an interaction was found between RAR and the Family PIR. In the subgroup with a Family PIR of 1.1–3.0, higher RAR was significantly associated with an increased risk of all-cause mortality (HR: 3.62, 95% CI: 2.45–5.33). Sensitivity Analysis Unweighted data were used, and PSM was employed to further control for confounding factors in order to compare the impact of RAR on survival. There were statistically significant differences between the high RAR and low RAR groups in terms of age, sex, race/ethnicity, education level, marital status, family PIR, BMI, drinking Status, physical activity, diabetes, hypertension, and cardiovascular disease (Supplementary Table 2). After balancing the differences between the two groups through PSM, 735 matched pairs of participants were obtained. Compared to the low RAR group, the high RAR group had worse all-cause mortality and cancer mortality (P < 0.05) (Fig. 5 ). RAR-Based Predictive Model for All-Cause Mortality in Cancer Survivors Unweighted data were used to identify independent predictors for cancer survivors and construct a predictive model. In the univariate Cox regression analysis, variables with a P-value less than 0.05 were included in the subsequent multivariate Cox regression analysis (Supplementary Table 3). Ultimately, age, sex, race, marital status, family PIR, BMI, physical activity, hypertension, diabetes, cardiovascular disease, and RAR were included in the final predictive model (Fig. 6 ). The C-index for the model was 0.76 (95% CI: 0.74–0.78), with AUC values of 0.77 (95% CI: 0.73–0.81) for 3 years, 0.77 (95% CI: 0.74–0.80) for 5 years, and 0.83 (95% CI: 0.80–0.85) for 10 years (Supplementary Fig. 1). Discussion This study is the first to explore the relationship between RAR, cancer incidence, and cancer survivors' prognosis, using a representative sample of American adults from across the nation. The results showed that RAR levels were not significantly associated with cancer incidence. However, during long-term follow-up of the cancer survivors, we found a significant positive linear relationship between RAR and both all-cause mortality and cancer-specific mortality. Subgroup analyses further indicated that the association between RAR and all-cause mortality, as well as cancer mortality, remained consistent across all subgroups. For each additional unit increase in RAR, the increase in mortality risk (HR = 2.42) was significantly greater than that associated with RDW (HR = 1.28), suggesting that RAR, as a composite index, may more effectively capture the pathophysiological changes associated with poor prognosis. Propensity score matching analysis further validated the robustness of the relationship between RAR levels and cancer prognosis, enhancing the reliability of our findings. Systemic inflammation is considered a key factor in promoting tumor cell proliferation and survival, inducing angiogenesis, facilitating metastasis, and altering drug responses 26 , 27 . RDW and albumin reflect conditions such as inflammation, oxidative stress, and malnutrition. Previous studies have primarily focused on RDW or albumin, with findings suggesting that RDW can serve as a predictive marker for several types of tumors 28 – 31 . However, in our study, based on a large population dataset, no significant association was found between RAR and tumor incidence. Studies have reported that RDW is associated with tumor staging, pathological grading, and malignancy 32 – 34 , and it has been found to be related to the prognosis of several cancers, including lung cancer 35 , colon cancer 36 , urological tumors 37 , and breast cancer 38 . Podhorecka et al. found that RDW did not change significantly during disease progression, suggesting that RDW could serve as a stable prognostic marker 39 . Additionally, an increase in RDW is linked to shortened telomere length 40 , which in turn is associated with cancer incidence and mortality 41 . Albumin not only reflects inflammation but is also associated with malnutrition. Lower albumin levels are associated with worse cancer prognosis, and albumin levels are closely related to malnutrition and poor postoperative outcomes in cancer patients 42 – 46 . Albumin may inhibit tumor progression by stabilizing DNA replication and enhancing immune responses 47 , and its accumulation at inflammation and tumor sites helps deliver anti-inflammatory and anticancer drugs 48 , 49 . Additionally, albumin can downregulate the expression and transport of inflammatory factors, thereby alleviating inflammation 50 . Lower albumin concentrations indicate stronger cancer-related inflammation, which may promote tumor progression 51 , 52 . Serum albumin has also been used as a prognostic marker for cancer survival. RAR has also been associated with cancer-related symptoms, such as cachexia, pain, and insomnia in cancer patients 53 . Notably, our study found that albumin was associated with all-cause mortality in cancer survivors, although the association with cancer-specific mortality was not significant. However, when RDW and albumin were combined, RAR showed a significant association with cancer-specific mortality, suggesting that RAR has greater predictive value than individual markers. This finding is consistent with similar results from other studies, such as the fibrinogen-to-albumin ratio, which has been used as a prognostic marker for cancer survivors 54 . The exact mechanisms by which RAR influences cancer prognosis remain unclear, but they may be closely related to factors such as telomere shortening, inflammation, oxidative stress, and malnutrition. Future basic research is needed to explore these mechanisms in greater detail. Previous Studies on the Relationship Between RAR and Cancer Survivors' Prognosis. A retrospective study involving 907 patients undergoing radical hysterectomy found that preoperative RAR was an independent risk factor for intraoperative blood transfusion and poor prognosis, including prolonged hospital stay and reduced 5-year survival (HR: 1.50, 95% CI: 1.04–2.17, p = 0.033) 22 . A second retrospective study using the Medical Information Mart for Intensive Care III (MIMIC-III) database also identified RAR as an independent risk factor for all-cause mortality in cancer patients 55 . However, this study included fewer variables and did not adjust for key confounders such as smoking, alcohol consumption, physical activity, hypertension, and diabetes, which may influence mortality. It is also important to note that the cancer survivors in this study were all from the ICU. Many cancer patients are admitted to the ICU due to non-cancer-related acute complications, such as infections, sepsis, acute respiratory distress syndrome, and acute kidney failure. Studies have shown that RAR is significantly associated with the prognosis of sepsis 56 , diabetic ketoacidosis 57 , acute respiratory distress syndrome 58 , and severe pneumonia 59 . In our study, we used NHANES data, which represent the health status of over 14 million people in the American and can be generalized to a broader population. Furthermore, the all-cause mortality risk model for cancer survivors based on RAR has a C-index of 0.76, with AUC values of 0.77 for 5 years and 0.83 for 10 years, making it a useful tool for predicting cancer survival. This study has several limitations. First, the prognostic value of RAR was assessed based on prospective cohort data, which cannot fully eliminate selection bias, although propensity score matching has partially mitigated the impact of confounding factors. Second, a single RAR measurement may not adequately capture dynamic changes during treatment. Future studies with repeated measurements are needed to validate the predictive value of RAR trajectories. Finally, despite adjustments for key metabolic and demographic variables, residual confounding factors (e.g., tumor staging, molecular subtypes) may still affect the interpretation of the results. Conclusion We found that RAR is significantly associated with both all-cause and cancer-specific mortality in cancer survivors. As a simple and cost-effective biomarker, a cancer prediction model based on RAR can provide valuable guidance for a large population of cancer survivors, particularly for those with poor prognosis, allowing for early intervention. However, future studies should include various cancer types and additional predictive markers, such as tumor staging, molecular subtypes, and treatment modalities, to enhance the model's accuracy and applicability. Abbreviations IARC International Agency for Research on Cancer ALB Albumin RDW Red Cell Distribution Width RAR Red Blood Cell Distribution Width to Albumin Ratio HDI high Human Development Index MetS metabolic syndrome ACS acute coronary syndrome BMI Body Mass Index OR Odds Ratio HR Hazard Ratio CI Confidence Interval ICD-10 International Classification of Diseases, 10th Revision IQR Interquartile Range PIR Poverty Income Ratio RCS Restricted Cubic Splines PSM Propensity Score Matching SBP Systolic Blood Pressure TC Total Cholesterol LDL-C low-density lipoprotein cholesterol HDL-C high-density lipoprotein cholesterol OS Overall Survival CSS Cancer-Specific Survival Declarations Declaration of competing interest The authors declare no competing interests. Author Contribution W L and ZM Q contributed to writing the manuscript and creating the figures and tables. XT W, MJ W, DX Z, J Y, and QZ W were responsible for data collation and statistical analysis. T Y was responsible for project design. All authors have read and approved the final manuscript. Data Availability The dataset used for this study analysis can be found on the official website of the National Health and Nutrition Examination Survey (https://wwwn.cdc.gov/nchs/nhanes/). References Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 74 (3), 229–263 (2024). Bray, F., Laversanne, M., Weiderpass, E. & Soerjomataram, I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer 127 (16), 3029–3030 (2021). Bizuayehu, H. M. et al. Global Disparities of Cancer and Its Projected Burden in 2050. JAMA Netw. open. 7 (11), e2443198 (2024). Salvagno, G. L., Sanchis-Gomar, F., Picanza, A. & Lippi, G. Red blood cell distribution width: A simple parameter with multiple clinical applications. Crit. Rev. Clin. Lab. Sci. 52 (2), 86–105 (2015). Wang, P. F. et al. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. J. Cancer . 10 (18), 4305–4317 (2019). Arques, S. Human serum albumin in cardiovascular diseases. Eur. J. Intern. Med. 52 , 8–12 (2018). Cohen, A. A. et al. Detection of a novel, integrative aging process suggests complex physiological integration. PLoS One . 10 (3), e0116489 (2015). Beydoun, M. A. et al. Red Cell Distribution Width Is Directly Associated with Poor Cognitive Performance among Nonanemic, Middle-Aged, Urban Adults. J. Nutr. 150 (1), 128–139 (2020). Yu, B. et al. Red blood cell distribution width to albumin ratio (RAR) is associated with low cognitive performance in American older adults: NHANES 2011–2014. BMC Geriatr. 25 (1), 157 (2025). Ding, J., Zhang, Y. & Chen, X. Red cell distribution width to albumin ratio is associated with asthma risk: a population-based study. Front. Med. (Lausanne) . 11 , 1493463 (2024). Wu, L. et al. Association of Red Blood Cell Distribution Width to Albumin Ratio With the Prevalence of Kidney Stones Among the General Adult Population. Immun. Inflamm. Dis. 12 (11), e70070 (2024). Guo, H., Wang, Y., Miao, Y. & Lin, Q. Red cell distribution width/albumin ratio as a marker for metabolic syndrome: findings from a cross-sectional study. BMC Endocr. disorders . 24 (1), 227 (2024). Xu, Y. & Li, S. J-Shaped relationship between the red cell distribution width to albumin ratio and erectile dysfunction: a cross-sectional study from NHANES 2001–2004. Front. Endocrinol. 16 , 1545272 (2025). Zhu, X. F., Hu, Y. Q., Long, Z. W. & Cao, M. Z. Association between RAR and the prevalence and prognosis of depression: A population-based study. J. Affect. Disord. 380 , 1–9 (2025). Liu, H., Xiang, R. & Chen, Z. The association between red blood cell distribution width-to-albumin ratio and risk of depression: A cross-sectional analysis of NHANES. J. Affect. Disord. 379 , 250–257 (2025). Jia, H., Yin, K., Zhao, J. & Che, F. Association of inflammation/nutrition-based indicators with Parkinson's disease and mortality. Front. Nutr. 11 , 1439803 (2024). Liu, J. et al. Red blood cell distribution width to albumin ratio associates with prevalence and long-term diabetes mellitus prognosis: an overview of NHANES 1999–2020 data. Front. Endocrinol. 15 , 1362077 (2024). Coussens, L. M. & Werb, Z. Inflammation and cancer. Nature 420 (6917), 860–867 (2002). Mantovani, A. Cancer: inflammation by remote control. Nature 435 (7043), 752–753 (2005). Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144 (5), 646–674 (2011). Ishii, M. et al. Risk Prediction Score for Cancer Development in Patients With Acute Coronary Syndrome. Circulation journal: official J. Japanese Circulation Soc. 88 (2), 234–242 (2024). Sim, J. H., Jang, D. M., Cho, H. S., Park, J. Y. & Choi, W. J. Association of red cell distribution width/albumin ratio with intraoperative blood transfusion in cervical cancer patients. PLoS One . 17 (11), e0277481 (2022). Outland, B., Newman, M. M. & William, M. J. Health Policy Basics: Implementation of the International Classification of Disease, 10th Revision. Annals of internal medicine. ;163(7):554–556. (2015). Whelton, P. K. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 71 (19), e127–e248 (2018). Addendum. ;44(Suppl. 1):S15-S33. Diabetes care. 2021;44(9):2182.2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care (2021). Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. Cancer-related inflammation. Nature 454 (7203), 436–444 (2008). Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, inflammation, and cancer. Cell 140 (6), 883–899 (2010). Song, B. et al. Utility of red cell distribution width as a diagnostic and prognostic marker in non-small cell lung cancer. Sci. Rep. 10 (1), 15717 (2020). Thakur, A. S., Indoria, C., Sahu, R., Kujur, P. & Gahine, R. Preoperative evaluation of red blood cell distribution width as a promising biomarker for discriminating between benign and malignant breast tumors and assessing breast cancer activity. Indian. J. Pathol. Microbiol. 67 (2), 324–327 (2024). He, Q. et al. Blood cell indices and inflammation-related markers with kidney cancer risk: a large-population prospective analysis in UK Biobank. Front. Oncol. 14 , 1366449 (2024). Han, F. et al. Diagnosis and survival values of neutrophil-lymphocyte ratio (NLR) and red blood cell distribution width (RDW) in esophageal cancer. Clin. Chim. Acta . 488 , 150–158 (2019). Huang, D. P., Ma, R. M. & Xiang, Y. Q. Utility of Red Cell Distribution Width as a Prognostic Factor in Young Breast Cancer Patients. Medicine 95 (17), e3430 (2016). Seretis, C., Seretis, F., Lagoudianakis, E., Gemenetzis, G. & Salemis, N. S. Is red cell distribution width a novel biomarker of breast cancer activity? Data from a pilot study. J. Clin. Med. Res. 5 (2), 121–126 (2013). Wang, F. M., Xu, G., Zhang, Y. & Ma, L. L. Red cell distribution width is associated with presence, stage, and grade in patients with renal cell carcinoma. Dis. Markers . 2014 , 860419 (2014). Wang, Y., Zhou, Y., Zhou, K., Li, J. & Che, G. Prognostic value of pre-treatment red blood cell distribution width in lung cancer: a meta-analysis. Biomarkers: biochemical indicators of exposure, response, and susceptibility to chemicals. ;25(3):241–247. (2020). Wen, Z. L., Zhou, X. & Xiao, D. C. Is red blood cell distribution width a prognostic factor for colorectal cancer? A meta-analysis. Front. Surg. 9 , 945126 (2022). Cao, W. et al. Pretreatment red blood cell distribution width may be a potential biomarker of prognosis in urologic cancer: a systematic review and meta-analysis. Biomark. Med. 16 (18), 1289–1300 (2022). Yin, J. M. et al. Is red cell distribution width a prognostic factor in patients with breast cancer? A meta-analysis. Front. Surg. 10 , 1000522 (2023). Podhorecka, M. et al. Assessment of red blood cell distribution width as a prognostic marker in chronic lymphocytic leukemia. Oncotarget 7 (22), 32846–32853 (2016). Kozlitina, J. & Garcia, C. K. Red blood cell size is inversely associated with leukocyte telomere length in a large multi-ethnic population. PLoS One . 7 (12), e51046 (2012). Willeit, P. et al. Telomere length and risk of incident cancer and cancer mortality. Jama 304 (1), 69–75 (2010). Pacelli, F. et al. Is malnutrition still a risk factor of postoperative complications in gastric cancer surgery? Clin. Nutr. 27 (3), 398–407 (2008). Huang, J. et al. Preoperative serum pre-albumin as an independent prognostic indicator in patients with localized upper tract urothelial carcinoma after radical nephroureterectomy. Oncotarget 8 (22), 36772–36779 (2017). Kao, H. K. et al. Nomogram based on albumin and neutrophil-to-lymphocyte ratio for predicting the prognosis of patients with oral cavity squamous cell carcinoma. Sci. Rep. 8 (1), 13081 (2018). Toiyama, Y. et al. Clinical impact of preoperative albumin to globulin ratio in gastric cancer patients with curative intent. Am. J. Surg. 213 (1), 120–126 (2017). Ayhan, A. et al. The preoperative albumin level is an independent prognostic factor for optimally debulked epithelial ovarian cancer. Arch. Gynecol. Obstet. 296 (5), 989–995 (2017). Bağırsakçı, E. et al. Role of Albumin in Growth Inhibition in Hepatocellular Carcinoma. Oncology 93 (2), 136–142 (2017). Sleep, D. Albumin and its application in drug delivery. Expert Opin. Drug Deliv. 12 (5), 793–812 (2015). Bern, M., Sand, K. M., Nilsen, J., Sandlie, I. & Andersen, J. T. The role of albumin receptors in regulation of albumin homeostasis: Implications for drug delivery. J. controlled release: official J. Controlled Release Soc. 211 , 144–162 (2015). Utariani, A., Rahardjo, E. & Perdanakusuma, D. S. Effects of Albumin Infusion on Serum Levels of Albumin, Proinflammatory Cytokines (TNF-α, IL-1, and IL-6), CRP, and MMP-8; Tissue Expression of EGRF, ERK1, ERK2, TGF-β, Collagen, and MMP-8; and Wound Healing in Sprague Dawley Rats. Int. J. Inflamm. 2020 , 3254017 (2020). Seaton, K. Albumin concentration controls cancer. J. Natl Med. Assoc. 93 (12), 490–493 (2001). Laursen, I., Briand, P. & Lykkesfeldt, A. E. Serum albumin as a modulator on growth of the human breast cancer cell line, MCF-7. Anticancer Res. 10 (2a), 343–351 (1990). Ozkarafakili, M. A., Kara, Z. M. Y., Musluman, A. M. & Bek, T. T. The Association of Plasma Asymmetric Dimethylarginine Concentrations and Inflammation Markers in Non-small Cell Lung Cancer. Sisli Etfal Hastanesi tip bulteni . 58 (4), 460–467 (2024). Zhang, Y. & Xiao, G. Prognostic significance of the ratio of fibrinogen and albumin in human malignancies: a meta-analysis. Cancer Manage. Res. 11 , 3381–3393 (2019). Lu, C. et al. Red blood cell distribution width-to-albumin ratio is associated with all-cause mortality in cancer patients. J. Clin. Lab. Anal. 36 (5), e24423 (2022). Xu, W. et al. Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study. Front. Nutr. 9 , 1019502 (2022). Zhou, D., Wang, J. & Li, X. The Red Blood Cell Distribution Width-Albumin Ratio Was a Potential Prognostic Biomarker for Diabetic Ketoacidosis. Int. J. Gen. Med. 14 , 5375–5380 (2021). Yoo, J. W. et al. Red cell distribution width/albumin ratio is associated with 60-day mortality in patients with acute respiratory distress syndrome. Infect. Dis. (London England) . 52 (4), 266–270 (2020). Jeong, J. H. et al. Clinical Usefulness of Red Cell Distribution Width/Albumin Ratio to Discriminate 28-Day Mortality in Critically Ill Patients with Pneumonia Receiving Invasive Mechanical Ventilation, Compared with Lacate/Albumin Ratio: A Retrospective Cohort Study. Diagnostics (Basel, Switzerland). ;11(12). (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf 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-6664819","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470326551,"identity":"ec53e366-4d62-496c-94cf-1c5b35c8e698","order_by":0,"name":"Wei Li","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":470326552,"identity":"5cee8027-b1ef-44f5-9357-82e7e32512d3","order_by":1,"name":"Zheng-mei Qiao","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zheng-mei","middleName":"","lastName":"Qiao","suffix":""},{"id":470326554,"identity":"e2c8fcb7-e6d9-44cb-9467-df9194d0f4c5","order_by":2,"name":"Xiao-ting Wei","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-ting","middleName":"","lastName":"Wei","suffix":""},{"id":470326557,"identity":"18e3c724-6600-4f86-abc8-146bd3304f79","order_by":3,"name":"Miao-jia Wang","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Miao-jia","middleName":"","lastName":"Wang","suffix":""},{"id":470326559,"identity":"6278f35c-95ab-45b0-a1ac-4501d026c353","order_by":4,"name":"Duo-xiang Zhao","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Duo-xiang","middleName":"","lastName":"Zhao","suffix":""},{"id":470326560,"identity":"6c747549-fc29-431c-b918-9f0980a43625","order_by":5,"name":"Jie Yang","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":470326561,"identity":"269cca9e-6c8a-42b7-b293-363c9aa618ab","order_by":6,"name":"Qin-zhe Wu","email":"","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qin-zhe","middleName":"","lastName":"Wu","suffix":""},{"id":470326562,"identity":"037c74a0-dfaa-4b2c-aef2-2c481714a8b4","order_by":7,"name":"Tao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNvbmgw8+VNjI8TMzH3yQUFFDWAsfz7Fkwxln0owl29uSDR6cOUZYi5xEjpk0b9vhxA1nzphJPmxhJsJhPAfMJIBajA1uJJhVJDawMfC3dycQ8EtDsoXEuXQ5yRsJaTcSd8gwSJw5u4GQLQdvGJRZG/PdSDh2I/EMG4OBRC4BLRKJDRIJbMyJDTcS2woS25iJ0ZLMJHGgzTlxwpnDbAzEaeE5xmzYAAlkZomEM8d4CPpFvr3/4+M/4Kjk//jxR0WNHH97L34tGICHNOWjYBSMglEwCrACAGrBT0lC7wY8AAAAAElFTkSuQmCC","orcid":"","institution":"Baoji High-Tech Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-05-14 13:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6664819/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6664819/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84696513,"identity":"2e71dd1b-c641-46df-9ab7-d2dfd3ba05a4","added_by":"auto","created_at":"2025-06-16 10:39:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":264752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/1c5efb1dd5cf031183bc7819.png"},{"id":84696514,"identity":"d49d80f4-6cf1-4885-a097-d45c5515d422","added_by":"auto","created_at":"2025-06-16 10:39:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":217170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of RAR with OS (A) and CSS (B) in cancer survivors. RAR, red blood cell distribution width to albumin ratio. OS, Overall Survival; CSS, Cancer-Specific Survival.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/f54c2272d44a62bcd4867cab.png"},{"id":84696512,"identity":"47010f1c-dca1-406c-8a9e-7f2806fa1e7d","added_by":"auto","created_at":"2025-06-16 10:39:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose–response relationship of RAR with OS (A) and CSS (B) in cancer survivors. RAR, red blood cell distribution width to albumin ratio. OS, Overall Survival; CSS, Cancer-Specific Survival.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/e5e097db26d3c8149c855c91.png"},{"id":84697097,"identity":"13f01322-a88e-41af-b171-2154373f8fae","added_by":"auto","created_at":"2025-06-16 10:47:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":572503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of RAR with all-cause mortality and cancer-specific mortality risk in cancer survivors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/a3b7b9f8f6d8cd5b385d60e7.png"},{"id":84697095,"identity":"add15269-6b06-409c-92d2-08ffd412941a","added_by":"auto","created_at":"2025-06-16 10:47:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":235695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of RAR with OS (A) and CSS (B) in cancer survivors after PSM. RAR, red blood cell distribution width to albumin ratio. OS, Overall Survival; CSS, Cancer-Specific Survival; PSM, propensity score matching.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/d335f3c5bfbaab32af82bddd.png"},{"id":84696524,"identity":"123372ac-fa3a-42d7-aeff-34e3a173ae83","added_by":"auto","created_at":"2025-06-16 10:39:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":282264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRAR-based nomogram for all-cause mortality prediction in cancer survivors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/76e07a0e5f745b4119ec02dd.png"},{"id":86401724,"identity":"77c0f154-8c8f-447f-8627-4295d943478e","added_by":"auto","created_at":"2025-07-10 09:02:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3287100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/c7b572b3-65fd-4918-ae02-6bdd1a9f4ad8.pdf"},{"id":84696517,"identity":"53e63db2-ca28-461d-b97b-86f59f79bca1","added_by":"auto","created_at":"2025-06-16 10:39:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":179115,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664819/v1/0d507b42fbdb8decbd78000d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is one of the most pressing public health challenges worldwide and is the second leading cause of death globally, following cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In most countries with a very high Human Development Index (HDI), cancer has already surpassed cardiovascular diseases as the leading cause of death\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to the International Agency for Research on Cancer (IARC)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, by 2050, the number of cancer cases and deaths worldwide is projected to reach 35.3\u0026nbsp;million and 18.5\u0026nbsp;million, respectively, representing increases of 76.6% and 89.7% compared to 2022. As cancer incidence and mortality continue to rise, it is expected to become the leading cause of death globally in the future. Therefore, there is an urgent need for effective indicators to predict the occurrence of cancer and to monitor and improve long-term health outcomes for cancer patients.\u003c/p\u003e \u003cp\u003eOxidative stress and inflammation can impair erythropoiesis and lead to abnormal red blood cell survival, resulting in increased Red Cell Distribution Width (RDW). Additionally, nutritional deficiencies can also cause elevated RDW\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. RDW is associated with the incidence and prognosis of various diseases\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Albumin possesses antioxidant, anti-inflammatory, antiplatelet aggregation, and anticoagulant properties\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A decrease in albumin levels usually indicates a high level of inflammation, poor nutritional status, and unfavorable therapeutic outcomes. RDW is positively correlated with chronological age, while albumin is negatively correlated with age\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. RDW and albumin are common laboratory markers, and the red blood cell distribution width to albumin ratio (RAR) combines the benefits of both markers, providing a comprehensive reflection of the body\u0026rsquo;s inflammatory and nutritional status. As a novel inflammatory biomarker, RAR has been significantly correlated with the prevalence of various diseases, including dementia\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, asthma\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, kidney stones\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, metabolic syndrome (MetS)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and erectile dysfunction\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, RAR has also been significantly associated with the prevalence and prognosis of depression\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, Parkinson\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and diabetes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInflammation plays a crucial role in the occurrence, development, and prognosis of tumors\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As a novel inflammatory biomarker, whether RAR is associated with the incidence and prognosis of cancer remains inconclusive. A study in Japan involving 573 patients with acute coronary syndrome (ACS) found that elevated RAR levels were an independent positive predictor of cancer occurrence\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Another study showed that RAR was associated with the postoperative prognosis of cervical cancer patients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, these studies had small sample sizes, and some did not sufficiently adjust for confounding factors. Given the continued rise in cancer incidence and mortality, along with the growing global cancer burden, this study utilizes data from the National Health and Nutrition Examination Survey (NHANES) to conduct a nationwide representative study to assess whether RAR can effectively predict cancer occurrence and the prognosis of cancer survivors. As a potential biomarker, if RAR can accurately predict cancer onset and assess long-term outcomes for cancer survivors, it could help improve early cancer screening, monitoring, and intervention strategies, ultimately enhancing the quality of life and survival rates of cancer survivors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is an investigation initiated by the National Center for Health Statistics (NCHS) aimed at assessing the health and nutritional status of American adults and children through interviews, physical examinations, and laboratory tests. The survey uses a complex, multi-stage probability sampling strategy, randomly selecting participants from different populations and geographic regions across the United States. This sampling method ensures that the survey results are representative of the entire American population. The study protocol was approved by the NCHS Research Ethics Review Board, and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003eThis study analyzed data from six NHANES cycles (2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, and 2015\u0026ndash;2016), which included 60,936 participants. To ensure the accuracy of the study, strict exclusion criteria were applied: 1) Age less than 20 years (N\u0026thinsp;=\u0026thinsp;26,756); 2) Missing RAR data (N\u0026thinsp;=\u0026thinsp;3,432); 3) Missing cancer and mortality status information (N\u0026thinsp;=\u0026thinsp;70); 4) Missing covariate data (N\u0026thinsp;=\u0026thinsp;9,226). Ultimately, 21,452 participants were included in the study for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRAR Assessment\u003c/h3\u003e\n\u003cp\u003eIn the NHANES survey, healthcare personnel at the Mobile Examination Center (MEC) measured participants' peripheral blood Red Cell Distribution Width (RDW, %) using a Coulter Analyzer. Albumin concentration was determined using the DcX 800 method, a two-color endpoint method. In this method, albumin reacts with bromocresol purple (BCP) reagent to form a complex, and the system measures the absorbance change at 600 nm. This absorbance is proportional to the albumin concentration in the blood sample, allowing the reflection of serum albumin levels (g/dL). The RAR was calculated using the following formula: [RDW (%) / serum albumin (g/dL)].\u003c/p\u003e\n\u003ch3\u003eCancer Survivors and Mortality Status\u003c/h3\u003e\n\u003cp\u003eIn the medical conditions section of the interview, data on cancer diagnosis history and cancer types were collected. Participants were asked: \u0026ldquo;Has a doctor or other healthcare professional ever told you that you had cancer or any type of malignant tumor?\u0026rdquo; Those who answered \"yes\" were defined as cancer survivors, and the specific types of cancer they had (up to three types) were recorded.\u003c/p\u003e \u003cp\u003eNHANES data were linked with the National Death Index (NDI), recording participants' mortality status and causes of death from the date of the survey to December 31, 2019. The causes of death were differentiated using International Classification of Diseases (ICD-10) codes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, categorizing deaths as all-cause mortality, cancer-related deaths, cardiovascular deaths, and other types of death.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe following covariates were selected for analysis based on relevant literature: age, sex, race/ethnicity, education level, marital status, Family Poverty Income Ratio (PIR), body mass index (BMI), drinking Status, smoking status, physical activity, diabetes, hypertension, hyperlipidemia, and cardiovascular disease. Race/ethnicity: Non-Hispanic White, Non-Hispanic Black, or Other. Marital Status: Married/cohabiting, Never Married, Widowed/divorced/separated. Education Level: Less than high school, High school, College or higher. Smoking Status: \"Yes\" (smoked more than 100 cigarettes in their lifetime) and \"No\" (never smoked or smoked fewer than 100 cigarettes). Drinking Status: Confirmed by the question: \"In any given year, have you consumed at least 12 alcoholic drinks?\" Participants who answered \"yes\" were defined as drinkers. Family PIR: Categorized into three levels based on the Family Poverty Income Ratio (PIR): \u0026lt;1.3, 1.3\u0026ndash;3.5, and \u0026ge;\u0026thinsp;3.5. BMI: Categorized into three levels: \u0026lt;25, 25\u0026ndash;30, and \u0026ge;\u0026thinsp;30 kg/m\u0026sup2;. Physical Activity: Categorized as vigorous activity, moderate activity, or inactivity. Hypertension: Defined based on self-reported hypertension history, use of antihypertensive medications, or a systolic blood pressure (SBP)\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg or diastolic blood pressure (DBP)\u0026thinsp;\u0026gt;\u0026thinsp;80 mmHg\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e (PMID: 29146535). Diabetes: Defined based on self-reported diabetes history, use of insulin or antidiabetic medications, or laboratory results showing HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or 2-hour postprandial glucose (2hPG)\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L\u003csup\u003e25\u003c/sup\u003e (PMID: 34135016). Hyperlipidemia: Defined based on self-reported high cholesterol, use of cholesterol-lowering medications, or laboratory results showing triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, low-density lipoprotein cholesterol (LDL-C)\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL, high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (male) or \u0026lt;\u0026thinsp;50 mg/dL (female), and total cholesterol (TC)\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL\u003csup\u003e9\u003c/sup\u003e. Cardiovascular Disease: Defined based on the medical history of heart failure, coronary heart disease, angina pectoris, heart attack, and stroke reported in the survey.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo ensure the study population is representative of the American population, we accounted for the complex sampling design and sampling weights in the analysis. Normality tests were conducted for continuous variables. For normally distributed data, the mean (standard error [SE]) is presented, and for skewed data, the median (interquartile range [IQR]) is presented. For group comparisons, continuous variables with a normal distribution were analyzed with the Student's t-test, and continuous variables with a skewed distribution were analyzed with the Mann-Whitney U test. Categorical variables were presented as weighted percentages and compared using the chi-square test.\u003c/p\u003e \u003cp\u003eTo assess the association between RAR, cancer incidence, and prognosis, we used weighted logistic regression and Cox regression models to calculate odds ratios (OR) and hazard ratios (HR), respectively. RAR was also converted into categorical variables based on quartiles, and trend P-values were calculated. To ensure comprehensive analysis, this study used three models: Model 1, an unadjusted crude model; Model 2, adjusted for age, sex, race, education level, marital status, family PIR, and BMI; Model 3, a fully adjusted model that further adjusted for Drinking Status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases in addition to the variables in Model 2.\u003c/p\u003e \u003cp\u003eConsidering that the relationship between RAR and cancer survival may not be a simple linear one, we used Restricted Cubic Splines (RCS) to capture complex nonlinear relationships, thereby improving model fit and predictive accuracy. Additionally, we performed subgroup analyses and interaction analyses based on different covariates for RAR and cancer survival. Although NHANES uses a complex sampling method to improve the representativeness and applicability of the results, conclusions drawn from weighted and unweighted analyses may differ. Therefore, we also used unweighted data and performed propensity score matching (PSM) to further control for confounding factors, to compare the impact of RAR on survival. Finally, we constructed a prognostic model for cancer survivors based on RAR. All statistical analyses were performed using R statistical software (version 4.2.2). A P-value of \u0026lt;\u0026thinsp;0.05 (two-sided) was considered statistically significant for all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eThis study included 21,452 adult participants from the 2005\u0026ndash;2016 NHANES database (weighted to represent 154,935,519 American adults), of whom 1,910 had cancer (Supplementary Table 1). Statistically significant differences were observed between the cancer and non-cancer groups in terms of RAR values, as well as in age, sex, race, education level, Family PIR, smoking status, blood pressure, hyperlipidemia, hyperglycemia, and cardiovascular disease. However, no significant differences were found between the two groups in terms of Drinking Status, BMI, and marital status. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the 1,910 cancer survivors, which correspond to 14,805,089 American adults in the weighted population. The median age of the participants was 64 years [IQR: 53, 73], with 58.3% female and 1,367 (88.0%) non-Hispanic White. Participants with higher RAR values were generally characterized by being older, female, non-Hispanic Black, unmarried or single, with lower family PIR, higher BMI, and less physical activity. Additionally, participants with higher RAR values were more likely to have hypertension, diabetes, and cardiovascular disease.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of Cancer Survivors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eQuartiles of RAR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall,N\u0026thinsp;=\u0026thinsp;1910\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.93),N\u0026thinsp;=\u0026thinsp;478\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(2.93\u0026ndash;3.13),N\u0026thinsp;=\u0026thinsp;461\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(3.14\u0026ndash;3.41),N\u0026thinsp;=\u0026thinsp;491\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.41),N\u0026thinsp;=\u0026thinsp;480\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (53, 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(48, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(53, 74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(56, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(57, 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e858 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224 ( 44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230 ( 45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218 ( 42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 ( 32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1052 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254 ( 55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231 ( 54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273 ( 57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294 ( 67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace/ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1367 (88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381 ( 91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335 ( 87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e357 ( 90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294 ( 79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 ( 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 ( 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 ( 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 ( 9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 ( 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 ( 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 ( 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 ( 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326 ( 9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 ( 6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 ( 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 ( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 ( 16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 ( 19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 ( 15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 ( 21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 ( 20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1175 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316 ( 74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 ( 76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299 ( 70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 ( 63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1158 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 ( 71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306 ( 70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280 ( 60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252 ( 56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e752 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 ( 28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 ( 29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 ( 39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 ( 43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily PIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 ( 11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 ( 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 ( 14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 ( 21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026ndash;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162 ( 27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183 ( 35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 ( 38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 ( 39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e734 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 ( 61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191 ( 55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 ( 47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 ( 38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e916 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 ( 52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 ( 46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220 ( 46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229 ( 49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e994 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227 ( 47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 ( 53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 ( 53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 ( 50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m2), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 ( 41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 ( 31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 ( 24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 ( 17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e671 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191 ( 40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 ( 37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 ( 31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 ( 28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e698 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106 ( 18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 ( 31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 ( 43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246 ( 53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 ( 10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 ( 9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 ( 11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 ( 15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1625 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e416 ( 90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 ( 90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417 ( 88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390 ( 84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1100 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 ( 48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 ( 50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296 ( 54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309 ( 62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 ( 25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 ( 21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 ( 22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 ( 23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 ( 26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 ( 28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 ( 22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 ( 14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e550 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171 ( 42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 ( 32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 ( 29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 ( 27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307 ( 57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 ( 67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361 ( 70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 ( 72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1470 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402 ( 89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366 ( 82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e386 ( 82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316 ( 67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 ( 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 ( 17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 ( 17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 ( 32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 ( 17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 ( 16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 ( 14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 ( 17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1580 (83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396 ( 82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378 ( 83.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e411 ( 85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395 ( 82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1502 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419 ( 92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367 ( 83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377 ( 80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 ( 75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 ( 7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 ( 16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 ( 19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 ( 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1443 (82.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406 ( 90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367 ( 86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 ( 79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305 ( 70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 ( 9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 ( 15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 ( 20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 ( 29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecancer mortality ,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1757 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 ( 95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 ( 94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e452 ( 94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429 ( 90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 ( 5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 ( 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 ( 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 ( 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 ( 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation Between RAR and Cancer Incidence\u003c/h3\u003e\n\u003cp\u003eTable 2 presents the results of the logistic regression analysis, which examined the association between RAR and adult cancer incidence. In Model 1 (crude model), when RAR was treated as a continuous variable, higher RAR was associated with a 44% increased risk of cancer (OR: 1.44 [1.29, 1.59], P \u0026lt; 0.001). When RAR was treated as a categorical variable, the Q1 group was used as the reference, with the OR for the Q2 group being 1.34 (1.12, 1.62) (P = 0.002), the OR for the Q3 group being 1.75 (1.46, 2.10) (P \u0026lt; 0.001), and the OR for the Q4 group being 2.07 (1.74, 2.47) (P \u0026lt; 0.001), with a trend P-value of \u0026lt;0.001. However, after further adjustment for covariates, the difference between RAR and cancer risk was no longer statistically significant (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The Association Between RAR and Cancer Incidence\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11( 1.08,1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03( 0.98,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03( 0.98,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59( 0.51,0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86( 0.70,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85( 0.69,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAR(Continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44( 1.29,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11( 0.97,1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11( 0.97,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartiles of RAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2(2.83\u0026ndash;3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34(1.12,1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93( 0.76,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93( 0.76,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3(3.05\u0026ndash;3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75(1.46,2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.82,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.82,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07(1.74,2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15(0.94,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15( 0.94,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eModel 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family poverty-income ratio (PIR), and body mass index (BMI); Model 3: Fully adjusted model, further adjusted for drinking status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases, in addition to the variables in Model 2.\u003c/p\u003e\n\u003cp\u003eResults are presented as odds ratios (OR) and 95% confidence intervals (CI). OR, odds ratio; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between RAR and Prognosis of Cancer Survivors\u003c/strong\u003eThe median follow-up time was 84 months (IQR: 55, 123), during which 467 participants died, including 153 from cancer. Kaplan-Meier curves showed that patients with higher RAR had significantly higher all-cause mortality and cancer-specific mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Cox proportional hazards models indicated that in all models, patients with higher RAR had a significantly increased risk of death (Table 3). Specifically, in the multivariable-adjusted Model 3, higher levels of RAR were significantly associated with both all-cause mortality and cancer-specific mortality in cancer patients. When RAR was treated as a continuous variable, each additional unit increase in RAR was associated with a 2.42-fold increase in all-cause mortality and a 2.49-fold increase in cancer-specific mortality. For all-cause mortality, with Q1 as the reference group, the HR for Q2 was 1.05 (0.74, 1.50), for Q3 was 1.56 (1.10, 2.20), and for Q4 was 2.62 (1.86, 3.69), showing a significant upward trend (P trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001); for cancer-specific mortality, with Q1 as the reference group, the HR for Q2 was 1.23 (0.67, 2.27), for Q3 was 1.30 (0.71, 2.37), and for Q4 was 2.43 (1.26, 4.67), also showing a significant upward trend (P trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, in all models, RAR had a higher HR value than RDW and albumin.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003eTable 3. The Relationship Between RAR and Mortality in Cancer Survivors\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-cause mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34( 1.22,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28( 1.20,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28( 1.19,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31( 0.21,0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39( 0.25,0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40( 0.26,0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAR(Continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90( 2.31,3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.483(1.99,3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42(1.93,3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartiles of RAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2(2.93\u0026ndash;3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68( 1.15,2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12( 0.78,1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05(0.74,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3(3.14\u0026ndash;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64( 1.83,3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67( 1.17,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56(1.10,2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72( 3.33,6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78( 1.96,3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (1.86,3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer-specific mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32( 1.20,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29( 1.17,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29( 1.17,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39( 0.17,0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40( 0.16,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41( 0.16,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAR(Continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78( 2.09,3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50( 1.82,3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49 (1.79,3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartiles of RAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1(\u0026lt;2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2(2.93\u0026ndash;3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55( 0.83,2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25(0.68,2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23( 0.67,2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3(3.14\u0026ndash;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75( 0.93,3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33( 0.72,2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 ( 0.71,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4(\u0026gt;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25( 1.77,5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48(1.30,4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43 (1.26,4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eModel 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family poverty-income ratio (PIR), and body mass index (BMI); Model 3: Fully adjusted model, further adjusted for drinking status, smoking status, physical activity, hypertension, hyperlipidemia, diabetes, and cardiovascular diseases, in addition to the variables in Model 2.\u003c/p\u003e\n\u003cp\u003eResults are presented as hazard ratios (HR) and 95% confidence intervals (CI). HR, hazard ratios; CI, confidence interval; PIR, poverty-income ratio; BMI, body mass index; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose-Response Analysis of RAR and Mortality in Cancer Survivors\u003c/strong\u003eRCS analysis explored the dose-response relationship between RAR levels and all-cause mortality as well as cancer-specific mortality in the study population (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The results showed a significant statistical relationship between RAR levels and both all-cause mortality and cancer-specific mortality (P overall\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with a positive linear correlation (P nonlinear\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis\u003c/strong\u003eThis study evaluated the relationship between RAR levels and the mortality risk of cancer survivors in different subgroups and explored potential interactions between RAR and other variables (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In all subgroups, higher RAR was significantly associated with increased all-cause mortality; additionally, most cancer survivor subgroups also showed statistically significant differences in cancer-specific mortality, with other subgroups showing a higher trend of mortality. Notably, an interaction was found between RAR and the Family PIR. In the subgroup with a Family PIR of 1.1\u0026ndash;3.0, higher RAR was significantly associated with an increased risk of all-cause mortality (HR: 3.62, 95% CI: 2.45\u0026ndash;5.33).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003eUnweighted data were used, and PSM was employed to further control for confounding factors in order to compare the impact of RAR on survival. There were statistically significant differences between the high RAR and low RAR groups in terms of age, sex, race/ethnicity, education level, marital status, family PIR, BMI, drinking Status, physical activity, diabetes, hypertension, and cardiovascular disease (Supplementary Table 2). After balancing the differences between the two groups through PSM, 735 matched pairs of participants were obtained. Compared to the low RAR group, the high RAR group had worse all-cause mortality and cancer mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRAR-Based Predictive Model for All-Cause Mortality in Cancer Survivors\u003c/strong\u003eUnweighted data were used to identify independent predictors for cancer survivors and construct a predictive model. In the univariate Cox regression analysis, variables with a P-value less than 0.05 were included in the subsequent multivariate Cox regression analysis (Supplementary Table 3). Ultimately, age, sex, race, marital status, family PIR, BMI, physical activity, hypertension, diabetes, cardiovascular disease, and RAR were included in the final predictive model (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The C-index for the model was 0.76 (95% CI: 0.74\u0026ndash;0.78), with AUC values of 0.77 (95% CI: 0.73\u0026ndash;0.81) for 3 years, 0.77 (95% CI: 0.74\u0026ndash;0.80) for 5 years, and 0.83 (95% CI: 0.80\u0026ndash;0.85) for 10 years (Supplementary Fig. 1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to explore the relationship between RAR, cancer incidence, and cancer survivors\u0026apos; prognosis, using a representative sample of American adults from across the nation. The results showed that RAR levels were not significantly associated with cancer incidence. However, during long-term follow-up of the cancer survivors, we found a significant positive linear relationship between RAR and both all-cause mortality and cancer-specific mortality. Subgroup analyses further indicated that the association between RAR and all-cause mortality, as well as cancer mortality, remained consistent across all subgroups. For each additional unit increase in RAR, the increase in mortality risk (HR\u0026thinsp;=\u0026thinsp;2.42) was significantly greater than that associated with RDW (HR\u0026thinsp;=\u0026thinsp;1.28), suggesting that RAR, as a composite index, may more effectively capture the pathophysiological changes associated with poor prognosis. Propensity score matching analysis further validated the robustness of the relationship between RAR levels and cancer prognosis, enhancing the reliability of our findings.\u003c/p\u003e\n\u003cp\u003eSystemic inflammation is considered a key factor in promoting tumor cell proliferation and survival, inducing angiogenesis, facilitating metastasis, and altering drug responses\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. RDW and albumin reflect conditions such as inflammation, oxidative stress, and malnutrition. Previous studies have primarily focused on RDW or albumin, with findings suggesting that RDW can serve as a predictive marker for several types of tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, in our study, based on a large population dataset, no significant association was found between RAR and tumor incidence. Studies have reported that RDW is associated with tumor staging, pathological grading, and malignancy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and it has been found to be related to the prognosis of several cancers, including lung cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, colon cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, urological tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and breast cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Podhorecka et al. found that RDW did not change significantly during disease progression, suggesting that RDW could serve as a stable prognostic marker\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additionally, an increase in RDW is linked to shortened telomere length\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, which in turn is associated with cancer incidence and mortality\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Albumin not only reflects inflammation but is also associated with malnutrition. Lower albumin levels are associated with worse cancer prognosis, and albumin levels are closely related to malnutrition and poor postoperative outcomes in cancer patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Albumin may inhibit tumor progression by stabilizing DNA replication and enhancing immune responses\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and its accumulation at inflammation and tumor sites helps deliver anti-inflammatory and anticancer drugs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Additionally, albumin can downregulate the expression and transport of inflammatory factors, thereby alleviating inflammation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Lower albumin concentrations indicate stronger cancer-related inflammation, which may promote tumor progression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Serum albumin has also been used as a prognostic marker for cancer survival. RAR has also been associated with cancer-related symptoms, such as cachexia, pain, and insomnia in cancer patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Notably, our study found that albumin was associated with all-cause mortality in cancer survivors, although the association with cancer-specific mortality was not significant. However, when RDW and albumin were combined, RAR showed a significant association with cancer-specific mortality, suggesting that RAR has greater predictive value than individual markers. This finding is consistent with similar results from other studies, such as the fibrinogen-to-albumin ratio, which has been used as a prognostic marker for cancer survivors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The exact mechanisms by which RAR influences cancer prognosis remain unclear, but they may be closely related to factors such as telomere shortening, inflammation, oxidative stress, and malnutrition. Future basic research is needed to explore these mechanisms in greater detail.\u003c/p\u003e\n\u003cp\u003ePrevious Studies on the Relationship Between RAR and Cancer Survivors\u0026apos; Prognosis. A retrospective study involving 907 patients undergoing radical hysterectomy found that preoperative RAR was an independent risk factor for intraoperative blood transfusion and poor prognosis, including prolonged hospital stay and reduced 5-year survival (HR: 1.50, 95% CI: 1.04\u0026ndash;2.17, p\u0026thinsp;=\u0026thinsp;0.033)\u003csup\u003e22\u003c/sup\u003e. A second retrospective study using the Medical Information Mart for Intensive Care III (MIMIC-III) database also identified RAR as an independent risk factor for all-cause mortality in cancer patients \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. However, this study included fewer variables and did not adjust for key confounders such as smoking, alcohol consumption, physical activity, hypertension, and diabetes, which may influence mortality. It is also important to note that the cancer survivors in this study were all from the ICU. Many cancer patients are admitted to the ICU due to non-cancer-related acute complications, such as infections, sepsis, acute respiratory distress syndrome, and acute kidney failure. Studies have shown that RAR is significantly associated with the prognosis of sepsis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, diabetic ketoacidosis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, acute respiratory distress syndrome\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, and severe pneumonia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. In our study, we used NHANES data, which represent the health status of over 14\u0026nbsp;million people in the American and can be generalized to a broader population. Furthermore, the all-cause mortality risk model for cancer survivors based on RAR has a C-index of 0.76, with AUC values of 0.77 for 5 years and 0.83 for 10 years, making it a useful tool for predicting cancer survival.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the prognostic value of RAR was assessed based on prospective cohort data, which cannot fully eliminate selection bias, although propensity score matching has partially mitigated the impact of confounding factors. Second, a single RAR measurement may not adequately capture dynamic changes during treatment. Future studies with repeated measurements are needed to validate the predictive value of RAR trajectories. Finally, despite adjustments for key metabolic and demographic variables, residual confounding factors (e.g., tumor staging, molecular subtypes) may still affect the interpretation of the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found that RAR is significantly associated with both all-cause and cancer-specific mortality in cancer survivors. As a simple and cost-effective biomarker, a cancer prediction model based on RAR can provide valuable guidance for a large population of cancer survivors, particularly for those with poor prognosis, allowing for early intervention. However, future studies should include various cancer types and additional predictive markers, such as tumor staging, molecular subtypes, and treatment modalities, to enhance the model\u0026apos;s accuracy and applicability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIARC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Agency for Research on Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRDW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Cell Distribution Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRAR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Blood Cell Distribution Width to Albumin Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh Human Development Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eACS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute coronary syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICD-10\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, 10th Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePoverty Income Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRCS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Splines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePSM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCancer-Specific Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW L and ZM Q contributed to writing the manuscript and creating the figures and tables. XT W, MJ W, DX Z, J Y, and QZ W were responsible for data collation and statistical analysis. T Y was responsible for project design. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used for this study analysis can be found on the official website of the National Health and Nutrition Examination Survey (https://wwwn.cdc.gov/nchs/nhanes/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCancer J. Clin.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e (3), 229\u0026ndash;263 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray, F., Laversanne, M., Weiderpass, E. \u0026amp; Soerjomataram, I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. \u003cem\u003eCancer\u003c/em\u003e \u003cb\u003e127\u003c/b\u003e (16), 3029\u0026ndash;3030 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBizuayehu, H. M. et al. Global Disparities of Cancer and Its Projected Burden in 2050. \u003cem\u003eJAMA Netw. open.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (11), e2443198 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvagno, G. L., Sanchis-Gomar, F., Picanza, A. \u0026amp; Lippi, G. Red blood cell distribution width: A simple parameter with multiple clinical applications. \u003cem\u003eCrit. Rev. Clin. Lab. Sci.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (2), 86\u0026ndash;105 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, P. F. et al. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. \u003cem\u003eJ. Cancer\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (18), 4305\u0026ndash;4317 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArques, S. Human serum albumin in cardiovascular diseases. \u003cem\u003eEur. J. Intern. Med.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 8\u0026ndash;12 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, A. A. et al. Detection of a novel, integrative aging process suggests complex physiological integration. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (3), e0116489 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeydoun, M. A. et al. Red Cell Distribution Width Is Directly Associated with Poor Cognitive Performance among Nonanemic, Middle-Aged, Urban Adults. \u003cem\u003eJ. Nutr.\u003c/em\u003e \u003cb\u003e150\u003c/b\u003e (1), 128\u0026ndash;139 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, B. et al. Red blood cell distribution width to albumin ratio (RAR) is associated with low cognitive performance in American older adults: NHANES 2011\u0026ndash;2014. \u003cem\u003eBMC Geriatr.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (1), 157 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, J., Zhang, Y. \u0026amp; Chen, X. Red cell distribution width to albumin ratio is associated with asthma risk: a population-based study. \u003cem\u003eFront. Med. (Lausanne)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 1493463 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, L. et al. Association of Red Blood Cell Distribution Width to Albumin Ratio With the Prevalence of Kidney Stones Among the General Adult Population. \u003cem\u003eImmun. Inflamm. Dis.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (11), e70070 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, H., Wang, Y., Miao, Y. \u0026amp; Lin, Q. Red cell distribution width/albumin ratio as a marker for metabolic syndrome: findings from a cross-sectional study. \u003cem\u003eBMC Endocr. disorders\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e (1), 227 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, Y. \u0026amp; Li, S. J-Shaped relationship between the red cell distribution width to albumin ratio and erectile dysfunction: a cross-sectional study from NHANES 2001\u0026ndash;2004. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1545272 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, X. F., Hu, Y. Q., Long, Z. W. \u0026amp; Cao, M. Z. Association between RAR and the prevalence and prognosis of depression: A population-based study. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cb\u003e380\u003c/b\u003e, 1\u0026ndash;9 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H., Xiang, R. \u0026amp; Chen, Z. The association between red blood cell distribution width-to-albumin ratio and risk of depression: A cross-sectional analysis of NHANES. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cb\u003e379\u003c/b\u003e, 250\u0026ndash;257 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, H., Yin, K., Zhao, J. \u0026amp; Che, F. Association of inflammation/nutrition-based indicators with Parkinson's disease and mortality. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1439803 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J. et al. Red blood cell distribution width to albumin ratio associates with prevalence and long-term diabetes mellitus prognosis: an overview of NHANES 1999\u0026ndash;2020 data. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1362077 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoussens, L. M. \u0026amp; Werb, Z. Inflammation and cancer. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e420\u003c/b\u003e (6917), 860\u0026ndash;867 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani, A. Cancer: inflammation by remote control. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e435\u003c/b\u003e (7043), 752\u0026ndash;753 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan, D. \u0026amp; Weinberg, R. A. Hallmarks of cancer: the next generation. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e144\u003c/b\u003e (5), 646\u0026ndash;674 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshii, M. et al. Risk Prediction Score for Cancer Development in Patients With Acute Coronary Syndrome. \u003cem\u003eCirculation journal: official J. Japanese Circulation Soc.\u003c/em\u003e \u003cb\u003e88\u003c/b\u003e (2), 234\u0026ndash;242 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim, J. H., Jang, D. M., Cho, H. S., Park, J. Y. \u0026amp; Choi, W. J. Association of red cell distribution width/albumin ratio with intraoperative blood transfusion in cervical cancer patients. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (11), e0277481 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOutland, B., Newman, M. M. \u0026amp; William, M. J. Health Policy Basics: Implementation of the International Classification of Disease, 10th Revision. \u003cem\u003eAnnals of internal medicine.\u003c/em\u003e ;163(7):554\u0026ndash;556. (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhelton, P. K. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (19), e127\u0026ndash;e248 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAddendum. ;44(Suppl. 1):S15-S33. \u003cem\u003eDiabetes care.\u003c/em\u003e 2021;44(9):2182.2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani, A., Allavena, P., Sica, A. \u0026amp; Balkwill, F. Cancer-related inflammation. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e454\u003c/b\u003e (7203), 436\u0026ndash;444 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrivennikov, S. I., Greten, F. R. \u0026amp; Karin, M. Immunity, inflammation, and cancer. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e140\u003c/b\u003e (6), 883\u0026ndash;899 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, B. et al. Utility of red cell distribution width as a diagnostic and prognostic marker in non-small cell lung cancer. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 15717 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur, A. S., Indoria, C., Sahu, R., Kujur, P. \u0026amp; Gahine, R. Preoperative evaluation of red blood cell distribution width as a promising biomarker for discriminating between benign and malignant breast tumors and assessing breast cancer activity. \u003cem\u003eIndian. J. Pathol. Microbiol.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (2), 324\u0026ndash;327 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Q. et al. Blood cell indices and inflammation-related markers with kidney cancer risk: a large-population prospective analysis in UK Biobank. \u003cem\u003eFront. Oncol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1366449 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, F. et al. Diagnosis and survival values of neutrophil-lymphocyte ratio (NLR) and red blood cell distribution width (RDW) in esophageal cancer. \u003cem\u003eClin. Chim. Acta\u003c/em\u003e. \u003cb\u003e488\u003c/b\u003e, 150\u0026ndash;158 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, D. P., Ma, R. M. \u0026amp; Xiang, Y. Q. Utility of Red Cell Distribution Width as a Prognostic Factor in Young Breast Cancer Patients. \u003cem\u003eMedicine\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e (17), e3430 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeretis, C., Seretis, F., Lagoudianakis, E., Gemenetzis, G. \u0026amp; Salemis, N. S. Is red cell distribution width a novel biomarker of breast cancer activity? Data from a pilot study. \u003cem\u003eJ. Clin. Med. Res.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), 121\u0026ndash;126 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, F. M., Xu, G., Zhang, Y. \u0026amp; Ma, L. L. Red cell distribution width is associated with presence, stage, and grade in patients with renal cell carcinoma. \u003cem\u003eDis. Markers\u003c/em\u003e. \u003cb\u003e2014\u003c/b\u003e, 860419 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Zhou, Y., Zhou, K., Li, J. \u0026amp; Che, G. Prognostic value of pre-treatment red blood cell distribution width in lung cancer: a meta-analysis. \u003cem\u003eBiomarkers: biochemical indicators of exposure, response, and susceptibility to chemicals.\u003c/em\u003e ;25(3):241\u0026ndash;247. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen, Z. L., Zhou, X. \u0026amp; Xiao, D. C. Is red blood cell distribution width a prognostic factor for colorectal cancer? A meta-analysis. \u003cem\u003eFront. Surg.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 945126 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, W. et al. Pretreatment red blood cell distribution width may be a potential biomarker of prognosis in urologic cancer: a systematic review and meta-analysis. \u003cem\u003eBiomark. Med.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (18), 1289\u0026ndash;1300 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, J. M. et al. Is red cell distribution width a prognostic factor in patients with breast cancer? A meta-analysis. \u003cem\u003eFront. Surg.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1000522 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodhorecka, M. et al. Assessment of red blood cell distribution width as a prognostic marker in chronic lymphocytic leukemia. \u003cem\u003eOncotarget\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (22), 32846\u0026ndash;32853 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozlitina, J. \u0026amp; Garcia, C. K. Red blood cell size is inversely associated with leukocyte telomere length in a large multi-ethnic population. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (12), e51046 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilleit, P. et al. Telomere length and risk of incident cancer and cancer mortality. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e304\u003c/b\u003e (1), 69\u0026ndash;75 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacelli, F. et al. Is malnutrition still a risk factor of postoperative complications in gastric cancer surgery? \u003cem\u003eClin. Nutr.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (3), 398\u0026ndash;407 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. et al. Preoperative serum pre-albumin as an independent prognostic indicator in patients with localized upper tract urothelial carcinoma after radical nephroureterectomy. \u003cem\u003eOncotarget\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (22), 36772\u0026ndash;36779 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKao, H. K. et al. Nomogram based on albumin and neutrophil-to-lymphocyte ratio for predicting the prognosis of patients with oral cavity squamous cell carcinoma. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 13081 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToiyama, Y. et al. Clinical impact of preoperative albumin to globulin ratio in gastric cancer patients with curative intent. \u003cem\u003eAm. J. Surg.\u003c/em\u003e \u003cb\u003e213\u003c/b\u003e (1), 120\u0026ndash;126 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyhan, A. et al. The preoperative albumin level is an independent prognostic factor for optimally debulked epithelial ovarian cancer. \u003cem\u003eArch. Gynecol. Obstet.\u003c/em\u003e \u003cb\u003e296\u003c/b\u003e (5), 989\u0026ndash;995 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBağırsak\u0026ccedil;ı, E. et al. Role of Albumin in Growth Inhibition in Hepatocellular Carcinoma. \u003cem\u003eOncology\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e (2), 136\u0026ndash;142 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSleep, D. Albumin and its application in drug delivery. \u003cem\u003eExpert Opin. Drug Deliv.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (5), 793\u0026ndash;812 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBern, M., Sand, K. M., Nilsen, J., Sandlie, I. \u0026amp; Andersen, J. T. The role of albumin receptors in regulation of albumin homeostasis: Implications for drug delivery. \u003cem\u003eJ. controlled release: official J. Controlled Release Soc.\u003c/em\u003e \u003cb\u003e211\u003c/b\u003e, 144\u0026ndash;162 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUtariani, A., Rahardjo, E. \u0026amp; Perdanakusuma, D. S. Effects of Albumin Infusion on Serum Levels of Albumin, Proinflammatory Cytokines (TNF-α, IL-1, and IL-6), CRP, and MMP-8; Tissue Expression of EGRF, ERK1, ERK2, TGF-β, Collagen, and MMP-8; and Wound Healing in Sprague Dawley Rats. \u003cem\u003eInt. J. Inflamm.\u003c/em\u003e \u003cb\u003e2020\u003c/b\u003e, 3254017 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeaton, K. Albumin concentration controls cancer. \u003cem\u003eJ. Natl Med. Assoc.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e (12), 490\u0026ndash;493 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaursen, I., Briand, P. \u0026amp; Lykkesfeldt, A. E. Serum albumin as a modulator on growth of the human breast cancer cell line, MCF-7. \u003cem\u003eAnticancer Res.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (2a), 343\u0026ndash;351 (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzkarafakili, M. A., Kara, Z. M. Y., Musluman, A. M. \u0026amp; Bek, T. T. The Association of Plasma Asymmetric Dimethylarginine Concentrations and Inflammation Markers in Non-small Cell Lung Cancer. \u003cem\u003eSisli Etfal Hastanesi tip bulteni\u003c/em\u003e. \u003cb\u003e58\u003c/b\u003e (4), 460\u0026ndash;467 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u0026amp; Xiao, G. Prognostic significance of the ratio of fibrinogen and albumin in human malignancies: a meta-analysis. \u003cem\u003eCancer Manage. Res.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 3381\u0026ndash;3393 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, C. et al. Red blood cell distribution width-to-albumin ratio is associated with all-cause mortality in cancer patients. \u003cem\u003eJ. Clin. Lab. Anal.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (5), e24423 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, W. et al. Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1019502 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, D., Wang, J. \u0026amp; Li, X. The Red Blood Cell Distribution Width-Albumin Ratio Was a Potential Prognostic Biomarker for Diabetic Ketoacidosis. \u003cem\u003eInt. J. Gen. Med.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 5375\u0026ndash;5380 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo, J. W. et al. Red cell distribution width/albumin ratio is associated with 60-day mortality in patients with acute respiratory distress syndrome. \u003cem\u003eInfect. Dis. (London England)\u003c/em\u003e. \u003cb\u003e52\u003c/b\u003e (4), 266\u0026ndash;270 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong, J. H. et al. Clinical Usefulness of Red Cell Distribution Width/Albumin Ratio to Discriminate 28-Day Mortality in Critically Ill Patients with Pneumonia Receiving Invasive Mechanical Ventilation, Compared with Lacate/Albumin Ratio: A Retrospective Cohort Study. \u003cem\u003eDiagnostics (Basel, Switzerland).\u003c/em\u003e ;11(12). (2021).\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":"The red blood cell distribution width to albumin ratio (RAR), cancer incidence, cancer prognosis, cancer survivors, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6664819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6664819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWith the rising incidence and mortality rates of cancer, there is an urgent need for effective biomarkers to predict cancer occurrence and monitor its prognosis. The red blood cell distribution width to albumin ratio (RAR), a novel inflammatory biomarker, has unclear associations with both cancer occurrence and prognosis. This study aims to explore the relationship between RAR and cancer incidence, as well as the prognosis of cancer survivors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This study included 21,452 adult participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2016, of whom 1,910 had cancer. Weighted multivariable logistic regression was used to assess the association between RAR and cancer incidence. To evaluate the relationship between RAR and cancer prognosis, weighted multivariable Cox regression, restricted cubic splines (RCS), and subgroup analysis were employed. Additionally, propensity score matching (PSM) was conducted for sensitivity analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the unadjusted model, RAR was significantly positively correlated with cancer incidence; however, this association became non-significant after adjusting for confounding factors. After fully adjusting for potential confounders, RAR was significantly associated with both all-cause and cancer-specific mortality in cancer survivors. Specifically, each additional unit increase in RAR was associated with a 2.42-fold increase in all-cause mortality (HR 2.42, 95% CI: 1.93, 3.03) and a 2.49-fold increase in cancer-specific mortality (HR 2.49, 95% CI: 1.79, 3.47). Subgroup analysis showed that higher RAR was associated with increased mortality risk across all subgroups. The prognostic model based on RAR had a C-index of 0.76, with AUC values of 0.77 for 5 years and 0.83 for 10 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRAR is significantly positively correlated with both all-cause and cancer-specific mortality in cancer survivors. The prognostic model based on RAR effectively predicts cancer survival and provides a basis for early intervention, particularly for populations at higher risk of poor outcomes.\u003c/p\u003e","manuscriptTitle":"Association of Red Blood Cell Distribution Width to Albumin Ratio (RAR) with Cancer Incidence and Prognosis in American Adults: NHANES 2005-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 10:39:54","doi":"10.21203/rs.3.rs-6664819/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":"cc699aaf-625b-476a-ba9f-264451aae56b","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49943417,"name":"Biological sciences/Cancer/Cancer models"},{"id":49943418,"name":"Biological sciences/Cancer/Cancer prevention"},{"id":49943419,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2025-07-10T08:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 10:39:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6664819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6664819","identity":"rs-6664819","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}