Abstract
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 inconclusive 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. we included 21,452 U.S. adults from the National Health and Nutrition Examination Survey 2005 to 2016, of whom 1910 were cancer survivors. The association between RAR and cancer incidence was assessed using weighted multivariable logistic regression. The relationship between RAR and mortality in cancer survivors was evaluated using weighted multivariable Cox proportional hazards models and sensitivity analyses. The outcomes assessed were all-cause and cancer-specific mortality. After full adjustment for confounders, no significant association was found between RAR and cancer incidence. However, each unit increase in RAR was significantly associated with increased all-cause mortality (hazard ratio 2.42, 95% confidence interval [CI]: 1.93–3.03) and cancer-specific mortality (hazard ratio 2.49, 95% CI: 1.79–3.47) in cancer survivors. This positive association was consistent across all predefined subgroups. A prognostic model incorporating RAR demonstrated moderate discriminative ability, with a C-index of 0.76 and time-dependent areas under the curve for 5- and 10-year survival of 0.77 and 0.83, respectively. RAR is an independent prognostic factor for both all-cause and cancer-specific mortality in cancer survivors, suggesting its potential utility as a prognostic biomarker. The model shows moderate accuracy, warranting external validation to confirm its clinical utility.
Keywords: cancer incidence, cancer prognosis, cancer survivors, NHANES, Red blood cell distribution width to albumin ratio
1. 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] According to the International Agency for Research on Cancer,[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] Albumin possesses antioxidant, anti-inflammatory, antiplatelet aggregation, and anticoagulant properties.[5] 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.[6] 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 is significantly associated with the incidence and prognosis of various diseases, including depression,[7,8] Parkinson disease,[9] and diabetes.[10]
Inflammation plays a crucial role in the occurrence, development, and prognosis of tumors.[11–13] Although preliminary studies have suggested an association between RAR and cancer incidence,[14] as well as postoperative prognosis in cervical cancer patients,[15] these studies are limited by small sample sizes and inadequate control of confounding factors, leaving the overall relationship between RAR and cancer unclear. Given the continued rise in cancer burden, this study uses data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cohort, to accurately assess the association of RAR with cancer incidence and the prognosis of cancer survivors.
2. Methods
2.1. Study population
The NHANES is an investigation initiated by the National Center for Health Statistics 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 National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent.
This study analyzed data from 6 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 <20 years (N = 26,756); 2) Missing RAR data (N = 3432); 3) Missing cancer and mortality status information (N = 70); 4) Missing covariate data (N = 9226). Ultimately, 21,452 participants were included in the study for subsequent analysis (Fig. 1).
Figure 1.
Flow chart of the study participants.
2.2. RAR assessment
In the NHANES survey, healthcare personnel at the Mobile Examination Center measured participants’ peripheral blood RDW using a Coulter Analyzer. Albumin concentration was determined using the DxC 800 method, a 2-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]).
2.3. 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 3 types) were recorded.
NHANES data were linked with the national death index, recording participants’ mortality status and causes of death from the date of the survey to December 31, 2019. The survival time was calculated from the date of the NHANES interview to the date of death or the end of the follow-up period (December 31, 2019), whichever occurred first. The primary outcomes for prognostic assessment were all-cause mortality and cancer-specific mortality. All-cause mortality was defined as death from any cause, ascertained through the national death index linkage. Cancer-specific mortality was defined based on the underlying cause of death variable UCOD_LEADING in the public-use Linked Mortality File. Cases were classified as cancer deaths if the code was 002 (Malignant neoplasms, corresponding to ICD-10 codes C00-C97).[16]
2.4. Covariates
To control for potential confounding bias, we a priori adjusted for the following covariates based on established knowledge. These covariates and their categorization are as follows: 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 3 levels based on the Family PIR: <1.3, 1.3–3.5, and ≥3.5. BMI: Categorized into 3 levels: <25, 25 to 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 >130 mm Hg or diastolic blood pressure >80 mm Hg.[17] Diabetes: Defined based on self-reported diabetes history, use of insulin or antidiabetic medications, or laboratory results showing HbA1c ≥6.5%, fasting blood glucose ≥7.0 mmol/L, or 2-hour postprandial glucose (2hPG) ≥11.1 mmol/L.[18] 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 ≥200 mg/dL.[19] Cardiovascular Disease: Defined based on the medical history of heart failure, coronary heart disease, angina pectoris, heart attack, and stroke reported in the survey.
2.5. Statistical analysis
To ensure national representativeness and account for the complex survey design in our pooled analysis of 6 NHANES cycles, we applied a modified examination weight (WTMEC2YR/6), as per the analytical guidelines for multi-cycle data. Normality tests were conducted for continuous variables. For normally distributed data, the mean (standard error) 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 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 3 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 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 performed propensity score matching (PSM) using a 1:1 ratio with a caliper of 0.2 to control for confounding factors and 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 <.05 (two-sided) was considered statistically significant for all tests.
3. Results
3.1. Baseline characteristics
This study included 21,452 adult participants from the 2005 to 2016 NHANES database (weighted to represent 154,935,519 American adults), of whom 1910 had cancer (Table S1, Supplemental Digital Content, https://links.lww.com/MD/R491). Statistically significant differences were observed between the cancer and noncancer 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 2 groups in terms of drinking status, BMI, and marital status. Table 1 presents baseline characteristics significantly associated with RAR quartiles in the study cohort of 1910 cancer survivors (weighted to 14,805,089 U.S. adults). The median age of the participants was 64 years [IQR: 53, 73], with 58.3% female and 1367 (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. The complete dataset is available in Table S2, Supplemental Digital Content, https://links.lww.com/MD/R491.
Table 1.
Characteristics of cancer survivors.
| Characteristic | Overall, N = 1910 | Quartiles of RAR | ||||
|---|---|---|---|---|---|---|
| Q1 (<2.93), N = 478 | Q2 (2.93–3.13), N = 461 | Q3 (3.14–3.41), N = 491 | Q4 (>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) | .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) | 35 (87.6) | 57 (90.1) | 294 (79.6) | <.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) | <.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) | <.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) | <.001 |
| 1.1–3.0 | 751 (34.7) | 162 (27.7) | 183 (35.8) | 203 (38.1) | 203 (39.8) | |
| > 3.0 | 734 (51.7) | 228 (61.1) | 191 (55.0) | 181 (47.3) | 134 (38.5) | |
| BMI (kg/m2), n (%) | ||||||
| < 25 | 541 (30.1) | 181 (41.2) | 144 (31.5) | 127 (24.8) | 89 (17.8) | <.001 |
| 25–30 | 671 (35.0) | 191 (40.0) | 171 (37.0) | 164 (31.7) | 145 (28.9) | |
| > 30 | 698 (34.9) | 106 (18.8) | 146 (31.5) | 200 (43.5) | 246 (53.3) | |
| Physical activity, n (%) | ||||||
| Inactive | 1100 (53.2) | 240 (48.1) | 255 (50.7) | 296 (54.9) | 309 (62.2) | .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) | .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) | <.001 |
| Yes | 440 (18.3) | 76 (10.7) | 95 (17.1) | 105 (17.8) | 164 (32.3) | |
| Cardiovascular disease, n (%) | ||||||
| No | 1502 (83.7) | 419 (92.2) | 367 (83.5) | 377 (80.3) | 339 (75.1) | <.001 |
| Yes | 408 (16.3) | 59 (7.8) | 94 (16.5) | 114 (19.7) | 141 (24.9) | |
BMI = body mass index, PIR = poverty-income ratio.
3.2. 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 <.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 = .002), the OR for the Q3 group being 1.75 (1.46, 2.10) (P <.001), and the OR for the Q4 group being 2.07 (1.74, 2.47) (P <.001), with a trend P-value of <.001. However, after further adjustment for covariates, the difference between RAR and cancer risk was no longer statistically significant (P >.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) | <.001 | 1.03 (0.98–1.08) | .249 | 1.03 (0.98–1.08) | .300 |
| ALB | 0.59 (0.51–0.69) | <.001 | 0.86 (0.70–1.07) | .179 | 0.85 (0.69–1.06) | .152 |
| RAR (continuous) | 1.44 (1.29–1.59) | <.001 | 1.11 (0.97–1.27) | .131 | 1.11 (0.97–1.28) | .133 |
| Quartiles of RAR | ||||||
| Q1 (<2.83) | Reference | – | Reference | – | Reference | – |
| Q2 (2.83–3.04) | 1.34 (1.12–1.62) | .002 | 0.93 (0.76–1.13) | .448 | 0.93 (0.76–1.13) | .462 |
| Q3 (3.05–3.31) | 1.75 (1.46–2.10) | <.001 | 1.00 (0.82–1.22) | .985 | 1.00 (0.82–1.22) | .976 |
| Q4 (>3.31) | 2.07 (1.74–2.47) | <.001 | 1.15 (0.94–1.41) | .179 | 1.15 (0.94–1.42) | .180 |
| P trend | <.001 | – | .068 | – | .072 | – |
Model 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family PIR, and 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.
ALB = albumin, BMI = body mass index, CI = confidence interval, HR = hazard ratio, PIR = poverty-income ratio, RAR = red blood cell distribution width to albumin ratio, RDW = red cell distribution width.
Results are presented as OR and 95% confidence intervals (CI). OR, odds ratio; CI; PIR; BMI; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio.
3.3. 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 <.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 <.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 <.001). Additionally, in all models, RAR had a higher HR value than RDW and albumin.
Figure 2.
Association of RAR with OS (A) and CSS (B) in cancer survivors. CSS = cancer-specific survival, OS = overall survival, RAR = red blood cell distribution width to albumin ratio.
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) | <.001 | 1.28 (1.20–1.37) | <.001 | 1.28 (1.19–1.37) | <.001 |
| ALB | 0.31 (0.21–0.46) | <.001 | 0.39 (0.25–0.58) | <.001 | 0.40 (0.26–0.61) | <.001 |
| RAR (continuous) | 2.90 (2.31–3.63) | <.001 | 2.483 (1.99–3.10) | <.001 | 2.42 (1.93–3.03) | <.001 |
| Quartiles of RAR | ||||||
| Q1 (<2.93) | Reference | – | Reference | – | Reference | – |
| Q2 (2.93–3.13) | 1.68 (1.15–2.46) | .008 | 1.12 (0.78–1.60) | .539 | 1.05 (0.74–1.50) | .779 |
| Q3 (3.14–3.41) | 2.64 (1.83–3.81) | <.001 | 1.67 (1.17–2.37) | .005 | 1.56 (1.10–2.20) | .013 |
| Q4 (>3.41) | 4.72 (3.33–6.70) | <.001 | 2.78 (1.96–3.94) | <.001 | 2.62 (1.86–3.69) | <.001 |
| P trend | <.001 | <.001 | <.001 | |||
| Cancer-specific mortality | ||||||
| RDW | 1.32 (1.20–1.46) | <.001 | 1.29 (1.17–1.41) | <.001 | 1.29 (1.17–1.42) | <.001 |
| ALB | 0.39 (0.17–0.86) | .019 | 0.40 (0.16–0.99) | .048 | 0.41 (0.16–1.05) | .063 |
| RAR (continuous) | 2.78 (2.09–3.70) | <.001 | 2.50 (1.82–3.43) | <.001 | 2.49 (1.79–3.47) | <.001 |
| Quartiles of RAR | ||||||
| Q1 (<2.93) | Reference | – | Reference | – | Reference | – |
| Q2 (2.93–3.13) | 1.55 (0.83–2.89) | .172 | 1.25 (0.68–2.30) | .476 | 1.23 (0.67–2.27) | .502 |
| Q3 (3.14–3.41) | 1.75 (0.93–3.31) | .083 | 1.33 (0.72–2.46) | .365 | 1.30 (0.71–2.37) | .396 |
| Q4 (>3.41) | 3.25 (1.77–5.95) | <.001 | 2.48 (1.30–4.75) | .006 | 2.43 (1.26–4.67) | .008 |
| P trend | <.001 | .005 | <.001 | |||
Model 1: Unadjusted crude model; Model 2: Adjusted for age, sex, race, education level, marital status, family PIR, and 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.
ALB = albumin, BMI = body mass index, CI = confidence interval, HR = hazard ratio, PIR = poverty-income ratio, RAR = red blood cell distribution width to albumin ratio, RDW = red cell distribution width.
Results are presented as HRs and 95% CI. HRs; CI; PIR; BMI; RDW, Red Cell Distribution Width; ALB, Albumin; RAR, red blood cell distribution width to albumin ratio.
3.4. Dose–response analysis of RAR and mortality in cancer survivors
Restricted cubic splines analysis explored the dose–response relationship between RAR levels and all-cause mortality as well as cancer-specific mortality in the study population (Fig. 3A and B). The results showed a significant statistical relationship between RAR levels and both all-cause mortality and cancer-specific mortality (P overall <.0001), with a positive linear correlation (P nonlinear >.05).
Figure 3.
Dose–response relationship of RAR with OS (A) and CSS (B) in cancer survivors. CSS = cancer-specific survival, OS = overall survival, RAR = red blood cell distribution width to albumin ratio.
3.5. 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 to 3.0, higher RAR was significantly associated with an increased risk of all-cause mortality (HR: 3.62, 95% CI: 2.45–5.33).
Figure 4.
Subgroup analysis of RAR with all-cause mortality and cancer-specific mortality risk in cancer survivors. RAR = red blood cell distribution width to albumin ratio.
3.6. 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 (Table S3, Supplemental Digital Content, https://links.lww.com/MD/R491). After balancing the differences between the 2 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 <.05) (Fig. 5).
Figure 5.
Association of RAR with OS (A) and CSS (B) in cancer survivors after PSM. CSS = cancer-specific survival, OS = overall survival, PSM = propensity score matching, RAR = red blood cell distribution width to albumin ratio.
3.7. 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 <.05 were included in the subsequent multivariate Cox regression analysis (Table S4, Supplemental Digital Content, https://links.lww.com/MD/R491). 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 (Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/R491).
Figure 6.
RAR-based nomogram for all-cause mortality prediction in cancer survivors. RAR = red blood cell distribution width to albumin ratio.
4. Discussion
Our analysis revealed a distinct prognostic role for the RAR among cancer survivors. While we observed no significant association between RAR and cancer incidence, elevated RAR levels were strongly and linearly associated with increased all-cause and cancer-specific mortality during long-term follow-up. This association remained robust across all predefined subgroups and was further validated by PSM. Notably, the hazard associated with each unit increase in RAR (HR = 2.42) substantially exceeded that of RDW alone (HR = 1.28). This suggests that the composite RAR index captures the synergistic impact of inflammatory and nutritional deficits on survival more effectively than its individual components, offering superior prognostic utility.
The biological plausibility of our findings is supported by the central role of systemic inflammation and nutritional status in cancer progression.[20,21] Whereas previous research has often examined RDW and albumin in isolation, our findings highlight the value of their integration. Evidence from the literature indicates that RDW is significantly associated with tumor stage, pathological grade, malignancy, and adverse prognosis in a spectrum of cancers, including lung, colon, urological, and breast cancers.[22–28] Podhorecka et al found that RDW did not change significantly during disease progression, suggesting that RDW could serve as a stable prognostic marker.[29] Additionally, an increase in RDW is linked to shortened telomere length,[30] which in turn is associated with cancer incidence and mortality.[31] Lower albumin levels, often reflecting underlying malnutrition, are consistently associated with worse cancer prognosis.[32–36] Beyond its role as a nutritional marker, albumin may exert direct antitumor effects by stabilizing DNA replication and enhancing immune responses.[37] Furthermore, its capacity to accumulate at sites of inflammation and tumors facilitates the targeted delivery of anti-inflammatory and anticancer agents,[38,39] while its ability to downregulate the expression and transport of inflammatory factors contributes to the alleviation of the systemic inflammatory state.[40–42] Furthermore, the systemic inflammation and nutritional imbalance reflected by RAR manifest clinically through cancer-related symptoms such as cachexia, pain, and insomnia.[43] Our results align with this concept, as the combination into RAR yielded a significant association with cancer-specific mortality, whereas albumin alone did not.
Building on prior research into the prognostic value of RAR, 1 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 = .033).[15] 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.[44] 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 noncancer-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,[45] diabetic ketoacidosis,[46] acute respiratory distress syndrome,[47] and severe pneumonia.[48] Our study also found that elevated RAR is associated with overweight, hypertension, diabetes, and cardiovascular diseases, suggesting that an elevated RAR is more likely to serve as a marker of underlying frailty, comorbid burden, or disease severity, rather than a direct causal factor.
This study has several limitations. First, the cancer diagnosis was based on self-report, which is susceptible to recall bias and potential misclassification. Second, survival time was calculated from the date of the NHANES interview rather than the date of cancer diagnosis; this may introduce bias into the estimation of the association between RAR and survival. Third, the lack of key clinical information, such as cancer stage, molecular subtype, and treatment details, limited our adjustment for these confounders and may affect the interpretation of the results. Fourth, despite the use of multivariable regression and PSM, residual confounding or selection bias cannot be excluded. Finally, the RAR measurement was obtained at a single time point, failing to capture its dynamic nature; future studies with repeated measurements are needed to validate its prognostic value.
5. Conclusion
RAR was independently associated with increased all-cause and cancer-specific mortality among cancer survivors in NHANES. While the prognostic model incorporating RAR showed moderate discriminative ability, further studies with clinical cancer data and repeated biomarker measures are needed before clinical implementation.
Author contributions
Data curation: Xiao-Ting Wei, Miao-Jia Wang, Duo-Xiang Zhao, Jie Yang, Qin-Zhe Wu.
Methodology: Tao Yang.
Project administration: Qin-Zhe Wu.
Software: Xiao-Ting Wei, Miao-Jia Wang, Duo-Xiang Zhao, Jie Yang.
Visualization: Wei Li, Zheng-Mei Qiao, Tao Yang.
Writing – original draft: Wei Li, Zheng-Mei Qiao.
Writing – review & editing: Tao Yang.
Supplementary Material
Abbreviations:
- ALB
- albumin
- BMI
- body mass index
- CI
- confidence interval
- CSS
- cancer-specific survival
- HDL-C
- high-density lipoprotein cholesterol
- HR
- hazard ratio
- IARC
- International Agency for Research on Cancer
- ICD-10
- International Classification of Diseases, 10th revision
- IQR
- interquartile range
- LDL-C
- low-density lipoprotein cholesterol
- OR
- odds ratio
- OS
- overall survival
- PIR
- poverty-income ratio
- PSM
- propensity score matching
- RAR
- red blood cell distribution width to albumin ratio
- RCS
- restricted cubic splines
- RDW
- red cell distribution width
- SBP
- systolic blood pressure
- TC
- total cholesterol
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available for this article.
How to cite this article: Li W, Qiao Z-M, Wei X-T, Wang M-J, Zhao D-X, Yang J, Wu Q-Z, Yang T. Red blood cell distribution width to albumin ratio and cancer prognosis in U.S. adults: NHANES 2005 to 2016. Medicine 2026;105:10(e47923).
Contributor Information
Wei Li, Email: 547845892@qq.com.
Zheng-Mei Qiao, Email: qiaozhengmei0000@163.com.
Xiao-Ting Wei, Email: 552414290@qq.com.
Miao-Jia Wang, Email: 474225162@qq.com.
Duo-Xiang Zhao, Email: 271335857@qq.com.
Jie Yang, Email: 18292998902@163.com.
Qin-Zhe Wu, Email: 2097425415@qq.com.
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