Kaplan-Meier survival curves by quintiles of red blood cell distribution width (RDW) according to age group. A, Participants 45 to 64 years old; B, participants 65 years or older.
Risk of death by red blood cell distribution width (RDW) according to age group and cause of death. Panels A, C, E, and G show hazard ratios and 95% confidence intervals (CIs; error bars) for participants aged 45 to 64 years; panels B, D, F, and H show hazard ratios and 95% CIs for participants 65 years or older. Panels A and B indicate all deaths; C and D, cardiovascular deaths; E and F, cancer deaths; and G and H, other deaths. Hazard ratios were adjusted for age, sex, race/ethnicity, education, body mass index, smoking status, cancer, congestive heart failure, diabetes, heart attack, pulmonary disease, stroke, overnight hospitalization, estimated glomerular filtration rate, hemoglobin concentration, mean corpuscular volume, and C-reaction protein level.
Probability of death by red blood cell distribution width (RDW) values in the reference range among adults 45 years or older. CI indicates confidence interval.
Patel KV, Ferrucci L, Ershler WB, Longo DL, Guralnik JM. Red Blood Cell Distribution Width and the Risk of Death in Middle-aged and Older Adults. Arch Intern Med. 2009;169(5):515-523. doi:10.1001/archinternmed.2009.11
Red blood cell distribution width (RDW), a component of an electronic complete blood count, is a measure of heterogeneity in the size of circulating erythrocytes. In patients with symptomatic cardiovascular disease (CVD), RDW is associated with mortality. However, it has not been demonstrated that RDW is a predictor of mortality independent of nutritional deficiencies or in the general population.
Red blood cell distribution width was measured in a national sample of 8175 community-dwelling adults 45 years or older who participated in the 1988-1994 National Health and Nutrition Examination Survey; mortality follow-up occurred through December 31, 2000. Deaths from all causes, CVD, cancer, and other causes were examined as a function of RDW.
Higher RDW values were strongly associated with an increased risk of death. Compared with the lowest quintile of RDW, the following were adjusted hazard ratios (HRs) for all-cause mortality (and 95% confidence intervals [CIs]): second quintile, HR, 1.1 (95% CI, 0.9-1.3); third quintile, HR, 1.2 (95% CI, 1.0-1.4); fourth quintile, HR, 1.4 (95% CI, 1.2-1.8); and fifth quintile, HR, 2.1 (95% CI, 1.7-2.6). For every 1% increment in RDW, all-cause mortality risk increased by 22% (HR, 1.22; 95% CI, 1.15-1.30; P < .001). Even when analyses were restricted to nonanemic participants or to those in the reference range of RDW (11%-15%) without iron, folate, or vitamin B12 deficiency, RDW remained strongly associated with mortality. The prognostic effect of RDW was observed in both middle-aged and older adults for multiple causes of death.
Red blood cell distribution width is a widely available test that is a strong predictor of mortality in the general population of adults 45 years or older.
Red blood cell distribution width (RDW) is routinely assessed as part of the complete blood count (CBC) to gather information on the heterogeneity in the size of circulating erythrocytes. Computationally, RDW is the coefficient of variation of red blood cell (RBC) volume, and therefore higher RDW values reflect greater heterogeneity in RBC size (anisocytosis), which is usually caused by perturbation in erythrocyte maturation or degradation. The RDW is used as an auxiliary index to help diagnose different types of anemia but has also been evaluated as a potential screening marker for colon cancer and celiac disease because of its responsiveness to subtle nutrient deficiency.1,2 Recently, researchers have reported higher mortality risk associated with higher RDW in patient populations with cardiovascular disease (CVD).3- 5 However, none of these prospective studies was able to account for nutritional status or levels of inflammation.
Given that the RDW is routinely reported by clinical laboratories as a component of the CBC and it is available for most patients, understanding its prognostic significance could be very valuable for risk stratification in clinical decision making. However, whether the prognostic effect of RDW is specific to patients with CVD or rather is also valid in the general population is unknown. Therefore, we sought to determine whether higher RDW levels are associated with increased risk of death in a large, nationally representative sample of middle-aged and older adults.
The Third National Health and Nutrition Examination Survey, 1988-1994 (NHANES III)6 was designed to provide health status information on a nationally representative sample of the civilian noninstitutionalized US population. Adult participants provided written informed consent before entering the study and then were interviewed in their homes and subsequently underwent a physical examination, including phlebotomy, either in a mobile examination center or at home. Data were collected in 2 phases (1988-1991 and 1991-1994), each of which provided a nationally representative sample. Both phases of data collection included CBCs, but assays for serum nutrients were performed only in phase 2 (1991-1994). Details of the NHANES III study design and data collection protocols have been published previously.6
Of the 9787 NHANES III participants who were 45 years or older, 8178 had their blood drawn. Participants missing RDW values were significantly older (P < .001), more likely to be female than male (P = .03), less likely to be Mexican American than non-Hispanic white (P < .001), and more likely to die duringr the follow-up period compared with those not missing RDW values (P < .001). Vital status through December 31, 2000, was ascertained for 8175 participants using the National Death Index (NDI); 3 participants were excluded because insufficient information was available to perform an NDI search. Analyses restricted to phase 2 were performed for 4087 adults.
The automated Coulter Counter (model S-Plus Jr; Coulter Electronics, Hialeah, Florida) was used to measure RDW, mean corpuscular volume (MCV), and hemoglobin concentration. Quality control procedures and calibration were performed daily and verified directly via telephone with Coulter Electronics headquarters. The maximum acceptable coefficients of variation for RDW, MCV, and hemoglobin concentration were 3.2%, 2.0%, and 1.0%, respectively. Laboratory methods used in NHANES III have been described in detail in previous publications.7
A total of 2428 deaths were identified through the NDI search (median follow-up, 7.9 years; maximum, 12.1 years). Over the follow-up period, coding for the underlying causes of death switched in 1999 from the ninth revision of the International Classification of Diseases (ICD-9) to the 10th revision (ICD-10). For comparability, the National Center for Health Statistics recoded deaths into 113 groups using ICD-10.8 To examine cause-specific mortality, cardiovascular deaths were identified with ICD-10 codes I00 to I78 (n = 1195), whereas codes C00 to C97 were used for cancer deaths (n = 518). All other causes of deaths were grouped into a third category (n = 713).
During the home interview, participants were asked to report their date of birth, race/ethnicity, and highest grade of education completed. Smoking status (never, former, or current) was determined through a standard set of questions. Measured height and weight were used to calculate body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared). For medical conditions, participants were asked if a doctor had ever told them that they had any of the following: cancer (non–skin-related), congestive heart failure, diabetes, heart attack, pulmonary diseases (asthma, chronic bronchitis, or emphysema), and stroke. In addition, overnight hospitalization stays within the past year were assessed by questionnaire. To assess renal function, serum creatinine values were recalibrated to the Cleveland Clinic Research Laboratory standard by applying the following formula: [standardized creatinine = −0.184 + (0.960 × uncalibrated serum creatinine in milligrams per deciliter)].9 (To convert serum creatinine to micromoles per liter, multiply values by 88.4.) Estimated glomerular filtration rate (eGFR) was then calculated using the following formula: eGFR = [175 × (standardized creatinine)-1.154 × (age)-0.203 × (0.742 if the participant is female) × (1.212 if the participant is black)].10 Latex-enhanced nephelometry was used to quantify C-reactive protein (CRP) level (Behring Diagnostics, Somerville, New Jersey). Fibrinogen was measured in citrated plasma using an automated coagulation autoanalyzer (Organon Teknika, Durham, North Carolina). White blood cell count was obtained as part of the CBC (Coulter Electronics). The bromocresol purple method was used to measure albumin.
In phase 2 of the data collection, the requisite assays for identifying iron, folate, and vitamin B12 deficiency were available.7 As previously defined, iron deficiency was considered present in participants with at least 2 of 3 criteria: transferrin saturation less than 15%, serum ferritin level less than 12 ng/mL (29.96 pmol/L), and erythrocyte protoporphyrin level greater than 69.8 μm (1.24 μmol/L).11 Participants with serum vitamin B12 level less than 200 pg/dL (147.56 pmol/L) were considered to have vitamin B12 deficiency.7 To identify folate deficiency, an RBC folate level of less than 102.6 ng/mL (232.49 nmol/L) was used in participants examined in the mobile examination center, whereas a serum folate level of less than 2.6 ng/mL (5.89 nmol/L) was used in those examined at home (RBC folate was not measured in blood collected in the home).7
To evaluate the association of RDW with mortality outcomes, RDW values were examined as a continuous variable as well as categorized into quintiles using the following cutoffs: less than 12.60%, 12.60% to 12.95%, 13.0% to 13.40%, 13.45% to 14.05%, and greater than 14.05%. Participant characteristics were first examined across quintiles of RDW (Table 1). Kaplan-Meier survival curves were then plotted by quintiles of RDW in participants aged 45 to 64 years and in those 65 years or older (Figure 1). The log-rank test was used to test the equality of survivor functions across RDW quintiles in each age group. Cox proportional hazards models were used to assess the association of RDW with mortality adjusting for multiple risk factors (Table 2). The proportional hazards assumption was confirmed by examining plots of Schoenfeld residuals. Four mortality outcomes were examined: all-cause, cardiovascular, cancer, and other. For each mortality outcome, 3 models were tested. The first model included RDW, age, sex, and race/ethnicity as predictors (model 1), whereas all other risk factors that might confound the RDW-to-mortality association were added in the second model (model 2). In phase 2 participants, a third model was tested that included the predictors in model 2 as well as the nutrient deficiency variables (model 3). Finally, to determine whether the effect of RDW on mortality occurs in both middle-aged and older adults, model 2 was stratified by age group for each mortality outcome (Figure 2). Tests of trend were performed by entering RDW as an ordinal variable. Using Stata/SE statistical software (version 9.2; StataCorp LP, College Station, Texas), all of these analyses were weighted and accounted for nonresponse bias and the complex sampling methods designed to provide estimates for the US population.
The added ability of RDW to predict all-cause mortality was evaluated using methods recommended by Cook12,13 and Pencina et al14 (Table 3). To compare the global fit of models with and without RDW as a continuous variable predicting death, the −2 log likelihood and Bayes information criterion (BIC) were examined. Lower values of these sensitive measures indicate better model fit, and the difference between −2 log likelihoods of nested models can be compared using a χ2 distribution, known as the likelihood ratio test. The Hosmer-Lemeshow statistic was used to assess model calibration by comparing predicted and observed probabilities of death across categories of 5% increments in predicted risk. To assess discrimination, the area under the receiver operating characteristic (ROC) curve as well as the C statistic, which is similar to the area under the ROC curve but allows for time to event analysis, were examined (higher values indicate better discrimination). In addition, the integrated discrimination improvement (IDI) statistic was calculated, which compares improvement in the integral of sensitivity with any change in the intergral of the false-positive rate (1-specificity) when a new marker (RDW) is added.14 Higher IDI values indicate better discrimination in risk prediction. Unweighted Cox models were used to obtain −2 log likelihood, BIC, and C statistics, whereas the Hosmer-Lemeshow, area under the ROC curve, and IDI were obtained with unweighted logistic regression models that examined deaths through 6 years of follow-up, which was available for all study participants.
The RDW values ranged from 11.0% to 30.6% (median, 13.15%; interquartile range, 12.65%-13.85%). Baseline participant characteristics are shown by quintiles of RDW in Table 1. Participants with higher RDW values were more likely to be older, less educated, currently smoking, and had higher BMI than those with lower RDW values. The proportion of non-Hispanic blacks increased from the lowest to highest quintiles of RDW. With the exception of cancer, the prevalence of age-associated diseases and hospitalization in the past year increased with higher RDW quintiles. In addition, eGFR, hemoglobin concentration, and MCV decreased with higher RDW, whereas CRP, fibrinogen, and WBC increased with higher RDW. As expected, a higher prevalence of nutrient deficiencies was observed in participants with higher RDW, although a clear gradient across RDW quintiles was not seen with vitamin B12 deficiency.
Figure 1 graphically displays the probability of survival over the follow-up period by quintiles of RDW for middle-aged and older adults. Middle-aged adults in the highest quintile of RDW had poorer survival relative to those with a lower RDW. Although the number of deaths was relatively low among middle-aged participants in the lower quintiles of RDW, an intermediate survival pattern was observed in those with an RDW of 13.45% to 14.05% (fourth quintile). In older adults, there was a clear survival gradient across RDW quintiles sustained over the 12 years of follow-up. Higher RDW was associated with poorer survival among older participants, particularly for those in the fourth and fifth quintiles of RDW.
After adjusting for age, sex, and race/ethnicity, there remained a stepwise, graded association between RDW levels and all-cause mortality in adults 45 years or older (model 1, Table 2). Participants in the fourth and fifth quintiles of RDW were 1.6 and 2.7 times more likely to die, respectively, compared with those in the lowest quintile of RDW. Further adjustment for major age-associated diseases as well as education, BMI, smoking status, hospitalizations, renal function, hemoglobin concentration, MCV, and CRP level only partially attenuated the effect of RDW on all-cause mortality (model 2). Even when the study population was restricted to those with serum markers of nutrition (participants in phase 2), higher RDW remained strongly associated with increased risk of death while adjusting for all potential confounding factors including iron, folate, and vitamin B12 deficiency (model 3). When RDW was examined as a continuous variable, mortality risk increased by 22% for every 1% increment in RDW adjusting for model 2 covariates (hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.15-1.30). The association of RDW with mortality did not vary significantly by sex (P = .45 for interaction term).
In addition to overall mortality, RDW was a particularly strong predictor of cardiovascular deaths (Table 2). The risk of dying from CVDs was nearly 2- and 3-fold higher in participants with RDW values in the fourth and fifth quintiles, respectively, compared with those in the bottom quintile (model 1). Adjustment for other risk factors did not substantially diminish this association. For cancer mortality, there was also a significant risk gradient across RDW quintiles, although the HRs for the upper quintiles were no longer statistically significant when adjusting for nutrient deficiencies in phase 2 participants (model 3). Mortality resulting from other causes was also significantly associated with RDW. Participants in the highest quintile had a 2-fold increased risk of death from noncardiovascular and noncancer causes compared with those in the lowest quintile of RDW.
Figure 2 illustrates that the association of RDW with mortality adjusted for multiple risk factors occurs in both middle-aged and older adults. There was a significant trend of increased risk of death from all causes with higher RDW in both age groups. Although the number of events was much smaller in participants aged 45 to 64 years than in those 65 years or older, risk of death from CVDs, cancer, and other causes was significantly higher in participants in the highest RDW quintile compared with those in the lowest quintile for both age groups.
The added prognostic value of RDW to mortality risk prediction is demonstrated by the summary statistics of model performance shown in Table 3. In both middle-aged and older adults, the addition of RDW significantly improved model fit as indicated by lower −2 log likelihood and BIC values as well as by the significant likelihood ratio test results. The Hosmer-Lemeshow statistic comparing predicted with observed mortality risk indicated excellent calibration when RDW was included in the model. In addition, the C statistic and area under the ROC curve values improved in models with RDW, reflecting better discrimination in risk prediction. Furthermore, the significant IDI value of 1.27% in middle-aged adults resulted from a significant increase in the mean sensitivity of 1.19% (P < .001) and a decrease in the mean false-positive rate of 0.08% (P = .06) when RDW was included as a predictor of mortality. In older adults, the mean sensitivity increased by 1.07% (P < .001), whereas the mean false-positive rate decreased by 0.48% (P < .001).
To further evaluate the association of RDW with mortality, a series of sensitivity analyses were performed. When participants with RDW greater than 15.0% were excluded, RDW remained significantly associated with all-cause mortality (comparing the highest RDW quintile to lowest; multivariate adjusted HR, 2.0; 95% CI, 1.6-2.4; P value for trend <.01). In fact, Figure 3 illustrates that there was a continuous rise in mortality risk among participants with RDW values of 11.0% to 15.0%. For every percentage point increase in RDW, the risk of death increased by 37%, adjusting for demographic, behavioral, and biomedical risk factors (HR, 1.37; 95% CI, 1.24-1.51). The actual observed proportion of deaths for each 0.5% interval of RDW (individual points) closely matched the modeled mortality risk represented by the solid line in Figure 3, supporting the validity of the risk modeling. Further exclusion of participants with iron, folate, or vitamin B12 deficiency did not eliminate the association (comparing the highest RDW quintile to the lowest, multivariate adjusted HR, 2.2; 95% CI, 1.5-3.2; P value for trend <.01). In addition, RDW was a significant predictor of all-cause mortality among nonanemic participants (baseline hemoglobin level >13.0 g/dL in women and >14.0 g/dL in men) (comparing the highest RDW quintile to the lowest, multivariate-adjusted HR, 2.1; 95% CI,1.6-2.8; P value for trend <.01). Finally, the effect of RDW on survival was only slightly reduced and remained statistically significant after further adjusting for WBC, fibrinogen, and serum albumin (comparing the highest RDW quintile to the lowest, multivariate adjusted HR, 1.9; 95% CI, 1.6-2.4; P value for trend <.01). (To convert hemoglobin to grams per liter, multiply by 10.0.)
In this nationally representative study, RDW was a strong predictor of mortality in both middle-aged and older adults. Mortality rates were graded across the entire distribution of RDW and were particularly elevated in participants with RDWs greater than 13.4%. Deaths from CVD, cancer, and other causes were all associated with RDW, although the effect was stronger for CVD mortality. Associations were independent of multiple potential confounding factors. Even when participants with high RDW values or nutritional deficiencies were excluded, RDW remained strongly associated with mortality. Importantly, RDW predicted mortality among participants who were clearly nonanemic. Summary measures of global model fit, model calibration, and model discrimination further showed that RDW significantly improved mortality risk prediction. The magnitude and robustness of these associations indicate that RDW is an age-associated biomarker that is prognostic in adults 45 years or older.
Although the exact physiologic mechanisms that underlie the association of RDW with survival are unknown, systemic factors that alter erythrocyte homeostasis, such as inflammation and oxidative stress, likely play a role. Inflammation might contribute to increased RDW levels by not only impairing iron metabolism but also by inhibiting the production of or response to erythropoietin or by shortening RBC survival.15,16 Using an index comprised of multiple proinflammatory cytokines, Ferrucci et al17 showed that higher levels of inflammation were associated with higher erythropoietin concentration among nonanemic older adults, whereas an inverse association was observed in anemic persons. This suggests that in a proinflammatory state the increase in erythropoietin is a compensatory mechanism for maintaining normal hemoglobin concentration and that anemia occurs when the compensatory increment in erythropoietin production is unsustainable. Indeed, a number of studies have shown that proinflammatory cytokines suppress erythropoietin gene expression, inhibit proliferation of erythroid progenitor cells, downregulate erythropoietin receptor expression, and reduce erythrocyte life span.16 Ershler et al18 demonstrated in a longitudinal cohort that erythropoietin increased with aging, which suggests that increased erythropoietin production is a compensatory mechanism for decreasing bone marrow response and/or RBC survival. In the current study, older age as well as elevated values for CRP, fibrinogen, and WBC count were strongly associated with higher RDW levels (Table 1). Even though RDW predicted mortality in participants who were nonanemic, it is possible that there are fluctuations in erythropoiesis caused by inflammation that lead to greater heterogeneity in erythrocyte size as younger erythrocytes are usually larger and more variable in size than older ones.
In addition to inflammation, oxidative stress might also contribute importantly to anisocytosis. While erythrocytes have tremendous antioxidant capacity and serve as the primary “oxidative sink,” they are prone to oxidative damage that reduces cell survival.19 In a population-based study, higher RDW values were independently associated with poorer pulmonary function, a condition associated with oxidative stress.20 Among persons with Down syndrome, a condition characterized by accelerated aging, levels of oxidative stress were significantly higher than in healthy controls.21 The Down syndrome group in that study had higher RDW values than controls (14.5% vs 12.8%), but this difference was not viewed as important because both means were within the reference range.21 In light of our findings, however, these differences in RDW are not trivial in terms of mortality risk. Patients who undergo hemodialysis also experience oxidative stress and inflammation owing to blood contact with dialysis membranes. A small randomized trial of these patients showed that a variety of outcomes, including RDW values, improved over 1 year in patients treated with a vitamin E–bonded cellulose membrane hemodialyzer compared with those treated with a standard cellulose membrane.22 Although more mechanistic research is needed, these studies indicate that variation in erythrocyte size is associated with oxidative stress, possibly through increased RBC turnover.
Findings from the current study are consistent with those of other recently published studies. In a randomized controlled trial of patients with heart failure, Felker et al4 showed that patients with an RDW greater than 15.8% had nearly a 2-fold increased risk of CVD death or hospitalization as well as death from any cause compared with those with an RDW less than 13.3%. As a validation, Felker et al4 also showed a 2-fold increased mortality risk comparing the highest RDW quintile (>15.5%) with the lowest (<13.0%) in a separate clinical cohort of patients with heart failure. Similarly, Tonelli et al5 analyzed data from a randomized controlled trial of patients with coronary artery disease and reported that death was twice as likely in patients with an RDW greater than 13.8% vs those with an RDW less than 12.6%. Finally, Anderson et al3 reported that the risk of death over 1 year was 3 times higher in patients undergoing cardiac catheterization with an RDW greater than 14.0% relative to those with an RDW of less than12.7%. All of these studies were able to adjust for major CVD risk factors, but data on nutritional status or inflammation were not available.
As with other studies, a limitation of the NHANES III6 is that laboratory assessments were made on a single occasion and therefore fluctuations in the CBC could not be evaluated. In addition, erythropoietin, reticulocyte count, markers of oxidative stress, and more sensitive assays of CRP and other proinflammatory cytokines were not available but could provide valuable clues as to the pathophysiologic mechanisms underlying anisocytosis (exploratory analyses showed that serum antioxidants, such as carotenoids, selenium, and vitamin E, were strongly associated with RDW, but practically did not change the effect of RDW on mortality). Furthermore, the sampling occurred in an era that preceded folate food supplementation, which could lower RDW. However, the relationship of RDW with mortality remained after adjusting for folate deficiency and exclusion of participants with nutritional deficiencies, so it is likely that this relationship would persist in the postsupplementation era. The current study has several strengths, including the large, community-based sample that is representative of nearly 73 million US adults 45 years or older. The relatively long follow-up period provided a sufficient number of deaths to examine cause-specific mortality. Finally, unlike previous studies, the current analysis adjusted for a comprehensive set of potential confounders, including iron, folate, and vitamin B12 deficiency as well as CRP levels.
Red blood cell distribution width is a widely available and inexpensive test that is prognostic in the general population of adults 45 years or older. Given that RDW rises with age and strongly predicts mortality, it is conceivable that anisocytosis might reflect impairment of multiple physiologic systems related to the aging process or is caused by inflammation and age-associated diseases. While further research is needed to elucidate the mechanisms, RDW provides prognostic information that can be used to improve risk stratification. Future studies should further evaluate the clinical usefulness of RDW for risk prediction as well as characterize change in RDW over time and define associations with other age-associated outcomes, such as the onset of anemia, physical disability, and cognitive impairment.
Correspondence: Kushang V. Patel, PhD, MPH, National Institute on Aging, 7201 Wisconsin Ave, Ste 3C309, Bethesda, MD 20814 (firstname.lastname@example.org).
Accepted for Publication: September 28, 2008.
Author Contributions: Dr Patel had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Patel, Ferrucci, Ershler, and Guralnik. Analysis and interpretation of data: Patel, Longo, and Guralnik. Drafting of the manuscript: Patel. Critical revision of the manuscript for important intellectual content: Ferrucci, Ershler, Longo, and Guralnik. Statistical analysis: Patel and Guralnik. Administrative, technical, and material support: Patel and Longo. Study supervision: Ferrucci, Longo, and Guralnik.
Financial Disclosure: None reported.
Funding/Support: This study was supported by the Intramural Research Program of the US National Institute on Aging, National Institutes of Health.