Abstract
Background
We tested whether greater variation in red blood cell size, measured by red cell distribution width (RDW), may predict aging-related degenerative conditions and therefore, serve as a marker of biological aging.
Methods
Three thousand six hundred and thirty-five community-dwelling older men were enrolled in the prospective Osteoporotic Fractures in Men Study. RDW was categorized into 4 groups (≤13.0%, 13.1%–14.0%, 14.1%–15.0%, and ≥15.1%). Functional limitations, frailty, strength, physical performance, and cognitive function were measured at baseline and 7.4 years later. Falls were recorded in the year after baseline; hospitalizations were obtained for 2 years after baseline. Mortality was assessed during a mean of 8.3 years of follow-up.
Results
Participants with greater variability in red cell size were weaker, walked more slowly, and had a worse cognitive function. They were more likely to have functional limitations (35.2% in the highest RDW category vs 16.0% in the lowest, p < .001) and frailty (30.3% vs 11.3%, p < .001). Those with greater variability in red cell size were more likely to develop new functional limitations and to become frail. The risk of having 2 or more falls was also greater (highest 19.2% vs lowest 10.3%, p < .001). The risk of hospitalization was higher in those with the highest variability (odds ratio [95% confidence interval], 1.8 [1.3–2.5]) compared with the lowest. Variability in red cell size was related to total and cause-specific mortality.
Conclusion
Greater variability in red cell size is associated with diverse aging-related outcomes, suggesting that it may have potential value as a marker for biological aging.
Keywords: Aging, Biomarker, Complete blood cell count, Red cell distribution width
Variation in red blood cell size, measured as the red cell distribution width (RDW), which is part of the complete blood count (CBC) (1,2), can be used clinically in the differential diagnosis of anemia. Recently, greater variability in red cell size has been associated with several diseases whose incidence increases with age, including ischemic heart disease, stroke, heart failure, atrial fibrillation, renal insufficiency, and fractures, as well as in-hospital mortality (3–10).
Variability in the size of red blood cells increases with age (11), raising the possibility that it reflects a fundamental process of aging (12,13). Therefore, we evaluated the association between RDW and a wide array of aging-related phenotypes, including functional limitation, frailty, muscle strength, physical performance, cognitive function, falls, hospitalizations, and total and cause-specific mortality in a large cohort of older men.
Method
Study Sample
Participants were enrolled in the Osteoporotic Fractures in Men (MrOS) Study (14,15). Briefly, 5994 ambulatory community-dwelling men aged 65 years were enrolled between March 2000 and April 2002 at 6 U.S. sites. Further information about the MrOS cohort can be found at the MrOS online website (http://mrosdata.sfcc-cpmc.net). Between March 2007 and March 2009, 3790 participants completed a clinic visit and examination, including a CBC (Visit 3, referred to as baseline in this report); excluding the 155 participants without CBC data left 3635 for analysis (Supplementary Appendix Figure 1). A repeat examination (Visit 4, follow-up) was done an average of 7.4 years later in 2091 participants (1183 participants had died and 361 participants terminated or were lost to follow-up). Visit 4 included measurements of functional limitation, frailty, muscle strength, physical performance, and cognitive function.
Baseline (Visit 3) Measurements
Blood was drawn in the morning following an overnight fast. Complete blood counts were performed at local Quest Diagnostic laboratories or at Stanford Outreach. General clinical information included demographic characteristics, anthropometric parameters, education level, physical activity status, and medical history. Body mass index was calculated as weight (kg) divided by height-squared (m2). A comorbidity index was calculated as the number of the following self-reported conditions: stroke, myocardial infarction, angina, chronic obstructive pulmonary disease, congestive heart failure, Parkinson’s disease, osteoarthritis, diabetes, thyroid disease, and osteoporosis.
Participants were asked about their ability to complete activities of daily living and instrumental activities of daily living, including the ability to walk 2–3 blocks, climb 10 steps, bathe/shower, and get in and out of bed or chairs (16). Participants with at least one difficulty in any of these activities were classified as having a functional limitation. Frailty status was evaluated using criteria proposed by Fried based on 5 components: shrinking, weakness, poor energy, slowness, and low physical activity (17). Participants with none of these criteria were categorized as robust, those with 1 or 2 components as intermediate, and those with 3 or more components as frail. Physical activity was evaluated by the Physical Activity Scale for the Elderly (PASE) (18).
Physical performance was assessed as the mean walking speed (m/s) at the usual pace over a 6-m course during 2 attempts (19). Time to complete 5 chair stands with arms crossed at the chest was recorded in seconds. Grip strength (kg) was measured as the average value from 2 tests of each hand using Jamar dynamometers (Sammons Preston Rolyan, Bolingbrook, IL) (20). Leg power in watts was measured for bilateral leg extensor muscles using the Nottingham Power Rig; the maximum power achieved after up to 9 trials was used (21). Global cognitive function was assessed using the Modified Mini-Mental Status Examination (3MS) (22).
Follow-Up Measurements
Functional limitations, frailty, physical performance, and cognitive function were measured again at Visit 4; the differences in absolute scores between Visit 3 and Visit 4 were calculated.
Participants were asked at regular intervals whether they had fallen during the prior 4-month period using mailed questionnaires. Incident and recurrent (2 or more) falls were defined as those that occurred during the first year after Visit 3. Information about hospitalization was obtained by self-reported questionnaires 2 years after Visit 3. Participants were asked whether they had been admitted to the hospital within the past year and the number of such hospitalizations. Recurrent hospitalization was defined as 2 or more admissions.
Participants were contacted every 4 months through February 2019 (a mean of 8.3 ± 3.1 years) to ascertain vital status. Next of kin was contacted if the participants did not return postcard questionnaires and did not respond to telephone follow-up. Reported deaths were confirmed by review of death certificates and/or medical records. The underlying cause of mortality was adjudicated centrally and classified as cardiovascular, cancer-related, and other.
Statistical Analysis
Baseline characteristics are presented as mean ± SD for continuous values and as number (%) for categorical values. Participants were classified into 4 groups according to the absolute RDW values (≤13.0%, 13.1%–14.0%, 14.1%–15.0%, and ≥15.1%) (23,24). Anemia was defined by the WHO criteria for men (hemoglobin concentration <13.0 g/dL) (25). New onset of functional limitation or frailty was defined as the development of these conditions in someone who had no limitation or who was robust at baseline (Visit 3). Because frailty and mortality are competing events, we created 4 potential categories at follow-up: robust, intermediate, frail, and dead.
Chi-square tests were used to compare proportions across the RDW groups; the Cochran–Armitage test was used to test for the linear trends. Continuous outcomes were compared by analysis of variance, and linear regression was performed to examine trends across RDW groups (26). Age-adjusted and multivariate-adjusted logistic regression analyses were used to estimate the relative risks of categorical outcomes in each RDW category. Kaplan–Meier survival curves were plotted for all and cause-specific mortality outcomes and the mortality rates were compared by log-rank-sum test across the RDW groups. Cox-proportional hazard models were used to estimate hazard ratios for mortality in each RDW category. All analyses were further adjusted for age, body mass index, numbers of comorbidities, and PASE score (referred to collectively as “other confounders”). Education levels were adjusted in analyses of cognitive function. Anemia can cause greater variability in red cell size, thus we tested potential interactions between RDW and anemia (hemoglobin ≤13.0 g/dL or higher) by including an interaction term. All statistical analyses were performed using Stata/MP version 15 (StataCorp, College Station, TX), and p values less than .05 were considered statistically significant.
Results
There was substantial variability in red blood cell size among the 3635 participants, with a median RDW value of 14.0% (range, 11.3%–32.9%). Greater variability was associated with increased age, higher multi-comorbidity score, lower PASE scores, and lower hemoglobin level (Table 1, all p < .001). About 1 in 5 participants (714, 19.6%) were anemic at baseline.
Table 1.
Baseline Characteristics of Study Participants by Category of RDW
| RDW (≤ 13.0%) (n = 370) | RDW (13.1–14.0%) (n = 1449) | RDW (14.1–15.0%) (n = 1120) | RDW (≥ 15.1%) (n = 696) | p Trend | |
|---|---|---|---|---|---|
| RDW, % | 12.8 (12.6–12.9) | 13.6 (13.3–13.8) | 14.5 (14.2–14.7) | 15.8 (15.3–16.6) | |
| Age, years | 78.2 (5.0) | 78.8 (4.9) | 79.4 (5.2) | 79.9 (5.4) | <.001 |
| Body mass index, kg/m2 | 26.9 (3.6) | 27.1 (3.7) | 27.0 (3.9) | 27.5 (4.3) | .05 |
| Hemoglobin, g/dL | 14.4 (1.2) | 14.4 (1.2) | 14.1 (1.3) | 13.3 (1.6) | <.001 |
| Anemia | 44 (11.9%) | 159 (11.0%) | 222 (19.8%) | 289 (41.5%) | |
| Numbers of comorbid conditions | |||||
| 0 | 165 (44.6%) | 625 (43.1%) | 418 (37.3%) | 232 (33.3%) | <.001 |
| 1 | 123 (33.2%) | 493 (34.0%) | 388 (34.6%) | 219 (31.5%) | |
| 2 | 50 (13.5%) | 215 (14.8%) | 187 (16.7%) | 143 (20.5%) | |
| ≥3 | 32 (8.6%) | 116 (8.0%) | 127 (11.3%) | 102 (14.7%) | |
| PASE score | 142.1 (66.9) | 134.2 (66.8) | 133.5 (71.6) | 119.3 (66.8) | <.001 |
Notes: PASE = Physical Activity Scale in Elderly; RDW = red cell distribution width. Data were provided as mean (SD) or median (min–max) for continuous values or N (%) for categorical values. P trends were calculated by linear regression analysis for continuous values and Cochran–Armitage test for categorical variables.
Functional Limitation and Frailty
The prevalence and severity of functional limitations at baseline rose progressively with greater variability in red cell size (Figure 1A; Supplementary Appendix Figure 2; p trend < .001). More than twice as many of those with the highest RDW values had functional limitations than in the lowest RDW category (32.5% vs 16.0%). Variability in red cell size was also associated with a greater likelihood of developing functional limitations during follow-up (Figure 1B), even after adjusting for age (p trend < .05) and body mass index, number of comorbidities, and PASE scores (Table 2, p trend < .05). Participants with greater variability in red cell size were almost 3 times more likely to be frail at baseline (30.3% in highest RDW group vs 11.3% in the lowest group, p trend < .001, Figure 1C). Those with the greatest variability were also more likely to die or become frail and were less likely to remain robust, than those with lower RDW (Figure 1D). These observations remained significant after adjusting for age (p trend < .05) and other confounders (Table 2, p trend < .05).
Figure 1.
Functional limitation and frailty status at baseline, and changes during follow-up, by red cell distribution width (RDW) category. (A) Proportion of participants with one or more functional limitations at baseline. (B) Development of one or more new functional limitations during follow-up in those (n = 2805) without any functional limitation at baseline. (C) Frailty status at baseline. (D) Proportion of participants who progressed to being intermediate, frail, or dead during follow-up in those (n = 857) who were robust at baseline.
Table 2.
Adjusted ORs for Outcomes and HRs for Mortality According to Each RDW Category
| Age-Adjusted Model | Age and Other Covariates*-Adjusted Model | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| OR/HR | 95% CI | p Value | p Trend | OR/HR | 95% CI | p Value | p Trend | ||
| Functional limitation | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | <.001 | 1.0 | Referent | <.001 | ||
| 13.1%–14.0% | 1.1 | 0.8–1.5 | .64 | 1.0 | 0.7–1.4 | .94 | |||
| 14.1%–15.0% | 1.5 | 1.1–2.0 | .02 | 1.3 | 0.9–1.8 | .11 | |||
| ≥15.1% | 2.6 | 1.9–3.5 | .00 | 2.0 | 1.4–2.8 | .00 | |||
| Progression of functional limitation | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | .01 | 1.0 | Referent | .02 | ||
| 13.1%–14.0% | 1.0 | 0.7–1.4 | .85 | 1.0 | 0.7–1.4 | .82 | |||
| 14.1%–15.0% | 1.2 | 0.8–1.7 | .45 | 1.2 | 0.8–1.7 | .36 | |||
| ≥15.1% | 1.5 | 1.0–2.3 | .05 | 1.4 | 0.9–2.1 | .14 | |||
| Frailty | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | <.001 | 1.0 | Referent | <.001 | ||
| 13.1%–14.0% | 1.5 | 1.0–2.2 | .04 | 1.4 | 0.9–2.2 | .11 | |||
| 14.1%–15.0% | 1.9 | 1.3–2.8 | .00 | 1.9 | 1.2–2.9 | .01 | |||
| ≥15.1% | 2.9 | 2.0–4.3 | .00 | 2.3 | 1.5–3.7 | .00 | |||
| Progression to “frail or die” | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | <.001 | 1.0 | Referent | <.001 | ||
| 13.1%–14.0% | 1.0 | 0.7–1.3 | .85 | 1.0 | 0.7–1.2 | .72 | |||
| 14.1%–15.0% | 1.3 | 1.0–1.7 | .07 | 1.2 | 0.9–1.6 | .13 | |||
| ≥15.1% | 2.0 | 1.5–2.7 | .00 | 1.9 | 1.4–2.5 | .00 | |||
| Recurrent falls | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | .001 | 1.0 | Referent | .02 | ||
| 13.1%–14.0% | 1.4 | 1.0–2.0 | .06 | 1.4 | 1.0–2.0 | .07 | |||
| 14.1%–15.0% | 1.5 | 1.1–2.2 | .02 | 1.4 | 1.0–2.1 | .06 | |||
| ≥15.1% | 1.9 | 1.3–2.8 | .00 | 1.7 | 1.1–2.5 | .01 | |||
| Recurrent hospitalizations | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | .001 | 1.0 | Referent | .02 | ||
| 13.1%–14.0% | 0.9 | 0.5–1.7 | .85 | 0.9 | 0.5–1.6 | .70 | |||
| 14.1%–15.0% | 1.4 | 0.8–2.4 | .25 | 1.2 | 0.7–2.1 | .51 | |||
| ≥15.1% | 1.9 | 1.1–3.4 | .03 | 1.5 | 0.8–2.6 | .20 | |||
| Mortality | |||||||||
| RDW | ≤13.0% | 1.0 | Referent | <.001 | 1.0 | Referent | |||
| 13.1%–14.0% | 1.0 | 0.8–1.2 | .93 | 1.0 | 0.8–1.2 | .93 | <.001 | ||
| 14.1%–15.0% | 1.2 | 1.0–1.5 | .03 | 1.2 | 1.0–1.5 | .05 | |||
| ≥15.1% | 1.7 | 1.4–2.1 | .00 | 1.7 | 1.4–2.0 | .00 |
Notes: CI = confidence interval; BMI = body mass index; HR = hazard ratio; OR = odd ratio; PASE = Physical Activity Scale in Elderly; RDW = red cell distribution width. Age-adjusted and fully adjusted ORs were estimated using logistic regression and HRs were calculated using the Cox-proportional hazard model for mortality.
*Further adjusted for other covariates of BMI, numbers of comorbidities, and PASE score.
Muscle Strength, Physical Performance, and Cognitive Function
Greater variability in red cell size at baseline was associated with weaker grip strength, poorer lower extremity power, slower gait speed and time to rise from a chair, and lower 3MS score (Figure 2). All of these associations remained significant after adjusting for age (p trend < .001) and other confounders (all p trend < .05). However, associations between RDW and changes in these measures during follow-up were not statistically significant.
Figure 2.
Muscle strength, physical performance, and cognitive function (mean ± SD) by red cell distribution width (RDW) group at baseline. (A) Grip strength (kg), (B) leg extension power (kg), (C) times for 5 chair-rises (s), (D) gait speed (m/s), and (E) cognitive function by modified Mini-Mental Status Examination (3MS).
Falls, Hospitalizations, Mortality, and Loss to Follow-up
Participants with greater variability in red cell size had a higher risk of falling, as well as having 2 or more falls, during follow-up (p trend < .001, Figure 3). Those with the greatest variability had a twofold greater risk of recurrent falls when compared with the group with the least variability (odds ratio = 2.1, 95% confidence interval 1.4–3.5). This association remained significant after adjusting for age (p trend < .001) and other confounders (p trend < .05, Table 2). Red cell distribution width was also significantly associated with the risks of hospitalization and recurrent hospitalizations (Figure 3) after adjusting for age (p trend < .001) and other confounders (p trend < .05, Table 2).
Figure 3.
Incident falls and hospitalizations by red cell distribution width (RDW) group. (A) Falls during 12 months of follow-up. (B) Proportions of participants with one or recurrent hospitalizations between 1 and 2 years of follow-up.
Those in the highest RDW category had twofold greater rates of total and cause-specific mortality than those in the lowest group (Figure 4, p trend <.001), even after adjusting for age (p < .001 for all) and other confounders (p < .001 for all, Table 2). The differences between RDW categories were greatest for deaths due to other (ie, noncardiovascular, noncancer) causes. Those with the greatest variability in red cell size had a 1.7-fold greater risk (odds ratio = 1.7, 95% confidence interval 1.1–2.7) of being lost to follow-up or terminating from the study when compared with those with the least variability.
Figure 4.
Kaplan–Meier survival curves with unadjusted hazard ratio (95% CI) by red cell distribution width (RDW) groups. (A) All-cause mortality, (B) cardiovascular mortality, (C) cancer mortality, and (D) other (noncardiovascular, noncancer) mortality.
Interactions Between Variability in Red Cell Size and Anemia
Significant associations between greater variability in red cell size and both baseline frailty (p = .001) and cognitive function (p = .01) were observed only in participants without anemia (Supplementary Appendix Figure 3). We found no significant interactions between RDW and anemia for any of the other outcomes.
Discussion
In this prospective study, we found that variation in red blood cell size, as measured by RDW values obtained from a CBC, was significantly associated with diverse aging-related conditions, including prevalent and incident functional limitation, presence and progression of frailty, low muscle power, poor physical performance, and impaired cognitive function, even after adjusting for chronological age and other aging-related confounders. Greater variability in red cell size was also associated with the risks of falls, hospitalization, and death. The present study extends previous reports that greater variability in red cell size is associated with many other aging-related diseases and mortality. Together with those findings, our results support the hypothesis that RDW may have a potential value as a marker for biological aging.
The underlying reasons for the associations between variability in red cell size and aging-related measures are not known. Clonal hematopoiesis, which is characterized by an overrepresentation of blood cells derived from a single clone, results from the accumulation of aging-related mutations in hematopoietic stem cells and leads to greater variation in red cell size (27). Clonal hematopoiesis is known to be associated with the risks of diseases that are representative of aging-related chronic diseases, such as diabetes, cardiovascular disease, and ischemic stroke (20,27), but has not been reported with the other aging-related outcomes included in our analysis. Additionally, cellular aging leads to changes in cell size due to alterations in the extracellular membrane and cytoskeleton (28). Chronic inflammation might also play a role, but the anemia associated with inflammation typically has a normal RDW (29,30). Iron and vitamin B12 deficiency influence variability in red cell size, but the association between RDW values and health-related outcomes appears to be independent of nutritional status (8). Genetic analyses have suggested the potential associations between specific diseases and variation of RDW value (13). Other studies might further examine whether genetic variants or accumulation of DNA damage partially accounts for changes in RDW with aging and its association with aging outcomes. Basic experiments would be worthwhile to identify the cellular mechanisms for increasing RDW with aging; they might discover new molecular targets for interventions to slow aging and reduce risks of the attendant diseases.
This study has several limitations. Our sample included predominantly Caucasian men, so our findings may not generalize to women or other ethnic groups. Additional studies should confirm the associations of RDW with aging-related conditions in other diverse populations. The somewhat greater loss to follow-up among those with higher RDW values may have limited our ability to detect associations with the development of new losses in muscle strength, physical performance, and cognitive function. Furthermore, other blood-based measures that were previously reported to have an association with poorer health-related outcomes in the older population including inflammatory markers were not available in the present study. Therefore, we could not evaluate the predictive value of RDW in the comparisons with those parameters.
RDW is part of a routine CBC and, therefore, it is already available for almost all patients. Our results suggest that the RDW value could be a simple gauge to identify patients at increased risk of many aging-related conditions and diseases. To confirm the role of RDW as a biomarker of aging, trials of interventions to slow aging or its pleiotropic effects should include RDW to determine whether those interventions also reduce RDW values.
Supplementary Material
Funding
The Osteoporotic Fractures in Men (MrOS) Study is supported by the National Institute on Aging, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Center for Advancing Translational Sciences, and the National Institutes of Health (NIH) Roadmap for Medical Research (AG027810, AG042124, AG042139, AG042140, AG042143, AG042145, AG042168, AR066160, and TR000128).
Conflict of Interest
The authors state that they have no conflicts of interest.
Author Contributions
Study design: K.M.K. and S.R.C. Study conduct: K.M.K., L.Y.L., and S.R.C. Data collection: J.A.C., K.E.E., E.S.O., and S.R.C. Data analysis: K.M.K. and L.Y.L. Data interpretation: K.M.K., L.Y.L., W.S.B., and S.R.C. Drafting manuscript: K.M.K., W.S.B., and S.R.C. Critical revision of the article for important intellectual content: All authors. Approving final version of the manuscript: All authors. K.M.K. and S.R.C. take responsibility for the integrity of the data analysis.
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