Abstract
Lifestyle changes have led to a rising incidence of sarcopenia, which adversely affects quality of life and health. Inflammation and malnutrition play key roles in its development. The hemoglobin-to-red cell distribution width ratio (HRR) is a blood marker reflecting these factors. This study explores the relationship between HRR and sarcopenia risk and its potential as a predictive tool. We utilized National Health and Nutrition Examination Survey datasets spanning 2011 to 2018, comprising 10,492 adults aged 20 years and older. Sarcopenia was defined according to standard criteria for muscle mass. HRR was divided into 4 quartile groups (Q1 to Q4). We conducted multivariate logistic regression to examine the relationship between HRR and the risk of sarcopenia. To assess potential nonlinear associations, restricted cubic spline models were applied. Additionally, subgroup analyses were carried out to test the stability of the results. We identified 931 cases of sarcopenia, representing 8.87% of all participants. Multivariable logistic regression showed that HRR as a continuous variable was significantly inversely associated with sarcopenia risk (Model 3: odds ratio [OR] = 0.39, 95% confidence interval: 0.24–0.66, P < .001). Compared with the Q1 group, the Q3 and Q4 groups had significantly lower risks of sarcopenia (Q3: OR = 0.78, P = .029; Q4: OR = 0.68, P = .002). The trend tests were statistically significant in all models (P for trend < .001). Restricted cubic spline analysis revealed no significant nonlinear relationship, indicating a linear inverse association between HRR and sarcopenia. Subgroup analysis revealed no significant interactions, supporting the robustness of the results. Lower HRR levels may be an independent risk factor for sarcopenia. HRR might contribute to the early recognition and risk assessment of populations prone to sarcopenia. Prospective investigations are essential to substantiate its predictive capacity and reveal the mechanisms involved.
Keywords: cross-sectional study, epidemiology, hemoglobin-to-red blood cell distribution width ratio, NHANES, sarcopenia
1. Introduction
Sarcopenia is an age-related geriatric syndrome characterized by a decline in muscle mass, muscle strength, and/or physical performance.[1] It is associated with increased risks of frailty, falls, disability, and even mortality, posing a significant public health burden.[2,3] Current data suggest that 10% to 27% of individuals over the age of 60 are affected by sarcopenia worldwide. With the accelerating global aging population, the number of people affected by sarcopenia is projected to reach 500 million by 2050.[4] Current diagnostic methods, such as dual-energy X-ray absorptiometry and electromyography, are often costly and technically demanding, limiting their use in primary care and community settings.[5,6] Consequently, identifying a practical, economical, and widely implementable biomarker for the early identification of sarcopenia is urgently required.
Recent studies have shown that chronic inflammation and malnutrition play critical roles in the pathogenesis of sarcopenia.[7,8] Several hematological markers derived from routine blood tests have been proposed to reflect an individual’s inflammatory status and nutritional condition. Among them, the hemoglobin-to-red cell distribution width ratio (HRR) has emerged as a novel indicator and has received increasing attention in recent years.[9,10] Red cell distribution width (RDW), which quantifies fluctuations in erythrocyte dimensions, is strongly associated with oxidative and inflammatory processes.[11,12] HRR integrates information from both parameters and has demonstrated significant predictive value for various age-related diseases, including cardiovascular diseases, chronic kidney disease, and cancer.[13–15] However, research investigating the link between HRR and sarcopenia is still scarce, and evidence from large-scale population-based studies is insufficient.
To address this research gap, we analyzed cross-sectional data from the 2011 to 2018 cycles of the National Health and Nutrition Examination Survey (NHANES) to explore the relationship between HRR and sarcopenia risk. We hypothesized that lower HRR levels are independently associated with an increased risk of sarcopenia, and that this association would remain significant even after adjusting for confounding factors.
2. Methods
2.1. Study population
This study was a retrospective cross-sectional analysis based on data from NHANES, a nationally representative epidemiological survey conducted by the National Center for Health Statistics, part of the U.S. Centers for Disease Control and Prevention. The NHANES database has been extensively described in previous studies.[16] Data for this analysis were sourced from 4 NHANES cycles covering the period 2011 to 2018. Ethical approval for NHANES was granted by the National Center for Health Statistics Ethics Review Board, and informed consent in writing was secured from all enrolled individuals (https://www.cdc.gov/nchs/nhanes/about/erb.html?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/irba98.htm).
The following standardized exclusion criteria were rigorously implemented during sample selection: participants under 20 years old; participants without data for sarcopenia assessment; individuals missing hemoglobin or RDW measurements;and pregnant individuals. Initially, 39,156 participants from the NHANES 2011 to 2018 cycles were included. After excluding 16,539 participants under 20 years old, 11,756 without sarcopenia assessments, 369 with incomplete hemoglobin or RDW data, and no pregnant individuals, the study ultimately analyzed 10,492 participants (Fig. 1).
Figure 1.
Flow chart of participants’ enrollment process. NHANES = National Health and Nutrition Examination Survey, HRR = hemoglobin-to-red cell distribution width ratio.
2.2. Definition of sarcopenia
In the NHANES program, appendicular skeletal muscle mass was measured using dual-energy X-ray absorptiometry, and all pregnant participants were excluded from the analysis.[17] According to the recommendations of the Foundation for the National Institutes of Health Sarcopenia Project, the skeletal muscle index was used to standardize skeletal muscle mass, calculated as appendicular skeletal muscle mass (kg) divided by body mass index (BMI, kg/m2).[18,19] Based on Foundation for the National Institutes of Health criteria, low skeletal muscle mass was defined as an skeletal muscle index of <0.789 for men or <0.512 for women. To more comprehensively assess sarcopenia, muscle strength was also considered in this study. Handgrip strength was used as an indicator of muscle strength, with recommended cutoffs of <26 kg for men and <16 kg for women. A diagnosis of sarcopenia was made only when both low skeletal muscle mass and reduced grip strength were present.
2.3. Measurement of HRR
In this study, the HRR was calculated using complete blood count data from the NHANES 2011 to 2018 cycles. Hemoglobin concentration (g/dL) and RDW (%) were measured using the Beckman Coulter DxH 900 automated hematology analyzer. HRR was calculated using the following formula: HRR = hemoglobin (g/dL)/RDW (%). Both parameters were obtained from the same blood sample to ensure consistency in measurement.
2.4. Covariates
The covariates included in the analysis covered various demographic and health behavior characteristics. Demographic information, including age, sex, race/ethnicity, educational attainment, and the poverty income ratio (PIR), was extracted from the NHANES demographic dataset. PIR, defined as the ratio of household income to the federal poverty threshold, was used to classify socioeconomic status into 3 groups: low income (<1.3), middle income (1.3–3.5), and high income (≥3.5). BMI was determined by dividing weight (kg) by height squared (m2) and categorized as underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), or obese (≥30 kg/m2). Smoking status was determined based on participants’ responses to the question “Do you now smoke cigarettes?” Alcohol consumption was categorized based on average daily intake over the past 12 months: nondrinkers, those who drank 1 to 3 drinks per day, and those who drank ≥4 drinks per day. Additionally, diagnoses of diabetes and hypertension were identified based on participants’ medical history records.
2.5. Statistical analyses
Means and standard deviations were used to describe continuous data, while categorical data were summarized as frequencies and proportions. The chi-square test and Student t test were applied to evaluate differences across groups. First, boxplots were generated to compare HRR levels between participants with and without sarcopenia. HRR was then categorized into quartiles to further explore its association with disease risk. We then used multivariate logistic regression models to analyze the association between HRR and the risk of sarcopenia, calculating the estimated odds ratios (ORs) and their 95% confidence intervals (CIs). Potential confounders were adjusted for stepwise. Model 1 was unadjusted; Model 2 was adjusted for age, sex, and race/ethnicity; and Model 3 was further adjusted for education level, PIR, smoking status, past-year alcohol drinking, BMI, diabetes mellitus, and hypertension. To investigate potential nonlinear dose–response relationships, restricted cubic spline regression models were employed, with knots placed at the 10th, 50th, and 90th percentiles of the log-transformed HRR distribution. In addition, stratified analyses and interaction tests were conducted across subgroups to assess the consistency and potential heterogeneity of the HRR–sarcopenia association.
Statistical computations were executed using R software version 4.4.1 (The R Foundation for Statistical Computing, Vienna, Austria), with significance defined as 2-tailed P-values under .05.
3. Results
3.1. Baseline characteristics of participants
Table 1 summarizes the baseline characteristics of 10,492 participants drawn from the NHANES 2011 to 2018 cycles, among whom 931 were diagnosed with sarcopenia, yielding a prevalence of 8.87%. Participants were divided into 4 groups based on HRR quartiles: Q1 (HRR ≤ 0.9779, n = 2629), Q2 (0.9779 < HRR ≤ 1.0775, n = 2630), Q3 (1.0775 < HRR ≤ 1.1679, n = 2619), and Q4 (HRR > 1.1679, n = 2614). Significant differences were observed among the 4 groups in terms of age, sex, race/ethnicity, education level, PIR, smoking status, past-year alcohol consumption, BMI, diabetes, and hypertension (all P-values < .05). Figure 2 presents a significant reduction in HRR levels among individuals with sarcopenia compared to non-sarcopenic subjects (P < .01).
Table 1.
Baseline characteristics of the study population stratifed according to HRR.
| Total (n = 10492) | Q1 (n = 2629) | Q2 (n = 2630) | Q3 (n = 2619) | Q4 (n = 2614) | P-value | |
|---|---|---|---|---|---|---|
| Age, n (%), yr | <.001 | |||||
| 20–40 | 5262 (50.15) | 1176 (44.73) | 1245 (47.34) | 1339 (51.13) | 1502 (57.46) | |
| ≥40 | 5230 (49.85) | 1453 (55.27) | 1385 (52.66) | 1280 (48.87) | 1112 (42.54) | |
| Gender, n%) | <.001 | |||||
| Male | 5159 (49.17) | 375 (14.26) | 870 (33.08) | 1627 (62.12) | 2287 (87.49) | |
| Female | 5333 (50.83) | 2254 (85.74) | 1760 (66.92) | 992 (37.88) | 327 (12.51) | |
| Race, n (%) | <.001 | |||||
| Mexican American | 1586 (15.12) | 360 (13.69) | 382 (14.52) | 407 (15.54) | 437 (16.72) | |
| Other Hispanic | 1098 (10.47) | 299 (11.37) | 291 (11.06) | 264 (10.08) | 244 (9.33) | |
| Non-Hispanic White | 3640 (34.69) | 554 (21.07) | 912 (34.68) | 1032 (39.40) | 1142 (43.69) | |
| Non-Hispanic Black | 2182 (20.80) | 974 (37.05) | 585 (22.24) | 394 (15.04) | 229 (8.76) | |
| Other race | 1986 (18.93) | 442 (16.81) | 460 (17.49) | 522 (19.93) | 562 (21.50) | |
| PIR, n (%) | <.001 | |||||
| <1.3 | 3449 (32.87) | 962 (36.59) | 844 (32.09) | 783 (29.90) | 860 (32.90) | |
| 1.3–3.5 | 3776 (35.99) | 971 (36.93) | 914 (34.75) | 942 (35.97) | 949 (36.30) | |
| ≥3.5 | 3267 (31.14) | 696 (26.47) | 872 (33.16) | 894 (34.14) | 805 (30.80) | |
| Smoking status, n (%) | .002 | |||||
| Every day | 4166 (39.71) | 1062 (40.40) | 1030 (39.16) | 996 (38.03) | 1078 (41.24) | |
| Some days | 1246 (11.88) | 310 (11.79) | 277 (10.53) | 311 (11.87) | 348 (13.31) | |
| Not at all | 5080 (48.42) | 1257 (47.81) | 1323 (50.30) | 1312 (50.10) | 1188 (45.45) | |
| Past-year alcohol drinking, n (%) | <.001 | |||||
| Nondrinker | 3120 (29.74) | 896 (34.08) | 862 (32.78) | 743 (28.37) | 619 (23.68) | |
| 1–3 drinks | 4608 (43.92) | 1186 (45.11) | 1162 (44.18) | 1178 (44.98) | 1082 (41.39) | |
| ≥4 drinks | 2764 (26.34) | 547 (20.81) | 606 (23.04) | 698 (26.65) | 913 (34.93) | |
| BMI, n (%) | <.001 | |||||
| Underweight | 182 (1.73) | 42 (1.60) | 50 (1.90) | 53 (2.02) | 37 (1.42) | |
| Normal weight | 3123 (29.77) | 663 (25.22) | 835 (31.75) | 834 (31.84) | 791 (30.26) | |
| Overweight | 3313 (31.58) | 694 (26.40) | 790 (30.04) | 863 (32.95) | 966 (36.95) | |
| Obese | 3874 (36.92) | 1230 (46.79) | 955 (36.31) | 869 (33.18) | 820 (31.37) | |
| Diabetes mellitus, n (%) | <.001 | |||||
| Yes | 777 (7.41) | 263 (10.00) | 195 (7.41) | 163 (6.22) | 156 (5.97) | |
| No | 9520 (90.74) | 2300 (87.49) | 2393 (90.99) | 2410 (92.02) | 2417 (92.46) | |
| Borderline | 195 (1.86) | 66 (2.51) | 42 (1.60) | 46 (1.76) | 41 (1.57) | |
| Hypertension, n (%) | <.001 | |||||
| Yes | 2477 (23.61) | 778 (29.59) | 590 (22.43) | 562 (21.46) | 547 (20.93) | |
| No | 8015 (76.39) | 1851 (70.41) | 2040 (77.57) | 2057 (78.54) | 2067 (79.07) | |
| Education level, n (%) | .023 | |||||
| <12th grade | 1908 (18.19) | 465 (17.69) | 468 (17.79) | 469 (17.91) | 506 (19.36) | |
| High school | 2292 (21.85) | 572 (21.76) | 523 (19.89) | 600 (22.91) | 597 (22.84) | |
| College or more | 6292 (59.97) | 1592 (60.56) | 1639 (62.32) | 1550 (59.18) | 1511 (57.80) | |
| Sarcopenia, n(%) | .212 | |||||
| Yes | 931 (8.87) | 254 (9.66) | 242 (9.20) | 221 (8.44) | 214 (8.19) | |
| No | 9561 (91.13) | 2375 (90.34) | 2388 (90.80) | 2398 (91.56) | 2400 (91.81) | |
| Hemoglobin (g/dL, mean ± SD) | 14.11 ± 1.54 | 12.34 ± 1.22 | 13.75 ± 0.71 | 14.62 ± 0.73 | 15.74 ± 0.86 | <.001 |
| RDW (%, mean ± SD) | 13.47 ± 1.36 | 14.84 ± 1.88 | 13.33 ± 0.65 | 13.03 ± 0.61 | 12.68 ± 0.59 | <.001 |
| HRR | 1.06 ± 0.16 | 0.85 ± 0.13 | 1.03 ± 0.03 | 1.12 ± 0.03 | 1.24 ± 0.06 | <.001 |
BMI = body mass index, HRR = hemoglobin-to-red blood cell distribution width ratio, PIR = family poverty income ratio, Q = quartile, RDW = red blood cell distribution width.
Figure 2.
Comparison of HRR concentration between sarcopenia and non-sarcopenia groups. HRR = hemoglobin-to-red cell distribution width ratio, **P < .01.
3.2. The relationship between HRR and sarcopenia
Logistic regression showed an negative relationship between HRR and sarcopenia risk, which persisted as statistically significant after controlling for confounding factors. According to Table 2, when treated as a continuous variable, HRR was significantly inversely associated with sarcopenia in Model 1 (OR = 0.55, 95% CI: 0.37–0.82, P = .003). This association remained robust after adjusting for additional covariates in Model 2 (OR = 0.27, 95% CI: 0.16–0.44, P < .001) and Model 3 (OR = 0.39, 95% CI: 0.24–0.66, P < .001). When HRR was categorized into quartiles, with Q1 (lowest quartile) as the reference, the Q2 group showed a significantly lower risk of sarcopenia in Model 2 (OR = 0.81, 95% CI: 0.67–0.99, P = .036), although the association did not reach statistical significance in Models 1 and 3. The Q3 group exhibited a significantly reduced risk in both Model 2 (OR = 0.65, 95% CI: 0.52–0.81, P < .001) and Model 3 (OR = 0.78, 95% CI: 0.62–0.98, P = .029). Similarly, the Q4 group demonstrated a consistent downward trend in risk across all models, with statistically significant associations in Model 2 (OR = 0.58, 95% CI: 0.46–0.74, P < .001) and Model 3 (OR = 0.68, 95% CI: 0.53–0.87, P = .002). Additionally, trend analysis indicated a significant linear trend between HRR quartiles and sarcopenia risk in all models (P for trend <.001), further supporting the conclusion that higher HRR levels may be associated with a reduced risk of sarcopenia.
Table 2.
Multivariate logistic regression analysis of HRR for risk of sarcopenia (n = 10,492).
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| HRR | 0.55 (0.37–0.82) | .003 | 0.27 (0.16–0.44) | <.001 | 0.39 (0.24–0.66) | <.001 |
| Q1 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Q2 | 0.95 (0.79–1.14) | .568 | 0.81 (0.67–0.99) | .036 | 0.93 (0.75–1.14) | .461 |
| Q3 | 0.86 (0.71–1.04) | .123 | 0.65 (0.52–0.81) | <.001 | 0.78 (0.62–0.98) | .029 |
| Q4 | 0.83 (0.69–1.01) | .061 | 0.58 (0.46–0.74) | <.001 | 0.68 (0.53–0.87) | .002 |
| P for trend | <.001 | <.001 | <.001 | |||
Model 1: no covariates were adjusted; Model 2: adjusted for age,gender and race; and Model 3: adjusted for age, gender, race, education level, PIR, smoking status, past-year alcohol drinking, BMI, diabetes mellitus, and hypertension.
BMI = body mass index, CI = confidence interval, HRR = hemoglobin-to-red blood cell distribution width ratio, OR = odds ratio, PIR = family poverty income ratio, Q = quartile.
Restricted cubic spline models were applied to examine the potential nonlinear association between HRR and sarcopenia risk. As shown in Figure 3A, the unadjusted model revealed a significant overall relationship (P for overall = .023), with no evidence of a nonlinear pattern (P for nonlinear = .705). After adjusting for confounders (Fig. 3B), the association remained statistically significant (P for overall = .004), while the nonlinear component remained nonsignificant (P for nonlinear = .561). These results suggest a likely linear inverse association between HRR and sarcopenia risk.
Figure 3.
Odds ratio of sarcopenia according to HRR levels in the overall population. The solid line represents the odds ratio for HRR; the shaded area indicates the 95% confidence interval. (A) No covariates adjusted, (B) all covariates adjusted. HRR = hemoglobin-to-red cell distribution width ratio.
3.3. Subgroup analysis
The results of subgroup analyses demonstrated a consistent association between HRR and sarcopenia risk across different populations. Stratified analyses and interaction tests were performed for variables including age, sex, race/ethnicity, education level, PIR, smoking status, past-year alcohol consumption, BMI, diabetes, and hypertension status. As illustrated in Figure 4, all interaction terms yielded P-values above .05, suggesting that these variables did not substantially alter the HRR–sarcopenia association (Fig. 4), thereby further confirming the robustness and generalizability of this association.
Figure 4.
The relationship between HRR and risk of sarcopenia across subgroups. BMI = body mass index, CI = confidence interval, HRR = hemoglobin-to-red cell distribution width ratio, OR = odds ratio, PIR = family poverty income ratio.
4. Discussion
This study is the first to investigate the association between HRR and sarcopenia risk. The results demonstrated that Individuals affected by sarcopenia exhibited significantly reduced HRR levels in comparison to non-sarcopenic participants. Multivariable logistic regression and restricted cubic spline analyses both suggested a potential linear inverse relationship between HRR levels and sarcopenia risk. Further subgroup analyses found no significant interactions, confirming the robustness of this association.
Although there are no direct studies on the relationship between HRR and sarcopenia to date, existing literature has highlighted the protective value of HRR in various diseases. For example, a study using Kendall tau-b correlation analysis found that HRR was negatively associated with frailty in elderly patients with coronary heart disease (K = –0.296, P < .001), consistent with HRR reflecting inflammation and nutritional status.[20] Additionally, pulmonary function indicators such as FVC, FEV1, and PEF were found to significantly improve with increasing HRR levels.[21] Research by Yongchun Xiao et al also demonstrated a linear positive correlation between HRR and femoral bone mineral density.[22] Although these studies focus on other diseases or physiological states, they collectively underscore the potential value of HRR in disease risk assessment and health prediction. Recent studies have begun to explore the role of HRR and related hematological markers in frailty and muscle dysfunction, which share overlapping mechanisms with sarcopenia. For instance, Kinoshita et al reported that a lower hemoglobin-to-RDW ratio was significantly associated with physical frailty in older Japanese outpatients.[23] Cheng et al analyzed data from both U.S. and Chinese populations and found that hematological and inflammatory indices, including HRR, were linked to sarcopenia risk.[24] Picca et al identified overlapping biomarkers of inflammation and oxidative stress in frailty and sarcopenia through systematic review.[25] Furthermore, Ma et al revealed that the RDW/albumin ratio was a strong predictor of mortality in individuals with sarcopenic obesity, further supporting the involvement of red cell variability and nutritional markers in muscle health.[26] These studies underscore the growing recognition of hematological indicators, including HRR, as potential predictors of sarcopenia and related conditions.
Inflammation and malnutrition are key mechanisms in the development of sarcopenia.[7,8] The HRR combines hemoglobin levels and RDW to reflect oxygen delivery capacity and erythrocyte heterogeneity, indirectly indicating an individual’s nutritional and inflammatory status.[21] A decrease in hemoglobin may indicate underlying anemia or malnutrition, leading to insufficient oxygen supply to skeletal muscle tissue, thereby impairing muscle synthesis and functional maintenance.[27]
Conversely, elevated RDW is commonly associated with chronic inflammation, oxidative stress, and bone marrow stress responses, all of which are key mechanisms in sarcopenia pathogenesis.[28–30] Research suggests that chronic low-grade inflammation accelerates the degradation of muscle proteins while suppressing their production, ultimately leading to declines in both muscle mass and strength.[31] Additionally, increased RDW may be related to deficiencies in iron, vitamin B12, or folate—nutrients crucial for erythropoiesis and muscle metabolism.[32–34] Therefore, a lower HRR may reflect a state of inflammation activation, inadequate oxygen supply, and nutritional imbalance, which can promote the occurrence and progression of sarcopenia through multiple pathological pathways.
Given that HRR is a readily available parameter from routine blood tests, characterized by simplicity, low cost, and ease of implementation, its potential utility in early risk assessment of sarcopenia warrants further investigation. In the future, HRR could be incorporated into sarcopenia screening tools, particularly suitable for resource-limited community settings or elderly populations.
A major strength of this study lies in its novelty, as it is the first to uncover the link between HRR and sarcopenia risk. Additionally, it is based on a nationally representative US database with a large sample size, ensuring good external validity. Nevertheless, some limitations should be acknowledged. First, the cross-sectional design prevents establishing causality. Second, the diagnosis of sarcopenia was based on muscle mass measurements, which may have inherent measurement errors. Moreover, despite being an integrated marker, HRR is susceptible to interference from factors like anemia and systemic inflammation. Third, although several major covariates were adjusted for, some known factors associated with sarcopenia, such as physical inactivity, rheumatoid arthritis, and chronic obstructive pulmonary disease (COPD), were not included in the analysis due to data availability and missingness in NHANES, which may have resulted in residual confounding. Fourth, the data used in this study were derived exclusively from a U.S. population, which may limit the generalizability of the findings to populations with different racial, ethnic, or cultural backgrounds. Future longitudinal studies are warranted to further validate HRR’s predictive value for sarcopenia development and to explore its interactions with inflammatory and nutritional markers.
5. Conclusion
This study found that lower HRR levels were significantly associated with an increased risk of sarcopenia. As a convenient, cost-effective indicator obtainable from routine blood tests, HRR may serve as a potential auxiliary screening tool for early identification of populations at risk of sarcopenia. Prospective studies are warranted to further validate these findings and to explore the clinical utility of HRR in practice.
Acknowledgments
We sincerely thank all the participants for their valuable contributions to this study.
Author contributions
Data curation: Weijun Zhang.
Writing – original draft: Weijun Zhang, Kun Li.
Writing – review & editing: Weijun Zhang, Kun Li.
Abbreviations:
- BMI
- body mass index
- CIs
- confidence intervals
- HRR
- hemoglobin-to-red cell distribution width ratio
- NHANES
- National Health and Nutrition Examination Survey
- ORs
- odds ratios
- PIR
- poverty income ratio
- RDW
- red cell distribution width
The study protocol (Protocol number: Protocol #2011-17 and Protocol #2018-01) was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB) (https://www.cdc.gov/nchs/nhanes/about/erb.html?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/irba98.htm). The Institutional Review Board of Hubei Provincial Hospital of Traditional Chinese Medicine confirmed that no additional ethical approval was required for this study, as the NHANES dataset is publicly available and contains no identifiable personal information.
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.
How to cite this article: Zhang W, Li K. Association of lower hemoglobin-to-RDW ratio with sarcopenia: Insights from a nationally representative NHANES 2011–2018 study in the United States. Medicine 2026;105:8(e47756).
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