ABSTRACT
This study aimed to investigate the relationship between the hemoglobin‐to‐red blood cell distribution width (RDW) ratio (HRR), a composite marker of inflammation and oxidative stress, and arterial stiffness. A total of 3657 participants from Health examination center, the Department of General Practice and Geriatrics at the First Affiliated Hospital of Fujian Medical University were included in a cross‐sectional analysis conducted between January 2016 and December 2023. Arterial stiffness was defined as a carotid‐femoral pulse wave velocity (cfPWV) of ≥10 m/s. HRR was calculated by dividing the hemoglobin concentration by the RDW. Participants were categorized into quartiles (Q1–Q4) based on their HRR values. Associations between HRR and arterial stiffness were evaluated using linear regression analysis, logistic regression models, stratified analyses, and restricted cubic splines (RCS) to identify potential non‐linear associations. Age and cfPWV increased significantly across decreasing HRR quartiles. In a fully adjusted model, compared with Q1, participants in Q3 (OR 0.95, 95% CI: 0.91–0.99, p = 0.024) and Q4 (OR 0.93, 95% CI: 0.88–0.97, p < 0.001) exhibited a progressive reduction in arterial stiffness. RCS analysis revealed a linear association between HRR and arterial stiffness. Stratified analysis indicated a stronger inverse association between higher HRR and lower arterial stiffness in individuals with diabetes or hypertension. This study offers additional evidence that supports the role of inflammation and oxidative stress in arterial stiffness.
Keywords: arterial stiffness, carotid‐femoral pulse wave velocity, hemoglobin‐to‐red blood cell distribution width ratio, inflammation
1. Introduction
Cardiovascular disease, a leading global cause of mortality and morbidity, imposes substantial economic burdens on patients, families, and healthcare systems [1]. Arterial stiffness, characterized by reduced vascular elasticity, represents a key contributor to cardiovascular disease and predicts adverse clinical outcomes [2]. Although multiple methods exist for assessing arterial stiffness, carotid‐femoral pulse wave velocity (cfPWV) remains the gold‐standard measurement [3]. Clinically, it is well established that cfPWV is correlated to some cardiovascular risk factors including aging, obesity, hypertension in our previous study [4, 5]. Pathophysiologically, arterial stiffness involves interrelated mechanisms, including chronic inflammation, oxidative stress, and metabolic dysregulation [6, 7, 8].
Chronic inflammation has been established as a causal mechanism underlying vascular stiffness [9]. Systemic inflammation is routinely assessed using biomarkers such as high‐sensitivity C reactive protein (hs‐CRP) and neutrophil to lymphocyte ratio (NLR) [10, 11]. Recently, hemoglobin‐to‐red blood cell distribution width (RDW) ratio (HRR) has emerged as a novel inflammatory indicator [12]. Lower HRR correlates with heightened inflammatory states. As HRR is derived from routine complete blood count parameters, this metric is readily obtainable even in community healthcare settings. Systemic inflammation theoretically disrupts red blood cell (RBC) production, maturation, and function. Elevated RDW is mechanistically associated with increased pro‐inflammatory cytokines, for example, interleukin‐6 (IL‐6), tumor necrosis factor‐alpha (TNF‐α), which promote endothelial dysfunction, collagen deposition, and elastin degradation—core pathological processes in arterial stiffening [13, 14, 15, 16]. Moreover, oxidative stress is well‐established driver of arterial stiffness, and elevated hemoglobin levels may attenuate this process through iron accumulation or via hemoglobin's intrinsic antioxidant properties [17]. Furthermore, elevated HRR reflects improved oxygen transport capacity of RBCs and attenuated systemic inflammation [18]. Clinically, lower HRR demonstrates consistent associations with higher risks for stroke, cancer, and heart failure [19, 20, 21]. However, the relationship between HRR and arterial stiffness remains unestablished. This study therefore aimed to elucidate the association between HRR and arterial stiffness.
2. Methods
2.1. Statement of Ethics
This single‐center cross‐sectional study was conducted in subjects from the database of the research (Fuzhou Study): Target organ damage and related risk factors in the patients with hypertension (registered number: ChiCTR2000039448 (28/10/2020), URL:http://www.chictr.org.cn/index.aspx), a clinical study conducted in Fuzhou city of China. The present study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Approval no.[2020]306).
2.2. Subjects
Subjects aged ≥ 18 years were recruited from Health examination center, the Department of General Practice, and Geriatrics at the First Affiliated Hospital of Fujian Medical University between January 2016 and December 2023. Inclusion criteria were as follows: (1) Outpatient: Individuals attending regular follow‐ups for hypertension or diabetes, or seeking screening for target organ damage related to these conditions. (2) Health examination center: Adults undergoing routine health check‐ups who volunteered to participate in the research after being informed of the study objectives. (3) Inpatient: Patients hospitalized for the stable management of chronic conditions including coronary heart disease, peptic ulcer disease, Alzheimer's disease, and renal cysts with no acute exacerbations at the time of enrollment.
A total of 5886 subjects who underwent cfPWV measurement were initially enrolled. According to the predefined exclusion criteria, 2229 subjects were excluded according to the following criteria: (1) History of acute cardiovascular or cerebrovascular events within 6 months, such as acute coronary syndrome, cerebral infarction, cerebral hemorrhage, or transient ischemic attack (n = 574); (2) Congestive heart failure, severe arrhythmias, hypertrophic obstructive cardiomyopathy, valvular heart disease, or restrictive cardiomyopathy (n = 67); (3) Serum creatinine > 2.5 mg/dL (1 mg/dL = 88.4 umol/L), serum aminotransferase levels > 3 × upper limit of normal, malignancy, autoimmune diseases, acute infectious diseases, current pregnancy, and use of folic acid and vitamin B12 (n = 208); (4) Missing HRR data (n = 1380). Consequently, 3657 participants were included in the final analysis (Figure 1).
FIGURE 1.
The flow chart of the study. cfPWV indicates carotid‐femoral pulse wave velocity; HRR, hemoglobin to red blood cell distribution width ratio.
2.3. Demographic and Clinical Data
Self‐reported data on chronological age, biological sex, tobacco use, and alcohol consumption were collected. Height and body weight were measured using calibrated instruments with participants wearing light clothing without shoes. Smokers were defined as participants who smoked ≥ 1 cigarette/day for ≥ 12 consecutive months [22]. Alcohol consumers were categorized based on their drinking frequency, with individuals who consumed alcohol > 1 occasion/month defined as drinkers [23]. Body mass index (BMI) was calculated using the formulas: BMI = body weight (kg)/[height (m)]2. Blood pressure was measured three times using an automated oscillometric device (HBP‐1300; Omron Healthcare, Kyoto, Japan) after ≥ 5 min of seated rest, with three consecutive measurements taken and the average value was recorded. Mean arterial pressure (MAP) was calculated using the formulas:MAP = [(systolic blood pressure (SBP) + 2 × diastolic blood pressure (DBP)]/3.
2.4. Laboratory Measurement
Venous blood samples were obtained after overnight fasting (≥ 8 h). Complete blood count analysis were performed using standardized methodologies. Hemoglobin and RDW values were obtained as part of the automated complete blood count utilizing an ADVIA 2120 (Siemens, Erlangen, Germany). Biochemical parameters including fasting plasma glucose (FPG), albumin, total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), triglyceride, creatinine, were quantified on an automated analyzer (Siemens, ADVIA2400, USA). Glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease (MDRD) formula: estimated GFR (eGFR) [mL/(min·1.73 m2)] = 186 × [creatinine (mmol/L)/88.41]−1.154 × (Age)−0.203, and the adjustments of the equation were 1 for males and 0.742 for females. Glycosylated hemoglobin A1c (HbA1c) was quantified by high‐performance liquid chromatography using VariantTM II system (Bio‐Rad Laboratories, Hercules, CA, USA). Urinary creatinine concentration was detected by colorimetry assay (Boehringer Mannheim/Hitachi 717 analyzer), and urinary albumin was determined by immunoturbidimetry method (Roche P800 automatic analyzer). The value of urinary albumin‐to‐creatinine ratio (UACR) was obtained by calculating the ratio of urinary albumin (mg) to urinary creatinine (g).
2.5. Measurement of cfPWV
cfPWV was measured by Complior Analyzer (Alam Medical, Saint Quentin Fallavier, France) with the participants in supine position [24, 25, 26]. The subject rested for 10 min. The distance between the right common carotid artery and the right common femoral artery was measured using a tape. The baroreceptor was then placed in the positions of the two arteries and properly fine‐tuned until the system displayed correct pulse wave shape. Eight consecutive cardiac cycles were observed at each measurement, which was repeated twice, and the average value was recorded. According to the formula: PWV = d/t (m/s), where d represents the adjusted distance and t represents the time delay between the carotid and femoral pulse waves, cfPWV was calculated. The recorded distance (d) was calculated as the measured distance multiplied by 0.8 [27]. Arterial stiffness was defined as cfPWV ≥ 10 m/s.
2.6. Definition
Hypertension was defined as blood pressure ≥ 140/90 mmHg, use of antihypertensive medications or a self‐reported history of hypertension [28]. Diabetes was defined as the use of antihyperglycemic medications or establishing a new diagnosis of diabetes [29].
HRR was defined as hemoglobin concentration (g/dL) divided by RDW (%). The study participants were categorized into quartiles based on their HRR values: the lowest quartile (Q1) was defined as HRR < 9.57, the second quartile (Q2) as 9.57 ≤ HRR < 10.55, the third quartile (Q3) as 10.55 ≤ HRR < 11.50, and the highest quartile (Q4) as HRR ≥ 11.50.
2.7. Statistical Analysis
Missing values including FPG, triglyceride, TC, LDL‐C, HDL‐C, and BMI were imputed using a random forest multiple imputation method in R Studio via the “mice” package. Continuous variables with a normal distribution were presented as means ± standard deviation (SD), while those with non‐normal distribution were expressed as medians and interquartiles range (IQR). Categorical variables were presented as percentages. Clinical characteristics among groups were compared using one‐way ANOVA or Kruskal–Wallis test for continuous variables, and Chi‐square test for categorical variables. Linear regression analysis was used to determine the independent variables related to cfPWV. Continuous variables were transformed by z‐score for logistic analysis. Logistic regression was performed to calculate odds ratio (OR) with 95% CI for arterial stiffness. Restricted cubic splines (RCS) model was used to assess whether a non‐linear relationship existed. Statistical analyses were performed using SPSS statistical software (version 22.0, USA) and R software (version 4.3.3, available at http://www.R‐project.org.). p < 0.05 was considered statistically significant.
3. Results
3.1. Clinical Characteristics
A cross‐sectional analysis was conducted on 3657 participants with a mean age of 59.6 ±12.0 years, including 2294 males (62.7%). Table 1 summarizes the clinical characteristics of the study population categorized by quartiles of HRR. The participants in the lower HRR quartiles were more likely to be female, nonsmoker, nondrinker, and exhibited higher levels of age, cfPWV, while presenting lower levels of TC, triglyceride, LDL‐C, eGFR. A lower prevalence of diabetes was observed in the lower HRR quartiles. Notably, a significant decreasing trend in arterial stiffness was observed across quartiles (Q1 to Q4: 30.0% vs. 25.0% vs. 24.5% vs. 20.5%, χ 2 = 35.88, p < 0.001) (Figure 2).
TABLE 1.
Clinical characteristics of the participants by HRR quartiles.
| Q1 (HRR<9.57) | Q2 (9.57 ≤ HRR < 10.55) | Q3 (10.55 ≤ HRR < 11.50) | Q4 (HRR ≥ 11.50) | X 2/F/Z | p | |
|---|---|---|---|---|---|---|
| n | 915 | 914 | 917 | 911 | ||
| Age (years) | 64.1 ± 12.4 | 60.8 ± 10.7 a | 59.0 ± 11.2 a , b | 54.3 ± 11.7 a , b , c | 113.81 | <0.001 |
| Gender | ||||||
| Male (%) | 337 (36.8) | 503 (55.0) a | 648 (70.7) a , b | 806 (88.5) a , b , c | 568.63 | <0.001 |
| Smoker (%) | 138 (15.1) | 190 (20.8) a | 251 (27.4) a , b | 258 (28.3) a , b | 61.57 | <0.001 |
| Drinker (%) | 71 (7.8) | 114 (12.5) a | 165 (18.0) a , b | 171 (18.8) a , b | 59.67 | <0.001 |
| BMI (kg/m2) | 24.2 ± 3.3 | 25.0 ± 3.3 a | 25.0 ± 4.4 a | 25.4 ± 3.0 a , b , c | 23.97 | <0.001 |
| Systolic blood pressure (mm Hg) | 134.2 ± 21.0 | 132.2 ± 19.0 | 133.4 ± 19.2 | 134.3 ± 17.9 | 2.26 | 0.087 |
| Diastolic blood pressure (mm Hg) | 76.0 ± 12.3 | 79.5 ± 11.0 a | 81.2 ± 11.6 a , b | 84.6 ± 11.9 a , b , c | 86.13 | <0.001 |
| Hypertension (%) | 621 (67.9) | 576 (63.0) | 590 (64.3) | 572 (62.8) | 6.78 | 0.086 |
| Diabetes (%) | 341 (37.3) | 298 (32.6) | 266 (29.0) a | 211 (23.2) a , b | 45.77 | <0.001 |
| Arterial stiffness (%) | 388 (42.2) | 323 (35.3) a | 316 (34.5) a | 265 (29.1) a | 35.88 | <0.001 |
| Fasting plasma glucose (mmol/L) | 5.1 (4.6, 6.0) | 5.3 (4.8, 6.1) a | 5.1 (4.6, 6.0) a | 5.4 (4.9, 6.2) a , b , c | 51.96 | <0.001 |
| HbA1c (%) | 6.4 ± 1.5 | 6.2 ± 1. 3 a | 6.2 ± 1.4 a | 6.1 ± 1.5 a , b , c | 53.14 | <0.001 |
| Triglyceride (mmol/L) | 1.1 (0.8, 1.6) | 1.3 (0.9, 1.8) a | 1.4 (1.0, 1.9) a , b | 1.5 (1.1, 2.1) a , b , c | 130.85 | <0.001 |
| Total cholesterol (mmol/L) | 4.4 (3.6, 5.2) | 4.6 (3.8, 5.3) a | 4.6 (3.9, 5.3) a | 4.8 (4.0, 5.4) a , b | 47.43 | <0.001 |
| LDL‐C (mmol/L) | 2.7 (2.0, 3.4) | 2.8 (2.2, 3.5) a | 2.9 (2.3, 3.7) a | 3.1 (2.5, 3.8) a , b , c | 88.44 | <0.001 |
| HDL‐C (mmol/L) | 1.2 (1.0, 1.5) | 1.2 (1.0, 1.5) | 1.2 (1.0, 1.4) | 1.2 (1.0, 1.4) b | 9.15 | <0.001 |
| eGFR (mg/min/1.73 m2) | 95.9 (81.6, 108.1) | 99.5 (89.9, 110.9) a | 100.1 (89.7, 111.4) a | 99.8 (90.0, 109.4) a | 44.75 | <0.001 |
| UACR (mg/g) | 8.24 (4.66, 19.35) | 7.01 (4.45, 14.58) a | 7.13 (4.35, 16.36) a | 7.28 (4.34, 16.46) a | 13.05 | 0.005 |
| cfPWV (m/s) | 10.2 ± 3.9 | 9.6 ± 2.6 a | 9.6 ± 2.5 a | 9.3 ± 2.2 a , b , c | 15.88 | <0.001 |
| Antihypertensive medications (%) | 387 (42.3) | 385 (42.1) | 402 (43.8) | 386 (42.4) | 0.70 | 0.874 |
| Antidiabetic medications (%) | 182 (19.9) | 161 (17.6) | 165 (18.0) | 128 (14.1) a , c | 11.32 | 0.010 |
| Lipid‐lowering medications (%) | 274 (30.0) | 286 (31.3) | 284 (31.1) | 276 (30.3) | 0.52 | 0.912 |
Abbreviations: BMI, body mass index; cfPWV, carotid‐femoral pulse wave velocity; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; HRR, hemoglobin to red blood cell distribution width ratio; LDL‐C, low‐density lipoprotein cholesterol; UACR, urinary albumin‐to‐creatinine ratio.
p < 0.05 versus Q1 group.
p < 0.05 versus Q2 group.
p < 0.05 versus Q3 group.
FIGURE 2.
The prevalence of arterial stiffness in the participants by HRR quartiles. HRR indicates hemoglobin to red blood cell distribution width ratio; Q, quartile. a p < 0.05 versus Q1 group.
Hypertension and diabetes were significantly more prevalent in the arterial stiffness group, compared to the non‐arterial stiffness group (p < 0.001). Medication use was also significantly higher in the arterial stiffness group, including antihypertensives (52.6% vs 37.3%), antidiabetic medications (25.9% vs 12.7%), and lipid‐lowering medications (36.2% vs 27.7%) (all p < 0.001) (Table S1).
3.2. Univariate and Multivariate Linear Regression Analysis of cfPWV
As shown in Table 2, after adjustment for HRR, age, gender, smoking status, MAP, TC, LDL‐C, HDL‐C, eGFR, HbA1c, UCAR, the following variables were independently associated with cfPWV: HRR (β: −0.12, 95% CI: −0.17 to −0.06, p < 0.001), age (β: 0.08, 95% CI: 0.07–0.08, p < 0.001), female (β: −0.41, 95% CI: −0.60 to −0.21, p < 0.001), MAP (β: 0.05, 95% CI: 0.04–0.05, p < 0.001), eGFR (β: −0.01, 95% CI: −0.01 to −0.01, p < 0.001), HbA1c (β: 0.25, 95% CI: 0.19–0.31, p < 0.001), UACR (β: 0.01, 95% CI: 0.01–0.01, p < 0.001).
TABLE 2.
Univariate linear analysis and multivariate linear analysis of relative factors for cfPWV (n = 3657).
| Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|
| Variables | β (95% CI) | p | β (95% CI) | p |
| HRR | −0.19 (−0.24, −0.13) | <0.001 | −0.12 (−0.17, −0.06) | <0.001 |
| Age | 0.08 (0.07, 0.09) | <0.001 | 0.08 (0.07, 0.08) | <0.001 |
| BMI | 0.01 (−0.01, 0.04) | 0.305 | — | — |
| Gender | ||||
| Male | ref | ref | ||
| Female | −0.34 (−0.53, −0.15) | <0.001 | −0.41 (−0.60, −0.21) | <0.001 |
| Smoking status | ||||
| Nonsmoker | ref | — | ||
| Smoker | 0.12 (−0.10, 0.34) | 0.301 | — | — |
| Mean arterial pressure | 0.04 (0.03, 0.04) | <0.001 | 0.05 (0.04, 0.05) | <0.001 |
| Triglyceride | 0.05 (−0.02, 0.11) | 0.185 | — | — |
| Total cholesterol | −0.14 (−0.22, −0.06) | <0.001 | 0.04 (−0.13, 0.21) | 0.671 |
| LDL‐C | −0.14 (−0.23, −0.05) | 0.003 | −0.00 (−0.19, 0.18) | 0.969 |
| HDL‐C | −0.61 (−0.85, −0.36) | <0.001 | −0.20 (−0.46, 0.06) | 0.131 |
| eGFR | −0.03 (−0.03, −0.02) | <0.001 | −0.01 (−0.01, −0.01) | <0.001 |
| HbA1C | 0.35 (0.29, 0.41) | <0.001 | 0.25 (0.19, 0.31) | <0.001 |
| UACR | 0.01 (0.01,0.01) | <0.001 | 0.01 (0.01, 0.01) | <0.001 |
Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; HRR, hemoglobin to red blood cell distribution width ratio; LDL‐C, low‐density lipoprotein cholesterol; UACR, urinary albumin‐to‐creatinine ratio.
3.3. Univariate and Multivariate Logistic Analyses of Relative Factors for Arterial Stiffness
As shown in Table 3, after adjustment for HRR, age, gender, smoking status, MAP, TC, LDL‐C, HDL‐C, eGFR, HbA1c, UCAR, the following variables were independently associated with arterial stiffness: HRR (OR: 0.88, 95% CI: 0.81–0.96, p = 0.003), age (OR: 2.25, 95% CI: 2.04–2.48, p < 0.001), female (OR: 0.71, 95% CI: 0.59–0.84, p < 0.001), MAP (OR: 0.99, 95% CI: 0.99–0.99, p = 0.035), eGFR (OR: 0.88, 95% CI: 0.81–0.96, p < 0.001), HbA1c (OR: 1.39, 95% CI: 1.29–1.51, p < 0.001), UACR (OR: 1.10, 95% CI: 1.01–1.19, p = 0.034).
TABLE 3.
Univariate logistic analysis and multivariate logistic analysis of relevant factors for arterial stiffness (n = 3657).
| Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|
| Variables | 0R (95% CI) | p | 0R (95% CI) | p |
| HRR | 0.81 (0.75, 0.86) | <0.001 | 0.88 (0.81, 0.96) | 0.003 |
| Age | 2.17 (2.00, 2.35) | <0.001 | 2.25 (2.04, 2.48) | <0.001 |
| Gender | ||||
| Male | ref | ref | ||
| Female | 0.77 (0.67, 0.89) | <0.001 | 0.71 (0.59, 0.84) | <0.001 |
| Smoking status | ||||
| Nonsmoker | ref | — | ||
| Smoker | 0.99 (0.96, 1.03) | 0.760 | — | — |
| BMI | 1.01 (0.95, 1.08) | 0.702 | — | — |
| Mean arterial pressure | 1.45 (1.35, 1.55) | <0.001 | 0.99 (0.99, 0.99) | 0.035 |
| Triglyceride | 1.03 (0.96, 1.10) | 0.423 | — | — |
| Total cholesterol | 0.86 (0.80, 0.92) | <0.001 | 0.94 (0.79, 1.13) | 0.521 |
| LDL‐C | 0.89 (0.83, 0.95) | 0.001 | 1.08 (0.91, 1.29) | 0.361 |
| HDL‐C | 0.82 (0.76, 0.89) | <0.001 | 0.92 (0.84, 1.01) | 0.080 |
| eGFR | 0.62 (0.58, 0.67) | <0.001 | 0.88 (0.81, 0.96) | <0.001 |
| HbA1C | 1.46 (1.36, 1.57) | <0.001 | 1.39 (1.29, 1.51) | <0.001 |
| UACR | 1.17 (1.07, 1.27) | <0.001 | 1.10 (1.01, 1.19) | 0.034 |
Note: Continuous variables were transformed by z‐score for logistic analysis.
Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; HRR, hemoglobin to red blood cell distribution width ratio; LDL‐C, low‐density lipoprotein cholesterol; UACR, urinary albumin‐to‐creatinine ratio.
3.4. Relationship Between HRR and Arterial Stiffness
The association between HRR and arterial stiffness is presented in Table 4. A negative correlation was observed between HRR quartiles and arterial stiffness, which persisted in both unadjusted and fully adjusted models. Specifically, the OR for each increase in quartile was 0.95 (95% CI: 0.93–0.96, p < 0.001) in the unadjusted model and 0.97 (95% CI: 0.95–0.99, p < 0.001) in the fully adjusted model. In the unadjusted model (Table 4, Model 1), compared with participants in Q1 for arterial stiffness, the ORs for Q2, Q3, and Q4 were 0.93 (95% CI: 0.89–0.97, p = 0.002), 0.92 (95% CI: 0.88–0.96, p < 0.001), and 0.88 (95% CI: 0.84–0.91, p < 0.001), respectively. After full adjustment for covariates including age, gender, smoking status, MAP, HDL‐C, eGFR, HbA1c, and UCAR (Table 4, Model 3), Q3 had an OR of 0.95 (95% CI: 0.91–0.99, p = 0.024) and Q4 had an OR of 0.93 (95% CI: 0.88–0.97, p < 0.001) compared to Q1. These data underscore the independent association between HRR and arterial stiffness.
TABLE 4.
The quartile of HRR associated with arterial stiffness (n = 3657).
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| 0R (95% CI) | p | 0R (95% CI) | p | 0R (95% CI) | p | |
| HRR_per_IQR | 0.95 (0.93, 0.96) | <0.001 | 0.98 (0.96, 0.99) | 0.010 | 0.97 (0.95, 0.99) | <0.001 |
| HRR quartiles | ||||||
| Q1 (HRR< 9.57) | Ref | Ref | Ref | |||
| Q2 (9.57 ≤ HRR < 10.55) | 0.93 (0.89, 0.97) | 0.002 | 0.95 (0.92, 0.99) | 0.030 | 0.96 (0.94, 1.02) | 0.063 |
| Q3 (10.55 ≤ HRR < 11.50) | 0.92 (0.88, 0.96) | <0.001 | 0.95 (0.91, 0.99) | 0.028 | 0.95 (0.91, 0.99) | 0.024 |
| Q4 (HRR ≥ 11.50) | 0.88 (0.84, 0.91) | <0.001 | 0.94 (0.90, 0.98) | 0.011 | 0.93 (0.88, 0.97) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
Note: Model 1: unadjusted model; Model 2: adjustment for age, gender; Model 3 adjustment for age, gender, smoking status, mean arterial pressure, high‐density lipoprotein cholesterol, estimated glomerular filtration rate, glycosylated hemoglobin A1c, urinary albumin‐to‐creatinine ratio.
Abbreviation: HRR, hemoglobin to red blood cell distribution width ratio.
A linear relationship was observed between HRR and arterial stiffness in RCS (p for non‐linear = 0.277, Figure 3).
FIGURE 3.
Restricted cubic spline for the relationship between HRR and arterial stiffness. Adjustment for age, gender, smoking status, mean arterial pressure, high‐density lipoprotein cholesterol, estimated glomerular filtration rate, glycosylated hemoglobin A1c, urinary albumin‐to‐creatinine ratio. CI indicates confidence interval; HRR, hemoglobin to red blood cell distribution width ratio; OR, odds ratio. n = 3657.
3.5. Stratified Analyses
Stratified analyses are presented in Figure 4. Age stratification showed significant associations between HRR and arterial stiffness in both participants aged ≥ 60 years (OR: 0.88, 95% CI: 0.77–1.00, p = 0.047) and those aged < 60 years (OR: 0.86, 95% CI: 0.74–0.99, p = 0.042), with a non‐significant age interaction (p for interaction = 0.923). Sex stratification revealed an association between HRR and arterial stiffness in men (OR: 0.84, 95% CI: 0.75–0.95, p = 0.005), whereas no association was observed in women (OR: 0.91, 95% CI: 0.77–1.07, p = 0.265). However, the sex interaction was non‐significant (p for interaction = 0.448). Similarly, significant associations were observed across BMI categories (BMI < 25 kg/m2, OR: 0.82, 95% CI: 0.72–0.94, p = 0.004; BMI ≥ 25 kg/m2: OR: 0.91, 95% CI: 0.80–1.04, p = 0.158) with a non‐significant BMI interaction (p for interaction = 0.297). In hypertension stratification, a significant association was observed in participants with hypertension (OR: 0.84, 95% CI: 0.75–0.94, p = 0.002), but no association in those without hypertension (OR: 0.96, 95% CI: 0.80–1.16, p = 0.667), with a significant interaction effect (p for interaction = 0.030). Similarly, stratification by diabetes status showed that higher HRR were significantly associated with lower arterial stiffness in participants with diabetes (OR: 0.79, 95% CI: 0.67–0.94, p = 0.006), but no association in those without diabetes (OR: 0.94, 95% CI: 0.84–1.06, p = 0.328). The interaction by diabetes status was statistically significant (p for interaction = 0.031).
FIGURE 4.
Stratified analysis for the association of HRR with arterial stiffness. BMI indicates body mass index; CI, confidence interval; HRR, hemoglobin to red blood cell distribution width ratio; OR, odds ratio. n = 3657.
4. Discussion
This study systematically examined the association between HRR and arterial stiffness, as assessed by cfPWV, in a cohort of 3657 participants. Cross‐sectional analyses revealed an inverse association between HRR and arterial stiffness. Stratified analyses showed a stronger inverse relationship between HRR and arterial stiffness in individuals with diabetes or hypertension. Additionally, the data implicate the role of inflammation in the pathogenesis of arterial stiffness.
Accumulating evidence identifies HRR as an independent biomarker associated with cardiovascular disease outcomes [30, 31, 32]. The inverse relationship between HRR and arterial stiffness may be mediated through interconnected pathways: First, elevated RDW and abnormal hemoglobin levels are jointly associated with a chronic inflammatory state. In this state, pro‐inflammatory factors such as IL‐6 and TNF‐α impair erythrocyte production, maturation, and function, resulting in increased RBC size variability and consequently elevated RDW [33]. Inflammation further dysregulates iron metabolism, contributing to decreased hemoglobin levels and increased erythrocyte heterogeneity, which collectively lower HRR [34]. Concurrently, it promotes pathological vascular remodeling through excessive abnormal collagen deposition and reduced elastin synthesis, ultimately driving the progression of arterial stiffness [35]. Xin‐Da Wang et al. reported an inverse linear relationship between HRR and the risk of coronary artery disease [36]. In a cross‐sectional survey, hs‐CRP showed a positive association with cf‐PWV in apparently healthy adults undergoing a routine health examinations [37]. Similarly, Jinlian Li et al. demonstrated that CRP was associated with increased risk of arterial stiffness in the National Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study [38]. Notably, a meta‐analysis revealed cfPWV improvement following TNF‐α antagonist therapy [39]. In this study, we employed HRR as a novel inflammatory marker to investigate its association with arterial stiffness. In multivariable‐adjusted logistic regression analyses, HRR remained independently associated with arterial stiffness. These findings support the mechanistic link between systemic inflammation and arterial stiffness. Second, HRR may be linked to antioxidant processes, and decreased hemoglobin and elevated RDW are associated with impaired erythrocyte antioxidant capacity. Mechanistically, reduced RBC deformability and increased cellular heterogeneity disrupt microvascular perfusion, promoting vascular oxidative stress through: (1) hemoglobin‐quenched nitric oxide; (2) iron‐catalyzed superoxide formation; and (3) compromised antioxidant defense [40, 41]. Collectively, these mechanisms create a vicious cycle that accelerates vascular aging and stiffening. Consistent with this mechanistic framework, our findings revealed a significant association between HRR and arterial stiffness, reinforcing the idea that HRR acts as a composite marker reflecting the cumulative burden of inflammation and oxidative stress on the vasculature. This observed relationship supports the potential utility of HRR in identifying the individuals susceptible to arterial stiffness, as it integrates multiple pathogenic pathways known to contribute to vascular dysfunction.
Chronic hyperglycemia exacerbates arterial stiffness through oxygen free radical generation and inflammatory promotion [42]. Meixin Yu et al. reported the RDW/albumin, an established inflammatory marker, correlates with an elevated risks of lower‐extremity atherosclerosis and diabetic nephropathy in diabetic population [43]. Moreover, diminished HRR in diabetic individuals has been independently associated with elevated risk of cardiovascular mortality [44]. In this study, a more pronounced inverse association was observed between higher HRR levels and arterial stiffness measurements in participants with diabetes compared to those without diabetes. The data indicate that inflammation and oxidative stress assume more prominent roles in the pathogenic mechanism of arterial stiffness within the diabetic population.
Arterial stiffness and hypertension are pathophysiologically interrelated. Mechanistically, long‐term elevated blood pressure induces endothelial dysfunction and oxidative stress, thereby activating pro‐inflammatory signaling cascade. Stiffened arteries exhibit impaired dampening of blood pressure fluctuations, leading to elevated systolic pressure. The rise further exacerbates arterial stiffening, establishing a self‐perpetuating vicious cycle of vascular inflammation and hemodynamic dysregulation [45]. A 9‐year cohort study of 3274 normotensive middle‐aged Japanese men demonstrated that sustained elevation in serum CRP levels was associated with an accelerated progression in PWV, and this accelerated arterial stiffening was significantly correlated with subsequent increases in blood pressure [46]. In the logistic regression analysis, it was found that HRR, MAP were independently associated with arterial stiffness. Subgroup analysis revealed a significantly stronger inverse association between HRR and arterial stiffness in hypertensive participants compared to normotensive ones.
Building on established links between hematologic dysfunction and vascular health, this study introduces HRR as a composite biomarker for arterial stiffness. Our findings suggest that HRR may provide clinically relevant insights beyond those offered by isolated hematologic parameters, though further validation is warranted. However, several limitations should be acknowledged. Firstly, as an observational, cross‐sectional, single‐center study, it cannot confirm causal relationships between HRR and arterial stiffness. Secondly, the research enrolled only participants from the southern region, and a limitation that constrains the extent to which the results can be generalized to other demographic or geographical groups. Additionally, the observed inverse association between HRR and conventional lipid parameters in our cohort contradicts established findings. This discrepancy may be explained by the more frequent use of lipid‐lowering therapy among patients with arterial stiffness.
5. Conclusion
This study positions the HRR as an integrative, cost‐effective biomarker that exhibits a robust inverse association with arterial stiffness, particularly in individuals with diabetes or hypertension. This association remained significant after adjustment for covariates, suggesting that the HRR may capture unique aspects of the hematologic‐vascular interplay. While complete blood count‐derived parameters offer practical clinical advantages, future longitudinal studies should determine whether repeated HRR tests can predict changes in arterial stiffness and inform targeted interventions.
Author Contributions
Fang Liu contributed to the conceptualization, data analysis and interpretation, and drafting of the manuscript. Beijia Lin contributed to the acquisition and analysis of data. Wenhui Huang, Jingrong Dai, Yangfan Hu, Ziheng Wu, Guoyan Xu, and Liangdi Xie contributed to critical revision of the manuscript. Tingjun Wang contributed to the conceptualization, critical revision of the manuscript, and final approval of the version to be published, funding acquisition.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supplementary Table 1 Clinical characteristics of participants by all patients with and without arterial stiffness
Acknowledgments
The authors would like to express their gratitude to all researchers and all participants in the study.
Liu F., Lin B., Huang W., et al. “Association Between Hemoglobin‐to‐Red Blood Cell Distribution Width Ratio and Arterial Stiffness.” The Journal of Clinical Hypertension 27, no. 9 (2025): e70141. 10.1111/jch.70141
Funding: This study was supported by Fujian Provincial Health Technology Project (2023CXA025).
Fang Liu and Beijia Lin contribute equally to this work.
Contributor Information
Liangdi Xie, Email: ldxield@163.com.
Tingjun Wang, Email: 1870311076@qq.com.
Data Availability Statement
The data of this study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table 1 Clinical characteristics of participants by all patients with and without arterial stiffness
Data Availability Statement
The data of this study are available from the corresponding author on reasonable request.