Methods

The National Health and Nutrition Examination Survey (NHANES) is intended to provide information regarding the health status of a nationally representative sample of the civilian noninstitutionalized United States population. The study participants were interviewed at home and subsequently underwent a physical examination, including phlebotomy, at a mobile examination center after providing informed consent. We evaluated the study participants of the NHANES 1999 to 2000, 2001 to 2002, 2003 to 2004, and 2005 to 2006. The details of the NHANES studies from 1999 to 2006 have been previously published. The National Center for Health Statistics ethics/institutional review board approved the study protocols for NHANES 1999 to 2006.

A total of 41,474 participants were screened and 13,569 subjects identified for further analysis, after applying the exclusion criteria. The exclusion criteria were as follows: age <20 years (n = 21,162) or >79 years (n = 1,876); history of self-reported coronary artery disease (n = 664), myocardial infarction (n = 358), congestive heart failure (n = 197), or stroke (n = 367); self-reported diabetes mellitus (n = 1,303); hemoglobin A1c >6.5 (n = 1,867); and angina pectoris (n = 111). The participants were also excluded from the analysis if they had missing values for the study variables, including systolic blood pressure (n = 791), history of hypertension (n = 17), history of dyslipidemia (n = 47), chronic kidney disease (n = 20), C-reactive protein (CRP) (n = 95), blood pressure medication use (n = 4,452), current cigarette use (n = 7), family history of coronary artery disease (n = 230), hemoglobin A1c (n = 42), or RDW (n = 354). The remaining 7,556 participants were included in the final analysis. To further assess the effect of nutritional factors such as iron, vitamin B ₁₂ , and folic acid, we ran a subgroup analysis of 4,932 subjects, all of whom had complete information for these variables; data were missing for serum iron (n = 1,809), serum vitamin B ₁₂ (n = 812), and serum folic acid (n = 3).

The Beckman Automated Coulter Counter method of counting and sizing, combined with an automatic diluting and mixing device for sample processing, and a single-beam photometer for hemoglobinometry, were used to measure the RDW, mean corpuscular volume, and hemoglobin respectively. Plasma hemoglobin A1c was measured on whole blood with high-performance liquid chromatography, and CRP levels were quantified with latex-enhanced nephelometry. To ensure the accuracy and reproducibility of each measured laboratory parameter, control specimens with a known assigned value were run with each sample, and testing was repeated on a 2% random specimen sample. Standardized creatinine was calculated according to the NHANES guidelines using the following formula: standard creatinine = 1.013 × serum creatinine (mg/dl) + 0.147. The estimated glomerular filtration rate was calculated using the following formula: estimated glomerular filtration rate = [175 × (standardized creatinine) ^−1.154 × (age) ^−0.203 × (0.742, if the participant were female) × (1.212 if the participant were black)]. Detailed descriptions of the laboratory methods used in NHANES 1999 to 2006 have been previously published.

The Framingham risk score (FRS) was calculated for all subjects, assigning gender-specific points as recommended by the National Cholesterol Education Program Expert Panel on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) guidelines. The participants were then divided into 3 categories of 10-year CHD risk: low risk (<10%, reference category), intermediate risk (10% to 20%), and high risk (>20%). The baseline characteristics were then compared across these categories. The categorical variables were compared using the chi-square-test (reported as proportions) and one-way analysis of variance test for continuous variables (reported as the mean ± SD). The test of trend was used to analyze the linearity of the association between the continuous variables and the Framingham risk categories. Log-transformations were applied to meet the assumption of normality for variables such as CRP, estimated glomerular filtration rate, and serum folic acid. The body mass index was categorized as normal (<25 kg/m ² ), overweight (25 to 29.9 kg/m ² ), and obese (≥30 kg/m ² ). The vitamin B ₁₂ levels were divided into 3 categories: <200, 200 to 900, and >900 pg/ml. Spearman’s correlation analysis was performed between the FRS and RDW values. To evaluate the role of RDW as a predictor of greater 10-year risk of hard CHD events, unadjusted and adjusted logistic regression analyses were performed. An estimated 10-year CHD risk of <10% served as the reference category for the analysis. The following models were generated: model 1, unadjusted (RDW alone); model 2, adjusted for race, body mass index, history of dyslipidemia, estimated glomerular filtration rate, and CRP; model 3, the variables in model 2 plus blood hemoglobin and mean corpuscular volume; and model 4, the variables in model 3 plus the serum vitamin B ₁₂ , folic acid, and iron levels. Furthermore, to evaluate the effect of anemia on the association between RDW and greater 10-year CHD risk, all the study participants were divided into 2 categories according to their anemia status (hemoglobin <13 g/dl in men and <12 g/dl in women, as defined by the World Health Organization criteria). Subsequently, the subjects were also divided into 2 categories according to the distribution of RDW in both anemic and nonanemic subjects (RDW at the seventy-fifth percentile or less vs greater than the seventy-fifth percentile). An RDW level at the seventy-fifth percentile or less in those without anemia served as the reference category for the analysis. Adjustments were applied similar to those for models 3 and 4, as described earlier. For all the analyses, p <0.05 was considered statistically significant. All statistical analyses was performed using Stata/SE statistical software, version 10 (StataCorp, College Station, Texas).

Results

Table 1 lists the baseline characteristics distribution for the entire cohort stratified according to the 3 studied FRS risk categories. The participants in the high 10-year CHD risk category were significantly older and more likely to be men, obese, hypertensive, and dyslipidemic compared to the reference category.

A linear increase was seen in the RDW value with a greater CHD risk category. Blood hemoglobin, mean corpuscular volume, total cholesterol, hemoglobin A1c, serum folic acid, serum creatinine, and blood urea nitrogen levels were also greater in the higher risk CHD risk categories (p <0.001). Similarly, serum CRP levels were high and serum high-density lipoprotein levels low in those in the high-risk category.

We performed Spearman’s correlation analysis between RDW and FRS ( Figure 1 ), which showed a statistically significant linear positivity between them (rho = 0.14, p <0.001). Subsequently, unadjusted and adjusted multivariate logistic regression models ( Table 2 ) were generated. These essentially represented the odds of being in higher risk categories, with higher RDW values for 10-year CHD risk compared to the reference category (<10%). The risk of being in the intermediate-risk category (FRS 10% to 20%) compared to the reference category increased significantly by 1.16 times (95% confidence interval [CI] 1.09 to 1.22) after the adjustments shown in model 2. Additional adjustments with blood hemoglobin and mean corpuscular volume levels resulted in increase in the odds ratio (OR) to 1.35 times for classification in the intermediate-risk category (95% CI 1.27 to 1.45, p <0.001). With adjustment for nutritional factors, as shown in model 4, the adjusted risk of being in the intermediate-risk category was 1.53 times (95% CI 1.38 to 1.69, p <0.001). The association was even stronger for those in the high-risk category (FRS >20%). Adjustments showed an OR of 1.23 (95% CI 1.12 to 1.35, p <0.001) using model 2, 1.38 (95% CI 1.25 to 1.53, p <0.001) using model 3, and 1.57 (95% CI 1.37 to 1.80, p <0.001) using model 4 for being classified in the high-risk category relative to the reference category.

Spearman’s correlation between RDW and FRS.

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