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. Author manuscript; available in PMC: 2012 Apr 1.
Published in final edited form as: Arch Ophthalmol. 2011 Apr;129(4):490–497. doi: 10.1001/archophthalmol.2011.57

Complete Blood Count and Retinal Vessel Diameters

Barbara E K Klein 1, Ronald Klein 1, Chelsea E Myers 1, Kristine E Lee 1
PMCID: PMC3075413  NIHMSID: NIHMS238703  PMID: 21482874

Abstract

Objective

To examine the cross-sectional associations of components of the complete blood count with retinal vessel diameters.

Methods

The data are from the 1988–1990 baseline examination of the Beaver Dam Eye Study cohort (n=4730). Blood pressure was measured, a medical history including questions on smoking was obtained, and fundus photographs centered on the optic disc were taken and digitized. Retinal arteriole and venule diameters were measured using computer-assisted software. The central retinal arteriole equivalent (CRAE) and central retinal venule equivalent (CRVE) were computed. A complete blood count was done.

Results

In age and sex adjusted analyses, red blood cell count (RBC), hemoglobin, hematocrit, and white blood cell (WBC) count were all statistically significantly associated with CRVE and CRAE, while platelet count was associated only with CRVE. These relationships persisted in more fully adjusted models, except platelet count became statistically significantly associated with both CRAE and CRVE.

Conclusions

Blood components as measured in a complete blood count are significant correlates of retinal vessel diameters and should be considered in analyses where retinal blood vessel diameters are outcomes.

INTRODUCTION

Retinal vessel diameters are associated with a variety of systemic traits and diseases. For example, narrower retinal arterioles are associated with higher blood pressure,1–4 myocardial perfusion,5 left ventricular remodeling,6 and stroke.7 Ikram and colleagues have found that wider retinal arterioles are associated with impaired fasting glucose and diabetes.8 Wider retinal venules are associated with severity of diabetic retinopathy9 and relatively smaller retinal venular diameter was found in persons with diabetes compared to those of similar age without type 2 diabetes9 and with incident proteinuria in persons with type 2 diabetes.10 Larger retinal venule diameters were associated with a variety of atherosclerosis indicators, such as higher carotid plaques scores and greater aortic calcifications, and risk factors such as body mass index and lower HDL cholesterol in the Rotterdam Study.11,12

It has been reported that hematocrit is significantly associated with retinal venular diameter13 although in the few studies that have investigated this, relationships are not consistent.14 Also, white blood cell (WBC) count has been found to be associated with retinal venular diameters in a large population based study.15 High WBC count, as is characteristic of leukemia,16 can alter the microvessel diameters due to the high concentration of cells. Furthermore, it has been demonstrated that leukocytosis in the peripheral and retinal circulation is associated with dilation of retinal arterioles and retinal venules in an experimental setting.17 When retinal arterioles are embolized with glass microspheres in a laboratory setting, the diameters of the embolized vessels are clearly dilated to accommodate the particulate emboli.18

Since retinal vessels (arterioles and venules) are parts of dynamic microvascular beds that may reflect many physiologic factors as well as the substances within them, we hypothesize that these structures may be influenced by the cells that are measured in the complete blood count in addition to other physiologic factors. We examine this hypothesis in population based data collected as part of the Beaver Dam Eye Study.

METHODS

Population

There were 4926 persons 43–86 years of age who participated in the baseline examination of the Beaver Dam Eye Study in 1988–2000. Ninety-nine percent of the population was of European ancestry.19 Informed consent was obtained from participants, and all examinations followed a similar protocol that was approved by the institutional Human Subjects Committee of the University of Wisconsin and conformed to the tenets of the Declaration of Helsinki.

Medical History

A history was obtained during the study evaluation during which questions were asked about smoking.

Laboratory and Examination Methods

A complete blood count (Coulter counter method) as well as a standardized medical history, measurements of blood pressure by standard protocol20 and ocular examination with retinal photographs were obtained.20 Photographs of Early Treatment Diabetic Retinopathy Study field 1 were digitized and retinal vessel diameters were measured with a computer assisted standardized grading protocol from these images.21,22

Measurement of retinal vessel diameters

Stereoscopic 30° color retinal photographs were converted to digital images by a high-resolution scanner (Nikon LS2000, Nikon Inc, Japan) using standard settings for all photographs. Images were displayed on 21-inch monitors set to 1600 × 1200 pixels. The diameters of all arterioles and venules coursing through a standard area 0.5 to 1.0 disc diameters from the optic disc margin (Zone B) were measured using a custom computer program (Retinal Analysis, University of Wisconsin-Madison), according to a standard protocol. Each vessel was identified as an arteriole or venule by a trained grader, using the original color photograph for reference. The grader selected a segment of the vessel in Zone B for measurement and used various tools to determine the validity of the measurement. A measurement was considered invalid if the grader could not get an accurate measurement after three attempts. The entire image was rejected if more than one arteriole or venule larger than 40 microns (µm) in diameter could not be measured accurately. This cut-off was based on preliminary data that showed vessels smaller than this diameter had no substantial impact on the summary values.21

Definitions

Cigarette smoking status was determined according to the following criteria: a never smoker had smoked fewer than 100 cigarettes in his/her lifetime; a past smoker had smoked 100 or more cigarettes in his/her lifetime but reported that he/she was no longer smoking; a current smoker had smoked more than 100 cigarettes in his/her lifetime and reported that he/she was still smoking. Pack years was calculated by first dividing the average number of cigarettes smoked per day by 20 and then multiplying by the number of years smoked.

Statistical Methods

Statistical analyses were conducted in SAS, version 9 (SAS Institute Inc., Cary, NC). Means were compared for statistically significant differences by the t-test and analysis of variance when two or more than two groups, respectively, were involved. Because the distributions of the blood elements were highly skewed and differed by sex, each blood element was analyzed using sex-specific quintiles. We computed the quintiles for blood elements for each sex separately but analyzed the data with both sexes together (i.e., Q1 for men and Q1 for women were grouped together as Q1 for the entire group). Central retinal arteriolar equivalents and central retinal venule equivalents are computed based on the diameters of the six largest of each vessel type in standardized digitized photographs of the optic discs.21 Univariate analyses between each variable and central retinal arteriole equivalent (CRAE) and central retinal venule equivalent (CRVE) were conducted using simple linear regression. In additional analyses, age, sex, smoking status, diabetes status, and the other vessel measurement were added into a model sequentially to determine the impact on model fit. A final model for each blood element was developed using stepwise model selection.

RESULTS

Of the 4926 individuals who were interviewed and examined at the baseline exam, individuals were excluded from the analyses if there was no measurement for CRAE or CRVE in either eye (N=114), if data were missing for any of the laboratory values used in the analysis (N=14), or if the individual had a condition or disease such as leukemia, thrombocythemia or polycythemia (N=68), leaving 4730 individuals eligible for analysis.

Individuals who were excluded were more likely to be older, female, hypertensive, and more likely to have lower diastolic blood pressure, higher levels of glycosylated hemoglobin and all other blood elements except platelet count than those included. There were no differences in systolic blood pressure, pack-years smoked, platelet count, or current smoking between the two groups (Table 1).

Table 1.

Characteristics of Those Included and Excluded from Analysis.

Included Excluded
Risk factor N Mean SD N Mean SD P value
Age (years) 4730 61.74 11.07 196 69.14 11.74 <0.001
Systolic BP (mmHg) 4729 132.03 20.38 194 134.76 22.86 0.07
Diastolic BP (mmHg) 4729 77.39 10.99 194 75.69 11.42 0.04
Pack years smoked 4702 17.62 26.83 192 18.97 30.69 0.50
Glycosylated hemoglobin (%) 4727 6.08 1.56 176 6.94 2.63 <0.001
Hematocrit (%) 4730 43.04 3.73 177 44.64 6.63 <0.001
Hemoglobin (g/dL) 4730 14.48 1.37 177 15.04 2.35 <0.001
Platelet count (k/µL) 4727 288.51 73.53 177 298.43 151.96 0.10
White blood cell count (k/µL) 4729 7.35 2.08 177 8.48 3.61 <0.001
Red blood cell count (M/µL) 4730 4.66 0.44 177 4.82 0.78 <0.001
Sex (male) 2105 44.50 59 30.05 <0.001
Hypertension 2374 50.23 115 60.21 0.007
Diabetes 413 8.75 33 18.54 <0.001
Current smoker 926 19.59 44 23.68 0.29
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SD = standard deviation; BP = blood pressure; CRAE = central retinal arteriole equivalent; CRVE = central retinal venule equivalent.

An increase of one sex-specific quintile of WBC, red blood cell (RBC) count, hemoglobin, and hematocrit is significantly associated with larger CRAE and CRVE (Table 2). An increase of one sex-specific quintile of platelet count was also significantly associated with larger CRVE. Additional adjustment for age does not change the significance of these relationships. When the elements are considered as continuous variables the relationships are unchanged from when they are considered as quintiles.

Table 2.

Relationships of Blood Elements with Central Retinal Arteriole Equivalent and Central Retinal Venule Equivalent.

Central Retinal Arteriole Equivalent Central Retinal Venule Equivalent
Blood Element Sex Rangea Nb Meanb SDb P value Nb Meanb SDb P value
Hematocrit (%) Q1 W 16.60–38.90 945 148.40 13.30 <0.001 948 223.79 19.76 <0.001
M 26.50–42.60
Q2 W 39.00–40.60 943 149.13 13.74 944 226.51 21.03
M 42.70–44.40
Q3 W 40.70–42.10 945 149.22 13.79 943 229.78 20.95
M 44.50–46.00
Q4 W 42.20–43.90 952 149.75 13.87 952 232.36 21.41
M 46.10–47.90
Q5 W 44.00–49.90 938 151.22 14.16 940 237.32 23.83
M 48.00–56.30
Platelet count Q1 W 60.00–243.00 936 149.96 13.14 0.77 936 227.22 20.83 <0.001
(k/µL) M 47.00–217.00
Q2 W 244.00–278.00 944 149.11 13.44 944 228.74 21.76
M 218.00–250.00
Q3 W 279.00–311.00 954 149.54 14.32 954 229.68 22.37
M 251.00–279.00
Q4 W 312.00–354.00 937 149.61 14.14 939 231.38 22.43
M 280.00–318.00
Q5 W 355.00–848.00 949 149.52 13.95 951 232.61 21.85
M 319.00–885.00
Hemoglobin Q1 W 4.90–12.90 895 148.25 13.54 <0.001 898 223.54 20.05 <0.001
(g/dL) M 9.00–14.30
Q2 W 13.00–13.50 992 149.17 13.32 992 227.12 20.76
M 14.40–15.00
Q3 W 13.60–14.10 963 149.35 13.92 963 229.54 21.16
M 15.10–15.50
Q4 W 14.20–14.70 876 149.86 13.57 875 232.53 21.08
M 15.60–16.20
Q5 W 14.80–16.50 997 151.02 14.46 999 236.64 23.92
M 16.30–18.50
Red blood cell Q1 W 2.28–4.18 945 149.10 13.56 0.004 949 224.35 19.98 <0.001
count (M/µL) M 2.88–4.55
Q2 W 4.19–4.40 953 149.70 13.41 954 227.98 20.76
M 4.56–4.77
Q3 W 4.41–4.59 942 148.70 14.53 940 229.63 21.44
M 4.78–4.97
Q4 W 4.60–4.79 934 149.22 13.24 935 232.01 22.44
M 4.98–5.20
Q5 W 4.80–6.68 949 151.01 14.14 949 235.78 23.24
M 5.21–6.43
White blood cell Q1 W 2.20–5.50 914 148.68 12.97 <0.001 913 224.57 19.48 <0.001
count (k/µL) M 2.50–5.70
Q2 W 5.60–6.50 946 149.35 13.43 948 227.82 20.91
M 5.80–6.70
Q3 W 6.60–7.50 983 148.63 13.69 984 228.62 21.42
M 6.80–7.60
Q4 W 7.60–8.80 915 149.41 14.27 915 232.08 21.36
M 7.70–8.70
Q5 W 8.90–43.70 964 151.60 14.40 969 236.45 24.25
M 8.80–24.10
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Q = quintile; W = women; M = men; SD = standard deviation.

a

Range of blood values for women and men included in each quintile.

b

For women and men combined in each quintile.

In order to investigate the relative strengths of the relationships of the blood elements to the vessel diameters, we developed models in which we sequentially included other known correlates of those diameters (Table 3). For all models, each element except platelet count was significantly associated with CRAE. The models indicate that including more of the additional variables increases the informativeness of the model measured by the change in R2. For all models, each blood element was significantly associated with CRVE. Additional adjustments for additional variables increased the R2 of the model. The presence of diabetes has a small incremental effect on the fit of the models.

Table 3.

Multivariate Relationships Between Blood Elements and Central Retinal Arteriole and Venule Equivalents

Central Retinal Arteriole Equivalent
Model 1 a Model 2 Model 3 Model 4 Model 5
Blood Element Est b P
valuec 		R2d Est P
value		 R2 Est P value R2 Est P
value		 R2 Est P
value		 R2
Hematocrit 0.65 <0.001 0.013 0.44 0.002 0.037 0.73 <0.001 0.094 0.70 <0.001 0.096 −0.44 <0.001 0.435
Hemoglobin 0.61 <0.001 0.012 0.39 0.006 0.036 0.71 <0.001 0.094 0.68 <0.001 0.096 −0.39 <0.001 0.434
Platelet count 0.34 0.02 0.009 0.31 0.03 0.036 0.50 <0.001 0.091 0.40 0.007 0.092 0.04 0.72 0.432
White blood cell count −0.14 0.34 0.008 −0.24 0.09 0.035 −0.16 0.24 0.089 −0.15 0.28 0.091 −0.56 <0.001 0.436
Red blood cell count 0.59 <0.001 0.012 0.22 0.12 0.035 0.52 <0.001 0.092 0.49 <0.001 0.093 −0.31 0.006 0.434
Central Retinal Venule Equivalent
Model 1 e Model 2 Model 3 Model 4 Model 5
Blood Element Est P value R2 Est P
value 		R2 Est P value R2 Est P
value 		R2 Est P
value 		R2
Hematocrit 3.31 <0.001 0.076 2.82 <0.001 0.130 2.93 <0.001 0.133 2.90 <0.001 0.134 2.24 <0.001 0.459
Hemoglobin 3.16 <0.001 0.072 2.63 <0.001 0.126 2.76 <0.001 0.129 2.72 <0.001 0.130 2.07 <0.001 0.456
Platelet count 1.01 <0.001 0.035 0.96 <0.001 0.101 1.01 <0.001 0.102 0.89 <0.001 0.103 0.52 0.005 0.439
White blood cell count 1.22 <0.001 0.037 0.98 <0.001 0.101 1.00 <0.001 0.103 1.01 <0.001 0.104 1.18 <0.001 0.445
Red blood cell count 2.80 <0.001 0.063 1.96 <0.001 0.113 2.06 <0.001 0.115 2.03 <0.001 0.116 1.56 <0.001 0.448
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a

Model 1 adjusted for age and sex; Model 2 adjusted for Model 1 and smoking status; Model 3 adjusted for Model 2 and systolic blood pressure; Model 4 adjusted for Model 3 and diabetes status; Model 5 adjusted for Model 4 and central retinal venule equivalent.

b

Beta estimate for an increase in 1 sex-specific quintile of risk factor

c

For risk factor in model

d

For model fit

e

Models are same as in a above, except Model 5 adjusted for Model 4 and central retinal arteriole equivalent.

We next performed the modeling use a stepwise approach including as possible variables all those considered in model 4 of Table 3 (Tables 4 and 5). For CRAE, systolic blood pressure had the highest partial R2, followed by smoking, the blood element, and diabetes for each blood element. In the model assessing the contribution of platelets to the other variables associated with CRAE, platelets no longer contributed a significant amount of information. For CRVE, smoking history has the greatest partial R2, followed by the blood element and then sex and systolic blood pressure. Because of the importance of relative diameter, we repeated the analyses in table 4, this time including CRVE in the models for CRAE and CRAE in models for CRVE (Table 5). These additions markedly improved the R2 of all models. In all models, the relative importance of smoking was diminished. The blood elements are the third most informative variable for CRVE or fourth most informative variable for CRAE when controlling for the other vessel measurement.

Table 4.

Stepwise Selection for Variables with Central Retinal Arteriole Equivalent and Central Retinal Venule Equivalent.

Central Retinal Arteriole Equivalent
Risk Factor Model Terms Selected Est (SE) a P value Partial R2 Model R2
Hematocrit Systolic BP, per 10 mmHg −1.72 (0.09) <0.001 0.0611 0.10
Smoking history, current vs past/never 6.73 (0.49) <0.001 0.0269
Hematocrit, per sex-specific quintile 0.58 (0.14) <0.001 0.0056
Diabetes, present vs absent 2.25 (0.68) 0.004 0.0017
Hemoglobin Systolic BP, per 10 mmHg −1.71 (0.09) <0.001 0.0611 0.10
Smoking history, current vs past/never 6.73 (0.49) <0.001 0.0269
Hemoglobin, per sex-specific quintile 0.55 (0.14) <0.001 0.0054
Diabetes, present vs absent 2.24 (0.68) 0.005 0.0016
Red blood cells Systolic BP, per 10 mmHg −1.72 (0.10) <0.001 0.0611 0.09
Smoking history, current vs past/never 7.04 (0.48) <0.001 0.0269
Red blood cells, per sex-specific quintile 0.58 (0.14) <0.001 0.0048
Diabetes, present vs absent 2.17 (0.68) 0.003 0.0017
White blood cells Systolic BP, per 10 mmHg −1.70 (0.09) <0.001 0.0611 0.09
Smoking history, current vs past/never 6.77 (0.50) <0.001 0.0269
White blood cells, per sex-specific quintile 0.39 (0.14) <0.001 0.0029
Diabetes, present vs absent 2.32 (0.68) 0.003 0.0018
Platelet count Systolic BP, per 10 mmHg −1.68 (0.09) <0.001 0.0610 0.09
Smoking history, current vs past/never 2.99 (0.25) <0.001 0.0269
Diabetes, present vs absent 2.25 (0.68) 0.001 0.0021
Sex, women vs men −1.23 (0.40) 0.002 0.002
Hematocrit Smoking history, current vs past/never 13.67 (0.78) <0.001 0.0889 0.13
Hematocrit, per sex-specific quintile 2.69 (0.22) <0.001 0.0304
Sex, women vs men 6.02 (0.60) <0.001 0.0081
Systolic BP, per 10 mmHg −0.62 (0.15) <0.001 0.0045
Age, per year −0.95 (0.30) 0.006 0.0017
Diabetes, present vs absent 2.47 (1.07) 0.05 0.0007
Hemoglobin Smoking history, current vs past/never 13.71 (0.79) <0.001 0.0889 0.13
Hemoglobin, per sex-specific quintile 2.48 (0.22) <0.001 0.0267
Sex, women vs men 6.06 (0.60) <0.001 0.0083
Systolic BP, per 10 mmHg −0.62 (0.16) <0.001 0.0044
Age, per year −0.84 (0.30) 0.01 0.0013
Diabetes, present vs absent 2.40 (1.08) 0.06 0.0007
Red blood cells Smoking history, current vs past/never 15.10 (0.77) <0.001 0.0889 0.13
Red blood cells, per sex-specific quintile 2.71 (0.21) <0.001 0.0307
Sex, women vs men 6.05 (0.60) <0.001 0.0071
Systolic BP, per 10 mmHg −0.64 (0.16) <0.001 0.0047
White blood cells Smoking history, current vs past/never 13.75 (0.80) <0.001 0.0889 0.12
White blood cells, per sex-specific quintile 1.91 (0.22) <0.001 0.0133
Sex, women vs men 6.00 (0.61) <0.001 0.0083
Systolic BP, per 10 mmHg −0.55 (0.16) <0.001 0.0035
Age, per year −0.91 (0.30) 0.003 0.0016
Diabetes, present vs absent 2.70 (1.08) 0.03 0.0009
Platelets Smoking history, current vs past/never 8.24 (0.41) <0.001 0.0885 0.11
Sex, women vs men 3.38 (0.63) <0.001 0.0070
Platelet count, per sex-specific quintile 0.98 (0.22) <0.001 0.0045
Systolic BP, per 10 mmHg −0.41 (0.16) 0.001 0.0021
Diabetes, present vs absent 3.12 (1.08) 0.01 0.0014
Age, per year −0.81 (0.30) 0.01 0.0011
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a

Beta estimate and standard error from linear regression model.

Table 5.

Stepwise Selection for Variables with Central Retinal Arteriole Equivalent (CRAE) and Central Retinal Venule Equivalent (CRVE), with CRVE considered for CRAE models and CRAE considered for CRVE models.

Central Retinal Arteriole Equivalent
Risk Factor Model Terms Selected Est (SE)a P value Partial R2 Model R2
Hematocrit CRVE, per 25 µm 9.64 (0.18) <0.001 0.3763 0.44
Systolic BP, per 10 mmHg −1.50 (0.08) <0.001 0.0462
Sex women vs men −2.77 (0.31) <0.001 0.0096
Hematocrit, per sex-specific quintile −0.39 (0.11) <0.001 0.0014
Age, per year 0.40 (0.15) 0.01 0.0010
Diabetes, present vs absent 1.20 (0.55) 0.03 0.0006
Hemoglobin CRVE, per 25 µm 9.62 (1.97) <0.001 0.3763 0.44
Systolic BP, per 10 mmHg −1.50 (0.08) <0.001 0.0462
Sex, women vs men −2.77 (0.31) <0.001 0.0096
Age, per year 0.38 (0.15) 0.004 0.0010
Hemoglobin, per sex-specific quintile −0.32 (0.11) 0.007 0.0009
Diabetes, present vs absent 1.21 (0.55) 0.03 0.0006
Red blood cells CRVE, per 25 µm 9.63 (0.18) <0.001 0.3763 0.44
Systolic BP, per 10 mmHg −1.49 (0.08) <0.001 0.0462
Sex, women vs men −2.77 (0.31) <0.001 0.0096
Red blood cells, per sex-specific quintile −0.42 (0.11) <0.001 0.0017
Age, per year 0.37 (0.15) 0.01 0.0008
Diabetes, present vs absent 1.30 (0.55) 0.02 0.0007
White blood cells CRVE, per 25 µm 9.58 (1.96) <0.001 0.3763 0.43
Systolic BP, per 10 mmHg −1.51 (0.08) <0.001 0.0463
Sex, women vs men −2.75 (0.31) <0.001 0.0096
Age, per year 0.39 (0.15) 0.005 0.0010
White blood cells, per sex-specific quintile −0.23 (0.11) 0.04 0.0005
Diabetes, present vs absent 1.17 (0.55) 0.03 0.0006
Platelet count CRVE, per 25 µm 9.58 (0.18) <0.001 0.38 0.44
Systolic BP, per 10 mmHg −1.51 (0.08) <0.001 0.0460
Sex, women vs men −2.75 (0.31) <0.001 0.0096
Platelet count, per age-specific quintile −0.51 (0.11) <0.001 0.003
Age, per year 0.33 (0.15) 0.02 0.0007
Diabetes, present vs absent 1.09 (0.55) 0.04 0.0005
Hematocrit CRAE, per 10 µm 9.46 (0.18) <0.001 0.3763 0.45
Hematocrit, per sex-specific quintile 2.17 (0.17) <0.001 0.0294
Sex, women vs men 6.44 (0.47) <0.001 0.0254
(CRVE) Smoking history, current vs past/never 7.10 (0.64) <0.001 0.0158
Systolic BP, per 10 mmHg 1.02 (0.13) <0.001 0.0060
Age, per year −1.11 (0.24) <0.001 0.0019
Hemoglobin CRAE, per 10 µm 9.48 (0.19) <0.001 0.3763 0.45
Hemoglobin, per sex-specific quintile 2.05 (0.17) <0.001 0.0267
Sex, women vs men 6.48 (0.48) <0.001 0.0256
Smoking history, current vs past/never 6.72 (0.63) <0.001 0.0155
Systolic BP, per 10 mmHg 1.02 (0.13) <0.001 0.0062
Age, per year −1.02 (0.24) <0.001 0.0016
Red blood cells CRAE, per 10 µm 9.46 (0.18) <0.001 0.3763 0.46
Smoking history, current vs past/never 8.36 (0.63) <0.001 0.0387
Red blood cells, per sex-specific quintile 2.12 (0.17) <0.001 0.0240
Sex, women vs men 6.42 (0.48) <0.001 0.0133
Systolic BP, per 10 mmHg 0.98 (0.24) <0.001 0.0057
Age, per year −0.90 (0.24) 0.013 0.0012
White blood cells CRAE, per 10 µm 9.54 (0.19) <0.001 0.3763 0.45
Smoking history, current vs past/never 7.10 (0.65) <0.001 0.0387
White blood cells, per sex-specific quintile 1.58 (0.18) <0.001 0.0105
Sex, women vs men 6.44 (0.48) <0.001 0.0119
Systolic BP, per 10 mmHg 1.09 (0.13) <0.001 0.0072
Age, per year −1.07 (0.24) <0.001 0.0019
Platelets CRAE, per 10 µm 9.64 (0.19) <0.001 0.3769 0.44
Smoking history, current vs past/never 8.13 (0.64) <0.001 0.0385
Sex, women vs men 6.38 (0.48) <0.001 0.0132
Systolic BP, per 10 mmHg 1.20 (0.13) <0.001 0.0095
Platelets, per sex-specific quintile 1.18 (0.17) <0.001 0.0057
Age, per year −0.90 (0.24) <0.001 0.0013
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a

Beta estimate and standard error from linear regression model.

While the previous analyses consider each blood element individually, we next developed models when they are included together. We chose only one of the three measures of red blood cell status (RBC, hemoglobin and hematocrit) for the purposes of these analyses. We modeled the outcomes as was done in Tables 4 and 5. In models where CRAE is the outcome, and CRVE is not included, each of the blood element components adds significantly to the models with the red blood cell variable being most informative and WBC and platelet count adding less information. When CRVE is included the WBC is no longer significant in the models. In models where CRVE is the outcome, and CRAE is not included, each of the blood element components adds significantly to the models and, again, the red blood cell variables being most informative and WBC and platelets adding less information. When we add CRAE, all of the three different blood components remain significant in most models. The relative importance of most of the other variables that were used in the models in Tables 4 and 5 remain unchanged.

DISCUSSION

We have found that hematocrit, hemoglobin, RBC count, WBC count and platelet count are significantly associated with retinal vessel diameters. In addition, in our population these factors outweigh the relative importance of age, sex and diabetes status in all models for both CRAE and CRVE. The effects related to RBC are greater than those for WBC. There are relatively smaller effects of platelet count. The associations are direct, i.e., higher values are associated with wider diameters. While these data may indicate that these effects are related to that actual burden imposed by more particulate matter in the blood, it may be more informative to consider the physiologic factors related to the blood elements (counts). It is possible that relative systemic factors such as chronic hypoxia due to systemic diseases or exposures may be associated with relatively higher RBC count (and hemoglobin, hematocrit). An obvious exposure that is compatible with this notion is smoking, which we found to be associated with both CRAE and CRVE in analyses not adjusting for the diameter of either. Smoking, aside from potential effects on systemic hypoxia, is also associated with systemic inflammation. This mechanism has been invoked to explain previously described associations of WBC counts to CRVE. In the Atherosclerosis Risk in Communities Study, higher WBC count was also associated with larger CRAE.13 Diabetes status, while significant in its effect, had a relatively small contribution compared to cigarette smoking for both CRAE and CRVE in our study.

Microvascular diameters undergo structural adaptation related to the local metabolic conditions.23 Hematocrit influences local hemodynamics.23 Vessel diameters are thought to change shear stress on vessel walls, dependent in part upon oxygen deficit and RBC signaling.23 In the Rotterdam Study, retinal venule diameter was greater in persons with decreased cerebral blood flow and with decreased arteriolar oxygen saturation.11,24 Oxygen stimulation is associated with decreased flow mediated diameters in large vessels.25 While this may have implications for cardiovascular events, it is uncertain what the health impact of changes in the microvascular beds may be.

Another possible effect (or correlate) of hematocrit is to increase blood viscosity.26 This increase in viscosity has been shown to be associated with risk factors for cardiovascular disease such as blood lipids and blood pressure27 and may also be a pathway whereby the blood factors we examined influence retinal vessel diameters.

Our study is limited in that it is a cross-sectional analysis and these findings may not be applicable to longitudinal data. Also, while we have controlled for the most important confounders, there may be others that we did not measure or that were not important in our analyses but may be important in other studies. Lastly, variability of our outcome (as well as our covariate) measurements may have influenced our findings, although we suspect that these would have led to our underestimate of the relationships we report.

In summary, we have found that blood cell counts (and related measures of hematocrit and hemoglobin) are significant correlates of retinal vessel diameters with greater effects than have been reported for some other correlates of these measures. We suggest that in research where retinal vessel diameters are the outcomes of interest, blood counts should be included as important determinants or correlates.

ACKNOWLEDGMENTS

This research is supported by National Institutes of Health grant EY06594 (Drs B. E. K. Klein, R. Klein). The National Eye Institute provided funding for entire study including collection and analyses of data. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the National Eye Institute or the National Institutes of Health. Additional support was provided by Senior Scientific Investigator Awards from Research to Prevent Blindness (Drs B. E. K. Klein, R. Klein). Dr B. E. K. Klein had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors thank Ms. Heidi Gutt, BA, and Mary Kay Aprison, BS, for their assistance in editing and preparing the manuscript.

Footnotes

Financial Disclosures

None.

Author contributions

Conception and design (BK, RK), acquisition of data (BK, RK), analysis and interpretation of data (BK, CM, KL), drafting of the manuscript (BK), critical revision of the manuscript for important intellectual content (RK, CM, KL), statistical expertise (CM, KL), obtaining funding (BK, RK), administrative/technical/material support (BK, CM, KL), supervision (BK).

REFERENCES

  • 1.Gunn M. On Ophthalmoscopic Evidence of General Arterial Disease. Trans Ophthal Soc Uk. 1898:18356–18381. [Google Scholar]
  • 2.Friedenwald H. The Doyne Memorial Lecture: Pathological Changes in the Retinal Blood-vessels in Arterio-sclerosis and Hypertension. Trans Ophthal Soc Uk. 1930:50452–50531. [Google Scholar]
  • 3.Wagener HP, Clay GE, Gipner JF. Classification of Retinal Lesions in the Presence of Vascular Hypertension. Trans Am Ophthalmol Soc. 1947:4557–4573. [PMC free article] [PubMed] [Google Scholar]
  • 4.Klein R, Sharrett AR, Klein BE, et al. Are retinal arteriolar abnormalities related to atherosclerosis?: The Atherosclerosis Risk in Communities Study. Arterioscler Thromb Vasc Biol. 2000;20(6):1644–1650. doi: 10.1161/01.atv.20.6.1644. [DOI] [PubMed] [Google Scholar]
  • 5.Wang L, Wong TY, Sharrett AR, Klein R, Folsom AR, Jerosch-Herold M. Relationship between retinal arteriolar narrowing and myocardial perfusion: multi-ethnic study of atherosclerosis. Hypertension. 2008;51(1):119–126. doi: 10.1161/HYPERTENSIONAHA.107.098343. [DOI] [PubMed] [Google Scholar]
  • 6.Cheung N, Bluemke DA, Klein R, et al. Retinal arteriolar narrowing and left ventricular remodeling: the multi-ethnic study of atherosclerosis. J Am Coll Cardiol. 2007;50(1):48–55. doi: 10.1016/j.jacc.2007.03.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wong TY, Klein R, Couper DJ, et al. Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet. 2001;358(9288):1134–1140. doi: 10.1016/S0140-6736(01)06253-5. [DOI] [PubMed] [Google Scholar]
  • 8.Ikram MK, Janssen JA, Roos AM, et al. Retinal vessel diameters and risk of impaired fasting glucose or diabetes: the Rotterdam study. Diabetes. 2006;55(2):506–510. doi: 10.2337/diabetes.55.02.06.db05-0546. [DOI] [PubMed] [Google Scholar]
  • 9.Klein R, Klein BE, Moss SE, Wong TY, Sharrett AR. Retinal vascular caliber in persons with type 2 diabetes: the Wisconsin Epidemiological Study of Diabetic Retinopathy: XX. Ophthalmology. 2006;113(9):1488–1498. doi: 10.1016/j.ophtha.2006.03.028. [DOI] [PubMed] [Google Scholar]
  • 10.Klein R, Klein BE, Moss SE, Wong TY. Retinal vessel caliber and microvascular and macrovascular disease in type 2 diabetes: XXI: the Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmology. 2007;114(10):1884–1892. doi: 10.1016/j.ophtha.2007.02.023. [DOI] [PubMed] [Google Scholar]
  • 11.Ikram MK, de Jong FJ, Vingerling JR, et al. Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study. Invest Ophthalmol Vis Sci. 2004;45(7):2129–2134. doi: 10.1167/iovs.03-1390. [DOI] [PubMed] [Google Scholar]
  • 12.Ikram MK, Witteman JC, Vingerling JR, Breteler MM, Hofman A, de Jong PT. Retinal vessel diameters and risk of hypertension: the Rotterdam Study. Hypertension. 2006;47(2):189–194. doi: 10.1161/01.HYP.0000199104.61945.33. [DOI] [PubMed] [Google Scholar]
  • 13.Klein R, Klein BE, Knudtson MD, Wong TY, Tsai MY. Are inflammatory factors related to retinal vessel caliber? The Beaver Dam Eye Study. Arch Ophthalmol. 2006;124(1):87–94. doi: 10.1001/archopht.124.1.87. [DOI] [PubMed] [Google Scholar]
  • 14.Kofoed PK, Sander B, Zubieta-Calleja G, Kessel L, Larsen M. Retinal vessel diameters in relation to hematocrit variation during acclimatization of highlanders to sea level altitude. Invest Ophthalmol Vis Sci. 2009;50(8):3960–3963. doi: 10.1167/iovs.08-3217. [DOI] [PubMed] [Google Scholar]
  • 15.Liew G, Sharrett AR, Kronmal R, et al. Measurement of retinal vascular caliber: issues and alternatives to using the arteriole to venule ratio. Invest Ophthalmol Vis Sci. 2007;48(1):52–57. doi: 10.1167/iovs.06-0672. [DOI] [PubMed] [Google Scholar]
  • 16.Norén-Nyström U, Heyman M, Frisk P, et al. Vascular density in childhood acute lymphoblastic leukaemia correlates to biological factors and outcome. Br J Haematol. 2009;146(5):521–530. doi: 10.1111/j.1365-2141.2009.07796.x. [DOI] [PubMed] [Google Scholar]
  • 17.Kolodjaschna J, Berisha F, Lasta M, Polska E, Fuchsjager-Mayrl G, Schmetterer L. Reactivity of retinal blood flow to 100% oxygen breathing after lipopolysaccharide administration in healthy subjects. Exp Eye Res. 2008;87(2):131–136. doi: 10.1016/j.exer.2008.05.006. [DOI] [PubMed] [Google Scholar]
  • 18.Klein R, Klein B, Henkind P, Bellhorn R. Retinal collateral vessel formation. Invest Ophthalmol. 1971;10(7):471–480. [PubMed] [Google Scholar]
  • 19.Klein R, Klein BE, Linton KL, De Mets DL. The Beaver Dam Eye Study: visual acuity. Ophthalmology. 1991;98(8):1310–1315. doi: 10.1016/s0161-6420(91)32137-7. [DOI] [PubMed] [Google Scholar]
  • 20.The hypertension detection and follow-up program: Hypertension Detection and Follow-up Program Cooperative Group. Prev Med. 1976;5(2):207–215. doi: 10.1016/0091-7435(76)90039-6. [DOI] [PubMed] [Google Scholar]
  • 21.Knudtson MD, Lee KE, Hubbard LD, Wong TY, Klein R, Klein BE. Revised formulas for summarizing retinal vessel diameters. Curr Eye Res. 2003;27(3):143–149. doi: 10.1076/ceyr.27.3.143.16049. [DOI] [PubMed] [Google Scholar]
  • 22.Wong TY, Klein R, Sharrett AR, et al. The prevalence and risk factors of retinal microvascular abnormalities in older persons: The Cardiovascular Health Study. Ophthalmology. 2003;110(4):658–666. doi: 10.1016/S0161-6420(02)01931-0. [DOI] [PubMed] [Google Scholar]
  • 23.Reglin B, Secomb TW, Pries AR. Structural adaptation of microvessel diameters in response to metabolic stimuli: where are the oxygen sensors? Am J Physiol Heart Circ Physiol. 2009;297(6):H2206–H2219. doi: 10.1152/ajpheart.00348.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.de Jong FJ, Vernooij MW, Ikram MK, et al. Arteriolar oxygen saturation, cerebral blood flow, and retinal vessel diameters. The Rotterdam Study. Ophthalmology. 2008;115(5):887–892. doi: 10.1016/j.ophtha.2007.06.036. [DOI] [PubMed] [Google Scholar]
  • 25.Frøbert O, Holmager P, Jensen KM, Schmidt EB, Simonsen U. Effect of acute changes in oxygen tension on flow-mediated dilation. Relation to cardivascular risk. Scand Cardiovasc J. 2008;42(1):38–47. doi: 10.1080/14017430701466402. [DOI] [PubMed] [Google Scholar]
  • 26.Cinar Y, Demir G, Pac M, Cinar AB. Effect of hematocrit on blood pressure via hyperviscosity. Am J Hypertens. 1999;12(7):739–743. doi: 10.1016/s0895-7061(99)00011-4. [DOI] [PubMed] [Google Scholar]
  • 27.de Simone G, Devereux RB, Chien S, Alderman MH, Atlas SA, Laragh JH. Relation of blood viscosity to demographic and physiologic variables and to cardiovascular risk factors in apparently normal adults. Circulation. 1990;81(1):107–117. doi: 10.1161/01.cir.81.1.107. [DOI] [PubMed] [Google Scholar]

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