Comparison of the simple red blood cell adhesiveness/aggregation test with the laser-assisted optical rotational cell analyzer: Red blood cell aggregation in patients with coronary artery disease and a healthy control group

Abstract

BACKGROUND: Red blood cell (RBC) aggregation influences blood flow properties, impacts blood microcirculation and consequently oxygen delivery. Different methods are established to determine RBC aggregation: under static conditions (i.e. the RBC adhesiveness/aggregation test (EAAT)) or under shear conditions (i.e. the laser-assisted optical rotational cell analyzer (LORCA)).

OBJECTIVE: Comparison of these two different methods in detecting the RBC aggregation of patients with coronary artery disease (CAD) and of healthy controls.

METHODS: RBC aggregation was quantified in peripheral venous blood of patients with CAD and healthy controls using EAAT and LORCA.

RESULTS: Both methods detected an increased RBC aggregation in patients with CAD compared to the healthy control group: the ratio of clot-free area to whole area (rCFA) detected with EAAT (15.65 vs. 11.30%), and aggregation index (66.33 vs. 53.90%), shear rate of disaggregation (SDA) (105.59 vs. 69.21 s^–1), and upstroke/t_top (0.03 vs. 0.02 au/s) detected with LORCA device were increased, aggregation half time (detected with LORCA) was decreased (2.11 vs. 3.60 s). rCFA (EAAT) correlated with SDA (LORCA).

CONCLUSIONS: Both methods determine an increased RBC aggregation in patients with CAD. However, only one measurement parameter of the LORCA seems to reflect the same RBC aggregation properties as the EAAT.

Keywords

Coronary artery disease measurement devices red blood cell aggregation

1 Introduction

Red blood cells (RBC) characteristics like the deformability and the aggregation influence blood flow properties in all vessel types [6 , 26]. Increased RBC aggregation is accompanied by an increased blood viscosity, increased blood flow resistance [42], reduced blood flow velocity [15, 38], and reduced functional capillary density [38]. All these facts result in reduced oxygen delivery - especially in microcirculation [4]. Increased RBC aggregation is associated with different cardiovascular risk factors: age [37], obesity [9, 41], arterial hypertension [11], diabetes mellitus [28] or coronary artery disease (CAD) [33]. An increased RBC aggregation is also observed in patients undergoing stent implantation with a distal occlusion/aspiration device [3], but also in patients with acute myocardial infarction or stroke [25, 35].

Several methods are established to determine RBC aggregation [5]. There are methods determining RBC aggregation under static conditions, other methods use shear stress conditions, simulating the RBC aggregation in vivo. The very simple adhesiveness/aggregation test (EAAT) [34] determines RBC aggregation under static conditions. Here, a ratio of RBC clot-free area to whole area (rCFA) of a dried blood drop is used as a measurement parameter. Automatized devices like the Laser-assisted optical rotational cell analyzer (LORCA), the Myrenne Aggregometer, the RheoScan-A, or the Erythroaggregometer, determine RBC aggregation under different shear stress conditions. All these methods use optical measured values and algorithms to calculate the measurement parameters, respectively. The measurement parameters of these different automatized devices have already been compared and result in very similar estimation of RBC aggregation [7 , 43].

We here compare the rCFA with the different measurement parameters determined with the automatic LORCA device by using peripheral venous blood of patients with CAD and healthy controls. Aim of the study was to analyze whether rCFA indeed reflects one or more parameter describing RBC aggregation under different shear stress conditions.

2 Material and methods

2.1 Study cohort

Seventeen patients with CAD were recruited from Aachen University Hospital Medical Clinic I. All subjects were screened by clinical history, physical examination, blood pressure measurement and routine chemical analysis. CAD was proved by coronary angiography in all patients. Medication was discontinued on the day of the investigations. Patients with renal insufficiency requiring hemodialysis (stage 4 and 5 according to the national kidney foundation kidney disease outcomes quality initiative (NFK-KDOQI) [22], a malignant or an inflammatory disease, women in premenopause, cardiac arrhythmia or heart failure according to the New York Heart Association stage III and IV [18] and patients treated with NO-donors (glyceryl trinitrate, isosorbide mononitrate, isosorbide dinitrate, sodium nitroprusside, molsidomine) within the last 12 hours before the examination were excluded. Standard blood count was determined in routine blood analysis during the hospital stay. Nineteen healthy volunteers, students and employees of the University Hospital Aachen, were used as the healthy control group. Written informed consent was obtained from all study subjects before enrolment. The study protocol was approved by the local ethics committee.

2.2 Sample preparation

All study subjects were overnight fasted. Blood was drawn in the morning after 15 min rest in supine position from the antecubital vein. Heparin was used for anticoagulation (10 IE * mL^–1, Liquemin 5000 IE, Roche, Grenzach-Wyhlen, Germany). For EAAT measurement there was no further preparation of the blood samples after anticoagulation. For LORCA measurement 2 ml blood was oxygenated for 15 min in a 50 mL tube (Greiner bio-one, Frickenhausen, Germany) by rotating according to the manufacturer’s instruction [17]. RBC aggregation was determined immediately after oxygenation. The total volume of the anticoagulated blood was used for RBC aggregation measurement [17].

2.3 Measurement of RBC aggregation

2.3.1 Simple slide test: EAAT (according to Rotstein et al. [34])

Blood was drawn into a syringe containing sodium citrate (one volume of 3.8% sodium citrate and three volumes of whole blood). One drop of the citrated whole blood (10 μL) was trickled onto an object slide inclined at an angle of 30° and allowed to run down by gravity, leaving a fine film. The object slides were left to dry in that position, at room temperature for minimal 2 h. A technician, who was blinded to the clinical and laboratory results of the patients, took photos (100 fold magnification) from 7 sequential sections of each smear using a digital camera (Canon Power Shot S70, 7.1 megapixels, 3.6x zoom, Canon Inc., Tokio, Japan) and attached these to the microscope (Leica DM IL, Leica Microsystems, Wetzlar, Germany). RBC aggregation was determined as the rCFA via Adobe Photoshop 7.0 (Adobe systems incorporated, San Jose, USA). The degree of RBC aggregability of each sample was expressed as mean of the 5 single pictures.

2.3.2 Automatic device: LORCA (R&R Mechatronics, Hoorn, The Netherlands) measurement (according to Hardeman et al. [17])

According to the instructions of the manufacturer, one ml of the oxygenated blood was placed into the gap of a preheated (37°C) couette system, which consists of two concentric cylinders with a gap of about 3 mm. Different RBC aggregation parameters were assessed during the outer cylinder rotates stepwise and after an abrupt rotation stop at 500 s^–1. A photo diode, integrated in the fixed inner cylinder, detects the intensity of a so called backscattered light during the processes of RBC aggregation and disaggregation. Changes in backscatter intensity are displayed in a syllectogram curve, and predefined parameters were automatically calculated during the processes of RBC aggregation and disaggregation [17]. Here we select parameters, which are described to reflect the different characteristics of RBC aggregation and disaggregation: “AI”, the aggregation index, calculated as integral of the total syllectogram curve, reflects the RBC aggregegability; “SDA”, the measured shear rate which is needed for a disaggregation of the RBC; “t_1/2”, the aggregation halftime, reflects the kinetics of RBC aggregation after disaggregation; “Upstroke/t_top”, reflects the kinetics of RBC disaggregation. For further details of underlying data and the calculation of the parameter, respectively, please see Hardemanet al. [17].

2.4 Statistical analysis

Data are expressed as mean±standard deviation (SD). Differences between study groups were tested by unpaired t test in case of continuous data and by Fisher’s exact test in case of categorical data (IBM SPSS Statistics 20.0, Armonk, USA). rCFA values and LORCA aggregation parameter in patients with CAD and a healthy control group were presented in a box plot as minimum and maximum (crosses), interquartile range from 25 to 75% (box), mean (square), and median (line). Linear regression curves and correlation coefficients were calculated according to the least squares method (Origin 7 G SR2, OriginLab, Northampton, USA). P-values ≤0.05 were accepted as statistically significant.

3 Results

3.1 Clinical characteristics of CAD patients and healthy volunteers

CAD patients and the healthy control group differed in age, body mass index, blood pressure, lipid profile (triglycerides, high-density-lipoprotein), plasma glucose and HbA1c. Mean corpuscular volume and mean corpuscular hemoglobin were different between the two groups, whereas mean corpuscular hemoglobin concentration was not. Hemoglobin, hematocrit and number of reticulocytes were comparable between the two groups (Table 1).

Table 1
Patient characteristics

CAD patients Healthy controls

Number [n] 17 19

Age [years] 62.6±6.9^* 24.3±3.7

Body mass index [kg/m²] 29.5±5.3^* 22.7±2.4

Hemoglobin [g/l] 142.6±10.2 138.7±13.9

Hematocrit [l/l] 0.42±0.03 0.41±0.04

Mean corpuscular volume [fl] 89.6±2.8^* 86.7±5.1

Mean corpuscular hemoglobin [pg] 30.9±1.3^* 29.5±2.1

Mean corpuscular hemoglobin concentration [g/l RBC] 343.2±7.4 340.4±10.0

Reticulocytes [% ] 9.8±3.9 9.3±2.8

White blood cell count [109/l] 6.5±1.8 5.6±1.2

Platelets [109/l] 236.9±75.3 249.3±55.5

Triglycerides [mg/dl] 141.1±54.6^* 80.7±31.5

Cholesterol [mg/dl] 178.1±42.4 169.7±33.3

Low density lipoprotein [mg/dl] 108.7±35.4 92.3±28.4

High density protein [mg/dl] 52.7±13.1^* 71.0±25.4

Glucose [mg/dl] 121.1±34.6^* 86.6±5.5

Hemoglobin A1c [% ] 6.3±1.0^* 5.2±0.2

Diabetes mellitus [n] 11^* 0

C-reactive protein [mg/dl] 5.32±1.49 <5

Systolic blood pressure [mmHg] 147.7±23.3^* 114.8±15.9

Diastolic blood pressure [mmHg] 87.8±10.7^* 68.9±9.9

Hypertension [n] 15^* 0

CAD patients	Healthy controls
Number	[n]	17	19
Age	[years]	62.6±6.9^*	24.3±3.7
Body mass index	[kg/m²]	29.5±5.3^*	22.7±2.4
Hemoglobin	[g/l]	142.6±10.2	138.7±13.9
Hematocrit	[l/l]	0.42±0.03	0.41±0.04
Mean corpuscular volume	[fl]	89.6±2.8^*	86.7±5.1
Mean corpuscular hemoglobin	[pg]	30.9±1.3^*	29.5±2.1
Mean corpuscular hemoglobin concentration	[g/l RBC]	343.2±7.4	340.4±10.0
Reticulocytes	[% ]	9.8±3.9	9.3±2.8
White blood cell count	[109/l]	6.5±1.8	5.6±1.2
Platelets	[109/l]	236.9±75.3	249.3±55.5
Triglycerides	[mg/dl]	141.1±54.6^*	80.7±31.5
Cholesterol	[mg/dl]	178.1±42.4	169.7±33.3
Low density lipoprotein	[mg/dl]	108.7±35.4	92.3±28.4
High density protein	[mg/dl]	52.7±13.1^*	71.0±25.4
Glucose	[mg/dl]	121.1±34.6^*	86.6±5.5
Hemoglobin A1c	[% ]	6.3±1.0^*	5.2±0.2
Diabetes mellitus	[n]	11^*	0
C-reactive protein	[mg/dl]	5.32±1.49	<5
Systolic blood pressure	[mmHg]	147.7±23.3^*	114.8±15.9
Diastolic blood pressure	[mmHg]	87.8±10.7^*	68.9±9.9
Hypertension	[n]	15^*	0

Continuous data are presented as mean±SD, categorical data as absolute numbers. Comparison between patients with CAD and healthy control by unpaired t test (continuous data) and by 2-tailed Fisher’s exact test (categorical data), ^*p≤0.05 vs. healthy controls; CAD = coronary artery disease, RBC = red blood cell.

3.2 RBC aggregation

Both methods detected increased RBC aggregation of CAD patients compared to the healthy control group. The rCFA measured with EAAT determined an increased RBC aggregation of patients with CAD (15.65±7.68% vs. 11.30±4.31%). All analysis parameters of the LORCA device detected an increased RBC aggregation: AI (66.33±10.61% vs. 53.90±8.33%), SDA (105.59±31.32 s^–1 vs. 69.21±19.10 s^–1) and Upstroke/t_top (0.03±0.01 au/s vs. 0.02±0.01 au/s) were increased in CAD patients in comparison to the healthy control group. Accordingly, t_1/2 exhibited a reduction (2.11±0.67 vs. 3.60±1.46 s) (Fig. 1).

Fig.1

EAAT and LORCA aggregation parameter in patients with CAD and a healthy control group. Values are presented as minimum and maximum (crosses), interquartile range from 25 to 75% (box), mean (square), and median (line) in a box plot. Comparison between patients with CAD and a healthy control group was done by unpaired t test (^*p≤0.05 vs. healthy controls); AI = aggregation index; CAD = coronary artery disease; rCFA = ratio of clot-free area to whole area; SDA = shear rate of disaggregation; t_1/2 = aggregation halftime.

3.3 Correlation between EAAT and LORCA parameters

rCFA (EAAT) correlated with SDA (LORCA) in patients with CAD and in the healthy control group (Table 2) and also in both study groups (Table 2, Fig. 2). However, there was no correlation between rCFA and the other LORCA parameters (AI, Upstroke/t_top, t_1/2) (Table 2).

Table 2
Correlation between EAAT parameter rCFA and LORCA parameters

CAD patients Healthy controls Both study groups

R p R p R p

AI 0.19 0.47 –0.05 0.84 0.30 0.07

SDA 0.56 0.02^* 0.55 0.01^* 0.68 <0.0001^*

t_1/2 –0.29 0.25 0.09 0.71 –0.25 0.14

Upstroke/t_top –0.37 0.15 –0.02 0.95 –0.09 0.61

CAD patients	Healthy controls	Both study groups
AI	0.19	0.47	–0.05	0.84	0.30	0.07
SDA	0.56	0.02^*	0.55	0.01^*	0.68	<0.0001^*
t_1/2	–0.29	0.25	0.09	0.71	–0.25	0.14
Upstroke/t_top	–0.37	0.15	–0.02	0.95	–0.09	0.61

AI = aggregation index; CAD = coronary artery disease; p = p-value of correlation; R = Pearson’s correlation coefficient; SDA = shear rate disaggregation; t_1/2 = aggregation halftime; ^*statistically significant p-value.

Fig.2

Correlation between rCFA (EAAT) and SDA (LORCA). Linear regression (black line) between rCFA and SDA of both patient groups (r = 0.68, p < 0.0001); CAD = coronary artery disease; rCFA = ratio of clot-free area to whole area, SDA = shear rate of disaggregation.

4 Discussion

Confirming prior studies [21, 32], the RBC aggregation was increased in patients with CAD compared to the healthy control group. We determined this increased RBC aggregation independently of the used measurement system (EAAT and LORCA) and the measurement parameters (rCFA, AI, SDA, t_1/2 and Upstroke/t_top). Prior studies, using the EAAT have demonstrated that an increased RBC aggregation is generally associated with the presence of cardiovascular risk factors such as familial and primary hypercholesterolemia [19], asymptomatic carotid stenosis [2], ST-elevation myocardial infarction and acute ischemic stroke [25]. In studies using the LORCA, increased RBC aggregation was described in patients with arterial hypertension [12], acute myocardial infarction [39] and obesity [10 , 34].

When comparing the EAAT measurement parameter rCFA, determined under static conditions, with the different measurement parameters of the LORCA, determined under different shear stress conditions, rCFA correlated only with one parameter of the LORCA, the SDA. Different factors influence the RBC aggregation: plasmatic macromolecules, like fibrinogen [8 , 36], RBC membrane charging distribution [16, 30] or the composition of RBC membrane proteins [14 , 29]. These factors may influence the RBC aggregation by various mechanisms. The measurement methods and their detection parameters may reflect different RBC aggregation mechanisms/properties in a different manner. Prior studies have demonstrated a correlation of LORCA aggregation parameters with cardiovascular risk factors [40, 41]. Not all changes in blood parameters, which are associated with cardiovascular risk factors, are accompanied with an increase in RBC aggregation. Total cholesterol levels correlate positively with the AI and negatively with t_1/2, whereas levels of lipoproteins or triglycerides do not correlate with RBC aggregation parameters [41]. The correlation between rCFA (EAAT) and SDA (LORCA) may reflect the fact, that these both parameters display the same underlying mechanisms for the increased RBC aggregation.

Taken together, despite of the different techniques, detection parameters and conditions (static condition versus shear condition), both methods EAAT and LORCA are useful to determine an increased RBC aggregation. Future studies are necessary to identify RBC aggregation properties, which are relevant for patient microcirculatory disorders. Epidemiological studies for a clinical evaluation of RBC aggregation as cardiovascular risk factor are also lacking so far. A simple, quick, and feasible method determining RBC aggregation, however, is a prerequisite for patient studies investigating pathogenic and therapeutic potential of RBC aggregation [1].

5 Study limitations

The number of included patients and volunteers is small. Differences in RBC aggregation of CAD patients and young healthy volunteers are not generally transferrable to other collectives. The risk factors may influence the RBC aggregation in a different manner causing an unequal effect on the measurement parameters. In this descriptive study, we could not identify the underlying mechanisms causing the correlation of EAAT parameter with just one LORCA parameter. Further studies are necessary to characterize the underlying mechanisms. We here compare just one automatic device with the EAAT.

Funding

This work was supported by the Dr. Heinz-Horst Deichmann Foundation.

Conflict of interest

The authors declare no conflict of interest.

Footnotes

Acknowledgments

The technical assistance of Katharina Lysaja is gratefully acknowledged.

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Comparison of the simple red blood cell adhesiveness/aggregation test with the laser-assisted optical rotational cell analyzer: Red blood cell aggregation in patients with coronary artery disease and a healthy control group

Abstract

Keywords

1 Introduction

2 Material and methods

2.1 Study cohort

2.2 Sample preparation

2.3 Measurement of RBC aggregation

2.3.1 Simple slide test: EAAT (according to Rotstein et al. [34])

2.3.2 Automatic device: LORCA (R&R Mechatronics, Hoorn, The Netherlands) measurement (according to Hardeman et al. [17])

2.4 Statistical analysis

3 Results

3.1 Clinical characteristics of CAD patients and healthy volunteers

Table 2 Correlation between EAAT parameter rCFA and LORCA parameters CAD patients Healthy controls Both study groups R p R p R p AI 0.19 0.47 –0.05 0.84 0.30 0.07 SDA 0.56 0.02* 0.55 0.01* 0.68 <0.0001* t1/2 –0.29 0.25 0.09 0.71 –0.25 0.14 Upstroke/ttop –0.37 0.15 –0.02 0.95 –0.09 0.61

5 Study limitations

Funding

Conflict of interest

Footnotes

Acknowledgments

References