The effect of exercise on blood fluidity: Use of the capillary model to assess the clogginess of blood

Abstract

AIM: The goal was to evaluate the effects of exercise on the clogginess of blood as well as the effect of increased blood cell count and hematocrit levels after exercise. We also investigated the mechanisms underlying the clogginess of blood.

METHODS: The time required for blood to pass through microchannels was measured. We focused on assessing the consecutive passage times for serial 20 μL volumes. We used heparinized peripheral blood obtained from subjects after exercise conducted at three intensity levels. Blood samples were also adjusted to achieve specific hematocrit levels or supplemented by addition of adenosine diphosphate (ADP).

RESULTS: The sequential blood passage times of consecutive 20 μL volumes increased with platelet aggregation and adhesion of white blood cells (WBC). We also observed an increase with blood cell counts and hematocrit levels. These changes occurred after high intensity exercise but not after low or medium intensity exercise. Furthermore, the sequential blood passage times of 20 μL volumes increased with platelet aggregation and adhesion of WBC at an ADP concentration at the threshold of aggregation but not at higher levels of hematocrit.

CONCLUSIONS: These findings suggested that high intensity exercise might induce the clogginess of blood by enhanced platelet aggregation and adhesion of WBC.

Keywords

Blood fluidity exercise intensity clogginess of blood white blood cells platelets

1 Introduction

Exercise is necessary for the promotion of health [18]. However, inappropriate exercise might be harmful, and cardiovascular events during exercise might actually lead to sudden death [16]. Gas and energy exchange during exercise occurs in the microcirculation of the peripheral capillaries. Blood fluidity is one of the factors that affect the microcirculation. Dysfunction within the microcirculation, especially a decline in blood fluidity is reportedly related to the occurrence of cardiovascular events [9, 10]. Gauddard et al. reported exercise-induced impairment of blood fluidity. They described a 50-year-old marathonrunner who underwent a central retinal vein thrombosis after a marathon run and investigated his rheological response to submaximal exercise [4].

Several factors, including dehydration, an increase of hematocrit, plasma viscosity, red blood cell (RBC) aggregation and a decrease of RBC deformability induced by strenuous exercise might cause a decline in blood fluidity [4, 8]. An increase of blood fluidity during exercise is necessary for nitric oxide production and adequate vasodilation [3]. However, there would be a threshold of vasodilation above which vascular function is not able to further adapt to a rise in blood fluidity. The threshold of vasodilation might be dependent on an endothelial dysfunction related to an atherosclerotic disease [19] or a physiological situation such as high intensity exercise. Therefore, clarifying the relationship between exercise and blood fluidity might be helpful for the prevention of cardiovascular events during exercise.

The MC-FAN is a blood rheology measurement device developed by Kikuchi et al. to evaluate blood fluidity [11 –13]. The MC-FAN device can precisely measure the time required for 100 μL of blood to pass through narrow channels and thereby evaluates blood fluidity at the capillary level. The factors that have an influence on blood passage time are mainly hematocrit levels and blood cell counts. Previous reports have demonstrated that blood passage time increases as a result of the increase of blood cell count and hematocrit levels after exercise [1 , 14].

Recently, it was reported that blood passage time was prolonged by an increase in the number of obstructed microchannels as well as increases in the blood cell count and hematocrit levels in the MC-FAN capillary model [7]. There are relatively few reports that have found that exercise increases the blood passage time caused by changes in the cloggy status of blood and the increase of blood cell counts. Ikeda et al. reported that high intensity exercise, but not low intensity exercise, prolonged blood passage time. This observation was accompanied by an increase in the number of obstructed microchannels as well as an increase in hematocrit levels [6]. Our preliminary study also showed that when we measured consecutive blood passage times of sequential 20 μL samples (0–20 μL, 20–40 μL, 40–60 μL, 60–80 μL and 80–100 μL) after high intensity exercise, the blood passage time of the later 20 μL fractions gradually increased [15]. The image display unit revealed the obstruction of the microchannels by aggregation and adherent or clogging platelet and WBC in the later 20 μL fractions. Based on these results, we hypothesized that it would be important to clarify the relationship between exercise intensity and the cloggy status of blood as well as the increase of blood cell count and hematocrit levels to avoid cardiovascular events during exercise.

In the present study, we termed the above mentioned cloggy status of blood in the MC-FAN capillary model “the clogginess of blood”. We evaluated the effect of that parameter as well as the effect of blood cell count and hematocrit levels on blood passage time in the capillary model using blood samples from the same subjects after a single bout of exercise at different intensities. In addition, to investigate the mechanisms contributing to the clogginess of blood, we examined whether hematocrit levels and addition of adenosine diphosphate (ADP) affected blood fluidity, especially with regard to the clogginess of blood.

2 Methods

2.1 Effects of exercise on blood fluidity

2.1.1 Subjects

The subjects included eight healthy, non-smoking male volunteers (age: 22.2 ± 1.6 years, height: 170.8 ± 8.1 cm, weight: 61.2 ± 13.3 kg, mean ± SD). The subjects undertook regular physical activity or exercise more than five times a week during high school. At the time of this study, they did not undertake regular physical activity or exercise as they used to do, but were not sedentary. This protocol was approved by the ethics committee of the Hiroshima University Graduate School of Health Sciences (No. 0530). Written informed consent was obtained from all subjects.

2.1.2 Exercise protocol

We performed exercise testing with a load of 20 watts/min by using a bicycle ergometer (STB-2400; Combi Co., Ltd., Tokyo, Japan). The subjects pedaled the bicycle ergometer at 50 rpm. We estimated the peak oxygen intake (peak $\dot{V}$ O₂) and the anaerobic threshold (AT) from the results of the analysis of expired gas (Aero monitor AE300s; Minato Medical Co., Ltd., Osaka, Japan). We used three levels of exercise intensity. Low intensity exercise was 80% of exercise levels at the AT. Medium intensity exercise was at the AT. High intensity exercise was at [AT+(peak $\dot{V}$ O₂-AT) ×0.5]) based upon preliminaryresearch [5].

One of the three intensity levels of exercise was performed randomly, by subject choice, at the same time of the day once a week. We asked the subjects to only engage in their usual activities and not to change their eating habits or life styles during the period of the experiment. The subjects were prohibited from drinking water during the measurements. The three exercise intensities were carried out for 30 min at room temperature (293.15K). The subjects could stop the exercise if they felt severe exhaustion or had other problems before reaching 30 min.

2.1.3 Blood collection

Blood was sampled from an antecubital vein with anticoagulation by heparin solution (5% v/v 1000 IU/mL heparin and 95% blood) in a seated position. The volume of the blood sample was 10 mL (5 mL ×2 vacuum tubes). The measurement of blood fluidity was done within five minutes of drawing samples.

2.1.4 Determination of blood passage time using a 100 μL sample

Blood fluidity was assessed with Kikuchis’ microchannel method [11 –13]. Microgrooves (width, seven μm; length, 30 μm; depth, 4.5 μm) were photo-fabricated on the surface of a single crystal silicon substrate (chip dimensions 15×15 mm). We converted microgrooves into leak-proof microchannels by tightly covering them with an optically flat glass plate. Because the volume of fluid that flows through one flow path is extremely small, 8736 flow paths of the same size were created to make it possible to measure the flow velocity. The silicon single crystal substrate was then mounted onto the microchannel flow system, MC-FAN (Hitachi Haramachi Electronics Co., Ltd, Ibaragi, Japan). This system makes it possible to observe directly the flow of blood cell elements through the microchannels under a microscope connected to an image display unit. Using this system, flow can be continuously viewed while the passage time for a given volume of blood is determined automatically. We measured blood passage time and observed the state of blood flow. Our revised value of blood passage time was expressed as a function of the actual whole blood passage time. Initially, the system was primed by determining the passage time for 100 μL of saline (typically 12 sec) at a pressure of 20 cm H₂O. The volume of each specimen was 100 μL. $Blood passage time (revised value; \sec) = \frac{Whole blood passage time (actual value)}{Saline solution passage time} \times 12$

2.1.5 Determination of split blood passage time and definition of the clogginess of blood

We measured not only the blood passage time of 100 μL, but also the sequential blood passage times for consecutive 20 μL volumes (0–20 μL, 20–40 μL, 40–60 μL, 60–80 μL and 80–100 μL). The image display unit connected to the microscope revealed whether or not red blood cells (RBC), WBC and platelets were passing through the microgrooves. The extent to which platelets and WBC clumped together and blocked the microgrooves in the MC-FAN capillary model was defined as the clogginess of blood. The clogginess of blood was indicated by the prolongation of blood passage time in the later 20 μL fractions until finally nothing could pass through the microgrooves.

2.1.6 Blood cell count and body weight

An automatic cell counter (Beckman Coulter Co., Ltd.) was used to quantitate RBC, WBC, hemoglobin levels, hematocrit levels, and platelets. We measured body weight (to 50 g precision) before and after exercise to check the effect of dehydration.

2.2 Effect of hematocrit and ADP on blood fluidity

2.2.1 Determination of hematocrit levels

Blood samples were centrifuged at 1000 rpm for ten min and separated into plasma and RBC. We prepared samples with hematocrit levels of 40% , 50% , and 60% using their own RBC and plasma.

2.2.2 Addition of ADP

To promote platelet aggregation, ADP was added to samples so that the final concentration of the ADP was 0.01 μM, 0.03 μM, 0.05 μM, 0.08 μM and 0.1 μM. Samples rested for ten min before measuring.

2.2.3 Blood collection the determination of blood passage time and the clogginess of blood

These steps were done in the same way as described for the experiments assessing the effect of exercise on blood fluidity.

2.3 Statistics

The measured values were compared using two-way analysis of variance with repeated measures, with exercise intensities (low, medium and high) and with trials (levels of hematocrit or ADP). Blood passage time, blood cell counts, and body weight were analyzed using paired t-tests for comparison before and after exercise. The initial blood passage time (0–20 μL) and blood passage time of 100 μL were analyzed using a Tukey’s HSD post hoc test for comparison of exercise intensities, levels of hematocrit or ADP.

These values included means ± standard deviation (SD). Differences associated with P < 0.05 were considered statistically significant. We used SPSS 21.0 (IBM Japan) to calculate statistics.

3 Results

3.1 Effect of exercise on blood fluidity

3.1.1 Exercise time

All subjects completed 30-min low and medium intensity exercises. However, six of eight subjects were not able to complete the 30-min high intensity exercise because of exhaustion. Exercise duration at high intensity averaged 22.8 ± 6.2 min.

3.1.2 Effect of exercise on blood passage time of 100 μL

Figure 1 shows the blood passage time of 100 μL before and after exercise. The blood passage time was not significantly changed after low and medium intensity exercises (low intensity exercise, baseline: 43.9 ± 3.7 sec versus after exercise: 43.0 ± 2.4 sec, p = 0.474; medium intensity exercise, baseline: 41.3 ± 3.9 versus after exercise: 42.8 ± 5.0 sec, p = 0.095). On the other hand, blood passage time was significantly increased after high intensity exercise (baseline: 44.6 ± 2.0 sec versus after exercise: 54.3 ± 2.7 sec, p < 0.01). A significant interaction between exercise intensity and time was observed for blood passage time (p < 0.01).

3.1.3 Effect of exercise on sequential blood passage times for 20 μL volumes

Figure 2A shows the sequential blood passage times every 20 μL after exercise. The blood passage times every 20 μL after low and medium intensity exercise had essentially constant values. On the other hand, sequential blood passage times every 20 μL after high intensity exercise increased. There were significant differences in initial blood passage times (0–20 μL) between low and high intensity exercise, as well as medium and high intensity exercise (all, p < 0.01), but no significant differences between low and medium intensity exercises. A significant interaction between exercise intensity and time was observed for blood passage time (p < 0.01). Obstruction of the microchannels by platelet aggregation and adhesion of WBC was observed in the later 20 μL fractions after high intensity exercise (Fig. 2B), but not after low or medium intensity exercise (Fig. 2C).

3.1.4 Effect of exercise on blood cell counts

The blood cell counts were not significantly changed after low and medium intensity exercises (Table 1). WBC numbers, RBC numbers, hemoglobin levels, and hematocrit levels were significantly increased after high intensity exercise (p < 0.01, p < 0.05, p < 0.05 and p < 0.01, respectively). The numbers of platelets were not significantly changed after any intensity of exercise. A significant interaction between exercise intensity and time was observed for WBC numbers, RBC numbers, hemoglobin levels, hematocrit levels, and platelet numbers (p < 0.05, p < 0.05, p < 0.01, p < 0.01 and p < 0.05, respectively).

3.1.5 Effect of exercise on body weight

At all three intensity levels of exercise, body weight was significantly decreased after exercise. Specifically, in low intensity exercise, the baseline was 61.8 ± 12.6 kg whereas after exercise, body weights averaged 61.6 ± 12.6 kg, p < 0.01. For middle intensity exercise, the baseline was 62.0 ± 12.6 kg versus 61.7 ± 12.6 kg after exercise, p < 0.01. Finally for high intensity exercise, the baseline was 61.3 ± 12.5 kg whereas post-exercise, body weights averaged 60.8 ± 12.4, p < 0.01. A significant interaction between exercise intensity and time was observed for body weight (p < 0.01).

3.2 Effects of hematocrit and ADP on blood fluidity

3.2.1 Effect of hematocrit levels on blood passage time

Blood passage times of a 100 μL sample at 40% , 50% and 60% hematocrit levels were 36.2 ± 2.7 sec, 43.9 ± 4.2 sec, and 48.5 ± 3.9 sec, respectively. As the hematocrit level was increased, the blood passage time of 100 μL tended to increase. Significant differences between hematocrits of 40% vs. 60% and 40% vs. 50% were observed in the blood passage time of 100 μL (all, p < 0.01).

Figure 3 shows the sequential blood passage time every 20 μL after the preparation of samples. There were significant differences in the initial blood passage time (0–20 μL) between the groups (all, p < 0.01). No significant interaction between hematocrit levels and time was observed (p = 0.666), but the effect of hematocrit levels was observed (p < 0.05). There was no obstruction of the microchannels by aggregation of platelets or adhesion of WBC in any time course at any hematocrit level.

3.2.2 Effect of ADP concentrations on blood passage time

The image display unit revealed that platelets gradually clumped and blocked the microgrooves when platelet aggregation was induced by ADP. Eventually, samples could not pass through the microgrooves.

In this study, we defined the aggregation threshold for ADP as the maximal concentration of ADP that would still allow the sample to pass through the microchannels without complete blockage. The aggregation thresholds were different in each subject and were 0.03 μM for one subject, 0.05 μM for one subject, 0.08 μM for four subjects, and 0.1 μM for two subjects. Blood passage times for 100 μL without ADP addition, 0.01 μM ADP and aggregation threshold were 40.1 ± 3.1 sec, 41.3 ± 2.7 sec, 68.0 ± 13.1 sec, respectively. Significant differences in blood passage times for 100 μL were observed between no addition and the aggregation threshold, as well as 0.01 μM ADP and the aggregation threshold (all, p < 0.01). Figure 4A shows sequential blood passage times every 20 μL after the addition of ADP. Significant differences in initial blood passage times (0–20 μL) were also observed between no addition and the aggregation threshold, as well as 0.01 μM ADP and the aggregation threshold (all, p < 0.01). A significant interaction between ADP levels and time was observed for blood passage time (p < 0.05). Obstruction of the microchannels by aggregation of platelets and adhesion of WBC was observed in the later 20 μL fractions at the aggregation threshold (Fig. 4B).

4 Discussion

In the present study, we examined the clogginess of blood as well as the effect of blood cell count and hematocrit levels on blood passage time using blood samples from the same subject after a single bout of exercise at three intensity levels. The sequential measurements of the duration of blood passage for consecutive 20 μL volumes increased with platelet aggregation and adhesion of WBC as well as the increase of blood cell count and hematocrit levels after high intensity exercise, but not after low or medium intensity exercise. Furthermore, the sequential blood passage times at 20 μL intervals increased with platelet aggregation and adhesion of WBC at the concentration of aggregation threshold of ADP, but not at higher levels of hematocrit. These findings suggested that high intensity exercise might increase the clogginess of blood through platelet aggregation and adhesion of WBC.

Although there have been many studies of the effects of exercise on blood fluidity such as the changes in hematocrit, plasma viscosity, RBC deformability and RBC aggregation [3], few reports have assessed the clogginess of blood using the MC-FAN capillary model. Blankfield analyzed the velocity of blood flow and turbulence in their preliminary study, but it was based on theoretical calculations [2].Ikeda et al. reported that high intensity exercise prolonged blood passage time, a change that was accompanied by an increase in the number of obstructed microchannels and an increase in the blood cell count and hematocrit levels [6]. Kamada et al. reported that ADP-induced activated platelets and erythrocytes were trapped within a fibrin meshwork, and thrombi occluded the upper channel of the microgrooves [7].

In the present study, we measured sequential passage times for consecutive 20 μL volumes (0–20 μL, 20–40 μL, 40–60 μL, 60–80 μL and 80–100 μL) as well as the blood passage time for 100 μL. The sequential blood passage times for consecutive 20 μL volumes after high intensity exercise increased until the microchannels were obstructed by platelet aggregation and adhesion of WBC. This was also observed after an increase of blood cell count and hematocrit levels. In contrast, the sequential blood passage times of 20 μL volumes were stable after low and medium intensity exercise. Under those conditions, no obstruction of the microchannels by platelet aggregation or adhesion of WBC was observed. Our study suggested that there might be a relationship between exercise intensity and the clogginess of blood.

To clarify the mechanisms contributing to changes in clogginess of blood, we performed experiments that showed the effect of the addition of ADP and the elevation of hematocrit levels. The sequential blood passage time for consecutive 20 μL volumes increased at the ADP-mediated aggregation threshold resulting in the obstruction of the microchannels by platelet aggregation with adhesion of WBC. The sequential blood passage times for consecutive 20 μL volumes were essentially constant at higher hematocrits with no obstruction of the microchannels by platelet aggregation and with no adhesion of WBC. The change in clogginess of blood might be induced by platelet aggregation with adhesion of WBC.

Our data suggest that studying the sequential blood passage times of consecutive 20 μL volumes might be useful to detect the effect of platelet aggregation and the adhesion of WBC induced by addition of ADP. The blood passage time of the full 100 μL volume was increased by the elevation of hematocrit levels and by the addition of ADP. The sequential blood passage times of consecutive 20 μL volumes were sequentially increased at a threshold concentration of ADP, but not at higher hematocrits. The sequentially increased blood passage times after high intensity exercise might be caused by platelet aggregation and adhesion of WBC rather than an increase of blood cell counts and hematocrits. The initial blood passage time (0–20 μL) might be increased due to elevation of hematocrit levels as well as addition of ADP. In the present study, the initial blood passage times (0–20 μL) differed significantly between the aggregation threshold of ADP and lower ADP levels as well as between a 40% hematocrit level and more than 50% . The increase of the initial blood passage time at the aggregation threshold of ADP might be caused by several mechanisms, including increased clogginess of blood but not through an increase of hematocrit levels. This novel method of looking at consecutive blood passage times is a new strategy to assess clogginess of blood in the MC-FAN capillary model.

With regard to the effects of exercise intensity on blood passage time, previous studies have reported that it increased during high intensity exercise [8, 14], whereas other studies reported that low or medium intensity exercise could also influence blood fluidity [1]. We believe that these discordant results were a result of several factors, including different methods of exercise, different intensities of exercise and different types of subjects (healthy and young vs. obese and middle aged). Connes et al. also reported that the discrepancy of previous data on the effects of exercise on hemorheology seemed to be related to the exercise protocol or the population tested [3]. Therefore, it is necessary to perform exercise under consistent experimental conditions to evaluate the precise effects of exercise on blood fluidity. Simmonds et al. have evaluated the effect of a single bout of cycling exercise on blood fluidity at different intensities in the same subjects [17]. They reported a significant increase of blood viscosity after a single bout of high-intensity cycling. In the present study, we examined the effect on blood fluidity of a single bout of three intensity levels of exercise in the same subjects. Our data suggested that high intensity exercise (but not low or medium intensity exercise) increased blood cell counts and hematocrit levels. These changes were observed as increased blood passage time and the increased clogginess of blood. These findings suggest that low and medium intensity exercise might be more suitable for health promotion from the standpoint of blood fluidity.

This study was limited by the fact that we did not clarify which type of blood cells nor which plasma component might have reduced the clogginess of blood. In addition, the number of subjects was small.

In conclusion, our study demonstrated that high intensity exercise might increase the clogginess of blood by platelet aggregation and adhesion of WBC as well as by the elevation of hematocrit. Low and medium intensity exercise might be more suitable for health promotion in respect to blood fluidity.

Footnotes

Acknowledgments

This research was supported in the part by Yasuda Educational Foundation Scientific Research Grant. The authors are particularly grateful for the technical assistance given by Mr. Masahiro Nishikawa and Ms. Haruna Kono.

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