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8CORRELATION OF HEMORHEOLOGIC PARAMETERS MEASURED IN VITRO AND IN VIVO BY DIFFERENT OPTICAL TECHNIQUES IN PATIENTS SUFFERING FROM VARIOUS SOCIALLY IMPORTANT DISEASES
ALEXANDER PR1EZZHEV1'2. ANDREI LUGOVTSOV12. ANASTASIA MASLYANITSINA1. PETR ERMOLINSKIY1 AND YURI GURFINKEL3
'PhysicsDepartment, Lomonosov Moscow State University, Russia 2International Laser Centre, Lomonosov Moscow State University, Russia 3Medical Research-Educational Centre, Lomonosov Moscow State University, Russia
avp2@mail.ru
Abstract
In this work. in vivo and in vitro optical methods were used to study the microrheologic properties of red blood cells (RBC) in patients suffering the coronary heart disease (CHD) and comorbidities such as type 2 diabetes mellitus (T2DM). The main focus of our study was the aggregation of RBC. which significantly influences the viscosity of blood [1]. Aggregation is a reversible process of formation of linear and more complex structures of RBC. It promotes the formation of peripheral cell-poor fluid layer that lowers the hydrodynamic vessel resistance to the blood flow [1]. In vitro as well as in vivo. the process of RBC aggregation can be described in several ways: by the amount of cells aggregating during a given time interval. by how many aggregates are observed. or by how fast a couple of RBCs can form a doublet. These parameters require specialized tools in order to be measured. so they are not used widely in clinical conditions.
Many methods can be applied to study RBC including the micropipette aspiration [2]. but optical techniques have several advantages: non-invasiveness and lack of direct mechanical contact with the cells; the option to study both individual cells and their ensembles; the possibility of in vivo and in vitro application. The last point can pose a challenge in terms of comparing results for these different conditions - in vitro measurements require anticoagulants for stabilizing the blood samples and storing blood during sample preparation. which can influence the measured parameters; while in vivo methods allow for measuring a different set of parameters that may be difficult to correlate with those measured in vitro.
The aim of this work was to find correspondence between in vivo and in vitro optical methods by studying patients with cardiovascular and associated pathologies. Understanding the link between the RBC aggregation and widespread cardiovascular diseases is vital to create new methods of diagnosis and treatment. The age. state of health and lifestyle of an individual determine in part the aggregation parameters of RBC [3]. as does the medicine intake [1.4].
The study enrolled 81 adults. including 25 healthy volunteers and 56 patients with CHD and arterial hypertension. The patients with CHD were divided into two groups depending on the presence of T2DM. First groups included 42 CHD patients without T2DM. second group included 14 CHD patients with T2DM.The healthy volunteers had an average age 27.5 and body mass index (BMI) 22.1. they were non-smokers. and had not been taking any medication. They were divided into two control groups (n = 10 and 15) that were studied independently by two different research teams.
Laser aggregometrymethod implemented in microchip stirring type RheoScan aggregometer (RheoMedTech, Rep. of Korea) was used to measure the aggregation parameters of RBC in vitro in whole blood samples [5]. The measurement technique is based on the diffused light scattering of a laser beam by the blood sample [6].The RBC aggregation kinetics is reflected in the dependence of scattered light intensity on time and several parameters are calculated based on it. Firstly. T12 characterizes the time interval. during which the signal (scattered light intensity) reaches half of the maximum value. Smaller Ty2 corresponds to greater curve slope and faster aggregation of the cells. Secondly. the aggregation index (AI) characterizes the fraction of cells aggregated during the first 10 seconds of measurement. It is calculated as a ratio between the area under the intensity curve to the total area above and below it. Higher AI values correspond to more numerous RBC aggregates in the sample.
Lasers tweezers were used to measure the duration of the process of spontaneous aggregation of two individual RBCs [7]. The measurements are carried out on a highly diluted blood suspension inside a glass microcuvette with a 100 ^m gap [8]. Patients' autological platelet poor plasma was used as the suspension medium. The time of doublet aggregate formation Tagg was measured in real time. Smaller values of Tagg correspond to faster aggregate formation. It's worth noting that the Tagg parameter corresponds to the initial stage of the RBC aggregation process. while laser aggregometry parameters (Ty2 and AI) represent the whole aggregation process. including the later stage of complex 3D structures formation.
Digital capillaroscopy (DC) [9] was used to evaluate the capillary blood flow parameters in vivo. Using the device Kapillaroskan-1 (AET. Russia) a quantitative assessment of the blood flow characteristics was carried out in the
capillaries of the nail bed. Several nail bed capillaries of each individual were recorded at a high frame fate and then used to assess the average capillary blood flow velocity (CBV), which was calculated by frame-by-frame analysis. The cases of absence or presence of aggregates in the bloodstream were defined as individual RBC or groups of several RBC separated by empty layers of plasma respectively.
For each blood sample, the parameters AI, Ti/2 and Tagg were measured 5 times. The calculation of the CBV and the detection of aggregates for the DC method was carried out using original software that analyzed recordings of the nail bed capillaries. Videos of at least 6 capillaries were used for calculations with the video duration of 3 to 5 seconds (at a recording rate of 100 frames per second, i.e., from 1800 to 3000 frames per patient).
The parameters assessed with all used methods for all CHD patients are shown different from those for the control groups. The first and second control groups do not show statistically significant differences between themselves. Our results show not only a significant difference of the aggregation parameters characteristic of patients compared to those of healthy people, but also a correspondence between the parameters measured in vivo and in vitro. RBC aggregate in CHD patients faster and more numerously, in particular the aggregation index increases by 20±7%.
The comorbidity of T2DM also significantly elevates aggregation in CHD patients. In particular, in CHD patients compared to the control group AI is higher by 20 ± 7% (p < 0.05), meaning more numerous aggregation, T1/2 is lower by 14 ± 9% (p < 0.05), meaning faster aggregation, and Tagg is lower by 27 ± 7% (p < 0.05), showing faster doublet formation. As for CBV, it is smaller than in the control. The two groups CHD patients (with and without T2DM) show significant (p < 0.05) differences. The AI parameter for all studied groups decreases with an increase in CBV, while T1/2, on the other hand, increases. Tagg remains constant for the whole CBV range. These results show that for patients with high CBV, the aggregation process in vitro is weaker compared to that for the patients with low CBV: it is less numerous and the doublet formation takes longer time. No statistically significant difference in aggregation was found between several patient subgroups, including the division by gender and smoking habits. AI weakly correlated with BMI and did not correlate with age.
The novelty of this work consists in a complex analysis of the parameters measured in vitro and in vivo for different groups of people. The results of studies performed by alternative methods do not contradict our conclusions and show increased aggregation of RBC in patients with CHD (including complications) compared to healthy donors [1, 10]. One of the limitations of the study is the small number of patients with both CHD and T2DM; in the future, we plan to increase this number. Another point that can be improved is the observation of additional factors that influence the blood flow, for example plasma components [1]. Also, BMI and other factors influence platelet activation and aggregation, which can indirectly affect the aggregation of RBCs; this was not accounted for in this work.
Acknowledgment: The authors acknowledge the financial support provided to this study by RFBR grant № 19-5251015.
References
[1] O. Baskurt,B. Neu, H. Meiselman, Red Blood Cell Aggregation, CRC Press, Boca Raton, 2012.
[2] R. Hochmuth, Micropipette aspiration of living cells. J. Biomechanics 33(1), 15-22, 2000.
[3] A. Priezzhev, K. Lee, N. Firsov, J. Lademann, Optical Study of RBC Aggregation in Whole Blood Samples and Single Cells. In: Handbook on Optical Biomedical Diagnostics, 2nd ed., V.V. Tuchin - editor, SPIE Press: Bellingham, Washington, pp. 536, 2016.
[4] A. Muravyov, I. Tikhomirova, A. Maimistova, S. Bulaeva, P. Mikhailov, N. Kislov, Red blood cell aggregation changes are dependent on its initial value: Effect of long-term drug treatment and short-term cell incubation with drug, Clin. Hemorheol. Microcirc. 48(4), 231-240, 2011.
[5] S. Shin, Y. Yang, J. Suh, Measurement of erythrocyte aggregation in a microchip stirring system by light transmission, Clin. Hemorheol. Microcirc. 41(3), 197-207, 2009.
[6] V.V. Lopatin, A.V. Priezzhev, Multiple light scattering by suspensions of erythrocytes in geometrical optics approximation, Proc. SPIE 4749, 267-274, 2002.
[7] P. Ermolinskiy, A. Lugovtsov, A. Maslyanitsina, A. Semenov, L. Dyachuk, A. Priezzhev, Interaction of erythrocytes in the process of pair aggregation in blood samples from patients with arterial hypertension and healthy donors: measurements with laser tweezers, J. Biomed. Photonics & Eng. 4(3), 030303,2018.
[8] K. Lee, M. Kinnunen, M. Khokhlova, E. Lyubin, A. Priezzhev, I. Meglinski, A. Fedyanin, Optical tweezers study of red blood
cell aggregation and disaggregation in plasma and protein solutions, J. Biomed. Opt. 21(3), 035001, 2016.
[9] Yu. Gurfinkel, O. Suchkova, M. Sasonko, A. Priezzhev, Implementation of digital optical capillaroscopy for quantifying and estimating the microvascular abnormalities in diabetes mellitus, Proc. SPIE 9917, 991703, 2015.
[10] Q. Yang, J.H. Wang, D.D. Huang, D.G. Li, B. Chen, L.M. Zhang, C.L. Yuan, L.J. Cai, Clinical significance of analysis of the level of blood fat, CRP and hemorheological indicators in the diagnosis of elder coronary heart disease, Saudi J. Biol. Sciences 25(8), 1812-1816, 2018.
